Proteomic and metabolomic investigation of serum lactate dehydrogenase elevation in COVID‐19 patients

  1. Haixi Yan
  2. Xiao Liang
  3. Juping Du
  4. Zebao He
  5. Yu Wang
  6. Mengge Lyu
  7. Liang Yue
  8. Fangfei Zhang
  9. Zhangzhi Xue
  10. Luang Xu
  11. Guan Ruan
  12. Jun Li
  13. Hongguo Zhu
  14. Jiaqin Xu
  15. Shiyong Chen
  16. Chao Zhang
  17. Dongqing Lv
  18. Zongmei Lin
  19. Bo Shen
  20. Yi Zhu
  21. Biyun Qian
  22. Haixiao Chen
  23. Tiannan Guo

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Abstract

Serum lactate dehydrogenase (LDH) has been established as a prognostic indicator given its differential expression in COVID‐19 patients. However, the molecular mechanisms underneath remain poorly understood. In this study, 144 COVID‐19 patients were enrolled to monitor the clinical and laboratory parameters over 3 weeks. Serum LDH was shown elevated in the COVID‐19 patients on admission and declined throughout disease course, and its ability to classify patient severity outperformed other biochemical indicators. A threshold of 247 U/L serum LDH on admission was determined for severity prognosis. Next, we classified a subset of 14 patients into high‐ and low‐risk groups based on serum LDH expression and compared their quantitative serum proteomic and metabolomic differences. The results showed that COVID‐19 patients with high serum LDH exhibited differentially expressed blood coagulation and immune responses including acute inflammatory responses, platelet degranulation, complement cascade, as well as multiple different metabolic responses including lipid metabolism, protein ubiquitination and pyruvate fermentation. Specifically, activation of hypoxia responses was highlighted in patients with high LDH expressions. Taken together, our data showed that serum LDH levels are associated with COVID‐19 severity, and that elevated serum LDH might be consequences of hypoxia and tissue injuries induced by inflammation.

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  1. Version published to 10.1002/pmic.202100002
  2. Version published to 10.1101/2021.01.10.21249333v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.10.21249333: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study is approved by the institutional medical ethics review board of Taizhou Hospital of Zhejiang Province and Westlake University.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The proteomics data were analyzed with Proteome Discoverer (Version 2.4.1.15, Thermo Fisher Scientific).
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    Statistical analysis: Statistical clinical data analyses were performed using SPSS software (version 22.0).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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.

  4. Version published to 10.1101/2021.01.10.21249333v1 on medRxiv