NCT01252550.

The gut microbiota (GM) consists of resident commensals and transient microbes conveyed by the diet but little is known about the role of the latter on GM homeostasis. Here we show, by a conjunction of quantitative metagenomics, in silico genome reconstruction and metabolic modeling, that consumption of a fermented milk product containing dairy starters and Bifidobacterium animalis potentiates colonic short chain fatty acids production and decreases abundance of a pathobiont Bilophila wadsworthia compared to a milk product in subjects with irritable bowel syndrome (IBS, n = 28). The GM changes parallel improvement of IBS state, suggesting a role of the fermented milk bacteria in gut homeostasis. Our data challenge the view that microbes ingested with food have little impact on the human GM functioning and rather provide support for beneficial health effects.

Objective Brain-gut-microbiota interactions may play an important role in human health and behavior. However, while 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. Methods Forty 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. Results Two bacterial genus-based clusters were identified, one with greater Bacteroides abundance (n=33), 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. Conclusions These 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.

Resident gut microbes co-exist with transient bacteria to form the gut microbiota. Despite increasing evidence suggesting a role for transient microbes on gut microbiota function, the interplay between resident and transient members of this microbial community is poorly defined. We aimed to determine the extent to which a host's autochthonous gut microbiota influences niche permissivity to transient bacteria using a fermented milk product (FMP) as a vehicle for five food-borne bacterial strains. Using conventional and gnotobiotic rats and gut microbiome analyses (16S rRNA genes pyrosequencing and reverse transcription qPCR), we demonstrated that the clearance kinetics of one FMP bacterium, Lactococcus lactis CNCM I-1631, were dependent on the structure of the resident gut microbiota. Susceptibility of the resident gut microbiota to modulation by FMP intervention correlated with increased persistence of L. lactis. We also observed gut microbiome configurations that were associated with altered stability upon exposure to transient bacteria. Our study supports the concept that allochthonous bacteria have transient and subject-specific effects on the gut microbiome that can be leveraged to re-engineer the gut microbiome and improve dysbiosis-related diseases.

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present , a novel multivariate data analysis framework for metagenomic biomarker discovery. accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.