Age-dependent regulation of SARS-CoV-2 cell entry genes and cell death programs correlates with COVID-19 severity

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Abstract

Changes in lung ACE2 expression and apoptotic priming throughout life span may affect COVID severity.

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  1. SciScore for 10.1101/2020.09.13.276923: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIACUC: Animal care and use: Mouse tissue immunofluorescence experiments described in these studies were approved by the Johns Hopkins University Animal Care and Use Committee (Protocol Number: M019M332) and were performed according to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health.
    IRB: Human infant lung samples were obtained and processed at autopsy from either patients with necrotizing enterocolitis or age-matched infants that died from unrelated conditions that did not affect the lungs, with approval from the University of Pittsburgh Institutional Review Board (CORID No. 491) and in accordance with the University of Pittsburgh anatomical tissue procurement guidelines.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableMale and female C57BL/6J mice were obtained from the Jackson Laboratory (

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Black 6 mice, strain C57BL/6J, (WT) (Jackson Laboratories) were used for tissue collection.
    C57BL/6J
    suggested: RRID:IMSR_JAX:000664)
    Software and Algorithms
    SentencesResources
    Analysis of public gene expression databases: Mouse and human microarray data were analyzed using Genevestigator (NEBION, Zurich, Switzerland)
    Genevestigator
    suggested: (Genevestigator, RRID:SCR_002358)
    RNA-seq data was obtained from the LGEA32 and LungMAP33 databases, with corresponding protein levels in extrapulmonary tissues confirmed using the Human Proteome Map49 database.
    LungMAP33
    suggested: None
    These tiles were stitched using ASHLAR, a novel stitching and registration algorithm (https://github.com/labsyspharm/ashlar).
    ASHLAR
    suggested: (ASHLAR, RRID:SCR_016266)

    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.
    • 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.