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Commit 7d518773 authored by Vishwaa Kannan's avatar Vishwaa Kannan
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added contributions, revamped description, finalized vcell engineering, added...

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<h3>EDIT SUBHEADING</h3>
<p>EDIT TEXT</p>
</div>
<div id="cool">
<div id="parts">
<h4>EDIT SUB SUBHEADING</h4>
<p>EDIT TEXT</p>
</div>
<div id="cool2">
<h4>EDIT SUB SUBHEADING</h4>
<p>EDIT TEXT</p>
<div id="parts">
<h4>Parts</h4>
<table>
<tr>
<th>Part link</th>
<th>Description</th>
</tr>
<tr>
<td>https://parts.igem.org/Part:BBa_K5114823</td>
<td>Device encoding superfolder GFP under prmA. After transforming into E. coli and not observing fluorescence, it is our hypothesis that the prmA promoter does not work in E. coli, likely due to lack of transcriptional machinery specific to Rhodococcus jostii.</td>
</tr>
<tr>
<td>https://parts.igem.org/Part:BBa_K5114227</td>
<td>Coding sequence for human liver fatty acid binding protein conjugated with circularly permuted GFP (hlFAB-GFP or FAB-GFP).</td>
</tr>
<tr>
<td>https://parts.igem.org/Part:BBa_K5114228</td>
<td>Expression Device for hlFAB-GFP</td>
</tr>
<tr>
<td>TBD</td>
<td>Coding sequence for a synthetic, estradiol-induced transcription factor that binds to the LexA operator DNA region. </td>
</tr>
<tr>
<td>TBD</td>
<td>Expression device for the synthetic transcription factor</td>
</tr>
<tr>
<td>TBD</td>
<td>GFP with RBS and Terminator under control of synthetic promoter bound by the synthetic transcription factor</td>
</tr>
</table>
</div>
<div id="EDIT-ID-2">
<h3>EDIT SUBHEADING</h3>
<p>EDIT TEXT</p>
<div id="MD">
<h3>Molecular Dynamics</h5>
<p>Using Amber, ChimeraX, and AutoDock Vina, we engineered and tested various mutations on hlFAB to enhance its lower detection limit. To streamline the calculation of the dissociation constant (Kd) using MMPBSA, we developed an automated pipeline. This pipeline outputs an Excel-compatible data file and a PDB file from the final simulation step, allowing for easy visualization of charge contributions. It integrates all critical steps of molecular dynamics simulation, including LEaP for system parameterization, a minimization step using steepest descent, a heating phase to bring the system to 300K, a density equilibration step to stabilize the system and allows for RMSD tracking, followed by equilibration, and finally, a 10-nanosecond production run, from which data for MMPBSA analysis is extracted. The pipeline also includes MMPBSA calculations, and a custom Python script that formats the raw output data into a user-friendly format. It is available in our team's software repository along with all prerequisite files. This pipeline is free to use and can assist teams in determining the effectiveness of ligand-receptor interactions. Additionally, it is fully customizable, making it adaptable for use with other ligands beyond PFOA.
<br>
Instructions to download and use the pipeline can be found on our wiki and our software tools repository:
https://gitlab.igem.org/2024/software-tools/gcm-ky
</p>
</div>
<div id="Vcell">
......@@ -70,7 +104,231 @@
<p>We have created a VCell BioModel that simulates our entire gene circuit. This model includes all components for the pRMA_GFP, FAB_GFP, and the Synt_Tran factor construct, all extremely valuable systems in synthetic biology that do not yet have a VCell model. With a few modifications, our model can be used to model different types of genetic circuits involving the LuxR-LuxI gene regulatory system.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/hefiefbiruebf.png" alt="Vcell_Contributions_Image">
<p>The models are hosted on VCell servers and are shared publicly in the “Uncurated” folder. They are completely free to use and modify for anyone with VCell.</p>
<p>Our most up-to-date models can be found in a table on the experiments page. These models are free for anyone to tinker with and use.</p>
<p>Our most up-to-date models can be found in a table on the experiments page. These models are free for anyone to tinker with and use.
Additionally, with the help of the VCell support team, we debugged VCell’s ability to export and import simulation data. This allows future users to pick up where they left off on previous simulations.
A document that outlines how to do so is attached below:
</p>
<div id="Tutorial_to_access_vcell_models">
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<body>
<h2 style="text-align:center">How to Access Our Vcell Models</h2>
<div class="container">
<div class="mySlides">
<div class="numbertext">1 / 9</div>
<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-114425.png" style="width:50%">
</div>
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<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-114725.png" style="width:50%">
</div>
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</div>
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<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115146.png" style="width:50%">
</div>
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<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115341.png" style="width:50%">
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<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115555.png" style="width:50%">
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<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115736.png" style="width:50%">
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<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115856.png" style="width:50%">
</div>
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<img src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-120005.png" style="width:50%">
</div>
<a class="prev" onclick="plusSlides(-1)">❮</a>
<a class="next" onclick="plusSlides(1)">❯</a>
<div class="caption-container">
<p id="caption"></p>
</div>
<div class="row">
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-114425.png" style="width:50%" onclick="currentSlide(1)" alt="Go to vcell.org and download the latest version of Vcell that is compatible with your device">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-114725.png" style="width:50%" onclick="currentSlide(2)" alt="Once you download Vcell and signed in go to the bottom left hand box and click the plus sign, and input the username of the creator of the biomodel you wish to see">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/hefiefbiruebf.png" style="width:50%" onclick="currentSlide(3)" alt="After putting in the username click on the Uncurated folder near the bottom on the box">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115146.png" style="width:50%" onclick="currentSlide(4)" alt="Click on the folder with the same username as the one you searched for & then click the biomodel you wish to see">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115341.png" style="width:50%" onclick="currentSlide(5)" alt="You can now look at the reaction diagram and simulation results but if you wish to have a copy of this biomodel you need to click on file(top left) and then click save as">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115555.png" style="width:50%" onclick="currentSlide(6)" alt="A window will then pop up and simply input the name you wish to call the biomodel in the text bar near the bottom and then click save">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115736.png" style="width:50%" onclick="currentSlide(7)" alt="If you wish to see simulation data click on application(shown in image) and then click on simulation. ">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-115856.png" style="width:50%" onclick="currentSlide(8)" alt="To access to data you need to click on the simulation you wish to see the data for and the click the graph icon on the top right">
</div>
<div class="column">
<img class="demo cursor" src="https://static.igem.wiki/teams/5114/images-arjun/2/screenshot-2024-09-29-120005.png" style="width:50%" onclick="currentSlide(9)" alt="Finally if you wish to see the data in a table format click on the table icon near the bottom right(shown in image)">
</div>
</div>
</div>
<script>
let slideIndex = 1;
showSlides(slideIndex);
function plusSlides(n) {
showSlides(slideIndex += n);
}
function currentSlide(n) {
showSlides(slideIndex = n);
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<p> </p>
<p> </p>
<p>Our most up-to-date models can be found in a table on the experiments page. These models are free for anyone to tinker with and use.</p>
</div>
<hr>
</div>
</div>
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wiki/pages/description.html
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<div id="pfas-problem" class="mt-4">
<h2>Our Approach to PFAS</h2>
<p>Our team will tackle the growing problem of PFAS (poly and per-fluoroalkyl substances) contamination/pollution. PFAS, due to its chemical inertness, are extremely hard to decompose and have been named “forever chemicals”. These substances have become more and more widespread to the point where 97% of Americans have detectable amounts of them in their blood (NHANES 2015). The health risk of PFAS is not fully understood but has been implicated in liver diseases, cancers, increased cholesterol levels, and other complications. Our team attempts to help combat this problem by engineering bacteria to produce an observable signal when they encounter PFAS. PFAS are usually detected with liquid or gas chromatography and mass spectrometry, which is time-consuming, expensive, and inaccessible to the public. PFAS-sensitive bacteria have the potential for extremely high-throughput testing for PFAS. We hope to be able to make such a system based on a genetic circuit transformed into E. coli. We will attempt three different approaches to determining an optimal circuit for PFAS detection. The first uses a previously discovered promoter, prmA, that has been documented to upregulate gene expression in the presence of PFAS. We will test the limit of detection of this promoter to quantitatively measure its efficacy. Second, we will use a gene circuit containing a FAB:GFP conjugated molecule; in the presence of PFAS, this molecule has been shown to change confirmation to allow for the GFP to fluoresce. Finally, we will use a synthetic transcription factor that responds to estradiol in yeast; because PFOA has been shown to be a strong estrogen receptor agonist, we hope that we can recreate the synthetic transcription factor into E. coli. In addition, to better characterize the possible impacts of PFAS, our team is using computer simulations and wet lab testing (via protein assays) to determine different proteins that can bind to PFAS, along with the possibility of computer-aided protein mutations to improve binding.</p>
<!-- <p>Our team will tackle the growing problem of PFAS (poly and per-fluoroalkyl substances) contamination/pollution. PFAS, due to its chemical inertness, are extremely hard to decompose and have been named “forever chemicals”. These substances have become more and more widespread to the point where 97% of Americans have detectable amounts of them in their blood (NHANES 2015). The health risk of PFAS is not fully understood but has been implicated in liver diseases, cancers, increased cholesterol levels, and other complications. Our team attempts to help combat this problem by engineering bacteria to produce an observable signal when they encounter PFAS. PFAS are usually detected with liquid or gas chromatography and mass spectrometry, which is time-consuming, expensive, and inaccessible to the public. PFAS-sensitive bacteria have the potential for extremely high-throughput testing for PFAS. We hope to be able to make such a system based on a genetic circuit transformed into E. coli. We will attempt three different approaches to determining an optimal circuit for PFAS detection. The first uses a previously discovered promoter, prmA, that has been documented to upregulate gene expression in the presence of PFAS. We will test the limit of detection of this promoter to quantitatively measure its efficacy. Second, we will use a gene circuit containing a FAB:GFP conjugated molecule; in the presence of PFAS, this molecule has been shown to change confirmation to allow for the GFP to fluoresce. Finally, we will use a synthetic transcription factor that responds to estradiol in yeast; because PFOA has been shown to be a strong estrogen receptor agonist, we hope that we can recreate the synthetic transcription factor into E. coli. In addition, to better characterize the possible impacts of PFAS, our team is using computer simulations and wet lab testing (via protein assays) to determine different proteins that can bind to PFAS, along with the possibility of computer-aided protein mutations to improve binding.</p>
-->
<p>PFAS are a large and diverse family of synthetic chemicals manufactured for industrial and consumer products beginning in the 1950s. These chemicals all have at least one fully fluorinated carbon bond that gives them a great resistance to breakdown in the environment. Originally developed for their water- and grease-resistant properties, PFAS are found in products that range from nonstick cookware to food wrappings, water-repellent fabrics, and firefighting foams. As they are found in these products, they are serious health and environmental concerns since they might be toxic and persist in the environment. They also tend to bioaccumulate over time without degradation. Of the many PFAS compounds, some like PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonate) have been linked to serious health problems such as cancer, liver damage, and developmental problems for children.
While both PFOA and PFOS have been largely phased out of commercial use in the United States, they continue to persist in the environment. Newer alternatives, such as GenX, have been developed but also carry risks; studies have linked them to liver and kidney damage. Other PFAS chemicals include the use of PFBS and PFHxS, though most of those remain under scrutiny over health effects. The problem is that its overwhelming application in industries contaminated drinking water, soil, and even the atmosphere. This calls for regulatory authorities like the EPA to set a regulation aimed at reducing the environmental and health risks caused by these chemicals. However, due to the very low thresholds for toxicity, detection of PFAS is extremely expensive and inaccessible to most.
</p>
<br>
<p>In this project, we aim to create a more accessible method to detect PFAS. The current standard is liquid chromatography/mass spectroscopy, whicih requires extremely expensive machines that are usually exclusive to large research institutions. Since PFAS often endangers rural and agricultural areas that are far from such machines, it is important for PFAS testing methods to be more accessible.</p>
<p>We combined wet lab approaches with dry lab modeling to tackle the problem of PFAS detection.</p>
</div>
<div id="Part design">
<h2>Part design</h2>
<p>
To approach the issue of PFAS, we ultimately created 3 approaches.
Last year, our team utilized a gene circuit that relied on the inducible promoter, prmA, from Rhodococcus jostii and a positive feedback loop to try and detect PFAS. However, we weren’t able to get significant results. This year, our team decided to move one step back and test the efficacy of the prmA promoter in Eschecheria coli. To test this, our first construct simply contained a superfolder GFP gene under the influence of the prmA promoter; if the promoter was effective, the E. coli would fluoresce when exposed to a high enough concentration of PFAS.
</p>
<img src="https://static.igem.wiki/teams/5114/contributions-and-design-images/construct-1-illustration.png" alt="construct 1 illustration" width="80%">
<p>
Our second construct utilized a FAB-GFP conjugate molecule created by Dr. Berger from the University of Virginia. This molecule was originally developed to react to fatty acids; when fatty acids was present, the molecule would change confirmation to activate the GFP portion of the conjugate. We had previously seen, through reverse screening of databases, that PFAS had a likely chance of binding to fatty acid receptors in the cell. Thus, we decided to try the FAB-GFP conjugate molecule with PFAS to see if a response would occur. In this construct, we had the FAB-GFP gene under a constitutive promoter, which allowed for the constant creation of the conjugated molecule; in the presence of different PFOA concentrations, the FAB-GFP would theoretically change confirmation and fluoresce.
</p>
<img src="https://static.igem.wiki/teams/5114/fabgfp-device-registry-files/construct-2-illustration.png" alt="construct-2-illustration" width="80%">
<p>
Our final construct utilized a synthetic transcription factor to trigger a hybrid gene. Dr. Dossani and his colleagues created this transcription factor in Saccharomyces cerevisiae, using a protein called LexA combined with a viral activator domain VP16. This transcription factor was meant to be activated by estradiol, which would then allow the transcription factor to enter the nucleus of S. cerevisiae and attach to a hybrid promoter; this hybrid promoter was created by finding new operator regions for existing promoters that would allow for the synthetic transcription factor to bind. Once again, in our reverse screening research, we found that it was highly likely that PFAS interacted with estradiol receptors. Thus, we wanted to test whether the synthetic transcription factor would respond to PFOA as well. This construct required two plasmids because of its length; one of the plasmids housed the synthetic transcription factor, which was put downstream of a constitutive promoter. The second plasmid contained the hybrid promoter and a superfolder GFP gene under its influence. We wanted to test whether the transcription factor would work in E. coli rather than the original host and to what extent it would detect PFOA.
</p>
<img src="https://static.igem.wiki/teams/5114/contributions-and-design-images/igem-2024-25-part-design-c3-c4.jpg" alt="construct 3 illustration" width="80%">
<p>To test our constructs, we would transform them into E. coli and incubate them in varying levels of PFOA (perfluorooctanoic acid) in 96-well plates. Fluorescence readings were taken of each well at 90 minute time intervals and recorded. More information can be seen on the Experiments and Results page.
</p>
</div>
<div id="modeling">
<h2>Modeling</h2>
<p>To better understand how our constructs would work, we employed two types of modeling: stochastic kinetic modeling with Virtual Cell (VCell) and Molecualr Dyanmics</p>
<h3>Kinetic Modeling</h3>
<p>We used VCell to model how our genetic circuits behaved over time. The simulations could tell us how each circuit behaves when in contact with different amounts of PFAS over time.</p>
<p>Virtual Cell, or VCell, is a software platform used to model cellular systems. We are using VCell to model the four primary constructs(names listed in experiments) we researched this year to detect PFAS, as well as other pathways. VCell is a valuable platform because it allows us to run simulations on the constructs we built. Additionally, VCell is user-friendly and easy to learn, as most functions are self-explanatory.
VCell allows us to simulate reaction rates either stochastically or deterministically. Deterministic simulations are based on partial differential equations. Stochastic simulations are based on the Gibson-Bruck solver, which essentially probabilistically samples individual reactions to occur and the expected time between reactions. Stochastic simulations are more accurate at the single-cellular level because they take into account singular molecules.
</p>
<img src="https://static.igem.wiki/teams/5114/contributions-and-design-images/vcelllogocrop-1.webp" alt="vcell logo">
<h3>Molecular Dynamics</h3>
<p>Molecular dynamics simulates the energetics of proteins in solution and of ligands interacting with proteins. This allows us to find where a ligand binds to a protein and the relative contributions of different amino acids to the strength of binding, which could allow us to determine which protein residues could be mutated to increase binding affinities.</p>
</div>
<div>
<h2>Human Practices</h2>
<p>To ensure that our project would have a positive impact, we reached out to stakeholders in various areas affected by PFAS. We interviewed a KY state representative, water quality professionals, and contacted other students within KY that had first hand experience with PFAS. Through our contacts, we learned that our project idea would be useful to the world and we made modifications to our project plan to best fit what is needed.</p>
</div>
<div id="references" class="mt-4">
<h2>References</h2>
<hr>
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wiki/pages/engineering.html
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<h5>Overview</h5>
<ul class="nav flex-column"> <!--YOU CAN ADD MORE OVERVIEW LINKS HERE, SIMPLY DUPLICATE AN UNORDERED LIST-->
<li class="nav-item">
<a class="nav-link" href="#Vcell-Documentation">Vcell Documentation</a>
<a href="#Part-engineering">Part Engineering</a>
<a class="nav-link" href="#Vcell-Documentation">Vcell Engineering</a>
<ul class="nav flex-column ml-3">
<li class="nav-item">
<a class="nav-link" href="#Cycle-1">Cycle 1</a>
<ul class="nav flex-column ml-3">
<li class="nav-item">
<a class="nav-link" href="#prma-GFP">Construct 1: prma_GFP</a>
<a class="nav-link" href="#prma-GFP">Construct 1: prma_GFP</a>
<li class="nav-item">
<a class="nav-link" href="#fab-GFP">Construct 2: FAB_GFP</a>
</li>
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</ul>
</li>
<li class="nav-item">
<a class="nav-link" href="#construct_2">Cycle 2 (Construct 3)</a>
<a class="nav-link" href="#Cycle-2">Cycle 2</a>
<a class="nav-link" href="#Cycle-3">Cycle 3</a>
</li>
</ul>
<a href="#AI">AI initiatives</a>
</li>
</ul>
<ul class="nav flex-column">
<li>
</li>
</ul>
</div>
</div>
<div class="col-lg-9">
<div id="Part-engineering">
<h3>Design</h3>
<p><u>Goal:</u> Our project’s goal was to effectively detect PFAS (specifically, PFOA) by utilizing e. coli for detection.</p>
<p>With this, we design 3 main pathways:
</p>
<ol>
<li>prmA Promoter construct: We tested the pRMA last year with Rhodococcus jostii, however we weren’t able to get significant results. We retested this promoter this year with e. coli, placing it upstream of a superfolder GFP gene to see if e. coli would fluoresce when exposed to PFOA.</li>
<li>FAB-GFP Conjugate construct: Using the FAB-GFP conjugate molecule created by Dr. Bryan Berger, initially meant to react to fatty acids, we designed a system that would theoretically detect PFAS binding, initiating GFP fluorescence. We hypothesized that PFAS would bind similarly to fatty acids due to receptor similarities.
</li>
<li>Synthetic Transcription Factor construct: Based on work done by Dr. Dossani in yeast, we designed a system of two plasmids: one containing a synthetic transcription factor that would respond to estradiol and another containing a hybrid promoter linked to GFP. Since there was already some research indicating that PFAS could interact with estradiol receptors, we wanted to see whether this system would function in e. coli as a method of PFAS detection.
</li>
</ol>
<h3>Build</h3>
<p>All part sequences are deposited into the registry and are publicly available. All genes were printed from Genscript and subcloned into pUC57-Kan or pUC57</p>
<ol>
<li>pRMA Promoter construct: The pRMA promoter construct was transformed into e. coli and plated on ampicillin-selective agar plates. After heat shock transformation, successful colonies were identified via colony screening, followed by plasmid extraction using a miniprep. Nanodrop analysis indicated DNA concentrations ranging from 200-300 ng/µL, confirming successful plasmid isolation for further testing in PFAS detection experiments.
</li>
<li>FAB-GFP Conjugate construct: The FAB-GFP conjugate plasmid was transformed into e. coli and plated on kanamycin-selective agar. White colonies were selected through blue-white screening, indicating successful transformation. Plasmid DNA was extracted using miniprep, with concentrations ranging from 170-350 ng/µL. A restriction digest using Eco31i enzyme confirmed the integrity of the construct, preparing it for PFAS-binding efficacy tests.
</li>
<li>Synthetic Transcription Factor construct 1 (Kanamycin): The synthetic transcription factor (STF) construct was transformed into e. coli and screened via blue-white colony screening. White colonies resistant to both antibiotics were selected, and plasmid DNA concentrations ranged from 160-250 ng/µL after miniprep. A restriction digest confirmed the proper assembly of the STF construct, facilitating its use in estradiol receptor interaction experiments for PFAS detection.
</li>
<li>Synthetic Transcription Factor construct 2 (Ampicillin): The STF construct with ampicillin resistance was transformed into e. coli. Following colony screening, white colonies were cultured, and plasmid DNA was extracted using miniprep. Nanodrop analysis confirmed DNA concentrations, and a restriction digest verified the structural integrity of the construct, readying it for testing in PFAS detection through synthetic transcription factor mechanisms.
</li>
</ol>
<h3>Test</h3>
<ol>
<li>pRMA Promoter construct:After successfully transforming the e. coli with the pRMA promoter construct, we tested its ability to induce GFP expression in response to PFAS exposure. A 96-well plate was prepared with E. coli cultures containing the pRMA promoter construct and exposed to varying concentrations of PFOA. Fluorescence readings were taken every 90 minutes using a plate reader over a 12-hour period.
</li>
<li>FAB-GFP Conjugate construct: To test the efficacy of the FAB-GFP construct in detecting PFAS, e. coli cultures containing the construct were exposed to increasing concentrations of PFOA in a 96-well plate format. Fluorescence was measured at 90-minute intervals for a 12-hour period.
</li>
<li>Synthetic Transcription Factor construct 1 (Kanamycin): The synthetic transcription factor (STF) construct with kanamycin resistance was tested for its response to estradiol receptor activation in the presence of PFAS. Cultures of e. coli containing the STF construct were grown in 96-well plates and exposed to various concentrations of PFOA (0 to 1000 ppm). Fluorescence measurements were recorded every 90 minutes using a plate reader.
</li>
<li>Synthetic Transcription Factor construct 2 (Ampicillin): The synthetic transcription factor (STF) construct with ampicillin resistance was tested for its response to estradiol receptor activation in the presence of PFAS. Cultures of e. coli containing the STF construct were grown in 96-well plates and exposed to various concentrations of PFOA (0 to 1000 ppm). Fluorescence measurements were recorded every 90 minutes using a plate reader.
</li>
</ol>
<p>After testing, there was no significant difference in fluorescence as the amount of PFOA added increased. This applies to every construct we tested (pRMA, FAB-GFP, STF 1/2). For a detailed look at results, please refer to our results page.
</p>
<h3>Learn</h3>
<p>Since there was no significant difference in fluorescence as the amount of PFOA added increased, more testing is needed. Additionally, we needed to validate that the proteins were correctly expressed.
</p>
</div>
<div id="Vcell-Documentation" class="mt-4">
<h2>VCell Engineering</h2>
<h3>Overall Purpose</h3>
......@@ -66,13 +113,14 @@
<p> </p>
<hr>
<div id="Cycle-1">
<h2>Overview</h2>
<h2>Cycle 1</h2>
<p>In Cycle 1, we first had to build the structure for the future cycles. First, we had to research the constructs we had to design to find out how to start constructing them. We decided to first look at the pRMA_GFP construct and end with the pLex_GFP construct. We first started with the design of the constructs. Each construct had the same geometry.</p>
<p> </p>
<p>Geometry:</p>
<p style="text-indent: 5%;"> Cell Size: 3.5 uM^3</p>
<p style="text-indent: 5%;"> Environment: 1000 uM^3</p>
<p> </p>
<p> Cell size was bassed on <a href="https://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=3&id=100003 ">Harvard Bionumbers</a></p>
<p>Environment size was determined by taking the reciprocal of the approximate max density of E. coli in batch culture, about 10^9 cells per milliliter of water (Tuttle et al., 2021), which is about 1000um^3.</p>
<p>Once a construct was designed we would input rate constants, and run stochastic simulations with the construct. In the simulations, we would use varying amounts of PFAS and see how much GFP was produced.</p>
<p> </p>
<p>Simulation Parameters: </p>
......@@ -148,7 +196,7 @@
<p> </p>
<h3>Build</h3>
<p>SyntTranscriptionFactor_GFP Construct #3:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105200.png" alt="Synt_Tran biomodel">
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105200.png" alt="Synt_Tran biomodel" >
<p> </p>
<p> </p>
<h3>Test & Results</h3>
......@@ -214,9 +262,93 @@
</div>
</div>
<div id="Cycle-2">
<h2>VCell Modeling Cycle 2</h2>
<h2>Construct 3: SyntTranscriptionFactor_GFP</h2>
<p> </p>
<h3>Design</h3>
<p>Further research shows that a combination of constructs 3 & 4 enhances the production of GFP in the presence of PFAS. Due to an error in the creation of constructs 3 and 4 which were supposed to work together to produce the GFP we had to recreate construct 3 which is a combination of the 2 constructs. When PFAS is introduced into the system and binds with the Synthetic Transcription factor it allows it to bind to the pLex promoter which induces transcription. Using this information a new model was developed using previous rate constants from the models and new simulations were run.
</p>
<p> </p>
<p>Cell volume was chosen as 3.5 uM^3 while the Environment volume was set to 1000 uM^3 and time was measured in seconds.</p>
<p> </p>
<p>Model name: Construct 3_Cycle 2</p>
<p>Owner: Aryanshah16</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>SyntTranscriptionFactor Construct:</p>
<img src="https://static.igem.wiki/teams/5114/aryan/kvekvk.png" alt="SyntTranscriptionFactor_GFP">
<p>The previous construct 3 and 4 were combined into one Biomodel.</p>
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>We ran stochastic simulations on the reaction diagram with varying micromolars of PFAS and ran to see its effect on the GFP protein production. Different rate constants were used which caused variance in the amount of GFP produced between the constructs.</p>
<img src="https://static.igem.wiki/teams/5114/aryan/vjvejievrji.png" alt="GFP_Graph">
<p>The graph was made using the Google Sheets platform by extracting the data tables from VCell and pasting them onto sheets. We were then able to create a graph with multiple independent variables.</p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p> </p>
<p>Going through the rate constant, where PFOA binds to the synthetic transcription factor, it directly correlates with the amount of GFP produced. Multiplying the rate constant by ½ would result in a 50% decrease in GFP production, etc. GFP was produced at similar amounts based on each volume of PFAS.</p>
</div>
<div id="Cycle-3">
<h2>Cycle 3</h2>
<h3>Design</h3>
<p>In the previous iterations, there were rate constants that were fully estimated without sources. In this cycle, a new source was added to each rate constant that did not have one previously. All sources can be found within our publicly available VCell Biomodels.
PFAS testing ranges were recomputed. Regulations are on the parts per trillion (ppt) level, so we used 1 ppt as our minimum concentration. Assuming 1 ppt=1 ng/ml and the molar weight of PFOA, a common model PFAS, is 414 g/mol, that means 1ppt is approximately 2E-6 uM of PFOA.
Additionally, we realized stationary phase E. coli are smaller than 3.5 um^3 (Azam et al., 1999) and are closer to 1 um^3.
Since real life application of our biosensor would likely use cells that are already at steady state equilibrium, we ran simulations where protein and mRNA is at steady state equilibrium before PFAS is introduced. Steady state concentrations were determined using deterministic models that ran until concentrations ceased fluctuations.
For the synthetic transcription factor Biomodel, a new degradation pathway for synthetic transcription factor molecules bound to PFOA to be degraded was introduced. For the FAB-GFP model, PFAS is made to be regenerated when the PFAS.FAB-GFP protein is degraded.
</p>
<h3>Build</h3>
<p>Model name: Construct3_Cycle3_dgl
Model owner: dglVcell
</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/stf-pathway.png" alt="stf pathway" width="70%" height="70%">
<p>Model name: FAB_GFP_Construct_v1.1_Douglas
Model owner: dglVcell
</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/fab-pathway.png" alt="fab pathway" width="70%" height="70%">
<h3>Test</h3>
<p>Simulations were run for 100 minutes (6000 seconds). Every plot shown is stochastic using the Gibson solver.</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/vcellplots.jpg" alt="vcell plots" width="80%" height="80%">
<h3>Learn</h3>
<p>The improved models showed very intriguing results.
A PFAS-inducible transcription factor with a relatively strong binding affinity appears to produce the greatest amount of signal in response to a very small amount of PFAS. Even when there is only about a single PFAS molecule per cell (at 10^-6 uM), the synthetic transcription factor system can produce a large amount of GFP (about 5000 molecules per cell).
The FAB-GFP system appears to be limited by the amount of PFAS that can bind to the GFP. At extremely low concentrations of PFAS, like those set as the minimal safety limits, there may be only one bound FAB-GFP producing fluorescence in a cell.
For both constructs, it appears that the cell actually depletes the amount of PFAS that is available externally due to strong binding affinities. It is important to note that while the binding affinity of FAB-GFP has been experimentally characterized by the original creators, the binding affinity of the synthetic transcription factor was set to be the empirical binding affinity of human estrogen receptor alpha to PFOA, which may not accurately reflect the actual binding affinity of PFOA or other PFAS to the entire synthetic transcription factor.
</p>
</div>
</div>
<div id="AI">
<h2>AI Initiatives</h2>
<p>At an earlier stage in the project, we attempted to use AI to design proteins that could be used to bind to PFAS, acting as a biosensor. While we were not able to incorporate a fully functional model into the final project, we have documented our efforts and progress so that the work could be expanded on.</p>
<object data="https://static.igem.wiki/teams/5114/vcelleng/igem-documentation-on-ai-initiatives-1.pdf" type="application/PDF" width="90%" height="600px"></object>
</div>
<div id="References">
<h2>References</h2>
<ul>
<li>Blinov, M. L., J. C. Schaff, D. Vasilescu, Moraru, II, J. E. Bloom, and L. M. Loew. 2017.
Compartmental and Spatial Rule-Based Modeling with Virtual Cell. Biophysical journal
113:1365-1372. PMC5627391
</li>
<li>Cai, L., Friedman, N., & Xie, X. S. (2006). Stochastic protein expression in individual cells at the single molecule level. Nature, 440(7082), 358–362. https://doi.org/10.1038/nature04599
</li>
<li>Cowan, A. E., Moraru, II, J. C. Schaff, B. M. Slepchenko, and L. M. Loew. 2012. Spatial modeling of cell signaling networks. Methods Cell Biol 110:195-221. PMC3288182
</li>
<li>Schaff, J., C. C. Fink, B. Slepchenko, J. H. Carson, and L. M. Loew. 1997. A general computational framework for modeling cellular structure and function. Biophysical journal 73:1135-1146. PMC1181013
</li>
<li>Ali Azam T, Iwata A, Nishimura A, Ueda S, Ishihama A. Growth phase-dependent variation in protein composition of the Escherichia coli nucleoid. J Bacteriol. 1999 Oct;181(20):6361-70. doi: 10.1128/JB.181.20.6361-6370.1999. PMID: 10515926; PMCID: PMC103771.</li>
<li>Tuttle, A. R., Trahan, N. D., & Son, M. S. (2021). Growth and maintenance of escherichia coli laboratory strains. Current Protocols, 1(1). https://doi.org/10.1002/cpz1.20 </li>
</ul>
</div>
</div>
</div>
</div>
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<h5>Overview</h5>
<ul class="nav flex-column"> <!--YOU CAN ADD MORE OVERVIEW LINKS HERE, SIMPLY DUPLICATE AN UNORDERED LIST-->
<li class="nav-item">
<a class="nav-link" href="#EDIT-ID">EDIT OVERVIEW TEXT</a>
<a class="nav-link" href="#Virtual-cell">Virtual Cell</a>
<ul class="nav flex-column ml-3">
<li class="nav-item">
<a class="nav-link" href="#EDIT-ID-1">EDIT SUBHEADING 1</a>
<a class="nav-link" href="#Description-and-purpose-vcell">Description and Purpose</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#Construction-vcell">Construction</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#Results-vcell">Results</a>
<ul class="nav flex-column ml-3">
<li class="nav-item">
<a class="nav-link" href="#cool">EDIT SUB SUBHEADING 1</a>
<a class="nav-link" href="#Construct-1">Construct 1</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#cool2">EDIT SUB SUB HEADING 2</a>
<a class="nav-link" href="#Construct-2">Construct 2</a>
</li>
<li class="nav-item">
<a href="#Construct-3">Construct 3</a>
</li>
</ul>
</li>
</ul>
</li>
<li class="nav-item">
<a href="#Molecular-dynamics" class="nav-link">Molecular Dynamics</a>
<ul class="nav flex-column ml-3">
<li class="nav-item">
<a href="#Description-and-purpose-md">Description and Purpose</a>
</li>
<li class="nav-item">
<a href="#Construction-md">Construction</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#EDIT-ID-2">EDIT SUBHEADING 2</a>
<a href="#Results-md">Results</a>
</li>
</ul>
</li>
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<div class="col-lg-9">
<div id="EDIT-ID" class="mt-4">
<h2>EDIT HEADER</h2>
<p>EDIT TEXT</p>
<hr>
<div id="EDIT-ID-1">
<h3>EDIT SUBHEADING</h3>
<p>EDIT TEXT</p>
<div id="Virtual-cell" class="mt-4">
<h2>Virtual Cell Kinetic modeling</h2>
<div id="Description-and-purpose-vcell">
<h3>Description and Purpose</h3>
<p>To get an idea of how our system would play out and determine the effect size of different perturbations on the system, we modeled our genetic constructs in the Virtual Cell software using the Gibson-Bruck stochastic solver. The stochastic solver is more accurate for nanoscale reactions than deterministic differential equations, and Virtual Cell provides a view-friendly user interface for designing reaction networks.</p>
</div>
<div id="Construction-vcell">
<h3>Construction</h3>
<p>Whenever possible, we extracted rate constants from existing literature.
Due to inconclusive experimental results, all our rate constants were extracted from literature or estimated.
All our models are publicly available on VCell and are also saved to our team’s software gitlab. You will need to download Virtual Cell to see our models.
See our Engineering page for more details on how we constructed and refined our models, and see our Contributions page for how to access our models.
</p>
<h2>Constructs Table</h2>
<table>
<tr>
<th>Construct Name</th>
<th>Biomodel Name</th>
<th>Creator of Biomodel</th>
<th>Short Description</th>
</tr>
<tr>
<td>Prma_GFP Construct</td>
<td>pRMA_GFP construct 1 v1.3</td>
<td>Pillow123</td>
<td>This construct explored the use of the pRMA promoter in response to PFAS to produce SF_GFP</td>
</tr>
<tr>
<td>FAB_GFP Construct</td>
<td>Construct 3_Cycle3_dgl</td>
<td>dglVcell</td>
<td>This construct explored the FAB protein mechanism in expressing FAB_GFP in response to PFAS</td>
</tr>
<tr>
<td>Synthetic_Transcription_Factor Construct</td>
<td>FAB_GFP_Construct_v1.1_Douglas</td>
<td>dglVcell</td>
<td>In this construct we modeled the dynamics of GFP under the control of a potentially PFAS inducible synthetic transcription factor</td>
</tr>
</table>
</div>
<div id="Results-vcell">
<h3>Results</h3>
<h4 id="Construct-1">Construct 1: prmA-GFP</h4>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105032.png" alt="pRMA_GFP Biomodel Image">
<p>This simulates GFP production when under the control of the prmA promoter, which has been shown to upregulate transcription in the presence of PFAS. This is a model of our part BBa_K5114823.
Because the mechanism of the prmA promoter is poorly understood, we approximated its response to PFAS as the DNA binding to PFAS molecules which increases its rate of transcription.
This model also takes into account the leakiness of the promoter. Experimental evidence suggests PFAS presence only increases expression of prmA controlled proteins by about 3 fold.
</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105104.png" alt="pRMA_GFP graph">
<p>This graph demonstrates the dynamics of the simple genetic circuit. The presence of GFP in the absence of PFAS is due to the basal production rate of the promoter. As shown, the amount of GFP by the end of the simulation increases as the amount of PFAS added increases. There appears to be a significant increase in the amount of GFP produced compared to basal production in the presence of as little as 0.01 uM (micromolar) of PFAS. As the amount of initial PFAS increases, the increase in GFP production slows down, likely due to saturation of DNA with PFAS. Thus, the highest curves represent maximal GFP production rate.
</p>
<br>
<p>
This model served as our first experience with modeling genetic circuits to detect PFAS, however we were skeptical that prmA would work in our chassis of E. coli because the transcriptional machinery that regulate the prmA promoter appears to be unique to Rhodococcus, as we found in a BLAST search. Additionally, chemicals very rarely bind directly to DNA to modulate transcriptional activity, so this model should not be taken as entirely true.
</p>
<h4 id="Construct-2">Construct 2: Conjugated human liver fatty acid binding protein and GFP (hlFAB-GFP or FAB-GFP)</h4>
<p>hlFAB-GFP is a protein made of circularly permuted GFP and human liver fatty acid binding protein, created by Mann and Berger in 2023. It acts as a PFAS sensor by fluorescence upon binding with PFAS. When no PFAS is bound, water can infiltrate the hydrophobic barrel of GFP that shields its chromophore and quench the fluorescence. When PFAS is bound to the hlFAB domain, less water can infiltrate and fluorescence is stronger. This models our part BBa_K5114228.</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/fab-pathway.png" alt="fab pathway" width="70%" height="70%">
<p>Simulations were carried out for 100 minutes (6000) seconds at various concentrations of initial PFAS. The environment was made to be 1000 um^3 in volume because that is the expected amount of water “available” to each individual cell (max density of <i>E. coli</i> is around 10^9 cells/ml). Cell volume was set to 1 um^3 based on established cell sizes of E. coli at stationary phase. Simulations were carried out at non-steady-state and steady-state (concentration of FAB_GFP and FAB_GFP_mRNA are stable) conditions, found by deterministic ODE modeling.
Regulations for PFAS are on the parts per trillion (ppt) level, so we used 1 ppt as our minimum concentration. Assuming 1 ppt=1 ng/L and the molar weight of PFOA, a common model PFAS, is 414 g/mol, that means 1 ppt is approximately 2E-6 uM of PFOA. Therefore, we simulated from 1E-6 uM initial PFAS in the environment up to 1E-2 uM, which would correlate to roughly 5 ppb.
</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/fabgfp-plots.jpg" alt="fabgfp plots" width="70%" height="70%">
<p>Interestingly, steady state modeling does not yield significant differences in maximal fluorescence. This is likely because the fluorescence is actually limited by the amount of PFAS available to bind to hlFAB-GFP. This can be seen in the graphs below. Since the environment is set to be 1000 times larger in volume than the cell, the final concentration of bound hlFAB-GFP is 1000 times the initial concentration of PFAS, indicating nearly every molecule of PFAS is bound to a hlFAB-GFP.
</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/fabgfp-pfas-depletion-at-non-steady-state.jpg" alt="pfas depletion plots", width="70%",height=70%>
<p>From 1E-6 to 1E-4, the stochastic nature of the simulation is evident: each step up on the graph represents one PFAS molecule diffusing into the cell from the environment and binding to one molecule of hlFAB-GFP.
<br>
Thus, our model indicates that biosensors that directly fluoresce upon binding to PFAS are limited by the amount of PFAS available to them, and not necessarily by the binding affinity of the protein. Future work should focus on increasing the effective fluorescence of the molecule.
</p>
<h4 id="Construct-3">Construct 3: Synthetic estradiol transcription factor</h4>
<p>A synthetic estradiol transcription factor (synTrans or STF for short) was originally created by fusing the hormone binding domain of human estrogen receptor alpha with the DNA binding domain of the Lex transcription factor and the Herpes-Simplex Virus protein VP16 to recruit ribosomes. The STF has been successfully used in yeast as a gratuitous transcription factor. Since PFAS has been shown to be an agonist for human estrogen receptor, we attempted to express it in E. coli to see if we could use the STF as a transcription factor inducible by PFAS. This is a model for our parts TBD and TBD.
</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/stf-pathway.png" alt="stf pathway" width="70%" height="70%">
<p>Cell and environment volumes were kept at 1 and 1000 um^3, respectively. Initial PFAS concentrations were also kept the same. Simulations were carried out in ideal settings that were not at steady state and had no promoter leakage (expression of GFP without the STF binding to the DNA), as well as in more realistic conditions where there was promoter leakage and the concentrations of the STF and its mRNA were constant. </p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/stf-plots.jpg" alt="stf plots" width="70%" height="70%">
<p>Compared to hlFAB-GFP, this construct appears to be much more sensitive to small quantities of PFAS. Each initial PFAS concentration resulted in much more GFP produced compared to the hlFAB-GFP. The increase in GFP production as initial PFAS concentration increases does not appear to increase logarithmically as did the hlFAB-GFP construct. However, the graphs imply the amount of GFP produced has yet to reach a steady state, indicating that longer exposure times to PFAS may produce more differentiated levels of GFP.
When considering GFP production in the presence of leaking promoters, it appears that it is more difficult to distinguish concentrations below 1E-4, as the shapes of the graph are very similar. It is possible that longer incubation time will make for more differentiated graphs, although more testing is required. The differentiability of the graphs will also depend on the sensitivity of the fluorimeter used.
Steady state conditions appear to slightly increase the rate of GFP at any given time, likely because there is more free STF available to bind to PFAS as soon as PFAS diffuses into the cell.
In summary, a synthetic transcription factor inducible by PFAS will likely produce much more fluorescence than something similar to hlFAB-GFP, with the fluorescence more limited by time than by the amount of PFAS available. However, promoter leakage must be kept to a minimum to better separate very low concentrations of PFAS, or be incubated for longer periods of time.
Our modeling demonstrates that steady state conditions do not significantly affect the fluorescence time or maximal fluorescence for hlFAB-GFP, and affects the STF by simply increasing the rate at which PFAS can be bound and GFP can be produced.
</p>
</div>
<div id="cool">
<h4>EDIT SUB SUBHEADING</h4>
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<p>EDIT TEXT</p>
</div>
</div>
<div id="Molecular-dynamics">
<h2>Molecular Dynamics</h2>
<div id="Description-and-purpose-md">
<h3>Description</h3>
<p>After using hlFAB in the lab and receiving a negative result, no GFP expression, we wanted to understand more about what happened when FAB-GFP interacted with PFOA. To accomplish this, we would need to simulate PFOA binding to FAB and analyze the bind to determine its effectiveness. We used AutoDock Vina, a docking software, to speed up the process, to place PFOA in the FAB binding domain. After the docking study, the best pose was selected, and the resulting complex was brought into Amber. From there, a quick implicit simulation was run to confirm that PFOA did stay in the FAB binding domain, confirming a bind. After we confirmed that PFOA was bound to FAB, we used the Molecular Mechanics Poisson-Boltzmann Surface Area method (MMPBSA) to evaluate the strength of the bind. MMPBSA works by removing the ligand (PFOA) from the receptor (FAB) and calculates the force required to do so; a higher force (ΔG) means a stronger bond. The MMPBSA method is based on the free energy equation: ΔG_bind = ΔE_vdw + ΔE_elec + ΔG_solv - TΔS, where each term represents van der Waals energy, electrostatic energy, solvation free energy, and entropy, respectively. After running the MMPBSA, the charge contribution was analyzed with a custom Python script, allowing us to determine what residues played the most significant roles in the bind. As an added benefit, inding the ΔG also allows us to calculate accurate rate constants for Vcell.
Furthermore, we wanted to see how to improve the bind of PFOA and hlFAB, potentially decreasing the lower detection limit of FAB. Using ChimeraX and Rotamers, we could accurately mutate hlFAB by swapping amino acid residues. To optimize the MMPBSA calculation, we created a pipeline that allows us to retrieve the ΔG of any hlFAB mutation and PFOA.
</p>
</div>
<div id="Construction-md">
<h3>Construction</h3>
<p>We used AlphaFold to fold the FAB-GFP structure using the sequence found in Dr. Berger’s paper. We then retrieved the SDF file for PFOA from its PubChem page. The charges for PFOA were generated from Antechamber, the industry standard. In AutoDock Vina, we selected the entire FAB domain to ensure we tried all the possibilities.
</p>
<p>We then took the resulting pose, and split it into 3 files, the receptor, the ligand, and the complex. The files were then fed through the pipeline to get the MMPBSA results. The MMPBSA pipeline contains x stages: Creating topology in LEaP, Minimizing the energy, Heating the structure, Equilibrating the density, Equilibrating the structure, A production run, the MMPBSA, and finally the data curation script.
</p>
</div>
<div id="#Results-md">
<h3>Results</h3>
</div>
</div>
</div>
</div>
</body>
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