DESCRIPTION
OUR PROJECT DESCRIPTION AND THE DEPTHS OF PFAS
OUR PROJECT DESCRIPTION AND THE DEPTHS OF PFAS
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.
In this project, we aim to create a more accessible method to detect PFAS. The current standard is liquid chromatography/mass spectroscopy, which 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.
We combined wet lab approaches with dry lab modeling to tackle the problem of PFAS detection.
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.
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.
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.
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. Each gene insert was flanked with BsaI and SapI sites and then printed from Genscript in pUC57 family carrier plasmids.
↑ Mann, M. M., & Berger, B. W. (2023, September 13). A genetically-encoded biosensor for direct detection of perfluorooctanoic acid. Nature News. https://www.nature.com/articles/s41598-023-41953-1
↑ Smathers, R. L., & Petersen, D. R. (2011, March 1). The human fatty acid-binding protein family: Evolutionary divergences and functions - human genomics. BioMed Central. https://humgenomics.biomedcentral.com/articles/10.1186/1479-7364-5-3-170
↑ 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
↑ 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.
Dossani, Z. Y., Reider Apel, A., Szmidt-Middleton, H., Hillson, N. J., Deutsch, S., Keasling, J. D., & Mukhopadhyay, A. (2018, March). A combinatorial approach to synthetic transcription factor-promoter combinations for yeast strain engineering. Yeast (Chichester, England). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873372/
ACS Publications: Chemistry journals, books, and references published ... (n.d.). http://pubs.acs.org/doi/full/10.1021/ja026939x?mobileUi=0
To better understand how our constructs would work, we employed two types of modeling: stochastic kinetic modeling with Virtual Cell (VCell) and Molecualar Dynamics
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.
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.
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 PMID: 9284281 DOI: 10.1016/S0006-3495(97)78146-3
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. PMC3519356 PMID: 22482950 DOI: 10.1016/B978-0-12-388403-9.00008-4
Molecular Dynamics (MD) is a computer simulation method used to study the physical movements of atoms and molecules over time. In an MD simulation, the atoms in a system (like a protein or a drug molecule) are treated as if they are moving according to the laws of physics. The atoms vibrate, rotate, and translate as if in a natural environment, allowing researchers to watch how the molecules behave in response to different forces. MD simulations help predict how a protein folds, how two molecules interact, or how a system reacts under specific conditions. This technique is valuable for studying how molecules behave over time, making it possible to observe interactions that would take seconds, minutes, or even days in real life.
Docking is another computational technique, but it's more focused on figuring out how two molecules fit together, like puzzle pieces. In biology, docking is often used to predict how a small molecule (like a drug) will interact with a larger molecule (like a protein). Imagine finding the best way a key fits into a lock—docking helps predict the best orientation and position for the molecules to bind. This technique is critical in drug discovery, as it helps scientists identify how well a drug binds to its target, which can influence the drug's effectiveness.
Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) is a method used to calculate the binding energy between two molecules. After running a Molecular Dynamics simulation to see how the molecules move and interact, MMPBSA helps researchers estimate how tightly these molecules bind together. The lower the energy, the more stable the interaction, which usually suggests that the molecules have a strong bond. This method is important because it combines the dynamic information from MD simulations with energy calculations, providing insights into how changes (like mutations in a protein) might affect binding strength. In simple terms, it’s a way to measure how well two molecules stick together, which is crucial for understanding processes like drug efficacy or protein interactions.
In order to carry out these processes, we used AutoDock Vina and Amber
Our human practices focus on how innovative detection methods can benefit communities. We engaged with a variety of stakeholders, including researchers, environmental advocates, local citizens, and went on water company tours to ensure that our project aligns with public health needs and ethical standards. By prioritizing transparency and community input, we want to address concerns about health risks posed by PFAS, especially in underrepresented and vulnerable populations.
We are also committed to educational outreach, raising awareness about PFAS contamination and the potential impact of detection technologies. By collaborating with environmental organizations and policy makers, we aim to provide the public with the knowledge and tools they need to advocate for cleaner environments. Through human practices, we hope to solidify the relationship between the scientific community and the public, ensuring that our innovations serve beneficially to change.