Protein Posttranslational Signatures Identified in COVID-19 Patient Plasma

  1. Pavan Vedula
  2. Hsin-Yao Tang
  3. David W. Speicher
  4. Anna Kashina
  5. The UPenn COVID Processing Unit

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a highly contagious virus of the coronavirus family that causes coronavirus disease-19 (COVID-19) in humans and a number of animal species. COVID-19 has rapidly propagated in the world in the past 2 years, causing a global pandemic. Here, we performed proteomic analysis of plasma samples from COVID-19 patients compared to healthy control donors in an exploratory study to gain insights into protein-level changes in the patients caused by SARS-CoV-2 infection and to identify potential proteomic and posttranslational signatures of this disease. Our results suggest a global change in protein processing and regulation that occurs in response to SARS-CoV-2, and the existence of a posttranslational COVID-19 signature that includes an elevation in threonine phosphorylation, a change in glycosylation, and a decrease in arginylation, an emerging posttranslational modification not previously implicated in infectious disease. This study provides a resource for COVID-19 researchers and, longer term, and will inform our understanding of this disease and its treatment.

Article activity feed

  1. Version published to 10.3389/fcell.2022.807149
  2. ScreenIT

    SciScore for 10.1101/2021.12.15.472822: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data Analysis: MS/MS data were analyzed using Thermo Proteome Discoverer v2.4.
    Thermo Proteome Discoverer
    suggested: (Resource Identification Portal, RRID:SCR_004098)
    Data Analysis: Peptide sequences were identified using MaxQuant 1.6.17.0 (Ref: PMID 19029910).
    MaxQuant
    suggested: (MaxQuant, RRID:SCR_014485)
    Two separate pFind searches were performed: Control (containing all 7 control samples) and Disease (containing all 6 samples).
    pFind
    suggested: (pFind, RRID:SCR_003011)
    Data deposition: All mass spectrometry raw data have been uploaded to the MassIVE public repository (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) with the accession MSV000088382, and the ProteomeXchange repository (http://www.proteomecentral.proteomexchange.org/cgi/) with the accession PXD029756.
    https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp
    suggested: (Mass spectrometry Interactive Virtual Environment (MassIVE, RRID:SCR_013665)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    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 10.1101/2021.12.15.472822v1 on bioRxiv