Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes

  1. Ruchi Jain
  2. Sathishkumar Ramaswamy
  3. Divinlal Harilal
  4. Mohammed Uddin
  5. Tom Loney
  6. Norbert Nowotny
  7. Hanan Alsuwaidi
  8. Rupa Varghese
  9. Zulfa Deesi
  10. Abdulmajeed Alkhajeh
  11. Hamda Khansaheb
  12. Alawi Alsheikh-Ali
  13. Ahmad Abou Tayoun

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Abstract

Characterizing key molecular and cellular pathways involved in COVID-19 is essential for disease prognosis and management. We perform shotgun transcriptome sequencing of human RNA obtained from nasopharyngeal swabs of patients with COVID-19, and identify a molecular signature associated with disease severity. Specifically, we identify globally dysregulated immune related pathways, such as cytokine-cytokine receptor signaling, complement and coagulation cascades, JAK-STAT, and TGF-β signaling pathways in all, though to a higher extent in patients with severe symptoms. The excessive release of cytokines and chemokines such as CCL2, CCL22, CXCL9 and CXCL12 and certain interferons and interleukins related genes like IFIH1, IFI44, IFIT1 and IL10 were significantly higher in patients with severe clinical presentation compared to mild and moderate presentations. Moreover, early induction of the TGF-β signaling pathway might be the primary cause of pulmonary fibrosis in patients with severe disease. Differential gene expression analysis identified a small set of regulatory genes that might act as strong predictors of patient outcome. Our data suggest that rapid transcriptome analysis of nasopharyngeal swabs can be a powerful approach to quantify host molecular response and may provide valuable insights into COVID-19 pathophysiology.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.28.316604: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Patient cohort and ethics statement: This study was approved by the Dubai Scientific Research Ethics Committee -Dubai Health Authority (approval number #DSREC-04/2020_02).
    Consent: The Ethics committee waived the requirement for informed consent since this study was part of a public health surveillance and outbreak investigation in the UAE.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: The sequencing read quality was checked using FastQC v0.11.815 and MultiQC v1.6.16 High quality reads (Q ≥ 30) were first mapped to rRNA sequences to remove potential rRNA reads using hisat v2-2.1.017 with the default parameter.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    The unmapped reads were then mapped to the SARS-CoV-2 genome (GenBank Accession number: NC_045512.2) using BWA v0.7.17.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    PCR duplicates were removed with Picard MarkDuplicates program v2.18.17.18 Samples with more than 6M reads aligned to the GRCh37 (hg19) were considered for further analysis.
    Picard MarkDuplicates
    suggested: None
    RNA sequencing data obtained from nasopharyngeal swabs of samples confirmed to be COVID-19 negative by RT-qPCR, hereafter referred to as controls (n = 32), were downloaded from GSE152075.11 The number of reads mapped to each gene in the genome (GRCh37) was calculated using the FeatureCounts program in the SubReads package v2.0.1.19 DESeq2 package v1.28.120 was applied to perform batch effects and normalization.
    FeatureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    In brief, DESeq2 uses the median of ratios method to normalize data and estimate size factors (which control for differences in the library size of the sequencing experiment).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Pathway enrichment analysis was performed using the clusterProfiler package v 3.16.021 to identify shared pathways among DEGs.
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    Heatmaps were generated using Morpheus22 and volcano plots were generated using VolcaNoseR package.
    VolcaNoseR
    suggested: None
    CPM values were used to compare expression between certain genes and violin plots were generated using GraphPad Prism v8.0.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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: 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.

    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. Version published to 10.1101/2020.09.28.316604v1 on bioRxiv