The specific metabolome profiling of patients infected by SARS-COV-2 supports the key role of tryptophan-nicotinamide pathway and cytosine metabolism

Abstract

The biological mechanisms involved in SARS-CoV-2 infection are only partially understood. Thus we explored the plasma metabolome of patients infected with SARS-CoV-2 to search for diagnostic and/or prognostic biomarkers and to improve the knowledge of metabolic disturbance in this infection. We analyzed the plasma metabolome of 55 patients infected with SARS-CoV-2 and 45 controls by LC-HRMS at the time of viral diagnosis (D0). We first evaluated the ability to predict the diagnosis from the metabotype at D0 in an independent population. Next, we assessed the feasibility of predicting the disease evolution at the 7th and 15th day. Plasma metabolome allowed us to generate a discriminant multivariate model to predict the diagnosis of SARS-CoV-2 in an independent population (accuracy > 74%, sensitivity, specificity > 75%). We identified the role of the cytosine and tryptophan-nicotinamide pathways in this discrimination. However, metabolomic exploration modestly explained the disease evolution. Here, we present the first metabolomic study in SARS-CoV-2 patients which showed a high reliable prediction of early diagnosis. We have highlighted the role of the tryptophan-nicotinamide pathway clearly linked to inflammatory signals and microbiota, and the involvement of cytosine, previously described as a coordinator of cell metabolism in SARS-CoV-2. These findings could open new therapeutic perspectives as indirect targets.

Figure 1

Univariate and multivariate analysis from plasma metabolome profile of C + versus C − patients. (A) Univariate analysis via volcano plot based on fold change and adjusted p-value, highlighting 2 metabolites, (B) Score scatter plot based on the PLS-DA models to explain the diagnosis (pink for C − and green for C +, (C) rank of the different metabolites (the top 15) identified by the PLS-DA according to the VIP score on the x-axis. Colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group, (D) pathway analysis based on enrichment analysis procedures, thus identifying the most relevant metabolic pathways via pathway impact and adjusted p-value. The figures were drawn via metaboanalyst software v 4.0 (https://www.metaboanalyst.ca/).

Figure 2

Example of the prediction of SARS-CoV-2 infection in an independent cohort (i.e. test set) from plasma metabolome profile of the patients from the training set. In a training set, ROC curves were obtained after PLS-DA models based on different numbers of metabolites. The model providing the highest Area Under the Curve with less than 10 metabolites was used to predict the diagnosis in the test set. Thus, the ROC curve in the test set enabled to compare the diagnosis prediction to the observed diagnosis. Finally the performances of 5 independent predictions determined by the 4 following performance criteria :sensitivity, specificity, Positive Predictive and Negative Predictive values were calculated. The figures were drawn via metaboanalyst software v 4.0 (https://www.metaboanalyst.ca/).

Figure 3

Multivariate analysis from plasma metabolome profile to explain clinical evolution in C + patients. (A) Score scatter plot based on the PLS-DA models to explain the evolution of the WHO ordinal scale at D7, red for worst score, green for a better score and purple for a similar score between D0 and D7, (B) rank of the different metabolites (the top 15) identified by the PLS-DA according to the VIP score on the x-axis. Colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group (− , + , = for the evolution of WHO ordinal scale; D: death, H: Home, and Hospi : hospitalization). (C) Score scatter plot based on the PLS-DA models to explain the evolution of patients at D15: red for death, green for hospital discharge and purple for hospitalization. Component 1 and 2 represent a linear combination of relevant metabolites expressing the maximum variance. After mean-centering and autoscaling, the data are used for the computation of the first principal component, that is the line in the K-dimensional space that best approximates the data in the least squares sense. Importantly one principal component is insufficient to model the systematic variation of a data set, and a second principal component is calculated. The second PC is also represented by a line in the K-dimensional variable space, which is orthogonal to the first PC. (D) rank of the different metabolites (the top 15) identified by the PLS-DA according to the VIP score on the x-axis. Colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group (− , + , = for the evolution of WHO ordinal scale; D: death, H: Home, and Hospi: hospitalization). The figures were drawn via metaboanalyst software v 4.0 (https://www.metaboanalyst.ca/).

Figure 4

Metabolites the most discriminant for the evaluation of clinical evolution in C + patients, (A) Venn diagram (Venny 2.1, https://bioinfogp.cnb.csic.es/tools/venny/) showing the 15 metabolites with the highest VIP used to explain the evolution of WHO ordinal scale (purple) and the evolution of patients (yellow) with the two common metabolites xanthine and thymine; (B) enrichment analysis based on the metabolites presented in the Venn diagram (A). Each node represents a metabolite set with its color based on its adjusted p-value and its size is based on fold enrichment. Two metabolite sets are connected by an edge if the number of their shared metabolites is over 25% of the total number of their combined metabolite sets. (C) Metabolites sets overview enrichment. The list of metabolic pathways is summarized under the network view. The enrichment analysis was implemented using the hypergeometric test to evaluate whether a particular metabolite set is represented more than expected by chance within the given compound list. One-tailed p values are provided after adjusting for multiple testing. The figures were drawn via metaboanalyst software v 4.0 (https://www.metaboanalyst.ca/).