Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19

  1. Sudhir Ghandikota
  2. Mihika Sharma
  3. Anil G. Jegga

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.patter.2021.100247
  2. ScreenIT

    SciScore for 10.1101/2020.08.27.270835: (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 second transcriptome signature is based on mRNA profiles of control (mock-infected) and 24h post-SARS-CoV-2-infection (USA-WA1/2020, MOI = 0.3) in Vero E6 cells (kidney epithelial cells extracted from an African green monkey (GSE153940; [16]).
    Vero E6
    suggested: None
    Differentially expressed genes from SARS-CoV-2 infection models and SARS-CoV-2-Human virus-host protein-protein interactome - Network analysis: For all conserved DEGs (genes differentially expressed unambiguously in at least 2 of the 3 transcriptomic sets compared, i.e., Calu-3, VeroE6, or mouse model - Ad5-hACE2) of SARS-CoV-2 infection models and 336 SARS-CoV-2-human PPI genes, we extracted interactions with a score of ≥ 0.9 or experimental interaction score of 0.7 or more from STRING v11 [26].
    Calu-3
    suggested: KCLB Cat# 30055, RRID:CVCL_0609)
    Software and Algorithms
    SentencesResources
    The raw data was downloaded from NCBI Sequence Read Archive (ProcessPublicData module) and the technical replicates were merged for individual samples before processing them (Process-RNASeq_SingleEnd module)
    NCBI Sequence Read Archive
    suggested: (NCBI Sequence Read Archive (SRA, RRID:SCR_004891)
    Quality checks [19] and quality trimming [20] were conducted prior to the transcript mapping/quantification step using the RSEM package [21].
    RSEM
    suggested: (RSEM, RRID:SCR_013027)
    RUVSeq [23] was used to remove potential variation and sequencing effects from the data before performing DE analysis using edgeR [24]
    RUVSeq
    suggested: (RUVSeq, RRID:SCR_006263)
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    For obtaining the human ortholog genes for mouse (Mus musculus) and the green monkey (Chlorocebus sabaeus), we used ortholog mappings from the NCBI’s HomoloGene and ENSEMBL databases.
    HomoloGene
    suggested: (HomoloGene, RRID:SCR_002924)
    ENSEMBL
    suggested: (Ensembl, RRID:SCR_002344)
    Differentially expressed genes from SARS-CoV-2 infection models and SARS-CoV-2-Human virus-host protein-protein interactome - Network analysis: For all conserved DEGs (genes differentially expressed unambiguously in at least 2 of the 3 transcriptomic sets compared, i.e., Calu-3, VeroE6, or mouse model - Ad5-hACE2) of SARS-CoV-2 infection models and 336 SARS-CoV-2-human PPI genes, we extracted interactions with a score of ≥ 0.9 or experimental interaction score of 0.7 or more from STRING v11 [26].
    STRING
    suggested: (STRING, RRID:SCR_005223)
    This DEG-PPI network was clustered (through Cytoscape v3.8.0 [27]) using Markov Clustering Algorithm (MCL) (available as part of the ClusterMaker Plugin [28] in Cytoscape) to identify gene clusters.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    Functional enrichment analysis: Functional enrichment analysis was carried out on the various DEG sets and gene clusters (from MCL clustering) using the ToppFun application of the ToppGene suite [30] and Enrichr [31].
    ToppGene
    suggested: ( ToppGene Suite , RRID:SCR_005726)
    Enrichr
    suggested: (Enrichr, RRID:SCR_001575)
    We also generated aging human lung and liver DEG sets from the GTEx data using the BioJupies tool [41].
    BioJupies
    suggested: (BioJupies, RRID:SCR_016346)
    Additionally, we also used a curated set of 307 human aging genes from GenAge database [42] (Build February 2020) for enrichment analysis (Supplementary File 2).
    GenAge
    suggested: (GenAge, RRID:SCR_010223)

    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: We detected the following sentences addressing limitations in the study:
    Our study holds certain limitations. The transcriptomics data used is from in vitro and in vivo model systems with no samples from human patients of COVID-19. Although there are emerging data sets from COVID-19 patients, they are currently limited. For example, from the study that reported SARS-CoV-2-triggered transcriptome in Calu-3 cells, transcriptomics data from lung samples of COVID-19 patient are available. However, these samples were from just one patient. Nevertheless, the conserved signature from our study can be used to compare with more robust transcriptomic signatures from COVID-19 patients as and when they are available. For example, comparing the DEGs from nasopharyngeal swabs from human patients (GSE152075) with the DEGs from the three SARS-CoV-2 infection models showed strong concordance with the Calu-3 and the humanized mouse model (Supplementary File 16). Surprisingly, there was no significant correlation between the VeroE6 upregulated genes and COVID-19 patient nasopharyngeal upregulated genes. Although Vero E6 is the most widely used cell line to replicate and isolate SARS-CoV-2 [4], the expression level of TMPRSS2, the receptor that SARS-CoV-2 uses to prime the spike protein of SARS-CoV-2 [80, 81] is reported to be quite low in this clone. Additionally, there were many DEGs (1180 upregulated and 1734 downregulated) that are specific to human patients suggesting the inherent limitations of current in vitro and in vivo models of COVID-19. The STRING-based P...

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 48, 51 and 52. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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

    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.

  3. Version published to 10.1101/2020.08.27.270835v1 on bioRxiv