Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics

Abstract

The pathogenesis of ME/CFS, a disease characterized by fatigue, cognitive dysfunction, sleep disturbances, orthostatic intolerance, fever, irritable bowel syndrome (IBS), and lymphadenopathy, is poorly understood. We report biomarker discovery and topological analysis of plasma metabolomic, fecal bacterial metagenomic, and clinical data from 50 ME/CFS patients and 50 healthy controls. We confirm reports of altered plasma levels of choline, carnitine and complex lipid metabolites and demonstrate that patients with ME/CFS and IBS have increased plasma levels of ceramide. Integration of fecal metagenomic and plasma metabolomic data resulted in a stronger predictive model of ME/CFS (cross-validated AUC = 0.836) than either metagenomic (cross-validated AUC = 0.745) or metabolomic (cross-validated AUC = 0.820) analysis alone. Our findings may provide insights into the pathogenesis of ME/CFS and its subtypes and suggest pathways for the development of diagnostic and therapeutic strategies.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1

Schematic figure describing the metabolomic and metagenomic analysis pipeline. Metabolomic data was pre-processed and compared between ME/CFS patients vs. controls, ME/CFS with IBS (ME/CFS + IBS) vs. controls and ME/CFS without IBS vs. controls, and female group only. Targeted and untargeted mass spectrometry platforms yielded data for 111 primary metabolites (PM), 103 biogenic amines (BA), 302 complex lipids (CL) and 46 bioactive oxylipins (OL). Statistical analyses were performed with Mann-Whitney U test and adjusted univariate logistic regression modeling on all metabolites except for exposomes/vitamins. After removal of 5-methoxytryptamine, metabolites with a p-value below 0.05 in both U test and univariate logistic regression were ranked with random forests. The top 10 random forests-ranked metabolites were used as predictors in the predictive multivariate logistic regression models. The goodness-of-fit and predictive performance for the predictive models were measured with ROC curves. Biochemical set enrichment and topological analyses were performed with MetaMapp and TDA software, respectively. Metagenomic data were incorporated to better understand the correlation between metabolites and bacterial abundance profiles.

Figure 2

Metabolites differentiate ME/CFS and controls. A MetaMapp network of all identified metabolites in the ME/CFS cohort was constructed by Tanimoto chemical similarity and Kyoto Encyclopedia of Genes and Genomes (KEGG) reaction pairs. Each node represents a metabolite. Up-regulated nodes are marked red and down-regulated nodes are marked blue. Node size reflects the magnitude of the effect. Only the compounds that pass a p-value cutoff of 0.05 are labeled. Red lines show the biochemical reactions and blue lines are chemical similarity scores above 0.85 Tanimoto similarity coefficients. The network was created using www.metamapp.fiehnlab.ucdavis.edu and visualized in Cytoscape using the organic layout algorithm. MetaMapp identified perturbations in tryptophan metabolism, carnitine shuttle/energy homeostasis and complex lipids. Metabolites representing the tryptophan and carnitine pathway were decreased in ME/CFS compared to controls. In contrast, threonic acid, amino acids (tyrosine, methionine and lysine), phenylacetylglutamine, pantothenic acid, hexaethylene glycol and ε-caprolactam were enriched in ME/CFS. Lipid analyses showed that whereas metabolites representing the SM, Cer/CE and PC/LPC pathways were decreased in ME/CFS, TG pathways were enriched. 5-MT: 5-methoxytryptamine, 9-HOTE: C18:3n3, 19,20-DiHDPE: C22H34O4, AC: acylcarnitine, Cer/CE: ceramide, DG: diacylglycerol, FA: fatty acid, LPC: lysophosphatidylcholine, PC: phosphatidylcholine, PE: phosphatidylethanolamine, SM: sphingomyelin, TG: triglyceride, Tyr Met Lys: tyrosine methionine lysine.

Figure 3

Topological data analysis (TDA) revealed altered metabolomic and metagenomic profiles in ME/CFS. The color scheme represents the strength of association with ME/CFS diagnosis (white: strongly associated with control, red: strongly associated with ME/CFS). Each node in a network comprises 1 or more subject(s) who share variables in multiple dimensions. Lines connect network nodes that contain shared variables and subjects. Unlike traditional network models wherein each node reflects only a single sample, the size of a node in the topological network is proportional to the number of variables with a similar profile. (A), (B) and (C) integrate plasma metabolomic, fecal metagenomic and plasma immune profiles, and symptom severity scores using the Jaccard metric to define multidimensional subgroups. Irrespective of the lenses used [(A) neighborhood lenses NL1 and NL2, (B) MDS coordinate 1 and 2, and (C) metric PCA 1 and 2], ME/CFS and control samples formed distinct networks. ME/CFS and control samples also formed distinct networks in TDA based on either fecal bacterial relative abundance or plasma metabolomic data in isolation using a variance normalized Euclidean distance metric with neighborhood lenses (NL1 and NL2). Fecal bacterial relative abundance features (D) were stronger drivers of the network distinction than plasma metabolomic features (E).