Team:NUS Singapore-A/shadow/Design
ILLUSTRATION (in progress)
Click on each segment of the illustration to discover what the results we have for each components of our system!
Glucose-xylose growth experiments
The cell pellets from Bl21*, BL21*-XylR, and BL21*-XylR*, both uninduced and induced, were analyzed. The thick bands observed at about 45kDa confirms the expression of XylR in induced BL21*-XylR, and XylR* in induced BL21*-XylR*. Tris-Gylcine was the protein ladder used.
To investigate the effectiveness of our xylose-utilizing module, we transformed the plasmid containing the native XylR gene and that containing the mutated XylR (XylR*) into E. coli BL21*. Since XylR acts as a co-activator of the xylose operon, we hypothesized that overexpressing native XylR would also help to enhance xylose utilization. Also, the effect would be more pronounced when XylR* was expressed, as it has a significantly higher binding affinity. BL21*, BL21*-XylR, and BL21*-XylR* were grown in 0.2% glucose, 0.2% xylose, as well as a mixture of 0.1% glucose and 0.1% xylose. Growth, as an indicator of sugar substrate utilization, was measured via absorbance at 600nm over 8-hours in a microplate reader.
To verify the expression of XylR and XylR*, SDS-PAGE was conducted for samples taken after induction. Thick bands at approximately 45kDa were observed for induced cells, with no corresponding band for the WT and uninduced samples, showing that XylR and XylR* are overexpressed respectively (Figure 1).
Our growth experiments displayed rather interesting results. BL21*-XylR* had noticeably the highest growth in all three conditions, while BL21*-XylR displayed considerably little or even no growth (Figure 2a, b, and c). Most importantly, overexpressing XylR* seemed to increase xylose utilization, where the growth of Bl21*-XylR* is significantly higher than the wild-type in xylose (Figure 2e), as well as in a mixture of glucose and xylose (Figure 2f). Our xylose-utilization module likely works!
Surprisingly, XylR* also appeared to enhance growth in glucose (Figure 1a). We expected it to have similar growth as the wild-type instead. Hence, it is difficult to ascertain whether the augmented growth in xylose is a result of improved utilization or other mechanisms, which is possible due to XylR’s role as a metabolic regulatory factor.
Nonetheless, BL21*-XylR* exhibited comparable growth rates in glucose and the mixture of glucose and xylose, suggesting co-utilization of sugar substrates occured (Figure 1f). In contrast, this was not observed in the wild-type, where having a mixture of sugars resulted in lower growth rates (Figure 1d). This is significant as we are one step closer to our vision of utilizing lignocellulosic waste as feedstock, which requires simultaneous utilization of glucose and xylose.
As for BL21*-XylR, there was little to no growth observed regardless of the sugar substrates (Figure 2e). Not only does XylR overexpression fail to improve xylose utilization as hypothesized, there appears to be a growth inhibitory effect. It is possible that other metabolic processes were compromised.
All in all, the XylR*-overexpressing xylose utilizing module is likely to be functioning as intended, but further tests would be necessary. To obtain a more definitive indication of glucose and xylose utilization, HPLC analysis could be carried out on the medium. If xylose utilization was not the contributing factor to enhanced growth, further tests could be conducted to elucidate the mechanism.
On the other hand, if XylR* overexpression does increase xylose utilization as expected, there are plenty of possibilities to be explored! To further demonstrate the applicability of our xylose utilizing module, crude lignocellulosic waste extracts can be fed as well. We can co-transform our current module with our biosynthesis plasmids, demonstrating the production of naringenin or even luteolin from xylose and bringing us closer to our final system. By varying inducer concentrations, the optimal level of XylR* expression can be determined. As such, other strategies could be employed to attain the level of expression without using chemical inducers - one of the key objectives of Coup Dy'état. This includes constitutive or light-regulated expression or even metabolic engineering. More excitingly, a xylose-based nutrient-sensing module could conceivably be developed, allowing for dynamic regulation via light induction!
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Characterization of stress promoter, PhtpG1
To show that our stress reporter part is sensitive to externally introduced constructs which produce foreign proteins (i.e., GFP or luteolin biosynthesis plasmids), we set up an experiment with different test constructs as in Figure 1. Cells were grown in 7 mL LB with relevant antibiotics, if necessary, in a 50 mL Falcon tube at 37°C in the shaking incubator at 220 rpm. 100 µL of each sample was extracted at 0, 2, 4, 5, 6, 24 h time points to measure fluorescence (GFP/RFP) and OD600 using microplate reader (BioTek). All values were corrected by using LB and respective antibiotics as blanks.
Demonstration of stress induced by biosynthesis plasmids
Four different experiments were carried out to prove the working of PhtpG1-mRFP stress reporter.
Experiment 1
Introduction: Using E. coli DH5α as the host we set up an experiment to show that PhtpG1-mRFP is sensitive to externally introduced constructs that produce foreign proteins. In our case, we decided to test this with GFP as it is a universal reporter to confirm if GFP does increase cell stress levels. For this experiment, we included two biological replicates to test our experimental strain (GFP+RFP A and GFP+RFP B).
Results: All three samples show an increase in RFP production/OD over time (Image A) and it is evident that the production of GFP increases the cell stress significant as there is an almost 50% increased production of RFP. This proves the working and the sensitivity of PhtpG1-mRFP to cell stress (Image B). We can observe a staggering difference in GFP between the control strain and in the strains GFP+RFP A and GFP+RFP B (Image D). This draws a strong correlation between cell stress and GFP production
Experiment 2
Introduction: Proving that production of GFP increases the production of RFP, we extended the to test PhtpG1-mRFP using larger constructs or those that express more foreign proteins. We used our deNovo plasmid to check if there would be a greater production of mRFP implying that the cell is more stressed.
Result: We were able to show that the de novo construct which expresses three enzymes OsPKS MCS and 4CL as compared to just GFP activated the stress promoter, PhtpG1, to a greater extent. This is manifested in higher levels of mRFP measured over a time period of 24 hours (see Figure 2A) in the cell expressing the three de novo enzymes as compared to the cell expressing GFP only. We also recorded GFU per OD600 to confirm that GFP was only expressed in RFP+GFP A and not deNovo B (see Figure 2C). From this data, we were able to deduce that larger externally induced construct which expresses larger or more foreign proteins cause greater cell stress than smaller constructs carrying genes of smaller proteins (i.e., GFP).
Experiment 3
Introduction: The next step in testing our stress reporter was to test it in different hosts. For testing different genetic background, we used the strain BL21 (DE3). This was done to show the versatility of our part so that users can use it for different experiments.
Results: Changing the host strain from DH5α does not affect the trend observed. The larger plasmid, in this case, deNovo is showed the highest production of mRFP which implies that it was the most stressed among our three samples. This proves that our stress reporter, PhtpG1-mRFP works efficiently in different genetic backgrounds.
Experiment 4
Introduction: To test our stress reporter's versatility, we decided to subject it to a different temperature. We used BL21 (DE3) at 25oC.
Result: Changing the temperature also did not change the end result observed at the 24 hour mark. However, the overall trend of mRFP levels per OD over the 24 hour period is slightly different compared to that 37. As 25 is lower, cell growth was much slower. This explains the downward trend of mRFP/OD values between 2-6 hours as the RFU values were divided by small OD. At the 6 hour mark, RFP values had increased significantly and so did the OD after which, the trend returns as predicted. This shows that our stress reporter, PhtpG1-mRFP is robust in different temperatures as well
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References
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We’re using synthetic biology to change the way the world thinks and functions.
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