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. 2022 Sep 29:9:995069.
doi: 10.3389/fmed.2022.995069. eCollection 2022.

Untargeted plasma metabolomic fingerprinting highlights several biomarkers for the diagnosis and prognosis of coronavirus disease 19

Céline Occelli  1   2   3 , Jean-Marie Guigonis  1 , Sabine Lindenthal  1 , Alexandre Cagnard  1 , Fanny Graslin  1 , Vesna Brglez  4   5 , Barbara Seitz-Polski  3   4   5 , Jean Dellamonica  3   4   6 , Jacques Levraut  2   3 , Thierry Pourcher  1
Affiliations

Untargeted plasma metabolomic fingerprinting highlights several biomarkers for the diagnosis and prognosis of coronavirus disease 19

Céline Occelli et al. Front Med (Lausanne). .

Abstract

Objectives: The COVID-19 pandemic has been a serious worldwide public health crisis since 2020 and is still challenging healthcare systems. New tools for the prognosis and diagnosis of COVID-19 patients remain important issues.

Design: Here, we studied the metabolome of plasma samples of COVID-19 patients for the identification of prognosis biomarkers.

Patients: Plasma samples of eighty-six SARS-CoV-2-infected subjects and 24 healthy controls were collected during the first peak of the COVID-19 pandemic in France in 2020.

Main results: Plasma metabolome fingerprinting allowed the successful discrimination of healthy controls, mild SARS-CoV-2 subjects, and moderate and severe COVID-19 patients at hospital admission. We found a strong effect of SARS-CoV-2 infection on the plasma metabolome in mild cases. Our results revealed that plasma lipids and alterations in their saturation level are important biomarkers for the detection of the infection. We also identified deoxy-fructosyl-amino acids as new putative plasma biomarkers for SARS-CoV-2 infection and COVID-19 severity. Finally, our results highlight a key role for plasma levels of tryptophan and kynurenine in the symptoms of COVID-19 patients.

Conclusion: Our results showed that plasma metabolome profiling is an efficient tool for the diagnosis and prognosis of SARS-CoV-2 infection.

Keywords: COVID-19; biomarkers; diagnostic; metabolomics (OMICS); prognostic.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of putative biomarkers of SARS-CoV-2 infection. PLS-DA analyses based on untargeted plasma metabolomic profiling discriminated healthy control subjects (CTRL) from all SARS-CoV-2 infected subjects (All, including mild, moderate and severe cases). Green and red dots represent CTRL and SARS-CoV-2 infected subjects, respectively. The score plot is represented with a confidence ellipse of 95% (A). Loading plots of the top 15 features (metabolites) selected on the average of the first five components of the PLS-DA model. Identified metabolites were validated by MS2 analyses and are shown as chemical names (B). Pairwise comparisons of areas under multivariate ROC curves (AUC) from each prognosis predictor (C).
FIGURE 2
FIGURE 2
Heatmap of the top 15 metabolites of SARS-CoV-2 infection. Metabolites were clustered using the ward method on t-test and ANOVA values. The molecular structures of the lipids are given.
FIGURE 3
FIGURE 3
Identification of putative biomarkers of mild SARS-CoV-2 infected subjects compared to healthy control subjects. PLS-DA analysis based on untargeted plasma metabolomic profiling discriminated healthy control subjects (CTRL) from mild SARS-CoV-2-infected cases (Mild). Green and red dots represent CTRL and mild SARS-CoV-2-infected subjects, respectively. The score plot is represented with a confidence ellipse of 95% (A). Loading plots of the top 15 features (metabolites) selected on the average of the first five components of the PLS-DA model. Identified features were MS2 validated and are shown as chemical names (B). Pairwise comparisons of areas under multivariate ROC curve (AUC) from each prognosis predictor (C).
FIGURE 4
FIGURE 4
Identification of the putative biomarkers of moderate and severe COVID-19 patients compared to mild SARS-CoV-2 infected subjects (A,B) and severe compared to moderate COVID-19 patients (C,D). PLS-DA analyses are shown in Supplementary Figure 2A and Figure 3A. Loading plots of the top 15 metabolites selected on the average of the first five components of the PLS-DA model. Identified features were MS2 validated and are shown as chemical names (A,C). Pairwise comparisons of areas under multivariate ROC curve (AUC) from each prognosis predictor (B,D).
FIGURE 5
FIGURE 5
Heatmap of plasma levels of published putative biomarkers of SARS-CoV-2 infection and COVID-19 severity. The values are available in Supplementary Data 5–13.
FIGURE 6
FIGURE 6
Venn diagram of significantly altered pathways in samples from healthy control subjects compared to SARS-CoV-2 infected subjects (CTRL vs. All), healthy control subjects compared to mild SARS-CoV-2 infected subjects (CTRL vs. Mild) and mild SARS-CoV-2 infected subjects compared to moderate and severe COVID-19 patients (Mild vs. Moderate and Severe). A value of p < 0.05 was considered significant.
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