Longitudinal Peripheral Blood Transcriptional Analysis Reveals Molecular Signatures of Disease Progression in COVID-19 Patients

  1. Qihong Yan
  2. Pingchao Li
  3. Xianmiao Ye
  4. Xiaohan Huang
  5. Bo Feng
  6. Tianxing Ji
  7. Zhilong Chen
  8. Feng Li
  9. Yudi Zhang
  10. Kun Luo
  11. Fengjuan Chen
  12. Xiaoneng Mo
  13. Jianhua Wang
  14. Liqiang Feng
  15. Fengyu Hu
  16. Chunliang Lei
  17. Linbing Qu
  18. Ling Chen

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Abstract

Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with some patients developing severe illness or even death. Disease severity has been associated with increased levels of proinflammatory cytokines and lymphopenia. To elucidate the atlas of peripheral immune response and pathways that might lead to immunopathology during COVID-19 disease course, we performed a peripheral blood RNA sequencing analysis of the same patient’s samples collected from symptom onset to full recovery. We found that PBMCs at different disease stages exhibited unique transcriptome characteristics. We observed that SARS-CoV-2 infection caused excessive release of inflammatory cytokines and lipid mediators as well as an aberrant increase of low-density neutrophils. Further analysis revealed an increased expression of RNA sensors and robust IFN-stimulated genes expression but a repressed type I IFN production. SARS-CoV-2 infection activated T and B cell responses during the early onset but resulted in transient adaptive immunosuppression during severe disease state. Activation of apoptotic pathways and functional exhaustion may contribute to the reduction of lymphocytes and dysfunction of adaptive immunity, whereas increase in IL2, IL7, and IL15 may facilitate the recovery of the number and function of lymphocytes. Our study provides comprehensive transcriptional signatures of peripheral blood response in patients with moderate COVID-19.

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  1. Version published to 10.4049/jimmunol.2001325
  2. ScreenIT

    SciScore for 10.1101/2020.05.05.20091355: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All patients signed informed consent for this study.
    IACUC: This study was approved by Review Committee of Guangzhou Eighth People’s Hospital of Guangzhou Medical University.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableHuman Subjects and Ethics: Peripheral blood mononuclear cells (PBMCs) from 4 female COVID-19 patients were obtained from Guangzhou Eighth People’s Hospital of Guangzhou Medical University.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Briefly, ribosomal RNAs were depleted using the (QIA seq FastSlect-rRNA HRM KIT, QIAGEN) according to the manufacturer’s instructions, Ribosomal RNA depletion was confirmed by using Agilent Bioanalyzer analysis and noting the absence of ribosomal peaks.
    Agilent Bioanalyzer
    suggested: None
    Pre-Processing of the Raw RNA-seq Data: Raw RNA-seq reads were filtered according to their base qualities, read sequences were trimmed at 3’end after reaching a 2-base sliding window with PHRED quality score lower than 20.
    PHRED
    suggested: (Phred, RRID:SCR_001017)
    Following filtering, Illumina adapter sequences at 3’end were removed using Trimmomatic v0.36 (58).
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Next, the trimmed reads were mapped to the human (hg38) and SARS-COV-2 viral (Wuhan-Hu-1) reference genomes (3) using HISAT v2.1 (59) with corresponding gene annotations (Gencode GRCh37/V32 for the human genome) with default settings RF, respectively.
    HISAT
    suggested: (HISAT2, RRID:SCR_015530)
    Total counts per mapped gene were determined using featureCounts function in SubReads package v1.5.3 (60) with default parameter.
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    SubReads
    suggested: None
    RNA-seq Data Analysis: Raw counts matrix obtained from featureCounts was used as input for differentially expression gene analysis with the bioconductor package edgeR v3.28 (61) or DESeq2 v1.26 (62) in R v3.6.
    bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    2C), and normalized using the DESeq2 method to remove the library-specific artefacts.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    We inferred the immune cell quantities in each blood sample using the CIBERSORT server (https://cibersortx.stanford.edu/).
    CIBERSORT
    suggested: (CIBERSORT, RRID:SCR_016955)
    Timeseries-Based Gene Expression Pattern Analysis and Gene Ontology (GO) Enrichment Analysis: To explore the gene expression pattern of the DEGs, we applied the R package Mfuzz v2.46 (63)for time-series analysis.
    Mfuzz
    suggested: (Mfuzz, RRID:SCR_000523)
    Here, we aligned the filtered reads (see in Pre-processing of the raw RNA-seq data) against reference V(D)J genes that download from IMGT (http://www.imgt.org/).
    http://www.imgt.org/
    suggested: (IMGT - the international ImMunoGeneTics information system, RRID:SCR_012780)
    Statistical analysis: All analyses were conducted by Prism v.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    8 (GraphPad Software, La Jolla, CA, USA).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your code and 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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT03314935Active, not recruitingA Phase 1/2 Study of INCB001158 in Combination With Chemothe…

    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.

  3. Version published to 10.1101/2020.05.05.20091355 on medRxiv