Profiling transcription factor sub-networks in type I interferon signaling and in response to SARS-CoV-2 infection

  1. Chilakamarti V. Ramana

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Abstract

Type I interferons (IFN α/β) play a central role in innate immunity to respiratory viruses, including coronaviruses. Genetic defects in type I interferon signaling were reported in a significant proportion of critically ill COVID-19 patients. Extensive studies on interferon-induced intracellular signal transduction pathways led to the elucidation of the Jak-Stat pathway. Furthermore, advances in gene expression profiling by microarrays have revealed that type I interferon rapidly induced multiple transcription factor mRNA levels. In this study, transcription factor profiling in the transcriptome was used to gain novel insights into the role of inducible transcription factors in response to type I interferon signaling in immune cells and in lung epithelial cells after SARS-CoV-2 infection. Modeling the interferon-inducible transcription factor mRNA data in terms of distinct sub-networks based on biological functions such as antiviral response, immune modulation, and cell growth revealed enrichment of specific transcription factors in mouse and human immune cells. The evolutionarily conserved core type I interferon gene expression consists of the inducible transcriptional factor mRNA of the antiviral response sub-network and enriched in granulocytes. Analysis of the type I interferon-inducible transcription factor sub-networks as distinct protein-protein interaction pathways revealed insights into the role of critical hubs in signaling. Interrogation of multiple microarray datasets revealed that SARS-CoV-2 induced high levels of IFN-beta and interferon-inducible transcription factor mRNA in human lung epithelial cells. Transcription factor mRNA of the three major sub-networks regulating antiviral, immune modulation, and cell growth were differentially regulated in human lung epithelial cell lines after SARS-CoV-2 infection and in the tissue samples of COVID-19 patients. A subset of type I interferon-inducible transcription factors and inflammatory mediators were specifically enriched in the lungs and neutrophils of COVID-19 patients. The emerging complex picture of type I IFN transcriptional regulation consists of a rapid transcriptional switch mediated by the Jak-Stat cascade and a graded output of the inducible transcription factor activation that enables temporal regulation of gene expression.

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    SciScore for 10.1101/2021.01.25.428122: (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
    Supplementary data was downloaded from the Journal publisher websites and from Geo datasets that were archived at Pubmed (NCBI).
    Pubmed
    suggested: (PubMed, RRID:SCR_004846)
    The identification of the differentially expressed genes in the transcription profile was analyzed using the GEO2R tool and differential expression analysis using DESeq2 using default parameters.
    GEO2R
    suggested: (GEO2R, RRID:SCR_016569)
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    The evaluation of the gene ontology (GO) enrichment and signaling pathway analysis was conducted using DAVID, KEGG, and Metascape software (30-32).
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    Protein interactions were interrogated and visualized in the STRING, WIKI-PI, and BIOGRID databases (33-35) Protein-protein interaction network centrality features were calculated according to https://www.sscnet.ucla.edu/soc/faculty/mcfarland/soc112/cent-ans.htm
    STRING
    suggested: (STRING, RRID:SCR_005223)
    BIOGRID
    suggested: (BioGrid Australia, RRID:SCR_006334)

    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:
    3.2 Time-course analysis of Type I Interferon signaling: Most of the gene expression profiling studies in response to type I IFN have some technical limitations (5). These studies were performed in transformed cells or cells cultured in the presence of high serum or in a limited number of cell types such as fibroblasts or epithelial cells. In addition, cells were subjected to prolonged interferon treatment of several hours to capture the maximum range of gene expression levels. Furthermore, antiviral response genes were overrepresented while transcription factor and growth-regulated genes were under-represented on many custom-designed arrays of interferon-regulated gene expression. These studies may have missed earlier dynamic changes in transcriptional factor mRNA levels and cell growth-related gene expression. Interrogation of gene expression datasets of early time points after type I Interferon treatment revealed that several transcription factor mRNA were expressed after 1-4 hour treatment of human PBMC and mouse B cells. (Figure 4A and 4B). Transcription factors involved in antiviral response including Stat1, Stat2, Irf1, Irf2, and Irf7 mRNA levels were rapidly induced in both PBMC and B cells (Figure 4). An intact type I IFN response is necessary to inhibit viral replication in cells, control the tissue restriction of virus, and increase the production of type I IFN acting as a feedback loop (4,36). A large number of type I interferon regulated genes like 2,5-oligoadeny...

    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.
    • No funding statement was detected.
    • 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.

  2. Version published to 10.1101/2021.01.25.428122 on bioRxiv
  3. Version published to 10.1515/cmb-2020-0128