Metabolic Snapshot of Plasma Samples Reveals New Pathways Implicated in SARS-CoV-2 Pathogenesis

  1. Oihane E. Albóniga
  2. Daniel Jiménez
  3. Matilde Sánchez-Conde
  4. Pilar Vizcarra
  5. Raquel Ron
  6. Sabina Herrera
  7. Javier Martínez-Sanz
  8. Elena Moreno
  9. Santiago Moreno
  10. Coral Barbas
  11. Sergio Serrano-Villar

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Abstract

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  1. Version published to 10.1021/acs.jproteome.1c00786
  2. ScreenIT

    SciScore for 10.1101/2021.09.29.462326: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Study approval: The study was carried out at the Ramón and Cajal University Hospital in Madrid (Spain) and was approved by the local Research Ethics Committee (ceic.hrc(at)salud.madrid.org,approval number 095/20).
    Consent: All subject unable to provide informed consent or witnessed oral consent with written consents by a representative were excluded.
    Sex as a biological variablenot detected.
    RandomizationThe prepared QCs were analyzed at the beginning of the run to condition the CE system and then every seven randomized samples to reduce any time-related effect.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: Then, the supervised methods PLS-DA and OPLS-DA were performed followed by model validation.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Then, raw data were aligned and processed with MassHunter Profinder software (version 10.0 SP1)
    MassHunter Profinder
    suggested: None
    The resulting list was imported in Microsoft Excel, and the data matrix was filtered before statistical analysis by removing metabolites with a percentage of coefficient of variation (% CV) greater than 30% in the QC samples.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    ), MATLAB software (The MathWorks, Maticks, MA, USA), MetaboAnalyst 5.0 and SPSS version 24 (IBM SPSS Statistics) for different purposes.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    MetaboAnalyst
    suggested: (MetaboAnalyst, RRID:SCR_015539)
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    This tool joins several databases, which are available online, such as METLIN (47), LIPIDMAPS (48), and KEGG (49), making the identification task faster and easier.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study is also subject to some limitations. First, the samples were collected during the first COVID-19 wave in Madrid. It is unknown yet whether the emerging SARS-CoV-2 variants could lead to different metabolic consequences. Second, as expected, cases in the severe group were older and had more comorbidities than milder cases, so we considered potential confounders in our statistical approach. Third, in the subgroup analyses separated by clinical severity, the statistical power to detect differences in metabolite abundances was lower due to the smaller sample sizes. In summary, in this work examining for the first time the metabolic changes associated with COVID-19 by CE-MS, we report the discovery of new plasma biomarkers for COVID-19 that provide mechanistic explanations for the clinical consequences of SARS-CoV-2, including mitochondrial and liver dysfunction as a consequence of hypoxemia (citrulline, citrate and BAIBA), energy production and amino acid catabolism (L-glycine, L-alanine, L-serine, L-proline, L-aspartic acid and L-histidine), and endothelial dysfunction and thrombosis (citrulline, L-ADMA, 2-AB, and Neu5Ac), and we found interconnections between these pathways (Figure 5). These biomarkers deserve further attention as biomarkers of SARS-CoV-2 susceptibility and COVID-19 clinical severity and as potential targets for interventions.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  3. Version published to 10.1101/2021.09.29.462326v1 on bioRxiv