Integrated fecal microbiome-metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome

• Published in: Gut microbes Vol. 14; no. 1; p. 2063016
• Main Authors: Mujagic, Zlatan, Kasapi, Melpomeni, Jonkers, Daisy MAE, Garcia-Perez, Isabel, Vork, Lisa, Weerts, Zsa Zsa R.M., Serrano-Contreras, Jose Ivan, Zhernakova, Alexandra, Kurilshikov, Alexander, Scotcher, Jamie, Holmes, Elaine, Wijmenga, Cisca, Keszthelyi, Daniel, Nicholson, Jeremy K, Posma, Joram M, Masclee, Ad AM
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 31.12.2022; Taylor & Francis Group
• Subjects: fecal metabolome; Feces - chemistry; Gastrointestinal Microbiome - physiology; gut metabolome; gut microbiota; gut-brain; host-microbiome interaction; Humans; Irritable bowel syndrome; Irritable Bowel Syndrome - metabolism; Metabolome; microbiome; Microbiota; Research Paper; serotonin; Serotonin - metabolism; stress; gut metabolome; stress; serotonin; gut-brain; Irritable bowel syndrome; host-microbiome interaction; fecal metabolome; microbiome; gut microbiota
• ISSN: 1949-0976; 1949-0984; 1949-0984
• DOI: 10.1080/19490976.2022.2063016

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS), several modalities of biological and clinical data must be combined. We aimed to identify profiles of fecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Fecal metabolites were measured using proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy and gut microbiome using shotgun metagenomic sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HCs) with extensive clinical, biological and phenotype information. Data were analyzed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on fecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal (GI) symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavorable stress response related to GI symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS.