Comprehensive Comparison of RNA-Seq Data of SARS-CoV-2, SARS-CoV and MERS-CoV Infections: Alternative Entry Routes and Innate Immune Responses

  1. Yingying Cao
  2. Xintian Xu
  3. Simo Kitanovski
  4. Lina Song
  5. Jun Wang
  6. Pei Hao
  7. Daniel Hoffmann

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Abstract

The pathogenesis of COVID-19 emerges as complex, with multiple factors leading to injury of different organs. Some of the studies on aspects of SARS-CoV-2 cell entry and innate immunity have produced seemingly contradictory claims. In this situation, a comprehensive comparative analysis of a large number of related datasets from several studies could bring more clarity, which is imperative for therapy development.

Methods

We therefore performed a comprehensive comparative study, analyzing RNA-Seq data of infections with SARS-CoV-2, SARS-CoV and MERS-CoV, including data from different types of cells as well as COVID-19 patients. Using these data, we investigated viral entry routes and innate immune responses.

Results and Conclusion

First, our analyses support the existence of cell entry mechanisms for SARS and SARS-CoV-2 other than the ACE2 route with evidence of inefficient infection of cells without expression of ACE2; expression of TMPRSS2/TPMRSS4 is unnecessary for efficient SARS-CoV-2 infection with evidence of efficient infection of A549 cells transduced with a vector expressing human ACE2. Second, we find that innate immune responses in terms of interferons and interferon simulated genes are strong in relevant cells, for example Calu3 cells, but vary markedly with cell type, virus dose, and virus type.

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  1. Version published to 10.3389/fimmu.2021.656433
  2. Version published to 10.1101/2021.01.07.425716v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.07.425716: (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.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The raw FASTQ data of different cell types infected with SARS-CoV-2, SARS-CoV and MERS-CoV, and lung samples of COVID-19 patients and healthy controls were retrieved from NCBI (57) (https://www.ncbi.nlm.nih.gov/) and ENA (58) (https://www.ebi.ac.uk/ena) (accession numbers GSE147507 (35), GSE56189, GSE148729 (41) and GSE153940 (59)).
    https://www.ncbi.nlm.nih.gov/
    suggested: (GENSAT at NCBI - Gene Expression Nervous System Atlas, RRID:SCR_003923)
    The preprocessed single cell RNA-Seq data of BALF samples from 6 severe COVID-19 patients and 3 moderate COVID-19 patients were downloaded from NCBI with accession number GSE145926 (44).
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Detailed information about these public datasets are available in the supplementary file: Supplementary.pdf For analysis, the human GRCh38 release 99 transcriptome and the green monkey (Chlorocebus sabaeus) ChlSab1.1 release 99 transcriptome and their corresponding annotation GTF files were downloaded from ENSEMBL (61) (https://www.ensembl.org).
    ENSEMBL
    suggested: (Ensembl, RRID:SCR_002344)
    https://www.ensembl.org
    suggested: (Homologous Sequences in Ensembl Animal Genomes, RRID:SCR_008356)
    The quality of the raw FASTQ data was examined with FastQC (62).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    The clean RNA sequencing reads were then pseudo-aligned to reference transcriptome and quantified using Kallisto (version 0.43.1) (64) with parameters “-b 30 –single −l 180 -s 20” for single-end sequencing data and with parameter “-b 30” for paired-end sequencing data.
    Kallisto
    suggested: (kallisto, RRID:SCR_016582)
    R packages EDASeq (66) and org.
    EDASeq
    suggested: (EDASeq, RRID:SCR_006751)
    The clean RNA-Seq data were also aligned to the virus genome with Bowtie 2 (69) (version 2.2.6) and the aligned BAM files were created, and the mapping rates to the virus genomes were obtained as well.
    Bowtie
    suggested: (Bowtie, RRID:SCR_005476)
    SAMtools (70) (version 1.5) was then used for sorting and indexing the aligned BAM files.
    SAMtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    3I was made by pheatmap R package (71), “complete” clustering method was used for clustering the rows and “euclidean” distance was used to measure the cluster distance.
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    The heatmap in Fig. 4A was made by ComplexHeatmap R package (72).
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  4. Version published to 10.1101/2021.01.07.425716v1 on bioRxiv