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Article

Increasing the Editing Efficiency of the MS2-ADAR System for Site-Directed RNA Editing

by
Jiarui Li
1,†,
Tomoko Oonishi
1,2,†,
Guangyao Fan
1,3,
Matomo Sakari
1 and
Toshifumi Tsukahara
1,*
1
Bioscience, Biotechnology and Biomedical Engineering Research Area, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Ishikawa, Japan
2
Division of Transdisciplinary Sciences, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Ishikawa, Japan
3
School of Medicine, Shaoxing University, Shaoxing 312000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2023, 13(4), 2383; https://doi.org/10.3390/app13042383
Submission received: 16 January 2023 / Revised: 6 February 2023 / Accepted: 10 February 2023 / Published: 13 February 2023
Browse Figures
Versions Notes

Abstract

:
Site-directed RNA editing (SDRE) technologies have great potential in gene therapy. Our group has developed a strategy to redirect exogenous adenosine deaminases acting on RNA (ADARs) to specific sites by making editable structures using antisense RNA oligonucleotides. Improving the editing efficiency of the MS2-ADAR system is important in treating undesirable G-to-A point mutations. This work demonstrates an effective strategy to enhance the editing efficiency of this SDRE system. The strategy involves changing the number of MS2 stem-loops on both sides of the antisense RNA and the mismatch base on the antisense part. The enhanced green fluorescent protein (EGFP) with W58X mutation is used as the reporter gene. Subsequently, we adjusted the amount of plasmids for transfection to tune the expression level of the guide RNA, and finally, we observed the fluorescence signal after transfection. After equalizing number of MS2 stem-loops at both sides of the antisense RNA, high editing efficiency was achieved. In the same level of guide RNA expression, when the paired base position was the target uridine, the editing efficiency was higher than cytidine, adenosine, and guanosine.

1. Introduction

Point mutation may cause single-amino acid substitutions. For instance, the point mutation of T1796A will result the substitution of valine to glutamic acid (V599E) in the BRAF gene. This mutation will lead to an activation of the BRAF protein which causes unlimited proliferative signaling in cancer cells [1]. Other diseases caused by point mutations include cystic fibrosis [2], chronic granulomatous disease [3], hepatic lipase deficiency, and hemophilia A [4]. To treat genetic diseases caused by point mutations, technologies related to genetic engineering are being gradually developed. Genetic engineering, which consists of a range of techniques for controlling the expression and activities of intracellular target genes, is widely employed in both basic research, and medicinal and therapeutic applications [5]. DNA editing is one genetic engineering technique. Generation of site-specific double-stranded DNA breaks (DSBs), and subsequent endogenous repair via error-prone non-homologous end-joining (NHEJ) or error-free homology-directed repair (HDR) pathways are required for DNA editing [6,7,8]. Third generation base editor (BE3) are the most frequently used base editor. BE3 uses a deaminase-CRISPR associated protein 9 (Cas9) fusion protein to induce cytidine to uridine or adenosine to inosine on target DNA [9,10,11]. These DNA editing technologies may cause insertion and deletion mutation bridging the break site or permanent mutations in DNAs, which may lead to cell death or oncogenic transformation [12,13,14].
Compared with DNA editing, RNA editing might be safer. RNA editing is a type of post-transcriptional modification of the gene-encoded sequence, which allows different forms of a protein to be produced from the same gene [15]. Mature messenger RNA (mRNA) consists only of exons; hence, intron editing does not occur, mRNAs are automatically degraded after translation is completed, and mutations are not passed on to offspring [16,17,18].
Different methodologies for site-directed RNA editing (SDRE) have been developed to achieve gene editing at the RNA level. For adenosine (A)-to-inosine (I) RNA editing, SNAP-tag [19], λN protein [20], MS2 system [21], and clustered regularly interspaced short palindromic repeats—associated protein 13 (Cas13) [22] systems have been reported. These technologies achieve SDRE in different ways, but they all follow the same principles.
In mammalian cells, the conversion of A to I and cytosine (C) to uridine (U) occurs in both coding and noncoding sequences [23,24,25]. In human cells, apolipoprotein B mRNA editing enzyme (APOBEC1) was first found to replace C with U in APOB transcripts [26]. Meanwhile, A to I conversion, with I acting as guanosine (G) during translation, is mediated by members of the ADAR family [27,28]. In recent years, ADAR2 has been used for performing C to U editing for certain mutation types [29].
In previous studies, we optimized the MS2 system for SDRE [30]. We replaced the original cytomegalovirus (CMV) promoter with the human U6 promoter for guide expression and incorporated a flexible linker in the fusion protein. Next, we decreased the MS2 stem-loop RNA from six to two copies and placed the guide RNAs between the two stem-loop RNA. The optimization resulted in a significant improvement in editing efficiency [31].
For the site-directed RNA editing system based on the MS2 system, previous studies did not compare the editing efficiency when the number of MS2 stem-loops and/or the mismatch base were changed. In the present study, we investigated the impact of these changes. We also explored the influence of different guide RNAs on guide RNA expression level and editing efficiency. The aim was to improve editing efficiency for practical applications.

2. Materials and Methods

2.1. Plasmid Construction

We used enhanced green fluorescent protein (EGFP) W58X as the target gene for SDRE, which was prepared in a previous study [21]. Expression vectors for MS2 coat protein (MCP) and ADAR1 deaminase domain (ADAR1 DD) were also prepared in previous work [30].
In the present study, MCP harbored an asparagine to lysine mutation at amino acid position 55 (N55K) because the mutation has higher binding affinity to MS2 RNA than to wild-type MCP [31]. The origin guide construct was prepared in a previous study [29]. The human U6 (hU6) promoter was substituted for the CMV IE94 promoter in pCS2+ (Addgene, Watertown, MA, USA), and the guide RNA was placed downstream of the hU6 promoter. In the guide RNA sequence (5′-gaacatgaggatcacccatgtctgggccagggcacgggcagctaacatgaggatcacccatgtctttt-3′), the underlined parts are the MS2 stem-loop RNA, and bold indicates the antisense RNA region. Guide RNAs with different numbers of MS2 stem-loop RNAs on the 5’side of the antisense RNA and the 3’side of the antisense RNA were engineered based on the original parent construct with a stem-loop on either side of the antisense RNA (1-1 stem-loop in Figure 1).
First, we prepared three types of constructs at downstream of the U6 promoter based on the original (1-1 stem-loop) guide RNA construct: 1-2 stem-loop, 2-1 stem-loop, and 2-2 stem-loop (Figure S1). The schematic diagram of their structure is shown in Figure 1. Second, to examine how each type of base pair in mismatches affects the editing efficiency, we used the 1-1 stem-loop guide RNA construct as a backbone, and replaced the C base in the mismatch position with A, U, or G. Then, all the six types of guide RNA construct were tested. Primers used for construction of these constructs are shown in Supplementary Table S1.
Plasmids were transformed into Escherichia coli DH5α competent cells (TaKaRa Bio, Shiga, Japan). Positive clones were cultured in 5 mL Lysogeny broth (LB) medium with ampicillin at 37 °C for 18 h with shaking and extracted using NucleoBond Xtra Midi (MACHEREY-NAGEL, Germany). The concentration of isolated plasmid was determined using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

2.2. Cell Culture

Human Embryonic Kidney Cells 293T (HEK293T) cells (from RIKEN BRO CELL BANK) were maintained in Dulbecco’s modified Eagle’s medium (Nacalai Tesque, Kyoto, Japan) with 10% fetal bovine serum (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) under 5% CO2 at 37 °C. After at least three passages from frozen stocks, cells were employed in investigations.

2.3. Transfection

Cells at a density of 2.0–3.5 × 105 cells per well were seeded in 24-well culture plates (TrueLine, Tokyo, Japan) with 500 µL DMEM containing 10% FBS (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), cultured for 24 h to 70% confluence, then subjected to transfection. Before transfection, 250 µL medium was removed so that the volume per well was 250 µL, and 250 µL fresh DMEM with 10%FBS was added. Next, 50 µL Opti-MEM (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), 2.5 µL of 1 µg/µL PEI MAX (Polysciences, Kyoto, Japan), and 10 ng of EGFP/EGFP W58X plasmid DNA, 100 ng of MS2-ADAR1 plasmid DNA, and the indicated amount of guide RNA plasmid DNA (Supplementary Tables S4–S6) were added, mixed, incubated for 20 min at room temperature, then added to each well. At 24 h after transfection, 300 µL of medium was removed from each well, and 300 µL of fresh DMEM with 10%FBS was added.

2.4. Cell Observation

At 48 h after transfection, cells were observed on Keyence Biozero-800 (Keyence Co., Ltd., Osaka, Japan) and BZ-X800 (Keyence Co., Ltd., Osaka, Japan) fluorescence microscopes under standard conditions. Fluorescence intensity was measured by ImageJ 1.51p software (Bethesda, Maryland, USA).

2.5. RNA Extraction and Complementary DNA Synthesis

Total RNAs were extracted from transfected cells in each well, respectively, using 200 µL of TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) per well according to the manufacturer’s protocol. The concentration of extracted RNA was measured using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Extracted RNA was treated with RNase-free Recombinant DNase I (TaKaRa Bio, Shiga, Japan) according to the manufacturer’s protocol. cDNA synthesis was performed by using 50 units of ReverTra Ace (TOYOBO, Osaka, Japan). When reverse transcription was performed, we split the extracted RNA into two, one used oligo dT as reverse transcription primer for editing efficiency confirmation. The other one used random hexamer primers as reverse transcription primer for guide RNA expression level confirmation. The reverse transcript reaction was incubated for 30 min at 42 °C, followed by incubation for 5 min at 90 °C.

2.6. Determination of Editing Efficiency

To confirm the restoration of the target sequence, PCR was performed with specific primer (Supplementary Table S3) and 50 ng of each well cDNA as template by using Go Taq Flexi DNA polymerase (Promega, Tokyo, Japan) to obtain the DNA region containing the target base in EGFP W58X. PCR products were purified using a NucleoSpin Gel and PCR Clean-up Kit (MACHEREY-ANGEL, Shiga, Japan).
Sanger sequencing was performed on purified products by Eurofins Genomics (Tokyo, Japan). The sequencing primer was the forward primer that had shown in Supplementary Table S3. Raw sequence data were analyzed by the SnapGene Viewer (Dotmatics, San Diego, CA, USA). When edited and unedited products were present together, a dual peak for G (edited) and A (unedited) was observed at the target site. Peak height was measured using ImageJ software (NIH, MD, USA). Editing was quantified based on the maximum peak height ratio of edited and unedited products using the formula [32]:
Editing   efficiency = 100 %   × G   height A   height   +   G   height .
Then, we obtained editing efficiencies by calculating the average of three individual transfection experiments.

2.7. Determination of Guide RNA Expression Level

To confirm the expression level of each type of guide RNA, we performed quantitative PCR (qPCR) with specific primers (Supplementary Table S2). Each type of guide cDNA was obtained by reverse transcription using random hexamer primers (Thermo Scientific, Waltham, MA, USA). Real-time PCR was performed in 20 µL volumes with TB Green Premix EX Taq II (TaKaRa Bio, Shiga, Japan) on an Mx-3000P instrument (Agilent Technologies, Santa Clara, CA, USA). Initial denaturation was at 95 °C for 30 s, followed by 50 cycles of 95 °C denaturation for 5 s, and 50 °C for 15 s, and 72 °C for 15 s, respectively. Data were collected during the annealing and extension steps. The cDNA of the human ACTB gene encoding β-actin served as an internal reference. The expression level of each type of guide RNA was obtained by calculating the average of three individual transfection experiments.

3. Results

3.1. The Performance of Different Numbers of MS2 Stem-Loop Constructs

In previous research, the guide RNA with 1-1 stem-loop was shown to possess high editing efficiency [30]. In the present work, we examined the effect of the number of MS2 stem-loop RNA regions on RNA editing efficiency.
We separately transfected each type of guide RNA construct into HEK 293T cells along with the other two factors (EGFP/EGFP W58X plasmid and MS2-ADAR1 plasmid). After 48 h, we observed the fluorescence signal (Figure 1a). From the fluorescence micrographs, the fluorescence intensity was estimated to be 6.06 arbitrary units (AU) with the 1-1 stem-loop, compared with 4.27 AU with the 2-2 stem-loop 3.78 AU with the 1-2 stem-loop, and 3.09 AU with the 2-1 stem-loop using ImageJ.
Next, we confirmed the editing efficiency by Sanger sequencing (Figure 1a,b) and measured the expression level of each type of guide RNA by qPCR (Figure 1c). The editing efficiency of the 1-1 stem-loop, the 1-2 stem-loop, the 2-1 stem-loop, and the 2-2 stem-loop after 48 h was 51.8%, 30.7%, 33.3%, and 39.1%, respectively (Figure 1b).
The qPCR results showed that the expression levels of different guide RNAs differed significantly. If we set the expression level of the original guide RNA with 1-1 stem-loop as one, then the relative expression level of the 1-2 stem-loop, the 2-1 stem-loop, and the 2-2 stem-loop was 1.6, 1.3, and 2.1, respectively (Figure 1c).

3.2. The Performance of Different Numbers of MS2 Stem-Loop Constructs with Adjusted Molar Ratio

Since the molar ratio of each guide RNA to the target gene is different, this can affect the editing efficiency and prevent accurate comparisons, hence we optimize the number of plasmids for transfection.
We calculated the average molar ratio of guide RNA to target gene (excluding the negative control) and optimized the amount of each type of guide RNA construct for transfection. Next, we transfected the constructs for the other three factors into HEK293T cells and observed the fluorescence signal 48 h later (Figure 2a). From the fluorescence micrographs, the fluorescence intensity was measured as 10.30 AU with the 1-1 stem-loop, 10.28 AU with the 2-2 stem-loop, 8.19 AU with the 1-2 stem-loop, and 8.62 AU with the 2-1 stem-loop using ImageJ.
The editing efficiencies and guide RNA expression levels were also calculated and shown as a bar graph (Figure 2b,c). After optimization, the editing efficiencies of 1-1 stem-loop, 1-2 stem-loop, 2-1 stem-loop, and 2-2 stem-loop constructs after 48 h were 53.3%, 33.3%, 33.8%, and 54.8%, respectively (Figure 2b).

3.3. The Types of Bases That Pair with Target Bases in SDRE

We transfected 390 ng of each mismatch guide RNA construct into HEK 293T cells along with the other two factors After 48 h, we measured fluorescence signals (Figure 3a). Fluorescence intensity was measured as 51.60 AU for mismatch C, 33.17 AU for mismatch U, 23.40 AU for mismatch A, and 18.38 AU for mismatch G.
Next, we performed Sanger sequencing (Figure 3a) for calculate the editing efficiency (Figure 3b), and we confirmed the expression level of each mismatch guide RNA (Figure 3c). Editing efficiencies were 53.0% for mismatch C, 18.9%, for mismatch A, 67.7% for mismatch U, and 9.1% for mismatch G (Figure 3b).
We also confirmed the expression levels of each mismatch guide RNA by qPCR. Here, we set the expression level of the mismatch C guide RNA as one. Expression levels of mismatch U, mismatch A, and mismatch G were 0.39, 0.25, and 0.19, respectively (Figure 3c).

3.4. The effect of Different Mismatch Guide RNAs on Editing Efficiency with Adjusted Molar Ratio

We also adjusted the molar ratio of guide RNA to target gene for transfection and compared the editing efficiency at the same molar ratio level.
Additionally, we calculated the average of molar ratio of each mismatch guide RNA to target gene. Meanwhile, the amount of each mismatch guide RNA for transfection was optimized. We transfected the constructs of the three factors into HEK293T cells, then the fluorescence signal was measured 48 h later (Figure 4a). The fluorescence intensity was measured as 59.31 AU in mismatch C, 33.07 AU for mismatch U, 21.12 AU for mismatch A, and 15.84 AU for mismatch G.
The editing efficiencies and guide RNA expression levels were also calculated and displayed as a bar graph (Figure 4b,c). After optimization, editing efficiencies were 73.5% for mismatch C, 52.8% for mismatch A, 88.3% for mismatch U, and 33.8% for mismatch G (Figure 4b).

4. Discussion

In this study, we showed that the number of stem-loops at both ends of the antisense RNA can affect the editing efficiency in SDRE (Figure 1 and Figure 2). By adjusting the amount of construct used for transfection, the expression level of guide RNA was similar. Thus, the results could be compared reliably. Although changes in editing efficiency were difficult to detect by fluorescence observation, changes in editing efficiency could be seen more intuitively using Sanger sequencing. For the 2-2 stem-loop, although the expression level was lower than for the original parent construct, the editing efficiency was increased (Figure 2b,c). By contrast, for 1-2 stem-loop and 2-1 stem-loop, the editing efficiency was not altered after optimizing the amount of construct used for transfection (Figure 1b and Figure 2b). These results suggest that when the number of stem-loops at both ends of the antisense RNA was the same, the MS2 SDRE system achieved higher editing efficiency.
In previous studies, the molar ratio of guide RNA to target gene was found to affect editing efficiency, and this may have an unavoidable impact on the effectiveness of gene therapy. Although a high editing efficiency (up to 57%) was reported in one recent study using λN, the molar ratio of guide RNA expression plasmid (U6 pENTR guide RNA vector) to target gene (CFTR Y122X) expression plasmid was 60:1, implying that the guide RNA was 60-fold more abundant than the target gene [25]. In another study on SDRE using Cas13, the molar ratio of guide RNA expression plasmid to target gene expression plasmid (RNA editing reporter) was 7.5:1 [22]. In our current study, the molar ratio of guide RNA expression plasmid to EGFP W58X was varied, the molar ratio of the 1-1 stem-loop in transfection was 59:1, compared with 36:1 for the 1-2 stem-loop, 44:1 for the 2-1 stem-loo, and 28:1 for the 2-2 stem-loop before optimization for transfection. After adjustment, the molar ratio was 39:1. Interestingly, after adjustment, the molar ratio of plasmid for transfection was similar, the 1-1 stem-loop and 2-2 stem-loop still achieved a similar level of editing efficiencies (Figure 2b), and the guide RNA expression level was similar for the 1-1 stem-loop and the 2-2 stem-loop (Figure 2c). It is difficult to explain this result, but it might be related to the interaction between the MS2 coat protein and the stem-loop of the MS2 RNA. It may also be due to the unequal number of stem-loops at both ends of the antisense RNA or the distance between the MS2 coat protein bound to the MS2 RNA and target sites will differ, thereby affecting the efficiency of RNA editing.
Next, we verified whether different base mismatch pairings could affect editing efficiency. In a previous study, with a C mismatch, the editing efficiency was highest [33]. By contrast, our results showed that introducing a U base pair achieved the highest editing efficiency (Figure 3b and Figure 4b). It is interesting because a highest editing efficiency may achieve when the bases are perfectly matched. It is difficult to explain this result, but it might be related to the structure of the ADAR1 catalytic domain. A perfectly matched double-stranded structure is more likely to bind to the ADAR1 catalytic domain and the target gene, thereby improving the catalytic efficiency.
In the previous study, the E1008Q variant of ADAR1 had increased the editing efficiency of RNA editing on natural substrates and artificial substrates [22,34]. For MS2-ADAR1 system, we guessed that the E1008Q variant of ADAR1 also affects editing efficiency. We performed the transfection of E1008Q variant of ADAR1 with 1-1 stem-loop guide RNA and reporter plasmid. However, the results showed that the editing efficiency of E1008Q variant of ADAR1 was the same as wild-type ADAR1. A possible explanation for this might be the distance between the mismatch base and the MS2 stem-loop RNA. The present study was designed to confirm the editing efficiency would be affected by the distance between the mismatch base and the stem-loop [22]. Further research should be undertaken to investigate the distance between the mismatch base and the MS2 stem-loop RNA.
There are still many unanswered questions from this study, including whether different mismatch bases affect protein translation, and whether the number of stem-loops at both ends of the guide RNA affects editing efficiency when the number is odd or even. It also remains to be verified whether the results of this experiment can be applied to MS2-APOBEC1 [35].

5. Conclusions

This study further explored and optimized the MS2-ADAR1 system. By adjusting the guide RNA, we achieved a higher editing efficiency (~80%), which bodes well for future applications in gene therapy. Based on the results, the MS2-ADAR1 system can be further optimized. Replacing ADAR1 with APOBEC1 may also achieve C to U replacement [35], indicating that the MS2 system can be manipulated to perform a wider range of RNA editing functions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app13042383/s1. Table S1: Oligos used for plasmid construction. Table S2: Oligo for qPCR. Table S3: Oligos for cDNA PCR and sequence. Table S4: The molar ratio of guide plasmid DNA to the EGFP W58X for transfection (Before optimization). Table S5: The molar ratio of guide plasmid DNA to the EGFP W58X for transfection (After optimization). Table S6: The molar ratio of base paired guide plasmid DNA to the EGFP W58X for transfection (After optimization). Figure S1: The sequences of MS2 stem-loops guide RNA in this article.

Author Contributions

Conceptualization, J.L., T.O. and T.T.; methodology, J.L. and T.O.; validation, J.L. and T.O.; formal analysis, G.F. and T.T.; investigation, G.F.; resources, T.T.; data curation, T.T.; writing—original draft preparation, J.L.; writing—review and editing, T.T. and M.S.; supervision, T.T.; project administration, T.T.; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported in part by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science 17H02204, 18K19288 and 21H02067 to Toshifumi Tsukahara).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data used in this study as stated in Materials and Methods.

Acknowledgments

This research was supported in part by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science. We thank Kazuaki Matsumura for their BZ-X800 fluorescence microscope.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Davies, H.; Bignell, G.; Cox, C.; Stephens, P.; Edkins, S.; Clegg, S.; Teague, J.; Woffendin, H.; Mathew J, G.; Bottomley, W.; et al. Mutations of the BRAF gene in human cancer. Nature 2002, 417, 949–954. [Google Scholar] [CrossRef] [PubMed]
  2. Ferec, C.; Audrezet, M.P.; Mercier, B.; Guillermit, H.; Moullier, P.; Quere, I.; Verlingue, C. Detection of over 98% cystic fibrosis mutations in a Celtic population. Nat. Genet. 1992, 1, 188–191. [Google Scholar] [CrossRef] [PubMed]
  3. Bolscher, B.G.; de Boer, M.; de Klein, A.; Weening, R.S.; Roos, D. Point mutations in the beta-subunit of cytochrome b558 leading to X-linked chronic granulomatous disease. Blood 1991, 77, 2482–2487. [Google Scholar] [CrossRef] [PubMed]
  4. Higuchi, M.; Kazazian, H.J.; Kasch, L.; Warren, T.C.; Mcginniss, M.J.; Phillips, J.R.; Kasper, C.; Janco, R.; Antonarakis, S.E. Molecular characterization of severe hemophilia A suggests that about half the mutations are not within the coding regions and splice junctions of the factor VIII gene. Proc. Natl. Acad. Sci. USA 1991, 88, 7405–7409. [Google Scholar] [CrossRef]
  5. Heidenreich, M.; Zhang, F. Applications of CRISPR-Cas systems in neuroscience. Nat. Rev. Neurosci. 2016, 17, 36–44. [Google Scholar] [CrossRef]
  6. Maeder, M.L.; Gersbach, C.A. Genome-editing Technologies for Gene and Cell Therapy. Mol. Ther. 2016, 24, 430–446. [Google Scholar] [CrossRef]
  7. Zhang, H.; Zhang, J.; Lang, Z.; Botella, J.R.; Zhu, J. Genome Editing-Principles and Applications for Functional Genomics Research and Crop Improvement. Crit. Rev. Plant Sci. 2017, 36, 291–309. [Google Scholar] [CrossRef]
  8. Shan, Q.; Wang, Y.; Li, J.; Gao, C. Genome editing in rice and wheat using the CRISPR/Cas system. Nat. Protoc. 2014, 9, 2395–2410. [Google Scholar] [CrossRef]
  9. Gaudelli, N.M.; Komor, A.C.; Rees, H.A.; Packer, M.S.; Badran, A.H.; Bryson, D.I.; David, L.R. Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature 2017, 551, 464–471. [Google Scholar] [CrossRef]
  10. Komor, A.C.; Kim, Y.B.; Packer, M.S.; Zuris, J.A.; Liu, D.R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 2016, 533, 420–424. [Google Scholar] [CrossRef] [Green Version]
  11. Komor, A.C.; Badran, A.H.; Liu, D.R. Editing the genome without double-stranded DNA Breaks. ACS Chem. Biol. 2018, 13, 383–388. [Google Scholar] [CrossRef]
  12. Zhang, X.H.; Tee, L.Y.; Wang, X.G.; Huang, Q.S.; Yang, S.H. Off-target Effects in CRISPR/Cas9-mediated Genome Engineering. Mol. Ther. Nucleic Acids 2015, 4, e264. [Google Scholar] [CrossRef]
  13. Cox, D.B.; Platt, R.J.; Zhang, F. Therapeutic genome editing: Prospects and challenges. Nat. Med. 2015, 21, 121–131. [Google Scholar] [CrossRef]
  14. Zhang, F.; Wen, Y.; Guo, X. CRISPR/Cas9 for genome editing: Progress, implications and challenges. Hum. Mol. Genet. 2014, 23, R40–R46. [Google Scholar] [CrossRef]
  15. Maas, S.; Rich, A. Changing genetic information through RNA editing. Bioessays 2000, 22, 790–802. [Google Scholar] [CrossRef]
  16. Vogel, P.; Stafforst, T. Critical review on engineering deaminases for site-directed RNA editing. Curr. Opin. Biotechnol. 2019, 55, 74–80. [Google Scholar] [CrossRef]
  17. Montiel-Gonzalez, M.F.; Diaz, Q.J.; Rosenthal, J. Current strategies for Site-Directed RNA Editing using ADARs. Methods 2019, 156, 16–24. [Google Scholar] [CrossRef]
  18. Chen, G.; Katrekar, D.; Mali, P. RNA-Guided Adenosine Deaminases: Advances and Challenges for Therapeutic RNA Editing. Biochemistry-US 2019, 58, 1947–1957. [Google Scholar] [CrossRef]
  19. Vogel, P.; Moschref, M.; Li, Q.; Merkle, T.; Selvasaravanan, K.D.; Li, J.B.; Stafforst, T. Efficient and precise editing of endogenous transcripts with SNAP-tagged ADARs. Nat. Methods 2018, 15, 535–538. [Google Scholar] [CrossRef]
  20. Vallecillo-Viejo, I.C.; Liscovitch-Brauer, N.; Montiel-Gonzalez, M.F.; Eisenberg, E.; Rosenthal, J. Abundant off-target edits from site-directed RNA editing can be reduced by nuclear localization of the editing enzyme. RNA Biol. 2018, 15, 104–114. [Google Scholar] [CrossRef] [Green Version]
  21. Azad, M.; Bhakta, S.; Tsukahara, T. Site-directed RNA editing by adenosine deaminase acting on RNA for correction of the genetic code in gene therapy. Gene Ther. 2017, 24, 779–786. [Google Scholar] [CrossRef] [PubMed]
  22. Cox, D.; Gootenberg, J.S.; Abudayyeh, O.O.; Franklin, B.; Kellner, M.J.; Joung, J.; Zhang, F. RNA editing with CRISPR-Cas13. Science 2017, 358, 1019–1027. [Google Scholar] [CrossRef] [PubMed]
  23. Nishikura, K. A-to-I editing of coding and non-coding RNAs by ADARs. Nat. Rev. Mol. Cell Biol. 2016, 17, 83–96. [Google Scholar] [CrossRef] [PubMed]
  24. Slotkin, W.; Nishikura, K. Adenosine-to-inosine RNA editing and human disease. Genome Med. 2013, 5, 105. [Google Scholar] [CrossRef]
  25. Rosenberg, B.R.; Hamilton, C.E.; Mwangi, M.M.; Dewell, S.; Papavasiliou, F.N. Transcriptome-wide sequencing reveals numerous APOBEC1 mRNA-editing targets in transcript 3’ UTRs. Nat. Struct. Mol. Biol. 2011, 18, 230–236. [Google Scholar] [CrossRef]
  26. Powell, L.M.; Wallis, S.C.; Pease, R.J.; Edwards, Y.H.; Knott, T.J.; Scott, J. A novel form of tissue-specific RNA processing produces apolipoprotein-B48 in intestine. Cell 1987, 50, 831–840. [Google Scholar] [CrossRef]
  27. Keegan, L.P.; Gallo, A.; O’Connell, M.A. The many roles of an RNA editor. Nat. Rev. Genet. 2001, 2, 869–878. [Google Scholar] [CrossRef]
  28. Maas, S.; Rich, A.; Nishikura, K. A-to-I RNA editing: Recent news and residual mysteries. J. Biol. Chem. 2003, 278, 1391–1394. [Google Scholar] [CrossRef]
  29. Abudayyeh, O.O.; Gootenberg, J.S.; Franklin, B.; Koob, J.; Kellner, M.J.; Ladha, A.; Joung, J.; Kirchgatterer, P.; Cox, D.; Zhang, F. A cytosine deaminase for programmable single-base RNA editing. Science 2019, 365, 382–386. [Google Scholar] [CrossRef]
  30. Tohama, T.; Sakari, M.; Tsukahara, T. Development of a Single Construct System for Site-Directed RNA Editing Using MS2-ADAR. Int. J. Mol. Sci. 2020, 21, 4943. [Google Scholar] [CrossRef]
  31. Lim, F.; Spingola, M.; Peabody, D.S. Altering the RNA binding specificity of a translational repressor. J. Biol. Chem. 1994, 269, 9006–9010. [Google Scholar] [CrossRef]
  32. Rinkevich, F.D.; Schweitzer, P.A.; Scott, J.G. Antisense sequencing improves the accuracy and precision of A-to-I editing measurements using the peak height ratio method. BMC Res. Notes. 2012, 24, 5–63. [Google Scholar] [CrossRef]
  33. Qu, L.; Yi, Z.; Zhu, S.; Wang, C.; Cao, Z.; Zhou, Z.; Yuan, P.; Yu, Y.; Tian, F.; Liu, Z.; et al. Programmable RNA editing by recruiting endogenous ADAR using engineered RNAs. Nat. Biotechnol. 2019, 37, 1059–1069. [Google Scholar] [CrossRef]
  34. Wang, Y.; Havel, J.; Beal, P.A. A Phenotypic Screen for Functional Mutants of Human Adenosine Deaminase Acting on RNA 1. ACS Chem. Biol, 2015; 10, 2512–2519. [Google Scholar]
  35. Bhakta, S.; Sakari, M.; Tsukahara, T. RNA editing of BFP, a point mutant of GFP, using artificial APOBEC1 deaminase to restore the genetic code. Sci. Rep. 2020, 10, 17304. [Google Scholar] [CrossRef]
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Figure 1. The performance of different numbers of MS2 stem-loop constructs. (a): Fluorescence micrographs of HEK293T obtained at 48 h after transfection. Images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The far-left panel shows schematic diagrams of the guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results are mean ± standard error of the mean (SEM n = 3). The editing level of EGFP W58X (negative control) was set at 0%. Asterisks indicate a statistically significant difference (p < −0.05). NS = not significant. (c): Bar graph showing the expression level of each type of guide RNA. The expression level of the 1-1 guide RNA (parent) construct was set as 1. Results are mean ± SEM (n = 3).
Figure 1. The performance of different numbers of MS2 stem-loop constructs. (a): Fluorescence micrographs of HEK293T obtained at 48 h after transfection. Images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The far-left panel shows schematic diagrams of the guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results are mean ± standard error of the mean (SEM n = 3). The editing level of EGFP W58X (negative control) was set at 0%. Asterisks indicate a statistically significant difference (p < −0.05). NS = not significant. (c): Bar graph showing the expression level of each type of guide RNA. The expression level of the 1-1 guide RNA (parent) construct was set as 1. Results are mean ± SEM (n = 3).
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Figure 2. The performance of different numbers of MS2 stem-loop constructs with adjusted molar ratio. (a): Fluorescence micrographs of HEK293T cells obtained at 48 h after transfection. The images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The panel in the far left shows schematic diagrams of guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results mean ± SEM (n = 3). The EGFP W58X (negative control) editing level was set as 0%. Asterisks indicate a statistically significant difference (p < 0.05). NS = not significant. (c): Bar graph showing expression levels for each type of guide RNA. The expression level of the 1-1 guide RNA was set as 1. Mean ± SEM (n = 3).
Figure 2. The performance of different numbers of MS2 stem-loop constructs with adjusted molar ratio. (a): Fluorescence micrographs of HEK293T cells obtained at 48 h after transfection. The images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The panel in the far left shows schematic diagrams of guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results mean ± SEM (n = 3). The EGFP W58X (negative control) editing level was set as 0%. Asterisks indicate a statistically significant difference (p < 0.05). NS = not significant. (c): Bar graph showing expression levels for each type of guide RNA. The expression level of the 1-1 guide RNA was set as 1. Mean ± SEM (n = 3).
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Figure 3. The performance of using different mismatch guide RNAs. (a): Fluorescence micrographs of HEK293T cells obtained at 48 h after transfection. The images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The panel in the far left shows schematic diagrams of guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results are mean ± SEM (n = 3). The EGFP W58X (negative control) editing level was set as 0%. Asterisks indicate a statistically significant difference (p < 0.05). NS = not significant. (c): Bar graph showing the expression levels for each type of guide RNA. The expression level of the mismatch C guide RNA was set as 1. Results are mean ± SEM (n = 3).
Figure 3. The performance of using different mismatch guide RNAs. (a): Fluorescence micrographs of HEK293T cells obtained at 48 h after transfection. The images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The panel in the far left shows schematic diagrams of guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results are mean ± SEM (n = 3). The EGFP W58X (negative control) editing level was set as 0%. Asterisks indicate a statistically significant difference (p < 0.05). NS = not significant. (c): Bar graph showing the expression levels for each type of guide RNA. The expression level of the mismatch C guide RNA was set as 1. Results are mean ± SEM (n = 3).
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Figure 4. The performance of using different mismatch guide RNAs with adjusted molar ratio. (a): Fluorescence micrographs of HEK293T cells obtained at 48 h after transfection. Images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The panel on the far left shows schematic diagrams of guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results are mean ± SEM (n = 3). The EGFP W58X (negative control) editing level was set as 0%. Asterisks indicate a statistically significant difference (p < 0.05). NS = not significant. (c): Bar graph showing expression levels for each type of guide RNA. The expression level for the mismatch C guide RNA expression level was set as 1. Results are mean ± SEM (n = 3).
Figure 4. The performance of using different mismatch guide RNAs with adjusted molar ratio. (a): Fluorescence micrographs of HEK293T cells obtained at 48 h after transfection. Images on the left panel are fluorescence images, the middle panel shows phase contrast images, and the right panel shows merged images. The panel on the far left shows schematic diagrams of guide RNAs. Sanger sequencing results. Black arrows indicate the target base for EGFP W58X. (b): Bar graph showing the editing efficiency (%) calculated from the sequencing results using the peak height ratio method. Results are mean ± SEM (n = 3). The EGFP W58X (negative control) editing level was set as 0%. Asterisks indicate a statistically significant difference (p < 0.05). NS = not significant. (c): Bar graph showing expression levels for each type of guide RNA. The expression level for the mismatch C guide RNA expression level was set as 1. Results are mean ± SEM (n = 3).
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MDPI and ACS Style

Li, J.; Oonishi, T.; Fan, G.; Sakari, M.; Tsukahara, T. Increasing the Editing Efficiency of the MS2-ADAR System for Site-Directed RNA Editing. Appl. Sci. 2023, 13, 2383. https://doi.org/10.3390/app13042383

AMA Style

Li J, Oonishi T, Fan G, Sakari M, Tsukahara T. Increasing the Editing Efficiency of the MS2-ADAR System for Site-Directed RNA Editing. Applied Sciences. 2023; 13(4):2383. https://doi.org/10.3390/app13042383

Chicago/Turabian Style

Li, Jiarui, Tomoko Oonishi, Guangyao Fan, Matomo Sakari, and Toshifumi Tsukahara. 2023. "Increasing the Editing Efficiency of the MS2-ADAR System for Site-Directed RNA Editing" Applied Sciences 13, no. 4: 2383. https://doi.org/10.3390/app13042383

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