Results
The outcomes of our project and our future plans.
The outcomes of our project and our future plans.
We were unable to fully ligate all 3 parts of our gene construct together. Our BioAnalyzer data did show minimal concentrations of ligated ABC insert, but this concentration wasn’t high enough for us to be able to use in the rest of the experiment (transformation+testing with PFAS). In the future, we will redesign our ligation procedure via more thorough research for the creation of the protocols and looking at other IGem teams to see attempts at a 3 part ligation. In addition, we will ensure that proper amounts of time are allocated for the digestion and ligation steps of our protocols, since this year, we had limited lab time and were therefore rushed in our experimentation. In addition, we will make sure to follow the steps of each protocol carefully, since that cost us a lot of time this year.
The construction of the storage vectors was a success. We were able to put our parts (A, B, and C) into different plasmids and transform these plasmids into bacteria. With these bacteria present for the harvesting of the parts of the insert, we will be able to have quick access to the inserts in the future without having to order them from Twist when we run out. However, there were many mishaps that occurred throughout our protocols and experimentation process. From these mishaps, we now know to follow each step of the protocols carefully. We also have increased knowledge of the different protocols needed for the digestion and ligation of inserts and plasmids, which will help us be independent and efficient in the future. In the future, we will use these storage vectors and miniprep + digest them for extraction of the inserts, which will allow us to create our full insert and experiment with PFAS more thoroughly.
Throughout our project, our team carried out literature research in order to derive a clear plan for our project. Thanks to the USAFA iGEM Team (1), we learned that the prmA promotor region can be induced with the presence of PFAS. However, there was no clear explanation on why this occured. Thus, our team actively read through more studies and established a potential conclusion to prmA promotor’s upregulatation by PFAS chemicals. We believe that PFAS presence causes a reduction in catalase efficacy (2) leading to an H2O2 accumulation. Subsequently, the H202 accumulation causes Fis transcriptional regulator or cAMP Receptor Protein to upregulate prmA promoter region. (3) (4) (5)
Also, thanks to the Stockholm 2020 iGEM team that attempted to detect PFAS using mRFP fluorescence, we were able to build our own genetic circuit based off of known inducers and promoters from their team’s research. (6)
In our research, we used reverse screening to achieve four goals: (1) Find proteins that interact with PFAS (PFOA and PFOS specifically), (2) look for similarities between location/function/sequence, (3) Tie into diseases linked to PFAS See this paper for one clear mechanism for liver and kidney damage (https://www.sciencedirect.com/science/article/pii/S0160412023002246), and lastly (4) look for bacterial protein hits, especially those that can cause stress because our prmA promoter seems to be general stress activated. If we could figure out what proteins PFAS interacts with to cause a stress response then we would uncover the mechanism of prmA promoter activation in response to PFAS, something not done before. It could improve future modeling and potentially open the door to engineering more sensitive methods of detection. After our research, We found targets that bound or were related to PFOA or PFAS which in the long run were very beneficial in working on this project as we could understand what types of proteins are related and we could use them to help build our gene circuit. These databases provided lists of proteins that are related and some of them like androgen receptors were very useful and we related them to our project. Some databases that provided promising information for this research are listed below.
We were also able to tie these targets to diseases or viruses and what part of the body they originate. The database searching also revealed the functions of these targets and how they could maybe be related to PFAS and PFOA as well as the probability of interaction between the proteins. This helped form the basis for the molecular modeling we would do in the future with platforms like V-Cell and OpenMM
We were finally able to run the OpenMM script, and the data that was outputed was quite reasonable. This proved useful as the lab part did not go according to plan, we were still able to test a part of our construct in some way. There were some slight discrepancies in the data, such as seemingly random temperature fluctuations, and beyond that almost no change was observed before and after the simulation. This might be caused by a faulty forcefield, in which we could spend more time researching and developing a proper one instead of blindly using an online server to generate these files. This may also be a true result, but we won’t know until more iterations of the docked complex have been tested, and the data examined. Over all, there are chances that the data might be off from faulty forcefields and other data file, in which more care will be taken, but the data does match our expectations, which means this data is likely true. If the results are true, we may not need to use OpenMM for observing conformation change after docking, but it can still be used to determine other changes and record useful values.
Using a stochastic solver provided by VCell and constants provided by Weber and Becut, we found that in a single cell, PFAS levels as low as 0.033 uM could trigger bursts of GFP production. These bursts are consistent with the well known phenomenon that transcription and translation happen in bursts and not as a continuous curve.
Despite the relatively low amount of GFP, this is occurring in only 1 cell. In real scenarios, this burst may occur in millions of cells at once that are all exposed to PFAS, potentially providing a detectable signal to a fluorimeter. More simulation will have to be conducted on multi cell systems and potentially how AHL can diffuse through membranes and affect multiple cells.
In experiments where the binding affinity between LuxR and AHL is extremely high, bursts of GFP are also seen in different concentrations of PFAS.
In both of the above simulation scenarios, the stochastic solver predicted higher GFP concentrations than the deterministic solver did (see Engineering for more graphs for comparison). In certain instances, the deterministic solver actually predicted less than 1 molecule of GFP would be produced at all. This all highlights the importance of stochastic solvers in microscopic environments such as intracellular reaction pathways.
In future works, further tinkering with the estimated reaction rate constants for PFAS’s effects on transcription could provide more accurate simulations. Inclusion of multiple cells and the interplay between the signaling molecules released between each individual would also be more representative of real field conditions. However, we hope our current results can serve as a positive proof of concept in silico model for our gene circuit and also illustrate the importance of stochastic simulations in synthetic biology.
With more time, we could also re-attempt lab work and apply the lessons we learned this year to ensure correct assembly of our gene parts. We can then compare the actual sensitivity and the pattern of GFP production with our simulated Virtual Cell models.