Blood Transcriptomes of Anti-SARS-CoV-2 Antibody-Positive Healthy Individuals Who Experienced Asymptomatic Versus Clinical Infection

  1. Petros P. Sfikakis
  2. Kleio-Maria Verrou
  3. Giannis Ampatziadis-Michailidis
  4. Ourania Tsitsilonis
  5. Dimitrios Paraskevis
  6. Efstathios Kastritis
  7. Evi Lianidou
  8. Paraskevi Moutsatsou
  9. Evangelos Terpos
  10. Ioannis Trougakos
  11. Vasiliki Chini
  12. Menelaos Manoloukos
  13. Panagiotis Moulos
  14. Georgios A. Pavlopoulos
  15. George Kollias
  16. Pantelis Hatzis
  17. Meletios A. Dimopoulos

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Abstract

The reasons behind the clinical variability of SARS-CoV-2 infection, ranging from asymptomatic infection to lethal disease, are still unclear. We performed genome-wide transcriptional whole-blood RNA sequencing, bioinformatics analysis and PCR validation to test the hypothesis that immune response-related gene signatures reflecting baseline may differ between healthy individuals, with an equally robust antibody response, who experienced an entirely asymptomatic (n=17) versus clinical SARS-CoV-2 infection (n=15) in the past months (mean of 14 weeks). Among 12.789 protein-coding genes analysed, we identified six and nine genes with significantly decreased or increased expression, respectively, in those with prior asymptomatic infection relatively to those with clinical infection. All six genes with decreased expression ( IFIT3, IFI44L, RSAD2, FOLR3, PI3, ALOX15 ), are involved in innate immune response while the first two are interferon-induced proteins. Among genes with increased expression six are involved in immune response ( GZMH, CLEC1B, CLEC12A ), viral mRNA translation ( GCAT ), energy metabolism ( CACNA2D2 ) and oxidative stress response ( ENC1 ). Notably, 8/15 differentially expressed genes are regulated by interferons. Our results suggest that subtle differences at baseline expression of innate immunity-related genes may be associated with an asymptomatic disease course in SARS-CoV-2 infection. Whether a certain gene signature predicts, or not, those who will develop a more efficient immune response upon exposure to SARS-CoV-2, with implications for prioritization for vaccination, warrant further study.

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  1. Version published to 10.3389/fimmu.2021.746203
  2. ScreenIT

    SciScore for 10.1101/2021.04.19.21255748: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The protocol was approved by the Ethics and Bioethics Committee of the School of Medicine, NKUA (protocol #312/02-06-2020) and study participants provided written informed consent.
    Consent: The protocol was approved by the Ethics and Bioethics Committee of the School of Medicine, NKUA (protocol #312/02-06-2020) and study participants provided written informed consent.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Blood collection and anti-SARS-CoV-2 antibody testing: Blood samples were collected from members of the NKUA, Athens, Greece in June–November 2020.
    anti-SARS-CoV-2
    suggested: None
    All plasma samples were analyzed as previously described [4] using, a) the CE-IVD Roche Elecsys® Anti-SARS-CoV-2 test, an electrochemiluminescence immunoassay (ECLIA) for the detection of total antibodies (IgG, IgM, and IgA; pan-Ig) to SARS-CoV-2 N-protein (Roche Diagnostics GmbH, Mannheim, Germany), and b) the CE-IVD Roche Elecsys® Anti-SARS-CoV-2 S, an ECLIA for the quantitative determination of antibodies (including IgGs) to the SARS-CoV-2 S-protein RBD (Roche Diagnostics)
    SARS-CoV-2 N-protein
    suggested: None
    Software and Algorithms
    SentencesResources
    The quality of FASTQ files was assessed using FastQC (version 0.11.9) [21].
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    The reads were mapped to the GRCh38 reference human genome using STAR, as part of a pipeline provided by Lexogen and BlueBee.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    Raw bam files, one for each sample, were summarized to a 3’UTR read counts table, using the Bioconductor package GenomicRanges [22], through metaseqR2 [23].
    GenomicRanges
    suggested: (GenomicRanges, RRID:SCR_000025)
    The gene counts table was normalized for inherent systematic or experimental biases (e.g., sequencing depth, gene length, GC content bias) using the Bioconductor package EDASeq [24].
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    EDASeq
    suggested: (EDASeq, RRID:SCR_006751)
    Differential gene expression: The resulting gene counts table was subjected to differential expression analysis (DEA) to compare individuals with a history of asymptomatic versus clinical (“symptomatic”) infection using the Bioconductor packages DESeq [26], edgeR [27], NOISeq[28]
    DESeq
    suggested: (DESeq, RRID:SCR_000154)
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    , limma [29], NBPSeq [30]
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    , baySeq [31].
    baySeq
    suggested: (baySeq, RRID:SCR_012795)
    DAVID analysis [32] was performed for the increased and decreased genes, both for enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and for biological processes [Gene Ontology (GO)].
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    For the prediction of enriched regulons in asymptomatic disease we used the TRRUST (v2) reference transcription factor (TF)–target interaction database [33] and enrichR [34] focusing on the ChEA prediction with the increased genes in asymptomatic disease as input.
    enrichR
    suggested: (Enrichr, RRID:SCR_001575)
    ChEA
    suggested: (ChEA, RRID:SCR_005403)

    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.
    • 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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  3. Version published to 10.1101/2021.04.19.21255748 on medRxiv