A-to-I RNA editing in SARS-COV-2: real or artifact?

  1. Ernesto Picardi
  2. Luigi Mansi
  3. Graziano Pesole

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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.

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  1. ScreenIT

    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.

    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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. ScreenIT

    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 Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    The raw data from infected Vero cells are available at the Open Science Framework (OSF) with accession number ​https://doi.org/10.17605/OSF.
    Vero
    suggested: None
    Software and Algorithms
    SentencesResources
    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.
    REDItools
    suggested: (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.
    BioProject
    suggested: (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)​.
    SAMtools
    suggested: (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.
    GSNAP
    suggested: (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.,
    RSeQC
    suggested: (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.
    GENCODE
    suggested: (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.
    DESeq2
    suggested: (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.
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Annotation of A-to-I editing events RNA editing events were annotated using ANNOVAR ​(Wang et al.,
    ANNOVAR
    suggested: (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.

  3. ScreenIT

    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
    SentencesResources
    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).
    H1299
    suggested: NCI-DTP Cat# NCI-H1299, CVCL_0060
    We 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-2
    suggested: CLS Cat# 300137/p1665_CaCo-2, CVCL_0025
    C) Enrichment of unique hyper editing positions in Vero cells.
    Vero
    suggested: CLS Cat# 605372/p622_VERO, CVCL_0059
    RNA 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-3
    suggested: BCRJ Cat# 0264, CVCL_0609
    Software and Algorithms
    SentencesResources
    Additionally, viral reads from PolyA+ data were about 4 orders of magnitude less abundant than total RNAseq data.
    PolyA+
    suggested: None
    While 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.
    WGS
    suggested: None
    Data and Code Availability The raw data are available at SRA under the following BioProject accessions: PRJNA625518, PRJNA616446, PRJNA601736, PRJNA605907 and PRJNA631753.
    BioProject
    suggested: (NCBI BioProject, SCR_004801)
    Method Details Filtering of RNAseq raw data Raw reads were cleaned using FASTP ​
    FASTP
    suggested: (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)​.
    SAMtools
    suggested: (Samtools, SCR_002105)
    Viral reads were also aligned onto the NC045512.2 assembly by GSNAP ​(Wu and Nacu, 2010) employing the transcriptome-guided strategy.
    GSNAP
    suggested: (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)​.
    RSeQC
    suggested: (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.
    STAR
    suggested: (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.
    Phred
    suggested: (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).
    REDItools
    suggested: (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.
    GENCODE
    suggested: (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.
    DESeq2
    suggested: (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.
    featureCounts
    suggested: (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.
    ANNOVAR
    suggested: (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.

  4. Version published to 10.1101/2020.07.27.223172v1 on bioRxiv