Multi-Omic Profiling of Plasma Identify Biomarkers and Pathogenesis of COVID-19 in Children

  1. Chong Wang
  2. Xufang Li
  3. Wanshan Ning
  4. Sitang Gong
  5. Fengxia Yang
  6. Chunxiao Fang
  7. Yu Gong
  8. Di Wu
  9. Muhan Huang
  10. Yujie Gou
  11. Shanshan Fu
  12. Yujie Ren
  13. Ruyi Yang
  14. Yang Qiu
  15. Yu Xue
  16. Yi Xu
  17. Xi Zhou

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Abstract

Although children usually develop less severe disease responding to COVID-19 than adults, little is known about the pathogenesis of COVID-19 in children. Herein, we conducted the plasma proteomic and metabolomic profiling of a cohort of COVID-19 pediatric patients with mild symptoms. Our data show that numerous proteins and metabolites involved in immune as well as anti-inflammatory processes were up-regulated on a larger scale in children than in adults. By developing a machine learning-based pipeline, we prioritized two sets of biomarker combinations, and identified 5 proteins and 5 metabolites as potential children-specific COVID-19 biomarkers. Further study showed that these identified metabolites not only inhibited the expression of pro-inflammatory factors, but also suppressed coronaviral replication, implying that these factors played key roles in protecting pediatric patients from both viral infection and infection-induced inflammation. Together, our study uncovered a protective mechanism responding to COVID-19 in children, and sheds light on potential therapies.

Teaser

Anti-inflammatory metabolites were highly elevated in the plasma of COVID-19 pediatric patients with mild symptoms.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.04.21252876: (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

    Experimental Models: Cell Lines
    SentencesResources
    At 12 hr post infection, the infected L2 cells were collected and total cellular RNAs were extracted.
    L2
    suggested: BCRC Cat# 60276, RRID:CVCL_0383)
    Software and Algorithms
    SentencesResources
    The sample was then fractionated into fractions by high pH reverse-phase HPLC using Agilent 300Extend C18 column (5 μm particles, 4.6 mm ID, 250 mm length).
    Agilent
    suggested: (Agilent Bravo NGS, RRID:SCR_019473)
    Protein database search: MS/MS raw data were analyzed with Proteome Discoverer (v2.4.1.15) using the Andromeda database search algorithm.
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    The reference database contained 20,380 Swiss-Prot/reviewed human protein sequences downloaded from the UniProt database (https://www.uniprot.org/proteomes/UP000005640, on November 15, 2019), and reverse decoy sequences were generated.
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    Metabolite structure analysis referred to some existing mass spectrometry public databases, mainly including massbank (http://www.massbank.jp/), knapsack (http://kanaya.naist.jp/knapsack/)
    massbank
    suggested: (MassBank, RRID:SCR_015535)
    HMDB (http://www.hmdb.ca/), and Metlin (http://metlin.scripps.edu/index.php).
    HMDB
    suggested: (HMDB, RRID:SCR_007712)
    Metlin
    suggested: (METLIN, RRID:SCR_010500)
    The statistical analyses were conducted using the ttest_ind function in scipy.stats.
    scipy
    suggested: (SciPy, RRID:SCR_008058)
    GO annotation files (released on 03 January 2020) were downloaded from the Gene Ontology Consortium Web site (http://www.geneontology.org/), and in total we obtained 19,288 human proteins annotated with at least one GO biological process term.
    http://www.geneontology.org/
    suggested: (Mouse Genome Informatics: The Gene Ontology Project, RRID:SCR_006447)
    The PLR algorithm was implemented in Python 3.7 with Scikit-learn 0.22.1.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Based on the pathway annotations in KEGG, working model included the 13 CC-specific DEPs and 25 CC-specific DEMs mainly involved in 6 KEGG pathways, including platelet activation, complement and coagulation cascades,
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    Results from OddPub: Thank you for sharing your code and 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 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

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  2. Version published to 10.1101/2021.03.04.21252876 on medRxiv
  3. Version published to 10.7150/thno.61832