Part:BBa_K5242024
sensorRNA_Chk1
1. Introduction
This part is the base design for all sensor RNAs and, in our experiments, was used in both the system optimization group and the splice isomer application group. When the target RNA is present, the sensor RNA is edited by ADAR, enabling the translation process of downstream genes.
Figure 1.The function of Sensor_Chk1
2. Design
2.1 Repoters
A schematic of the part is shown in Figure 1. In it, green fluorescent protein and red fluorescent protein are reporter genes, in which green fluorescent protein indicates the edited sensor RNA while red fluorescent protein indicates the total sensor RNA, and the editing efficiency of ADAR can be derived by calculating the ratio of the two fluorescence intensities. In practice, these two reporter genes can be replaced with other genes.
Figure 2.The diagram of Sensor_Chk1
2.2 ogRNA
The sequence of ogRNA is derived from the complementary sequence of the target RNA, which has one of the UGGs changed to a UAG, and 4 MS2 loops used to recruit ADARs gives the sequence of ogRNA. This site triggers an A-C mismatch and is the core of the ogRNA. When working, the ogRNA will be complementary paired with the target RNA, and with MS2 recruitment, the ADARs will mutate the base A at the mismatch site to I, which is regarded as G, allowing downstream translation to proceed normally.
2.3 2A Peptides and GSG linker
The 2A peptides is a small, self-cleaving sequence derived from the foot-and-mouth disease virus (FMDV). The 2A peptide works by causing a ribosome to skip the final glycine of the 2A sequence, leading to a cleavage event that separates the proteins encoded upstream and downstream of the 2A sequence. We use E2A and LV2A to separate two fluorescent proteins and intermediate peptides. The GSG linker is used to link the 2A peptides with upstream peptides.
3. Experimental Characterization
3.1 Impact of MS2 and ADARs on Editing
3.1.1 Plasmid Construction and Yeast Transformation
We planed to include a side-by-side comparison of the two ADAR proteins at the same time. In our experimental design, we introduced two parts into yeast. The first part is the ADAR protein expression, with four gene types: PDC promoter + ADAR1, PDC promoter + ADAR_MCP, TEF promoter + ADAR1, and TEF promoter + ADAR_MCP. The second part is the sensor, with three types containing 0, 2, or 4 MS2 sequences. The overall design is shown in Table 1. This method allows us to determine the combination with the highest editing efficiency. After construction, we observed these 12 engineered yeast strains using confocal microscopy and flow cytometry to calculate ADAR editing efficiency.
We used the Gibson Assembly to build our plasmids. The sequencing results of the two plasmids are shown in the Figure15, proving that the plasmid construction was successful.
Figure 3.The Sequencing results
After that, we performed a successful yeast transformation.
| Group | Promoter | ADARs | the Number of MS2 |
|---|---|---|---|
| 1 | TEF1 | ADAR1_p150 | 0 |
| 2 | TEF1 | ADAR1_p150 | 2 |
| 3 | TEF1 | ADAR1_p150 | 4 |
| 4 | TEF1 | ADAR2_MCP | 0 |
| 5 | TEF1 | ADAR2_MCP | 2 |
| 6 | TEF1 | ADAR2_MCP | 4 |
| 7 | PDC1 | ADAR1_p150 | 0 |
| 8 | PDC1 | ADAR1_p150 | 2 |
| 9 | PDC1 | ADAR1_p150 | 4 |
| 10 | PDC1 | ADAR2_MCP | 0 |
| 11 | PDC1 | ADAR2_MCP | 2 |
| 12 | PDC1 | ADAR2_MCP | 4 |
Figures 4 and 5 clearly show that the addition of MS2 sequences effectively enhances the editing efficiency of the entire system. However, an unexpected observation is that increasing the number of MS2 sequences leads to a decrease in editing efficiency for both TEF-ADAR1 and TEF-ADAR2. This outcome contrasts with the results observed for PDC-ADAR2. We hypothesize that the MS2 sequences may interfere with the transcription process, potentially causing premature termination of the mScarlet-EGFP fusion protein. When ADAR1/2 is driven by the PDC promoter, the number of MS2 sequences correlates with increased binding affinity, leading to enhanced editing efficiency. In contrast, under the control of the TEF promoter, the high expression levels of ADAR1/2 may lead to saturation, making the concentration of sensor RNA a limiting factor. Given that MS2 sequences might increase the likelihood of abortive transcription, a higher number of MS2 sequences could reduce the available concentration of sensor RNA, thereby decreasing editing efficiency.
Figure 4.Confocal Results of Editing Systems Containing Different MS2 sequences
Figure 5.FACs Results of Editing Systems Containing Different MS2 sequences
Furthermore, Figures 6 and 7 highlight that TEF-ADAR2 exhibits the highest editing efficiency, likely attributable to its elevated expression level.
Figure 6.Confocal Results of Editing Systems Containing Different ADAR Types and Promoters
Figure 7.FACs Results of Editing Systems Containing Different ADAR Types and Promoters
3.1.2 Conclusions of Impact of MS2 and ADARs on Editing
For ADAR2_MCP there was a significant increase in the editing efficiency after switching promoters, but for ADAR1_p150 the increase in the editing efficiency by switching promoters was not significant. Although the inclusion of MS2 in general favors ADAR editing, the effect of the amount of MS2 on the editing efficiency was more complex, which may imply that there is still an unknown mechanism in the process of ADAR and RNA action.
3.2 Application of sensor RNA for splice isomer detection
Knowing that Chk1 and Chk1s are a pair of splice heterodimers, we set up four sets of experiments to examine the potential of ADARs to detect splice heterodimers. In the experiments, we applied the same plasmid importing sensor RNA and target RNA. And we used the TEF1 promoter and 4 MS2 loops. The groupings and predicted results are shown in Table 2.
| Group | the Target of Sensor RNA | the Transcribed RNA | Green Fluorescence |
|---|---|---|---|
| kk | Chk1 | Chk1 | Yes |
| ks | Chk1 | Chk1s | No |
| sk | Chk1 | Chk1s | No |
| ss | Chk1s | Chk1s | Yes |
3.2.1 Plasmid Construction and Yeast Transformation
As before, we used the Gibson Assembly to build our plasmids. The sequencing results of the two plasmids are shown in the Figure 8,Figure 9, Figure 10,Figure 11, proving that the plasmid construction was successful.
Figure 8.The sequencing results
Figure 9.The sequencing results
Figure 10.The sequencing results
Figure 11.The sequencing results
After that, we performed a successful yeast transformation.
3.2.2 Fluoresence Observasion
3.2.2.1 LCFM Analysis
Figures 12 and 13 show the fluorescence microscopy observations. However, contrary to the expected results, for ADAR1, all four groups showed green fluorescence, while for ADAR2, all but the last group showed green fluorescence. We decided to use flow cytometry to count the percentage of each group that showed green fluorescence.
Figure 12.The LCFM results of ADAR1_p150
Figure 13.The LCFM results of ADAR2_MCP
3.2.2.2 FACs Analysis
We got nice data from Flow cytometry analysis, though the results were not exactly as what we expected. The editing efficiency are shown in Figure 14 and Figure 15.
Figure 14.The FACs results of ADAR1_p150
Figure 15.The FACs results of ADAR2_MCP
2.3.3 Conclusions of in vivo dynamic monitoring of splice variant
According to the datas, we came to some conclusions:
①ADAR2_MCP has the higher edit efficiency at about 80-95%, but this causes no evidence of selectivity. So we couldn't evaluate whether our sensor function well or not.
②The extremely high efficiency of TA2 in yeast shows the potential of becoming a powerful gene edit tool in yeast.
③ADAR1_p150 has the edit efficiency >15% according to the datas from our Optimization group, which is significantly higher than the stop codon readthrough or off target effect (edit efficiency is around 5%). So we could infer that TA1 has a strong selective edit effect for pSensor-Chk1 with Target-Chk1. But TA1 only has the edit effect on pSensor-target_Chk1-Chk1, that was beyond our expectations.
④Design of Sensor is important for the selectivity of ADAR. After the experiment, we put the sequences into IntaRNA and predict the binding energy and binding area of sensor and its corresponding target. According to the results differences, we inferred that the sensor might not have more than one potential binding area and the base around the A-C mismatch might be strictly complementary pairing (not pairing like CAU-AUC).
Figure 16.IntaRNA Simulation of Binding Energy of Chk1(s)-Chk1(s)
3. Referances
Jiang, K. et al. Programmable eukaryotic protein synthesis with RNA sensors by harnessing ADAR. Nature Biotechnology 41, 698-707 (2023).
Savva, Y. A., Rieder, L. E. & Reenan, R. A. The ADAR protein family. Genome Biology 13, 252 (2012).
Sun, J. et al. Cloning and characterization of a panel of constitutive promoters for applications in pathway engineering in Saccharomyces cerevisiae. Biotechnology and bioengineering 109, 2082-2092 (2012).
Martin Mann, Patrick R. Wright, and Rolf Backofen IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions Nucleic Acids Research, 2017, 45 (W1), W435–W439.
Patrick R. Wright, Jens Georg, Martin Mann, Dragos A. Sorescu, Andreas S. Richter, Steffen Lott, Robert Kleinkauf, Wolfgang R. Hess, and Rolf Backofen CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains Nucleic Acids Research, 2014, 42 (W1), W119-W123.
Anke Busch, Andreas S. Richter, and Rolf Backofen IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions Bioinformatics, 2008, 24 (24), 2849-56.
Martin Raden, Syed M Ali, Omer S Alkhnbashi, Anke Busch, Fabrizio Costa, Jason A Davis, Florian Eggenhofer, Rick Gelhausen, Jens Georg, Steffen Heyne, Michael Hiller, Kousik Kundu, Robert Kleinkauf, Steffen C Lott, Mostafa M Mohamed, Alexander Mattheis, Milad Miladi, Andreas S Richter, Sebastian Will, Joachim Wolff, Patrick R Wright, and Rolf Backofen Freiburg RNA tools: a central online resource for RNA-focused research and teaching Nucleic Acids Research, 46(W1), W25-W29, 2018. Sequence and Features
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