Proof of Concept

Goal

The goal of this project is to identify which molecule-promoter combinations will create an accurate, detectable fluorescent signals. This will serve as the basis of future biosensor development.

Design

In the first step of experiments to address the lack of biosensors in the scientific community, our team wanted to identify certain molecules that would induce promoters that then produced a fluorescent response. These efforts were made possible by a study from the Weizmann Institute of Science, which presented a comprehensive library of 2000 E. coli promoters fused to GFP [1]. The study provides a total of 21 promoter plates, each with 95 promoters and 1 negative control well.

Figure 1. Part of the library of promoters with their descriptions and position in the delivered 96-well plate

These promoters, some of which are shown in Figure 1, are reasonably well studied and activate known biological pathways in the organism. For example, Figure 2 shows the known regulatory influence of yabP and the role the promoter plays in E. coli gene expression. As a result, we expect the promoters to transcribe the same proteins when activated in our lab setting. Additionally, we created example biosensors using the significant promoters we found through the tool PlasMapper 3.0 [3].

Figure 2. Regulatory influences and genomic location of yabP, one of the promoters in the E. coli library [2]

Figure 3. pIGEM1, a theoretical plasmid created with the gadB promoter fused to sfGFP, serving as a potential biosensor for Butanoyl-Homoserine Lactone when transformed into E. coli [3]

Promoters are sequences of DNA that control the transcription of a gene. Since they were fused to GFP, these promoters produce the Green Fluorescent Protein when they are activated. The fluorescent signal can then be detected and quantified by a plate reader. The greater the amount of inducer, the more the promoter should be activated and therefore result in a greater fluorescent response.

In order to select molecules that would actually produce a greater fluorescent response when present in greater concentrations, our team decided to investigate compounds that have a high relevance to scientific research and are likely to induce some of the promoters in the E. coli library. The following abbreviations were used for each molecule compared against each of the 2,000 promoters: PBA represents 3-Phenoxybenzoic Acid, LOV represent Lovastatin, PRO represents Propoxur, DEP represents Diethyl Phthalate, TAR represents Tartaric Acid, CAR represents Carbaryl, BHL represents Butanoyl-Homoserine Lactone, PGA represents Phenylglyoxylic Acid, PFS represents Perfluorooctane Sulfonate, and CND represents Cis-Naphthalene Dihydrodiol.

Figure 4. sfGFP and OD600 reading from plate reader for TAR molecule with Promoter Plate 1

Figure 5. sfGFP/OD600 for TAR with Promoter Plate 1

Based on our initial set of experiments, we confirmed our ability to grow the E. coli strains with various promoters (with cell density represented by the OD600 values) and the ability of the promoters to activate GFP transcription in the presence of the molecules (with fluorescence represented by the sfGFP value). The fluorescence was then normalized by cell density (sfGFP/OD600 values shown in Figure 5) because the greater the number of cells, the greater the amount of GFP produced and therefore greater fluorescence. The varying amounts of sfGFP/OD600 (fluorescence per cell) produced follow our expectations as each molecule should induce some of the promoters better than others. In order to select the best inducer-promoter combination, we focused on pairings that produced an sfGFP/OD600 value greater than 1.35. We also disregarded any well with an OD600 less than 0.1 as such a low value means that very little cell growth occurred and thus the data cannot be considered accurate.

Figure 6. sfGFP/OD600 values of the cyoA promoter when tested against each of the molecular compounds

The data from the initial screening was also analyzed by promoters. For the top four promoters that produced the most fluorescence with a certain molecule, its fluorescence value was also compared to that in the other molecules. For example, Figure 7 shows that cyoA, a promoter that produced a high fluorescence with PBA, has a much lower fluorescence when tested with the other molecules. It is highly unlikely that the same promoter is induced by two or more of the molecules. Therefore, these results validate the experiment since only PBA is a sufficient inducer for cyoA and this combination may make a good biosensor for the molecule. The same analysis conducted with other molecules and promoters produced varying results.

Figure 7. Comparison of the fluorescence of the top 4 promoters for PBA against all molecules in the screening

Figure 8. Comparison of the fluorescence of the top 4 promoters for PRO against all molecules in the screening

The figures show that some of the promoters seem to be induced by only one of the molecules, as expected, such as cyoA which has a 2.7 times fold compared to the next highest fluorescence with cyoA. However, other promoters seem to be activated at similar levels by the majority of the molecules, such as ypdA with PRO. These promoters are disregarded as their level of fluorescence doesn’t particularly change with the presence of a particular molecule, despite being initially considered high.

After selecting the ideal molecule-promoter pairs through our data analysis, the next series of experiments conducted for validation were the titrations. Different concentrations of the molecule, from 0 M to 10 mM in 8 increments, were added to the E. coli strain with the identified promoter.

Figure 9. The titration performed for the top 12 strains for PRO, where each strain was tested against different concentrations of PRO

With different concentrations of the molecules, the same promoter produced different levels of fluorescence, which also aligns with our hypothesis. This shows that the transcription of the promoter is dependent on the concentration of the molecules, so the molecules might be inducers for these E. coli. While all of the bacteria didn’t produce an increasing fluorescent signal with an increasing concentration of the molecule, several did have a positive trend and showed promising results for being the basis of a biosensor after several iterations. Variations may be due to experimental error or the effect of the molecules on other biological pathways in the bacterial.

In particular, the following molecules and strains were identified to have an increasing trend with increasing concentration of the molecule. BHL with the ydel promoter produced a 1.7 times fold increase. CND with the ybcK promoter produced a 2.2 times fold increase. CND with the aegA promoter produced a 2.8 times fold increase. Finally, DEP with the yfiF promoter produced a 2.0 times fold increase.

Figure 10-13. Final promoters that had an increasing trend in fluorescence with increasing concentration of the corresponding molecules

Overall, this research demonstrates a strong proof of concept for the future development of a biosensor detecting the presence of the identified molecules. Several molecule-promoter pairs demonstrated an increasing trend with increasing concentrations of the molecules that can be refined and quantified to detect the presence of that molecule in future applications.

References

[1] Zaslaver, A., Bren, A., Ronen, M., Itzkovitz, S., Kikoin, I., Shavit, S., Liebermeister, W., Surette, M. G., & Alon, U. (2006). A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nature Methods, 3(8), 623–628. DOI: 10.1038/nmeth895;
[2] Keseler, I. M., Gama-Castro, S., Mackie, A., Billington, R., Bonavides-Martínez, C., Caspi, R., Kothari, A., Krummenacker, M., Midford, P. E., Muñiz-Rascado, L., Ong, W. K., Paley, S., Santos-Zavaleta, A., Subhraveti, P., Tierrafría, V. H., Wolfe, A. J., Collado-Vides, J., Paulsen, I. T., & Karp, P. D. (2021). The EcoCyc Database in 2021. Frontiers in Microbiology, 12, 711077. DOI: 10.3389/fmicb.2021.711077;
[3] Wishart, D. S., Ren, L., Leong-Sit, J., Saha, S., Grant, J. R., Stothard, P., Singh, U., Kropielnicki, A., Oler, E., Peters, H., Gautam, V. (2023). PlasMapper 3.0 - A Web Server for Generating, Editing, Annotating and Visualizing Publication Quality Plasmid Maps. Nucleic Acids Research. DOI: 10.1093/NAR/GKAD276.