A-to-I RNA editing in SARS-COV-2: real or artifact?
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
ADAR1-mediated deamination of adenosines in long double stranded RNAs plays an important role in modulating the innate immune response. However, recent investigations based on metatranscriptomic samples of COVID-19 patients and SARS-COV-2 infected Vero cells have recovered contrasting findings. Using RNAseq data from time course experiments of infected human cell lines and transcriptome data from Vero cells and clinical samples, we prove that A-to-G changes observed in SARS-COV-2 genomes represent genuine RNA editing events, likely mediated by ADAR1. While the A-to-I editing rate is generally low, changes are distributed along the entire viral genome, are overrepresented in exonic regions and are, in the majority of cases, nonsynonymous. The impact of RNA editing on virus-host interactions could be relevant to identify potential targets for therapeutic interventions.
Article activity feed
-
SciScore for 10.1101/2020.07.27.223172: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank …
SciScore for 10.1101/2020.07.27.223172: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
-
SciScore for 10.1101/2020.07.27.223172: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources The raw data from infected Vero cells are available at the Open Science Framework (OSF) with accession number https://doi.org/10.17605/OSF. Verosuggested: NoneSoftware and Algorithms Sentences Resources Single nucleotide variants detected by REDItools (Picardi and Pesole, 2013) were called at an allelic fraction two … SciScore for 10.1101/2020.07.27.223172: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources The raw data from infected Vero cells are available at the Open Science Framework (OSF) with accession number https://doi.org/10.17605/OSF. Verosuggested: NoneSoftware and Algorithms Sentences Resources Single nucleotide variants detected by REDItools (Picardi and Pesole, 2013) were called at an allelic fraction two times higher than the error rate estimated by the overlap of read pairs. REDItoolssuggested: (REDItools, RRID:SCR_012133)Data and Code Availability The raw data are available at SRA under the following BioProject accessions: PRJNA625518, PRJNA616446, PRJNA601736, PRJNA605907 and PRJNA631753. BioProjectsuggested: (NCBI BioProject, RRID:SCR_004801)Unique and concordant reads mapping on the SARS-COV-2 genome were extracted by sambamba (Tarasov et al., 2015) and converted in BAM format by SAMtools (Li et al., 2009). SAMtoolssuggested: (Samtools, RRID:SCR_002105)Viral reads were also aligned onto the NC045512.2 assembly by GSNAP (Wu and Nacu, 2010) employing the transcriptome-guided strategy. GSNAPsuggested: (GSNAP, RRID:SCR_005483)The strand orientation per each sample was inferred by the infer_experiment.py script from the RSeQC package (Wang et al., RSeQCsuggested: (RSeQC, RRID:SCR_005275)Gene expression in cell lines Read counts per known gene were carried out using featureCounts (Liao et al., 2014) and GENCODE (v31lift37) annotations. GENCODEsuggested: (GENCODE, RRID:SCR_014966)Differential expression in time course experiments was done by DESeq2 (Love et al., 2014) while count normalization in FPKM for figures was performed by a custom script. DESeq2suggested: (DESeq, RRID:SCR_000154)Quantification of sense and antisense viral strands The quantification of sense and antisense viral strands was performed in strand oriented datasets only and using featureCounts (Liao et al., 2014) providing as annotations the list of known viral non overlapping coding regions from UCSC. featureCountssuggested: (featureCounts, RRID:SCR_012919)Annotation of A-to-I editing events RNA editing events were annotated using ANNOVAR (Wang et al., ANNOVARsuggested: (ANNOVAR, RRID:SCR_012821)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-
SciScore for 10.1101/2020.07.27.223172: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources The viral infection activated the type I interferon response in Calu-3 cells only and, consequently, ADAR1 did not appear deeply up regulated in Caco-2 and H1299 cells as also attested by the AEI index measured at all time points (Supp. Fig. 1). H1299suggested: NCI-DTP Cat# NCI-H1299, CVCL_0060We found A-to-G and T-to-C hyper edited reads only in Calu-3 and Caco-2 cells but the total number of edited reads was quite low as a result of the PolyA+ sequencing strategy in which mature viral transcripts rather … SciScore for 10.1101/2020.07.27.223172: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources The viral infection activated the type I interferon response in Calu-3 cells only and, consequently, ADAR1 did not appear deeply up regulated in Caco-2 and H1299 cells as also attested by the AEI index measured at all time points (Supp. Fig. 1). H1299suggested: NCI-DTP Cat# NCI-H1299, CVCL_0060We found A-to-G and T-to-C hyper edited reads only in Calu-3 and Caco-2 cells but the total number of edited reads was quite low as a result of the PolyA+ sequencing strategy in which mature viral transcripts rather than full genomic RNAs are captured. Caco-2suggested: CLS Cat# 300137/p1665_CaCo-2, CVCL_0025C) Enrichment of unique hyper editing positions in Vero cells. Verosuggested: CLS Cat# 605372/p622_VERO, CVCL_0059RNA editing and expression of key genes in PolyA+ RNAseq data from Calu-3, Caco-2 and H1299 infected cells at three time points post-infection (4h, 12h and 24h). Calu-3suggested: BCRJ Cat# 0264, CVCL_0609Software and Algorithms Sentences Resources Additionally, viral reads from PolyA+ data were about 4 orders of magnitude less abundant than total RNAseq data. PolyA+suggested: NoneWhile in the human transcriptome A-to-G changes due to RNA editing can be distinguished from SNPs by means of whole genome (WGS) and/or whole exome (WES) sequencing data (Diroma et al., 2019), in the SARS-COV-2 RNA genome this distinction cannot be done. WGSsuggested: NoneData and Code Availability The raw data are available at SRA under the following BioProject accessions: PRJNA625518, PRJNA616446, PRJNA601736, PRJNA605907 and PRJNA631753. BioProjectsuggested: (NCBI BioProject, SCR_004801)Method Details Filtering of RNAseq raw data Raw reads were cleaned using FASTP FASTPsuggested: (fastp, SCR_016962)Unique and concordant reads mapping on the SARS-COV-2 genome were extracted by sambamba (Tarasov et al., 2015) and converted in BAM format by SAMtools (Li et al., 2009). SAMtoolssuggested: (Samtools, SCR_002105)Viral reads were also aligned onto the NC045512.2 assembly by GSNAP (Wu and Nacu, 2010) employing the transcriptome-guided strategy. GSNAPsuggested: (GSNAP, SCR_005483)The strand orientation per each sample was inferred by the infer_experiment.py script from the RSeQC package (Wang et al., 2012). RSeQCsuggested: (RSeQC, SCR_005275)Additionally, human reads were also aligned onto the human reference genome by STAR (Dobin et al., 2013) and proving known GENCODE (v31lift37) annotations. STARsuggested: (STAR, SCR_015899)Dense clusters of high-quality (Phred ≥30) A-to-G (or T-to-C) mismatches are detected retaining reads in which the number of A-to-G changes was at least 5% of the read length and discarding reads having too dense A-to-G mismatch clusters (length <10% of the read length) or clusters contained within either the first or last 20% of the read or clusters with a particularly large percentage (>60%) of a single nucleotide. Phredsuggested: (Phred, SCR_001017)Detection of RNA editing at single nucleotide level We performed an initial variant calling by REDItools (version 2) (Picardi and Pesole, 2013) and same parameters used also in (Di Giorgio et al., 2020) (-os 4 -q 30 -bq 30 -l 0). REDItoolssuggested: (REDItools, SCR_012133)Gene expression in cell lines Read counts per known gene were carried out using featureCounts (Liao et al., 2014) and GENCODE (v31lift37) annotations. GENCODEsuggested: (GENCODE, SCR_014966)Differential expression in time course experiments was done by DESeq2 (Love et al., 2014) while count normalization in FPKM for figures was performed by a custom script. DESeq2suggested: (DESeq, SCR_000154)Quantification of sense and antisense viral strands The quantification of sense and antisense viral strands was performed in strand oriented datasets only and using featureCounts (Liao et al., 2014) providing as annotations the list of known viral non overlapping coding regions from UCSC. featureCountssuggested: (featureCounts, SCR_012919)Annotation of A-to-I editing events RNA editing events were annotated using ANNOVAR (Wang et al., 2010) providing the list of known SARS-COV-2 transcripts from UCSC. ANNOVARsuggested: (ANNOVAR, SCR_012821)Data from additional tools added to each annotation on a weekly basis.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-