Changes of urinary proteomic before and after QIV and COVID-19 vaccination

  1. Xuanzhen Pan
  2. Yongtao Liu
  3. Yijin Bao
  4. Lilong Wei
  5. Youhe Gao

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

We first collected a young people’s urine samples cohort of quadrivalent influenza vaccine. Urine protein at 24 hours after vaccination was enriched in immune-related pathways, though the specific pathways varied. Perhaps because different people may be in a previous life encountered some of the viruses in the vaccine, the second immunization was triggered. Or everyone has a different constitution, exposure to the same virus triggering different immunity. We then collected urine samples from several uninfected SARS-CoV-2 young people before and after the first, second, and third doses of the COVID-19 vaccine. We found that the differential protein compared between after the second dose (24h) and before the second dose enriched pathways were involved in regulated exocytosis and immune-related pathways, indicating not first exposure to antigen. Surprisingly, the urine differential protein-enriched pathways before and after the first dose were similar to those before and after the second dose. We assume that although the volunteers have not been infected with SARS-CoV-2, they might have been exposed to other coimmunogenic coronaviruses. 2~4h after the third vaccination, the differentially expressed protein also enriched regulated exocytosis and immune-related pathways, indicating that the body has triggered the immune response in a very short time after vaccination, and urine proteome is a good window to monitor the changes of human immune function.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2022.03.25.485748: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study’s ethics approval was approved by the China-Japan Friendship Hospital review boards, and each participant signed informed consent.
    Consent: This study’s ethics approval was approved by the China-Japan Friendship Hospital review boards, and each participant signed informed consent.
    Sex as a biological variableMass spectrometry data processing: The Ms data of QIV cohort and COVID-19 (1) male cohort(DDA MS data) is performed label-free quantitative comparisons.
    RandomizationMissing values were assumed to be biased toward low abundance proteins that were below the MS detection limit, referred to as “missing not at random”, an assumption that is frequently made in proteomics studies12,13.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Base peak chromatograms were inspected visually in Xcalibur Qual Brower version 4.0.27.19(Thermo Fisher Scientific).
    Xcalibur Qual Brower
    suggested: None
    RAW files were processed by MaxQuant version 1.6.17.0 (http://www.maxquant.org) using default parameters unless otherwise specified5–7.
    MaxQuant
    suggested: (MaxQuant, RRID:SCR_014485)
    Data processing was using Perseus version 1.6.14.0 (
    Perseus
    suggested: (Perseus, RRID:SCR_015753)
    To generate a spectral library, ten DDA raw files were first searched by Proteome Discoverer (version 2.1; Thermo Scientific) with SEQUEST HT against the Uniprot human sequence database (November 27, 2020; 196,211 sequences).
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    The differential proteins were analyzed by Gene Ontology (GO) based on biological processes(BP), cellular components(CC), and molecular functions(MF) using DAVID18, and biological process from WebGestalt (
    WebGestalt
    suggested: None
    (http://www.webgestalt.org).
    http://www.webgestalt.org
    suggested: (WebGestalt: WEB-based GEne SeT AnaLysis Toolkit, RRID:SCR_006786)
    Protein interaction network analysis was performed using the STRING database (https://string-db.org/cgi/input.pl) and visualized by Cytoscape (V.3.7.1)19 and OmicsBean work-bench (http://www.omicsbean.cn).
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    http://www.omicsbean.cn
    suggested: (OmicsBean, RRID:SCR_016322)

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2022.03.25.485748v1 on bioRxiv