Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

  1. Ivayla Roberts
  2. Marina Wright Muelas
  3. Joseph M. Taylor
  4. Andrew S. Davison
  5. Yun Xu
  6. Justine M. Grixti
  7. Nigel Gotts
  8. Anatolii Sorokin
  9. Royston Goodacre
  10. Douglas B. Kell

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Abstract

Introduction

The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.

Objectives

Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient’s infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased).

Methods

High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created.

Results

The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74–0.91) and 0.76 (CI 0.67–0.86).

Conclusion

Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

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  1. Version published to 10.1007/s11306-021-01859-3
  2. Version published to 10.1101/2020.12.09.20246389v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.12.09.20246389: (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

    Software and Algorithms
    SentencesResources
    From those compounds 267 in ESI + and 142 in ESI-had identification in mzCloud with score higher than 70 % and full match on Predicted Composition.
    mzCloud
    suggested: (mzCloud, RRID:SCR_014669)
    Pathway enrichment analysis: Pathway enrichment analysis was performed in MUMMICHOG (Li et al., 2013) version 2 incorporated in MetaboAnalyst (Pang et al., 2020).
    MetaboAnalyst
    suggested: (MetaboAnalyst, RRID:SCR_015539)

    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:
    However, it is important to note a limitation of cytosine:uracil ratio usage given that cytosine degradation pathway goes through uracil. To acknowledge this relationship, the kinetics of the degradation process need to be incorporated in a potential viral activity predictive model. Moreover, as highlighted by (Migaud et al., 2020) the levels of certain nucleobases are also increased in patients who die from sepsis and acute respiratory failure. Further investigation would be required to tease out the contributions of SARS-CoV-2 viral replication to the levels of these pyrimidines against the secondary effects of the virus on the host (human) health. Pseudouridine, was also noted to be increased in severe cases and poor outcome. As mentioned previously pseudouridine (Figure 4) is a marker of cell (rRNA) turnover (Nakano et al., 1993), for instance in heart failure (Dunn et al., 2007). When adjusting for cardiovascular disease this compound remained significant but showed slight correlation with hypertension and age. Finally, when adjusted for all factors pseudouridine 95 % CI lost significance indicating correlation with more than one factor. Interestingly, growing evidence points toward complications in COVID-19 arising through a vasculopathy and coagulopathy elicited by the infection (Pretorius et al., 2020) and may be indicative of this process. Tryptophan - kynurenine degradation: The degradation of tryptophan to kynurenine is a well-studied pathway associated with increa...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

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  4. Version published to 10.1101/2020.12.09.20246389v1 on medRxiv