Enhancing gene expression is vital for downstream applications such as the biosynthesis of valuable chemicals or manipulating behaviors of designed gene circuits against intrinsic noise. The traditional means is to adopt promoters with higher activity to increase the inflow of mRNA and protein translation, which inevitably increases metabolic pressure for the chassis, thus limiting the scale and complexity of artificial biological circuits. Instead of increasing gene expression, we seek to modulate the degradation rate of mRNA by degradation-tuning RNAs (dsRNAs) on the 5’UTR region of the mRNAs. Degradation-tuning RNAs(dtRNAs) are hairpin-shaped RNA structures placed on the 5' untranslated region of the mRNA, and they could modulate the degradation rate constant of prokaryotic mRNA by resisting endocellular RNase attack. It allows us to enhance protein expressions without posting an extra metabolic burden to the host, and we introduce these functional structures on mRNA to synthetic biology as a new type of standard biological parts, wishing to provide a useful toolbox for the iGEM community and facilitate downstream circuit constructions.
Figure 1. Conceiving of our project and the applications of dtRNAs
Before any experiments, we want to test the assumption of enhancing gene expression by decreasing degradation. Therefore, we established a model based on biochemical kinetics to simulate the influence of mRNA degradation rates on cell growth and protein expression. We use the model to describe the relationship between bacterial growth, gene expression, and other related indexes over time, then adjusted the degradation coefficient of mRNA to simulate the effect of dtRNA. In this model, the growth pressure of bacteria comes from the decrease of survival resources in the environment, and gene expression will also consume resources.
We assume that:
1. the amount of resources in the culture medium environment is never negative.
2. consumption of resources only comes from bacterial division and intracellular gene expression.
3. the effect of expression of other genes in the system is not considered as it is simplified as cell division.
Figure 2. system of ordinary differential equations in our model
We estimate the parameters according to the literature and used MATLAB to calculate the numerical solution of the ODEs. Firstly, we tested the effect of adopting promoters with different strengths, which is the most conventional method for enhancing expression. The results accord well with our acknowledgment, that this mean comes with the prize of sacrificing bacterial growth under high expression conditions.
Figure 2. Changes in bacterial growth and GFP concentration at different transcription rates
Next, we test the effect of changing mRNA degradation rates. The negative effect on bacterial growth is significantly decreased and become negligible in this context, but the tunable range of GFP concentration is still large enough. While both increased promoter strength and decreased mRNA degradation rate lead to higher expression, the latter gives much lower metabolic pressure to the host cell.
Figure 3. Changes in bacterial growth and GFP concentration at different mRNA degradation rates
Model simulations confirm our concept of modulating RNA degradation to enhance protein expression without posting too much burden to the host cell, while traditional means of increasing downstream products, such as adopting promoters with higher strength, result in a significant rise in metabolic pressure. We, therefore, regard modulating mRNA degradation rates as a viable approach for our frame of the project and worth for using functional molecules to achieve this goal.
Although several reports are showing that special secondary structures at the 5’ UTR region of mRNA would resist degradation from endogenous RNase, these structures are not comprehensively designed and tested until the report published by Zhang et al. in 2021. We thought these degradation-tuning structures might be perfect materials for modulating degradation. For wet lab work, we integrate six dtRNA structures into expression cassettes under the control of the J23106 promoter with medium strength.
Figure 4. Schematic diagram of gene circuit
After HiFi assembly, transformation, and microplate reading, the obtained fluorescent curves and growth curves are used to characterize the anti-degradation effect of dtRNAs and the metabolic pressure, respectively. We found that changes in dtRNA structure led to negligible deviation in the cell growth curve but a significant range in fluorescence. Compared with the previous report from the literature, we observed comparable fluorescent signals, indicating the effect of degradation-tuning RNAs is indeed transferrable to DH5α strains, the only difference is that the introduction of dtRNA82 did not abolish the GFP fluorescence, which may come from the difference of Rnase targeting sites between the two strains.
Figure.4 GFP fluorescent curve under the control of J23106
Figure.5 Comparison of dtRNA fluorescent fold change with different host strains
On the other hand, the measured OD600 curve shows a similar but even faster growth curve when using dtRNAs, no matter whether its function is to resist or facilitate degradation. This interesting observation deserves further investigation in the future. These experiment results solidified dtRNAs’ ability to change expressions and confirm our simulation results of not posting burden to the host.
Figure.6 Bacterial growth curve under the control of J23106
For a wider use range of dtRNAs, we have to test whether this part type is compactable and performs well in different parts combinations or genetic contexts, as unexpected crosstalk could happen. To test its universality with various biological parts, we changed the promoter type, but the effectiveness of dtRNAs remains roughly the same. After changing the promoter to weaker J23116, we still observe nearly three-fold changes in fluorescence, but the noise would become significant when using J23109, as it is too weak to express enough GFP.
Figure.7 GFP fluorescent curve under the control of different promoters
Figure.8 growth curve under the control of different promoters
Furthermore, we also used a forward-engineering principle to construct a variant dtRNA that shows slightly higher fluorescence compared to the former version, demonstrating the potential to improve dtRNA designs iteratively. Firstly, we analyzed the structure of these six dtRNAs using NUPACK:
Figure.9 structure and structural factors of dtRNAs
The effectiveness has a good correlation with the structural factor concluded in the article, so we further optimized the best-performed dtRNA1 and designed a second-generation dtRNA(dtRNA1v2) with optimal structural parameters based on the structure of dtRNA1 and used NUPACK to simulate the secondary structure.
Figure.6 Simulated secondary structure of dtRNA1v2
The structure parameters of dtRNA1v2:
4. stem length: 11 bp; structural factor: 100%
5. stem GC content: 54.5%; structural factor: 91%
6. loop size: 6 nt; structural factor:100%
The further-modified version of dtRNA1 has even perfect structural factors compared to other ones. Then we experimentally integrate dtRNA1v2 into GFP-expressing cassettes and measure the fluorescence and OD value.
Figure.7 Fluorescence curve of dtRNA1v2
The result shows a slightly higher fluorescence value compare to the original version of dtRNA1, with the GFP fold change up to 3.96. Although this value may not be statistically significant when adding repeating groups, it shows the potential of designing second-generation dtRNAs using forward-engineering principles.
Based on the aforementioned experiments and investigation, we have successfully proven our concept of using dtRNA as a structural component to modulate mRNA degradation rates, as well as the most direct application of increasing protein concentrations without significantly affecting bacteria growth. We anticipate further integration of dtRNA into higher-level devices and complex circuits to explore the full potential of this new part type.