Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets

  1. Chen Su
  2. Simon Rousseau
  3. Amin Emad

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Abstract

Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, we combined a series of recently developed computational tools to identify transcriptional regulatory networks involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus. In addition, using network-guided analyses, we identified signaling pathways that are associated with these networks and kinases that may regulate them. The results identified classical antiviral response pathways including Interferon response factors (IRFs), interferons (IFNs), and JAK-STAT signaling as key elements upregulated by SARS-CoV-2 in comparison to mock-treated cells. In addition, comparing SARS-Cov-2 infection of airway epithelial cells to other respiratory viruses identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory networks identified herein reflect a combination of experimentally validated hits and novel pathways supporting the computational pipeline to quickly narrow down promising avenue of investigations when facing an emerging and novel disease such as COVID-19.

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    SciScore for 10.1101/2020.12.23.424177: (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
    For the reconstruction of SARS-CoV-2 versus other viruses (SvOV) TRN, we used 33 samples corresponding to independent biological replicates of A549 cells infected with SARS-CoV-2, RSV, IAV, and HPIV3, NHBE cells infected with SARS-CoV-2, IAV, and IAVdNS, A549-ACE2 cells infected with SARS-CoV-2, and Calu3 cells infected with SARS-CoV-2.
    A549-ACE2
    suggested: None
    Calu3
    suggested: KCLB Cat# 30055, RRID:CVCL_0609)
    LINCS Level 5 consensus signatures (‘trt_sh.cgs’) corresponding to shRNA knockdowns in A549 cell line were obtained from GEO with the accession number (GSE92742).
    A549
    suggested: None
    Software and Algorithms
    SentencesResources
    Data collection: We downloaded mock-treated and infected RNA-seq gene expression profiles of human lung epithelial cells from the Gene Expression Omnibus (GEO) database (accession number: GSE147507).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    We downloaded the list of human TFs from AnimalTFDB [52].
    AnimalTFDB
    suggested: (AnimalTFDB, RRID:SCR_001624)
    Experimentally verified protein-protein interaction network from the STRING database [14] and HumanNet integrated network [21] were downloaded from KnowEnG’s knowledge network (version 17.06) available at the address https://github.com/KnowEnG/KN_Fetcher/blob/master/Contents.md.
    STRING
    suggested: (STRING, RRID:SCR_005223)
    The list of target genes for the top TFs (identified using ChIP-seq) was downloaded from the GTRD database (http://gtrd.biouml.org/downloads/20.06/intervals/target_genes/Homo%20sapiens/genes%20promoter%5b-1000,+100%5d/).
    ChIP-seq
    suggested: (ChIP-seq, RRID:SCR_001237)
    To construct the TRNs using InPheRNo, we first performed differential expression analysis using EdgeR [54] with the cell type as a confounding factor.
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)

    Results from OddPub: Thank you for sharing your code.

    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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  2. Version published to 10.1101/2020.12.23.424177v1 on bioRxiv