Distinct immune responses in patients infected with influenza or SARS-CoV-2, and in COVID-19 survivors, characterised by transcriptomic and cellular abundance differences in blood

  1. Jelmer Legebeke
  2. Jenny Lord
  3. Rebekah Penrice-Randal
  4. Andres F. Vallejo
  5. Stephen Poole
  6. Nathan J. Brendish
  7. Xiaofeng Dong
  8. Catherine Hartley
  9. John W. Holloway
  10. Jane S. Lucas
  11. Anthony P. Williams
  12. Gabrielle Wheway
  13. Fabio Strazzeri
  14. Aaron Gardner
  15. James P.R. Schofield
  16. Paul J. Skipp
  17. Julian A. Hiscox
  18. Marta E. Polak
  19. Tristan W. Clark
  20. Diana Baralle

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Abstract

Background

The worldwide pandemic caused by SARS-CoV-2 has claimed millions of lives and has had a profound effect on global life. Understanding the pathogenicity of the virus and the body’s response to infection is crucial in improving patient management, prognosis, and therapeutic strategies. To address this, we performed functional transcriptomic profiling to better understand the generic and specific effects of SARS-CoV-2 infection.

Methods

Whole blood RNA sequencing was used to profile a well characterised cohort of patients hospitalised with COVID-19, during the first wave of the pandemic prior to the availability of approved COVID-19 treatments and who went on to survive or die of COVID-19, and patients hospitalised with influenza virus infection between 2017 and 2019. Clinical parameters between patient groups were compared, and several bioinformatic tools were used to assess differences in transcript abundances and cellular composition.

Results

The analyses revealed contrasting innate and adaptive immune programmes, with transcripts and cell subsets associated with the innate immune response elevated in patients with influenza, and those involved in the adaptive immune response elevated in patients with COVID-19. Topological analysis identified additional gene signatures that differentiated patients with COVID-19 from patients with influenza, including insulin resistance, mitochondrial oxidative stress and interferon signalling. An efficient adaptive immune response was furthermore associated with patient survival, while an inflammatory response predicted death in patients with COVID-19. A potential prognostic signature was found based on a selection of transcript abundances, associated with circulating immunoglobulins, nucleosome assembly, cytokine production and T cell activation, in the blood transcriptome of COVID-19 patients, upon admission to hospital, which can be used to stratify patients likely to survive or die.

Conclusions

The results identified distinct immunological signatures between SARS-CoV-2 and influenza, prognostic of disease progression and indicative of different targeted therapies. The altered transcript abundances associated with COVID-19 survivors can be used to predict more severe outcomes in patients with COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.12.21257086: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics and consent: The study was approved by the South Central - Hampshire A Research Ethics Committee (
    Consent: Patients gave written informed consent or consultee assent was obtained where patients were unable to give consent.
    Sex as a biological variablenot detected.
    RandomizationThe FluPOC study was a multicentre randomised controlled trial evaluating the clinical impact of mPOCT for influenza in hospitalised adult patients with acute respiratory illness, during influenza season, using the BioFire FilmArray platform with the Respiratory Panel 2.1 (28).
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Raw fastq files were trimmed to remove Illumina adapter sequences using Cutadapt v1.2.1 (29).
    Cutadapt
    suggested: (cutadapt, RRID:SCR_011841)
    Data QC and alignment: Quality control (QC) of read data was performed using FastQC (31) (v0.11.9) and compiled and visualised with MultiQC (32) (v1.5).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    The STAR index was created with STAR’s (33) (v2.7.6a) genomeGenerate function using GRCh38.primary_assembly.genome.fa and gencode.v34.annotation.gtf (34) (both downloaded from GENCODE), with –sjdbOverhang 149 and all other settings as default.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    STAR’s
    suggested: None
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    Systems immunology-based analysis of blood transcript modules: Blood transcript module (BTM) analysis was performed with molecular signatures derived from 5 vaccine trials (41) as a reference dataset, and BTM activity was calculated using the BTM package (41) (v1.015) in Python (42) (v3.7.2) using the normalized counts as input.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Unbiased gene clustering analysis: Gene co-expression analysis was performed with BioLayout (44) (v3.4) using a correlation value of 0.95, other settings were kept at default.
    BioLayout
    suggested: (BioLayout Express 3D, RRID:SCR_007179)
    Genes were subsequently analysed with ToppGene (45)
    ToppGene
    suggested: ( ToppGene Suite , RRID:SCR_005726)
    Differential gene expression analysis between patient groups: HTSeq (46) (v0.11.2) count was used to assign counts to RNA-seq reads in the Samtools sorted BAM file using GENCODE v34 annotation.
    HTSeq
    suggested: (HTSeq, RRID:SCR_005514)
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    EdgeR (47) (v3.30.3) was used for differential gene expression analysis with R (v4.0.2) in RStudio (v1.3.959).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    Metadata comparison plots were made with the R package ggplot2 (50) (3.3.2) and statistical testing with the R package ggpubr (51) (v0.4.0).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    ) assembled 537 genes differentially expressed (EdgeR, FDR < 0.5 and |log2 fold change > 1|) in blood taken on admission between patients with COVID-19 who either survived or died of COVID-19 within 30 days of admission to hospital.
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    ISRCTN14966673NANA
    ISRCTN17197293NANA

    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 10.1101/2021.05.12.21257086v1 on medRxiv