Yumiko Nakanishi

Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells

Gut commensal microbes shape the mucosal immune system by regulating the differentiation and expansion of several types of T cell. Clostridia, a dominant class of commensal microbe, can induce colonic regulatory T (Treg) cells, which have a central role in the suppression of inflammatory and allergic responses. However, the molecular mechanisms by which commensal microbes induce colonic Treg cells have been unclear. Here we show that a large bowel microbial fermentation product, butyrate, induces the differentiation of colonic Treg cells in mice. A comparative NMR-based metabolome analysis suggests that the luminal concentrations of short-chain fatty acids positively correlates with the number of Treg cells in the colon. Among short-chain fatty acids, butyrate induced the differentiation of Treg cells in vitro and in vivo, and ameliorated the development of colitis induced by adoptive transfer of CD4+ CD45RBhi T cells in Rag1−/− mice. Treatment of naive T cells under the Treg-cell-polarizing conditions with butyrate enhanced histone H3 acetylation in the promoter and conserved non-coding sequence regions of the Foxp3 locus, suggesting a possible mechanism for how microbial-derived butyrate regulates the differentiation of Treg cells. Our findings provide new insight into the mechanisms by which host–microbe interactions establish immunological homeostasis in the gut.

Bifidobacteria can protect from enteropathogenic infection through production of acetate

The human gut is colonized with a wide variety of microorganisms, including species, such as those belonging to the bacterial genus Bifidobacterium, that have beneficial effects on human physiology and pathology. Among the most distinctive benefits of bifidobacteria are modulation of host defence responses and protection against infectious diseases. Nevertheless, the molecular mechanisms underlying these effects have barely been elucidated. To investigate these mechanisms, we used mice associated with certain bifidobacterial strains and a simplified model of lethal infection with enterohaemorrhagic Escherichia coli O157:H7, together with an integrated ‘omics’ approach. Here we show that genes encoding an ATP-binding-cassette-type carbohydrate transporter present in certain bifidobacteria contribute to protecting mice against death induced by E. coli O157:H7. We found that this effect can be attributed, at least in part, to increased production of acetate and that translocation of the E. coli O157:H7 Shiga toxin from the gut lumen to the blood was inhibited. We propose that acetate produced by protective bifidobacteria improves intestinal defence mediated by epithelial cells and thereby protects the host against lethal infection.

Statistical Indices for Simultaneous Large-Scale Metabolite Detections for a Single NMR Spectrum

NMR-based metabolomics has become a practical and analytical methodology for discovering novel genes, biomarkers, metabolic phenotypes, and dynamic cell behaviors in organisms. Recent developments in NMR-based metabolomics, however, have not concentrated on improvements of comprehensiveness in terms of simultaneous large-scale metabolite detections. To resolve this, we have devised and implemented a statistical index, the SpinAssign p-value, in NMR-based metabolomics for large-scale metabolite annotation and publicized this information. It enables simultaneous annotation of more than 200 candidate metabolites from the single (13)C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell extract.

Evaluation and Characterization of Bacterial Metabolic Dynamics with a Novel Profiling Technique, Real-Time Metabolotyping

Background Environmental processes in ecosystems are dynamically altered by several metabolic responses in microorganisms, including intracellular sensing and pumping, battle for survival, and supply of or competition for nutrients. Notably, intestinal bacteria maintain homeostatic balance in mammals via multiple dynamic biochemical reactions to produce several metabolites from undigested food, and those metabolites exert various effects on mammalian cells in a time-dependent manner. We have established a method for the analysis of bacterial metabolic dynamics in real time and used it in combination with statistical NMR procedures. Methodology/Principal Findings We developed a novel method called real-time metabolotyping (RT-MT), which performs sequential 1H-NMR profiling and two-dimensional (2D) 1H, 13C-HSQC (heteronuclear single quantum coherence) profiling during bacterial growth in an NMR tube. The profiles were evaluated with such statistical methods as Z-score analysis, principal components analysis, and time series of statistical TOtal Correlation SpectroScopY (TOCSY). In addition, using 2D 1H, 13C-HSQC with the stable isotope labeling technique, we observed the metabolic kinetics of specific biochemical reactions based on time-dependent 2D kinetic profiles. Using these methods, we clarified the pathway for linolenic acid hydrogenation by a gastrointestinal bacterium, Butyrivibrio fibrisolvens. We identified trans11, cis13 conjugated linoleic acid as the intermediate of linolenic acid hydrogenation by B. fibrisolvens, based on the results of 13C-labeling RT-MT experiments. In addition, we showed that the biohydrogenation of polyunsaturated fatty acids serves as a defense mechanism against their toxic effects. Conclusions RT-MT is useful for the characterization of beneficial bacterium that shows potential for use as probiotic by producing bioactive compounds.

In vitro evaluation method for screening of candidate prebiotic foods

The aim of this work was to develop a simple and rapid in vitro evaluation method for screening and discovery of uncharacterised and untapped prebiotic foods. Using a NMR-based metabolomic approach coupled with multivariate statistical analysis, the metabolic profiles generated by intestinal microbiota after in vitro incubation with feces were examined. The viscous substances of Japanese bunching onion (JBOVS) were identified as one of the candidate prebiotic foods by this in vitro screening method. The JBOVS were primarily composed of sugar components, especially fructose-based carbohydrates. Our results suggested that ingestion of JBOVS contributed to lactate and acetate production by the intestinal microbiota, and were accompanied by an increase in the Lactobacillus murinus and Bacteroidetes sp. populations in the intestine and fluctuation of the host-microbial co-metabolic process. Therefore, our approach should be useful as a rapid and simple screening tool for potential prebiotic foods.

Maternal High Fiber Diet during Pregnancy and Lactation Influences Regulatory T Cell Differentiation in Offspring in Mice

Short-chain fatty acids (SCFAs), the end products of dietary fiber, influence the immune system. Moreover, during pregnancy the maternal microbiome has a great impact on the development of the offspring’s immune system. However, the exact mechanisms by which maternal SCFAs during pregnancy and lactation influence the immune system of offspring are not fully understood. We investigated the molecular mechanisms underlying regulatory T cell (Treg) differentiation in offspring regulated by a maternal high fiber diet (HFD). Plasma levels of SCFAs in offspring from HFD-fed mice were higher than in those from no fiber diet–fed mice. Consequently, the offspring from HFD-fed mice had higher frequencies of thymic Treg (tTreg) and peripheral Tregs. We found that the offspring of HFD-fed mice exhibited higher autoimmune regulator (Aire) expression, a transcription factor expressed in the thymic microenvironment, suggesting SCFAs promote tTreg differentiation through increased Aire expression. Notably, the receptor for butyrate, G protein–coupled receptor 41 (GPR41), is highly expressed in the thymic microenvironment and Aire expression is not increased by stimulation with butyrate in GPR41-deficient mice. Our studies highlight the significance of SCFAs produced by a maternal HFD for Treg differentiation in the thymus of offspring. Given that Aire expression is associated with the induction of tTregs, the maternal microbiome influences Treg differentiation in the thymus of offspring through GPR41-mediated Aire expression.

Erratum: Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells (Nature (2013) 504 (446-450) DOI: 10.1038/nature12721)

This corrects the article DOI: 10.1038/nature12721

A Metabolomic-Based Evaluation of the Role of Commensal Microbiota throughout the Gastrointestinal Tract in Mice

Commensal microbiota colonize the surface of our bodies. The inside of the gastrointestinal tract is one such surface that provides a habitat for them. The gastrointestinal tract is a long organ system comprising of various parts, and each part possesses various functions. It has been reported that the composition of intestinal luminal metabolites between the small and large intestine are different; however, comprehensive metabolomic and commensal microbiota profiles specific to each part of the gastrointestinal lumen remain obscure. In this study, by using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS)-based metabolome and 16S rRNA gene-based microbiome analyses of specific pathogen-free (SPF) and germ-free (GF) murine gastrointestinal luminal profiles, we observed the different roles of commensal microbiota in each part of the gastrointestinal tract involved in carbohydrate metabolism and nutrient production. We found that the concentrations of most amino acids in the SPF small intestine were higher than those in the GF small intestine. Furthermore, sugar alcohols such as mannitol and sorbitol accumulated only in the GF large intestine, but not in the SPF large intestine. On the other hand, pentoses, such as arabinose and xylose, gradually accumulated from the cecum to the colon only in SPF mice, but were undetected in GF mice. Correlation network analysis between the gastrointestinal microbes and metabolites showed that niacin metabolism might be correlated to Methylobacteriaceae. Collectively, commensal microbiota partially affects the gastrointestinal luminal metabolite composition based on their metabolic dynamics, in cooperation with host digestion and absorption.

Liberation from equations: An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

Mapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics. We developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes. We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice. The complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.