Gene Expression Meta-Analysis Reveals Interferon-induced Genes

  1. Amber Park
  2. Laura K. Harris

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Abstract

Background

Severe Acute Respiratory Syndrome (SARS) corona virus (CoV) infections are a serious public health threat because of their pandemic-causing potential. This work analyzes mRNA expression data from SARS infections through meta-analysis of gene signatures, possibly identifying therapeutic targets associated with major SARS infections.

Methods

This work defines 37 gene signatures representing SARS-CoV, Middle East Respiratory Syndrome (MERS)-CoV, and SARS-CoV2 infections in human lung cultures and/or mouse lung cultures or samples and compares them through Gene Set Enrichment Analysis (GSEA). To do this, positive and negative infectious clone SARS (icSARS) gene panels are defined from GSEA-identified leading-edge genes between two icSARS-CoV derived signatures, both from human cultures. GSEA then is used to assess enrichment and identify leading-edge icSARS panel genes between icSARS gene panels and 27 other SARS-CoV gene signatures. The meta-analysis is expanded to include five MERS-CoV and three SARS-CoV2 gene signatures. Genes associated with SARS infection are predicted by examining the intersecting membership of GSEA-identified leading-edges across gene signatures.

Results

Significant enrichment (GSEA p<0.001) is observed between two icSARS-CoV derived signatures, and those leading-edge genes defined the positive (233 genes) and negative (114 genes) icSARS panels. Non-random significant enrichment (null distribution p<0.001) is observed between icSARS panels and all verification icSARSvsmock signatures derived from human cultures, from which 51 over- and 22 under-expressed genes are shared across leading-edges with 10 over-expressed genes already associated with icSARS infection. For the icSARSvsmock mouse signature, significant, non-random significant enrichment held for only the positive icSARS panel, from which nine genes are shared with icSARS infection in human cultures. Considering other SARS strains, significant, non-random enrichment (p<0.05) is observed across signatures derived from other SARS strains for the positive icSARS panel. Five positive icSARS panel genes, CXCL10, OAS3, OASL, IFIT3, and XAF1, are found across mice and human signatures regardless of SARS strains.

Conclusion

The GSEA-based meta-analysis approach used here identifies genes with and without reported associations with SARS-CoV infections, highlighting this approach’s predictability and usefulness in identifying genes that have potential as therapeutic targets to preclude or overcome SARS infections.

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  1. Version published to 10.1101/2020.11.14.382697v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.11.14.382697: (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
    Further, we selected GSE33267 because it had mock-, icSARS-, and dORF6-infected samples of Calu-3 cells, from which 2B-4 is a clonal derivative.
    Calu-3
    suggested: None
    Experimental Models: Organisms/Strains
    SentencesResources
    To determine if the molecular changes associated with SARS infection observed in human lung cell cultures are reproducible in an in vivo mouse model, we selected four datasets that examined lung samples from mock- or SARS-infected 20-week-old C57BL6 mice.
    C57BL6
    suggested: None
    GSE49262 had samples from mice infected with MA15 (105 inoculation dose) or dORF6 mutant strains, and GSE49263 had samples infected with MA15 (105 inoculation dose) or dNSP16 mutant strains.
    dNSP16
    suggested: None
    Software and Algorithms
    SentencesResources
    2.1 mRNA Expression Resources: To identify molecular changes associated with SARS infection in human lung cell cultures, we searched the Gene Expression Omnibus (GEO) repository (Edgar et al., 2002; Barrett et al., 2011; Barrett et al., 2013) to find seven datasets for use in our study (Table 1).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    GSEA uses the statistical metric used to rank genes in the reference signature (e.g., T-score) to calculate a running summation enrichment score where hits (i.e., matches between query set and reference signature) increase the enrichment score proportional to the ranking statistical metric (e.g., T-score) and a miss (i.e., non-matches between query set and reference signature) decreases the enrichment score.
    GSEA
    suggested: (SeqGSEA, RRID:SCR_005724)
    Pathway enrichment analysis was performed on both icSARS gene panels using Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8. 2.5 Verification of icSARS Gene Panels: To verify the icSARS gene panels, we performed GSEA between icSARS gene panels and GSE47962-derived, GSE37827-derived, GSE48142-derived, and GSE33267-derived icSARSvsmock signatures (Figure 2A).
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    Histogram data and associated graphs (e.g., distribution curves and box and whiskers plot) were calculated using XLStat version 2020.3 (XLSTAT, 2013; Addinsoft, 2019). 2.6 Comparison to icSARS-induced Gene Expression Changes in Mice and Across Other SARS Strains: To examine differential gene expression of genes from the icSARS gene panels in mice, we performed GSEA between icSARS gene panels (queries) and the GSE17400-derived icSARSvsmock signature from mock- or icSARS-infected mouse lung samples (reference, Figure 2B).
    XLSTAT
    suggested: (XLSTAT, RRID:SCR_016299)
    Heat maps were generated by Morpheus, https://software.broadinstitute.org/morpheus.
    Morpheus
    suggested: (Morpheus, RRID:SCR_014975)

    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.

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  3. Version published to 10.1101/2020.11.14.382697v1 on bioRxiv