Urine Proteome of COVID-19 Patients

  1. Yanchang Li
  2. Yihao Wang
  3. Huiying Liu
  4. Wei Sun
  5. Baoqing Ding
  6. Yinghua Zhao
  7. Peiru Chen
  8. Li Zhu
  9. Zhaodi Li
  10. Naikang Li
  11. Lei Chang
  12. Hengliang Wang
  13. Changqing Bai
  14. Ping Xu

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Abstract

The atypical pneumonia (COVID-19) caused by SARS-CoV-2 is an ongoing pandemic and a serious threat to global public health. The COVID-19 patients with severe symptoms account for a majority of mortality of this disease. However, early detection and effective prediction of patients with mild to severe symptoms remains challenging. In this study, we performed proteomic profiling of urine samples from 32 healthy control individuals and 6 COVID-19 positive patients (3 mild and 3 severe). We found that urine proteome samples from the mild and severe COVID-19 patients with comorbidities can be clearly differentiated from healthy proteome samples based on the clustering analysis. Multiple pathways have been compromised after the COVID-19 infection, including the dysregulation of immune response, complement activation, platelet degranulation, lipoprotein metabolic process and response to hypoxia. We further validated our finding by directly comparing the same patients’ urine proteome after recovery. This study demonstrates the COVID-19 pathophysiology related molecular alterations could be detected in the urine and the potential application of urinary proteome in auxiliary diagnosis, severity determination and therapy development of COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.02.20088666: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All participants have provided signed informed consent and samples were collected with ethics approval from institutional review board (IRB) from the Fifth Medical Center of Chinese PLA General Hospital and Beijing Proteome Research Center.
    IRB: All participants have provided signed informed consent and samples were collected with ethics approval from institutional review board (IRB) from the Fifth Medical Center of Chinese PLA General Hospital and Beijing Proteome Research Center.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableAmong the six analyzed samples, two of them are female.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data processing and label-free quantification: The raw files were searched with MaxQuant software (v1.5.3.0) against the database composed of Human fasta downloaded from Swiss-Prot (version released in 2020.02) and the SARS-CoV-2 virus fasta downloaded from NCBI (RefSeq GCF_009858895.2).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    The peptides were quantified by the peak area derived from their MS1 intensity with MaxQuant software [39].
    MaxQuant
    suggested: (MaxQuant, RRID:SCR_014485)
    Differential proteins were filtered using R package limma (version 3.34.9).
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Proteins were clustered using R package mFuzz (version 2.46.0) into 16 significant discrete clusters.
    mFuzz
    suggested: (Mfuzz, RRID:SCR_000523)
    Pathway analyses: The function of differential proteins was analyzed in David Bioinformatics Resources (https://david.ncifcrf.gov/) and Human Protein Atlas (http://www.proteinatlas.org/
    http://www.proteinatlas.org/
    suggested: (HPA, RRID:SCR_006710)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Given the possibility of using proteome as a diagnostic tool we have shown, we are also aware of several limitations in our COVID-19 urine proteomics study. First, only limited number of COVID-19 patient samples were included in our proteomics study, future study to include more samples will likely mitigate the possible sampling bias. Second, the current COVID-19 patients were only categorized as M-COVID and S-COVID. We could not obtain intermediate type of COVID-19 patient samples to improve the resolution to predict the trend and progress of atypical pneumonia caused by SARS-CoV-2 due to the limited number of patients. Third, the batch effect of the sample processing and proteomics analysis may cause some deviations. Some healthy control datasets were generated before, though with the similar experiment processes. We recommended that the urine proteomics researches of the healthy, COVID-19 and their corresponding recovery samples were performed meanwhile if possible. Fourth, patients were subjected to different antiviral drug treatments, compounded with their age, preexisting health conditions as well as wide range of days of onset symptoms, it might cause some bias to the conclusion at this stage. Altogether, our data demonstrate that a urine proteome-based proteomics study can reliably and sensitively differentiate COVID-19 patients from healthy people. It might be able to serve as a powerful tool to help scientists and clinicians fight the COVID-19 pandemic.

    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.

  2. Version published to 10.1101/2020.05.02.20088666v1 on medRxiv
  3. Version published to 10.1016/j.urine.2021.02.001