Proteomic and metabolomic signatures associated with the immune response in healthy individuals immunized with an inactivated SARS-CoV-2 vaccine

  1. Yi Wang
  2. Xiaoxia Wang
  3. Laurence Don Wai Luu
  4. Shaojin Chen
  5. Fu Jin
  6. Shufang Wang
  7. Xiaolan Huang
  8. Licheng Wang
  9. Xiaocui Zhou
  10. Xi Chen
  11. Xiaodai Cui
  12. Jieqiong Li
  13. Jun Tai
  14. Xiong Zhu

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Abstract

CoronaVac (Sinovac), an inactivated vaccine for SARS-CoV-2, has been widely used for immunization. However, analysis of the underlying molecular mechanisms driving CoronaVac-induced immunity is still limited. Here, we applied a systems biology approach to understand the mechanisms behind the adaptive immune response to CoronaVac in a cohort of 50 volunteers immunized with 2 doses of CoronaVac. Vaccination with CoronaVac led to an integrated immune response that included several effector arms of the adaptive immune system including specific IgM/IgG, humoral response and other immune response, as well as the innate immune system as shown by complement activation. Metabolites associated with immunity were also identified implicating the role of metabolites in the humoral response, complement activation and other immune response. Networks associated with the TCA cycle and amino acids metabolic pathways, such as phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and glycine, serine and threonine metabolism were tightly coupled with immunity. Critically, we constructed a multifactorial response network (MRN) to analyze the underlying interactions and compared the signatures affected by CoronaVac immunization and SARS-CoV-2 infection to further identify immune signatures and related metabolic pathways altered by CoronaVac immunization. These results suggest that protective immunity against SARS-CoV-2 can be achieved via multiple mechanisms and highlights the utility of a systems biology approach in defining molecular correlates of protection to vaccination.

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  1. ScreenIT

    SciScore for 10.1101/2021.07.21.21260959: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Written informed consent was obtained from each subject and protocols were approved by Institutional Review Boards of Sayan People’s Hospital.
    IRB: Written informed consent was obtained from each subject and protocols were approved by Institutional Review Boards of Sayan People’s Hospital.
    Sex as a biological variablenot detected.
    RandomizationPlasma proteomics: Ten of the fifty subjects were randomly selected for plasma proteomic analysis.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Plates were coated with either SARS-CoV-2 recombinant antigens or mouse anti-human IgM monoclonal antibody.
    anti-human IgM
    suggested: None
    Software and Algorithms
    SentencesResources
    To remove highly abundant interfering proteins in human plasma, a multiple-affinity removal system liquid chromatography (LC) column (High Select™ Top14 Abundant Protein Depletion Mini Spin Columns; Thermo Fisher Technologies, Santa Clara, CA, USA) was used.
    Thermo Fisher Technologies
    suggested: None
    The resultant mass spectrometry data were analyzed using Maxquant (Version 1.6.17) and the protein search database used was the Homo sapiens FASTA database downloaded from UniprotKB (UP000005640.fasta).
    FASTA
    suggested: (FASTA, RRID:SCR_011819)
    UniprotKB
    suggested: (UniProtKB, RRID:SCR_004426)
    All UPLC-MS/MS methods used the ACQUITY 2D UPLC system (Waters, Milford, MA, USA) and Q-Exactive Quadrupole-Orbitrap (Thermo Fisher Scientific™, San Jose, USA) and TripleTOF 5600+ (AB SCIEX, MA, USA) with ESI source and mass analyzer.
    Thermo Fisher Scientific™
    suggested: (Thermo Fisher Scientific, RRID:SCR_008452)
    Open database sources including KEGG and MetaboAnalyst, Human Metabolome Database, were used to identify metabolic pathways.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Orthogonal partial least squares discrimination analysis (OPLS-DA) and partial least squares-discriminate analysis (PLS-DA) was conducted using MetaboAnalyst 5.0 (http://www.metaboanalyst.ca/MetaboAnalyst/).
    MetaboAnalyst
    suggested: (MetaboAnalyst, RRID:SCR_015539)
    Metscape was used to build the network of metabolites, analyze the correlation of these different metabolites and visualize the networks.
    Metscape
    suggested: ( Metscape , RRID:SCR_014687)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 41. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

    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 10.1101/2021.07.21.21260959v1 on medRxiv