COVID-19-specific metabolic imprint yields insights into multiorgan system perturbations

Abstract

Corona disease 2019 (COVID-19) affects multiple organ systems. Recent studies have indicated perturbations in the circulating metabolome linked to COVID-19 severity. However, several questions pertain with respect to the metabolome in COVID-19. We performed an in-depth assessment of 1129 unique metabolites in 27 hospitalized COVID-19 patients and integrated results with large-scale proteomic and immunology data to capture multiorgan system perturbations. More than half of the detected metabolic alterations in COVID-19 were driven by patient-specific confounding factors ranging from comorbidities to xenobiotic substances. Systematically adjusting for this, a COVID-19-specific metabolic imprint was defined which, over time, underwent a switch in response to severe acute respiratory syndrome coronavirus-2 seroconversion. Integration of the COVID-19 metabolome with clinical, cellular, molecular, and immunological severity scales further revealed a network of metabolic trajectories aligned with multiple pathways for immune activation, and organ damage including neurological inflammation and damage. Altogether, this resource refines our understanding of the multiorgan system perturbations in severe COVID-19 patients.

Keywords: COVID-19; Metabolomics; Multi-omics; Multiorgan; Neuroinflammation; SARS-CoV-2.

Figure 1

Remodeling of all metabolic compartments in COVID‐19 and identification of confounding factors. (a) Study design. Cohort and key characteristics are described as well as the pipeline of ultraperformance liquid chromatography (UPLC)‐tandem mass spectrometry (MS/MS) electrospray ionization (ESI) used for metabolomics characterization of serum. (b) Landscape of quantified metabolites. Relative proportions of superpathways and the most common sub‐pathways are depicted (number of metabolites identified). (c) Differences in metabolite concentrations between healthy donors (HD, n = 17) and hospitalized COVID‐19 patients (n = 27). Each dot represents a single metabolite, FDR, false discovery rate; circular diagrams depict in blue the proportion of each superpathway significantly affected (q < 0.05). (d) Schematic view of the identification of xenobiotic‐metabolite interdependencies. R, Spearman coefficient of correlation, the thinner circular diagram depicts the number of highly significant correlations between all metabolites while the thicker circular diagrams show the main xenobiotic‐metabolite interdependencies and the related superpathways involved. (e) Impact of confounding factors on the metabolic landscape in hospitalized COVID‐19 patients. Differences in metabolite concentrations between patients with or without the indicated confounding factors (y‐axis) and healthy donors (x‐axis). Each dot represents a single metabolite. The number of most variable metabolites (>fold change of 2, blue) and most significant (FDR corrected “q,” or not “p”) are indicated above each graph. (f) Distinct and shared effects of confounders on the metabolic landscape in hospitalized COVID‐19 patients. Bar graphs show the number and the composition (superpathways, colored in the bars) of the metabolites affected (most variable on the top, most significant on the bottom plot) by the corresponding confounders (color‐coded) indicated on the left. Circular diagrams show the proportion of the metabolome affected by confounders (including all xenobiotics). Pie charts display the proportion of metabolites affected by one or several confounders with single or shared effects displayed inside the upsets plots with connected dots under the bars.

Figure 2

SARS‐CoV‐2‐specific metabolic imprint across COVID‐19 severities and multidimensional map of subclinical phenotypes. (a) Phylogeny deduced from serum metabolic assessment of healthy donors (#1‐17) and COVID‐19 patients (#18‐44) using the corrected and noncorrected metabolome. Euclidian distances are calculated in the multidimensional reduced metabolic space (all principal components) and clustered using Ward's method. (b) Analysis of metabolic switches across pseudotime of early COVID‐19 using the corrected and noncorrected metabolome. Metabolite concentrations are compared using Mann–Whitney test between healthy donors (n = 17) and hospitalized serum PCR positive/IgG negative (n = 5) or serum PCR negative/IgG positive (n = 12) COVID‐19 patients at the time of sampling. Pie charts show the overlap between the top 100 significant changes found using corrected and noncorrected metabolome. Waterfall plots display the magnitude of changes identified using the corrected metabolome. Each bar represents a single metabolite. (c) Identification of predictive models distinguishing COVID‐19 developing fatal cases using the corrected and noncorrected metabolome. Results from the best identified models using logistic regressions (0.05 as significance for entry and stay in the model, Akaike information criterion [AIC]) run either with the corrected or the noncorrected metabolome. (d) Schematic view of the structure and content of the Munsell chart of COVID‐19 severities containing 69 composite severity scales across layers of the pathophysiological spectrum. (e) Correlation‐based metabolic trajectories across the 69 composite severity scales of the Munsell chart. R, Pearson's coefficient of correlation. Combined clustering (using Ward's method) of severity scales and metabolites based on the three highest and three lowest Pearson correlation coefficients identified for each scale. (f) Correlations between single metabolites and selected serum proteome‐defined neurological severity scales. Indicated p‐values shown for linear regression calculations. (g) Map of the pathophysiological landscape of COVID‐19 based on principal component (PC) analysis.