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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 (1H-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.

Item does not contain fulltext, To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10(-8)) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10(-20)), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10(-10) < P < 5 × 10(-8)) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Aims: To study the microbiome-gut-brain axis we aimed to identify profiles of fecal microbiota and metabolites associated with irritable bowel syndrome (IBS) and to specific phenotypes of IBS, and used metabolic reaction networks integrated with microbiota data to study the host-microbiome interaction. Methods: Extensively phenotyped IBS patients (Rome III) and healthy controls (HC) were included in this cross-sectional analysis. Fecal metabolites were measured using proton-Nuclear-Magnetic-Resonance (1H-NMR) spectroscopy. Gut microbiome was assessed by shot-gun-metagenomic-sequencing (MGS). Data were analyzed using Support-Vector-Machines for classification and regression, and kernel t-SNE to obtain unsupervised clustering of IBS for comparison with biological and clinical metadata. Results: 314 subjects were included: 181 IBS, 133 HC. Fecal microbiome-metabolome profiles were associated to IBS and HC. These groups could be distinguished by metabolic phenotyping with an area-under-the-curve (AUC) of 79.5%, and by metagenomics data, with best AUC 77.3%, using microbial family taxonomic rank. This could substantially be improved by combining microbiota and metabolites; AUC 83.6% (Figure 1). There were no significant differences in predictive ability between three IBS subtypes based on predominant stool pattern when considering the microbiome-metabolome data. However, unsupervised clustering shows distinct subsets of IBS patients based on fecal microbiome and metabolome, associated to effects of psychological stress on gastrointestinal symptoms, onset of IBS after stressful events, levels of serotonin and its metabolite 5-hydroxyindoleacetate, medical history of previous abdominal surgery, dietary caloric intake and duration of IBS symptoms. Furthermore, metabolic reaction networks integrated with microbiota data provided insight in metabolic pathways. Conclusions: Fecal microbiome-metabolome signatures for IBS were associated with altered serotonin metabolism and unf

Contains fulltext : 202884.pdf (publisher's version ) (Closed access), Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity(1). However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available(2), then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using bidirectional Mendelian randomization (MR) analyses to assess causality(3), we found that the host-genetic-driven increase in gut production of the SCFA butyrate was associated with improved insulin response after an oral glucose-tolerance test (P = 9.8 x 10(-5)), whereas abnormalities in the production or absorption of another SCFA, propionate, were causally related to an increased risk of T2D (P = 0.004). These data provide evidence of a causal effect of the gut microbiome on metabolic traits and support the use of MR as a means to elucidate causal relationships from microbiome-wide association findings.

BACKGROUND: The Rome criteria for irritable bowel syndrome (IBS) have been revised and are expected to apply only to the subset of Rome III IBS subjects with abdominal pain as predominant symptom, occurring at least once a week. The aim of this study was to determine the percentage of Rome III IBS subjects that fulfills Rome IV criteria and to evaluate differences between Rome IV-positive and Rome IV-negative subjects.METHODS: Four hundred and four Rome III IBS subjects completed a 14-day end-of-day symptom diary, the Gastrointestinal Symptom Rating Scale (GSRS), Hospital Anxiety and Depression Scale, and RAND 36-item Short-Form Health Survey (SF-36). Diary-based surrogate Rome IV criteria were defined as occurrence of abdominal pain at least 1 day each week with a severity of ≥2 (mild; definition 1) or ≥3 (considerable; definition 2).KEY RESULTS: Using surrogate Rome IV criteria, 353 (87.4%, definition 1) and 249 (61.6%, definition 2) subjects were defined as Rome IV positive. These patients were more often female, younger, and recruited from secondary/tertiary care compared with Rome IV-negative subjects. They also presented with higher abdominal pain scores and gastrointestinal (GI) symptom severity on both end-of-day diary and GSRS, higher psychological symptom scores, and lower quality of life compared with Rome IV-negative subjects.CONCLUSIONS AND INFERENCES: The Rome IV IBS population likely reflects a subgroup of Rome III IBS patients with more severe GI symptomatology, psychological comorbidities, and lower quality of life. This implies that results from Rome III IBS studies may not be directly comparable to those from Rome IV IBS populations.

Contains fulltext : 176878.pdf (publisher's version ) (Open Access), Orally ingested bacteria interact with intestinal mucosa and may impact immunity. However, insights in mechanisms involved are limited. In this randomized placebo-controlled cross-over trial, healthy human subjects were given Lactobacillus plantarum supplementation (strain TIFN101, CIP104448, or WCFS1) or placebo for 7 days. To determine whether L. plantarum can enhance immune response, we compared the effects of three stains on systemic and gut mucosal immunity, by among others assessing memory responses against tetanus toxoid (TT)-antigen, and mucosal gene transcription, in human volunteers during induction of mild immune stressor in the intestine, by giving a commonly used enteropathic drug, indomethacin [non-steroidal anti-inflammatory drug (NSAID)]. Systemic effects of the interventions were studies in peripheral blood samples. NSAID was found to induce a reduction in serum CD4+/Foxp3 regulatory cells, which was prevented by L. plantarum TIFN101. T-cell polarization experiments showed L. plantarum TIFN101 to enhance responses against TT-antigen, which indicates stimulation of memory responses by this strain. Cell extracts of the specific L. plantarum strains provoked responses after WCFS1 and TIFN101 consumption, indicating stimulation of immune responses against the specific bacteria. Mucosal immunomodulatory effects were studied in duodenal biopsies. In small intestinal mucosa, TIFN101 upregulated genes associated with maintenance of T- and B-cell function and antigen presentation. Furthermore, L. plantarum TIFN101 and WCFS1 downregulated immunological pathways involved in antigen presentation and shared downregulation of snoRNAs, which may suggest cellular destabilization, but may also be an indicator of tissue repair. Full sequencing of the L. plantarum strains revealed possible gene clusters that might be responsible for the differential biological effects of the bacteria on host immunity. In conclusion, the impact of oral consumption L. plantarum on host

Contains fulltext : 171213.pdf (publisher's version ) (Closed access), Deep sequencing of the gut microbiomes of 1135 participants from a Dutch population-based cohort shows relations between the microbiome and 126 exogenous and intrinsic host factors, including 31 intrinsic factors, 12 diseases, 19 drug groups, 4 smoking categories, and 60 dietary factors. These factors collectively explain 18.7% of the variation seen in the interindividual distance of microbial composition. We could associate 110 factors to 125 species and observed that fecal chromogranin A (CgA), a protein secreted by enteroendocrine cells, was exclusively associated with 61 microbial species whose abundance collectively accounted for 53% of microbial composition. Low CgA concentrations were seen in individuals with a more diverse microbiome. These results are an important step toward a better understanding of environment-diet-microbe-host interactions.

Contains fulltext : 171719.pdf (publisher's version ) (Closed access), The gut microbiome is affected by multiple factors, including genetics. In this study, we assessed the influence of host genetics on microbial species, pathways and gene ontology categories, on the basis of metagenomic sequencing in 1,514 subjects. In a genome-wide analysis, we identified associations of 9 loci with microbial taxonomies and 33 loci with microbial pathways and gene ontology terms at P < 5 x 10-8. Additionally, in a targeted analysis of regions involved in complex diseases, innate and adaptive immunity, or food preferences, 32 loci were identified at the suggestive level of P < 5 x 10-6. Most of our reported associations are new, including genome-wide significance for the C-type lectin molecules CLEC4F-CD207 at 2p13.3 and CLEC4A-FAM90A1 at 12p13. We also identified association of a functional LCT SNP with the Bifidobacterium genus (P = 3.45 x 10-8) and provide evidence of a gene-diet interaction in the regulation of Bifidobacterium abundance. Our results demonstrate the importance of understanding host-microbe interactions to gain better insight into human health.

Purpose There is a critical need for population-based prospective cohort studies because they follow individuals before the onset of disease, allowing for studies that can identify biomarkers and disease-modifying effects, and thereby contributing to systems epidemiology. Participants This paper describes the design and baseline characteristics of an intensively examined subpopulation of the LifeLines cohort in the Netherlands. In this unique subcohort, LifeLines DEEP, we included 1539 participants aged 18 years and older. Findings to date We collected additional blood (n=1387), exhaled air (n=1425) and faecal samples (n=1248), and elicited responses to gastrointestinal health questionnaires (n=1176) for analysis of the genome, epigenome, transcriptome, microbiome, metabolome and other biological levels. Here, we provide an overview of the different data layers in LifeLines DEEP and present baseline characteristics of the study population including food intake and quality of life. We also describe how the LifeLines DEEP cohort allows for the detailed investigation of genetic, genomic and metabolic variation for a wide range of phenotypic outcomes. Finally, we examine the determinants of gastrointestinal health, an area of particular interest to us that can be addressed by LifeLines DEEP. Future plans We have established a cohort of which multiple data levels allow for the integrative analysis of populations for translation of this information into biomarkers for disease, and which will offer new insights into disease mechanisms and prevention.