SARS-CoV-2 Proteins Exploit Host’s Genetic and Epigenetic Mediators for the Annexation of Key Host Signaling Pathways

  1. Md. Abdullah-Al-Kamran Khan
  2. Abul Bashar Mir Md. Khademul Islam

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Abstract

The constant rise of the death toll and cases of COVID-19 has made this pandemic a serious threat to human civilization. Understanding of host-SARS-CoV-2 interaction in viral pathogenesis is still in its infancy. In this study, we utilized a blend of computational and knowledgebase approaches to model the putative virus-host interplay in host signaling pathways by integrating the experimentally validated host interactome proteins and differentially expressed host genes in SARS-CoV-2 infection. While searching for the pathways in which viral proteins interact with host proteins, we discovered various antiviral immune response pathways such as hypoxia-inducible factor 1 (HIF-1) signaling, autophagy, retinoic acid-inducible gene I (RIG-I) signaling, Toll-like receptor signaling, fatty acid oxidation/degradation, and IL-17 signaling. All these pathways can be either hijacked or suppressed by the viral proteins, leading to improved viral survival and life cycle. Aberration in pathways such as HIF-1 signaling and relaxin signaling in the lungs suggests the pathogenic lung pathophysiology in COVID-19. From enrichment analysis, it was evident that the deregulated genes in SARS-CoV-2 infection might also be involved in heart development, kidney development, and AGE-RAGE signaling pathway in diabetic complications. Anomalies in these pathways might suggest the increased vulnerability of COVID-19 patients with comorbidities. Moreover, we noticed several presumed infection-induced differentially expressed transcription factors and epigenetic factors, such as miRNAs and several histone modifiers, which can modulate different immune signaling pathways, helping both host and virus. Our modeling suggests that SARS-CoV-2 integrates its proteins in different immune signaling pathways and other cellular signaling pathways for developing efficient immune evasion mechanisms while leading the host to a more complicated disease condition. Our findings would help in designing more targeted therapeutic interventions against SARS-CoV-2.

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  1. Version published to 10.3389/fmolb.2020.598583
  2. ScreenIT

    SciScore for 10.1101/2020.05.06.050260: (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
    Analysis of microarray expression data: Microarray expression data on SARS-CoV infected 2B4 cells or uninfected controls for 24 hrs obtained from Gene Expression Omnibus (GEO), accession: GSE17400 (https://www.ncbi.nlm.nih.gov/geo) (Barrett et al., 2012)
    2B4
    suggested: None
    Software and Algorithms
    SentencesResources
    Analysis of microarray expression data: Microarray expression data on SARS-CoV infected 2B4 cells or uninfected controls for 24 hrs obtained from Gene Expression Omnibus (GEO), accession: GSE17400 (https://www.ncbi.nlm.nih.gov/geo) (Barrett et al., 2012)
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Quality of microarray experiment (data not shown) was verified by Bioconductor package “arrayQualityMetrics v3.2.4” (Kauffmann et al., 2009)
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Differentially expressed (DE) between two experimental conditions were called using Bioconductor package Limma (Smyth, 2005).
    Limma
    suggested: (LIMMA, RRID:SCR_010943)
    Probe annotations were converted to genes using in-house python script basing the Ensembl gene model (Biomart 99) (Flicek et al., 2007).
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    Mapping of reads was done with TopHat (tophat v2.1.1 with Bowtie v2.4.1) (Trapnell et al., 2009)
    TopHat
    suggested: (TopHat, RRID:SCR_013035)
    Bowtie
    suggested: (Bowtie, RRID:SCR_005476)
    After mapping, we used SubRead package featureCount v2.21 (Liao et al., 2013) to calculate absolute read abundance (read count, rc) for each transcript/gene associated to the Ensembl genes.
    SubRead
    suggested: (Subread, RRID:SCR_009803)
    featureCount
    suggested: None
    For differential expression (DE) analysis we used DESeq2 v1.26.0 with R v3.6.2 (2019-07-05) (Anders and Huber, 2010) that uses a model based on the negative binomial distribution.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    We have also performed the enrichment analysis based on KEGG pathway module of STRING database (Szklarczyk et al., 2019) for the 332 proteins (Supplementary file 1) retrieved from the analysis of Gordon et al. (2020) (Gordon et al., 2020) along with the deregulated genes analyzed from SARS-CoV-2 infected cell’s RNA-seq expression data. 2.5.
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Obtaining the transcription factors binds promoter regions: We have obtained the transcription factors (TFs) which bind to the given promoters from Cistrome data browser (Zheng et al., 2018) that provides TFs from experimental ChIP-seq data.
    Cistrome data browser
    suggested: (OMICtools, RRID:SCR_002250)
    Identification of the host epigenetic factors genes: We used EpiFactors database (Medvedeva et al., 2015) to find human genes related to epigenetic activity. 2.9.
    EpiFactors
    suggested: (EpiFactors , RRID:SCR_016956)
    Mapping of the human proteins in cellular pathways: We have utilized KEGG mapper tool (Kanehisa and Sato, 2020) for the mapping of deregulated genes SARS-CoV-2 interacting host proteins in different cellular pathways.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.05.06.050260v1 on bioRxiv