Cumulative effect of aging and SARS-CoV2 infection on poor prognosis in the elderly: Insights from transcriptomic analysis of lung and blood

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Abstract

Introduction

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) has affected millions of people worldwide and with notable heterogeneity in its clinical presentation. Probability of contracting this highly contagious infection is similar across age groups but disease severity and fatality among aged patients with or without comorbidities are higher. We hypothesized that SARS-CoV-2 infection may augment aging-related gene expression alterations resulting in severe outcomes in elderly patients.

Methodology

We performed a comparative analysis of publicly available transcriptome data from Broncho Alveolar Lavage Fluid (BALF)/lung/blood of healthy aging group with i) COVID-19 patients; and ii) data of host genes interacting with SARS-CoV-2 proteins.

Results

We observed i) a significant overlap of gene expression profiles of patients’ BALF and blood with lung and blood of the healthy group respectively; ii) a more pronounced overlap in blood compared to lung; and iii) a similar overlap between host genes interacting with SARS-CoV-2 and aging blood transcriptome.

Conclusions

Pathway enrichment analysis of overlapping gene sets suggests that infection alters expression of genes already dysregulated in the elderly, which together may lead to poor prognosis. eQTLs in these genes may also confer poor outcome in young patients worsening with age and co-morbidities. Furthermore, the pronounced overlap observed in blood may explain clinical symptoms including blood clots, strokes, heart attack, multi-organ failure etc. in severe cases. This model based on a limited patient dataset seems robust and holds promise for testing larger tissue specific datasets from patients with varied severity and across populations.

Article activity feed

  1. SciScore for 10.1101/2020.06.15.151761: (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 druggable targets: Finally, these genes identified above were screened for their druggability with FDA approved drugs using DGIdb website (http://dgidb.org).
    DGIdb
    suggested: (DGIdb, RRID:SCR_006608)

    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: We detected the following sentences addressing limitations in the study:
    Limitation: Our study is limited by non-availability of large tissue specific transcriptomic datasets of COVID19 patients across age groups, and with varying severity and from different populations to validate the hypothesis.

    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.

    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.