Persistent COVID-19 Symptoms Minimally Impact the Development of SARS-CoV-2-Specific T Cell Immunity

  1. Hengsheng Fang
  2. Adam D. Wegman
  3. Kianna Ripich
  4. Heather Friberg
  5. Jeffrey R. Currier
  6. Stephen J. Thomas
  7. Timothy P. Endy
  8. Adam T. Waickman

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Abstract

SARS-CoV-2 represents an unprecedented public health challenge. While the majority of SARS-CoV-2-infected individuals with mild-to-moderate COVID-19 resolve their infection with few complications, some individuals experience prolonged symptoms lasting for weeks after initial diagnosis. Persistent viral infections are commonly accompanied by immunologic dysregulation, but it is unclear if persistent COVID-19 impacts the development of virus-specific cellular immunity. To this end, we analyzed SARS-CoV-2-specific cellular immunity in convalescent COVID-19 patients who experienced eight days or fewer of COVID-19 symptoms or symptoms persisting for 18 days or more. We observed that persistent COVID-19 symptoms were not associated with the development of an overtly dysregulated cellular immune response. Furthermore, we observed that reactivity against the N protein from SARS-CoV-2 correlates with the amount of reactivity against the seasonal human coronaviruses 229E and NL63. These results provide insight into the processes that regulate the development of cellular immunity against SARS-CoV-2 and related human coronaviruses.

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  1. Version published to 10.3390/v13050916
  2. ScreenIT

    SciScore for 10.1101/2021.01.29.21250771: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was reviewed by the SUNY Upstate Medical University IRB, reviewed approved by the Western Institutional Review Board (IRB # 1587400), and performed under informed consent.
    Consent: This study was reviewed by the SUNY Upstate Medical University IRB, reviewed approved by the Western Institutional Review Board (IRB # 1587400), and performed under informed consent.
    Randomizationnot detected.
    BlindingSamples were de-identified following collection, and researchers conducting assays were blinded to clinical data until final comparative analysis.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Study design: Convalescent COVID-19 patients were recruited for this study at the SUNY Upstate Medical University Clinical Research Unit starting in March 2020 under the SUNY Upstate Convalescent Plasma Donor Program [27].
    Plasma Donor Program
    suggested: None
    Flow cytometry analysis was performed on a BD FACSAria II instrument and analyzed using FlowJo v10.7 software (Treestar).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    RNA, cDNA, and DNA during the library preparation process were quantified using Agilent Bioanalyzer, and final libraries were sequenced using a 75 cycle High Output NextSeq 500/550
    Agilent Bioanalyzer
    suggested: None
    Raw reads from FASTQ files were mapped to the human reference transcriptome (Ensembl, Home sapiens, GRCh38) using Kallisto [28] version 0.46.2.
    Kallisto
    suggested: (kallisto, RRID:SCR_016582)
    Transcript-level counts and abundance data were imported and summarized in R (version 4.0.2) using the TxImport package [29] and TMM normalized using the package EdgeR [30, 31].
    TxImport
    suggested: (tximport, RRID:SCR_016752)
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)
    Differential gene expression analysis performed using linear modeling and Bayesian statistics in the R package Limma [32].
    Limma
    suggested: (LIMMA, RRID:SCR_010943)
    Statistical analysis: Statistical analyses were performed using GraphPad Prism v8 Software (
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    (GraphPad Software, La Jolla, CA).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your 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: 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.

    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 10.1101/2021.01.29.21250771v1 on medRxiv