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(+) CD45RB(hi) 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.

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

Multivariate data analysis in pharmaceutics: a tutorial review.

Metabolites reflect the integration of gene expression, protein interaction and other different regulatory processes and are therefore closer to the phenotype than mRNA transcripts or proteins alone. Amongst all –omics technologies, metabolomics is the most transversal and can be applied to different organisms with little or no modifications. It has been successfully applied to the study of molecular phenotypes of plants in response to abiotic stress in order to find particular patterns associated to stress tolerance. These studies have highlighted the essential involvement of primary metabolites: sugars, amino acids and Krebs cycle intermediates as direct markers of photosynthetic dysfunction as well as effectors of osmotic readjustment. On the contrary, secondary metabolites are more specific of genera and species and respond to particular stress conditions as antioxidants, Reactive Oxygen Species (ROS) scavengers, coenzymes, UV and excess radiation screen and also as regulatory molecules. In addition, the induction of secondary metabolites by several abiotic stress conditions could also be an effective mechanism of cross-protection against biotic threats, providing a link between abiotic and biotic stress responses. Moreover, the presence/absence and relative accumulation of certain metabolites along with gene expression data provides accurate markers (mQTL or MWAS) for tolerant crop selection in breeding programs.

Metabolomics as a tool to investigate abiotic stress tolerance in plants.

Current approaches and challenges for the metabolite profiling of complex natural extracts

Kernel-based techniques (such as support vector machines, Bayes point machines, kernel principal component analysis, and Gaussian processes) represent a major development in machine learning algorithms. Support vector machines (SVM) are a group of supervised learning methods that can be applied to classification or regression. In a short period of time, SVM found numerous applications in chemistry, such as in drug design (discriminating between ligands and nonligands, inhibitors and noninhibitors, etc.), quantitative structure-activity relationships (QSAR, where SVM regression is used to predict various physical, chemical, or biological properties), chemometrics (optimization of chromatographic separation or compound concentration prediction from spectral data as examples), sensors (for qualitative and quantitative prediction from sensor data), chemical engineering (fault detection and modeling of industrial processes), and text mining (automatic recognition of scientific information). Support vector machines represent an extension to nonlinear models of the generalized portrait algorithm developed by Vapnik and Lerner. The SVM algorithm is based on the statistical learning theory and the Vapnik–Chervonenkis

Applications of Support Vector Machines in Chemistry

Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools The use of chemometrics tools, eg, principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, eg, OPLS discriminate analysis, having two or more classes The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of mu

Erik Johansson

Papers

Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models.

Multivariate Data Analysis (MVDA)

Partial Least Squares (PLS) in Cheminformatics