Contribution

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CONTRIBUTION

mPapaya


Introduction

While waiting for the parts we designed for our project to be synthesised, we wanted to get into the lab and familiarise ourselves with the equipment and techniques. Our PI suggested we practiced transforming, growing, and expressing proteins in bacteria with fluorescent proteins which are relatively non-toxic to bacteria and easy to visualise. During training on the imaging flow cytometer, we noticed some unusual results. These are presented below and are our bronze medal contribution to the iGEM registry (BBa_K4345003).


Methods

Expression of sfGFP [1], mCherry [2], and mPapaya1 [3], was under control of the strong constitutive promoter J23100, the strong RBS B0034, and terminated by the double B0015 terminator. The gene was carried on a medium copy number plasmid with ampicillin resistance and transformed in E. coli DH5a (protocol can be found on our Experiments Page). 5ml cultures were grown at 37 °C, 200 rpm for 18 h. Cell morphology was investigated using an Image Stream Mark II (Amnis-Luminex Corp.) Imaging Flow Cytometer configured with Bright Field (LED 33.92 mW), Side-Scatter (laxer 785 nm, collecting 762/35 nm) and either GFP (excitation laser 488 nm, emission 533/55 nm), RFP (excitation laser 561 nm, emission 702/85 nm) or YFP (excitation laser 488 nm, emission 533/55 nm).

Imaging Flow Cytometry data was graphically analysed using specialised software [4]. The software can graphically represent the distribution of labelled cell populations, which can then be gated (selecting the region of cells to be analysed). Only the main population of cells were gated, with the outliers and speed beads used to calibrate the Flow Cytometer being disregarded.


Results

Imaging Flow Cytometry collects two types of optical information: forward scatter (FSC) and side scatter (SSC) [4]. The main population of cells to be analysed was gated from a scatter plot of FSC against SSC (Figure 1).


Three scatter graphs of forward scatter against side scatter showing the gated cells used for further analysis
Figure 1:Scatter graphs of forward scatter against side scatter, with the gated regions selected. From left to right: mCherry, mPapaya1, sfGFP

Cell Length

The images in Figure 2 show that there is a distinct difference in cell length between cells expressing mPapaya1 in comparison to cells expressing mCherry and sfGFP. Cells expressing mCherry (Figure 2a) and sfGFP (Figure 2c) appear spherical and rod-shaped, which is a healthy shape for E. coli [5]; whereas cells expressing mPapaya1 appear much longer and 'noodly', (Figure 2b). The geometric mean of cell length for cells expressing mPapaya1 was larger (8.005 µm) than in cells expressing mCherry (2.544 µm) and cells expressing sfGFP (3.862 µm) (see Figure 3).


Flow Cytometry images of the cells expressing fluorescent proteins, displaying how the cells expressing mPapaya have different morphology in comparison to the others.
Figure 2: Flow Cytometry images of the cells expressing fluorescent proteins, displaying how the cells expressing mPapaya have different morphology in comparison to the others.
Figure 2a: Cells expressing mCherry. The first column is the brightfield, the second column is the forward scatter, and the third column is the side scatter.
Figure 2b: Cells expressing mPapaya1. The first column is the forward scatter, the second column is the brightfield, and the third column is the side scatter.
Figure 2c: Cells expressing sfGFP. The first column is the brightfield, the second column is the forward scatter, and the third column is the side scatter.

Histograms showing the distribution of cell length. The data shows that cells expressing mPapaya1 are on average longer (geometric mean=8.005) than the other cells( mCherry geometric mean=2.544, sfGFP geometric mean=3.862)
Figure 3: From left to right: mCherry, mPapaya1, sfGFP. Histograms showing the distribution of cell length. The data shows that cells expressing mPapaya1 are on average longer (geometric mean=8.005 µm) than the other cells( mCherry geometric mean=2.544 µm, sfGFP geometric mean=3.862 µm)

Fluorescence Intensity

Due to differences in quantum yield and extinction coefficient (i.e. brightness) between the three proteins it is not possible to compare expression levels of mPapaya1, sfGFP and mCherry without calibration. Furthermore, the practical brightness of FPs is determined by several factors, such as folding, maturation efficiency, pKa, as well as the optical properties of the imaging equipment, therefore molecular brightness may not reflect the actual brightness of a protein during an experiment [6]. However, it is possible to compare cell variability within a population of cells. The flow cytometric histograms in Figure 4 show normalised frequency against fluorescence intensity. The coefficient of variation values (CV, %) are 84 for mPapaya,1 76 for mCherry, and 57 for sfGFP, demonstrating that cultures expressing mPapaya1 displays the widest range of fluorescent intensity in comparison to cultures expressing sfGFP and mCherry.


Histograms showing the distribution of cell fluorescence intensity, showing that mPapaya displays the widest range of fluorescence intensity. However, practical brightness cannot be compared between the cells without calibration first.
Figure 4:From left to right: mCherry, mPapaya1, sfGFP. Histograms showing the distribution of cell fluorescence intensity, showing that mPapaya displays the widest range of fluorescence intensity. However, practical brightness cannot be compared between the cells without calibration first.

Discussion

Cell morphology is often characteristic of a particular bacterial species. E. coli cells for example are often reported as being rod-shaped 2 µm long and 1 µm diameter [5]. However, bacterial cell morphology can change in response to the stage of life cycle and environmental factors [7]. This was seen during our experiments where E. coli cells expression mPapaya1 were observed to be longer than cells expressing either sfGFP or mCherry (see Figure 2). This increased cell length which could be representative of unhealthy cells, may be the reason why there is a wider variability in fluorescence of individual cells in populations expressing mPapaya1. We unfortunately did not have time to investigate this further, but we hope that contributing our observations to the iGEM registry will inspire future teams' experiments.

RNAIII Inhibiting Peptide


Our original idea was centred around RNAIII Inhibiting Peptide (RIP) (BBa_K2616001), a protein that was thought to disrupt quorum sensing in Staphylococcus aureus, as demonstrated by iGEM Pasteur Paris 2018 [8]. RIP would have acted as a competitive inhibitor against RAP, thus preventing the phosphorylation of TRAP. As phosphorylated TRAP is involved in the upregulation of the accessory gene regulator (agr) system, therefore by preventing TRAP phosphorylation, the agr operon would not have been expressed. As a result, the production of autoinducing peptides (AIPs) would have been avoided. Since AIPs directly feedback into the agr system, the prevention of AIP formation in the first place would have resulted in the no upregulation of the agr operon, and so no quorum sensing.


However, through further literature searches, we discovered that this system did not work in the way we thought, and this was supported by our own modelling in Alphafold [9] and ChimeraX [10], which showed that it was doubtful whether RIP binds to TRAP. In the literature [11], we discovered that agr deletion mutants increased biofilm formation, therefore casting the whole RIP and agr operon system into doubt.


With this information we decided not to proceed with RIP, but instead we investigated other molecules that are crucial in quorum sensing and biofilm formation, such as AI-2.


REFERENCES

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  2. Pedelacq, J. D., Cabantous, S., Tran, T., Terwilliger, T. C. & Waldo, G. S. 2006. Engineering and characterization of a superfolder green fluorescent protein. Nat Biotechnol, 24, 79-88.
  3. Shaner, N. C., Campbell, R. E., Steinbach, P. A., Giepmans, B. N., Palmer, A. E. & Tsien, R. Y. 2004. Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat Biotechnol, 22, 1567-72.
  4. Jahan-Tigh, R. R., Ryan C., Obermoser G., Schwarzengerger K., 2012 Flow Cytometry Journal of Investigative Dermatology 132, DOI: 10.1038/jid.2012.282
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  7. Power, A. L., Barber, D. G., Groenhof, S. R. M., Wagley, S., Liu, P., Parker, D. A. & Love, J. 2021. The Application of Imaging Flow Cytometry for Characterisation and Quantification of Bacterial Phenotypes. Front Cell Infect Microbiol, 11, 716592.
  8. iGEM 2018 Pasteur Paris. Available at: https://2018.igem.org/Team:Pasteur_Paris/Fighting
  9. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583-9. https://doi.org/10.1038/s41586-021-03819-2.
  10. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 2022;50:D439-44. https://doi.org/10.1093/nar/gkab1061
  11. Cuong Vuong, Christiane Gerke, Greg A. Somerville, Elizabeth R. Fischer, Michael Otto, Quorum-Sensing Control of Biofilm Factors in Staphylococcus epidermidis, The Journal of Infectious Diseases, Volume 188, Issue 5, 1 September 2003, Pages 706-718, https://doi.org/10.1086/377239