Consensus transcriptional regulatory networks of coronavirus-infected human cells

  1. Scott A. Ochsner
  2. Rudolf T. Pillich
  3. Neil J. McKenna

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Abstract

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

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  1. Version published to 10.1038/s41597-020-00628-6
  2. Version published to 10.1101/2020.04.24.059527v5 on bioRxiv
  3. Version published to 10.1101/2020.04.24.059527v4 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.04.24.059527: (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

    Software and Algorithms
    SentencesResources
    Dataset biocuration: Datasets from Gene Expression Omnibus (SCR_005012) and Array Express (SCR_002964) were biocurated as previously described, with the incorporation of an additional classification of peptide ligands122 to supplement the existing mappings derived from the International Union of Pharmacology Guide To Pharmacology (SCR_013077).
    Gene Expression Omnibus
    detected: Gene Expression Omnibus (GEO ( RRID:SCR_005012)
    Array Express
    detected: ArrayExpress ( RRID:SCR_002964)
    of Pharmacology Guide Pharmacology
    detected: IUPHAR/BPS Guide to Pharmacology ( RRID:SCR_013077)
    Dataset processing and consensome analysis: Statistical analysis: High confidence transcript intersection analysis was performed using the Bioconductor GeneOverlap analysis package17 (SCR_018419) implemented in R.
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    GeneOverlap
    detected: GeneOverlap ( RRID:SCR_018419)
    A two tailed two sample t-test assuming equal variance was used to compare the mean percentile ranking of the EMT (12 degrees of freedom) and E2F (14 degrees of freedom) signatures in the MERS, SARS1, SARS2 and IAV consensomes using the PRISM software package (SCR_005375).
    PRISM
    detected: PRISM ( RRID:SCR_005375)
    To generate the ChIP-Seq consensomes, we first retrieved processed gene lists from ChIP-Atlas (SCR_015511), in which human genes are ranked based upon their average MACS2 occupancy across all publically archived datasets in which a given pathway node is the IP antigen.
    ChIP-Seq
    detected: ChIP-Atlas ( RRID:SCR_015511)
    We then organized the ranked lists into percentiles to generate the node ChIP-Seq consensomes.
    ChIP-Seq
    suggested: (ChIP-seq, RRID:SCR_001237)
    SPP web application: The SPP knowledgebase (SCR_018412) is a gene-centric Java Enterprise Edition 6, web-based application around which other gene, mRNA, protein and BSM data from external databases such as NCBI are collected.
    SPP web
    detected: Signaling Pathways Project ( RRID:SCR_018412)
    XML describing each dataset and experiment is generated and submitted to CrossRef (SCR_003217) to mint DOIs10.
    CrossRef
    detected: CrossRef ( RRID:SCR_003217)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The CoV consensomes have a number of limitations. Primarily, since they are predicated specifically on transcriptional regulatory technologies, they will assign low rankings to transcripts that may not be transcriptionally responsive to CoV infection, but whose encoded proteins nevertheless play a role in the cellular response. For example, MASP2, which encodes an important node in the response to CoV infection has either a very low consensome ranking (SARS1, MERS and IAV), or is absent entirely (SARS2), indicating that it is transcriptionally unresponsive to viral infection and likely activated at the protein level in response to upstream signals. This and similar instances therefore represent “false negatives” in the context of the impact of CoV infection on human cells. Another limitation of the transcriptional focus of the datasets is the absence of information on specific protein interactions and post-translational modifications, either viral-human or human-human, that give rise to the observed transcriptional responses. Although these can be inferred to some extent, the availability of existing32,68,109 and future proteomic and kinomic datasets will facilitate modeling of the specific signal transduction events giving rise to the downstream transcriptional responses. Finally, although detailed metadata are readily available on the underlying data points, the consensomes do not directly reflect the impact of variables such as tissue context or duration of infection on di...

    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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  5. Version published to 10.1101/2020.04.24.059527v3 on bioRxiv
  6. Version published to 10.1101/2020.04.24.059527v2 on bioRxiv
  7. Version published to 10.1101/2020.04.24.059527v1 on bioRxiv