Comprehensive analysis of next-generation sequencing data in COVID-19 and its secondary complications

  1. Muttanagouda Giriyappagoudar
  2. Basavaraj Vastrad
  3. Rajeshwari Horakeri
  4. Chanabasayya Vastrad

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Abstract

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health threat globally. To discover key molecular changes in COVID-19 and its secondary complications, we analyzed next-generation sequencing (NGS) data of COVID-19. NGS data (GSE163151) was screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were identified in the present study, using DESeq2 package in R programming software. Gene ontology (GO) and pathway enrichment analysis were performed, and the protein-protein interaction (PPI) network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network were established. Subsequently, receiver operating characteristic curve (ROC) analysis was used to validate the diagonostics valuesof the hub genes. Firstly, 954 DEGs (477 up regulated and 477 down regulated) were identified from the four NGS dataset. GO enrichment analysis revealed enrichment of DEGs in genes related to the immune system process and multicellular organismal process, and REACTOME pathway enrichment analysis showed enrichment of DEGs in the immune system and formation of the cornified envelope. Hub genes were identified from the PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, the ROC analysis indicate that COVID-19 and its secondary complications with following hub genes, namely, RPL10, FYN, FLNA, EEF1A1, UBA52, BMI1, ACTN2, CRMP1, TRIM42 and PTCH1, had good diagnostics values. This study identified several genes associated with COVID-19 and its secondary complications, which improves our knowledge of the disease mechanism.

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    SciScore for 10.1101/2022.02.03.478930: (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
    The Benjamini & Hochberg false discovery rate method was used as a correction factor for the adjusted P-value in DESeq2 [41].
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    ggplot2 and gplot in R software was subsequently performed to plot the volcano plot and heat map.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    GO and pathway enrichment analyses of DEGs: The g:Profiler (http://biit.cs.ut.ee/gprofiler/) [42] is an online functional annotation tool was used to perform Gene Ontology (GO) and REACTOME pathway enrichment analyses.
    g:Profiler
    suggested: (G:Profiler, RRID:SCR_006809)
    REACTOME
    suggested: (Reactome, RRID:SCR_003485)
    GO enrichment analysis (http://www.geneontology.org) [43] has three independent branches: biological process (BP), cellular component (CC).
    http://www.geneontology.org
    suggested: (Mouse Genome Informatics: The Gene Ontology Project, RRID:SCR_006447)
    MiRNA-hub gene regulatory network construction: The miRNet database (https://www.mirnet.ca/) [52] is an open-source platform mainly focusing on miRNA-hub gene interactions. miRNet utilizes fourteen established miRNA-hub gene prediction databases, including TarBase
    TarBase
    suggested: (TarBase, RRID:SCR_000577)
    , miRTarBase, miRecords,
    miRecords
    suggested: (miRecords, RRID:SCR_013021)
    miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0.
    miRanda
    suggested: (miRanda, RRID:SCR_017496)
    Subsequently, the network of the hub genes and their targeted miRNAs was visualized by Cytoscape software version 3.8.2 [46].
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    NetworkAnalyst database utilizes fourteen established TF-hub gene prediction 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. [(GSE163151) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163151)]
    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 10.1101/2022.02.03.478930 on bioRxiv