RESULTS
A SUMMARY OF OUR CONCLUSIONS AND OUTCOMES
A SUMMARY OF OUR CONCLUSIONS AND OUTCOMES
The initial docking of hlFAB with PFOA was successful, providing a solid foundation for further analysis. Using Autodock Vina, the docking procedure predicted an initial binding free energy (ΔG) of -11.3016 kcal/mol for the wild-type hlFAB bound to PFOA. This value reflects the strength of the interaction between the protein and the ligand, where a more negative ΔG indicates a stronger, more favorable binding.
To better understand the significance of this result, we calculated the dissociation constant (Kd) from the ΔG using the following equation:
ΔG = RT * ln(Kd)
Where:
R, ideal gas constant, (1.987 * 10^-3 cal/mol·K)
Temperature (300K)
ΔG is the binding free energy (-11.3016 Kcal/mol)
By rearranging the equation to solve for ΔG:
e(ΔG / RT) = Kd
Substituting in the values, we get:
5.836 nano MolarThis calculation gives a dissociation constant (Kd) of approximately 5.836 nM, which indicates strong binding. Since Kd means the concentration required of the ligand to bind to half of all the receptors, a low Kd, especially in the nanomolar range, signifies that the ligand (PFOA) binds tightly to the protein (hlFAB), making it an effective candidate for detection in our experimental work.
After running the first MMPBSA, we generated a graph from the python script, in order to see the charge decomposition:
We used this graph to determine likely residues to mutate.
To track the impact of various mutations on the binding affinity, we created a table comparing the ΔG values and calculated Kd for each mutation. These results show how each mutation influences the interaction between hlFAB and PFOA, and whether the changes enhance or weaken binding strength.
| Mutation | ΔG (kcal/mol) | Kd (M) | Effect on Binding Strength | Why we chose this mutation |
|---|---|---|---|---|
| Wild type + PFOA | -11.3016 | 5.8359 x 10^-9 | N/A | Baseline |
| Wild type + Palmitic Acid | -2.4944 | 0.0152 | N/A | To compare PFOA to it’s natural ligand. |
| ILE 52 to ARG + PFOA | -10.0965 | 4.4065 x 10^-8 | Slightly worse | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged resiude (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| ILE 308 to ARG + PFOA | -13.1878 | 2.4654 x 10^-10 | Great improvement. | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged resiude (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| SER 349 to THR + PFOA | -3.1394 | 0.0051 | Severly worse | In the binding pocket, There is a Serine and a Threonine that interact with the
oxygens on PFOA, and they form hydrogen bonds between FAB and PFOA. So the idea was to make the two
residues that interact with PFOA the same.
|
| PHE 50 to ARG + PFOA | -6.9105 | 9.2318 x 10^-6 | Severly worse | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged resiude (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| THR 351 to SER + PFOA | -12.2177 | 1.2550 x 10^-9 | Slightly worse | In the binding pocket, There is a Serine and a Threonine that interact with the
oxygens on PFOA, and they form hydrogen bonds between FAB and PFOA. So the idea was to make the two
residues that interact with PFOA the same.
|
This table organizes the results of each mutation, highlighting how the mutations affect the protein's ability to bind with PFOA. For example, the ILE 308 mutation shows an improvement in binding, while the SER 349 mutation significantly weakens the interaction. These findings provide clear direction on which mutations could improve the lower detection limit of hlFAB, and helps identify key residues such as SER 349 and THR 351.
Our iGEM Team’s research goal was to design an efficient mechanism to detect PFAS chemicals in the bacterial species DH5-Alpha E. coli. To do so, our team developed three potential pathway mechanisms: prmA Promoter, FAB-GFP, and Estrogen Synthetic Transcription Factor. After testing all three pathways, our results were inconclusive, suggesting that more testing and future research are needed.
PREFACE: We planned to collect data up to the 48-hour timestamp in 1.5-hour increments. However, the fluorometer we were using had technical issues, resulting in data points only up till the 3-hour timestamp.
Our first construct used the prmA promoter characterized by United States Air Force Academy (2019) and Stockholm (2020), as well as by previous literature.
The results of the fluorescence of our construct are shown below:
Graph 1
Graph 1 displays the fluorescence values over time of a colony from plate 1 AFTER subtracting the LB broth and PFOA solution’s fluorescence values at the corresponding times. From the graph, it’s evident that there was an increase in fluorescence as PFOA was added, as can be seen by the difference between the blue line, which represents 0 micromolar concentrations of PFOA, and the other lines, which represent higher concentrations. This difference indicates that PFOA may have led to an uptick in GFP production. However, when looking at the values of the fluorescence intensities themselves, which are all negative, show that the LB Broth with PFOA solution had a higher fluorescence than the fluorescence of the cells on their own. Although we aren’t sure of the reason behind this, it may be because of the addition of the cells, which reduced the natural fluorescence of the LB broth, or because of errors in the measurement of the fluorescence from the fluorometer. More testing is needed to determine if the constructs work.
Graph 2
The data in Graph 2 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The fluorescence may be affected by PFAS since the fluorescence at any given time point appears to be ordered by PFAS concentration, however, more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.
In addition to testing the prmA promoter with PFOA, we attempted to test it with H2O2 concentrations to determine if our proposed mechanism of action is plausible.
Graph 3
Graph 3 displays the fluorescence values over time of a colony that was taken from plate 1, containing E. coli transformed with the prmA gene construct, after subtracting the LB broth fluorescence values at the corresponding times. From this, we can see there was an uptick in GFP production with higher concentrations of H2O2 concentrations; the blue line, which represents a 0 micromolar concentration of hydrogen peroxide, has a lower fluorescence value than the other lines. However, once again we see, when looking at the values of the fluorescence intensities themselves, which are all negative, show that the LB Broth with PFOA solution had a higher fluorescence than the fluorescence of the cells on their own.
Graph 4
The data in Graph 4 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The small spacing between curves makes it very difficult to establish any causality between fluorescence and H2O2, so more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.
The FAB-GFP mechanism was taken from previous literature (https://www.nature.com/articles/s41598-023-41953-1). According to their results, they found that the FAB-GFP complex was capable of fluorescing in E. coli after exposure to concentrations of PFAS.
The results of the fluorescence for our construct are shown below:
Graph 5
Graph 5 displays the fluorescence values over time of a colony taken from plate 1, containing E. coli taken from the FAB-GFP construct after subtracting the LB broth fluorescence values at the corresponding times. From this, we can see there was an increase in fluorescence when PFOA was added. This is evident by comparing the heights at different points of the 0 uM PFOA concentration line to the others. However, this trend doesn’t compare with the other values. The 50 μM concentration line begins by being lower than the 5 μM concentration. Despite this, from the 1.5-hour mark onwards, it remains higher than both the 5 μM and the 250 μM concentration levels. Additionally, at 0 hours, the 250 μM was lower than the 5 μM, not following the traditional trend of a direct relationship between PFOA concentration and fluorescence intensity. Since we know that the concentration of PFAS doesn’t directly correlate to the fluorescence intensity (exemplified by this graph’s data), we can still confirm the fact that PFOA concentration increases the fluorescence intensity compared to no PFOA at all.
Graph 6
The data in Graph 6 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The fluorescence may be affected by PFAS since the fluorescence at any given time point appears to be ordered by PFAS concentration, however, more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.
The estrogen receptor synthetic transcription factor (STF) and its promoter was taken from previous literature (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873372/ ). These researchers primarily developed the STF to detect various concentrations of estradiol in yeast cells. They also developed a hybrid promoter dedicated to the binding sites of the STF to upregulate the transcription of GFP. Since PFAS is known to be an agonist for estrogen receptors, it was hypothesized that the estrogen synthetic transcription factor may also bind to PFAS chemicals, causing a conformational change that may also upregulate transcription in the hybrid promoter Plex.
The results of the fluorescence of our construct are shown below:
Graph 7
Graph 7 displays the fluorescence values over time of a colony that was taken from plate 1, containing E. coli taken from the Estrogen Receptor STF construct after subtracting the LB broth fluorescence values at the corresponding times. Since this graph doesn’t follow the previous and confirmed trend of fluorescence intensity increasing when PFAS (PFOA) is added, it can be hypothesized that when applied in real-time, this construct is less likely to be able to provide accurate results via fluorescence.
Graph 8
The data in Graph 8 implies that all cells produce a basal fluorescence over time based on the increasing fluorescence reading across all cells. The amount of fluorescence at any time point appears to be inversely related to PFAS concentration, however more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.
Overall, all three of our constructs had inconclusive results. A common reason for this inconclusiveness may be because of the technical issues our team faced in the lab with the fluorometer. However, more issues and areas of further experimentation are listed below.
After testing E. coli transformed with the prmA-GFP construct, the results were inconclusive. According to USAFA 2019, there has been a precedent that the PrmA promoter is sensitive to PFAS concentrations. However, those findings were tested in Rhodococcus jostii, suggesting that the prmA promoter may not be induced in the presence of PFAS due to the potential lack of transcriptional elements native to Rhodococcus and absent in E. coli. Based on an extensive literature review, we hypothesize that the prmA promoter requires R. jostii specific Fis family transcriptional regulators and cAMP-receptor proteins to function properly. We confirmed these results by conducting a BLAST of R. jostii cAMP-receptor proteins and Fis family transcriptional regulators against the E. coli genome, which did not return any sequences with >50% identity. E. coli likely lacks the transcriptional machinery for the prmA promoter to function as it does in R. jostii. It was also hypothesized that the prmA promoter region was induced by an abundance of H2O2 caused by oxidative stress from the presence of PFAS. So, when our team also tested prmA-GFP in the presence of H2O2, the results were inconclusive as well. Based on our team's results, it may be that the H2O2 abundance is not the correct mechanism that causes the prmA promoter to upregulate transcription, or there may have been human error when testing concentrations of H2O2, suggesting that further research must be completed to identify the mechanism of prmA’s sensitivity to PFAS.
When tested at our lab to confirm, the results came out inconclusive. One major reason may be that our genetic construct had a few missense mutations, which may be the leading cause of the lack of promising results. Based on the molecular dynamics simulations, there are many possibilities for the improvement of binding affinities through mutations, which may translate to better results in a practical setting. Additionally, according to Virtual Cell results, the limiting factor in the fluorescence of FAB-GFP is the availability of PFAS in the environment (using association constants given by the original author). Thus, this opens two avenues to explore: decreasing binding affinities even further, because of the simplicity of Virtual Cell and its models, and innovating a way to increase the fold change in fluorescence of the bound FAB-GFP compared to the unbound (essentially making the FAB-GFP fluoresce more when PFAS or a fatty acid is bound to it), which requires increased structural understanding of the protein.
There may be several reasons why our fluorescence results came out inconclusive. One major reason may be that the PFAS chemicals may not bind correctly to the STF, which prevents its activation of expression on the hybrid promoter. Another reason may be that the conformational change does not correctly occur to activate expression on the hybrid promoter. In addition, there may be native key transcription factors that are necessary to induce the hybrid promoter that is only found in yeast cells and absent in E. coli, suggesting that there is significantly less transcription in our E. coli. The last contributing factor to our inconclusive results may be that the VP16 activator domain may not have functioned as desired in our E. coli. VP16 is native to herpes simplex virus proteins which mainly target eukaryotic cells. The RNA polymerase in eukaryotic cells is fundamentally different compared to prokaryotic cells, which means that the VP16 may not have successfully recruited the polymerases to the DNA as desired.
We modeled the dynamics of part BBa_K5114228.
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 E. coli 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.
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. 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. 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.
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 when bound to PFAS.
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.
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 fluorometer 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.