Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway

  1. Andrea R. Daamen
  2. Prathyusha Bachali
  3. Katherine A. Owen
  4. Kathryn M. Kingsmore
  5. Erika L. Hubbard
  6. Adam C. Labonte
  7. Robert Robl
  8. Sneha Shrotri
  9. Amrie C. Grammer
  10. Peter E. Lipsky

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Abstract

SARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.

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  1. Version published to 10.1038/s41598-021-86002-x
  2. ScreenIT

    SciScore for 10.1101/2020.05.28.121889: (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
    RNA-seq data were processed using a consistent workflow using FASTQC, Trimmomatic
    FASTQC
    suggested: (FastQC, RRID:SCR_014583)
    Reads were mapped to the human reference genome hg38 using STAR, and the .sam files were converted to sorted .
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Read counts were summarized using the featureCounts function of the Subread package (v1.61.) The RNA-seq tools are all free, open source programs available at the following web addresses SRA toolkit - https://github.com/ncbi/sra-tools FastQC - https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Trimmomatic - http://www.usadellab.org/cms/?
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    STAR - https://github.com/alexdobin/STAR http://labshare.cshl.edu/shares/gingeraslab/www-data/dobin/STAR/STAR.posix/doc/STARmanual.pdf Sambamba - http://lomereiter.github.io/sambamba/ FeatureCounts - http://subread.sourceforge.net/ Differential gene expression and gene set enrichment analysis: The DESeq2 workflow was used for differential expression analysis.
    https://github.com/alexdobin/STAR
    suggested: (Hamilton Microlab STAR Automated Liquid Handling, RRID:SCR_019993)
    http://subread.sourceforge.net/
    suggested: (Subread, RRID:SCR_009803)
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    The filtered raw counts were normalized using the DESeq method and differentially expressed genes were determined by FDR < 0.2 (Anders and Huber, 2010).
    DESeq
    suggested: (DESeq, RRID:SCR_000154)
    Nine single-cell RNA-seq lung cell populations (AT1, AT2, Ciliated, Club, Endothelial, Fibroblasts, Immuno Monocytes, Immuno T Cells, and Lymphatic Endothelium) were downloaded from the Eils Lung Tissues set (Lukassen et al., 2020) accessed by the UC Santa Cruz Genome Browser (
    Santa Cruz Genome Browser
    suggested: None
    Briefly, STRING (version 1.3.2) generated networks were imported into Cytoscape (version 3.6.1) and partitioned with MCODE via the clusterMaker2 (version 1.2.1) plugin.
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    KEGG pathways, NCBI PubMed, and the Interactome.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Linear Regression Analysis: Simple linear regression between calculated myeloid subpopulation A1, A2, and A3 GSVA scores and biological functions or signaling pathway GSVA scores was performed in GraphPad Prism Version 8.4.2.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Drugs targeting gene products of interest by both direct and indirect targeting mechanisms were sourced by Combined Lupus Treatment Scoring (CoLTS)-scored drugs (Grammer et al., 2016), the Connectivity Map via the drug repurposing tool, DrugBank, and literature mining.
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    PBMC-CoV2 were used as input for the CMaP and LINCS Unified Environment (CLUE) cloud-based connectivity map analysis platform (
    CMaP
    suggested: (CMAP, RRID:SCR_009034)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In order to develop a model of SARS-CoV2 infection, we have utilized multiple orthogonal approaches to analyze gene expression from COVID-19 patients, but also acknowledge the limitations of this data. For example, we were only able to analyze 2-3 samples per experimental condition and this limited the statistical power of each of our bioinformatics techniques. In addition, the low number of samples meant that heterogeneity among patients in any given cohort had an increased impact on the overall outcome. One possible reason for this intra-cohort heterogeneity is that the patients may have exhibited varying levels of disease severity and, unfortunately, we did not have access to clinical information. These points highlight the need for additional studies on more patients, preferably accounting for differences in demographic information and disease status, in order to increase the power of downstream analyses. In conclusion, transcriptomic analysis has contributed critical insights into the pathogenesis of COVID-19. Diffuse Mo/MΦ activation is the likely primary driver of clinical pathology. Therefore, this work provides a rationale for placing greater focus on the detrimental effects of exaggerated activation of pathogenic Mo/MΦs and for targeting these populations as an effective treatment strategy for COVID-19 patients.

    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.

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  3. Version published to 10.1101/2020.05.28.121889v1 on bioRxiv