Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

  1. Bo Shen
  2. Xiao Yi
  3. Yaoting Sun
  4. Xiaojie Bi
  5. Juping Du
  6. Chao Zhang
  7. Sheng Quan
  8. Fangfei Zhang
  9. Rui Sun
  10. Liujia Qian
  11. Weigang Ge
  12. Wei Liu
  13. Shuang Liang
  14. Hao Chen
  15. Ying Zhang
  16. Jun Li
  17. Jiaqin Xu
  18. Zebao He
  19. Baofu Chen
  20. Jing Wang
  21. Haixi Yan
  22. Yufen Zheng
  23. Donglian Wang
  24. Jiansheng Zhu
  25. Ziqing Kong
  26. Zhouyang Kang
  27. Xiao Liang
  28. Xuan Ding
  29. Guan Ruan
  30. Nan Xiang
  31. Xue Cai
  32. Huanhuan Gao
  33. Lu Li
  34. Sainan Li
  35. Qi Xiao
  36. Tian Lu
  37. Yi Zhu
  38. Huafen Liu
  39. Haixiao Chen
  40. Tiannan Guo

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Abstract

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  1. Version published to 10.1016/j.cell.2020.05.032
  2. ScreenIT

    SciScore for 10.1101/2020.04.07.20054585: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study has been approved by the Ethical/Institutional Review Board of Taizhou Public Health Medical Center and Westlake University.
    RandomizationSerum samples of four patient groups from both training and validation cohorts were randomly distributed in eight different batches.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Digested peptides were cleaned-up with SOLAμ (Thermo Fisher Scientific™, San Jose, USA) following the manufacturer’s instructions, and lableled with TMTpro 16plex label reagents (Thermo Fisher Scientific™, San Jose, USA) as described previously.
    Thermo Fisher Scientific™
    suggested: (Thermo Fisher Scientific, RRID:SCR_008452)
    The resultant mass spectrometric data were analyzed using Proteome Discoverer (Version 2.4.1.15, Thermo Fisher Scientific) using a protein database composed of the Homo sapiens fasta database downloaded from UniprotKB on 07 Jan 2020 and the SARS-CoV-2 virus fasta downloaded from NCBI (version NC_045512.2).
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    UniprotKB
    suggested: (UniProtKB, RRID:SCR_004426)
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    From the training cohort, the important features were selected with mean decrease accuracy larger than 3 using random forest containing a thousand trees using R package randomForest (version 4.6.14) random forest analysis with 10-fold cross validation as binary classification of paired severe and non-severe group using combined differentially regulated proteins and metabolites features.
    randomForest
    suggested: (RandomForest Package in R, RRID:SCR_015718)
    The top Gene Ontology (GO) processes were enriched by Metascape web-based platform (Zhou et al., 2019).
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    The GO terms is enriched using the Cytoscape plug-in ClueGO (Bindea et al., 2009).
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    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.
    • 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 10.1101/2020.04.07.20054585v1 on medRxiv