Glucose-xylose growth experiments

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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To demonstrate that our stress reporter is sensitive to externally introduced constructs which produce recombinant proteins, we tested various constructs transformed into E. coli as illustrated in Figure E1. Cells were grown and samples were extracted at certain time points to measure fluorescence (GFP/RFP) and OD₆₀₀ in a microplate reader.

Characterization of burden-responsive promoter, P_htpG1

First, we set out to characterize the P_htpG1 promoter which was chosen to be the stress sensor. Using E. coli DH5α as the host, we introduced our stress reporter module along with a GFP-expressing construct (Figure E1b). The level of RFP expression was compared to cells transformed with the stress reporter module only, which served as a control (Figure E1a). Since the RFP levels for cells containing only the module should only correspond to the basal stress level and that induced by the module itself, additional levels of RFP detected would be a result of GFP expression. For this experiment, we included two replicates picked from different colonies (GFP+RFP A and GFP+RFP B) as our experimental strains (Figure E2).

There is an overall trend of increasing RFP/OD levels over time (Figure E2A), which is indicative of increased cell stress over time. By comparing RFP/OD of the control and experimental strains at the 24 h time point (Figure E2B), we demonstrated that GFP production in cells caused an approximately 0.5 fold increase in RFP/OD levels, suggesting an equivalent increase in cell stress.

To further confirm that GFP production contributed to stress and thus elevated RFP levels, we compared the production of both fluorescent proteins (Figure E2C). Indeed, GFP+RFP A, which had a higher GFP productivity, exhibited higher levels of stress as reported by its higher RFP expression.

These showed that our stress reporter module works as expected! It is not only able to report cell stress but is also sensitive and responsive to the presence of externally introduced constructs. With that, we moved on to bigger things: testing our module with the our constructs used for biosynthesis.

Demonstration of stress induced by biosynthesic constructs

Higher recombinant protein expression leads to more stress

To justify the need for our stress reporter module, we performed the same tests on our biosynthetic constructs (Figure E1C, D, E, and F). As the level of RFP expression corresponds to the level of stress, we hypothesized that larger constructs which expresses more proteins would induce a heavier burden on the cells, resulting in higher RFP levels. To examine this, we used our de novo plasmid, a significantly larger construct expressing three larger enzymes (Figure E1c). Again, two replicates picked from different colonies (de novo A and de novo B) were the experimental strains (Figure 3).

The results were as expected! Cells containing the de novo constructs exhibited higher RFP levels, indicating that the stress promoter, P_htpG1 was activated to a greater extent (Figure 3a and b). Hence, we were able to tentatively conclude that the level of stress, manifested in RFP expression, is correlated to higher recombinant protein expression. Stress experienced by cells is ineed something not to be taken lightly!

Robustness of our stress reporter module

Thus far, the investigation of our stress reporter module has been carried out in DH5α. Hence, to demonstrate the robustness of the module, we tested it in a different genetic background, i.e. BL21 (DE3) Star, which was also our choice strain for biosynthesis.

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 25_oC.

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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