Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

  1. Basavaraj Vastrad
  2. Chanabasayya Vastrad

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Abstract

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

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    SciScore for 10.1101/2021.09.24.461631: (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
    Identification of DEGs: The limma in R Bioconductor software package [48] was used to perform the identification of DEGs between COVID 19 samples and non COVID 19 samples.
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    The DEGs are presented as volcano plots, generated using ggplot2 in the R software.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    GO and REACTOME pathway enrichment analysis of DEGs: The g:Profiler (http://biit.cs.ut.ee/gprofiler/) [50] is an online functional annotation tool to provide a comprehensive understanding of biological information of genes and proteins.
    g:Profiler
    suggested: (G:Profiler, RRID:SCR_006809)
    GO term (http://www.geneontology.org) enrichment analysis is used broadly to classify the characteristic biological attributes of genes, gene products, and sequences, including biological process (BP), cell components (CC) and molecular function (MF) [51].
    http://www.geneontology.org
    suggested: (Mouse Genome Informatics: The Gene Ontology Project, RRID:SCR_006447)
    REACTOME (https://reactome.org/) [52] pathway enrichment analysis demonstrates the enriched signaling pathways in DEGs.
    REACTOME
    suggested: (Reactome, RRID:SCR_003485)
    Construction of the PPI network and module analysis: The IMEx interactome (https://www.imexconsortium.org/) is a public database harboring known and predicted protein-protein interactions [53].
    https://www.imexconsortium.org/
    suggested: (IMEx - The International Molecular Exchange Consortium, RRID:SCR_002805)
    The network was then visualized using the Cytoscape 3.8.2 (http://www.cytoscape.org/) [54].
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    We applied miRNet database (https://www.mirnet.ca/) [61] to integrate 14 miRNA databases (TarBase, miRTarBase, miRecords,
    miRecords
    suggested: (miRecords, RRID:SCR_013021)
    We applied NetworkAnalyst database (https://www.networkanalyst.ca/) [62] to integrate TF database (JASPER).
    NetworkAnalyst
    suggested: (NetworkAnalyst, RRID:SCR_016909)
    The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE156063) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156063)]
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)

    Results from OddPub: Thank you for sharing your data.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.09.24.461631v1 on bioRxiv