Mutational Signatures as Sensors of Environmental Exposures: Role of Smoking in COVID-19 Vulnerabilities

  1. Yoo-Ah Kim
  2. Ermin Hodzic
  3. Ariella Saslafsky
  4. Damian Wojtowicz
  5. Bayarbaatar Amgalan
  6. Teresa M. Przytycka

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Abstract

Background

Environmental exposures such as smoking are widely recognized risk factors in the emergence of lung diseases including lung cancer and acute respiratory distress syndrome (ARDS). However, the strength of environmental exposures is difficult to measure, making it challenging to understand their impacts. On the other hand, some COVID-19 patients develop ARDS in an unfavorable disease progression and smoking has been suggested as a potential risk factor among others. Yet initial studies on COVID-19 cases reported contradictory results on the effects of smoking on the disease – some suggest that smoking might have a protective effect against it while other studies report an increased risk. A better understanding of how the exposure to smoking and other environmental factors affect biological processes relevant to SARS-CoV-2 infection and unfavorable disease progression is needed.

Approach

In this study, we utilize mutational signatures associated with environmental factors as sensors of their exposure level. Many environmental factors including smoking are mutagenic and leave characteristic patterns of mutations, called mutational signatures, in affected genomes. We postulated that analyzing mutational signatures, combined with gene expression, can shed light on the impact of the mutagenic environmental factors to the biological processes. In particular, we utilized mutational signatures from lung adenocarcinoma (LUAD) data set collected in TCGA to investigate the role of environmental factors in COVID-19 vulnerabilities. Integrating mutational signatures with gene expression in normal tissues and using a pathway level analysis, we examined how the exposure to smoking and other mutagenic environmental factors affects the infectivity of the virus and disease progression.

Results

By delineating changes associated with smoking in pathway-level gene expression and cell type proportions, our study demonstrates that mutational signatures can be utilized to study the impact of exogenous mutagenic factors on them. Consistent with previous findings, our analysis showed that smoking mutational signature (SBS4) is associated with activation of cytokine-mediated signaling pathways, leading to inflammatory responses. Smoking related changes in cell composition were also observed, including the correlation of SBS4 with the expansion of goblet cells. On the other hand, increased basal cells and decreased ciliated cells in proportion were associated with the strength of a different mutational signature (SBS5), which is present abundantly but not exclusively in smokers. In addition, we found that smoking increases the expression levels of genes that are up-regulated in severe COVID-19 cases. Jointly, these results suggest an unfavorable impact of smoking on the disease progression and also provide novel findings on how smoking impacts biological processes in lung.

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    SciScore for 10.1101/2021.09.27.461855: (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
    To determine the predominant signatures being active in LUAD samples, we started with the initial sample exposures to mutational signatures from [24] (Synapse accession number: syn11804065).
    Synapse
    suggested: (Synapse, RRID:SCR_006307)
    Signature SBS45 was omitted from the analyses presented in this study as this signature is likely an artefact due to 8-oxo-guanine introduced during sequencing (see COSMIC Mutational Signatures website: https://cancer.sanger.ac.uk/signatures/).
    COSMIC Mutational Signatures
    suggested: None
    Expression data: TCGA LUAD RNAseq expression data were obtained from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/) on June 5th, 2020.
    https://portal.gdc.cancer.gov/
    suggested: (Genomic Data Commons Data Portal (GDC Data Portal, RRID:SCR_014514)
    HTseq counts were normalized and variance-stabilizing transformed (vst) using DESeq2 [63].
    HTseq
    suggested: (HTSeq, RRID:SCR_005514)
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)

    Results from OddPub: Thank you for sharing your code.

    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.

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

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  2. Version published to 10.1101/2021.09.27.461855v1 on bioRxiv