Small-molecule metabolome identifies potential therapeutic targets against COVID-19

  1. Sean Bennet
  2. Martin Kaufmann
  3. Kaede Takami
  4. Calvin Sjaarda
  5. Katya Douchant
  6. Emily Moslinger
  7. Henry Wong
  8. David E. Reed
  9. Anne K. Ellis
  10. Stephen Vanner
  11. Robert I. Colautti
  12. Prameet M. Sheth

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Abstract

Respiratory viruses are transmitted and acquired via the nasal mucosa, and thereby may influence the nasal metabolome composed of biochemical products produced by both host cells and microbes. Studies of the nasal metabolome demonstrate virus-specific changes that sometimes correlate with viral load and disease severity. Here, we evaluate the nasopharyngeal metabolome of COVID-19 infected individuals and report several small molecules that may be used as potential therapeutic targets. Specimens were tested by qRT-PCR with target primers for three viruses: Influenza A (INFA), respiratory syncytial virus (RSV), and SARS-CoV-2, along with unaffected controls. The nasopharyngeal metabolome was characterized using an LC–MS/MS-based screening kit capable of quantifying 141 analytes. A machine learning model identified 28 discriminating analytes and correctly categorized patients with a viral infection with an accuracy of 96% (R 2  = 0.771, Q 2  = 0.72). A second model identified 5 analytes to differentiate COVID19-infected patients from those with INFA or RSV with an accuracy of 85% (R 2  = 0.442, Q 2  = 0.301). Specifically, Lysophosphatidylcholines-a-C18:2 (LysoPCaC18:2) concentration was significantly increased in COVID19 patients ( P  < 0.0001), whereas beta-hydroxybutyric acid, Methionine sulfoxide, succinic acid, and carnosine concentrations were significantly decreased ( P  < 0.0001). This study demonstrates that COVID19 infection results in a unique nasopharyngeal metabolomic signature with carnosine and LysoPCaC18:2 as potential therapeutic targets.

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  1. Version published to 10.1038/s41598-022-14050-y
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    SciScore for 10.1101/2021.06.18.21259150: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics: All experimental protocols were approved by and conducted in accordance with the Queen’s University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (HSREB Files 6029794, 6029811).
    Consent: Under Ontario’s Personal Health Information Protection Act, all patients have the right to withhold or withdraw their consent for the use, access or disclosure of their Personal Health Information and patients are not disadvantaged if they refuse to participate.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    BlindingSamples were anonymized and de-identified so researchers were blind to the identity of the patients.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A limitation of our study was that extensive validation of the TMIC Prime kit for use with VTM was not conducted, including evaluation of matrix effects arising from VTM. While recovery of most synthetic metabolites was demonstrated at a single level in VTM, accuracy may have been affected by matrix interferences. For the purposes of this pilot study, we used the TMIC Prime kit as a rapid screening method to evaluate broad metabolite profiles in patients with respiratory diseases that will form the basis of more refined assays for individual metabolites in future studies. While internal standards corrected for recovery of analytes from VTM, we could not account for differences in yields of NP swabs. Furthermore, assay performance metrics for certain lipids such as LysoPCaC18:2 were not determined as we lacked a synthetic standard, and the internal standard used for this lipid was non-specific. In conclusion, we demonstrated that the metabolome of the nasopharynx can be measured from clinical nasal swabs, and that metabolite profiles identified using machine learning methods can differentiate patients with COVID-19 from other respiratory virus infections (e.g. INFA/RSV). Our study identified key metabolites specifically altered in COVID19 such as carnosine and LysoPCaC18:2 that have previously been implicated in viral replication and symptom generation. This enables us to propose mechanisms contributing to viral infection and propagation as well as potential targets for COVID-...

    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.

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  3. Version published to 10.1101/2021.06.18.21259150v1 on medRxiv