The aging transcriptome and cellular landscape of the human lung in relation to SARS-CoV-2

  1. Ryan D. Chow
  2. Sidi Chen

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Abstract

Since the emergence of SARS-CoV-2 in December 2019, Coronavirus Disease-2019 (COVID-19) has rapidly spread across the globe. Epidemiologic studies have demonstrated that age is one of the strongest risk factors influencing the morbidity and mortality of COVID-19. Here, we interrogate the transcriptional features and cellular landscapes of the aging human lung through integrative analysis of bulk and single-cell transcriptomics. By intersecting these age-associated changes with experimental data on host interactions between SARS-CoV-2 or its relative SARS-CoV, we identify several age-associated factors that may contribute to the heightened severity of COVID-19 in older populations. We observed that age-associated gene expression and cell populations are significantly linked to the heightened severity of COVID-19 in older populations. The aging lung is characterized by increased vascular smooth muscle contraction, reduced mitochondrial activity, and decreased lipid metabolism. Lung epithelial cells, macrophages, and Th1 cells decrease in abundance with age, whereas fibroblasts, pericytes and CD4+ Tcm cells increase in abundance with age. Several age-associated genes have functional effects on SARS-CoV replication, and directly interact with the SARS-CoV-2 proteome. Interestingly, age-associated genes are heavily enriched among those induced or suppressed by SARS-CoV-2 infection. These analyses illuminate potential avenues for further studies on the relationship between the aging lung and COVID-19 pathogenesis, which may inform strategies to more effectively treat this disease.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.07.030684: (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
    Single cell transcriptomes of human lungs were obtained from the Tissue Stability Cell Atlas (https://www.tissuestabilitycellatlas.org/) 19 and from the Human Lung Cell Atlas (https://github.com/krasnowlab/HLCA and https://www.synapse.org/#!Synapse:syn21041850/) 42.
    https://www.synapse.org/#
    suggested: (Multiple Myeloma survival predictor, RRID:SCR_017651)
    Identification of age-associated genes in human lung: To identify age-associated genes, the raw counts values were analyzed by DESeq2 (v1.24.0) 22, using the likelihood ratio test (LRT).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Gene ontology and pathway analysis of lung age-associated genes: Gene ontology and pathway enrichment analysis was performed using DAVID (v6.8) 70 (https://david.ncifcrf.gov/), separating the age-associated genes into the two clusters (increasing or decreasing with age), as described above.
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    Age-associated genes that are transcriptionally regulated upon SARS-CoV-2 infection: To assess whether the expression of lung age-associated genes is influenced by SARS-CoV-2 infection, we utilized the data from a preprint manuscript detailing the transcriptional response to SARS-CoV-2 infection 68, from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507) (accessed on April 13, 2020).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)

    Results from OddPub: Thank you for sharing your data.

    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.

  2. ScreenIT

    SciScore for 10.1101/2020.04.07.030684: (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 variableA total of 578 lung RNA-seq profiles were compiled, of which 31.66% were from women.Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    AT2 cells also frequently expressed ATP1B1 , ALG5 , NEU1 , and ATP6V1A ( 70.21 % , 50.64 % , 43.24 % , and 27.78 % ) .
    AT2
    suggested: None
    Volcano plots of differentially expressed genes in A549 cells ( a) , A549 cells transduced with an ACE2 vector ( A549-ACE2 ) ( b) , or Calu-3 cells ( c) .
    A549
    suggested: None
    Venn diagram of shared SARS-CoV-2 induced genes ( a ) or SARS-CoV-2 repressed genes ( b ) in A549 cells , A549-ACE2 cells , and Calu-3 cells .
    A549-ACE2
    suggested: None
    Table S24: Differential expression analysis in Calu-3 cells, infected with SARS-CoV-2 vs mock control, with age-association annotations.
    Calu-3
    suggested: BCRJ Cat# 0264, CVCL_0609
    Software and Algorithms
    SentencesResources
    Since functional screening data with SARS-CoV-2 has not yet been described ( as of March 30 , 2020) , we instead searched for data on SARS-CoV .
    SARS-CoV-2
    suggested: (Sino Biological Cat# 40143-R019, AB_2827973)
    Single cell transcriptomes of human lungs were obtained from the Tissue Stability Cell Atlas ( https://www.tissuestabilitycellatlas.org/ ) 19 and from the Human Lung Cell Atlas ( https://github.com/krasnowlab/HLCA and https://www.synapse.org/# ! Synapse:syn21041850/ ) 42 .
    https://www.synapse.org/#
    suggested: (Multiple Myeloma survival predictor, SCR_017651)
    Identification of age-associated genes in human lung To identify age-associated genes , the raw counts values were analyzed by DESeq2 ( v1.24.0 ) 22 , using the likelihood ratio test ( LRT) .
    DESeq2
    suggested: (DESeq, SCR_000154)
    Age-associated genes that are transcriptionally regulated upon SARS-CoV-2 infection To assess whether the expression of lung age-associated genes is influenced by SARSCoV-2 infection , we utilized the data from a preprint manuscript detailing the transcriptional response to SARS-CoV-2 infection 68 , from the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi ? acc=GSE147507 ) ( accessed on April 13 , 2020) .
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO), SCR_005012)
    Gene ontology and pathway enrichment analysis was performed using DAVID ( v6.8 ) 70 ( https://david.ncifcrf.gov/).
    DAVID
    suggested: (DAVID, SCR_001881)

    Results from OddPub: Thank you for sharing your data.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.04.07.030684 on bioRxiv