SUMMARY In inflammatory bowel disease, the relationship between a host and gut microbial community goes awry. We have characterized the fecal microbial communities in a mouse IBD model driven by T-bet deficiency in the innate immune system. 16S rRNA-based analysis of T-bet−/− × Rag2−/− and Rag2−/− mice revealed distinctive communities that correlate with host genotype. Culture-based surveys, invasion assays, antibiotic treatment, and TNF-α blockade disclosed that the presence of Klebsiella pneumoniae and Proteus mirabilis correlates with colitis in T-bet−/− × Rag2−/− animals, and that T-bet−/− × Rag2−/− derived strains can elicit colitis in Rag2−/− and wild-type adults. Cross-fostering experiments provided evidence for the role of these organisms in maternal transmission of disease. This model provides a foundation for defining how gut microbial communities work in concert with specific culturable colitogenic agents to cause IBD, and a foundation for conducting proof-of-concept tests of new preventative or therapeutic measures directed at components of the gut microbiota and/or host.

SUMMARYBacterial vaginosis (BV) is the most commonly reported microbiological syndrome among women of childbearing age. BV is characterized by a shift in the vaginal flora from the dominantLactobacillusto a polymicrobial flora. BV has been associated with a wide array of health issues, including preterm births, pelvic inflammatory disease, increased susceptibility to HIV infection, and other chronic health problems. A number of potential microbial pathogens, singly and in combinations, have been implicated in the disease process. The list of possible agents continues to expand and includes members of a number of genera, includingGardnerella,Atopobium,Prevotella,Peptostreptococcus,Mobiluncus,Sneathia,Leptotrichia,Mycoplasma, and BV-associated bacterium 1 (BVAB1) to BVAB3. Efforts to characterize BV using epidemiological, microscopic, microbiological culture, and sequenced-based methods have all failed to reveal an etiology that can be consistently documented in all women with BV. A careful analysis of the available data suggests that what we term BV is, in fact, a set of common clinical signs and symptoms that can be provoked by a plethora of bacterial species with proinflammatory characteristics, coupled to an immune response driven by variability in host immune function.

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users.