Lactulose Challenge Determines Visceral Sensitivity and Severity of Symptoms in Patients With Irritable Bowel Syndrome

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Fermented dairy products modulate Citrobacter rodentium-induced colonic hyperplasia.

We evaluated the protective effects of fermented dairy products (FDPs) in an infection model, using the mouse pathogen Citrobacter rodentium (CR). Treatment of mice with FDP formulas A, B, and C or a control product did not affect CR colonization, organ specificity, or attaching and effacing lesion formation. Fermented dairy product A (FDP-A), but neither the supernatant from FDP-A nor β-irradiated (IR) FDP-A, caused a significant reduction in colonic crypt hyperplasia and CR-associated pathology. Profiling the gut microbiota revealed that IR-FDP-A promoted higher levels of phylotypes belonging to Alcaligenaceae and a decrease in Lachnospiraceae (Ruminococcus) during CR infection. Conversely, FDP-A prevented a decrease in Ruminococcus and increased Turicibacteraceae (Turicibacter). Importantly, loss of Ruminococcus and Turicibacter has been associated with susceptibility to dextran sodium sulfate-induced colitis. Our results demonstrate that viable bacteria in FDP-A reduced CR-induced colonic crypt hyperplasia and prevented the loss of key bacterial genera that may contribute to disease pathology

Identification of an Intestinal Microbiota Signature Associated With Severity of Irritable Bowel Syndrome

BACKGROUND &amp; AIMS: We have limited knowledge about the association between the composition of the intestinal microbiota and clinical features of irritable bowel syndrome (IBS). We collected information on the fecal and mucosa-associated microbiota of patients with IBS and evaluated whether these were associated with symptoms. METHODS: We collected fecal and mucosal samples from adult patients who met the Rome III criteria for IBS at secondary or tertiary care outpatient clinics in Sweden, as well as from healthy subjects. The exploratory set comprised 149 subjects (110 with IBS and 39 healthy subjects); 232 fecal samples and 59 mucosal biopsy samples were collected and analyzed by 16S ribosomal RNA targeted pyrosequencing. The validation set comprised 46 subjects (29 with IBS and 17 healthy subjects); 46 fecal samples, but no mucosal samples, were collected and analyzed. For each subject, we measured exhaled H2 and CH4, oro-anal transit time, and the severity of psychological and gastrointestinal symptoms. Fecal methanogens were measured by quantitative polymerase chain reaction. Numeric ecology analyses and a machine learning procedure were used to analyze the data. RESULTS: Fecal microbiota showed covariation with mucosal adherent microbiota. By using classic approaches, we found no differences in fecal microbiota abundance or composition between patients with vs without IBS. A computational statistical technique-like machine learning procedure allowed us to reduce the 16S ribosomal RNA data complexity into a microbial signature for severe IBS, consisting of 90 bacterial operational taxonomic units. We confirmed the robustness of the intestinal microbial signature for severe IBS in the validation set. The signature was able to discriminate between patients with severe symptoms, patients with mild/moderate symptoms, and healthy subjects. By using this intestinal microbiota signature, we found IBS symptom severity to be associated negatively with microbial richness, exhaled CH4, presence of methanogens, and enterotypes enriched with Clostridiales or Prevotella species. This microbiota signature could not be explained by differences in diet or use of medications. CONCLUSIONS: In analyzing fecal and mucosal microbiota from patients with IBS and healthy individuals, we identified an intestinal microbiota profile that is associated with the severity of IBS symptoms. TRIAL REGISTRATION NUMBER: NCT01252550.CC BY-NC-ND 4.0</p

Fasting breath H2 and gut microbiota metabolic potential are associated with the response to a fermented milk product in irritable bowel syndrome.

ObjectivesAim of this study was to assess the effect of a fermented milk product containing Bifidobacterium lactis CNCM I-2494 (FMP) on gastrointestinal (GI) symptoms and exhaled H2 and CH4 during a nutrient and lactulose challenge in patients with irritable bowel syndrome (IBS).MethodsWe included 125 patients with IBS (Rome III). Fasted subjects were served a 400ml liquid test meal containing 25g lactulose. The intensity of eight GI symptoms and the amount of exhaled H2 and CH4 were assessed before and during 4h after meal intake. The challenge was repeated after 14 days consumption of FMP or a control product in a double-blind, randomized, parallel design. The metabolic potential of fecal microbiota was profiled using 16S MiSeq analysis of samples obtained before and after the intervention.Results106 patients with IBS were randomized. No difference between FMP or control groups was found on GI symptoms or breath H2 and CH4 in the whole cohort. A post-hoc analysis in patients stratified according to their fasting H2 levels showed that in high H2 producers (fasting H2 level≥10ppm, n = 35), FMP consumption reduced fasting H2 levels (p = 0.003) and H2 production during the challenge (p = 0.002) and tended to decrease GI discomfort (p = 0.05) vs. control product. The Prevotella/Bacteroides metabolic potential at baseline was higher in high H2 producers (pConclusionsThe response to a fermented milk product containing Bifidobacterium lactis CNCM I-2494 (FMP) in patients with IBS seems to be associated with the metabolic potential of the gut microbiota.Trial registrationClinicalTrial.gov NCT01252550. These results were presented as congress posters at Digestive Disease Week 2016 in San Diego, USA and United European Gastroenterology Week 2016 in Vienna, Austria

Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women

ObjectiveBrain-gut-microbiota interactions may play an important role in human health and behavior. Although rodent models have demonstrated effects of the gut microbiota on emotional, nociceptive, and social behaviors, there is little translational human evidence to date. In this study, we identify brain and behavioral characteristics of healthy women clustered by gut microbiota profiles.MethodsForty women supplied fecal samples for 16S rRNA profiling. Microbial clusters were identified using Partitioning Around Medoids. Functional magnetic resonance imaging was acquired. Microbiota-based group differences were analyzed in response to affective images. Structural and diffusion tensor imaging provided gray matter metrics (volume, cortical thickness, mean curvature, surface area) as well as fiber density between regions. A sparse Partial Least Square-Discrimination Analysis was applied to discriminate microbiota clusters using white and gray matter metrics.ResultsTwo bacterial genus-based clusters were identified, one with greater Bacteroides abundance (n = 33) and one with greater Prevotella abundance (n = 7). The Prevotella group showed less hippocampal activity viewing negative valences images. White and gray matter imaging discriminated the two clusters, with accuracy of 66.7% and 87.2%, respectively. The Prevotella cluster was associated with differences in emotional, attentional, and sensory processing regions. For gray matter, the Bacteroides cluster showed greater prominence in the cerebellum, frontal regions, and the hippocampus.ConclusionsThese results support the concept of brain-gut-microbiota interactions in healthy humans. Further examination of the interaction between gut microbes, brain, and affect in humans is needed to inform preclinical reports that microbial modulation may affect mood and behavior

Most diverse data, PCA sample plots.

<p><b>(a)</b> TSS and <b>(b)</b> TSS multilevel OTU log counts, <b>(c)</b> TSS-ILR and <b>(d)</b> TSS-ILR multilevel normalised log counts, <b>(e)</b> CSS and <b>(f)</b> CSS multilevel log counts.</p

Most diverse TSS+CLR data, sPLS-DA sample, contribution and cladogram plots.

<p><b>(a)</b> sample plot on the first two components with 95% confidence level ellipse plots, <b>(b)</b> and <b>(c)</b> represent the contribution of each OTU feature selected on the first (10 OTUs) and second component (120 OTUs), with OTU contribution ranked from bottom (important) to top. Colours indicate body site in which the OTU is most abundant. <b>(d)</b> Cladogram generated from the sPLS-DA result using GraphlAn.</p

Oral data, contribution and cladogram plots of the features selected for each sPLS-DA component.

<p><b>(a)</b> Component 1, <b>(b)</b> Component 2, <b>(c)</b> Component 3. In <b>(c)</b> only the top 150 OTU are represented. <b>(d)</b> Cladogram generated from the sPLS-DA results for components 1 and 2 using GraphlAn.</p