Immunopathogenic overlap between COVID-19 and tuberculosis identified from transcriptomic meta-analysis and human macrophage infection

  1. Dylan Sheerin
  2. Abhimanyu
  3. Nashied Peton
  4. William Vo
  5. Cody Charles Allison
  6. Xutao Wang
  7. W. Evan Johnson
  8. Anna Kathleen Coussens

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Abstract

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  1. Version published to 10.1016/j.isci.2022.104464
  2. ScreenIT

    SciScore for 10.1101/2020.11.25.20236646: (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.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A literature search of published and pre-print manuscripts was conducted on the NIH PubMed and bioRxiv, medRxiv, and SSRN servers uploaded/published between 01/02/20 and 20/09/20 (figure 1).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    The curatedTBData package,10 which includes 48 publicly available TB RNA-seq datasets, was used to identify eligible TB datasets, that included individuals who progressed to TB during the duration of study follow-up, with RNA-seq data at baseline and time of diagnosis, and patient-level meta-data including time to TB progression.
    curatedTBData
    suggested: None
    The eligible COVID-19 and influenza control signatures (appendix 2 p1) were evaluated independently against the patient-level TB RNA-seq data, generating individual-sample putative “COVID-19 risk score” using gene set variation analysis (GSVA)11 with the TBSignatureProfiler package12 (appendix 1 p2), and score significance, as compared to latent TB infection (LTBI) controls, calculated by Bonferroni-corrected t-test (appendix 2 p2–4).
    TBSignatureProfiler
    suggested: None
    Single cell (sc)RNA-seq integrative comparison was conducted using the identified COVID-19 bronchoalveolar lavage fluid (BALF) dataset13 downloaded from the NIH GEO database (GSE145926) and a TB PBMC dataset14 downloaded from NCBI Short Read Archive (SRA, SRR11038989-SRR11038995).
    NCBI Short Read Archive
    suggested: None

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We counteracted this limitation by including signatures from multiple studies for each immune cell population. In summary, we show for the first time through large scale meta-analysis of the available transcriptomic data, that advanced COVID-19 and TB disease states overlap at the gene, cell, and systems levels. These shared disease mechanisms could prove to be hotspots for immune exacerbation, inducing greater immunopathology, in the case of co-infection. We report a new 20-gene gene signature which distinguishes severe COVID-19 from active and LTBI that should be investigated further for its disease classification value in larger datasets as they become available. Taken together, the data presented here along with the emerging case reports identifying TB as a risk factor for severe COVID-19 suggesting that individuals with known previous TB history, recent TB exposures or LTBI with pre-existing lung pathology, are at increased risk of severe COVID-19 disease and, potentially, early progression to TB disease, following SARS-CoV-2 infection. Given the medical capacity to do so, we therefore propose that such individuals should 1) be closely followed to allow early detection of respiratory symptom onset, 2) be screened for SARS-CoV-2 and TB at symptom onset, and 3) be followed up for TB in the months subsequent to SARS-CoV-2 diagnosis. Diagnostic and clinical outcome data arising from early stages of the COVID-19 outbreak should be stratified by TB history in order to determin...

    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

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  3. Version published to 10.1101/2020.11.25.20236646v1 on medRxiv