Identification of potential biomarkers and inhibitors for SARS-CoV-2 infection

  1. Hanming Gu
  2. Gongsheng Yuan

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Abstract

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has overwhelmed many health systems globally. Here, we aim to identify biological markers and associated biological processes of COVID-19 using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profile of the GSE152418 dataset was originally produced by using the high-throughput Illumina NovaSeq 6000. Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) and Gene Ontology (GO) enrichment analyses were applied to identify functional categories and biochemical pathways. KEGG and GO results suggested that biological pathways such as “Cancer pathways” and “Insulin pathways” were mostly affected in the development of COVID-19. Moreover, we identified several genes including EP300, CREBBP, and POLR2A were involved in the virus activities in COVID-19 patients. We further predicted that some inhibitors may have the potential to block the SARS-CoV-2 infection based on the L1000FWD analysis. Therefore, our study provides further insights into the underlying pathogenesis of COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.15.20195487: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Cell culture and treatment: The RAW 264.7 cell lines were purchased from American Type Culture Collection (ATCC® TIB-71™).
    RAW 264.7
    suggested: None
    Software and Algorithms
    SentencesResources
    resources: Gene expression profile dataset GSE152418 was obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/).
    http://www.ncbi.nlm.nih.gov/geo/
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    GO analysis and KEGG pathway enrichment analysis of DEGs in this study were analyzed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (http://david.ncifcrf.gov/).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    The significant modules were from constructed PPI networks using MCODE.
    MCODE
    suggested: (MCODE, RRID:SCR_015828)
    Statistical analysis: For statistical analysis, Prism 7 software (GraphPad Software, USA) was used.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    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 10.1101/2020.09.15.20195487v1 on medRxiv