Konrad Aden1, Ateequr Rehman2, Silvio Waschina3, Wei-Hung Pan2, Alesia Walker4, Marianna Lucio4, Alejandro Mena Nunez2, Richa Bharti2, Johannes Zimmerman3, Johannes Bethge5, Berenice Schulte5, Dominik Schulte5, Andre Franke2, Susanna Nikolaus5, Johann Oltmann Schroeder5, Doris Vandeputte6, Jeroen Raes6, Silke Szymczak7, Georg H Waetzig8, Rainald Zeuner5, Philippe Schmitt-Kopplin4, Christoph Kaleta3, Stefan Schreiber9, Philip Rosenstiel10. 1. Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. 2. Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. 3. Institute for Experimental Medicine, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. 4. Research Unit Analytical BioGeoChemistry, Helmholtz ZentrumMünchen, German Research Centre for Environmental Health (GmbH), Neuherberg, Germany. 5. Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. 6. VIB-KU Leuven Center for Microbiology, Campus Gasthuisberg, Leuven, Belgium. 7. Institute of Medical Informatics and Statistics, University of Kiel, Kiel, Germany. 8. CONARIS Research Institute AG, Kiel, Germany. 9. Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. Electronic address: s.schreiber@mucosa.de. 10. Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. Electronic address: p.rosenstiel@mucosa.de.
Abstract
BACKGROUND & AIMS: Altered interactions between the mucosal immune system and intestinal microbiota contribute to pathogenesis of inflammatory bowel diseases (IBD). It is not clear how inhibitors of cytokines, such as antagonists of tumor necrosis factor (anti-TNF), affect the intestinal microbiome. We investigated the effects of anti-TNF agents on gut microbe community structure and function in a longitudinal 2-step study of patients with IBD. We correlated our findings with outcomes of treatment and investigated patterns of metabolites in fecal samples before and after anti-TNF therapy. METHODS: We performed a prospective study of 2 cohorts of patients in Germany; the discovery cohort comprised 12 patients with IBD, 17 patients with rheumatic disease, and 19 healthy individuals (controls); fecal samples were collected at baseline and 2, 6, and 30 weeks after induction of anti-TNF therapy. The validation cohort comprised 23 patients with IBD treated with anti-TNF or vedolizumab (anti-α4β7 integrin) and 99 healthy controls; fecal samples were collected at baseline and at weeks 2, 6, and 14. Fecal microbiota were analyzed by V3-V4 16S ribosomal RNA gene amplicon sequencing. Clinical response and remission were determined by clinical disease activity scores. Metabolic network reconstruction and associated fecal metabolite level inference was performed in silico using the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. Metabolomic analyses of fecal samples from a subset of patients were performed to validate metabolites associated with treatment outcomes. RESULTS: Anti-TNF therapy shifted the diversity of fecal microbiota in patients with IBD, but not with rheumatic disease, toward that of controls. Across timepoints, diversity indices did not vary significantly between patients with IBD who did or did not achieve clinical remission after therapy. In contrast, in silico modeling of metabolic interactions between gut microbes found metabolite exchange to be significantly reduced at baseline in fecal samples from patients with IBD and to be associated with later clinical remission. Predicted levels of butyrate and substrates involved in butyrate synthesis (ethanol or acetaldehyde) were significantly associated with clinical remission following anti-TNF therapy, verified by fecal metabolomic analyses. CONCLUSIONS: Metabolic network reconstruction and assessment of metabolic profiles of fecal samples might be used to identify patients with IBD likely to achieve clinical remission following anti-TNF therapy and increase our understanding of the heterogeneity of IBD.
BACKGROUND & AIMS: Altered interactions between the mucosal immune system and intestinal microbiota contribute to pathogenesis of inflammatory bowel diseases (IBD). It is not clear how inhibitors of cytokines, such as antagonists of tumor necrosis factor (anti-TNF), affect the intestinal microbiome. We investigated the effects of anti-TNF agents on gut microbe community structure and function in a longitudinal 2-step study of patients with IBD. We correlated our findings with outcomes of treatment and investigated patterns of metabolites in fecal samples before and after anti-TNF therapy. METHODS: We performed a prospective study of 2 cohorts of patients in Germany; the discovery cohort comprised 12 patients with IBD, 17 patients with rheumatic disease, and 19 healthy individuals (controls); fecal samples were collected at baseline and 2, 6, and 30 weeks after induction of anti-TNF therapy. The validation cohort comprised 23 patients with IBD treated with anti-TNF or vedolizumab (anti-α4β7 integrin) and 99 healthy controls; fecal samples were collected at baseline and at weeks 2, 6, and 14. Fecal microbiota were analyzed by V3-V4 16S ribosomal RNA gene amplicon sequencing. Clinical response and remission were determined by clinical disease activity scores. Metabolic network reconstruction and associated fecal metabolite level inference was performed in silico using the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. Metabolomic analyses of fecal samples from a subset of patients were performed to validate metabolites associated with treatment outcomes. RESULTS: Anti-TNF therapy shifted the diversity of fecal microbiota in patients with IBD, but not with rheumatic disease, toward that of controls. Across timepoints, diversity indices did not vary significantly between patients with IBD who did or did not achieve clinical remission after therapy. In contrast, in silico modeling of metabolic interactions between gut microbes found metabolite exchange to be significantly reduced at baseline in fecal samples from patients with IBD and to be associated with later clinical remission. Predicted levels of butyrate and substrates involved in butyrate synthesis (ethanol or acetaldehyde) were significantly associated with clinical remission following anti-TNF therapy, verified by fecal metabolomic analyses. CONCLUSIONS: Metabolic network reconstruction and assessment of metabolic profiles of fecal samples might be used to identify patients with IBD likely to achieve clinical remission following anti-TNF therapy and increase our understanding of the heterogeneity of IBD.