Computational genomic analysis of the lung tissue microenvironment in COVID-19 patients

  1. Krithika Bhuvaneshwar
  2. Subha Madhavan
  3. Yuriy Gusev

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Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 170 million people, and caused over 3.5 million deaths throughout the world as of May 2021. Although over 150 million people around the world have recovered from this disease, the long term effects of the disease are still under study. A year after the start of the pandemic, data from COVID-19 recovered patients shows multiple organs affected with a broad spectrum of manifestations. Long term effects of SARS-CoV-2 infection includes fatigue, chest pain, cellular damage, and robust innate immune response with inflammatory cytokine production. More clinical studies and clinical trials are needed to not only document, but also to understand and determine the factors that predispose certain people to the long term side effects of his infection.

In this manuscript, our goal was to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between SARS-CoV-2 viral infection, immune response and the lungs microbiome of COVID-19 patients. Each sample was analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2 we have analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus.

Our correlation analysis of three types of measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients.

Our exploratory study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.28.446250: (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
    For this paper, we applied the viGEN pipeline that uses Bowtie2 aligner [28] to find the viruses detected in the two datasets.
    Bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    This gene matrix was input into a public online tool CIBERSORT [33].
    CIBERSORT
    suggested: (CIBERSORT, RRID:SCR_016955)

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04634370Not yet recruitingFase I Clinical Trial on NK Cells for COVID-19

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.05.28.446250v1 on bioRxiv