Designed by: Grenoble 2011   Group: iGEM11_Grenoble   (2011-09-20)

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Post-transcriptional regulation<h2>

The RsmA system has a homologous in Escherichia Coli named CsrA. We know these two system are extremely closed on structural and functional sides. The most difference between this two regulation systems is on target regulon. For example, in Pseudomonas aeruginosa Rsma regulate many virulence genes as type III secretion system (anja Brencic and Stephen Lory). In Escherichia coli, RsmA homologous regulate metabolic network. Our goal is to integrate this translational regulation system in the toggle switch. We need to know whether it influence the bacteria’s life.

<a href="Growth_cur_final2.png"><img src="Growth_cur_final2.png" alt="logo iGEM"/></a>

Figure 1 RsmA influence on DH5α growth. Bacteria from overnight culture were reseeding in rich medium supplemented with tetracycline at 20mg/ml and also IPTG at the concentration given in the legend.

Figure 1 present growth curve of DH5α carries into a plasmid pVLT31 with or without rsmA and Natural RBS cloned downstream the Plac promoter. Two triads can be seen. The triad containing the strains with empty plasmid shows an upper growth curve compared to the second triad carries pVLT31-rsmA. But it’s important to say that the two groups start their growth not at the same value. That explains a time lag between these two groups. After normalization of all curves, no differences between these two kind of bacteria could be seen. We conclude that the RsmA overexpression hasn’t effects on the growth of bacteria.

<h2>Characterisation of the leader sequences

We extracted and cloned several leader sequences that comport a ribosome-binding site (RBS). We first wanted to verify whether we could use them with a reporter gene,in order to:

  • <a href="#lab">Characterise their RBS strength</a>
  • <a href="#lab">Use them for further testtomeasure the effect of rsmAlaterrsmA +rsmY transcription. </a>

We first tested the following constructions with a FACSCalibur flow cytometer:

<a href="Figure_2_construction.png"><img height="150px" src="Figure_2_construction.png" alt="figure2"/></a>

Figure 2:

:Constructions used to characterise the sequences RBS strength of magA and fha leader sequences.

We used the brick BBa_K256003 as a reference to compare our leader sequences to. Fha1 and maga leader sequences are cloned to the same components as BBa_K256003, and carried by the same plasmid. We used two negative controls:

  • <a href="#lab">A brick similar to BBa_K5450010 that differs only by the absence of promoter</a>
  • <a href="#lab">A cell culture containing no plasmid </a>

Cell were incubate one night at 37°C, 250rpm, during 14h. They were re-suspended tree hours before the test. Optic density at 600 nm was 3± 0,3 for all samples 15mn before the test. 50 μl of LB containing cellsand medium were diluted into 500 μl of filtered in 0,2 nm PBS, and tube were loader into the FACS.

<a href="Figure_3_facs.png"><img height="150px" src="Figure_3_facs.png" alt="figure3"/></a>

Figure 3:Dot plot of the water tube and one of the sample tubes. The measurements are done with 40000 events. FACS measurements were realised in duplicate (only one his shown).

The cytometer count each particle that pass through the light beam; It is necessary to select an analysis gate that corresponds to the bacteria size. We can then see what is the natural fluorescence of our control bacteria. This allows defining the windows of basal signal (M1) and positive signal (M2). We look at the average fluorescence within windows that comport the most cells.

<a href="Figure_4_flow_cytometre2.png"><img height="150px" src="Figure_4_flow_cytometre2.png" alt="figure4"/></a>

Figure 4: Individuals flow cytometer results for each construction. In red are theGFP’s fluorescence averages, in black the rate of bacteriawithin the M1 or M2 window.

<a href="Figure_5_flow_courbe.png"><img height="150px" src="Figure_5_flow_courbe.png" alt="figure4"/></a>

Figure 5: Compilation of the flow cytometer measurements. 1and 2 are the negative controls, 3 :magaleader sequence, 4: fha leader sequence, 5 : strongest biobrick RBS.

The two negatives controls (WT and without promoter) show a very little fluorescent signal as expected. The reference brick BBa_K25003 shows an impressive amount of GFP with about 77 % of the cell population that fluoresce more than the control.Its average fluorescent signal is 1030 vs 2,5(wild type). The signal from the brick containing maga leader sequence (BBA_K545006) doesn’t differ very much from the negative controls: four per cent of the cell population fluoresce more than the controls, at a very low level even. The fluorescence mean is 4,4 vs 2,5 from the WT control (window M1).

Part BBa_K545010 containing the fha leader sequence induce a fluorescence signal higher than the control for 90 % of the cell population. The average signal (M2 window) is 154 vs 2,5(WT) and 1029 (BBa_K25003).

<a href="Figure_6_rbs.png"><img height="150px" src="Figure_6_rbs.png" alt="figure4"/></a>

Figure 6: relative strength of maga(BBa_K545006) and fha(BBa_K545005) leader sequences RBS compared to BBa_K25003 RBS. The later is the strongest RBS used to compare others RBS of the registry. Maga has got very week RBS binding site strength,whereas fhaLS is still in the same order level.

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