We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

The gut microbiome plays an important role in immune function and has been implicated in several autoimmune disorders. Here we use 16S rRNA sequencing to investigate the gut microbiome in subjects with multiple sclerosis (MS, n=60) and healthy controls (n=43). Microbiome alterations in MS include increases in Methanobrevibacter and Akkermansia and decreases in Butyricimonas, and correlate with variations in the expression of genes involved in dendritic cell maturation, interferon signalling and NF-kB signalling pathways in circulating T cells and monocytes. Patients on disease-modifying treatment show increased abundances of Prevotella and Sutterella, and decreased Sarcina, compared with untreated patients. MS patients of a second cohort show elevated breath methane compared with controls, consistent with our observation of increased gut Methanobrevibacter in MS in the first cohort. Further study is required to assess whether the observed alterations in the gut microbiome play a role in, or are a consequence of, MS pathogenesis.

SUMMARY Mammals exhibit marked inter-individual variations in their gut microbiota, but it remains unclear if this is primarily driven by host genetics or by extrinsic factors like dietary intake. To address this, we examined the effect of dietary perturbations on the gut microbiota of five inbred mouse strains, mice deficient for genes relevant to host-microbial interactions (MyD88−/−, NOD2−/−, ob/ob, and Rag1−/−), and >200 outbred mice. In each experiment, consumption of a high-fat, high-sugar diet reproducibly altered the gut microbiota despite differences in host genotype. The gut microbiota exhibited a linear dose response to dietary perturbations, taking an average of 3.5 days for each diet-responsive bacterial groups to reach a new steady state. Repeated dietary shifts demonstrated that most changes to the gut microbiota are reversible, while also uncovering bacteria whose abundance depends on prior consumption. These results emphasize the dominant role that diet plays in shaping inter-individual variations in host-associated microbial communities.

We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions data from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network

Highlights d Phages can coexist over time with targeted gut bacteria d Phages induce cascading effects on microbiota species that are not directly targeted d Phage-induced bacterial modulation impacts the gut metabolome d Phages can modulate metabolites, which are known to affect the mammalian host

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.

Bacteria can be engineered to function as diagnostics or therapeutics in the mammalian gut but commercial translation of these technologies has been hindered by the susceptibility of synthetic genetic circuits to mutation and unpredictable function during extended gut colonization. Here we report stable, engineered bacterial strains that maintain their function for 6 months in the mouse gut. We engineered a commensal murine Escherichia coli strain to detect tetrathionate, which is produced during inflammation. Using our engineered diagnostic strain, which retains memory of exposure in the gut for analysis by fecal testing, we detected tetrathionate in both infection-induced and genetic mouse models of inflammation over 6 months. The synthetic genetic circuits in the engineered strain were genetically stable and functioned as intended over time. The robust, durable performance of these strains confirms the potential of engineered bacteria as living diagnostics.

The role of dysbiosis in food allergy (FA) remains unclear. We found that dysbiotic fecal microbiota in FA infants evolved compositionally over time and failed to protect against FA in mice. Infants and mice with FA had decreased IgA and increased IgE binding to fecal bacteria, indicative of a broader breakdown of oral tolerance than hitherto appreciated. Therapy with Clostridiales species impacted by dysbiosis, either as a consortium or as monotherapy with Subdoligranulum variabile , suppressed FA in mice, as did a separate immunomodulatory Bacteroidales consortium. Bacteriotherapy induced regulatory T (Treg) cells expressing the transcription factor ROR-γt in a MyD88-dependent manner, which were deficient in FA infants and mice and ineffectively induced by their microbiota. Deletion of Myd88 or Rorc in Treg cells abrogated protection by bacteriotherapy. Thus, commensals activate a MyD88/ROR-γt pathway in nascent Treg cells to protect against FA, while dysbiosis impairs this regulatory response to promote disease.