http://2012.igem.org/wiki/index.php?title=Special:Contributions&feed=atom&limit=250&target=Felixekn2012.igem.org - User contributions [en]2024-03-29T07:17:41ZFrom 2012.igem.orgMediaWiki 1.16.0http://2012.igem.org/Template:Team:Washington/Templates/TopTemplate:Team:Washington/Templates/Top2012-11-15T17:40:21Z<p>Felixekn: </p>
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</html></div>Felixeknhttp://2012.igem.org/Team:Washington/PlasticsTeam:Washington/Plastics2012-11-15T17:38:09Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“Plastics: made to last forever, designed to throw away”--5gyres.org</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:Washington_Sadfish.png|border|440px|right|thumb|Much of the plastic waste in the environment accumulates in the ocean, where the toxic plasticizers and additives contaminate the water.]]<br />
Playing an integral role in our modern world, plastics account for over a third of products made today. They have a relatively low cost of production, serve to promote the development of industry, lower the cost of consumer goods, and are easy to produce on a large scale. Plastics, regardless of disposability, compose 10% of the waste generated in the world and an even higher percentage is carelessly thrown into our environment<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. Plastic pollution discharges debris into the ocean that are ingested by wildlife, often resulting in injury or death<html><a href="#Sources"><font size="1"><sup>[2]</sup></font></a></html>.<br />
<br />
<br />
===The PUR Problem ===<br />
A commonly used plastic, Polyurethane (PUR), is used to create a wide range of products including dense solids, rigid insulating foam, and thermoset elastics. Because of its extreme versatility, the world consumption of PUR is continually increasing. According to current market analysis, the global production of PUR is estimated at 27 billion pounds per year and is expected to increase to 36 billion pounds by the year 2016<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. The leading method of recycling polyurethane is to mechanically grind the waste and rebind it into carpet cushioning, accounting for about 4% of waste polyurethane<html><a href="#Sources"><font size="1"><sup>[4]</sup></font></a></html>. However, due to the high costs of transportation and chemical degradation, most PURs are incinerated to recapture some of the energy used to make them<html><a href="#Sources"><font size="1"><sup>[5]</sup></font></a></html>.<br />
<br />
<br />
[[Image:Washington_PURBURN.png|border|280px|left|thumb|Incineration is currently the most popular means of waste PUR disposal. This method is not only harmful to our environment, the products have been shown to be carcinogenic.]]<br />
<br />
===Current Means of Disposal are Undesirable===<br />
Non-biodegradable plastics are often disposed of through waste incineration as it is the most efficient method of degradation. However, a major issue with this is that the products of plastic burning, which include polychlorinated di-benzo-p-dioxins/furans and carbon dioxide, are known to be carcinogenic<html><a href="#Sources"><font size="1"><sup>[6]</sup></font></a></html>. Upon incineration, these gaseous products are released into the atmosphere and have the potential to cause future problems to human health as well as contribute to global warming. As an additional concern, plastics constitute large volumes of space as they are habitually disposed of in landfills. It has been shown that plasticizers and plastic additives leak from the these plastics and contaminate aquatic environments<html><a href="#Sources"><font size="1"><sup>[7]</sup></font></a></html>. Thus, because of the environmental damage that current disposal methods incur, there is a large need for safer methods of plastic degradation.<br />
<br />
<br />
===Engineering Microbes to Degrade PUR===<br />
To solve the problem of PUR recycling, we propose a bacteria that is both able to degrade PUR and subsist off of the products of degradation as its sole carbon source. To this end, we propose a two plasmid system. The first plasmid would have two genes. The first gene, polyeurethane esterase, will encode an enzyme that is able to break down the PUR polymer structure into two molecules, one of which, ethylene glycol, can diffuse across the membrane of the bacterium<html><a href="#Sources"><font size="1"><sup>[8]</sup></font></a></html>. The second gene, osmotic inducible protein Y (osmY), would encode a protein that fuses to PUR esterase and exports the enzyme through the cell membrane and into the supernatant<html><a href="#Sources"><font size="1"><sup>[9]</sup></font></a></html>. The second plasmid would have an operon composed of, glycolaldehyde reductase (fucO) and glycoaldehyde dehydrogenase (aldA), that allows the bacterium to use ethylene glycol as its central metabolite<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. This system would allow the bacteria to turn the plastics plaguing our landfills into bacterial biomass which would in turn degrade more PUR. <br />
[[Image:Washington_Plastic_Overview.png|border|950px|center|thumb|The complete degradation pathway takes polyurethane, an undesirable waste product and converts it into a central metabolite for bacterium.]]<br />
<br />
<br />
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<h1 id='App'>Our App: The Turbidostat<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
<center>[http://students.washington.edu/cnt/pmwiki/pmwiki.php?n=Main.HomePage <b><font size='4'>Click here for the Turbidostat Wiki</font></b>]</center><br />
[[Image:Washington_Turbidostat.png|border|400px|left|thumb|Schematic diagram of our turbidostat. It uses the optical density measurements to modulate how much media is pumped into the culture vessel every minute to maintain a constant cell density. Through this method of cell density control, we can measure how fast the cells are growing and thus gain an understanding for relative fitness increases thorough out the course of an experiment.]]<br />
[[Image:Recycleapp.png|150px|right]]<br />
To characterize and optimize growth of specific parts of our overall polyurethane degradation pathway we developed a <i><b>homemade software application (App)</b></i>. However, devices that can accomplish this are not commercially available. Additionally, details for such tools are often sparsely detailed and frequently require exotic reagents and specialized training. To circumvent these issues, we instead decided to use our expertise in electrical, biological, and computer engineering to create an application that could control cheaply and readily available hardware to achieve the desired directed evolution and characterization functionality. <br />
<br />
<br />
Our App uniquely controls an assortment of hardware devices easily found online, creating a fully functional turbidostat. A turbidostat is a closed-loop continuous culture device that maintains a desired constant cell density and chemical environment. The constant environment allows for continuous log phase growth and constant selective pressures (in our case polyurethane or ethylene glycol), which enables proper characterization (seen through media input per dilution cycle) and reduces evolutionary trajectory drift. By maintaining constant selective pressures, the rate of evolution is also dramatically improved<html><a href="#Sources"><font size="1"><sup>[11]</sup></font></a></html>. By creating this turbidostat App (written in Python), given sufficient time, we are able to properly assess and optimize our individual parts as well as the entire polyurethane degrading circuit in a controlled and reliable manner. Please click the link for complete instructions on how to build your own turbidostat.<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[[Image:Washington_PUR.png|border|250px|left|thumb|To assemble the polyurethane esterase, we used 4 IDT gBlocks, each containing a unique set of homology sequences that only allow for one orientation of binding, and Gibsoned them together onto a pGA backbone.]]<br />
<br />
===PUR Esterase Assay=== <br />
Polyurethane esterase degrades polyurethane by cleaving the ester bonds to produce one of a variety of monomers with isocyanate groups on either end and an ethylene glycol. For our circuit, we have chosen to use the esterase estCS2 ([http://partsregistry.org/Part:BBa_K892012 BBa_K892012]). Originally isolated and characterized by Kang et al, the sequence of estCS2 is available at [http://www.ncbi.nlm.nih.gov/nuccore/283462288 GenBank (GU256649)]<html><a href="#Sources"><font size="1"><sup>[8]</sup></font></a></html>. Since their DNA constructs were unavailable and the authors did not respond to requests, we synthetically constructed the entire 1.7kb estCS2 gene from four 500-base gBlocks provided by IDT using Gibson cloning. <br />
<br />
<br />
[[Image:Washington_PUR_Assay.png|border|350px|right|thumb|To assemble for activity of polyurethane esterase, cells transformed with the PUR esterase plasmid were grown to <br />
saturation, sonicated, then a piece of pre-weighed polyurethane foam was inserted into the culture tube.]]<br />
To assay esterase activity we made one 50mL TB + kanamycin overnight culture for the cells containing the esterase plasmid and a 50mL TB overnight culture with untransformed cells. After incubating the cultures for a day, we spun them down to form a pellet. The supernatant was then poured out and then the remaining pellets were resuspended the in enough water for each culture to be equalized at an arbitrary OD of 1.4. Three milliliters of each resuspended culture was aliquoted out into three microcentrifuge tubes (1 mL of one culture in each tube, 6 total microcentrifuge tubes). These tubes, along with 6 microcentrifuge tubes that contained only 1mL of LB were then sonicated at an amplitude of 20 with 1 second pulses on and off for 30 seconds. After sonication we poured each lysate and LB aliquots into 25mL culture tubes containing 1mL of LB and a preweighed sample of foam. This gave us a total of 12, 25mL culture tubes containing a pre weighed foam piece with LB+lysate or LB+sonicated LB. These culture tubes were left on the bench top at room temperature overnight. In the morning we washed the foam with DI water and dried them in a 65º C incubator. After drying, we weighed the samples to see if there was any change in the mass of the foam samples.<br />
<br />
===Exporter Protein OsmY Assay===<br />
Since polyurethane is a large polymer, larger than the cell itself, it cannot be imported for degradation and therefore our esterase must be exported. It has been shown in Bolkinsky, Gregory et al that a protein, osmotically inducible protein Y (osmY, [http://partsregistry.org/Part:BBa_K892008 BBa_K892008]), is naturally exported by <i>E. coli</i> [9]. Furthermore this paper shows that osmY can be used as an export tag in <i>E. coli</i> when it is translationally fused to a protein. <br />
<br />
[[Image:Washington_osmY.png|border|348px|left|thumb|Our exporter protein (osmY) is linked to sfGFP through a serine glycine linker.]]<br />
[[Image:Washington_osmY_Assay.png|border|546px|right|thumb|We used osmY fused to sfGFP and as a control sfGFP fused to mamI in the same translational conformation to test for osmY functionality. These cultures were spun down and the fluorescence (480nm excitation, 509nm emission) of the cells+supernatant and supernatant were measured.]]<br />
<br />
<br />
To assay the exportation system we created a fusion protein. The fusion protein consisted of super folder GFP (sfGFP) fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]), which allowed for visual assessment of whether or not osmY was correctly exported into the supernatant. For the control, sfGFP fused to mamI ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K590016 BBa_K590016]), a protein that binds to the cell membrane, was used. The sfGFP-osmY and sfGFP-mamI plasmids were transformed into BL21 and then were grown up overnight (after picking a colony of each from their transformation plates) in M9 1% glucose media. To quantify whether or not osmY properly exported proteins outside of the cell membrane, 4, 500µL aliquots of each overnight culture was made. From each of these aliquots, 200µL of the culture was pipetted into a 96 well plate and then the remaining culture was spun down in a centrifuge at 3000g for 3 minutes. Carefully, from each of the spun down aliquots, 200µL of the supernatant was pipetted into the 96 well plate. The remaining supernatant for each aliquot was disposed of and the cells were resuspended with 300 µL of fresh M9 1% glucose media. From the each of the resuspended aliquots, 200µL was pipetted into the 96 well plate. The 96 well plate was then read with a plate reader; each well was excited with a 480+/-20 nm light and then a detector quantified how much 509 +/-20 nm light was give off. <br />
<br />
Using the information from the plate reader we could determine if osmY properly exports the fused protein. If the supernatant from the cells expressing the osmY construct had high fluorescence with respect to cells+supernatant while the supernatant of the control sfGFP-mamI had low fluorescence compared to its overall cells+supernatant fluorescence than it would be concluded that the protein was properly exported when fused to osmY. But if there was little to no supernatant fluorescence of the sfGFP-osmY construct or sfGFP-mamI and sfGFP-osmY illustrated the same ratio of supernatant fluorescence to cells+supernatant fluorescence then it would be concluded that osmY did not export proteins outside of the cell or our designed fusion between sfGFP and osmY disrupted sfGFP/osmY function.<br />
<br />
<br />
[[Image:Washington_fucO_aldA.png|border|350px|left|thumb|By putting fucO and aldA onto a single plasmids with a single promote, we were able to create an operon that when transcribed and translated allows microbes to utilize ethylene glycol as a sole carbon source [10].]]<br />
<br />
===The Turbidostat Assay===<br />
The media used for the turbidostat assay was comprised exclusively of M9 salts and ethylene glycol. Because of this choice of media, the only carbon source available to <i>E. coli</i> was ethylene glycol and because this cannot be digested by normal lab strains of <i>E. coli</i>, only <i>E. coli</i> that can digest ethylene glycol will survive<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. The turbidostat App tracks cell growth by first blanking (setting optical density (OD) to 0) and then measuring and recording OD values after inoculation. Because OD and cell density are both linearly related, OD is a proxy for cell density measurements. After blanking on the M9 30mM ethylene glycol media, MG1655 cells that were transformed with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]), which were grown in TB to log phase and then washed with PBS, were added to the turbidostat's culture vessel to an OD of 0.2. This allowed cells still in log phase to divide without exceeding the OD threshold of 0.3 (our desired constant cell density). After inoculation, the turbidostat was left to run overnight. Cell density and amount of media added per dilution cycle was captured in the morning. With this data, an understanding for how well the transformed <i>E. coli</i> subsists using ethylene glycol as its sole carbon source was gained.<br />
<br />
<br />
The next assay ran was to transform MG1655 with a fucO-aldA operon plasmid ([http://partsregistry.org/Part:BBa_K892013 BBa_K892013]). This operon consisted of fucO and aldA being on a single plasmid that contains a single promoter. With this present, transformed MG1655 cells would be theoretically more viable in ethylene glycol since the cells would not have to use energy to create two antibiotic resistance genes (although no antibiotics were used in the the M9 ethylene glycol media, the cells still transcribe and translate the genes). By running the turbidostat in the same way as the dual transformation experiment, an idea of how well the transformed operon faired versus how well the two plasmid transformation could be gained.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===PUR Esterase Assay Results===<br />
We ran the PUR esterase assay and we found that the foam we used did not degrade. We quantified this by measuring the weight of the foam pieces to the nearest milligram before and after we ran the assay. We actually ended up leaving the lysate with the foam for three days to improve degradation but this had no effect on the end result. The reason we think the foam was not degraded was because we did not know the full composition of the foam, it could of had other polymers mixed in with the polyurethane that prevented our enzyme from properly degrading it. Further testing will need to be done to properly assess the functionality of our PUR esterase.<br />
<br />
===Exporter Protein OsmY Assay Results===<br />
[[Image:Washington_osmY_Assay_Results.png|border|940px|center|thumb|Each histogram depicts the average normalized fluorescence values for cells plus supernatant, cells only, and supernatant only. The image in the center is a visual representation of the presented data.]]<br />
When running the osmY assay described in the methods section, we saw that sfGFP fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]) resulted in a much higher fluorescence ratio of cells+supernatant to supernatant than the fluorescence ratio of cells+supernatant to supernatant of sfGFP fused to mamI. Even though the cells only fluorescence of sfGFP-osmY plasmid exhibited nearly the same level of fluorescence as supernatant only, it does not go against the argument that osmY is properly exporting sfGFP into the supernatant. sfGFP-osmY is produced within the cell and it cannot be expected that all osmY will be secreted from the cell at a given moment. Thus from the above data, it can be gathered that <b>a protein fused to osmY will be exported into the supernatant.</b><br />
<br />
===Turbidostat Assay Results===<br />
[[Image:Washington_Turbidostat_Pump.png|border|450px|left|thumb|M9 30 mM ethylene glycol media injected into the culture vessel every minute to maintain the arbitrary optical density of 0.3.]]<br />
<br />
[[Image:Washington_Turbidostat_OD.png|border|450px|right|thumb|Optical density measurements of <i>E. coli</i> transformed with fucO pGA3K3 + aldA pGA1C3 within the turbidostat growing on M9 30 mM ethylene glycol media.]]<br />
<br />
<br />
Following the turbidostat assay protocol, we used transformed MG1655 with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) on a medium copy Kanamycin resistance backbone and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]) on a high copy Chloramphenicol resistance backbone and cultured it in the turbidostat. The turbidostat was operated overnight and the above data illustrates that the transformed MG1655 grew using ethylene glycol as its sole carbon source, which untransformed MG1655 cannot do. This is the case because the optical density (OD)/cell density rose from an initial arbitrary OD of 0.18 (time of inoculation) to an OD of 0.3, which was maintained throughout the time of the experiment (15 hours). The turbidostat maintained this OD by constantly varying the amount of new media put into the culture vessel. The amount added was reduced whenever the OD surpassed 0.3, which was quite frequently. Although time constraints did not allow us to transform MG1655 with our fucO-aldA operon and characterize it, it can be seen from the data above that when fucO and aldA genes are individually transformed and over expressed in MG1655 <i>E. coli</i>, <b><i>E. coli</i> gains the ability to utilize ethylene glycol as its sole carbon source.</b> The average time (based off of a 10mL culture volume and an average dilution rate of 30µL/min) for <i>E. coli</i> to undergo binary fission was 333 minutes, faster than the literature value of 360 minutes [10]. <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===Putting it All together===<br />
The parts as they are right now have not been put into one cell strain and tested as a system. The obvious next step would be to test the efficacy of the entire plastic degrading construct against polyurethane. Using the turbidostat, we have the luxury of having an optimized and continual growth environment for our system to mutate <i>E. coli</i> towards improved functionality. Better cell strains would be periodically saved and tested in harsher selective conditions until eventually the strain is able to survive and reproduce off of polyurethane as its sole carbon source. We recognize that this may take many iterations to complete but the potential end product could be a cell that is able to consume plastic much faster, safer and more economically than traditional recycling methods.<br />
<br />
===Trash to Treasure===<br />
After stable growth off of plastic is achieved the next step would be to clone in a third plasmid that would produce some valuable commodity. Washington 2011 demonstrated that diesel fuel can be synthesized from central metabolites using their Petrobrick platform. The addition of the Petrobrick to the system would allow <i>E. coli</i> to process plastic waste and turn it into biofuels. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892008 BBa_K892008 '''osmY''']<br />
<br />
The coding sequence for osmotically induced protein Y, a protein that when fused to another protein, gets exported out of <i>E. coli</i> cells.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009 '''fucO''']<br />
<br />
The gene fucO, which codes for glycolaldehyde reductase, is one of the genes required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010 '''aldA''']<br />
<br />
The gene aldA, which codes for glycolaldehyde dehydrogenase, is the other gene required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892011 BBa_K892011 '''sfGFP-osmY''']<br />
<br />
A composite part for osmotically induced protein Y fused downstream to sfGFP using a glycine - serine linker regulated by the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892012 BBa_K892012 '''PUR Esterase''']<br />
<br />
An enzyme that breaks down polyurethane plastic behind the control of the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892013 BBa_K892013 '''fucO-aldA''']<br />
<br />
The combination of BBa_K892009 and BBa_K892010 behind a strong biofab promoter.<br />
<br />
<br />
----<br />
<html><a name="Sources"></a></html><br />
<h1 id='Parts'>Sources <html><a href="#Sources"><font size="3">[Top]</font></a></html></h1><br />
<html><br />
<ol><br />
<li>Barnes, D. K. A., F. Galgani, R. C. Thompson, and M. Barlaz. "Accumulation and Fragmentation of Plastic Debris in Global Environments." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 1985-998. Print.</li><br />
<br />
<li>Gregory, M. R. "Environmental Implications of Plastic Debris in Marine Settings--entanglement, Ingestion, Smothering, Hangers-on, Hitch-hiking and Alien Invasions." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2013-025. Print.</li><br />
<br />
<li>"Global Polyurethane Market to Reach 9.6 Mln Tons by 2015." Plastemart.com. N.p., 30 Aug. 2011. Web. <a href="http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674">http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674</a> </li><br />
<br />
<li>"Polyurethane Recycling." Polyurethanes. American Chemistry Council, n.d. Web. <a href="http://polyurethane.americanchemistry.com/Sustainability/Recycling">http://polyurethane.americanchemistry.com/Sustainability/Recycling</a> </li><br />
<br />
<li>"Frequently Asked Questions on Polyurethanes." Polyurethanes.org. European Diisocyanate and Polyol Producers Association, n.d. Web. <a href="http://www.polyurethanes.org/index.php?page=faqs">http://www.polyurethanes.org/index.php?page=faqs</a> </li><br />
<br />
<li>Takasuga, T., N. Umetsu, T. Makino, K. Tsubota, KS Sajwan, and KS Kumar. "Role of Temperature and Hydrochloric Acid on the Formation of Chlorinated Hydrocarbons and Polycyclic Aromatic Hydrocarbons during Combustion of Paraffin Powder, Polymers, and Newspaper." Archives of Environmental Contamination and Toxicology (2007): 8-21. Print. </li><br />
<br />
<li>Teuten, E. L., J. M. Saquing, D. R. U. Knappe, M. A. Barlaz, S. Jonsson, A. Bjorn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha, and H. Takada. "Transport and Release of Chemicals from Plastics to the Environment and to Wildlife." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2027-045. Print. </li><br />
<br />
<li>Kang, Chul-Hyung. "A Novel Family VII Esterase with Industrial Potential from Compost Metagenomic Library." Microbial Cell Factories 10.41 (2011): n. pag. Print. </li><br />
<br />
<li>Bokinsky, Gregory, Et. Al. "Synthesis of Three Advanced Biofuels from Ionic Liquid-penetreated Switchgrass Using Engineered Escherichia Coli." Proceedings of the National Academy of Sciences of the United States of America 108.50 (2011): 19949-9954. Print. </li><br />
<br />
<li> Boronat, Albert, Estrella Caballero, and Juan Aguilar. "Experimental Evolution of a Metabolic Pathway for Ethylene Glycol Utilization by Escherichia Coli." Journal of Bacteriology Jan. (1983): 134-39. Web. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC217350/">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC217350/</a> </li><br />
<br />
<li> E. Toprak, A. Veres, J. B. Michel, R. Chait, D. L. Hartl, R. Kishony, “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection,” Nature Genetics, vol. 44, no. 1, pp. 101-105, Jan. 2012. </li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:WashingtonTeam:Washington2012-10-15T22:46:16Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<center>'''Overview:'''</center>==<br />
<p>Biological systems must often be painstakingly tuned before they will efficiently produce drugs or biofuels, degrade chemicals, or perform other useful tasks. Our team implemented broadly applicable methods to optimize biological systems through directed evolution, light-regulated gene expression, and computer aided protein design. We characterized light-inducible protein expression systems for multichromatic tuning of biological pathways. To provide an inexpensive method for tuning gene expression with light, we developed a tablet application that is freely available. We also used computer-aided design to develop proteins that more effectively bind isotypes of the flu protein Hemagglutinin. Finally, we implemented a continuous culture device (turbidostat) in order to apply directed evolution to the metabolism of ethylene glycol in E. coli. We have termed the research conducted this year “Apptogenetics” as all projects utilize purpose-built computational applications for biological research. </p><br />
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<div class="view view-tenth"> <br />
<img src="https://static.igem.org/mediawiki/2012/4/41/Recycleapp.png" /> <br />
<div class="mask"> <br />
<h2>Plastic Degredation</h2> <br />
<p>Turning trash into treasure</p> <br />
<a href="https://2012.igem.org/Team:Washington/Plastics" class="info">Read More</a> <br />
</div> <br />
</div><br />
<div class="view view-tenth"><br />
<img src="https://static.igem.org/mediawiki/2012/e/ef/Fluapp.png" /> <br />
<div class="mask"> <br />
<h2 style="padding-top: 20px;">Flu Binders</h2> <br />
<p>Targeting influenza - one protein at a time</p> <br />
<a href="https://2012.igem.org/Team:Washington/Flu" class="info">Read More</a> <br />
</div> <br />
</div><br />
<div class="view view-tenth"> <br />
<img src="https://static.igem.org/mediawiki/2012/e/e0/Lightapp.png" /> <br />
<div class="mask"> <br />
<h2 style="padding-top: 20px;">Optogenetics</h2> <br />
<p>Shine a light (or several)</p> <br />
<a href="https://2012.igem.org/Team:Washington/Optogenetics" class="info">Read More</a> <br />
</div> <br />
</div><br />
<div class="view view-tenth"> <br />
<img src="https://static.igem.org/mediawiki/2012/c/ce/Community.png" /> <br />
<div class="mask"> <br />
<h2>Community Outreach</h2> <br />
<p>See how we educated the community</p> <br />
<a href="https://2012.igem.org/Team:Washington/Outreach" class="info">Read More</a> <br />
</div> <br />
</div><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T03:59:11Z<p>Felixekn: /* Building Optogenetic Tools */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<center>[http://www.youtube.com/watch?v=8h4RbDjTDYg <b><font size='4'>See our app in action!</font></b>]</center><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size.<br />
<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:Picture1 wtf alex.png|thumb|right|400px|Explanation of a general light system]] <br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
Chromatic tuning is based on light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter P<sub>cpcG2</sub>, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
<br />
[[File:Picture 2.png|thumb|left|400px|Explanation of a general light system]]<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
<br />
<br />
===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
<br />
[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
<br />
<br />
Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
<br />
===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
<br />
[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
<br />
<br />
In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
<br />
When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
<br />
In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:20120927 big dots cropped experimentwells resized.png|border|350px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|350px|right|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating weaker gene expression.]]<br />
<br />
<br />
<br />
As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends. The well images were also put into ImageJ, which measured the average pixel value of each culture, as is shown in the graph on the right. The large uncertainty in the data is caused by uneven bacterial spreads in the agar, which can be solved by using soft agar.<br />
<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We believe that light inducible gene expression represents a field that could be of great interest to synthetic biologists, and the iGEM community. The work we carried out this summer has laid a foundation on which we hope future teams can build, but there is much more than can be done. To that end, we are currently trying to standardize more the light sensors, and submit them to the registry so that they are readily available for all interested teams in the future. The light sensor used in this year's research appears to have a rather leaky promoter in our hands, leading to more transcription than we would normally expect in dark conditions. We think that the E.colight app developed over the summer could be used to address this problem. In 96 well plate experiments we noticed what appeared to be leakiness between wells. In order to carry out experiments using liquid cultures in the future, we may need to modify the assay plates. We are currently exploring a variety of options to accomplish this. For example, a suggestion was made to use a rubber adaptor with appropriately placed holes that would fit under a 96 well plate. Currently, the blue light sensor has not been characterized, and the only confirmation of a working sensor available right now are the sequencing files. We hope to continue to work with this sensor, testing and characterizing the sensor and seeking to fix any problems with the light sensor if problems are to be found. We hope to accomplish this kind of testing within the next couple of weeks. We would also like to work with the light sensors that we currently have in order to increase control of genetic pathways. The light sensors can also be attached to different genes in order to control production of certain substrates. Finally, Taking current light sensors available and applying them to e.coli is another aspiration of the Washington iGEM team.<br />
<br />
<br />
The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
<br />
<br />
The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''Repaired LovTAP''']<br />
<br />
The LovTAP system, without the point mutation.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''LovTAP with reporter gene''']<br />
<br />
LovTAP system, with inverter and sfGFP output.<br />
<br />
----<br />
<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
<br><br />
[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
<br><br />
[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T03:58:46Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<center>[http://www.youtube.com/watch?v=8h4RbDjTDYg <b><font size='4'>See our app in action!</font></b>]</center><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size.<br />
<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:Picture1 wtf alex.png|thumb|right|400px|Explanation of a general light system]] <br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
Chromatic tuning is based on light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter P<sub>cpcG2</sub>, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
<br />
[[File:Picture 2.png|thumb|left|400px|Explanation of a general light system]]<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
<br />
===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
<br />
[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
<br />
<br />
Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
<br />
<br />
===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
<br />
[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
<br />
<br />
In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
<br />
When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
<br />
In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:20120927 big dots cropped experimentwells resized.png|border|350px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|350px|right|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating weaker gene expression.]]<br />
<br />
<br />
<br />
As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends. The well images were also put into ImageJ, which measured the average pixel value of each culture, as is shown in the graph on the right. The large uncertainty in the data is caused by uneven bacterial spreads in the agar, which can be solved by using soft agar.<br />
<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We believe that light inducible gene expression represents a field that could be of great interest to synthetic biologists, and the iGEM community. The work we carried out this summer has laid a foundation on which we hope future teams can build, but there is much more than can be done. To that end, we are currently trying to standardize more the light sensors, and submit them to the registry so that they are readily available for all interested teams in the future. The light sensor used in this year's research appears to have a rather leaky promoter in our hands, leading to more transcription than we would normally expect in dark conditions. We think that the E.colight app developed over the summer could be used to address this problem. In 96 well plate experiments we noticed what appeared to be leakiness between wells. In order to carry out experiments using liquid cultures in the future, we may need to modify the assay plates. We are currently exploring a variety of options to accomplish this. For example, a suggestion was made to use a rubber adaptor with appropriately placed holes that would fit under a 96 well plate. Currently, the blue light sensor has not been characterized, and the only confirmation of a working sensor available right now are the sequencing files. We hope to continue to work with this sensor, testing and characterizing the sensor and seeking to fix any problems with the light sensor if problems are to be found. We hope to accomplish this kind of testing within the next couple of weeks. We would also like to work with the light sensors that we currently have in order to increase control of genetic pathways. The light sensors can also be attached to different genes in order to control production of certain substrates. Finally, Taking current light sensors available and applying them to e.coli is another aspiration of the Washington iGEM team.<br />
<br />
<br />
The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
<br />
<br />
The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892801 BBa_K892801 '''Repaired LovTAP''']<br />
<br />
The LovTAP system, without the point mutation.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''LovTAP with reporter gene''']<br />
<br />
LovTAP system, with inverter and sfGFP output.<br />
<br />
----<br />
<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
<br><br />
[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
<br><br />
[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:WashingtonTeam:Washington2012-10-04T03:51:40Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<center>'''Overview:'''</center>==<br />
<p>Biological systems must often be painstakingly tuned before they will efficiently produce drugs or biofuels, degrade chemicals, or perform other useful tasks. Our team implemented broadly applicable methods to optimize biological systems through directed evolution, light-regulated gene expression, and computer aided protein design. We characterized light-inducible protein expression systems for multichromatic tuning of biological pathways. To provide an inexpensive method for tuning gene expression with light, we developed a tablet application that is freely available. We also used computer-aided design to develop proteins that more effectively bind isotypes of the flu protein Hemagglutinin. Finally, we implemented a continuous culture device (turbidostat) in order to apply directed evolution to the metabolism of ethylene glycol in E. coli. We have termed the research conducted this year “Apptogenetics” as all projects utilize purpose-built computational applications for biological research. </p><br />
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<h2>Plastic Degredation</h2> <br />
<p>Turning trash into treasure</p> <br />
<a href="https://2012.igem.org/Team:Washington/Plastics" class="info">Read More</a> <br />
</div> <br />
</div><br />
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<h2 style="padding-top: 20px;">Flu Binders</h2> <br />
<p>Targeting influenza - one protein at a time</p> <br />
<a href="https://2012.igem.org/Team:Washington/Flu" class="info">Read More</a> <br />
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<h2 style="padding-top: 20px;">Optogenetics</h2> <br />
<p>Shine a light (or several)</p> <br />
<a href="https://2012.igem.org/Team:Washington/Optogenetics" class="info">Read More</a> <br />
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<h2>Community Outreach</h2> <br />
<p>See how we educated the community</p> <br />
<a href="https://2012.igem.org/Team:Washington/Outreach" class="info">Read More</a> <br />
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</html></div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T03:26:30Z<p>Felixekn: /* Experimental Description */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
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<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<center>[http://www.youtube.com/watch?v=8h4RbDjTDYg <b><font size='4'>See our app in action!</font></b>]</center><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size.<br />
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<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:Picture1 wtf alex.png|thumb|right|400px|Explanation of a general light system]] <br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
Chromatic tuning is based off light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter P<sub>cpcG2</sub>, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
<br />
[[File:Picture 2.png|thumb|left|400px|Explanation of a general light system]]<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
<br />
<br />
===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
<br />
[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
<br />
<br />
Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
<br />
<br />
===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
<br />
[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
<br />
<br />
In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
<br />
When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
<br />
In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.[[File:20120927 big dots cropped experimentwells resized.png|border|350px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]<br />
<br />
As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends.<br />
<br />
<br />
[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|350px|left|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating a smaller amount of cut S-Gal.]]To create some kind of gradient for the system, we decided to use a 96 well plate and the app. There are rows with constant green light excitation, two rows in the middle that flash and end rows that have constant green light on. The plate is darkest on either end with a gradient that lightens up as it gets closer to the middle, where red and green light flash, inducing intermediary amounts of gene expression. Ideally there should only be lighter colors of wells in columns 6 and 7. However, there appears to be some leakiness with the utilization of the light app as some of this light has managed to leak to other wells, which creates a sort of gradient with darkest wells on either side and becoming lighter towards the middle. Pipetting error also affects the data as some of the wells have more colonies than others which leads to higher pixel values even if gene expression may not necessarily be higher. Using soft agar can help resolve this problem in the future. The quantified data above was made by using ImageJ and measuring average pixel value of each plate. Generally there is a gradient of increased pixel value as the well is further away from the red light. Both replicates show similar results, even though replicate one has slightly ,more dramatic increases while replicate one appears to have a steadier rise. Because of leakiness, some of the flashing red light that was supposed to be confined to wells 6 and 7 have leaked over to nearby wells, causing some gene repression even when there shouldn’t necessarily have been any. However the results show that gene repression is extremely sensitive to red light and a gradient can be created from various levels of red light relatively easily. That being said, gene control isn’t completely “on” or “off” but also holds the benefit of having various levels of “on” and “off”. <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
We want to work more with this biological system, we are working on biobricking a few more of the light sensors so that they are readily available. We suspect that a big problem with the light sensor right now is the leaky promoter, which leads to more transcription than we would normally expect in dark conditions. The promoter should be modified accordingly, either by fixing any present mutation or choosing to use a new promoter entirely for various reasons. Also to improve characterization of the sensor itself, in the future soft agar should be used which will allow more sufficient spread of bacteria, which should minimize differences in bacteria colonies in each well, allowing better data collection with imageJ. There also appears to be some leakiness between wells that should be addressed more appropriately in the future. A suggestion was made to use a rubber stopper with holes in it on top of the app to try to minimize leakiness of light between different wells.We also want to work with different light sensors to improve functioning and to use light sensors to promote various gene expression of different proteins.<br />
<br />
The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
<br />
The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''Blue Light Sensor, TetR Inverter and sfGFP Output''']<br />
<br />
The blue light sensing LovTAP system, engineered produce sfGFP as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892014 BBa_K892014 '''ccaS-ccaR-lacZ ''']<br />
<br />
The green light sensor, engineered to produce lacZ as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892801 BBa_K892801 '''Repaired LovTAP''']<br />
<br />
The LovTAP system, without the point mutation.<br />
<br />
<br />
----<br />
<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
<br><br />
[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
<br><br />
[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T03:25:58Z<p>Felixekn: /* Building Optogenetic Tools */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<center>[http://www.youtube.com/watch?v=8h4RbDjTDYg <b><font size='4'>See our app in action!</font></b>]</center><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size.<br />
<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:Picture1 wtf alex.png|thumb|right|400px|Explanation of a general light system]] <br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
Chromatic tuning is based off light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter P<sub>cpcG2</sub>, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
<br />
[[File:Picture 2.png|thumb|left|400px|Explanation of a general light system]]<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
<br />
<br />
===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
<br />
[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
<br />
<br />
Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
<br />
===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
<br />
[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
<br />
<br />
In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
<br />
When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
<br />
In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.[[File:20120927 big dots cropped experimentwells resized.png|border|350px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]<br />
<br />
As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends.<br />
<br />
<br />
[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|350px|left|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating a smaller amount of cut S-Gal.]]To create some kind of gradient for the system, we decided to use a 96 well plate and the app. There are rows with constant green light excitation, two rows in the middle that flash and end rows that have constant green light on. The plate is darkest on either end with a gradient that lightens up as it gets closer to the middle, where red and green light flash, inducing intermediary amounts of gene expression. Ideally there should only be lighter colors of wells in columns 6 and 7. However, there appears to be some leakiness with the utilization of the light app as some of this light has managed to leak to other wells, which creates a sort of gradient with darkest wells on either side and becoming lighter towards the middle. Pipetting error also affects the data as some of the wells have more colonies than others which leads to higher pixel values even if gene expression may not necessarily be higher. Using soft agar can help resolve this problem in the future. The quantified data above was made by using ImageJ and measuring average pixel value of each plate. Generally there is a gradient of increased pixel value as the well is further away from the red light. Both replicates show similar results, even though replicate one has slightly ,more dramatic increases while replicate one appears to have a steadier rise. Because of leakiness, some of the flashing red light that was supposed to be confined to wells 6 and 7 have leaked over to nearby wells, causing some gene repression even when there shouldn’t necessarily have been any. However the results show that gene repression is extremely sensitive to red light and a gradient can be created from various levels of red light relatively easily. That being said, gene control isn’t completely “on” or “off” but also holds the benefit of having various levels of “on” and “off”. <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
We want to work more with this biological system, we are working on biobricking a few more of the light sensors so that they are readily available. We suspect that a big problem with the light sensor right now is the leaky promoter, which leads to more transcription than we would normally expect in dark conditions. The promoter should be modified accordingly, either by fixing any present mutation or choosing to use a new promoter entirely for various reasons. Also to improve characterization of the sensor itself, in the future soft agar should be used which will allow more sufficient spread of bacteria, which should minimize differences in bacteria colonies in each well, allowing better data collection with imageJ. There also appears to be some leakiness between wells that should be addressed more appropriately in the future. A suggestion was made to use a rubber stopper with holes in it on top of the app to try to minimize leakiness of light between different wells.We also want to work with different light sensors to improve functioning and to use light sensors to promote various gene expression of different proteins.<br />
<br />
The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
<br />
The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''Blue Light Sensor, TetR Inverter and sfGFP Output''']<br />
<br />
The blue light sensing LovTAP system, engineered produce sfGFP as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892014 BBa_K892014 '''ccaS-ccaR-lacZ ''']<br />
<br />
The green light sensor, engineered to produce lacZ as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892801 BBa_K892801 '''Repaired LovTAP''']<br />
<br />
The LovTAP system, without the point mutation.<br />
<br />
<br />
----<br />
<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
<br><br />
[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
<br><br />
[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T03:25:30Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<center>[http://www.youtube.com/watch?v=8h4RbDjTDYg <b><font size='4'>See our app in action!</font></b>]</center><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size.<br />
<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:Picture1 wtf alex.png|thumb|right|400px|Explanation of a general light system]] <br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
Chromatic tuning is based off light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter P<sub>cpcG2</sub>, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
<br />
[[File:Picture 2.png|thumb|left|400px|Explanation of a general light system]]<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
<br />
===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
<br />
[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
<br />
<br />
Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
<br />
===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
<br />
[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
<br />
<br />
In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
<br />
When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
<br />
In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.[[File:20120927 big dots cropped experimentwells resized.png|border|350px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]<br />
<br />
As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends.<br />
<br />
<br />
[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|350px|left|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating a smaller amount of cut S-Gal.]]To create some kind of gradient for the system, we decided to use a 96 well plate and the app. There are rows with constant green light excitation, two rows in the middle that flash and end rows that have constant green light on. The plate is darkest on either end with a gradient that lightens up as it gets closer to the middle, where red and green light flash, inducing intermediary amounts of gene expression. Ideally there should only be lighter colors of wells in columns 6 and 7. However, there appears to be some leakiness with the utilization of the light app as some of this light has managed to leak to other wells, which creates a sort of gradient with darkest wells on either side and becoming lighter towards the middle. Pipetting error also affects the data as some of the wells have more colonies than others which leads to higher pixel values even if gene expression may not necessarily be higher. Using soft agar can help resolve this problem in the future. The quantified data above was made by using ImageJ and measuring average pixel value of each plate. Generally there is a gradient of increased pixel value as the well is further away from the red light. Both replicates show similar results, even though replicate one has slightly ,more dramatic increases while replicate one appears to have a steadier rise. Because of leakiness, some of the flashing red light that was supposed to be confined to wells 6 and 7 have leaked over to nearby wells, causing some gene repression even when there shouldn’t necessarily have been any. However the results show that gene repression is extremely sensitive to red light and a gradient can be created from various levels of red light relatively easily. That being said, gene control isn’t completely “on” or “off” but also holds the benefit of having various levels of “on” and “off”. <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
We want to work more with this biological system, we are working on biobricking a few more of the light sensors so that they are readily available. We suspect that a big problem with the light sensor right now is the leaky promoter, which leads to more transcription than we would normally expect in dark conditions. The promoter should be modified accordingly, either by fixing any present mutation or choosing to use a new promoter entirely for various reasons. Also to improve characterization of the sensor itself, in the future soft agar should be used which will allow more sufficient spread of bacteria, which should minimize differences in bacteria colonies in each well, allowing better data collection with imageJ. There also appears to be some leakiness between wells that should be addressed more appropriately in the future. A suggestion was made to use a rubber stopper with holes in it on top of the app to try to minimize leakiness of light between different wells.We also want to work with different light sensors to improve functioning and to use light sensors to promote various gene expression of different proteins.<br />
<br />
<br />
The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
<br />
<br />
The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
<br />
<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''Blue Light Sensor, TetR Inverter and sfGFP Output''']<br />
<br />
The blue light sensing LovTAP system, engineered produce sfGFP as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892014 BBa_K892014 '''ccaS-ccaR-lacZ ''']<br />
<br />
The green light sensor, engineered to produce lacZ as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892801 BBa_K892801 '''Repaired LovTAP''']<br />
<br />
The LovTAP system, without the point mutation.<br />
<br />
<br />
----<br />
<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
<br><br />
[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
<br><br />
[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T03:22:02Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
<br />
[http://www.youtube.com/watch?v=8h4RbDjTDYg <b>See our app in action!</b>]<br />
<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size. Follow [http://www.youtube.com/watch?v=8h4RbDjTDYg this link] for a video demonstration. <br />
<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:Picture1 wtf alex.png|thumb|right|400px|Explanation of a general light system]] <br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
Chromatic tuning is based off light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter P<sub>cpcG2</sub>, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
<br />
[[File:Picture 2.png|thumb|left|400px|Explanation of a general light system]]<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
<br />
===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
<br />
[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
<br />
<br />
Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
<br />
===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
<br />
[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
<br />
<br />
In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
<br />
When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
<br />
In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.[[File:20120927 big dots cropped experimentwells resized.png|border|350px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]<br />
<br />
As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends.<br />
<br />
<br />
[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|350px|left|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating a smaller amount of cut S-Gal.]]To create some kind of gradient for the system, we decided to use a 96 well plate and the app. There are rows with constant green light excitation, two rows in the middle that flash and end rows that have constant green light on. The plate is darkest on either end with a gradient that lightens up as it gets closer to the middle, where red and green light flash, inducing intermediary amounts of gene expression. Ideally there should only be lighter colors of wells in columns 6 and 7. However, there appears to be some leakiness with the utilization of the light app as some of this light has managed to leak to other wells, which creates a sort of gradient with darkest wells on either side and becoming lighter towards the middle. Pipetting error also affects the data as some of the wells have more colonies than others which leads to higher pixel values even if gene expression may not necessarily be higher. Using soft agar can help resolve this problem in the future. The quantified data above was made by using ImageJ and measuring average pixel value of each plate. Generally there is a gradient of increased pixel value as the well is further away from the red light. Both replicates show similar results, even though replicate one has slightly ,more dramatic increases while replicate one appears to have a steadier rise. Because of leakiness, some of the flashing red light that was supposed to be confined to wells 6 and 7 have leaked over to nearby wells, causing some gene repression even when there shouldn’t necessarily have been any. However the results show that gene repression is extremely sensitive to red light and a gradient can be created from various levels of red light relatively easily. That being said, gene control isn’t completely “on” or “off” but also holds the benefit of having various levels of “on” and “off”. <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
We want to work more with this biological system, improving the light sensor available so that we can biobrick them later and have those pieces readily available for use. We suspect that a big problem with the light sensor right now is the leaky promoter, which leads to more transcription than we would normally expect in dark conditions. The promoter should be modified accordingly, either by fixing any present mutation or choosing to use a new promoter entirely for various reasons. Also to improve characterization of the sensor itself, in the future soft agar should be used which will allow more sufficient spread of bacteria, which should minimize differences in bacteria colonies in each well, allowing better data collection with imageJ. There also appears to be some leakiness between wells that should be addressed more appropriately in the future. A suggestion was made to use a rubber stopper with holes in it on top of the app to try to minimize leakiness of light between different wells.We also want to work with different light sensors to improve functioning and to use light sensors to promote various gene expression of different proteins.<br />
<br />
<br />
The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
<br />
<br />
The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
<br />
<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''Blue Light Sensor, TetR Inverter and sfGFP Output''']<br />
<br />
The blue light sensing LovTAP system, engineered produce sfGFP as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892014 BBa_K892014 '''ccaS-ccaR-lacZ ''']<br />
<br />
The green light sensor, engineered to produce lacZ as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892801 BBa_K892801 '''Repaired LovTAP''']<br />
<br />
The LovTAP system, without the point mutation.<br />
<br />
<br />
----<br />
<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
<br><br />
[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
<br><br />
[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T03:21:28Z<p>Felixekn: /* Characterization of a light inducible protein expression system for the tuning of biological pathways */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
<br />
[http://www.youtube.com/watch?v=8h4RbDjTDYg <b>See our app in action!</b>]<br />
<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size. Follow [http://www.youtube.com/watch?v=8h4RbDjTDYg this link] for a video demonstration. <br />
<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:Picture1 wtf alex.png|thumb|right|400px|Explanation of a general light system]] <br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
Chromatic tuning is based off light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter P<sub>cpcG2</sub>, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
<br />
[[File:Picture 2.png|thumb|left|400px|Explanation of a general light system]]<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
<br />
===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
<br />
[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
<br />
<br />
Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
<br />
===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
<br />
[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
<br />
<br />
In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
<br />
When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
<br />
In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.[[File:20120927 big dots cropped experimentwells resized.png|border|350px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]<br />
<br />
As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends.<br />
<br />
<br />
[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|350px|left|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating a smaller amount of cut S-Gal.]]To create some kind of gradient for the system, we decided to use a 96 well plate and the app. There are rows with constant green light excitation, two rows in the middle that flash and end rows that have constant green light on. The plate is darkest on either end with a gradient that lightens up as it gets closer to the middle, where red and green light flash, inducing intermediary amounts of gene expression. Ideally there should only be lighter colors of wells in columns 6 and 7. However, there appears to be some leakiness with the utilization of the light app as some of this light has managed to leak to other wells, which creates a sort of gradient with darkest wells on either side and becoming lighter towards the middle. Pipetting error also affects the data as some of the wells have more colonies than others which leads to higher pixel values even if gene expression may not necessarily be higher. Using soft agar can help resolve this problem in the future. The quantified data above was made by using ImageJ and measuring average pixel value of each plate. Generally there is a gradient of increased pixel value as the well is further away from the red light. Both replicates show similar results, even though replicate one has slightly ,more dramatic increases while replicate one appears to have a steadier rise. Because of leakiness, some of the flashing red light that was supposed to be confined to wells 6 and 7 have leaked over to nearby wells, causing some gene repression even when there shouldn’t necessarily have been any. However the results show that gene repression is extremely sensitive to red light and a gradient can be created from various levels of red light relatively easily. That being said, gene control isn’t completely “on” or “off” but also holds the benefit of having various levels of “on” and “off”. <br />
<br />
<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
We want to work more with this biological system, improving the light sensor available so that we can biobrick them later and have those pieces readily available for use. We suspect that a big problem with the light sensor right now is the leaky promoter, which leads to more transcription than we would normally expect in dark conditions. The promoter should be modified accordingly, either by fixing any present mutation or choosing to use a new promoter entirely for various reasons. Also to improve characterization of the sensor itself, in the future soft agar should be used which will allow more sufficient spread of bacteria, which should minimize differences in bacteria colonies in each well, allowing better data collection with imageJ. There also appears to be some leakiness between wells that should be addressed more appropriately in the future. A suggestion was made to use a rubber stopper with holes in it on top of the app to try to minimize leakiness of light between different wells.We also want to work with different light sensors to improve functioning and to use light sensors to promote various gene expression of different proteins.<br />
<br />
The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
<br />
The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
<br />
<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892800 BBa_K892800 '''Blue Light Sensor, TetR Inverter and sfGFP Output''']<br />
<br />
The blue light sensing LovTAP system, engineered produce sfGFP as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892014 BBa_K892014 '''ccaS-ccaR-lacZ ''']<br />
<br />
The green light sensor, engineered to produce lacZ as a reporter.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892801 BBa_K892801 '''Repaired LovTAP''']<br />
<br />
The LovTAP system, without the point mutation.<br />
<br />
----<br />
<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
<br><br />
[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
<br><br />
[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/File:Washington_Appto.jpgFile:Washington Appto.jpg2012-10-04T03:18:22Z<p>Felixekn: uploaded a new version of &quot;File:Washington Appto.jpg&quot;</p>
<hr />
<div></div>Felixeknhttp://2012.igem.org/File:Washington_Appto.jpgFile:Washington Appto.jpg2012-10-04T03:07:09Z<p>Felixekn: </p>
<hr />
<div></div>Felixeknhttp://2012.igem.org/File:Washington_Turbidostat_OD.pngFile:Washington Turbidostat OD.png2012-10-04T03:02:23Z<p>Felixekn: uploaded a new version of &quot;File:Washington Turbidostat OD.png&quot;</p>
<hr />
<div></div>Felixeknhttp://2012.igem.org/File:Washington_Turbidostat_Pump.pngFile:Washington Turbidostat Pump.png2012-10-04T03:01:15Z<p>Felixekn: uploaded a new version of &quot;File:Washington Turbidostat Pump.png&quot;</p>
<hr />
<div></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-04T02:04:08Z<p>Felixekn: /* Turbidostat: Ethylene Glycol Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
<html><br />
<ol><br />
<li><b><font size="4">Turbidostat Preparation</font></b></li><br />
<ol><br />
<li> Created M9 30mM ethylene glycol media</li><br />
<ul><li> Sterile 200mL of 5X M9 salts </li><br />
<li> 2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li> 800mL of sterile double distilled water</li></ul><br />
<li> Autoclave the closed system of tubing, syringes, culture vessel, and empty bottle </li><br />
<ul><li> Wrap all open ends of the system with aluminium foil before autoclaving to prevent autoclave water from entering the closed tubing system </li></ul><br />
<li> Place closed system into the turbidostat apparatus</li><br />
<li> Carefully and sterilely decant previously made sterile M9 30mM ethylene glycol media into the media bottle </li> <br />
<li> Place the turbidostat into a 37º C incubator</li><br />
<li> Connect the turbidostat to a computer with the turbidostat software and related libraries installed</li><br />
<li> Start up turbidostat software program </li><br />
<ul><li>Be sure to close incubator door to reduce amount of ambient light</li></ul><br />
<li> Wait till program reads out optical density values - should see "0.000" as first optical density value</li><br />
</ol><br />
<li><b><font size="4">Cell Preparation</font></b></li><br />
<ol><br />
<li> Culture an overnight of MG1655 transformed with fucO pGA3K3 and aldA pGA1C3 in 2mL of TB with kanamycin and chloramphenicol</li><br />
<li> Take 1mL from the overnight culture and pipette it into a 1.5mL microcentrifuge tube </li><br />
<li> Pellet the 1mL aliquot at 4000g for 3 minutes</li><br />
<li> Pour out the supernatant </li><br />
<li> Resuspend the pelleted cells with 1mL of sterile PBS</li><br />
<li> Repeat steps 3-5 two more times </li><br />
</ol><br />
<li><b><font size="4">Turbidostat Inoculation and Operation</font></b></li><br />
<ol><br />
<li> Take a hypodermic needle attached to a 2.5mL syringe and aspirate 0.5mL of washed cells </li><br />
<li> Open incubator and through the soft stopper at the top of the culture vessel, inject the cells into the culture vessel to an OD ~0.2 </li><br />
<li> Close incubator door</li><br />
<li> Wait 12-24 hours </li><br />
<li> Stop turbidostat program </li><br />
<li> Retrieve data files from program folder and analyze them using your favorite mathematical software (ie: <a href=http://www.mathworks.com/products/matlab/><font size="2">Matlab</font></a> or <a href=http://www.wolfram.com/mathematica/><font size="2">Mathematica</font></a>)</li><br />
</ol><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T01:51:05Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
<br />
Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: E. colight<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
<br />
E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
<br />
The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size.<br />
<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
<br />
<br />
<br />
[[File:Washington_Green_Light_Sensor.png|thumb|right|250px|When exposed to λ=535nm light, the CcaR_CcaS light sensor phosphorylates and activates the P<sub>cpcG2</sub> promoter.]] Chromatic tuning is based off light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter pcpcg2, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
<br />
A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
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===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
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[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
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Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
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===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
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[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
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In order to test the functionality of the green light activator and red light repressor, we plated the same cells onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
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When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
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In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
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<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.<br />
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As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends. <br />
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To create some kind of gradient for the system, we decided to use a 96 well plate and the app. There are rows with constant green light excitation, two rows in the middle that flash and end rows that have constant green light on. The plate is darkest on either end with a gradient that lightens up as it gets closer to the middle, where red and green light flash, inducing intermediary amounts of gene expression. Ideally there should only be lighter colors of wells in columns 6 and 7. However, there appears to be some leakiness with the utilization of the light app as some of this light has managed to leak to other wells, which creates a sort of gradient with darkest wells on either side and becoming lighter towards the middle. Pipetting error also affects the data as some of the wells have more colonies than others which leads to higher pixel values even if gene expression may not necessarily be higher. Using soft agar can help resolve this problem in the future. The quantified data above was made by using ImageJ and measuring average pixel value of each plate. Generally there is a gradient of increased pixel value as the well is further away from the red light. Both replicates show similar results, even though replicate one has slightly ,more dramatic increases while replicate one appears to have a steadier rise. Because of leakiness, some of the flashing red light that was supposed to be confined to wells 6 and 7 have leaked over to nearby wells, causing some gene repression even when there shouldn’t necessarily have been any. However the results show that gene repression is extremely sensitive to red light and a gradient can be created from various levels of red light relatively easily. That being said, gene control isn’t completely “on” or “off” but also holds the benefit of having various levels of “on” and “off”. <br />
[[File:20120927 big dots cropped experimentwells resized.png|border|400px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]<br />
[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|500px|right|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating a smaller amount of cut S-Gal.]]<br />
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<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
We want to work more with this biological system, improving the light sensor available so that we can biobrick them later and have those pieces readily available for use. We suspect that a big problem with the light sensor right now is the leaky promoter, which leads to more transcription than we would normally expect in dark conditions. The promoter should be modified accordingly, either by fixing any present mutation or choosing to use a new promoter entirely for various reasons. Also to improve characterization of the sensor itself, in the future soft agar should be used which will allow more sufficient spread of bacteria, which should minimize differences in bacteria colonies in each well, allowing better data collection with imageJ. There also appears to be some leakiness between wells that should be addressed more appropriately in the future. A suggestion was made to use a rubber stopper with holes in it on top of the app to try to minimize leakiness of light between different wells.We also want to work with different light sensors to improve functioning and to use light sensors to promote various gene expression of different proteins.<br />
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The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
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The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
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<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
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[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
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[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:48:37Z<p>Felixekn: </p>
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<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
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<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
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[[File:Flu_virus.jpg|border|380px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]]<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
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We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly''' – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
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<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
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To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
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<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
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== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
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If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
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===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
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[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
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[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
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The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
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The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
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==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
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We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
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<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
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[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
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[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
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[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
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[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
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HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
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HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
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HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
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HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
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HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
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HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
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HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
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<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
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After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
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<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:48:22Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
<br />
<br />
[[File:Flu_virus.jpg|border|360px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]]<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly''' – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:47:48Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
<br />
<br />
[[File:Flu_virus.jpg|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]]<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly''' – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:47:33Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]]<br />
<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly''' – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:46:55Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]]<br />
<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly''' – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:45:33Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly''' – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:45:12Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly"" – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:20:37Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:20:16Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast for Yeast Surface Display testing.<br />
<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result in a variant of the binder with the desired amino acid shift. To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:14:35Z<p>Felixekn: /* Proposing Mutations based on FoldIt Models */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
[[Image:Washington H312A.jpg|border|600px|left|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
<br />
We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
<br />
To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
<br />
Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:13:44Z<p>Felixekn: /* Test: Yeast Surface Display */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington H312A.jpg|border|800px|center|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
<br />
We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
<br />
To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
<br />
Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997)<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:12:31Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:Washington H312A.jpg|border|800px|center|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. <br />
On the right: the histidine residue was mutated to an alanine, thus reducing clashing and predicting better binding.]]<br />
<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
<br />
We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
<br />
To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
<br />
Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997) [3]. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/PlasticsTeam:Washington/Plastics2012-10-04T01:10:34Z<p>Felixekn: /* PUR Esterase Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“Plastics: made to last forever, designed to throw away”--5gyres.org</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:Washington_Sadfish.png|border|440px|right|thumb|The increasing problem of plastic waste.]]<br />
Playing an integral role in our modern world, plastics account for over a third of products made today. They have a relatively low cost of production, serve to promote the development of industry, lower the cost of consumer goods, and are easy to produce on a large scale. Plastics, regardless of disposability, compose 10% of the waste generated in the world and an even higher percentage is carelessly thrown into our environment<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. Plastic pollution discharges debris into the ocean that are ingested by wildlife, often resulting in injury or death<html><a href="#Sources"><font size="1"><sup>[2]</sup></font></a></html>.<br />
<br />
<br />
===The PUR Problem ===<br />
A commonly used plastic, Polyurethane (PUR), is used to create a wide range of products including dense solids, rigid insulating foam, and thermoset elastics. Because of its extreme versatility, the world consumption of PUR is continually increasing. According to current market analysis, the global production of PUR is estimated at 27 billion pounds per year and is expected to increase to 36 billion pounds by the year 2016<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. The leading method of recycling polyurethane is to mechanically grind the waste and rebind it into carpet cushioning, accounting for about 4% of waste polyurethane<html><a href="#Sources"><font size="1"><sup>[4]</sup></font></a></html>. However, due to the high costs of transportation and chemical degradation, most PURs are incinerated to recapture some of the energy used to make them<html><a href="#Sources"><font size="1"><sup>[5]</sup></font></a></html>.<br />
<br />
<br />
[[Image:Washington_PURBURN.png|border|280px|left|thumb|Current methods of disposal, like burning is harmful for our environment.]]<br />
===Current Means of Disposal are Undesirable===<br />
Non-biodegradable plastics are often disposed of through waste incineration as it is the most efficient method of degradation. However, a major issue with this is that the products of plastic burning, which include polychlorinated di-benzo-p-dioxins/furans and carbon dioxide, are known to be carcinogenic<html><a href="#Sources"><font size="1"><sup>[6]</sup></font></a></html>. Upon incineration, these gaseous products are released into the atmosphere and have the potential to cause future problems to human health as well as contribute to global warming. As an additional concern, plastics constitute large volumes of space as they are habitually disposed of in landfills. It has been shown that plasticizers and plastic additives leak from the these plastics and contaminate aquatic environments<html><a href="#Sources"><font size="1"><sup>[7]</sup></font></a></html>. Thus, because of the environmental damage that current disposal methods incur, there is a large need for safer methods of plastic degradation.<br />
<br />
===Engineering Microbes to Degrade PUR===<br />
To solve the problem of PUR recycling, we propose a bacteria that is both able to degrade PUR and subsist off of the products of degradation as its sole carbon source. To this end, we propose a two plasmid system. The first plasmid would have two genes. The first gene, polyeurethane esterase, will encode an enzyme that is able to break down the PUR polymer structure into two molecules, one of which, ethylene glycol, can diffuse across the membrane of the bacterium<html><a href="#Sources"><font size="1"><sup>[8]</sup></font></a></html>. The second gene, osmotic inducible protein Y (osmY), would encode a protein that fuses to PUR esterase and exports the enzyme through the cell membrane and into the supernatant<html><a href="#Sources"><font size="1"><sup>[9]</sup></font></a></html>. The second plasmid would have an operon composed of, glycolaldehyde reductase (fucO) and glycoaldehyde dehydrogenase (aldA), that allows the bacterium to use ethylene glycol as its central metabolite<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. This system would allow the bacteria to turn the plastics plaguing our landfills into bacterial biomass which would in turn degrade more PUR. <br />
[[Image:Washington_Plastic_Overview.png|border|950px|center|thumb|The complete degradation pathway takes polyurethane, an undesirable waste product and converts it into a central metabolite for bacterium.]]<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: The Turbidostat<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Washington_Turbidostat.png|border|400px|left|thumb|Schematic diagram of our turbidostat. It uses the optical density measurements to modulate how much media is pumped into the culture vessel every minute to maintain a constant cell density. Through this method of cell density control, we can measure how fast the cells are growing and thus gain an understanding for relative fitness increases thorough out the course of an experiment.]]<br />
[[Image:Recycleapp.png|150px|right]]<br />
To characterize and optimize growth of specific parts of our overall polyurethane degradation pathway we developed a <i><b>homemade software application (App)</b></i>. However, devices that can accomplish this are not commercially available. Additionally, details for such tools are often sparsely detailed and frequently require exotic reagents and specialized training. To circumvent these issues, we instead decided to use our expertise in electrical, biological, and computer engineering to create an application that could control cheaply and readily available hardware to achieve the desired directed evolution and characterization functionality. <br />
<br />
<br />
Our App uniquely controls an assortment of hardware devices easily found online, creating a fully functional turbidostat. A turbidostat is a closed-loop continuous culture device that maintains a desired constant cell density and chemical environment. The constant environment allows for continuous log phase growth and constant selective pressures (in our case polyurethane or ethylene glycol), which enables proper characterization (seen through media input per dilution cycle) and reduces evolutionary trajectory drift. By maintaining constant selective pressures, the rate of evolution is also dramatically improved<html><a href="#Sources"><font size="1"><sup>[11]</sup></font></a></html>. By creating this turbidostat App (written in Python), given sufficient time, we are able to properly assess and optimize our individual parts as well as the entire polyurethane degrading circuit in a controlled and reliable manner.<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[[Image:Washington_PUR.png|border|250px|left|thumb|To assemble the polyurethane esterase, we used 4 IDT gBlocks, each containing a unique set of homology sequences that only allow for one orientation of binding, and Gibsoned them together onto a pGA backbone.]]<br />
<br />
===PUR Esterase Assay=== <br />
Polyurethane esterase degrades polyurethane by cleaving the ester bonds to produce one of a variety of monomers with isocyanate groups on either end and an ethylene glycol. For our circuit, we have chosen to use the esterase estCS2 ([http://partsregistry.org/Part:BBa_K892012 BBa_K892012]). Originally isolated and characterized by Kang et al, the sequence of estCS2 is available at [http://www.ncbi.nlm.nih.gov/nuccore/283462288 GenBank (GU256649)]<html><a href="#Sources"><font size="1"><sup>[8]</sup></font></a></html>. Since their DNA constructs were unavailable and the authors did not respond to requests, we synthetically constructed the entire 1.7kb estCS2 gene from four 500-base gBlocks provided by IDT using Gibson cloning. <br />
<br />
<br />
[[Image:Washington_PUR_Assay.png|border|350px|right|thumb|To assemble for activity of polyurethane esterase, cells transformed with the PUR esterase plasmid were grown to <br />
saturation, sonicated, then a piece of pre-weighed polyurethane foam was inserted into the culture tube.]]<br />
To assay esterase activity we made one 50mL TB + kanamycin overnight culture for the cells containing the esterase plasmid and a 50mL TB overnight culture with untransformed cells. After incubating the cultures for a day, we spun them down to form a pellet. The supernatant was then poured out and then the remaining pellets were resuspended the in enough water for each culture to be equalized at an arbitrary OD of 1.4. Three milliliters of each resuspended culture was aliquoted out into three microcentrifuge tubes (1 mL of one culture in each tube, 6 total microcentrifuge tubes). These tubes, along with 6 microcentrifuge tubes that contained only 1mL of LB were then sonicated at an amplitude of 20 with 1 second pulses on and off for 30 seconds. After sonication we poured each lysate and LB aliquots into 25mL culture tubes containing 1mL of LB and a preweighed sample of foam. This gave us a total of 12, 25mL culture tubes containing a pre weighed foam piece with LB+lysate or LB+sonicated LB. These culture tubes were left on the bench top at room temperature overnight. In the morning we washed the foam with DI water and dried them in a 65º C incubator. After drying, we weighed the samples to see if there was any change in mass of the foam samples.<br />
<br />
===Exporter Protein OsmY Assay===<br />
Since polyurethane is a large polymer, larger than the cell itself, it cannot be imported for degradation and therefore our esterase must be exported. It has been shown in Bolkinsky, Gregory et al that a protein, osmotically inducible protein Y (osmY, [http://partsregistry.org/Part:BBa_K892008 BBa_K892008]), is naturally exported by <i>E. coli</i> [9]. Furthermore this paper shows that osmY can be used as an export tag in <i>E. coli</i> when it is translationally fused to a protein. <br />
<br />
[[Image:Washington_osmY.png|border|348px|left|thumb|Our exporter protein (osmY) is linked to sfGFP through a serine glycine linker.]]<br />
[[Image:Washington_osmY_Assay.png|border|546px|right|thumb|We used osmY fused to sfGFP and as a control sfGFP fused to mamI in the same translational conformation to test for osmY functionality. These cultures were spun down and the fluorescence (480nm excitation, 509nm emission) of the cells+supernatant and supernatant were measured.]]<br />
<br />
<br />
To assay the exportation system we created a fusion protein. The fusion protein consisted of super folder GFP (sfGFP) fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]), which allowed for visual assessment of whether or not osmY was correctly exported into the supernatant. For the control, sfGFP fused to mamI ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K590016 BBa_K590016]), a protein that binds to the cell membrane, was used. The sfGFP-osmY and sfGFP-mamI plasmids were transformed into BL21 and then were grown up overnight (after picking a colony of each from their transformation plates) in M9 1% glucose media. To quantify whether or not osmY properly exported proteins outside of the cell membrane, 4, 500µL aliquots of each overnight culture was made. From each of these aliquots, 200µL of the culture was pipetted into a 96 well plate and then the remaining culture was spun down in a centrifuge at 3000g for 3 minutes. Carefully, from each of the spun down aliquots, 200µL of the supernatant was pipetted into the 96 well plate. The remaining supernatant for each aliquot was disposed of and the cells were resuspended with 300 µL of fresh M9 1% glucose media. From the each of the resuspended aliquots, 200µL was pipetted into the 96 well plate. The 96 well plate was then read with a plate reader; each well was excited with a 480+/-20 nm light and then a detector quantified how much 509 +/-20 nm light was give off. <br />
<br />
Using the information from the plate reader we could determine if osmY properly exports the fused protein. If the supernatant from the cells expressing the osmY construct had high fluorescence with respect to cells+supernatant while the supernatant of the control sfGFP-mamI had low fluorescence compared to its overall cells+supernatant fluorescence than it would be concluded that the protein was properly exported when fused to osmY. But if there was little to no supernatant fluorescence of the sfGFP-osmY construct or sfGFP-mamI and sfGFP-osmY illustrated the same ratio of supernatant fluorescence to cells+supernatant fluorescence then it would be concluded that osmY did not export proteins outside of the cell or our designed fusion between sfGFP and osmY disrupted sfGFP/osmY function.<br />
<br />
<br />
[[Image:Washington_fucO_aldA.png|border|350px|left|thumb|By putting fucO and aldA onto a single plasmids with a single promote, we were able to create an operon that when transcribed and translated allows microbes to utilize ethylene glycol as a sole carbon source [10].]]<br />
<br />
===The Turbidostat Assay===<br />
The media to used for the turbidostat assay was comprised exclusively of M9 salts and ethylene glycol. Because of this choice of media, the only carbon source available to <i>E. coli</i> was ethylene glycol and because this can't be digest by normal lab strains of <i>E. coli</i>, only <i>E. coli</i> that can digest ethylene glycol will survive<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. The turbidostat App tracks cell growth by first blanking (setting optical density (OD) to 0) and then measuring and recording OD values after inoculation. Because OD and cell density are both linearly related, OD is a proxy for cell density measurements. After blanking on the M9 30mM ethylene glycol media, MG1655 cells that were transformed with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]), which were grown in TB to log phase and then washed with PBS, were added to the turbidostat's culture vessel to an OD of 0.2. This allowed cells still in log phase to divide without exceeding the OD threshold of 0.3 (our desired constant cell density). After inoculation, the turbidostat was left running overnight and cell density and amount of media added per dilution cycle was captured in the morning. With this data, understanding for how well the transformed <i>E. coli</i> subsists using ethylene glycol as its sole carbon source was gained.<br />
<br />
<br />
The next assay ran was to transform MG1655 with a fucO-aldA operon plasmid ([http://partsregistry.org/Part:BBa_K892013 BBa_K892013]). This operon consisted of fucO and aldA being on a single plasmid that contains a single promoter. With this present, transformed MG1655 cells would be theoretically more viable in ethylene glycol since the cells would not have to spend energy on two antibiotic resistance genes (although no antibiotics were used in the the M9 ethylene glycol media, the cells still transcribe and translate the genes). By running the turbidostat in the same way as the dual transformation experiment, an idea for how well the transformed operon fairs versus how well the two plasmid transformation could be gained.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===PUR Esterase Assay Results===<br />
We ran the PUR esterase assay and we found that the foam we used did not degrade. We quantified this by measuring the weight of the foam pieces to the nearest milligram before and after we ran the assay. We actually ended up leaving the lysate with the foam for three days to improve degradation but this had no effect on the end result. The reason we think the foam was not degraded was because we did not know the full composition of the foam, it could of had other polymers mixed in with the polyurethane that prevented our enzyme from properly degrading it. Further testing will need to be done to properly assess the functionality of our PUR esterase.<br />
<br />
===Exporter Protein OsmY Assay Results===<br />
[[Image:Washington_osmY_Assay_Results.png|border|940px|center|thumb|Each histogram depicts the average normalized fluorescence values for cells plus supernatant, cells only, and supernatant only. The image in the center is a visual representation of the presented data.]]<br />
When running the osmY assay described in the methods section, we saw that sfGFP fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]) resulted in a much higher fluorescence ratio of cells+supernatant to supernatant than the fluorescence ratio of cells+supernatant to supernatant of sfGFP fused to mamI. Even though the cells only fluorescence of sfGFP-osmY plasmid exhibited nearly the same level of fluorescence as supernatant only, it does not go against the argument that osmY is properly exporting sfGFP into the supernatant. sfGFP-osmY is produced within the cell and it cannot be expected that all osmY will be secreted from the cell at a given moment. Thus from the above data, it can be gathered that <b>osmY, when fused to a protein, will export the protein out into the supernatant.</b><br />
<br />
===Turbidostat Assay Results===<br />
[[Image:Washington_Turbidostat_Pump.png|border|450px|left|thumb|M9 30 mM ethylene glycol media injected into the culture vessel every minute to maintain the arbitrary optical density of 0.3.]]<br />
<br />
[[Image:Washington_Turbidostat_OD.png|border|450px|right|thumb|Optical density measurements of <i>E. coli</i> transformed with fucO pGA3K3 + aldA pGA1C3 within the turbidostat growing on M9 30 mM ethylene glycol media.]]<br />
<br />
<br />
Following the turbidostat assay protocol, we used transformed MG1655 with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) on a medium copy Kanamycin resistant backbone and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]) on a high copy Chloramphenicol resistant backbone and cultured it in the turbidostat. The turbidostat was operated overnight and the above data illustrates that the transformed MG1655 grew using ethylene glycol as its sole carbon source, which normal untransformed MG1655 cannot do. This is the case because the optical density (OD)/cell density rose from an initial arbitrary OD of 0.18 (time of inoculation) to an OD of 0.3, which was maintained throughout the time of the experiment (15 hours). The turbidostat maintained this OD through constantly imputing amounts of new media into the culture vessel based whenever the OD surpassed 0.3, which was quite frequently. Although time constraints did not allow us to transform MG1655 with our fucO-aldA operon and characterize it, it can be seen from the data above that <b>when fucO and aldA genes are individually transformed and over expressed in MG1655 <i>E. coli</i>, <i>E. coli</i> gains the ability to utilize ethylene glycol as its sole carbon source. The average doubling time (based off of a 10mL culture volume and an average dilution rate of 30µL/min) was 333 minutes, slightly faster than the literature value of 360 minutes [10].</b> <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===Putting it All together===<br />
The parts as they are right now have not been put into one cell strain and tested as a system. The obvious next step would be to test the efficacy of the entire plastic degrading construct against polyurethane. Using the turbidostat, we have the luxury of having an optimized and continual growth environment for our system to mutate towards improved functionality. Better cell strains would be periodically saved and tested in harsher selective conditions until eventually the strain is able to survive and reproduce off of polyurethane as its sole carbon source. We recognize that this may take many iterations to complete but the end product could potentially be a cell that is able to consume plastic much faster and safer than traditional recycling methods.<br />
<br />
<br />
===Trash to Treasure===<br />
After stable growth off of plastic is achieved the next step would be to clone in a third plasmid that would produce some valuable commodity. Washington 2011 demonstrated that diesel fuel can be synthesized from central metabolites using their Petrobrick platform. The addition of the Petrobrick to the system would allow plastic waste to be processed by <i>E. coli</i> which would then turn it into biofuels. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892008 BBa_K892008 '''osmY''']<br />
<br />
The coding sequence for osmotically induced protein Y, a protein that when fused to another protein, gets exported out of <i>E. coli</i> cells.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009 '''fucO''']<br />
<br />
The gene fucO, which codes for glycolaldehyde reductase, is one of the genes required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010 '''aldA''']<br />
<br />
The gene aldA, which codes for glycolaldehyde dehydrogenase, is the other gene required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892011 BBa_K892011 '''sfGFP-osmY''']<br />
<br />
A composite part for osmotically induced protein Y fused downstream to sfGFP using a glycine - serine linker regulated by the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892012 BBa_K892012 '''PUR Esterase''']<br />
<br />
An enzyme that breaks down polyurethane plastic behind the control of the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892013 BBa_K892013 '''fucO-aldA''']<br />
<br />
The combination of BBa_K892009 and BBa_K892010 behind a strong biofab promoter.<br />
<br />
<br />
----<br />
<html><a name="Sources"></a></html><br />
<h1 id='Parts'>Sources <html><a href="#Sources"><font size="3">[Top]</font></a></html></h1><br />
<html><br />
<ol><br />
<li>Barnes, D. K. A., F. Galgani, R. C. Thompson, and M. Barlaz. "Accumulation and Fragmentation of Plastic Debris in Global Environments." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 1985-998. Print.</li><br />
<br />
<li>Gregory, M. R. "Environmental Implications of Plastic Debris in Marine Settings--entanglement, Ingestion, Smothering, Hangers-on, Hitch-hiking and Alien Invasions." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2013-025. Print.</li><br />
<br />
<li>"Global Polyurethane Market to Reach 9.6 Mln Tons by 2015." Plastemart.com. N.p., 30 Aug. 2011. Web. <http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674></li><br />
<br />
<li>"Polyurethane Recycling." Polyurethanes. American Chemistry Council, n.d. Web. <http://polyurethane.americanchemistry.com/Sustainability/Recycling>. </li><br />
<br />
<li>"Frequently Asked Questions on Polyurethanes." Polyurethanes.org. European Diisocyanate and Polyol Producers Association, n.d. Web. <http://www.polyurethanes.org/index.php?page=faqs>. </li><br />
<br />
<li>Takasuga, T., N. Umetsu, T. Makino, K. Tsubota, KS Sajwan, and KS Kumar. "Role of Temperature and Hydrochloric Acid on the Formation of Chlorinated Hydrocarbons and Polycyclic Aromatic Hydrocarbons during Combustion of Paraffin Powder, Polymers, and Newspaper." Archives of Environmental Contamination and Toxicology (2007): 8-21. Print. </li><br />
<br />
<li>Teuten, E. L., J. M. Saquing, D. R. U. Knappe, M. A. Barlaz, S. Jonsson, A. Bjorn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha, and H. Takada. "Transport and Release of Chemicals from Plastics to the Environment and to Wildlife." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2027-045. Print. </li><br />
<br />
<li>Kang, Chul-Hyung. "A Novel Family VII Esterase with Industrial Potential from Compost Metagenomic Library." Microbial Cell Factories 10.41 (2011): n. pag. Print. </li><br />
<br />
<li>Bokinsky, Gregory, Et. Al. "Synthesis of Three Advanced Biofuels from Ionic Liquid-penetreated Switchgrass Using Engineered Escherichia Coli." Proceedings of the National Academy of Sciences of the United States of America 108.50 (2011): 19949-9954. Print. </li><br />
<br />
<li> Boronat, Albert, Estrella Caballero, and Juan Aguilar. "Experimental Evolution of a Metabolic Pathway for Ethylene Glycol Utilization by Escherichia Coli." Journal of Bacteriology Jan. (1983): 134-39. Web. </li><br />
<br />
<li> E. Toprak, A. Veres, J. B. Michel, R. Chait, D. L. Hartl, R. Kishony, “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection,” Nature Genetics, vol. 44, no. 1, pp. 101-105, Jan. 2012. </li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/PlasticsTeam:Washington/Plastics2012-10-04T01:09:12Z<p>Felixekn: /* “Plastics: made to last forever, designed to throw away”--5gyres.org */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“Plastics: made to last forever, designed to throw away”--5gyres.org</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:Washington_Sadfish.png|border|440px|right|thumb|The increasing problem of plastic waste.]]<br />
Playing an integral role in our modern world, plastics account for over a third of products made today. They have a relatively low cost of production, serve to promote the development of industry, lower the cost of consumer goods, and are easy to produce on a large scale. Plastics, regardless of disposability, compose 10% of the waste generated in the world and an even higher percentage is carelessly thrown into our environment<html><a href="#Sources"><font size="1"><sup>[1]</sup></font></a></html>. Plastic pollution discharges debris into the ocean that are ingested by wildlife, often resulting in injury or death<html><a href="#Sources"><font size="1"><sup>[2]</sup></font></a></html>.<br />
<br />
<br />
===The PUR Problem ===<br />
A commonly used plastic, Polyurethane (PUR), is used to create a wide range of products including dense solids, rigid insulating foam, and thermoset elastics. Because of its extreme versatility, the world consumption of PUR is continually increasing. According to current market analysis, the global production of PUR is estimated at 27 billion pounds per year and is expected to increase to 36 billion pounds by the year 2016<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. The leading method of recycling polyurethane is to mechanically grind the waste and rebind it into carpet cushioning, accounting for about 4% of waste polyurethane<html><a href="#Sources"><font size="1"><sup>[4]</sup></font></a></html>. However, due to the high costs of transportation and chemical degradation, most PURs are incinerated to recapture some of the energy used to make them<html><a href="#Sources"><font size="1"><sup>[5]</sup></font></a></html>.<br />
<br />
<br />
[[Image:Washington_PURBURN.png|border|280px|left|thumb|Current methods of disposal, like burning is harmful for our environment.]]<br />
===Current Means of Disposal are Undesirable===<br />
Non-biodegradable plastics are often disposed of through waste incineration as it is the most efficient method of degradation. However, a major issue with this is that the products of plastic burning, which include polychlorinated di-benzo-p-dioxins/furans and carbon dioxide, are known to be carcinogenic<html><a href="#Sources"><font size="1"><sup>[6]</sup></font></a></html>. Upon incineration, these gaseous products are released into the atmosphere and have the potential to cause future problems to human health as well as contribute to global warming. As an additional concern, plastics constitute large volumes of space as they are habitually disposed of in landfills. It has been shown that plasticizers and plastic additives leak from the these plastics and contaminate aquatic environments<html><a href="#Sources"><font size="1"><sup>[7]</sup></font></a></html>. Thus, because of the environmental damage that current disposal methods incur, there is a large need for safer methods of plastic degradation.<br />
<br />
===Engineering Microbes to Degrade PUR===<br />
To solve the problem of PUR recycling, we propose a bacteria that is both able to degrade PUR and subsist off of the products of degradation as its sole carbon source. To this end, we propose a two plasmid system. The first plasmid would have two genes. The first gene, polyeurethane esterase, will encode an enzyme that is able to break down the PUR polymer structure into two molecules, one of which, ethylene glycol, can diffuse across the membrane of the bacterium<html><a href="#Sources"><font size="1"><sup>[8]</sup></font></a></html>. The second gene, osmotic inducible protein Y (osmY), would encode a protein that fuses to PUR esterase and exports the enzyme through the cell membrane and into the supernatant<html><a href="#Sources"><font size="1"><sup>[9]</sup></font></a></html>. The second plasmid would have an operon composed of, glycolaldehyde reductase (fucO) and glycoaldehyde dehydrogenase (aldA), that allows the bacterium to use ethylene glycol as its central metabolite<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. This system would allow the bacteria to turn the plastics plaguing our landfills into bacterial biomass which would in turn degrade more PUR. <br />
[[Image:Washington_Plastic_Overview.png|border|950px|center|thumb|The complete degradation pathway takes polyurethane, an undesirable waste product and converts it into a central metabolite for bacterium.]]<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: The Turbidostat<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Washington_Turbidostat.png|border|400px|left|thumb|Schematic diagram of our turbidostat. It uses the optical density measurements to modulate how much media is pumped into the culture vessel every minute to maintain a constant cell density. Through this method of cell density control, we can measure how fast the cells are growing and thus gain an understanding for relative fitness increases thorough out the course of an experiment.]]<br />
[[Image:Recycleapp.png|150px|right]]<br />
To characterize and optimize growth of specific parts of our overall polyurethane degradation pathway we developed a <i><b>homemade software application (App)</b></i>. However, devices that can accomplish this are not commercially available. Additionally, details for such tools are often sparsely detailed and frequently require exotic reagents and specialized training. To circumvent these issues, we instead decided to use our expertise in electrical, biological, and computer engineering to create an application that could control cheaply and readily available hardware to achieve the desired directed evolution and characterization functionality. <br />
<br />
<br />
Our App uniquely controls an assortment of hardware devices easily found online, creating a fully functional turbidostat. A turbidostat is a closed-loop continuous culture device that maintains a desired constant cell density and chemical environment. The constant environment allows for continuous log phase growth and constant selective pressures (in our case polyurethane or ethylene glycol), which enables proper characterization (seen through media input per dilution cycle) and reduces evolutionary trajectory drift. By maintaining constant selective pressures, the rate of evolution is also dramatically improved<html><a href="#Sources"><font size="1"><sup>[11]</sup></font></a></html>. By creating this turbidostat App (written in Python), given sufficient time, we are able to properly assess and optimize our individual parts as well as the entire polyurethane degrading circuit in a controlled and reliable manner.<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[[Image:Washington_PUR.png|border|250px|left|thumb|To assemble the polyurethane esterase, we used 4 IDT gBlocks, each containing a unique set of homology sequences that only allow for one orientation of binding, and Gibsoned them together onto a pGA backbone.]]<br />
<br />
===PUR Esterase Assay=== <br />
Polyurethane esterase degrades polyurethane by cleaving the ester bonds to produce one of a variety of monomers with isocyanate groups on either end and an ethylene glycol. For our circuit, we have chosen to use the esterase estCS2 ([http://partsregistry.org/Part:BBa_K892012 BBa_K892012]). Originally isolated and characterized by Kang et al [8], the sequence of estCS2 is available at [http://www.ncbi.nlm.nih.gov/nuccore/283462288 GenBank (GU256649)]. Since their DNA constructs were unavailable and the authors did not respond to requests, we synthetically constructed the entire 1.7kb estCS2 gene from four 500-base gBlocks provided by IDT using Gibson cloning. <br />
<br />
<br />
[[Image:Washington_PUR_Assay.png|border|350px|right|thumb|To assemble for activity of polyurethane esterase, cells transformed with the PUR esterase plasmid were grown to <br />
saturation, sonicated, then a piece of pre-weighed polyurethane foam was inserted into the culture tube.]]<br />
To assay esterase activity we made one 50mL TB + kanamycin overnight culture for the cells containing the esterase plasmid and a 50mL TB overnight culture with untransformed cells. After incubating the cultures for a day, we spun them down to form a pellet. The supernatant was then poured out and then the remaining pellets were resuspended the in enough water for each culture to be equalized at an arbitrary OD of 1.4. Three milliliters of each resuspended culture was aliquoted out into three microcentrifuge tubes (1 mL of one culture in each tube, 6 total microcentrifuge tubes). These tubes, along with 6 microcentrifuge tubes that contained only 1mL of LB were then sonicated at an amplitude of 20 with 1 second pulses on and off for 30 seconds. After sonication we poured each lysate and LB aliquots into 25mL culture tubes containing 1mL of LB and a preweighed sample of foam. This gave us a total of 12, 25mL culture tubes containing a pre weighed foam piece with LB+lysate or LB+sonicated LB. These culture tubes were left on the bench top at room temperature overnight. In the morning we washed the foam with DI water and dried them in a 65º C incubator. After drying, we weighed the samples to see if there was any change in mass of the foam samples.<br />
<br />
===Exporter Protein OsmY Assay===<br />
Since polyurethane is a large polymer, larger than the cell itself, it cannot be imported for degradation and therefore our esterase must be exported. It has been shown in Bolkinsky, Gregory et al that a protein, osmotically inducible protein Y (osmY, [http://partsregistry.org/Part:BBa_K892008 BBa_K892008]), is naturally exported by <i>E. coli</i> [9]. Furthermore this paper shows that osmY can be used as an export tag in <i>E. coli</i> when it is translationally fused to a protein. <br />
<br />
[[Image:Washington_osmY.png|border|348px|left|thumb|Our exporter protein (osmY) is linked to sfGFP through a serine glycine linker.]]<br />
[[Image:Washington_osmY_Assay.png|border|546px|right|thumb|We used osmY fused to sfGFP and as a control sfGFP fused to mamI in the same translational conformation to test for osmY functionality. These cultures were spun down and the fluorescence (480nm excitation, 509nm emission) of the cells+supernatant and supernatant were measured.]]<br />
<br />
<br />
To assay the exportation system we created a fusion protein. The fusion protein consisted of super folder GFP (sfGFP) fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]), which allowed for visual assessment of whether or not osmY was correctly exported into the supernatant. For the control, sfGFP fused to mamI ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K590016 BBa_K590016]), a protein that binds to the cell membrane, was used. The sfGFP-osmY and sfGFP-mamI plasmids were transformed into BL21 and then were grown up overnight (after picking a colony of each from their transformation plates) in M9 1% glucose media. To quantify whether or not osmY properly exported proteins outside of the cell membrane, 4, 500µL aliquots of each overnight culture was made. From each of these aliquots, 200µL of the culture was pipetted into a 96 well plate and then the remaining culture was spun down in a centrifuge at 3000g for 3 minutes. Carefully, from each of the spun down aliquots, 200µL of the supernatant was pipetted into the 96 well plate. The remaining supernatant for each aliquot was disposed of and the cells were resuspended with 300 µL of fresh M9 1% glucose media. From the each of the resuspended aliquots, 200µL was pipetted into the 96 well plate. The 96 well plate was then read with a plate reader; each well was excited with a 480+/-20 nm light and then a detector quantified how much 509 +/-20 nm light was give off. <br />
<br />
Using the information from the plate reader we could determine if osmY properly exports the fused protein. If the supernatant from the cells expressing the osmY construct had high fluorescence with respect to cells+supernatant while the supernatant of the control sfGFP-mamI had low fluorescence compared to its overall cells+supernatant fluorescence than it would be concluded that the protein was properly exported when fused to osmY. But if there was little to no supernatant fluorescence of the sfGFP-osmY construct or sfGFP-mamI and sfGFP-osmY illustrated the same ratio of supernatant fluorescence to cells+supernatant fluorescence then it would be concluded that osmY did not export proteins outside of the cell or our designed fusion between sfGFP and osmY disrupted sfGFP/osmY function.<br />
<br />
<br />
[[Image:Washington_fucO_aldA.png|border|350px|left|thumb|By putting fucO and aldA onto a single plasmids with a single promote, we were able to create an operon that when transcribed and translated allows microbes to utilize ethylene glycol as a sole carbon source [10].]]<br />
<br />
===The Turbidostat Assay===<br />
The media to used for the turbidostat assay was comprised exclusively of M9 salts and ethylene glycol. Because of this choice of media, the only carbon source available to <i>E. coli</i> was ethylene glycol and because this can't be digest by normal lab strains of <i>E. coli</i>, only <i>E. coli</i> that can digest ethylene glycol will survive<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. The turbidostat App tracks cell growth by first blanking (setting optical density (OD) to 0) and then measuring and recording OD values after inoculation. Because OD and cell density are both linearly related, OD is a proxy for cell density measurements. After blanking on the M9 30mM ethylene glycol media, MG1655 cells that were transformed with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]), which were grown in TB to log phase and then washed with PBS, were added to the turbidostat's culture vessel to an OD of 0.2. This allowed cells still in log phase to divide without exceeding the OD threshold of 0.3 (our desired constant cell density). After inoculation, the turbidostat was left running overnight and cell density and amount of media added per dilution cycle was captured in the morning. With this data, understanding for how well the transformed <i>E. coli</i> subsists using ethylene glycol as its sole carbon source was gained.<br />
<br />
<br />
The next assay ran was to transform MG1655 with a fucO-aldA operon plasmid ([http://partsregistry.org/Part:BBa_K892013 BBa_K892013]). This operon consisted of fucO and aldA being on a single plasmid that contains a single promoter. With this present, transformed MG1655 cells would be theoretically more viable in ethylene glycol since the cells would not have to spend energy on two antibiotic resistance genes (although no antibiotics were used in the the M9 ethylene glycol media, the cells still transcribe and translate the genes). By running the turbidostat in the same way as the dual transformation experiment, an idea for how well the transformed operon fairs versus how well the two plasmid transformation could be gained.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===PUR Esterase Assay Results===<br />
We ran the PUR esterase assay and we found that the foam we used did not degrade. We quantified this by measuring the weight of the foam pieces to the nearest milligram before and after we ran the assay. We actually ended up leaving the lysate with the foam for three days to improve degradation but this had no effect on the end result. The reason we think the foam was not degraded was because we did not know the full composition of the foam, it could of had other polymers mixed in with the polyurethane that prevented our enzyme from properly degrading it. Further testing will need to be done to properly assess the functionality of our PUR esterase.<br />
<br />
===Exporter Protein OsmY Assay Results===<br />
[[Image:Washington_osmY_Assay_Results.png|border|940px|center|thumb|Each histogram depicts the average normalized fluorescence values for cells plus supernatant, cells only, and supernatant only. The image in the center is a visual representation of the presented data.]]<br />
When running the osmY assay described in the methods section, we saw that sfGFP fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]) resulted in a much higher fluorescence ratio of cells+supernatant to supernatant than the fluorescence ratio of cells+supernatant to supernatant of sfGFP fused to mamI. Even though the cells only fluorescence of sfGFP-osmY plasmid exhibited nearly the same level of fluorescence as supernatant only, it does not go against the argument that osmY is properly exporting sfGFP into the supernatant. sfGFP-osmY is produced within the cell and it cannot be expected that all osmY will be secreted from the cell at a given moment. Thus from the above data, it can be gathered that <b>osmY, when fused to a protein, will export the protein out into the supernatant.</b><br />
<br />
===Turbidostat Assay Results===<br />
[[Image:Washington_Turbidostat_Pump.png|border|450px|left|thumb|M9 30 mM ethylene glycol media injected into the culture vessel every minute to maintain the arbitrary optical density of 0.3.]]<br />
<br />
[[Image:Washington_Turbidostat_OD.png|border|450px|right|thumb|Optical density measurements of <i>E. coli</i> transformed with fucO pGA3K3 + aldA pGA1C3 within the turbidostat growing on M9 30 mM ethylene glycol media.]]<br />
<br />
<br />
Following the turbidostat assay protocol, we used transformed MG1655 with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) on a medium copy Kanamycin resistant backbone and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]) on a high copy Chloramphenicol resistant backbone and cultured it in the turbidostat. The turbidostat was operated overnight and the above data illustrates that the transformed MG1655 grew using ethylene glycol as its sole carbon source, which normal untransformed MG1655 cannot do. This is the case because the optical density (OD)/cell density rose from an initial arbitrary OD of 0.18 (time of inoculation) to an OD of 0.3, which was maintained throughout the time of the experiment (15 hours). The turbidostat maintained this OD through constantly imputing amounts of new media into the culture vessel based whenever the OD surpassed 0.3, which was quite frequently. Although time constraints did not allow us to transform MG1655 with our fucO-aldA operon and characterize it, it can be seen from the data above that <b>when fucO and aldA genes are individually transformed and over expressed in MG1655 <i>E. coli</i>, <i>E. coli</i> gains the ability to utilize ethylene glycol as its sole carbon source. The average doubling time (based off of a 10mL culture volume and an average dilution rate of 30µL/min) was 333 minutes, slightly faster than the literature value of 360 minutes [10].</b> <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===Putting it All together===<br />
The parts as they are right now have not been put into one cell strain and tested as a system. The obvious next step would be to test the efficacy of the entire plastic degrading construct against polyurethane. Using the turbidostat, we have the luxury of having an optimized and continual growth environment for our system to mutate towards improved functionality. Better cell strains would be periodically saved and tested in harsher selective conditions until eventually the strain is able to survive and reproduce off of polyurethane as its sole carbon source. We recognize that this may take many iterations to complete but the end product could potentially be a cell that is able to consume plastic much faster and safer than traditional recycling methods.<br />
<br />
<br />
===Trash to Treasure===<br />
After stable growth off of plastic is achieved the next step would be to clone in a third plasmid that would produce some valuable commodity. Washington 2011 demonstrated that diesel fuel can be synthesized from central metabolites using their Petrobrick platform. The addition of the Petrobrick to the system would allow plastic waste to be processed by <i>E. coli</i> which would then turn it into biofuels. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892008 BBa_K892008 '''osmY''']<br />
<br />
The coding sequence for osmotically induced protein Y, a protein that when fused to another protein, gets exported out of <i>E. coli</i> cells.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009 '''fucO''']<br />
<br />
The gene fucO, which codes for glycolaldehyde reductase, is one of the genes required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010 '''aldA''']<br />
<br />
The gene aldA, which codes for glycolaldehyde dehydrogenase, is the other gene required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892011 BBa_K892011 '''sfGFP-osmY''']<br />
<br />
A composite part for osmotically induced protein Y fused downstream to sfGFP using a glycine - serine linker regulated by the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892012 BBa_K892012 '''PUR Esterase''']<br />
<br />
An enzyme that breaks down polyurethane plastic behind the control of the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892013 BBa_K892013 '''fucO-aldA''']<br />
<br />
The combination of BBa_K892009 and BBa_K892010 behind a strong biofab promoter.<br />
<br />
<br />
----<br />
<html><a name="Sources"></a></html><br />
<h1 id='Parts'>Sources <html><a href="#Sources"><font size="3">[Top]</font></a></html></h1><br />
<html><br />
<ol><br />
<li>Barnes, D. K. A., F. Galgani, R. C. Thompson, and M. Barlaz. "Accumulation and Fragmentation of Plastic Debris in Global Environments." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 1985-998. Print.</li><br />
<br />
<li>Gregory, M. R. "Environmental Implications of Plastic Debris in Marine Settings--entanglement, Ingestion, Smothering, Hangers-on, Hitch-hiking and Alien Invasions." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2013-025. Print.</li><br />
<br />
<li>"Global Polyurethane Market to Reach 9.6 Mln Tons by 2015." Plastemart.com. N.p., 30 Aug. 2011. Web. <http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674></li><br />
<br />
<li>"Polyurethane Recycling." Polyurethanes. American Chemistry Council, n.d. Web. <http://polyurethane.americanchemistry.com/Sustainability/Recycling>. </li><br />
<br />
<li>"Frequently Asked Questions on Polyurethanes." Polyurethanes.org. European Diisocyanate and Polyol Producers Association, n.d. Web. <http://www.polyurethanes.org/index.php?page=faqs>. </li><br />
<br />
<li>Takasuga, T., N. Umetsu, T. Makino, K. Tsubota, KS Sajwan, and KS Kumar. "Role of Temperature and Hydrochloric Acid on the Formation of Chlorinated Hydrocarbons and Polycyclic Aromatic Hydrocarbons during Combustion of Paraffin Powder, Polymers, and Newspaper." Archives of Environmental Contamination and Toxicology (2007): 8-21. Print. </li><br />
<br />
<li>Teuten, E. L., J. M. Saquing, D. R. U. Knappe, M. A. Barlaz, S. Jonsson, A. Bjorn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha, and H. Takada. "Transport and Release of Chemicals from Plastics to the Environment and to Wildlife." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2027-045. Print. </li><br />
<br />
<li>Kang, Chul-Hyung. "A Novel Family VII Esterase with Industrial Potential from Compost Metagenomic Library." Microbial Cell Factories 10.41 (2011): n. pag. Print. </li><br />
<br />
<li>Bokinsky, Gregory, Et. Al. "Synthesis of Three Advanced Biofuels from Ionic Liquid-penetreated Switchgrass Using Engineered Escherichia Coli." Proceedings of the National Academy of Sciences of the United States of America 108.50 (2011): 19949-9954. Print. </li><br />
<br />
<li> Boronat, Albert, Estrella Caballero, and Juan Aguilar. "Experimental Evolution of a Metabolic Pathway for Ethylene Glycol Utilization by Escherichia Coli." Journal of Bacteriology Jan. (1983): 134-39. Web. </li><br />
<br />
<li> E. Toprak, A. Veres, J. B. Michel, R. Chait, D. L. Hartl, R. Kishony, “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection,” Nature Genetics, vol. 44, no. 1, pp. 101-105, Jan. 2012. </li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/PlasticsTeam:Washington/Plastics2012-10-04T01:08:27Z<p>Felixekn: /* “Plastics: made to last forever, designed to throw away”--5gyres.org */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“Plastics: made to last forever, designed to throw away”--5gyres.org</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:Washington_Sadfish.png|border|440px|right|thumb|The increasing problem of plastic waste.]]<br />
Playing an integral role in our modern world, plastics account for over a third of products made today. They have a relatively low cost of production, serve to promote the development of industry, lower the cost of consumer goods, and are easy to produce on a large scale. Plastics, regardless of disposability, compose 10% of the waste generated in the world and an even higher percentage is carelessly thrown into our environment<html><a href="#Sources"><font size="1"><sup>[1]</font></sup></a></html>. Plastic pollution discharges debris into the ocean that are ingested by wildlife, often resulting in injury or death <html><a href="#Sources"><font size="1">[2]</font></a></html>.<br />
<br />
<br />
===The PUR Problem ===<br />
A commonly used plastic, Polyurethane (PUR), is used to create a wide range of products including dense solids, rigid insulating foam, and thermoset elastics. Because of its extreme versatility, the world consumption of PUR is continually increasing. According to current market analysis, the global production of PUR is estimated at 27 billion pounds per year and is expected to increase to 36 billion pounds by the year 2016<html><a href="#Sources"><font size="1"><sup>[3]</sup></font></a></html>. The leading method of recycling polyurethane is to mechanically grind the waste and rebind it into carpet cushioning, accounting for about 4% of waste polyurethane<html><a href="#Sources"><font size="1"><sup>[4]</sup></font></a></html>. However, due to the high costs of transportation and chemical degradation, most PURs are incinerated to recapture some of the energy used to make them<html><a href="#Sources"><font size="1"><sup>[5]</sup></font></a></html>.<br />
<br />
<br />
[[Image:Washington_PURBURN.png|border|280px|left|thumb|Current methods of disposal, like burning is harmful for our environment.]]<br />
===Current Means of Disposal are Undesirable===<br />
Non-biodegradable plastics are often disposed of through waste incineration as it is the most efficient method of degradation. However, a major issue with this is that the products of plastic burning, which include polychlorinated di-benzo-p-dioxins/furans and carbon dioxide, are known to be carcinogenic<html><a href="#Sources"><font size="1"><sup>[6]</sup></font></a></html>. Upon incineration, these gaseous products are released into the atmosphere and have the potential to cause future problems to human health as well as contribute to global warming. As an additional concern, plastics constitute large volumes of space as they are habitually disposed of in landfills. It has been shown that plasticizers and plastic additives leak from the these plastics and contaminate aquatic environments<html><a href="#Sources"><font size="1"><sup>[7]</sup></font></a></html>. Thus, because of the environmental damage that current disposal methods incur, there is a large need for safer methods of plastic degradation.<br />
<br />
===Engineering Microbes to Degrade PUR===<br />
To solve the problem of PUR recycling, we propose a bacteria that is both able to degrade PUR and subsist off of the products of degradation as its sole carbon source. To this end, we propose a two plasmid system. The first plasmid would have two genes. The first gene, polyeurethane esterase, will encode an enzyme that is able to break down the PUR polymer structure into two molecules, one of which, ethylene glycol, can diffuse across the membrane of the bacterium<html><a href="#Sources"><font size="1"><sup>[8]</sup></font></a></html>. The second gene, osmotic inducible protein Y (osmY), would encode a protein that fuses to PUR esterase and exports the enzyme through the cell membrane and into the supernatant<html><a href="#Sources"><font size="1"><sup>[9]</sup></font></a></html>. The second plasmid would have an operon composed of, glycolaldehyde reductase (fucO) and glycoaldehyde dehydrogenase (aldA), that allows the bacterium to use ethylene glycol as its central metabolite<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. This system would allow the bacteria to turn the plastics plaguing our landfills into bacterial biomass which would in turn degrade more PUR. <br />
[[Image:Washington_Plastic_Overview.png|border|950px|center|thumb|The complete degradation pathway takes polyurethane, an undesirable waste product and converts it into a central metabolite for bacterium.]]<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: The Turbidostat<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Washington_Turbidostat.png|border|400px|left|thumb|Schematic diagram of our turbidostat. It uses the optical density measurements to modulate how much media is pumped into the culture vessel every minute to maintain a constant cell density. Through this method of cell density control, we can measure how fast the cells are growing and thus gain an understanding for relative fitness increases thorough out the course of an experiment.]]<br />
[[Image:Recycleapp.png|150px|right]]<br />
To characterize and optimize growth of specific parts of our overall polyurethane degradation pathway we developed a <i><b>homemade software application (App)</b></i>. However, devices that can accomplish this are not commercially available. Additionally, details for such tools are often sparsely detailed and frequently require exotic reagents and specialized training. To circumvent these issues, we instead decided to use our expertise in electrical, biological, and computer engineering to create an application that could control cheaply and readily available hardware to achieve the desired directed evolution and characterization functionality. <br />
<br />
<br />
Our App uniquely controls an assortment of hardware devices easily found online, creating a fully functional turbidostat. A turbidostat is a closed-loop continuous culture device that maintains a desired constant cell density and chemical environment. The constant environment allows for continuous log phase growth and constant selective pressures (in our case polyurethane or ethylene glycol), which enables proper characterization (seen through media input per dilution cycle) and reduces evolutionary trajectory drift. By maintaining constant selective pressures, the rate of evolution is also dramatically improved<html><a href="#Sources"><font size="1"><sup>[11]</sup></font></a></html>. By creating this turbidostat App (written in Python), given sufficient time, we are able to properly assess and optimize our individual parts as well as the entire polyurethane degrading circuit in a controlled and reliable manner.<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[[Image:Washington_PUR.png|border|250px|left|thumb|To assemble the polyurethane esterase, we used 4 IDT gBlocks, each containing a unique set of homology sequences that only allow for one orientation of binding, and Gibsoned them together onto a pGA backbone.]]<br />
<br />
===PUR Esterase Assay=== <br />
Polyurethane esterase degrades polyurethane by cleaving the ester bonds to produce one of a variety of monomers with isocyanate groups on either end and an ethylene glycol. For our circuit, we have chosen to use the esterase estCS2 ([http://partsregistry.org/Part:BBa_K892012 BBa_K892012]). Originally isolated and characterized by Kang et al [8], the sequence of estCS2 is available at [http://www.ncbi.nlm.nih.gov/nuccore/283462288 GenBank (GU256649)]. Since their DNA constructs were unavailable and the authors did not respond to requests, we synthetically constructed the entire 1.7kb estCS2 gene from four 500-base gBlocks provided by IDT using Gibson cloning. <br />
<br />
<br />
[[Image:Washington_PUR_Assay.png|border|350px|right|thumb|To assemble for activity of polyurethane esterase, cells transformed with the PUR esterase plasmid were grown to <br />
saturation, sonicated, then a piece of pre-weighed polyurethane foam was inserted into the culture tube.]]<br />
To assay esterase activity we made one 50mL TB + kanamycin overnight culture for the cells containing the esterase plasmid and a 50mL TB overnight culture with untransformed cells. After incubating the cultures for a day, we spun them down to form a pellet. The supernatant was then poured out and then the remaining pellets were resuspended the in enough water for each culture to be equalized at an arbitrary OD of 1.4. Three milliliters of each resuspended culture was aliquoted out into three microcentrifuge tubes (1 mL of one culture in each tube, 6 total microcentrifuge tubes). These tubes, along with 6 microcentrifuge tubes that contained only 1mL of LB were then sonicated at an amplitude of 20 with 1 second pulses on and off for 30 seconds. After sonication we poured each lysate and LB aliquots into 25mL culture tubes containing 1mL of LB and a preweighed sample of foam. This gave us a total of 12, 25mL culture tubes containing a pre weighed foam piece with LB+lysate or LB+sonicated LB. These culture tubes were left on the bench top at room temperature overnight. In the morning we washed the foam with DI water and dried them in a 65º C incubator. After drying, we weighed the samples to see if there was any change in mass of the foam samples.<br />
<br />
===Exporter Protein OsmY Assay===<br />
Since polyurethane is a large polymer, larger than the cell itself, it cannot be imported for degradation and therefore our esterase must be exported. It has been shown in Bolkinsky, Gregory et al that a protein, osmotically inducible protein Y (osmY, [http://partsregistry.org/Part:BBa_K892008 BBa_K892008]), is naturally exported by <i>E. coli</i> [9]. Furthermore this paper shows that osmY can be used as an export tag in <i>E. coli</i> when it is translationally fused to a protein. <br />
<br />
[[Image:Washington_osmY.png|border|348px|left|thumb|Our exporter protein (osmY) is linked to sfGFP through a serine glycine linker.]]<br />
[[Image:Washington_osmY_Assay.png|border|546px|right|thumb|We used osmY fused to sfGFP and as a control sfGFP fused to mamI in the same translational conformation to test for osmY functionality. These cultures were spun down and the fluorescence (480nm excitation, 509nm emission) of the cells+supernatant and supernatant were measured.]]<br />
<br />
<br />
To assay the exportation system we created a fusion protein. The fusion protein consisted of super folder GFP (sfGFP) fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]), which allowed for visual assessment of whether or not osmY was correctly exported into the supernatant. For the control, sfGFP fused to mamI ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K590016 BBa_K590016]), a protein that binds to the cell membrane, was used. The sfGFP-osmY and sfGFP-mamI plasmids were transformed into BL21 and then were grown up overnight (after picking a colony of each from their transformation plates) in M9 1% glucose media. To quantify whether or not osmY properly exported proteins outside of the cell membrane, 4, 500µL aliquots of each overnight culture was made. From each of these aliquots, 200µL of the culture was pipetted into a 96 well plate and then the remaining culture was spun down in a centrifuge at 3000g for 3 minutes. Carefully, from each of the spun down aliquots, 200µL of the supernatant was pipetted into the 96 well plate. The remaining supernatant for each aliquot was disposed of and the cells were resuspended with 300 µL of fresh M9 1% glucose media. From the each of the resuspended aliquots, 200µL was pipetted into the 96 well plate. The 96 well plate was then read with a plate reader; each well was excited with a 480+/-20 nm light and then a detector quantified how much 509 +/-20 nm light was give off. <br />
<br />
Using the information from the plate reader we could determine if osmY properly exports the fused protein. If the supernatant from the cells expressing the osmY construct had high fluorescence with respect to cells+supernatant while the supernatant of the control sfGFP-mamI had low fluorescence compared to its overall cells+supernatant fluorescence than it would be concluded that the protein was properly exported when fused to osmY. But if there was little to no supernatant fluorescence of the sfGFP-osmY construct or sfGFP-mamI and sfGFP-osmY illustrated the same ratio of supernatant fluorescence to cells+supernatant fluorescence then it would be concluded that osmY did not export proteins outside of the cell or our designed fusion between sfGFP and osmY disrupted sfGFP/osmY function.<br />
<br />
<br />
[[Image:Washington_fucO_aldA.png|border|350px|left|thumb|By putting fucO and aldA onto a single plasmids with a single promote, we were able to create an operon that when transcribed and translated allows microbes to utilize ethylene glycol as a sole carbon source [10].]]<br />
<br />
===The Turbidostat Assay===<br />
The media to used for the turbidostat assay was comprised exclusively of M9 salts and ethylene glycol. Because of this choice of media, the only carbon source available to <i>E. coli</i> was ethylene glycol and because this can't be digest by normal lab strains of <i>E. coli</i>, only <i>E. coli</i> that can digest ethylene glycol will survive<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. The turbidostat App tracks cell growth by first blanking (setting optical density (OD) to 0) and then measuring and recording OD values after inoculation. Because OD and cell density are both linearly related, OD is a proxy for cell density measurements. After blanking on the M9 30mM ethylene glycol media, MG1655 cells that were transformed with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]), which were grown in TB to log phase and then washed with PBS, were added to the turbidostat's culture vessel to an OD of 0.2. This allowed cells still in log phase to divide without exceeding the OD threshold of 0.3 (our desired constant cell density). After inoculation, the turbidostat was left running overnight and cell density and amount of media added per dilution cycle was captured in the morning. With this data, understanding for how well the transformed <i>E. coli</i> subsists using ethylene glycol as its sole carbon source was gained.<br />
<br />
<br />
The next assay ran was to transform MG1655 with a fucO-aldA operon plasmid ([http://partsregistry.org/Part:BBa_K892013 BBa_K892013]). This operon consisted of fucO and aldA being on a single plasmid that contains a single promoter. With this present, transformed MG1655 cells would be theoretically more viable in ethylene glycol since the cells would not have to spend energy on two antibiotic resistance genes (although no antibiotics were used in the the M9 ethylene glycol media, the cells still transcribe and translate the genes). By running the turbidostat in the same way as the dual transformation experiment, an idea for how well the transformed operon fairs versus how well the two plasmid transformation could be gained.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===PUR Esterase Assay Results===<br />
We ran the PUR esterase assay and we found that the foam we used did not degrade. We quantified this by measuring the weight of the foam pieces to the nearest milligram before and after we ran the assay. We actually ended up leaving the lysate with the foam for three days to improve degradation but this had no effect on the end result. The reason we think the foam was not degraded was because we did not know the full composition of the foam, it could of had other polymers mixed in with the polyurethane that prevented our enzyme from properly degrading it. Further testing will need to be done to properly assess the functionality of our PUR esterase.<br />
<br />
===Exporter Protein OsmY Assay Results===<br />
[[Image:Washington_osmY_Assay_Results.png|border|940px|center|thumb|Each histogram depicts the average normalized fluorescence values for cells plus supernatant, cells only, and supernatant only. The image in the center is a visual representation of the presented data.]]<br />
When running the osmY assay described in the methods section, we saw that sfGFP fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]) resulted in a much higher fluorescence ratio of cells+supernatant to supernatant than the fluorescence ratio of cells+supernatant to supernatant of sfGFP fused to mamI. Even though the cells only fluorescence of sfGFP-osmY plasmid exhibited nearly the same level of fluorescence as supernatant only, it does not go against the argument that osmY is properly exporting sfGFP into the supernatant. sfGFP-osmY is produced within the cell and it cannot be expected that all osmY will be secreted from the cell at a given moment. Thus from the above data, it can be gathered that <b>osmY, when fused to a protein, will export the protein out into the supernatant.</b><br />
<br />
===Turbidostat Assay Results===<br />
[[Image:Washington_Turbidostat_Pump.png|border|450px|left|thumb|M9 30 mM ethylene glycol media injected into the culture vessel every minute to maintain the arbitrary optical density of 0.3.]]<br />
<br />
[[Image:Washington_Turbidostat_OD.png|border|450px|right|thumb|Optical density measurements of <i>E. coli</i> transformed with fucO pGA3K3 + aldA pGA1C3 within the turbidostat growing on M9 30 mM ethylene glycol media.]]<br />
<br />
<br />
Following the turbidostat assay protocol, we used transformed MG1655 with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) on a medium copy Kanamycin resistant backbone and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]) on a high copy Chloramphenicol resistant backbone and cultured it in the turbidostat. The turbidostat was operated overnight and the above data illustrates that the transformed MG1655 grew using ethylene glycol as its sole carbon source, which normal untransformed MG1655 cannot do. This is the case because the optical density (OD)/cell density rose from an initial arbitrary OD of 0.18 (time of inoculation) to an OD of 0.3, which was maintained throughout the time of the experiment (15 hours). The turbidostat maintained this OD through constantly imputing amounts of new media into the culture vessel based whenever the OD surpassed 0.3, which was quite frequently. Although time constraints did not allow us to transform MG1655 with our fucO-aldA operon and characterize it, it can be seen from the data above that <b>when fucO and aldA genes are individually transformed and over expressed in MG1655 <i>E. coli</i>, <i>E. coli</i> gains the ability to utilize ethylene glycol as its sole carbon source. The average doubling time (based off of a 10mL culture volume and an average dilution rate of 30µL/min) was 333 minutes, slightly faster than the literature value of 360 minutes [10].</b> <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===Putting it All together===<br />
The parts as they are right now have not been put into one cell strain and tested as a system. The obvious next step would be to test the efficacy of the entire plastic degrading construct against polyurethane. Using the turbidostat, we have the luxury of having an optimized and continual growth environment for our system to mutate towards improved functionality. Better cell strains would be periodically saved and tested in harsher selective conditions until eventually the strain is able to survive and reproduce off of polyurethane as its sole carbon source. We recognize that this may take many iterations to complete but the end product could potentially be a cell that is able to consume plastic much faster and safer than traditional recycling methods.<br />
<br />
<br />
===Trash to Treasure===<br />
After stable growth off of plastic is achieved the next step would be to clone in a third plasmid that would produce some valuable commodity. Washington 2011 demonstrated that diesel fuel can be synthesized from central metabolites using their Petrobrick platform. The addition of the Petrobrick to the system would allow plastic waste to be processed by <i>E. coli</i> which would then turn it into biofuels. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892008 BBa_K892008 '''osmY''']<br />
<br />
The coding sequence for osmotically induced protein Y, a protein that when fused to another protein, gets exported out of <i>E. coli</i> cells.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009 '''fucO''']<br />
<br />
The gene fucO, which codes for glycolaldehyde reductase, is one of the genes required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010 '''aldA''']<br />
<br />
The gene aldA, which codes for glycolaldehyde dehydrogenase, is the other gene required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892011 BBa_K892011 '''sfGFP-osmY''']<br />
<br />
A composite part for osmotically induced protein Y fused downstream to sfGFP using a glycine - serine linker regulated by the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892012 BBa_K892012 '''PUR Esterase''']<br />
<br />
An enzyme that breaks down polyurethane plastic behind the control of the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892013 BBa_K892013 '''fucO-aldA''']<br />
<br />
The combination of BBa_K892009 and BBa_K892010 behind a strong biofab promoter.<br />
<br />
<br />
----<br />
<html><a name="Sources"></a></html><br />
<h1 id='Parts'>Sources <html><a href="#Sources"><font size="3">[Top]</font></a></html></h1><br />
<html><br />
<ol><br />
<li>Barnes, D. K. A., F. Galgani, R. C. Thompson, and M. Barlaz. "Accumulation and Fragmentation of Plastic Debris in Global Environments." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 1985-998. Print.</li><br />
<br />
<li>Gregory, M. R. "Environmental Implications of Plastic Debris in Marine Settings--entanglement, Ingestion, Smothering, Hangers-on, Hitch-hiking and Alien Invasions." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2013-025. Print.</li><br />
<br />
<li>"Global Polyurethane Market to Reach 9.6 Mln Tons by 2015." Plastemart.com. N.p., 30 Aug. 2011. Web. <http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674></li><br />
<br />
<li>"Polyurethane Recycling." Polyurethanes. American Chemistry Council, n.d. Web. <http://polyurethane.americanchemistry.com/Sustainability/Recycling>. </li><br />
<br />
<li>"Frequently Asked Questions on Polyurethanes." Polyurethanes.org. European Diisocyanate and Polyol Producers Association, n.d. Web. <http://www.polyurethanes.org/index.php?page=faqs>. </li><br />
<br />
<li>Takasuga, T., N. Umetsu, T. Makino, K. Tsubota, KS Sajwan, and KS Kumar. "Role of Temperature and Hydrochloric Acid on the Formation of Chlorinated Hydrocarbons and Polycyclic Aromatic Hydrocarbons during Combustion of Paraffin Powder, Polymers, and Newspaper." Archives of Environmental Contamination and Toxicology (2007): 8-21. Print. </li><br />
<br />
<li>Teuten, E. L., J. M. Saquing, D. R. U. Knappe, M. A. Barlaz, S. Jonsson, A. Bjorn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha, and H. Takada. "Transport and Release of Chemicals from Plastics to the Environment and to Wildlife." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2027-045. Print. </li><br />
<br />
<li>Kang, Chul-Hyung. "A Novel Family VII Esterase with Industrial Potential from Compost Metagenomic Library." Microbial Cell Factories 10.41 (2011): n. pag. Print. </li><br />
<br />
<li>Bokinsky, Gregory, Et. Al. "Synthesis of Three Advanced Biofuels from Ionic Liquid-penetreated Switchgrass Using Engineered Escherichia Coli." Proceedings of the National Academy of Sciences of the United States of America 108.50 (2011): 19949-9954. Print. </li><br />
<br />
<li> Boronat, Albert, Estrella Caballero, and Juan Aguilar. "Experimental Evolution of a Metabolic Pathway for Ethylene Glycol Utilization by Escherichia Coli." Journal of Bacteriology Jan. (1983): 134-39. Web. </li><br />
<br />
<li> E. Toprak, A. Veres, J. B. Michel, R. Chait, D. L. Hartl, R. Kishony, “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection,” Nature Genetics, vol. 44, no. 1, pp. 101-105, Jan. 2012. </li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/PlasticsTeam:Washington/Plastics2012-10-04T01:04:24Z<p>Felixekn: /* The Turbidostat Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“Plastics: made to last forever, designed to throw away”--5gyres.org</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:Washington_Sadfish.png|border|440px|right|thumb|The increasing problem of plastic waste.]]<br />
Playing an integral role in our modern world, plastics account for over a third of products made today. They have a relatively low cost of production, serve to promote the development of industry, lower the cost of consumer goods, and are easy to produce on a large scale. Plastics, regardless of disposability, compose 10% of the waste generated in the world and an even higher percentage is carelessly thrown into our environment <html><a href="#Sources"><font size="1">[1]</font></a></html>. Plastic pollution discharges debris into the ocean that are ingested by wildlife, often resulting in injury or death <html><a href="#Sources"><font size="1">[2]</font></a></html>.<br />
<br />
<br />
===The PUR Problem ===<br />
A commonly used plastic, Polyurethane (PUR), is used to create a wide range of products including dense solids, rigid insulating foam, and thermoset elastics. Because of its extreme versatility, the world consumption of PUR is continually increasing. According to current market analysis, the global production of PUR is estimated at 27 billion pounds per year and is expected to increase to 36 billion pounds by the year 2016 <html><a href="#Sources"><font size="1">[3]</font></a></html>. The leading method of recycling polyurethane is to mechanically grind the waste and rebind it into carpet cushioning, accounting for about 4% of waste polyurethane <html><a href="#Sources"><font size="1">[4]</font></a></html>. However, due to the high costs of transportation and chemical degradation, most PURs are incinerated to recapture some of the energy used to make them <html><a href="#Sources"><font size="1">[5]</font></a></html>.<br />
<br />
<br />
[[Image:Washington_PURBURN.png|border|280px|left|thumb|Current methods of disposal, like burning is harmful for our environment.]]<br />
===Current Means of Disposal are Undesirable===<br />
Non-biodegradable plastics are often disposed of through waste incineration as it is the most efficient method of degradation. However, a major issue with this is that the products of plastic burning, which include polychlorinated di-benzo-p-dioxins/furans and carbon dioxide, are known to be carcinogenic <html><a href="#Sources"><font size="1">[6]</font></a></html>. Upon incineration, these gaseous products are released into the atmosphere and have the potential to cause future problems to human health as well as contribute to global warming. As an additional concern, plastics constitute large volumes of space as they are habitually disposed of in landfills. It has been shown that plasticizers and plastic additives leak from the these plastics and contaminate aquatic environments <html><a href="#Sources"><font size="1">[7]</font></a></html>. Thus, because of the environmental damage that current disposal methods incur, there is a large need for safer methods of plastic degradation.<br />
<br />
===Engineering Microbes to Degrade PUR===<br />
To solve the problem of PUR recycling, we propose a bacteria that is both able to degrade PUR and subsist off of the products of degradation as its sole carbon source. To this end, we propose a two plasmid system. The first plasmid would have two genes. The first gene, polyeurethane esterase, will encode an enzyme that is able to break down the PUR polymer structure into two molecules, one of which, ethylene glycol, can diffuse across the membrane of the bacterium <html><a href="#Sources"><font size="1">[8]</font></a></html>. The second gene, osmotic inducible protein Y (osmY), would encode a protein that fuses to PUR esterase and exports the enzyme through the cell membrane and into the supernatant <html><a href="#Sources"><font size="1">[9]</font></a></html>. The second plasmid would have an operon composed of, glycolaldehyde reductase (fucO) and glycoaldehyde dehydrogenase (aldA), that allows the bacterium to use ethylene glycol as its central metabolite <html><a href="#Sources"><font size="1">[10]</font></a></html>. This system would allow the bacteria to turn the plastics plaguing our landfills into bacterial biomass which would in turn degrade more PUR. <br />
[[Image:Washington_Plastic_Overview.png|border|950px|center|thumb|The complete degradation pathway takes polyurethane, an undesirable waste product and converts it into a central metabolite for bacterium.]]<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: The Turbidostat<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Washington_Turbidostat.png|border|400px|left|thumb|Schematic diagram of our turbidostat. It uses the optical density measurements to modulate how much media is pumped into the culture vessel every minute to maintain a constant cell density. Through this method of cell density control, we can measure how fast the cells are growing and thus gain an understanding for relative fitness increases thorough out the course of an experiment.]]<br />
[[Image:Recycleapp.png|150px|right]]<br />
To characterize and optimize growth of specific parts of our overall polyurethane degradation pathway we developed a <i><b>homemade software application (App)</b></i>. However, devices that can accomplish this are not commercially available. Additionally, details for such tools are often sparsely detailed and frequently require exotic reagents and specialized training. To circumvent these issues, we instead decided to use our expertise in electrical, biological, and computer engineering to create an application that could control cheaply and readily available hardware to achieve the desired directed evolution and characterization functionality. <br />
<br />
<br />
Our App uniquely controls an assortment of hardware devices easily found online, creating a fully functional turbidostat. A turbidostat is a closed-loop continuous culture device that maintains a desired constant cell density and chemical environment. The constant environment allows for continuous log phase growth and constant selective pressures (in our case polyurethane or ethylene glycol), which enables proper characterization (seen through media input per dilution cycle) and reduces evolutionary trajectory drift. By maintaining constant selective pressures, the rate of evolution is also dramatically improved <html><a href="#Sources"><font size="1">[11]</font></a></html>. By creating this turbidostat App (written in Python), given sufficient time, we are able to properly assess and optimize our individual parts as well as the entire polyurethane degrading circuit in a controlled and reliable manner.<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[[Image:Washington_PUR.png|border|250px|left|thumb|To assemble the polyurethane esterase, we used 4 IDT gBlocks, each containing a unique set of homology sequences that only allow for one orientation of binding, and Gibsoned them together onto a pGA backbone.]]<br />
<br />
===PUR Esterase Assay=== <br />
Polyurethane esterase degrades polyurethane by cleaving the ester bonds to produce one of a variety of monomers with isocyanate groups on either end and an ethylene glycol. For our circuit, we have chosen to use the esterase estCS2 ([http://partsregistry.org/Part:BBa_K892012 BBa_K892012]). Originally isolated and characterized by Kang et al [8], the sequence of estCS2 is available at [http://www.ncbi.nlm.nih.gov/nuccore/283462288 GenBank (GU256649)]. Since their DNA constructs were unavailable and the authors did not respond to requests, we synthetically constructed the entire 1.7kb estCS2 gene from four 500-base gBlocks provided by IDT using Gibson cloning. <br />
<br />
<br />
[[Image:Washington_PUR_Assay.png|border|350px|right|thumb|To assemble for activity of polyurethane esterase, cells transformed with the PUR esterase plasmid were grown to <br />
saturation, sonicated, then a piece of pre-weighed polyurethane foam was inserted into the culture tube.]]<br />
To assay esterase activity we made one 50mL TB + kanamycin overnight culture for the cells containing the esterase plasmid and a 50mL TB overnight culture with untransformed cells. After incubating the cultures for a day, we spun them down to form a pellet. The supernatant was then poured out and then the remaining pellets were resuspended the in enough water for each culture to be equalized at an arbitrary OD of 1.4. Three milliliters of each resuspended culture was aliquoted out into three microcentrifuge tubes (1 mL of one culture in each tube, 6 total microcentrifuge tubes). These tubes, along with 6 microcentrifuge tubes that contained only 1mL of LB were then sonicated at an amplitude of 20 with 1 second pulses on and off for 30 seconds. After sonication we poured each lysate and LB aliquots into 25mL culture tubes containing 1mL of LB and a preweighed sample of foam. This gave us a total of 12, 25mL culture tubes containing a pre weighed foam piece with LB+lysate or LB+sonicated LB. These culture tubes were left on the bench top at room temperature overnight. In the morning we washed the foam with DI water and dried them in a 65º C incubator. After drying, we weighed the samples to see if there was any change in mass of the foam samples.<br />
<br />
===Exporter Protein OsmY Assay===<br />
Since polyurethane is a large polymer, larger than the cell itself, it cannot be imported for degradation and therefore our esterase must be exported. It has been shown in Bolkinsky, Gregory et al that a protein, osmotically inducible protein Y (osmY, [http://partsregistry.org/Part:BBa_K892008 BBa_K892008]), is naturally exported by <i>E. coli</i> [9]. Furthermore this paper shows that osmY can be used as an export tag in <i>E. coli</i> when it is translationally fused to a protein. <br />
<br />
[[Image:Washington_osmY.png|border|348px|left|thumb|Our exporter protein (osmY) is linked to sfGFP through a serine glycine linker.]]<br />
[[Image:Washington_osmY_Assay.png|border|546px|right|thumb|We used osmY fused to sfGFP and as a control sfGFP fused to mamI in the same translational conformation to test for osmY functionality. These cultures were spun down and the fluorescence (480nm excitation, 509nm emission) of the cells+supernatant and supernatant were measured.]]<br />
<br />
<br />
To assay the exportation system we created a fusion protein. The fusion protein consisted of super folder GFP (sfGFP) fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]), which allowed for visual assessment of whether or not osmY was correctly exported into the supernatant. For the control, sfGFP fused to mamI ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K590016 BBa_K590016]), a protein that binds to the cell membrane, was used. The sfGFP-osmY and sfGFP-mamI plasmids were transformed into BL21 and then were grown up overnight (after picking a colony of each from their transformation plates) in M9 1% glucose media. To quantify whether or not osmY properly exported proteins outside of the cell membrane, 4, 500µL aliquots of each overnight culture was made. From each of these aliquots, 200µL of the culture was pipetted into a 96 well plate and then the remaining culture was spun down in a centrifuge at 3000g for 3 minutes. Carefully, from each of the spun down aliquots, 200µL of the supernatant was pipetted into the 96 well plate. The remaining supernatant for each aliquot was disposed of and the cells were resuspended with 300 µL of fresh M9 1% glucose media. From the each of the resuspended aliquots, 200µL was pipetted into the 96 well plate. The 96 well plate was then read with a plate reader; each well was excited with a 480+/-20 nm light and then a detector quantified how much 509 +/-20 nm light was give off. <br />
<br />
Using the information from the plate reader we could determine if osmY properly exports the fused protein. If the supernatant from the cells expressing the osmY construct had high fluorescence with respect to cells+supernatant while the supernatant of the control sfGFP-mamI had low fluorescence compared to its overall cells+supernatant fluorescence than it would be concluded that the protein was properly exported when fused to osmY. But if there was little to no supernatant fluorescence of the sfGFP-osmY construct or sfGFP-mamI and sfGFP-osmY illustrated the same ratio of supernatant fluorescence to cells+supernatant fluorescence then it would be concluded that osmY did not export proteins outside of the cell or our designed fusion between sfGFP and osmY disrupted sfGFP/osmY function.<br />
<br />
<br />
[[Image:Washington_fucO_aldA.png|border|350px|left|thumb|By putting fucO and aldA onto a single plasmids with a single promote, we were able to create an operon that when transcribed and translated allows microbes to utilize ethylene glycol as a sole carbon source [10].]]<br />
<br />
===The Turbidostat Assay===<br />
The media to used for the turbidostat assay was comprised exclusively of M9 salts and ethylene glycol. Because of this choice of media, the only carbon source available to <i>E. coli</i> was ethylene glycol and because this can't be digest by normal lab strains of <i>E. coli</i>, only <i>E. coli</i> that can digest ethylene glycol will survive<html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. The turbidostat App tracks cell growth by first blanking (setting optical density (OD) to 0) and then measuring and recording OD values after inoculation. Because OD and cell density are both linearly related, OD is a proxy for cell density measurements. After blanking on the M9 30mM ethylene glycol media, MG1655 cells that were transformed with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]), which were grown in TB to log phase and then washed with PBS, were added to the turbidostat's culture vessel to an OD of 0.2. This allowed cells still in log phase to divide without exceeding the OD threshold of 0.3 (our desired constant cell density). After inoculation, the turbidostat was left running overnight and cell density and amount of media added per dilution cycle was captured in the morning. With this data, understanding for how well the transformed <i>E. coli</i> subsists using ethylene glycol as its sole carbon source was gained.<br />
<br />
<br />
The next assay ran was to transform MG1655 with a fucO-aldA operon plasmid ([http://partsregistry.org/Part:BBa_K892013 BBa_K892013]). This operon consisted of fucO and aldA being on a single plasmid that contains a single promoter. With this present, transformed MG1655 cells would be theoretically more viable in ethylene glycol since the cells would not have to spend energy on two antibiotic resistance genes (although no antibiotics were used in the the M9 ethylene glycol media, the cells still transcribe and translate the genes). By running the turbidostat in the same way as the dual transformation experiment, an idea for how well the transformed operon fairs versus how well the two plasmid transformation could be gained.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===PUR Esterase Assay Results===<br />
We ran the PUR esterase assay and we found that the foam we used did not degrade. We quantified this by measuring the weight of the foam pieces to the nearest milligram before and after we ran the assay. We actually ended up leaving the lysate with the foam for three days to improve degradation but this had no effect on the end result. The reason we think the foam was not degraded was because we did not know the full composition of the foam, it could of had other polymers mixed in with the polyurethane that prevented our enzyme from properly degrading it. Further testing will need to be done to properly assess the functionality of our PUR esterase.<br />
<br />
===Exporter Protein OsmY Assay Results===<br />
[[Image:Washington_osmY_Assay_Results.png|border|940px|center|thumb|Each histogram depicts the average normalized fluorescence values for cells plus supernatant, cells only, and supernatant only. The image in the center is a visual representation of the presented data.]]<br />
When running the osmY assay described in the methods section, we saw that sfGFP fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]) resulted in a much higher fluorescence ratio of cells+supernatant to supernatant than the fluorescence ratio of cells+supernatant to supernatant of sfGFP fused to mamI. Even though the cells only fluorescence of sfGFP-osmY plasmid exhibited nearly the same level of fluorescence as supernatant only, it does not go against the argument that osmY is properly exporting sfGFP into the supernatant. sfGFP-osmY is produced within the cell and it can't be expected that all osmY will be secreted from the cell at a given moment. Thus from the above data, it can be gathered that <b>osmY, when fused to a protein, will export the protein out into the supernatant.</b><br />
<br />
===Turbidostat Assay Results===<br />
[[Image:Washington_Turbidostat_Pump.png|border|450px|left|thumb|M9 30 mM ethylene glycol media injected into the culture vessel every minute to maintain the arbitrary optical density of 0.3.]]<br />
<br />
[[Image:Washington_Turbidostat_OD.png|border|450px|right|thumb|Optical density measurements of <i>E. coli</i> transformed with fucO pGA3K3 + aldA pGA1C3 within the turbidostat growing on M9 30 mM ethylene glycol media.]]<br />
<br />
<br />
Following the turbidostat assay protocol, we used transformed MG1655 with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) on a medium copy Kanamycin resistant backbone and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]) on a high copy Chloramphenicol resistant backbone and cultured it up in the turbidostat. The turbidostat was operated overnight and the above data illustrates that the transformed MG1655 grew using ethylene glycol as its sole carbon source, which normal untransformed MG1655 can't do. This is the case because the optical density (OD)/cell density rose from an initial arbitrary OD of 0.18 (time of inoculation) to an OD of 0.3, which was maintained throughout the time of the experiment (15 hours). The turbidostat maintained this OD through constantly imputing amounts of new media into the culture vessel based whenever the OD surpassed 0.3, which was quite frequently. Although time constraints did not allow us to transform MG1655 with our fucO-aldA operon and characterize it, it can be seen from the data above that <b>when fucO and aldA genes are individually transformed and over expressed in MG1655 <i>E. coli</i>, <i>E. coli</i> gains the ability to utilize ethylene glycol as its sole carbon source. The average doubling time (based off of a 10mL culture volume and an average dilution rate of 30µL/min) was 333 minutes, slightly faster than the literature value of 360 minutes [10].</b> <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===Putting it All together===<br />
The parts as they are right now have not been put into one cell strain and tested as a system. The obvious next step would be to test the efficacy of the entire plastic degrading construct against polyurethane. Using the turbidostat, we have the luxury of having an optimized and continual growth environment for our system to mutate towards improved functionality. Better cell strains would be periodically saved and tested in harsher selective conditions until eventually the strain is able to survive and reproduce off of polyurethane as its sole carbon source. We recognize that this may take many iterations to complete but the end product could potentially be a cell that is able to consume plastic much faster and safer than traditional recycling methods.<br />
<br />
<br />
===Trash to Treasure===<br />
After stable growth off of plastic is achieved the next step would be to clone in a third plasmid that would produce some valuable commodity. Washington 2011 demonstrated that diesel fuel can be synthesized from central metabolites using their Petrobrick platform. The addition of the Petrobrick to the system would allow plastic waste to be processed by <i>E. coli</i> which would then turn it into biofuels. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892008 BBa_K892008 '''osmY''']<br />
<br />
The coding sequence for osmotically induced protein Y, a protein that when fused to another protein, gets exported out of <i>E. coli</i> cells.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009 '''fucO''']<br />
<br />
The gene fucO, which codes for glycolaldehyde reductase, is one of the genes required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010 '''aldA''']<br />
<br />
The gene aldA, which codes for glycolaldehyde dehydrogenase, is the other gene required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892011 BBa_K892011 '''sfGFP-osmY''']<br />
<br />
A composite part for osmotically induced protein Y fused downstream to sfGFP using a glycine - serine linker regulated by the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892012 BBa_K892012 '''PUR Esterase''']<br />
<br />
An enzyme that breaks down polyurethane plastic behind the control of the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892013 BBa_K892013 '''fucO-aldA''']<br />
<br />
The combination of BBa_K892009 and BBa_K892010 behind a strong biofab promoter.<br />
<br />
<br />
----<br />
<html><a name="Sources"></a></html><br />
<h1 id='Parts'>Sources <html><a href="#Sources"><font size="3">[Top]</font></a></html></h1><br />
<html><br />
<ol><br />
<li>Barnes, D. K. A., F. Galgani, R. C. Thompson, and M. Barlaz. "Accumulation and Fragmentation of Plastic Debris in Global Environments." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 1985-998. Print.</li><br />
<br />
<li>Gregory, M. R. "Environmental Implications of Plastic Debris in Marine Settings--entanglement, Ingestion, Smothering, Hangers-on, Hitch-hiking and Alien Invasions." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2013-025. Print.</li><br />
<br />
<li>"Global Polyurethane Market to Reach 9.6 Mln Tons by 2015." Plastemart.com. N.p., 30 Aug. 2011. Web. <http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674></li><br />
<br />
<li>"Polyurethane Recycling." Polyurethanes. American Chemistry Council, n.d. Web. <http://polyurethane.americanchemistry.com/Sustainability/Recycling>. </li><br />
<br />
<li>"Frequently Asked Questions on Polyurethanes." Polyurethanes.org. European Diisocyanate and Polyol Producers Association, n.d. Web. <http://www.polyurethanes.org/index.php?page=faqs>. </li><br />
<br />
<li>Takasuga, T., N. Umetsu, T. Makino, K. Tsubota, KS Sajwan, and KS Kumar. "Role of Temperature and Hydrochloric Acid on the Formation of Chlorinated Hydrocarbons and Polycyclic Aromatic Hydrocarbons during Combustion of Paraffin Powder, Polymers, and Newspaper." Archives of Environmental Contamination and Toxicology (2007): 8-21. Print. </li><br />
<br />
<li>Teuten, E. L., J. M. Saquing, D. R. U. Knappe, M. A. Barlaz, S. Jonsson, A. Bjorn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha, and H. Takada. "Transport and Release of Chemicals from Plastics to the Environment and to Wildlife." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2027-045. Print. </li><br />
<br />
<li>Kang, Chul-Hyung. "A Novel Family VII Esterase with Industrial Potential from Compost Metagenomic Library." Microbial Cell Factories 10.41 (2011): n. pag. Print. </li><br />
<br />
<li>Bokinsky, Gregory, Et. Al. "Synthesis of Three Advanced Biofuels from Ionic Liquid-penetreated Switchgrass Using Engineered Escherichia Coli." Proceedings of the National Academy of Sciences of the United States of America 108.50 (2011): 19949-9954. Print. </li><br />
<br />
<li> Boronat, Albert, Estrella Caballero, and Juan Aguilar. "Experimental Evolution of a Metabolic Pathway for Ethylene Glycol Utilization by Escherichia Coli." Journal of Bacteriology Jan. (1983): 134-39. Web. </li><br />
<br />
<li> E. Toprak, A. Veres, J. B. Michel, R. Chait, D. L. Hartl, R. Kishony, “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection,” Nature Genetics, vol. 44, no. 1, pp. 101-105, Jan. 2012. </li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/OptogeneticsTeam:Washington/Optogenetics2012-10-04T01:01:37Z<p>Felixekn: /* Optogenetics: A hands-free approach to protein regulation */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>Optogenetics: A hands-free approach to protein regulation</i>==<br />
<br />
[[File:Washington_Multichromatic_Peppers.png|border|180px|right|thumb|An image of two peppers by shining red and green light on bacteria with light receptors.[3]]]<br />
<h1 id='Background'>Background </h1><br />
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Bacterial metabolic pathways constitute a foundation on which to build biological processes that perform useful tasks such as the production of drugs, biofuels, or the degradation of harmful compounds. Often, to achieve an optimally tuned system, expression levels of each component must be varied over a wide range. The burgeoning field of optogenetics affords researchers the ability to control gene expression with light. In addition to being low cost, controlling of gene expression with light has a number of advantages over standard chemical methods of gene control. Among these are the ability to finely tune induction levels through changes in intensity, as well as the ability to quickly and completely remove the input. Further, many light-induced expression systems are reversible depending on the wavelength used for illumination. Thus one of the goals of the 2012 iGEM team is to implement a light inducible system which we hoped to apply to the tuning of multiple metabolic pathways. In addition, we wanted to make the tools to control optogenetic systems readily available and easier to access.<br />
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<h1 id='App'>Our App: E. colight<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[File:UWiGEM2012QR_size6.png|link=https://play.google.com/store/apps/details?id=com.UWIgem.test2d|left|text-top|alt=App QR Code|frame|We developed an [https://play.google.com/store/apps/details?id=com.UWIgem.test2d app for the Android OS] that allows up to control gene expression with light.]][[Image:Lightapp.png|150px|right]]<br />
At the beginning of our project we wanted to make a system for high-specificity light control without the common problems such systems came with: High cost, difficult assembly, and very little reproducibility. With these goals in mind, we worked with Max Gelb to create E. colight.<br />
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E. colight was designed entirely from scratch in order to illuminate bacterial cultures with different types of light. The light source is on as either blue, green, or red. In the app, any given color of light can be shone between 0 and 60,000 milliseconds, which will illuminate the culture accordingly. Intensity of the light can be adjusted between 0 and 100%. All three colors are individually controllable, making high-specificity multichromatic tuning very easy.<br />
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The app has two settings which allow it to control bacterial cultures: One is its petri dish setting, which is a single light source made for a petri dish with an 8.5mm diameter. The other is a 96-well plate, which means it has '''''96 individually-controllable light sources''''', which lends the potential to run huge numbers of tests in tandem. The size of both the wells and the petri dish is flexible and can be adjusted between 0 and 100% of its original size.<br />
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<h1 id='Methods'>Methods<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
===Characterization of a light inducible protein expression system for the tuning of biological pathways===<br />
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Chromatic tuning is based off light-induced protein interactions. The most basic of these systems is a monochromatic system, in which a specific wavelength of light changes a protein's conformation temporarily, causing it to interact differently with its environment. Our system involves the use of a photoreceptor CcaS attached to a chromophore and CcaR, a response regulator. The chromophore, when illuminated with green light (λ=535nm) will undergo a conformational change leading to autophosphorylation of the photoreceptor CcaS. Increased autophosphorylation leads to activation of the response regulator via phosphorylation. The activated response regulator will then bind to the promoter pcpcg2, activating transcription and producing beta-galactosidase. Beta-galactosidase will cleave the S-gal, creating a black precipitate and allowing us to know visually that our light sensor is working. Red light (λ=672nm), on the other hand, changes the protein conformation such that it resists phosphorylation, thus not binding to the promoter and not allowing gene expression. Any gene placed after the promoter can be theoretically controlled by the exposure of λ=535nm and λ=637nm light to the system.<br />
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A blue light sensor (Lov_Tap BBa_K191003) was found in the registry and transformed to be used as one of our main components to create a functioning blue light sensor, activated at 470 nm wavelengths of light. The part taken from the registry was joined with a TetR inverter, since originally the blue light sensor repressed gene expression. The newly added TetR inverter then suppressed the regulator that came with Lov_Tap, allowing transcription to occur under stimulation of blue light. We also included sfGFP which is our readout protein, allowing the used to know whether or not the light sensor was properly functioning. Near the end, once we had all the pieces we did a Gibson assembly to join our pieces. At that time we noticed that our blue light sensor had a point mutation that needed to be fixed. The point mutation was then fixed through several PCRs that retrieved two parts of the sensor without the mutation. Once those two parts were obtained, they were then fused together in the Gibson assembly along with being Gibson assembled together with the inverter and sfGFP. Future iGEM projects could be focused around detaching sfGFP as the output protein and attaching the light sensor to other parts of the e.coli gene, allowing light illuminated control of genetic pathways.<br />
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===Building Optogenetic Tools===<br />
The current swath of tools available to illuminate bacterial cultures in a controlled manner often necessitate the purchase of specialized equipment or materials. Further, devices must often be both constructed, and fully characterized by the researcher prior to conducting any experiments to ensure reproducibility. To circumvent these problems, we sought to develop an application whose accessibility played off of modern technological conveniences. In recent years, mobile technology has become increasingly ubiquitous. Each year sees the release of many new tablets, phones, and small computers, including some that are relatively cheap. Tablets are excellently-sized, cost-effective tools for use with bacterial cultures. <br />
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[[File:AMOLED diagram.png|thumb|left|top|250px|AMOLED displays have organic cathode layers that provide high-intensity light when stimulated by a TFT array.[1]]]After a bit of research we decided to install our app on the Samsung Galaxy Tab 7.7. We chose the tablet because of it's relatively low cost and because of the brightness and contrast of the display. The Samsung Galaxy Tab 7.7 uses a Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display, which emits brighter and more intense light than LCD displays used on other tablets. Contrast is also elevated under an AMOLED display, as the color black is represented by turning an LED off, instead of the dark-grey substitute LCD displays use. The downside to AMOLED displays is that the organic material in the screen decays over the course of years, which causes uneven color shifts and imprints in the screen, but we decided that the benefits of the AMOLED display outweighed this detriment. The app was programmed by Max Gelb.<br />
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Our solution was to design and build a software application for use on tablet devices that is able to shine light of different frequencies in different conformations (i.e. 96 well plate, petri dish) to enable controlled and reproducible characterization and testing of optogenetic pathways. The application that we designed can generate different wavelengths of light. To use the application we simply chose well or plates, and then chose wait time between flashes of colors in milliseconds of wait time and color intensities of each color from 0-100. The bacteria can then be grown overnight on top of the app. The app is currently available in the google play store for free and provides a convenient way for anyone interested in biological sciences to test their optogenetic systems. Because iGEM teams and labs will have a set budget, we hope that this tool becomes useful to all that want to pursue science.<br />
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[[File:App+Plate-in-action.jpeg|right|thumb|text-top|200px|An example petri dish on top of the working app.]]<br />
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===Experimental Description===<br />
E. coli was transformed with pJT122 and pJT106b, which contained the lacZ gene, the green light sensor, and the system's chromophores.[3] In order to characterize the genes, we put them through three experiments: An assay of pigment concentration based on bacterial concentration, an assay of pigment concentration based on absorbed light, and a test of light leakage between wells.<br />
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[[File:Washington_Concentration_Gradient.png|left|thumb|350px|In the bacterial concentration assay, as the amount of E. coli in a well increased, so too did the expression of lacZ.]]For the assay based on bacterial concentration, we grew an overnight culture of JT2 cells transformed with pJT122 and pJT106b overnight in buffered LB. Cultures were grown in tubes wrapped with aluminum foil to ensure no light interfered with gene expression during growth. We then diluted the cultures in a 1:5 serial dilution in a row on a 96-well plate, and exposed them only to green light from E. colight for a 15-hour incubation. As seen in the picture to the right, the cells visibly demonstrated an increase in pigment production (a sign of increased lacZ activity).[[File:09192012 cropped scaled.png|border|250px|right|thumb|lacZ is downregulated by red light in our modified bacteria. Layout of plates, from left to right: constant illumination under red light, green light, or grown in the dark.]][[File:09192012 cropped measurements plot.png|right|thumb|250px|Image analysis of the petri dishes above. The x axis indicates treatment (red light, green light, or darkness). The y axis indicates darkness. Red-treated cells are much lighter than the other treatments, indicating red light downregulation of lacZ expresion.]]<br />
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In order to test the functionality of the green light activator and red light repressor, we plated JT2 cells with pJT122 and pJT106b onto three petri dishes. One petri dish was incubated in green light, one in red light, and one in total darkness for 15 hours. The dish exposed to red light demonstrated very strong downregulation, while the dish exposed to green light showed strong upregulation.<br />
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When performing characterization tests on 96-well plates, we found that bacteria over a predominantly-green well but near predominantly-red wells had drastically decreased gene expression compared to predominantly-green wells that weren't near red light. After repeating trials, we decided this was due to light leaking from one well to another. An LED spreads light in all directions, unlike a laser which focuses only in a single area. This leak was causing the green light sensors to switch conformation and repress gene output, which meant that our tuning could lead to imprecise results. In order to compensate for this, we added a feature to the app, and made use of a rubber gasket. Hoping to keep our tools all electronic, we added a well-size modulator to the app in order to increase the gap spaces between wells and hopefully decrease the amount of light that moved from its generative well to another well. The rubber gasket was our physical fix, and simply involved placing a 96-well rubber cover on top of the app so that only light moving upwards would pass though.<br />
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In order to make more precise comparisons between data, we used ImageJ to quantify lacZ expression. In ImageJ, the images were converted to grayscale, and the average black pixel density was calculated for each plate or well. The quantified data gives a more careful look at our results, and adds details to results that cannot be told apart by the naked eye.<br />
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<h1 id='Results'>Results Summary <html><a href="#Results"><font size="3">[Top]</font></a></html></h1><br />
When originally characterizing the light sensor, we found that bacteria left in darkness turned the controlled gene on equal to or more than the green light-illuminated plate. This is possibly because P<sub>cpcG2</sub> is leaky, or possibly because the natural conformation of the green light sensor is its upregulation form. Either way, our tests demonstrated that only the red light repressor was truly functional. When dealing with light leakage, we found that even decreasing the size of the light source didn't help as much as simply covering the light with the rubber stop.<br />
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As a proof of concept of both the light sensor and E. colight, we created a gradient of gene expression by making use of the light leakage. In two rows of a 96-well plate, we programmed all the well lights to display green constantly except for two lights in the middle, which periodically flashed red. The red light-induced downregulation of gene expression was demonstrated as the least amount of visible pigment was in the center of the gradient, and the greatest amount was at the ends. <br />
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To create some kind of gradient for the system, we decided to use a 96 well plate and the app. There are rows with constant green light excitation, two rows in the middle that flash and end rows that have constant green light on. The plate is darkest on either end with a gradient that lightens up as it gets closer to the middle, where red and green light flash, inducing intermediary amounts of gene expression. Ideally there should only be lighter colors of wells in columns 6 and 7. However, there appears to be some leakiness with the utilization of the light app as some of this light has managed to leak to other wells, which creates a sort of gradient with darkest wells on either side and becoming lighter towards the middle. Pipetting error also affects the data as some of the wells have more colonies than others which leads to higher pixel values even if gene expression may not necessarily be higher. Using soft agar can help resolve this problem in the future. The quantified data above was made by using ImageJ and measuring average pixel value of each plate. Generally there is a gradient of increased pixel value as the well is further away from the red light. Both replicates show similar results, even though replicate one has slightly ,more dramatic increases while replicate one appears to have a steadier rise. Because of leakiness, some of the flashing red light that was supposed to be confined to wells 6 and 7 have leaked over to nearby wells, causing some gene repression even when there shouldn’t necessarily have been any. However the results show that gene repression is extremely sensitive to red light and a gradient can be created from various levels of red light relatively easily. That being said, gene control isn’t completely “on” or “off” but also holds the benefit of having various levels of “on” and “off”. <br />
[[File:20120927 big dots cropped experimentwells resized.png|border|400px|left|thumb|Plate Experiment: Columns 1-5: constant exposure to green light. Columns 6-7: flashing red light. Columns 8-12: constant exposure to green light. Rows are replicates.]]<br />
[[File:20120927 big dots cropped experimentwells firstroi ddply plot.png|border|500px|right|thumb|Image analysis of the plate experiment. The y axis is well darkness and the x axis is distance from the center of the plate. 0 is columns 6 and 7, 1 is 5 and 8, and so on. Wells closest to the center (red light, reduces lacZ expression) are lighter than wells that are farther away, indicating a smaller amount of cut S-Gal.]]<br />
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<h1 id='Future'>Future Directions<html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
We want to work more with this biological system, improving the light sensor available so that we can biobrick them later and have those pieces readily available for use. We suspect that a big problem with the light sensor right now is the leaky promoter, which leads to more transcription than we would normally expect in dark conditions. The promoter should be modified accordingly, either by fixing any present mutation or choosing to use a new promoter entirely for various reasons. Also to improve characterization of the sensor itself, in the future soft agar should be used which will allow more sufficient spread of bacteria, which should minimize differences in bacteria colonies in each well, allowing better data collection with imageJ. There also appears to be some leakiness between wells that should be addressed more appropriately in the future. A suggestion was made to use a rubber stopper with holes in it on top of the app to try to minimize leakiness of light between different wells.We also want to work with different light sensors to improve functioning and to use light sensors to promote various gene expression of different proteins.<br />
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The app is readily available and can be downloaded onto any android OS system through the Google play store. Currently we have access to the tablet system with the app on it so we will want to use this app to further test our bacteria in the future to validate for functionality and to control genetic expression. <br />
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The main idea of creating a light activated system would be to control gene transcription, either producing a wanted substrate or breaking substrates down. One of the ways we would like to work with our light systems in the future is to optimize it to control metabolism or production of needed substrates in e.coli. Doing that will allow us to optimally control the growth of our bacteria, only allowing it to grow under a certain wavelength of light and metabolism being repressed in the absent of light and slowed down without the right wavelength of light illuminating the bacteria. That way, if someone becomes contaminated with the bacteria and goes out into the environment, the bacteria will ideally not be able to thrive there. Therefore, by preventing bacteria growth through optimized gene control we can prevent the spread of bacteria therefore protecting the environment from potential contamination with mutant strains.<br />
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<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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<h1 id='Sources'>Sources <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[1]http://upload.wikimedia.org/wikipedia/commons/9/96/AMOLED_en.svg<br />
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[2]Levskaya, A. et al. Synthetic biology: Engineering Escherichia coli to see light. Nature 438, 441–442 (2005).<br />
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[3]Tabor, J. J., Levskaya, A. & Voigt, C. a Multichromatic control of gene expression in Escherichia coli. Journal of molecular biology 405, 315–24 (2011).</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-04T01:01:16Z<p>Felixekn: /* “For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General */</p>
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<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
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<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs [1]. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]|border|330px|left|thumb|Hemagglutinin is an viral protein that coats the surface of Influenza.]<br />
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One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
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We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
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<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
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To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
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<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
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== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
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If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
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===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
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===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu biunder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
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[[Image:Washington H312A.jpg|border|800px|center|thumb| On the left: A histidine residue on the HB36.5 binder (purple) clashed with a phenylalanine reside on H2 (green) and showed a positive-predicted score, which indicated poor binding. On the right: the histidine residue was mutated to an alanine, thus reducing clashing and and predicting better binding.]]<br />
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== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
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The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
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The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
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We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
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To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
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Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
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==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997) [3]. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
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We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
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<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
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<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/PlasticsTeam:Washington/Plastics2012-10-04T00:49:38Z<p>Felixekn: /* The Turbidostat Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“Plastics: made to last forever, designed to throw away”--5gyres.org</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:Washington_Sadfish.png|border|440px|right|thumb|The increasing problem of plastic waste.]]<br />
Playing an integral role in our modern world, plastics account for over a third of products made today. They have a relatively low cost of production, serve to promote the development of industry, lower the cost of consumer goods, and are easy to produce on a large scale. Plastics, regardless of disposability, compose 10% of the waste generated in the world and an even higher percentage is carelessly thrown into our environment <html><a href="#Sources"><font size="1">[1]</font></a></html>. Plastic pollution discharges debris into the ocean that are ingested by wildlife, often resulting in injury or death <html><a href="#Sources"><font size="1">[2]</font></a></html>.<br />
<br />
<br />
===The PUR Problem ===<br />
A commonly used plastic, Polyurethane (PUR), is used to create a wide range of products including dense solids, rigid insulating foam, and thermoset elastics. Because of its extreme versatility, the world consumption of PUR is continually increasing. According to current market analysis, the global production of PUR is estimated at 27 billion pounds per year and is expected to increase to 36 billion pounds by the year 2016 <html><a href="#Sources"><font size="1">[3]</font></a></html>. The leading method of recycling polyurethane is to mechanically grind the waste and rebind it into carpet cushioning, accounting for about 4% of waste polyurethane <html><a href="#Sources"><font size="1">[4]</font></a></html>. However, due to the high costs of transportation and chemical degradation, most PURs are incinerated to recapture some of the energy used to make them <html><a href="#Sources"><font size="1">[5]</font></a></html>.<br />
<br />
<br />
[[Image:Washington_PURBURN.png|border|280px|left|thumb|Current methods of disposal, like burning is harmful for our environment.]]<br />
===Current Means of Disposal are Undesirable===<br />
Non-biodegradable plastics are often disposed of through waste incineration as it is the most efficient method of degradation. However, a major issue with this is that the products of plastic burning, which include polychlorinated di-benzo-p-dioxins/furans and carbon dioxide, are known to be carcinogenic <html><a href="#Sources"><font size="1">[6]</font></a></html>. Upon incineration, these gaseous products are released into the atmosphere and have the potential to cause future problems to human health as well as contribute to global warming. As an additional concern, plastics constitute large volumes of space as they are habitually disposed of in landfills. It has been shown that plasticizers and plastic additives leak from the these plastics and contaminate aquatic environments <html><a href="#Sources"><font size="1">[7]</font></a></html>. Thus, because of the environmental damage that current disposal methods incur, there is a large need for safer methods of plastic degradation.<br />
<br />
===Engineering Microbes to Degrade PUR===<br />
To solve the problem of PUR recycling, we propose a bacteria that is both able to degrade PUR and subsist off of the products of degradation as its sole carbon source. To this end, we propose a two plasmid system. The first plasmid would have two genes. The first gene, polyeurethane esterase, will encode an enzyme that is able to break down the PUR polymer structure into two molecules, one of which, ethylene glycol, can diffuse across the membrane of the bacterium <html><a href="#Sources"><font size="1">[8]</font></a></html>. The second gene, osmotic inducible protein Y (osmY), would encode a protein that fuses to PUR esterase and exports the enzyme through the cell membrane and into the supernatant <html><a href="#Sources"><font size="1">[9]</font></a></html>. The second plasmid would have an operon composed of, glycolaldehyde reductase (fucO) and glycoaldehyde dehydrogenase (aldA), that allows the bacterium to use ethylene glycol as its central metabolite <html><a href="#Sources"><font size="1">[10]</font></a></html>. This system would allow the bacteria to turn the plastics plaguing our landfills into bacterial biomass which would in turn degrade more PUR. <br />
[[Image:Washington_Plastic_Overview.png|border|950px|center|thumb|The complete degradation pathway takes polyurethane, an undesirable waste product and converts it into a central metabolite for bacterium.]]<br />
<br />
<br />
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<h1 id='App'>Our App: The Turbidostat<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Washington_Turbidostat.png|border|400px|left|thumb|Schematic diagram of our turbidostat. It uses the optical density measurements to modulate how much media is pumped into the culture vessel every minute to maintain a constant cell density. Through this method of cell density control, we can measure how fast the cells are growing and thus gain an understanding for relative fitness increases thorough out the course of an experiment.]]<br />
[[Image:Recycleapp.png|150px|right]]<br />
We wanted a general system that would allow us to improve and characterize the function of a given strain of bacteria/expressed plasmid in a tightly controlled environment. However, devices that can accomplish this are not commercially available. Additionally, details for such tools are often sparsely detailed and frequently require exotic reagents and specialized training. To circumvent these issues, we instead decided to use our expertise in electrical, biological, and computer engineering to create a software tool that could control cheaply and readily available hardware to achieve the desired directed evolution and characterization functionality. <br />
<br />
<br />
To characterize and optimize growth of specific parts of our overall polyurethane degradation pathway we developed a <i><b>homemade software application (App)</b></i>. Our App uniquely controls an assortment of hardware devices easily found online, creating a fully functional turbidostat. A turbidostat is a closed-loop continuous culture device that maintains a desired constant cell density and chemical environment. The constant environment allows for continuous log phase growth and constant selective pressures (in our case polyurethane or ethylene glycol), which enables proper characterization (seen through media input per dilution cycle) and reduces evolutionary trajectory drift. By maintaining constant selective pressures, the rate of evolution is also dramatically improved <html><a href="#Sources"><font size="1">[11]</font></a></html>. By creating this turbidostat App (written in Python), we are able to properly asses and optimize, given enough time, our individual parts as well as the entire polyurethane degrading circuit in a controlled and reliable manner.<br />
<br />
<br />
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<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[[Image:Washington_PUR.png|border|250px|left|thumb|To assemble the polyurethane esterase, we used 4 IDT gBlocks, each containing a unique set of homology sequences that only allow for one orientation of binding, and Gibsoned them together onto a pGA backbone.]]<br />
<br />
===PUR Esterase Assay=== <br />
Polyurethane esterase degrades polyurethane by cleaving the ester bonds to produce one of a variety of monomers with isocyanate groups on either end and an ethylene glycol. For our circuit, we have chosen to use the esterase estCS2 ([http://partsregistry.org/Part:BBa_K892012 BBa_K892012]). Originally isolated and characterized by Kang et al [8], the sequence of estCS2 is available at [http://www.ncbi.nlm.nih.gov/nuccore/283462288 GenBank (GU256649)]. Since their DNA constructs were unavailable and the authors did not respond to requests, we synthetically constructed the entire 1.7kb estCS2 gene from four 500-base gBlocks provided by IDT using Gibson cloning. <br />
<br />
<br />
[[Image:Washington_PUR_Assay.png|border|350px|right|thumb|To assemble for activity of polyurethane esterase, cells transformed with the PUR esterase plasmid were grown to <br />
saturation, sonicated, then a piece of pre-weighed polyurethane foam was inserted into the culture tube.]]<br />
To assay esterase activity we made one 50mL TB + kanamycin overnight culture for the cells containing the esterase plasmid and a 50mL TB overnight culture with untransformed cells. After incubating the cultures for a day, we spun them down to form a pellet. The supernatant was then poured out and then the remaining pellets were resuspended the in enough water for each culture to be equalized at an arbitrary OD of 1.4. Three milliliters of each resuspended culture was aliquoted out into three microcentrifuge tubes (1 mL of one culture in each tube, 6 total microcentrifuge tubes). These tubes, along with 6 microcentrifuge tubes that contained only 1mL of LB were then sonicated at an amplitude of 20 with 1 second pulses on and off for 30 seconds. After sonication we poured each lysate and LB aliquots into 25mL culture tubes containing 1mL of LB and a preweighed sample of foam. This gave us a total of 12, 25mL culture tubes containing a pre weighed foam piece with LB+lysate or LB+sonicated LB. These culture tubes were left on the bench top at room temperature overnight. In the morning we washed the foam with DI water and dried them in a 65º C incubator. After drying, we weighed the samples to see if there was any change in mass of the foam samples.<br />
<br />
===Exporter Protein OsmY Assay===<br />
Since polyurethane is a large polymer, larger than the cell itself, it cannot be imported for degradation and therefore our esterase must be exported. It has been shown in Bolkinsky, Gregory et al that a protein, osmotically inducible protein Y (osmY, [http://partsregistry.org/Part:BBa_K892008 BBa_K892008]), is naturally exported by <i>E. coli</i> [9]. Furthermore this paper shows that osmY can be used as an export tag in <i>E. coli</i> when it is translationally fused to a protein. <br />
<br />
[[Image:Washington_osmY.png|border|348px|left|thumb|Our exporter protein (osmY) is linked to sfGFP through a serine glycine linker.]]<br />
[[Image:Washington_osmY_Assay.png|border|546px|right|thumb|We used osmY fused to sfGFP and as a control sfGFP fused to mamI in the same translational conformation to test for osmY functionality. These cultures were spun down and the fluorescence (480nm excitation, 509nm emission) of the cells+supernatant and supernatant were measured.]]<br />
<br />
<br />
To assay the exportation system we created a fusion protein. The fusion protein consisted of super folder GFP (sfGFP) fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]), which allowed for visual assessment of whether or not osmY was correctly exported into the supernatant. For the control, sfGFP fused to mamI ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K590016 BBa_K590016]), a protein that binds to the cell membrane, was used. The sfGFP-osmY and sfGFP-mamI plasmids were transformed into BL21 and then were grown up overnight (after picking a colony of each from their transformation plates) in M9 1% glucose media. To quantify whether or not osmY properly exported proteins outside of the cell membrane, 4, 500µL aliquots of each overnight culture was made. From each of these aliquots, 200µL of the culture was pipetted into a 96 well plate and then the remaining culture was spun down in a centrifuge at 3000g for 3 minutes. Carefully, from each of the spun down aliquots, 200µL of the supernatant was pipetted into the 96 well plate. The remaining supernatant for each aliquot was disposed of and the cells were resuspended with 300 µL of fresh M9 1% glucose media. From the each of the resuspended aliquots, 200µL was pipetted into the 96 well plate. The 96 well plate was then read with a plate reader; each well was excited with a 480+/-20 nm light and then a detector quantified how much 509 +/-20 nm light was give off. <br />
<br />
Using the information from the plate reader we could determine if osmY properly exports the fused protein. If the supernatant from the cells expressing the osmY construct had high fluorescence with respect to cells+supernatant while the supernatant of the control sfGFP-mamI had low fluorescence compared to its overall cells+supernatant fluorescence than it would be concluded that the protein was properly exported when fused to osmY. But if there was little to no supernatant fluorescence of the sfGFP-osmY construct or sfGFP-mamI and sfGFP-osmY illustrated the same ratio of supernatant fluorescence to cells+supernatant fluorescence then it would be concluded that osmY did not export proteins outside of the cell or our designed fusion between sfGFP and osmY disrupted sfGFP/osmY function.<br />
<br />
<br />
[[Image:Washington_fucO_aldA.png|border|350px|left|thumb|By putting fucO and aldA onto a single plasmids with a single promote, we were able to create an operon that when transcribed and translated allows microbes to utilize ethylene glycol as a sole carbon source [10].]]<br />
<br />
===The Turbidostat Assay===<br />
The media used for the turbidostat assay was comprised exclusively of M9 salts and ethylene glycol. Because of this choice of media, the only carbon source available to <i>E. coli</i> was ethylene glycol and because this can't be digest by normal lab strains of <i>E. coli</i>, only <i>E. coli</i> that can digest ethylene glycol can subsist <html><a href="#Sources"><font size="1"><sup>[10]</sup></font></a></html>. The turbidostat App tracks cell growth by first blanking (setting optical density (OD) to 0) and then measuring and recording OD values after inoculation. Because OD and cell density are both linearly related, OD is a proxy for cell density measurements. After blanking on pure M9 30mM ethylene glycol media, MG1655 cells that were transformed with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]), which were grown in TB to log phase and then were washed with PBS 3 times and then added to the turbidostat culture vessel to an OD of 0.2. This allowed for the cells still in log phase to divide without exceeding the OD threshold of 0.3 (our desired constant cell density). The turbidostat was then left running overnight and culture density and amount of fresh M9 ethylene glycol media added per dilution cycle were checked in the morning.<br />
<br />
<br />
The next assay ran was to transform MG1655 with a fucO-aldA operon plasmid ([http://partsregistry.org/Part:BBa_K892013 BBa_K892013]). This operon consisted of fucO and aldA being on a single plasmid that contains a single promoter. With this present, transformed MG1655 cells would be theoretically more viable in ethylene glycol since the cells would not have to spend energy on two antibiotic resistance genes (although no antibiotics were used in the the M9 ethylene glycol media, the cells still transcribe and translate the genes). By running the turbidostat in the same way as the dual transformation experiment, an idea for how well the transformed operon fairs versus how well the two plasmid transformation could be gained.<br />
<br />
<br />
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<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===PUR Esterase Assay Results===<br />
We ran the PUR esterase assay and we found that the foam we used did not degrade. We quantified this by measuring the weight of the foam pieces to the nearest milligram before and after we ran the assay. We actually ended up leaving the lysate with the foam for three days to improve degradation but this had no effect on the end result. The reason we think the foam was not degraded was because we did not know the full composition of the foam, it could of had other polymers mixed in with the polyurethane that prevented our enzyme from properly degrading it. Further testing will need to be done to properly assess the functionality of our PUR esterase.<br />
<br />
===Exporter Protein OsmY Assay Results===<br />
[[Image:Washington_osmY_Assay_Results.png|border|940px|center|thumb|Each histogram depicts the average normalized fluorescence values for cells plus supernatant, cells only, and supernatant only. The image in the center is a visual representation of the presented data.]]<br />
When running the osmY assay described in the methods section, we saw that sfGFP fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]) resulted in a much higher ratio between cells + supernatant and supernatant only fluorescence values than the ratio of cells + supernatant and supernatant fluorescence of sfGFP fused to mamI. Even though the cells only fluorescence of sfGFP-osmY plasmid exhibited nearly the same level of fluorescence as supernatant only, it does not go against the argument that osmY is properly exporting sfGFP into the supernatant because sfGFP-osmY is produced within the cell and it can't be expected that all osmY be secreted from the cell. Thus from the above data, it can be gathered that <b>osmY when fused to a protein will export the protein out into the supernatant.</b><br />
<br />
===Turbidostat Assay Results===<br />
[[Image:Washington_Turbidostat_Pump.png|border|450px|left|thumb|M9 30 mM ethylene glycol media injected into the culture vessel every minute to maintain the arbitrary optical density of 0.3.]]<br />
<br />
[[Image:Washington_Turbidostat_OD.png|border|450px|right|thumb|Optical density measurements of <i>E. coli</i> transformed with fucO pGA3K3 + aldA pGA1C3 within the turbidostat growing on M9 30 mM ethylene glycol media.]]<br />
<br />
<br />
Following the turbidostat assay protocol, we used transformed MG1655 with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) on a medium copy Kanamycin resistant backbone and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]) on a high copy Chloramphenicol resistant backbone and cultured it up in the turbidostat. The turbidostat was operated overnight and the above data illustrates that the transformed MG1655 grew using ethylene glycol as its sole carbon source, which normal untransformed MG1655 can't do. This is the case because the optical density (OD)/cell density rose from an initial arbitrary OD of 0.18 (time of inoculation) to an OD of 0.3, which was maintained throughout the time of the experiment (15 hours). The turbidostat maintained this OD through constantly imputing amounts of new media into the culture vessel based whenever the OD surpassed 0.3, which was quite frequently. Although time constraints did not allow us to transform MG1655 with our fucO-aldA operon and characterize it, it can be seen from the data above that <b>when fucO and aldA genes are individually transformed and over expressed in MG1655 <i>E. coli</i>, <i>E. coli</i> gains the ability to utilize ethylene glycol as its sole carbon source. The average doubling time (based off of a 10mL culture volume and an average dilution rate of 30µL/min) was 333 minutes, slightly faster than the literature value of 360 minutes [10].</b> <br />
<br />
<br />
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<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===Putting it All together===<br />
The parts as they are right now have not been put into one cell strain and tested as a system. The obvious next step would be to test the efficacy of the entire plastic degrading construct against polyurethane. Using the turbidostat, we have the luxury of having an optimized and continual growth environment for our system to mutate towards improved functionality. Better cell strains would be periodically saved and tested in harsher selective conditions until eventually the strain is able to survive and reproduce off of polyurethane as its sole carbon source. We recognize that this may take many iterations to complete but the end product could potentially be a cell that is able to consume plastic much faster and safer than traditional recycling methods.<br />
<br />
<br />
===Trash to Treasure===<br />
After stable growth off of plastic is achieved the next step would be to clone in a third plasmid that would produce some valuable commodity. Washington 2011 demonstrated that diesel fuel can be synthesized from central metabolites using their Petrobrick platform. The addition of the Petrobrick to the system would allow plastic waste to be processed by <i>E. coli</i> which would then turn it into biofuels. <br />
<br />
<br />
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<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892008 BBa_K892008 '''osmY''']<br />
<br />
The coding sequence for osmotically induced protein Y, a protein that when fused to another protein, gets exported out of <i>E. coli</i> cells.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009 '''fucO''']<br />
<br />
The gene fucO, which codes for glycolaldehyde reductase, is one of the genes required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010 '''aldA''']<br />
<br />
The gene aldA, which codes for glycolaldehyde dehydrogenase, is the other gene required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892011 BBa_K892011 '''sfGFP-osmY''']<br />
<br />
A composite part for osmotically induced protein Y fused downstream to sfGFP using a glycine - serine linker regulated by the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892012 BBa_K892012 '''PUR Esterase''']<br />
<br />
An enzyme that breaks down polyurethane plastic behind the control of the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892013 BBa_K892013 '''fucO-aldA''']<br />
<br />
The combination of BBa_K892009 and BBa_K892010 behind a strong biofab promoter.<br />
<br />
<br />
----<br />
<html><a name="Sources"></a></html><br />
<h1 id='Parts'>Sources <html><a href="#Sources"><font size="3">[Top]</font></a></html></h1><br />
<html><br />
<ol><br />
<li>Barnes, D. K. A., F. Galgani, R. C. Thompson, and M. Barlaz. "Accumulation and Fragmentation of Plastic Debris in Global Environments." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 1985-998. Print.</li><br />
<br />
<li>Gregory, M. R. "Environmental Implications of Plastic Debris in Marine Settings--entanglement, Ingestion, Smothering, Hangers-on, Hitch-hiking and Alien Invasions." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2013-025. Print.</li><br />
<br />
<li>"Global Polyurethane Market to Reach 9.6 Mln Tons by 2015." Plastemart.com. N.p., 30 Aug. 2011. Web. <http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674></li><br />
<br />
<li>"Polyurethane Recycling." Polyurethanes. American Chemistry Council, n.d. Web. <http://polyurethane.americanchemistry.com/Sustainability/Recycling>. </li><br />
<br />
<li>"Frequently Asked Questions on Polyurethanes." Polyurethanes.org. European Diisocyanate and Polyol Producers Association, n.d. Web. <http://www.polyurethanes.org/index.php?page=faqs>. </li><br />
<br />
<li>Takasuga, T., N. Umetsu, T. Makino, K. Tsubota, KS Sajwan, and KS Kumar. "Role of Temperature and Hydrochloric Acid on the Formation of Chlorinated Hydrocarbons and Polycyclic Aromatic Hydrocarbons during Combustion of Paraffin Powder, Polymers, and Newspaper." Archives of Environmental Contamination and Toxicology (2007): 8-21. Print. </li><br />
<br />
<li>Teuten, E. L., J. M. Saquing, D. R. U. Knappe, M. A. Barlaz, S. Jonsson, A. Bjorn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha, and H. Takada. "Transport and Release of Chemicals from Plastics to the Environment and to Wildlife." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2027-045. Print. </li><br />
<br />
<li>Kang, Chul-Hyung. "A Novel Family VII Esterase with Industrial Potential from Compost Metagenomic Library." Microbial Cell Factories 10.41 (2011): n. pag. Print. </li><br />
<br />
<li>Bokinsky, Gregory, Et. Al. "Synthesis of Three Advanced Biofuels from Ionic Liquid-penetreated Switchgrass Using Engineered Escherichia Coli." Proceedings of the National Academy of Sciences of the United States of America 108.50 (2011): 19949-9954. Print. </li><br />
<br />
<li> Boronat, Albert, Estrella Caballero, and Juan Aguilar. "Experimental Evolution of a Metabolic Pathway for Ethylene Glycol Utilization by Escherichia Coli." Journal of Bacteriology Jan. (1983): 134-39. Web. </li><br />
<br />
<li> E. Toprak, A. Veres, J. B. Michel, R. Chait, D. L. Hartl, R. Kishony, “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection,” Nature Genetics, vol. 44, no. 1, pp. 101-105, Jan. 2012. </li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/PlasticsTeam:Washington/Plastics2012-10-04T00:48:30Z<p>Felixekn: /* The Turbidostat Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“Plastics: made to last forever, designed to throw away”--5gyres.org</i>==<br />
<br />
<h1 id='Background'>Background</h1><br />
[[Image:Washington_Sadfish.png|border|440px|right|thumb|The increasing problem of plastic waste.]]<br />
Playing an integral role in our modern world, plastics account for over a third of products made today. They have a relatively low cost of production, serve to promote the development of industry, lower the cost of consumer goods, and are easy to produce on a large scale. Plastics, regardless of disposability, compose 10% of the waste generated in the world and an even higher percentage is carelessly thrown into our environment <html><a href="#Sources"><font size="1">[1]</font></a></html>. Plastic pollution discharges debris into the ocean that are ingested by wildlife, often resulting in injury or death <html><a href="#Sources"><font size="1">[2]</font></a></html>.<br />
<br />
<br />
===The PUR Problem ===<br />
A commonly used plastic, Polyurethane (PUR), is used to create a wide range of products including dense solids, rigid insulating foam, and thermoset elastics. Because of its extreme versatility, the world consumption of PUR is continually increasing. According to current market analysis, the global production of PUR is estimated at 27 billion pounds per year and is expected to increase to 36 billion pounds by the year 2016 <html><a href="#Sources"><font size="1">[3]</font></a></html>. The leading method of recycling polyurethane is to mechanically grind the waste and rebind it into carpet cushioning, accounting for about 4% of waste polyurethane <html><a href="#Sources"><font size="1">[4]</font></a></html>. However, due to the high costs of transportation and chemical degradation, most PURs are incinerated to recapture some of the energy used to make them <html><a href="#Sources"><font size="1">[5]</font></a></html>.<br />
<br />
<br />
[[Image:Washington_PURBURN.png|border|280px|left|thumb|Current methods of disposal, like burning is harmful for our environment.]]<br />
===Current Means of Disposal are Undesirable===<br />
Non-biodegradable plastics are often disposed of through waste incineration as it is the most efficient method of degradation. However, a major issue with this is that the products of plastic burning, which include polychlorinated di-benzo-p-dioxins/furans and carbon dioxide, are known to be carcinogenic <html><a href="#Sources"><font size="1">[6]</font></a></html>. Upon incineration, these gaseous products are released into the atmosphere and have the potential to cause future problems to human health as well as contribute to global warming. As an additional concern, plastics constitute large volumes of space as they are habitually disposed of in landfills. It has been shown that plasticizers and plastic additives leak from the these plastics and contaminate aquatic environments <html><a href="#Sources"><font size="1">[7]</font></a></html>. Thus, because of the environmental damage that current disposal methods incur, there is a large need for safer methods of plastic degradation.<br />
<br />
===Engineering Microbes to Degrade PUR===<br />
To solve the problem of PUR recycling, we propose a bacteria that is both able to degrade PUR and subsist off of the products of degradation as its sole carbon source. To this end, we propose a two plasmid system. The first plasmid would have two genes. The first gene, polyeurethane esterase, will encode an enzyme that is able to break down the PUR polymer structure into two molecules, one of which, ethylene glycol, can diffuse across the membrane of the bacterium <html><a href="#Sources"><font size="1">[8]</font></a></html>. The second gene, osmotic inducible protein Y (osmY), would encode a protein that fuses to PUR esterase and exports the enzyme through the cell membrane and into the supernatant <html><a href="#Sources"><font size="1">[9]</font></a></html>. The second plasmid would have an operon composed of, glycolaldehyde reductase (fucO) and glycoaldehyde dehydrogenase (aldA), that allows the bacterium to use ethylene glycol as its central metabolite <html><a href="#Sources"><font size="1">[10]</font></a></html>. This system would allow the bacteria to turn the plastics plaguing our landfills into bacterial biomass which would in turn degrade more PUR. <br />
[[Image:Washington_Plastic_Overview.png|border|950px|center|thumb|The complete degradation pathway takes polyurethane, an undesirable waste product and converts it into a central metabolite for bacterium.]]<br />
<br />
<br />
----<br />
<h1 id='App'>Our App: The Turbidostat<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Washington_Turbidostat.png|border|400px|left|thumb|Schematic diagram of our turbidostat. It uses the optical density measurements to modulate how much media is pumped into the culture vessel every minute to maintain a constant cell density. Through this method of cell density control, we can measure how fast the cells are growing and thus gain an understanding for relative fitness increases thorough out the course of an experiment.]]<br />
[[Image:Recycleapp.png|150px|right]]<br />
We wanted a general system that would allow us to improve and characterize the function of a given strain of bacteria/expressed plasmid in a tightly controlled environment. However, devices that can accomplish this are not commercially available. Additionally, details for such tools are often sparsely detailed and frequently require exotic reagents and specialized training. To circumvent these issues, we instead decided to use our expertise in electrical, biological, and computer engineering to create a software tool that could control cheaply and readily available hardware to achieve the desired directed evolution and characterization functionality. <br />
<br />
<br />
To characterize and optimize growth of specific parts of our overall polyurethane degradation pathway we developed a <i><b>homemade software application (App)</b></i>. Our App uniquely controls an assortment of hardware devices easily found online, creating a fully functional turbidostat. A turbidostat is a closed-loop continuous culture device that maintains a desired constant cell density and chemical environment. The constant environment allows for continuous log phase growth and constant selective pressures (in our case polyurethane or ethylene glycol), which enables proper characterization (seen through media input per dilution cycle) and reduces evolutionary trajectory drift. By maintaining constant selective pressures, the rate of evolution is also dramatically improved <html><a href="#Sources"><font size="1">[11]</font></a></html>. By creating this turbidostat App (written in Python), we are able to properly asses and optimize, given enough time, our individual parts as well as the entire polyurethane degrading circuit in a controlled and reliable manner.<br />
<br />
<br />
----<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[[Image:Washington_PUR.png|border|250px|left|thumb|To assemble the polyurethane esterase, we used 4 IDT gBlocks, each containing a unique set of homology sequences that only allow for one orientation of binding, and Gibsoned them together onto a pGA backbone.]]<br />
<br />
===PUR Esterase Assay=== <br />
Polyurethane esterase degrades polyurethane by cleaving the ester bonds to produce one of a variety of monomers with isocyanate groups on either end and an ethylene glycol. For our circuit, we have chosen to use the esterase estCS2 ([http://partsregistry.org/Part:BBa_K892012 BBa_K892012]). Originally isolated and characterized by Kang et al [8], the sequence of estCS2 is available at [http://www.ncbi.nlm.nih.gov/nuccore/283462288 GenBank (GU256649)]. Since their DNA constructs were unavailable and the authors did not respond to requests, we synthetically constructed the entire 1.7kb estCS2 gene from four 500-base gBlocks provided by IDT using Gibson cloning. <br />
<br />
<br />
[[Image:Washington_PUR_Assay.png|border|350px|right|thumb|To assemble for activity of polyurethane esterase, cells transformed with the PUR esterase plasmid were grown to <br />
saturation, sonicated, then a piece of pre-weighed polyurethane foam was inserted into the culture tube.]]<br />
To assay esterase activity we made one 50mL TB + kanamycin overnight culture for the cells containing the esterase plasmid and a 50mL TB overnight culture with untransformed cells. After incubating the cultures for a day, we spun them down to form a pellet. The supernatant was then poured out and then the remaining pellets were resuspended the in enough water for each culture to be equalized at an arbitrary OD of 1.4. Three milliliters of each resuspended culture was aliquoted out into three microcentrifuge tubes (1 mL of one culture in each tube, 6 total microcentrifuge tubes). These tubes, along with 6 microcentrifuge tubes that contained only 1mL of LB were then sonicated at an amplitude of 20 with 1 second pulses on and off for 30 seconds. After sonication we poured each lysate and LB aliquots into 25mL culture tubes containing 1mL of LB and a preweighed sample of foam. This gave us a total of 12, 25mL culture tubes containing a pre weighed foam piece with LB+lysate or LB+sonicated LB. These culture tubes were left on the bench top at room temperature overnight. In the morning we washed the foam with DI water and dried them in a 65º C incubator. After drying, we weighed the samples to see if there was any change in mass of the foam samples.<br />
<br />
===Exporter Protein OsmY Assay===<br />
Since polyurethane is a large polymer, larger than the cell itself, it cannot be imported for degradation and therefore our esterase must be exported. It has been shown in Bolkinsky, Gregory et al that a protein, osmotically inducible protein Y (osmY, [http://partsregistry.org/Part:BBa_K892008 BBa_K892008]), is naturally exported by <i>E. coli</i> [9]. Furthermore this paper shows that osmY can be used as an export tag in <i>E. coli</i> when it is translationally fused to a protein. <br />
<br />
[[Image:Washington_osmY.png|border|348px|left|thumb|Our exporter protein (osmY) is linked to sfGFP through a serine glycine linker.]]<br />
[[Image:Washington_osmY_Assay.png|border|546px|right|thumb|We used osmY fused to sfGFP and as a control sfGFP fused to mamI in the same translational conformation to test for osmY functionality. These cultures were spun down and the fluorescence (480nm excitation, 509nm emission) of the cells+supernatant and supernatant were measured.]]<br />
<br />
<br />
To assay the exportation system we created a fusion protein. The fusion protein consisted of super folder GFP (sfGFP) fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]), which allowed for visual assessment of whether or not osmY was correctly exported into the supernatant. For the control, sfGFP fused to mamI ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K590016 BBa_K590016]), a protein that binds to the cell membrane, was used. The sfGFP-osmY and sfGFP-mamI plasmids were transformed into BL21 and then were grown up overnight (after picking a colony of each from their transformation plates) in M9 1% glucose media. To quantify whether or not osmY properly exported proteins outside of the cell membrane, 4, 500µL aliquots of each overnight culture was made. From each of these aliquots, 200µL of the culture was pipetted into a 96 well plate and then the remaining culture was spun down in a centrifuge at 3000g for 3 minutes. Carefully, from each of the spun down aliquots, 200µL of the supernatant was pipetted into the 96 well plate. The remaining supernatant for each aliquot was disposed of and the cells were resuspended with 300 µL of fresh M9 1% glucose media. From the each of the resuspended aliquots, 200µL was pipetted into the 96 well plate. The 96 well plate was then read with a plate reader; each well was excited with a 480+/-20 nm light and then a detector quantified how much 509 +/-20 nm light was give off. <br />
<br />
Using the information from the plate reader we could determine if osmY properly exports the fused protein. If the supernatant from the cells expressing the osmY construct had high fluorescence with respect to cells+supernatant while the supernatant of the control sfGFP-mamI had low fluorescence compared to its overall cells+supernatant fluorescence than it would be concluded that the protein was properly exported when fused to osmY. But if there was little to no supernatant fluorescence of the sfGFP-osmY construct or sfGFP-mamI and sfGFP-osmY illustrated the same ratio of supernatant fluorescence to cells+supernatant fluorescence then it would be concluded that osmY did not export proteins outside of the cell or our designed fusion between sfGFP and osmY disrupted sfGFP/osmY function.<br />
<br />
<br />
[[Image:Washington_fucO_aldA.png|border|350px|left|thumb|By putting fucO and aldA onto a single plasmids with a single promote, we were able to create an operon that when transcribed and translated allows microbes to utilize ethylene glycol as a sole carbon source [10].]]<br />
<br />
===The Turbidostat Assay===<br />
The media used for the turbidostat assay was comprised exclusively of M9 salts and ethylene glycol. Because of this choice of media, the only carbon source available to <i>E. coli</i> was ethylene glycol and because this can't be digest by normal lab strains of <i>E. coli</i>, only <i>E. coli</i> that can digest ethylene glycol can subsist <html><a href="#Sources"><font size="1">[10]</font></a></html>. The turbidostat App tracks cell growth by first blanking (setting optical density (OD) to 0) and then measuring and recording OD values after inoculation. Because OD and cell density are both linearly related, OD is a proxy for cell density measurements. After blanking on pure M9 30mM ethylene glycol media, MG1655 cells that were transformed with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]), which were grown in TB to log phase and then were washed with PBS 3 times and then added to the turbidostat culture vessel to an OD of 0.2. This allowed for the cells still in log phase to divide without exceeding the OD threshold of 0.3 (our desired constant cell density). The turbidostat was then left running overnight and culture density and amount of fresh M9 ethylene glycol media added per dilution cycle were checked in the morning.<br />
<br />
<br />
The next assay ran was to transform MG1655 with a fucO-aldA operon plasmid ([http://partsregistry.org/Part:BBa_K892013 BBa_K892013]). This operon consisted of fucO and aldA being on a single plasmid that contains a single promoter. With this present, transformed MG1655 cells would be theoretically more viable in ethylene glycol since the cells would not have to spend energy on two antibiotic resistance genes (although no antibiotics were used in the the M9 ethylene glycol media, the cells still transcribe and translate the genes). By running the turbidostat in the same way as the dual transformation experiment, an idea for how well the transformed operon fairs versus how well the two plasmid transformation could be gained.<br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===PUR Esterase Assay Results===<br />
We ran the PUR esterase assay and we found that the foam we used did not degrade. We quantified this by measuring the weight of the foam pieces to the nearest milligram before and after we ran the assay. We actually ended up leaving the lysate with the foam for three days to improve degradation but this had no effect on the end result. The reason we think the foam was not degraded was because we did not know the full composition of the foam, it could of had other polymers mixed in with the polyurethane that prevented our enzyme from properly degrading it. Further testing will need to be done to properly assess the functionality of our PUR esterase.<br />
<br />
===Exporter Protein OsmY Assay Results===<br />
[[Image:Washington_osmY_Assay_Results.png|border|940px|center|thumb|Each histogram depicts the average normalized fluorescence values for cells plus supernatant, cells only, and supernatant only. The image in the center is a visual representation of the presented data.]]<br />
When running the osmY assay described in the methods section, we saw that sfGFP fused to osmY ([http://partsregistry.org/Part:BBa_K892008 BBa_K892011]) resulted in a much higher ratio between cells + supernatant and supernatant only fluorescence values than the ratio of cells + supernatant and supernatant fluorescence of sfGFP fused to mamI. Even though the cells only fluorescence of sfGFP-osmY plasmid exhibited nearly the same level of fluorescence as supernatant only, it does not go against the argument that osmY is properly exporting sfGFP into the supernatant because sfGFP-osmY is produced within the cell and it can't be expected that all osmY be secreted from the cell. Thus from the above data, it can be gathered that <b>osmY when fused to a protein will export the protein out into the supernatant.</b><br />
<br />
===Turbidostat Assay Results===<br />
[[Image:Washington_Turbidostat_Pump.png|border|450px|left|thumb|M9 30 mM ethylene glycol media injected into the culture vessel every minute to maintain the arbitrary optical density of 0.3.]]<br />
<br />
[[Image:Washington_Turbidostat_OD.png|border|450px|right|thumb|Optical density measurements of <i>E. coli</i> transformed with fucO pGA3K3 + aldA pGA1C3 within the turbidostat growing on M9 30 mM ethylene glycol media.]]<br />
<br />
<br />
Following the turbidostat assay protocol, we used transformed MG1655 with fucO ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009]) on a medium copy Kanamycin resistant backbone and aldA ([http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010]) on a high copy Chloramphenicol resistant backbone and cultured it up in the turbidostat. The turbidostat was operated overnight and the above data illustrates that the transformed MG1655 grew using ethylene glycol as its sole carbon source, which normal untransformed MG1655 can't do. This is the case because the optical density (OD)/cell density rose from an initial arbitrary OD of 0.18 (time of inoculation) to an OD of 0.3, which was maintained throughout the time of the experiment (15 hours). The turbidostat maintained this OD through constantly imputing amounts of new media into the culture vessel based whenever the OD surpassed 0.3, which was quite frequently. Although time constraints did not allow us to transform MG1655 with our fucO-aldA operon and characterize it, it can be seen from the data above that <b>when fucO and aldA genes are individually transformed and over expressed in MG1655 <i>E. coli</i>, <i>E. coli</i> gains the ability to utilize ethylene glycol as its sole carbon source. The average doubling time (based off of a 10mL culture volume and an average dilution rate of 30µL/min) was 333 minutes, slightly faster than the literature value of 360 minutes [10].</b> <br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
===Putting it All together===<br />
The parts as they are right now have not been put into one cell strain and tested as a system. The obvious next step would be to test the efficacy of the entire plastic degrading construct against polyurethane. Using the turbidostat, we have the luxury of having an optimized and continual growth environment for our system to mutate towards improved functionality. Better cell strains would be periodically saved and tested in harsher selective conditions until eventually the strain is able to survive and reproduce off of polyurethane as its sole carbon source. We recognize that this may take many iterations to complete but the end product could potentially be a cell that is able to consume plastic much faster and safer than traditional recycling methods.<br />
<br />
<br />
===Trash to Treasure===<br />
After stable growth off of plastic is achieved the next step would be to clone in a third plasmid that would produce some valuable commodity. Washington 2011 demonstrated that diesel fuel can be synthesized from central metabolites using their Petrobrick platform. The addition of the Petrobrick to the system would allow plastic waste to be processed by <i>E. coli</i> which would then turn it into biofuels. <br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892008 BBa_K892008 '''osmY''']<br />
<br />
The coding sequence for osmotically induced protein Y, a protein that when fused to another protein, gets exported out of <i>E. coli</i> cells.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892009 BBa_K892009 '''fucO''']<br />
<br />
The gene fucO, which codes for glycolaldehyde reductase, is one of the genes required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892010 BBa_K892010 '''aldA''']<br />
<br />
The gene aldA, which codes for glycolaldehyde dehydrogenase, is the other gene required for <i>E. coli</i> to utilize ethylene glycol as a food source. The coding sequence is put behind a strong biofab promoter and RBS.<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892011 BBa_K892011 '''sfGFP-osmY''']<br />
<br />
A composite part for osmotically induced protein Y fused downstream to sfGFP using a glycine - serine linker regulated by the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892012 BBa_K892012 '''PUR Esterase''']<br />
<br />
An enzyme that breaks down polyurethane plastic behind the control of the lacI promoter ([http://partsregistry.org/wiki/index.php?title=Part:BBa_R0011 BBa_R0011]) and the standard Elowitz RBS ([http://partsregistry.org/wiki/index.php?title=Part:BBa_B0034 BBa_B0034]).<br />
<br />
[http://partsregistry.org/wiki/index.php?title=Part:BBa_K892013 BBa_K892013 '''fucO-aldA''']<br />
<br />
The combination of BBa_K892009 and BBa_K892010 behind a strong biofab promoter.<br />
<br />
<br />
----<br />
<html><a name="Sources"></a></html><br />
<h1 id='Parts'>Sources <html><a href="#Sources"><font size="3">[Top]</font></a></html></h1><br />
<html><br />
<ol><br />
<li>Barnes, D. K. A., F. Galgani, R. C. Thompson, and M. Barlaz. "Accumulation and Fragmentation of Plastic Debris in Global Environments." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 1985-998. Print.</li><br />
<br />
<li>Gregory, M. R. "Environmental Implications of Plastic Debris in Marine Settings--entanglement, Ingestion, Smothering, Hangers-on, Hitch-hiking and Alien Invasions." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2013-025. Print.</li><br />
<br />
<li>"Global Polyurethane Market to Reach 9.6 Mln Tons by 2015." Plastemart.com. N.p., 30 Aug. 2011. Web. <http://www.plastemart.com/Plastic-Technical-Article.asp?LiteratureID=1674></li><br />
<br />
<li>"Polyurethane Recycling." Polyurethanes. American Chemistry Council, n.d. Web. <http://polyurethane.americanchemistry.com/Sustainability/Recycling>. </li><br />
<br />
<li>"Frequently Asked Questions on Polyurethanes." Polyurethanes.org. European Diisocyanate and Polyol Producers Association, n.d. Web. <http://www.polyurethanes.org/index.php?page=faqs>. </li><br />
<br />
<li>Takasuga, T., N. Umetsu, T. Makino, K. Tsubota, KS Sajwan, and KS Kumar. "Role of Temperature and Hydrochloric Acid on the Formation of Chlorinated Hydrocarbons and Polycyclic Aromatic Hydrocarbons during Combustion of Paraffin Powder, Polymers, and Newspaper." Archives of Environmental Contamination and Toxicology (2007): 8-21. Print. </li><br />
<br />
<li>Teuten, E. L., J. M. Saquing, D. R. U. Knappe, M. A. Barlaz, S. Jonsson, A. Bjorn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha, and H. Takada. "Transport and Release of Chemicals from Plastics to the Environment and to Wildlife." Philosophical Transactions of the Royal Society B: Biological Sciences 364.1526 (2009): 2027-045. Print. </li><br />
<br />
<li>Kang, Chul-Hyung. "A Novel Family VII Esterase with Industrial Potential from Compost Metagenomic Library." Microbial Cell Factories 10.41 (2011): n. pag. Print. </li><br />
<br />
<li>Bokinsky, Gregory, Et. Al. "Synthesis of Three Advanced Biofuels from Ionic Liquid-penetreated Switchgrass Using Engineered Escherichia Coli." Proceedings of the National Academy of Sciences of the United States of America 108.50 (2011): 19949-9954. Print. </li><br />
<br />
<li> Boronat, Albert, Estrella Caballero, and Juan Aguilar. "Experimental Evolution of a Metabolic Pathway for Ethylene Glycol Utilization by Escherichia Coli." Journal of Bacteriology Jan. (1983): 134-39. Web. </li><br />
<br />
<li> E. Toprak, A. Veres, J. B. Michel, R. Chait, D. L. Hartl, R. Kishony, “Evolutionary paths to antibiotic resistance under dynamically sustained drug selection,” Nature Genetics, vol. 44, no. 1, pp. 101-105, Jan. 2012. </li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-04T00:24:48Z<p>Felixekn: /* Turbidostat: Ethylene Glycol Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
<html><br />
<ol><br />
<li><b><font size="4">Turbidostat Preparation</font></b></li><br />
<ol><br />
<li> Created M9 30mM ethylene glycol media</li><br />
<ul><li> Sterile 200mL of 5X M9 salts </li><br />
<li> 2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li> 800mL of sterile double distilled water</li></ul><br />
<li> Autoclave the closed system of tubing, syringes, culture vessel, and empty bottle </li><br />
<ul><li> Wrap all open ends of the system with aluminium foil before autoclaving to prevent autoclave water from entering the closed tubing system </li></ul><br />
<li> Place closed system into the turbidostat apparatus</li><br />
<li> Carefully and sterilely decant previously made sterile M9 30 mM ethylene glycol media into the media bottle </li> <br />
<li> Place turbidostat into a 37º C incubator</li><br />
<li> Connect turbidostat to a computer with the turbidostat software and related libraries installed</li><br />
<li> Start up turbidostat Program </li><br />
<ul><li>Be sure to close incubator door to reduce amount of ambient light</li></ul><br />
<li> Wait till program reads out optical density values - should see "0.000" as first optical density value</li><br />
</ol><br />
<li><b><font size="4">Cell Preparation</font></b></li><br />
<ol><br />
<li> Culture an overnight of MG1655 transformed with fucO pGA3K3 and aldA pGA1C3 in 2mL of TB with kanamycin and chloramphenicol</li><br />
<li> Take 1mL from the overnight culture and pipette it into a 1.5mL microcentrifuge tube </li><br />
<li> Pellet the 1mL aliquot at 4000g for 3 minutes</li><br />
<li> Pour out the supernatant </li><br />
<li> Resuspend the pelleted cells with 1mL of sterile PBS</li><br />
<li> Repeat steps 3-5 two more times </li><br />
</ol><br />
<li><b><font size="4">Turbidostat Inoculation and Operation</font></b></li><br />
<ol><br />
<li> Take a hypodermic needle attached to a 2.5mL syringe and aspirate 0.5mL of washed cells </li><br />
<li> Open incubator and through the soft stopper at the top of the culture vessel, inject the cells into the culture vessel to an OD ~0.2 </li><br />
<li> Close incubator door</li><br />
<li> Wait 12-24 hours </li><br />
<li> Stop turbidostat program </li><br />
<li> Retrieve data files from program folder and analyze them using your favorite mathematical software (ie: <a href=http://www.mathworks.com/products/matlab/><font size="2">Matlab</font></a> or <a href=http://www.wolfram.com/mathematica/><font size="2">Mathematica</font></a>)</li><br />
</ol><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-04T00:24:32Z<p>Felixekn: /* Turbidostat: Ethylene Glycol Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
<html><br />
<ol><br />
<li><b><font size="4">Turbidostat Preparation</font></b></li><br />
<ol><br />
<li> Created M9 30mM ethylene glycol media</li><br />
<ul><li> Sterile 200mL of 5X M9 salts </li><br />
<li> 2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li> 800mL of sterile double distilled water</li></ul><br />
<li> Autoclave the closed system of tubing, syringes, culture vessel, and empty bottle </li><br />
<ul><li> Wrap all open ends of the system with aluminium foil before autoclaving to prevent autoclave water from entering the closed tubing system </li></ul><br />
<li> Place closed system into the turbidostat apparatus</li><br />
<li> Carefully and sterilely decant previously made sterile M9 30 mM ethylene glycol media into the media bottle </li> <br />
<li> Place turbidostat into a 37º C incubator</li><br />
<li> Connect turbidostat to a computer with the turbidostat software and related libraries installed</li><br />
<li> Start up turbidostat Program </li><br />
<ul><li>Be sure to close incubator door to reduce amount of ambient light</li></ul><br />
<li> Wait till program reads out optical density values - should see "0.000" as first optical density value</li><br />
</ol><br />
<li><b><font size="4">Cell Preparation</font></b></li><br />
<ol><br />
<li> Culture an overnight of MG1655 transformed with fucO pGA3K3 and aldA pGA1C3 in 2mL of TB with kanamycin and chloramphenicol</li><br />
<li> Take 1mL from the overnight culture and pipette it into a 1.5mL microcentrifuge tube </li><br />
<li> Pellet the 1mL aliquot at 4000g for 3 minutes</li><br />
<li> Pour out the supernatant </li><br />
<li> Resuspend the pelleted cells with 1mL of sterile PBS</li><br />
<li> Repeat steps 3-5 two more times </li><br />
</ol><br />
<li><b><font size="4">Turbidostat Inoculation and Operation</font></b></li><br />
<ol><br />
<li> Take a hypodermic needle attached to a 2.5mL syringe and aspirate 0.5mL of washed cells </li><br />
<li> Open incubator and through the soft stopper at the top of the culture vessel, inject the cells into the culture vessel to an OD ~0.2 </li><br />
<li> Close incubator door</li><br />
<li> Wait 12-24 hours </li><br />
<li> Stop turbidostat program </li><br />
<li> Retrieve data files from program folder and analyze them using your favorite mathematical software (ie: <a href=http://www.mathworks.com/products/matlab/><font size="2">Matlab</font></a> <a href=http://www.wolfram.com/mathematica/><font size="2">Mathematica</font></a></li><br />
</ol><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-04T00:19:03Z<p>Felixekn: /* Turbidostat: Ethylene Glycol Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
<html><br />
<ol><br />
<li><b><font size="4">Turbidostat Preparation</font></b></li><br />
<ol><br />
<li> Created M9 30mM ethylene glycol media</li><br />
<ul><li> Sterile 200mL of 5X M9 salts </li><br />
<li> 2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li> 800mL of sterile double distilled water</li></ul><br />
<li> Autoclave the the closed system of tubing, syringes, culture vessel, and empty bottle </li><br />
<ul><li> Wrap all open ends of the system with aluminium foil before autoclaving to prevent autoclave water from entering the closed tubing system </li></ul><br />
<li> Place closed system into the turbidostat apparatus</li><br />
<li> Carefully and sterilely decant previously made sterile M9 30 mM ethylene glycol media into the media bottle </li> <br />
<li> Place turbidostat into a 37º C incubator</li><br />
<li> Connect turbidostat to a computer with the turbidostat software and related libraries installed</li><br />
<li> Start up turbidostat Program </li><br />
<ul><li>Be sure to close incubator door to reduce amount of ambient light</li></ul><br />
<li> Wait till program reads out optical density values - should see "0.000" as first optical density value</li><br />
</ol><br />
<li><b><font size="4">Cell Preparation</font></b></li><br />
<ol><br />
<li> Culture an overnight of MG1655 transformed with fucO pGA3K3 and aldA pGA1C3 in 2mL of TB with kanamycin and chloramphenicol</li><br />
<li> Take 1mL from the overnight culture and pipette it into a 1.5mL microcentrifuge tube </li><br />
<li> Pellet the 1mL aliquot at 4000g for 3 minutes</li><br />
<li> Pour out the supernatant </li><br />
<li> Resuspend the pelleted cells with 1mL of sterile PBS</li><br />
<li> Repeat steps 3-5 two more times </li><br />
</ol><br />
<li><b><font size="4">Turbidostat Inoculation and Operation</font></b></li><br />
<ol><br />
<li> Take a hypodermic needle attached to a 2.5mL syringe and aspirate 0.5mL of washed cells </li><br />
<li> Open incubator and through the soft stopper at the top of the culture vessel, inject the cells into the culture vessel to an OD ~0.2 </li><br />
<li> Close incubator door</li><br />
<li> Wait 12-24 hours </li><br />
<li> Stop turbidostat program </li><br />
<li> Retrieve data files from program folder and analyze them using your favorite mathematical software (ie: [http://www.mathworks.com/products/matlab/ Matlab] or [http://www.wolfram.com/mathematica/ Mathematica]</li><br />
</ol><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-04T00:14:58Z<p>Felixekn: /* Turbidostat: Ethylene Glycol Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
<html><br />
<ol><br />
<li><b><font size="4">Turbidostat Preparation</font></b></li><br />
<ol><br />
<li> Created M9 30mM ethylene glycol media</li><br />
<ul><li> Sterile 200mL of 5X M9 salts </li><br />
<li> 2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li> 800mL of sterile double distilled water</li></ul><br />
<li> Autoclave the the closed system of tubing, syringes, culture vessel, and empty bottle </li><br />
<ul><li> Wrap all open ends of the system with aluminium foil before autoclaving to prevent autoclave water from entering the closed tubing system</li></ul><br />
<li> Place closed system into the turbidostat apparatus</li><br />
<li> Carefully and sterilely decant previously made sterile M9 30 mM ethylene glycol media into the media bottle </li> <br />
<li> Place turbidostat into a 37º C incubator</li><br />
<li> Connect turbidostat to a computer with the turbidostat software and related libraries installed</li><br />
<li> Start up turbidostat Program <li><br />
<ul><li>Be sure to close incubator door to reduce amount of ambient light</li></ul><br />
<li> Wait till program reads out optical density values - should see "0.000" as first optical density value</li><br />
</ol><br />
<li><b><font size="4">Cell Preparation</font></b></li><br />
<ol><br />
<li> Culture an overnight of MG1655 transformed with fucO pGA3K3 and aldA pGA1C3 in 2mL of TB with kanamycin and chloramphenicol</li><br />
<li> Take 1mL from the overnight culture and pipette it into a 1.5mL microcentrifuge tube </li><br />
<li> Pellet the 1mL aliquot at 4000g for 3 minutes</li><br />
<li> Pour out the supernatant </li><br />
<li> Resuspend the pelleted cells with 1mL of sterile PBS</li><br />
<li> Repeat steps 3-5 two more times </li><br />
</ol><br />
<li><b><font size="4">Turbidostat Inoculation and Operation</font></b></li><br />
<ol><br />
<li> Take a hypodermic needle attached to a 2.5mL syringe and aspirate 0.5mL of washed cells </li><br />
<li> Open incubator and through the soft stopper at the top of the culture vessel, inject the cells into the culture vessel to an OD ~0.2 </li><br />
<li> Close incubator door</li><br />
<li> Wait 12-24 hours </li><br />
<li> Stop turbidostat program </li><br />
<li> Retrieve data files from program folder and analyze them using your favorite mathematical software (ie: [http://www.mathworks.com/products/matlab/ Matlab] or [http://www.wolfram.com/mathematica/ Mathematica]</li><br />
</ol><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-04T00:14:23Z<p>Felixekn: /* Turbidostat: Ethylene Glycol Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
<html><br />
<ol><br />
<li><b><font size="4">Turbidostat Preparation</font></b></li><br />
<ol><br />
<li> Created M9 30mM ethylene glycol media</li><br />
<ul><li> Sterile 200mL of 5X M9 salts </li><br />
<li> 2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li> 800mL of sterile double distilled water</li></ul><br />
<li> Autoclave the the closed system of tubing, syringes, culture vessel, and empty bottle </li><br />
<ul><li> Wrap all open ends of the system with aluminium foil before autoclaving to prevent autoclave water from entering the closed tubing system</li></ul><br />
<li> Place closed system into the turbidostat apparatus</li><br />
<li> Carefully and sterilely decant previously made sterile M9 30 mM ethylene glycol media into the media bottle </li> <br />
<li> Place turbidostat into a 37º C incubator</li><br />
<li> Connect turbidostat to a computer with the turbidostat software and related libraries installed</li><br />
<li> Start up turbidostat Program <li><br />
<ul><li>Be sure to close incubator door to reduce amount of ambient light</li></ul><br />
<li> Wait till program reads out optical density values - should see "0.000" as first optical density value</li><br />
</ol><br />
<li><b><font size="4">Cell Preparation</font></b><li><br />
<ol><br />
<li> Culture an overnight of MG1655 transformed with fucO pGA3K3 and aldA pGA1C3 in 2mL of TB with kanamycin and chloramphenicol</li><br />
<li> Take 1mL from the overnight culture and pipette it into a 1.5mL microcentrifuge tube </li><br />
<li> Pellet the 1mL aliquot at 4000g for 3 minutes</li><br />
<li> Pour out the supernatant </li><br />
<li> Resuspend the pelleted cells with 1mL of sterile PBS</li><br />
<li> Repeat steps 3-5 two more times </li><br />
</ol><br />
<li><b><font size="4">Turbidostat Inoculation and Operation</font></b></li><br />
<ol><br />
<li> Take a hypodermic needle attached to a 2.5mL syringe and aspirate 0.5mL of washed cells </li><br />
<li> Open incubator and through the soft stopper at the top of the culture vessel, inject the cells into the culture vessel to an OD ~0.2 </li><br />
<li> Close incubator door</li><br />
<li> Wait 12-24 hours </li><br />
<li> Stop turbidostat program </li><br />
<li> Retrieve data files from program folder and analyze them using your favorite mathematical software (ie: [http://www.mathworks.com/products/matlab/ Matlab] or [http://www.wolfram.com/mathematica/ Mathematica]</li><br />
</ol><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-04T00:04:46Z<p>Felixekn: /* Turbidostat: Ethylene Glycol Assay */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
===Turbidostat Preparation===<br />
<html><br />
<ol><br />
<li> Created M9 30mM ethylene glycol media</li><br />
<ul><li> Sterile 200mL of 5X M9 salts </li><br />
<li> 2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li> 800mL of sterile double distilled water</li></ul><br />
<li> Autoclave the the closed system of tubing, syringes, culture vessel, and empty bottle </li><br />
<ul><li> Wrap all open ends of the system with aluminium foil before autoclaving to prevent autoclave water from entering the closed tubing system</li></ul><br />
<li> Place closed system into the turbidostat apparatus</li><br />
<li> Carefully and sterilely decant previously made sterile M9 30 mM ethylene glycol media into the media bottle </li> <br />
<li> Place turbidostat into a 37º C incubator</li><br />
<li> Connect turbidostat to a computer with the turbidostat software and related libraries installed</li><br />
<li> Start up turbidostat Program <li><br />
<ul><li>Be sure to close incubator door to reduce amount of ambient light</li></ul><br />
<li> Wait till program reads out optical density values - should see "0.000" as first optical density value</li><br />
</ol><br />
</html><br />
<br />
===Cell Preparation===<br />
<html><br />
<ol><br />
<li> Culture an overnight of MG1655 transformed with fucO pGA3K3 and aldA pGA1C3 in 2mL of TB with kanamycin and chloramphenicol</li><br />
<li> Take 1mL from the overnight culture and pipette it into a 1.5mL microcentrifuge tube </li><br />
<li> Pellet the 1mL aliquot at 4000g for 3 minutes</li><br />
<li> Pour out the supernatant </li><br />
<li> Resuspend the pelleted cells with 1mL of sterile PBS</li><br />
<li> Repeat steps 3-5 two more times </li><br />
</ol><br />
</html>.<br />
<br />
===Turbidostat Inoculation and Operation===<br />
<html><br />
<ol><br />
<li> Take a hypodermic needle attached to a 2.5mL syringe and aspirate 0.5mL of washed cells </li><br />
<li> Open incubator and through the soft stopper at the top of the culture vessel, inject the cells into the culture vessel to an OD ~0.2 </li><br />
<li> Close incubator door</li><br />
<li> Wait 12-24 hours </li><br />
<li> Stop turbidostat program </li><br />
<li> Retrieve data files from program folder and analyze them using your favorite mathematical software (ie: [http://www.mathworks.com/products/matlab/ Matlab] or [http://www.wolfram.com/mathematica/ Mathematica]</li><br />
</ol><br />
</html></div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-03T23:49:45Z<p>Felixekn: /* Proposing Mutations based on FoldIt Models */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
----<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|[1] There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]]<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu binder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:foldit_flu_final.png|link=http://fold.it/portal/|border|800px|center|thumb|Mutating histidine to alanine on HB36.5 in FoldIt; <br />
<br />
Left: clashes between the histines of HB36.5 and that of H2; <br />
<br />
Right: The score (the energy of the protein structure) decreases and the binding increases.]]<br />
<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
<br />
We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
<br />
To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
<br />
Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
<br />
<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
<br />
<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997) [3]. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-03T23:49:14Z<p>Felixekn: /* Proposing Mutations based on FoldIt Models */</p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
----<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|[1] There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]]<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu binder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:foldit_flu_final.png|link=http://fold.it/portal/|border|center|thumb|Mutating histidine to alanine on HB36.5 in FoldIt; <br />
<br />
Left: clashes between the histines of HB36.5 and that of H2; <br />
<br />
Right: The score (the energy of the protein structure) decreases and the binding increases.]]<br />
<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
<br />
We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
<br />
To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
<br />
Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
<br />
<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
<br />
<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997) [3]. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We have submitted six parts to the registry. HB80.4, HB36.5 and four of our most efficacious mutants. A short description of each part is provided below.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-03T23:29:37Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
----<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|[1] There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]]<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|380px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
[[Image: Washing_Foldit.png|150px|right|link=http://fold.it/portal/]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu binder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:foldit_flu_final.png|link=http://fold.it/portal/|border|800px|center|thumb|Mutating histidine to alanine on HB36.5 in FoldIt; <br />
<br />
Left: clashes between the histines of HB36.5 and that of H2; <br />
<br />
Right: The score (the energy of the protein structure) decreases and the binding increases.]]<br />
<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
<br />
The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
<br />
The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
<br />
We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
<br />
To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
<br />
Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
<br />
<br />
<br />
<br />
<br />
<br />
==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
<br />
<br />
To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997) [3]. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
<br />
<br><br />
We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
<br />
<br />
----<br />
<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
<br />
[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
<br />
<br />
[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
<br />
[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
<br />
[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
<br />
HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
<br />
HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
<br />
HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
<br />
HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
<br />
HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
<br />
HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
<br />
<br />
----<br />
<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<p><br />
We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
<br />
After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
</p><br />
<br />
<br />
----<br />
<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
<br />
Flu Binders submitted six parts to the registry: Starting HB80.4 and HB36.5 Flu binders and four of our promising mutants. A short description for each part is provided below.<br />
<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
<br />
The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
<br />
A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
<br />
The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
<br />
[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
<br />
A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
<br />
<br />
----<br />
<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
<br />
[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
<br />
[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
<br />
[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/File:Washing_Foldit.pngFile:Washing Foldit.png2012-10-03T23:28:56Z<p>Felixekn: </p>
<hr />
<div></div>Felixeknhttp://2012.igem.org/Team:Washington/Protocols/EG_AssayTeam:Washington/Protocols/EG Assay2012-10-03T23:27:26Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
<br />
=Turbidostat: Ethylene Glycol Assay=<br />
<html><br />
<ol><br />
<li>Created M9 30mM ethylene glycol media</li><br />
<ul><li>Sterile 200mL of 5X M9 salts </li><br />
<li>2.8mL of filter sterilized 99.99% ethylene glycol</li><br />
<li>800mL of sterile double distilled water</li></ul><br />
<li>Autoclaved the tubing, syringes, and media vessels</li></div>Felixeknhttp://2012.igem.org/Team:Washington/FluTeam:Washington/Flu2012-10-03T23:24:51Z<p>Felixekn: </p>
<hr />
<div>{{Template:Team:Washington/Templates/Top}}<br />
__NOTOC__<br />
==<i>“For the first time in history we can track the evolution of a pandemic in real time. Influenza viruses are notorious for their rapid mutation and unpredictable behavior.”--Margaret Chan, WHO Director General</i>==<br />
<br />
----<br />
<h1 id='Background'>Background</h1><br />
[[Image:FluTree.jpg|border|330px|right|thumb|[1] There are 16 subtypes of Hemagglutinin(HA). They are divided into two groups based on their phylogenicity. We focused our efforts on the Group I HAs. ]]<br />
The influenza virus, also known as the flu, is a deadly virus that has decimated large populations of various species including humans. Even with the advent of vaccinations, high rates of mortality still occur because of the constant evolution of the virus. Variations within each strain of influenza are found within the viral surface proteins that coat their surfaces. <br />
[[File:Flu_virus.jpg]]<br />
<br />
One such protein is Hemagglutinin (HA), which is represented by the H when describing flu subtypes (H1N1, H2N3, etc). Hemagglutinin is primarily involved in viral transfer as it mediates the binding and entry into a host cell through a sialic acid linkage. Each strain of Influenza is characterized by it's variant of Hemagglutinin. There are 16 known subtypes of Hemagglutinin; these subtypes give rise to the vast multitude of strains that exist to today and usually only differ by a relatively few number of amino acid mutations. The World Health Organization uses the history of influenza breakouts to predict upcoming strains which could threaten to cause future pandemics and vaccinations are made using these predictions.<br />
[[Image:Washington_HB36.png|border|350px|left|thumb|HB36.5 original flu binder, shown as a four-bundle helix in yellow, bound to a monomer subunit of the trimer Hemagglutinin in purple. We focused our design efforts on finding differences near the HB36.5 binding epitope(same as HB80.4 binding epitope) on the different HAs tested.]]<br />
In 2011, Fleishman et al., detailed two small proteins which had binding capabilities to various subtypes of hemagglutinin. In 2012, Whitehead et al., optimized the flu binders and showed that HB80.4 was broadly binding, meaning it bound to multiple HAs with high affinity. They also showed that HB36.5 bound preferentially to HA 1.<br />
<br />
We aimed to further optimize the binding of HB80.4 to the group 1 HAs, but knowing that HB80.4 was already broadly binding to many HAs, we wanted to focus our efforts on the flu binder HB36.5 which until had not yet been tested against any other HAs is group 1. Therefore, we wanted to test HB36.5 against the other group 1 HAs and asses its native binding capabilities. After testing, we discovered that in addition to HA1, HB36.5 bound to H5, H5, H9 and H13. Binding was not observed to HA2 or HA12. '''Through application based design using FoldIt and real world testing we were able to find single amino acid mutations to the binders that improved binding to the flu subtypes HA2, HA6, and HA9. Having two broadly – binding flu binders, with subtype specific capabilities would allow for enhanced rapid and reliable global tracking of current and future Influenza strains.<br />
'''<br />
<br />
<br />
----<br />
<h1 id='App'>Foldit - A protein folding application<html><a href="#App"><font size="3">[Top]</font></a></html></h1><br />
[[Image:Fluapp.png|150px|right]]<br />
[[Image: Washington Folditsheetoutofplace.png|border|280px|left|thumb|If you are unfamiliar with the rules of Foldit, there is a freely downloadable game-tutorial which will show you the basics!. ]]<br />
<br />
To design our binder mutants we employed the purpose-built application (app), [http://fold.it/portal/ FoldIt]. This program enables you to view a desired protein complex and score conformational or mutational changes to the structure. FoldIt also indicates potential issues within a protein by indicating clashes between resides and offers many options of resolving it. Clashing residues, for example, can be manually resolved by mutating a particular amino acid to a different one, the effect of your choice of mutation will then be evaluated immediately and given a score. This program was employed in designing mutants of the native binders that would bind better to the different flu subtypes. By utilizing the computational application Foldit, we were able to design many mutants and then quickly assess whether or not they would produce the desired binding affect. <br />
<br />
<br />
<br />
<h1 id='Methods'>Methods <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
[[Image:flu buildtestdesign.png|border|1500px|center|thumb|An overview of our our project.]]<br />
<br />
== Design: Method of computational design ==<br />
===Gathering information about the structures of Hemagglutinin===<br />
Since we wanted to improve the binding of HB80.4 and HB36.5 to specific HAs, we had to understand the structural differences of the many subtypes of hemagglutinin. Initially, we searched for specific hemagglutinin structures on the [http://www.rcsb.org/pdb/home/home.do '''Protein Data Bank'''] (PDB). If we found the desired hemagglutinin structures, we downloaded the associated PDB file and made a model of the binder-HA complex in Pymol. These model would then later be used in [http://fold.it/portal/ '''Foldit''']. <br />
<br />
If we could not find the exact structure of a particular hemagglutinin, we then searched for the protein sequence of that hemagglutinin on the National Center for Biotechnology Information (www.ncbi.nih.gov). We then aligned the protein sequence of the structurally unknown hemagglutinin we obtained to that of the H1 HA (which has a known structure) by using [http://www.ebi.ac.uk/Tools/msa/clustalw2/ '''Clustalw2''']. Next, we created homology models in FoldIt of the desired hemaggluttinin by changing the side chains of the H1 HA in Foldit according to the protein sequence differences we found.<br />
<br />
===Using the computational application Foldit to design our models===<br />
Crystal stuctures of HB36.3 and HB80.4 in complex with H1HA are available on the PDB as PDB ID 3R2X and 4EEF, respectively. To create models our the binder bound to different HAs, we aligned our alternative HA structures to the H1HA in the published crystal structures using PYMOL. Then, we would record the difference in side chains between that specific hemagglutinin and H1HA and use FoldIt to implement those changes on the known crystal structures. The models we created are linked below: <br />
{|align="center"<br />
![[File:HB80_H12_model.zip]] | [[File:HB80_H9_model.zip ]] | [[File:HB80_H5_model.zip]] | [[File:HB36_H13_model.zip ]] | [[File:HB36_H12_model.zip]]<br />
|}<br />
{|align="center"<br />
![[File:HB36_H9_model.zip ]] | [[File:HB36_H6_model.zip ]] | [[File:HB36_H5_model.zip ]] | [[File:HB36_H2_model.zip]]<br />
|}<br />
<br />
===Proposing Mutations based on FoldIt Models===<br />
After preparing the FoldIt model, we proposed mutations on the original flu binder in order to improve its binding to the desired hemagglutinin. There is a score total in FoldIt which indicates the energy of the whole protein structure. It is our understanding that the protein structure is more stable when its energy is lower. Thus, our primary goal was to decrease the score total in FoldIt. We did that in three different ways, filling the holes, making space, and balancing the electrostatic. Holes that did not exist in H1HA would be created with the replacement of different side chains. We would mutate the corresponding side chain on the flu binder so that it extends out and fill a hole on the hemagglutinin. Making space is the exact opposite of filling the holes. Some side chains of certain hemaggluttinins would be more projected out in space than that of hemagluttinin one. Therefore, we would mutate the corresponding side chains of the flu binder in order to provide room for the more extended side chains. Some variations of side chain on hemaggluttinin causes some electrostatic changes. We would mutate the corresponding side chains of the flu binder so as to counter balance the electrostatic changes. For example, if a certain area on the helix is changed to be more electronegative, we would introduce an electropositive side chain on the flu binder for improving the binding and vice versa.<br />
<br />
[[Image:foldit_flu_final.png|link=http://fold.it/portal/|border|800px|center|thumb|Mutating histidine to alanine on HB36.5 in FoldIt; <br />
<br />
Left: clashes between the histines of HB36.5 and that of H2; <br />
<br />
Right: The score (the energy of the protein structure) decreases and the binding increases.]]<br />
<br />
== Build: Kunkel Mutagenesis - Mutagenizing HB80.4 and HB36.5 flu binders ==<br />
[https://2012.igem.org/Team:Washington/Protocols/Kunkel '''Kunkel mutagenesis'''] is a classic procedure for incorporating targeted mutations into a plasmid and we use it to create many variants of original starting flu binders. <br />
[[Image:Washington Kunkels.png|500px|thumb|left|Overview of how Kunkel Mutagenesis works]]<br />
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The proteins tested were encoded on the shuttle vector pETCON which contained the yeast display components and was compatible with expression both in ''E. Coli'' (bacteria) and ''S. Cerevisiae'' (Yeast). This allowed us to do Kunkel Mutagenesis in ''E. Coli'' and not have to switch vectors when we transform into yeast in the testing phase of our experiment when we perform Yeast Surface Display.<br />
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The first step to producing our specially designed flu binders was to change the original flu binding protein with our desired amino acid substitutions. <br />
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We designed mutagenic oligonucleotide primers that would anneal to the original flu binding protein and incorporate point mutations that, when expressed, would result a variant of the binder with the desired amino acid shift.<br />
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To incorporate these mutations, we first isolated single stranded DNA (ssDNA) of our vector harboring the original binding sequence. To do this we infected cells with bacteriophage M13, which packages its own ssDNA genome identified by length, and so in tandem packaged our vector in single stranded form. We then harvested the phage from the lysed culture of ''E. Coli'', and extracted our single stranded vector DNA. <br />
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Next, we annealed and extended our mutagenic oligos to incorporate the specified mutations into the newly synthesized antisense strand. This hybrid vector was transformed into ''E. Coli'' that degraded the original uracil-containing DNA and replaced it with sections complementary to the mutagenized strand.<br />
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==Test: Yeast Surface Display==<br />
[[Image:Washington ysdexplanation Screen shot 2012-10-03 at 2.45.23 PM.png|border|450px|right|thumb|Overview of Yeast Surface Display protocol.]]<br />
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To test our designs, we [https://2012.igem.org/Team:Washington/Protocols/Yeast '''transformed'''] our mutants into the organism ''Saccharomyces cerevisiae'' in order to perform surface display and binding efficiency analysis. We then used [https://2012.igem.org/Team:Washington/Protocols/Display '''yeast surface display'''], a method adapted from Boder and Wittrup (1997) [3]. This method allowed us to analyze a subset of our constructs at 4 different antigen (HA) concentrations: 0,1, 10, and 100 nM. In this process, our mutant plasmids were first expressed in yeast and grown in SDCAA growth media. Next, cells were grown in SCGAA media which contained glucose and galactose, required for the activation of the galactose inducible promoter on the yeast plasmid. This allows the mutant protein to be expressed on the yeast surface. All yeast cell samples were standardized to an OD600 of 2, and then labeled using different amounts of a single type of biotinylated antigen (HA). After an incubation step, the samples were then washed to remove unbound antigen. The samples were then labeled with Streptavidin-PE (to monitor binding of antigen, see in red in the diagram below) and anti-cymc FITC (to monitor surface expression, seen in green in the diagram below). <br />
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We then analyzed the fluorescent signature of the yeast cells using flow cytometery. Flow cytometry beams a laser into a stream of the samples containing the mutant and uses fluorescence detectors to measure the volume of fluorescently labeled cells. This generates graphs of the mean PE fluorescence of the surface displaying the population of cells as a function of antigen, and the KD is calculated that fits to that data assuming 1:1 binding stoichiometry. <br />
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<h1 id='Results'>Results Summary <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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We were able to design and test 6 HB36.5 variants and 14 HB80.4 variants. Including the starting designs we tested over 150 binder/HA combinations (see below spreadsheet). The results showed that most of the suggested mutations we found did not change the binding affinity to the different hemagglutinins. However, we did find 7 variants (listed below the table), that had an effect on binding. Our most dramatic results were with HB36.5 H312A, which was able to bind H2HA strongly where the original design variant could not. H2HA is particularly important as it has not been seen in humans since 1958. As it is known to still circulate in birds and has the potential to cross back over into humans it can be considered a future pandemic strain. We hope that our mutations to HB36.5 will be useful in a diagnostic that can rapidly test for the presence of H2 hemagglutinin which would be useful in global health monitoring. We have not been able to find any other protein that can differentiate between H1(the most common) and H2HA and believe we have created the first such example. <br />
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[[Image:UW FLU HB36 results graph.png|border|800px|center|thumb|HB36.5 and its variants tested against hemagglutinin subtype 2 at four different concentrations. The binding signal was determined by taking the geometric mean of a labeled population of 25,000 yeast cells at three different HA concentrations. The H312A mutation shows particularly strong binding signal at 100nM. ]]<br />
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[[Image:UWashington_HB36cytograph.jpg|border|500px|center|thumb|HB36.5 tested using Yeast surface display at both 0 nM H2 on the left, and 100 nM H2 on the right. Both cytometry plots show little to no binding to H2.]]<br />
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[[Image:UWashington HB36cytomutant.jpg|border|500px|center|thumb|HB36.5 flu binder tested using Yeast surface display at 100 nM H2 on the left, H312A mutant tested on the right at 100 nM H2 on the right. HB36.5 Shows little to no binding at this HA concentration. H312A dramatically increases binding to H2.]]<br />
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[[Image:Results All .png|border|500px|center|thumb|Summary of the results, NC (No changes for the binding), + (increased binding), - (decreased binding)]]<br />
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HB36.5_F317H was predicted to increase HA5 binding but it decreased the binding to HA9. Nevertheless, it can be used for specific hemagglutinin subtypes binder. <br />
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HB36.5_H312A was predicted to increase HA2 binding and it did improve binding to HA2. <br />
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HB80.4_A280Vwas predicted to decrease HA2 binding but it improved the binding to HA2. <br />
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HB80.4_K298E was predicted to increase HA2, HA5, HA6 and HA12 binding and it did improve binding to HA2.<br />
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HB80.4_S276H was predicted to increase HA6 binding but it decreased the binding to HA6.<br />
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HB80.4_K298S was predicted to increase HA12 binding but it decreased the binding to HA6.<br />
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HB80.4_S286K was predicted to increase HA12 binding and it increased the binding to HA12. However, it decreased the binding to HA6.<br />
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<h1 id='Future'>Future Directions <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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We intend to combine the HB36.5 single-point mutants H312A and W313A, both successfully having increased binding to HA2, to create a superior combinatorial mutant with multiple-fold improvement. Simultaneously, we intend to create a mutant of HB36.5 that will bind H12 preferentially, as HB36.5 does not currently do so. <br />
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After obtaining a version of HB36.5 that binds to HA12 more efficiently, a diagnostic system can be made to track the types of hemagglutinin on the surface of influenza. Specifically, this system can identify HA2 and HA12 from the 16 subtypes of hemagglutinin. <br />
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<h1 id='Parts'>Parts Submitted <html><a href="#Background"><font size="3">[Top]</font></a></html></h1><br />
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Flu Binders submitted six parts to the registry: Starting HB80.4 and HB36.5 Flu binders and four of our promising mutants. A short description for each part is provided below.<br />
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[http://partsregistry.org/wiki/index.php/Part:BBa_K892401 BBa_K892401: '''Starting HB36.5''']<br />
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The starting HB36.5 flu binder originally shown to bind to H1 by Whitehead et al.<br />
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[http://partsregistry.org/wiki/index.php/Part:BBa_K892402 BBa_K892402: '''HB36.5_F317S''']<br />
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A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Serine<br />
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[http://partsregistry.org/wiki/index.php/Part:BBa_K892403 BBa_K892403: '''HB36.5_H312A''']<br />
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A mutated HB80.4 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 312 from Histidine to Alanine, which dramatically increases the binding to H2 over the native HB36.5 binder.<br />
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[http://partsregistry.org/wiki/index.php/Part:BBa_K892404 BBa_K892404: '''HB36.5_A326E''']<br />
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A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 326 from Alanine to Glutamate<br />
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[http://partsregistry.org/wiki/index.php/Part:BBa_K892405 BBa_K892405: '''Starting HB80.4''']<br />
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The starting HB80.4 flu binder originally shown to bind to various subtypes of Hemagglutinin in group 1. Originally identifited by Fleishman et al., further optimized by Whitehead et al. <br />
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[http://partsregistry.org/wiki/index.php/Part:BBa_K892410 BBa_K892410: '''HB36.5_F317H''']<br />
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A mutated HB36.5 flu binder aimed to increase binding to H2, a subtype of hemagglutinin on the surface of the Influenza virus. This mutant has a point mutation at residue 317 from Phenylalanine to Histidine<br />
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<h1 id='Parts'>References<html><a href="#References"><font size="3">[Top]</font></a></html></h1><br />
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[1] Whitehead, Timothy, et al. "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing." Nature Biotechnology 30.6 (2012):543-548.<br />
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[2] Fleishman, Sarel, et al."Computational design of proteins targeting the conserved stem region of influenza hemagglutinin." Science 332.6031 (2011): 816-821.<br />
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[3] Boder, Eric and Wittrup, Dane. "Yeast surface display for screening combinatorial polypeptide libraries." Nature Biotechnology 15.6 (1997):553-557.</div>Felixeknhttp://2012.igem.org/File:Foldit_flu_final.pngFile:Foldit flu final.png2012-10-03T23:13:55Z<p>Felixekn: uploaded a new version of &quot;File:Foldit flu final.png&quot;</p>
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<div></div>Felixeknhttp://2012.igem.org/File:Foldit_flu_final.pngFile:Foldit flu final.png2012-10-03T23:09:56Z<p>Felixekn: uploaded a new version of &quot;File:Foldit flu final.png&quot;</p>
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