Abnormal upregulation of cardiovascular disease biomarker PLA2G7 induced by proinflammatory macrophages in COVID-19 patients

  1. Yang Li
  2. Yongzhong Jiang
  3. Yi Zhang
  4. Naizhe Li
  5. Qiangling Yin
  6. Linlin Liu
  7. Xin Lv
  8. Yan Liu
  9. Aqian Li
  10. Bin Fang
  11. Jiajia Li
  12. Hengping Ye
  13. Gang Yang
  14. Xiaoxian Cui
  15. Yang Liu
  16. Yuanyuan Qu
  17. Chuan Li
  18. Jiandong Li
  19. Dexin Li
  20. Zhongtao Gai
  21. Shiwen Wang
  22. Faxian Zhan
  23. Mifang Liang

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Abstract

High rate of cardiovascular disease (CVD) has been reported among patients with coronavirus disease 2019 (COVID-19). Importantly, CVD, as one of the comorbidities, could also increase the risks of the severity of COVID-19. Here we identified phospholipase A2 group VII (PLA2G7), a well-studied CVD biomarker, as a hub gene in COVID-19 though an integrated hypothesis-free genomic analysis on nasal swabs (n = 486) from patients with COVID-19. PLA2G7 was further found to be predominantly expressed by proinflammatory macrophages in lungs emerging with progression of COVID-19. In the validation stage, RNA level of PLA2G7 was identified in nasal swabs from both COVID-19 and pneumonia patients, other than health individuals. The positive rate of PLA2G7 were correlated with not only viral loads but also severity of pneumonia in non-COVID-19 patients. Serum protein levels of PLA2G7 were found to be elevated and beyond the normal limit in COVID-19 patients, especially among those re-positive patients. We identified and validated PLA2G7, a biomarker for CVD, was abnormally enhanced in COVID-19 at both nucleotide and protein aspects. These findings provided indications into the prevalence of cardiovascular involvements seen in patients with COVID-19. PLA2G7 could be a potential prognostic and therapeutic target in COVID-19.

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  1. Version published to 10.1038/s41598-021-85848-5
  2. ScreenIT

    SciScore for 10.1101/2020.08.16.20175505: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Human studies: The research protocol was approved by the human bioethics committee of the Chinese Center for Disease Control and Prevention, and all participants provided written informed consent.
    Consent: Human studies: The research protocol was approved by the human bioethics committee of the Chinese Center for Disease Control and Prevention, and all participants provided written informed consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisAfter removing outliners, the soft-thresholding power was then calculated, with the type of network set to signed.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collection: In brief, data were obtained from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) in July 2020 using the keyword “SARS-COV-2”.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Differentially expressed genes screening: When the batch-corrected count matrix of GSE152075 was compared in SARS-COV-2 positive cases with negative cases, DESeq2(19) was applied for differential expressed genes (DEGs).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Graph-based clustering was performed on the PCA-reduced data for clustering analysis with Seurat v.3.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Statistical analysis: R (version 4.0.2) was used for most analyses, with hub gene selection being performed using XGBoost and Scikit in Python (version 3.6).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: We detected the following sentences addressing limitations in the study:
    Nevertheless, our study has certain limitations. The expression of PLA2G7 was not validated in nasal swabs from the patients with severe COVID-19. In addition, the plasma levels of Lp-PLA2 in pneumonia patients were also not tested. Due to limited information of clinical traits of samples, confounding factors, such as pre-existing CVDs, which could increase the likelihood of PLA2G7 positivities, were not controlled. The in-house qRT-PCR assay of PLA2G7 was not optimized, leading to inadequate diagnostic performance. Most of the samples were retrospectively collected in Wuhan, China. It has been almost several months passed since all the samples were collected. The freshness of samples could also undermine the performance of the in-house PLA2G7 targeted qRT-PCR and commercial Lp-PLA2 ELISA toolkit. Taken together, we firstly identified proinflammatory monocytes-driven macrophages specific PLA2G7 in lungs, a typically biomarker of CVDs, which leaded to abnormal levels of plasma Lp-PLA2, was enhanced in COVID-19. Importantly, PLA2G7 was detected in nasal swabs from patients with pneumonia. Although levels of PLA2G7 was significantly elevated in severe pneumonia comparing to moderate pneumonia, there were no significant differences between that in COVID-19 and severe pneumonia. Moreover, increased levels of Lp-PLA2 in plasma could provide insights to higher mortality was seen in patients underlying comorbidities (e.g. hypertension, diabetes mellitus, cardiovascular disease) and s...

    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 10.1101/2020.08.16.20175505v1 on medRxiv