Symbiotic bacterial metabolites regulate gastrointestinal barrier function via the xenobiotic sensor PXR and Toll-like receptor 4.

Intestinal microbial metabolites are conjectured to affect mucosal integrity through an incompletely characterized mechanism. Here we showed that microbial-specific indoles regulated intestinal barrier function through the xenobiotic sensor, pregnane X receptor (PXR). Indole 3-propionic acid (IPA), in the context of indole, is a ligand for PXR in vivo, and IPA downregulated enterocyte TNF-α while it upregulated junctional protein-coding mRNAs. PXR-deficient (Nr1i2(-/-)) mice showed a distinctly "leaky" gut physiology coupled with upregulation of the Toll-like receptor (TLR) signaling pathway. These defects in the epithelial barrier were corrected in Nr1i2(-/-)Tlr4(-/-) mice. Our results demonstrate that a direct chemical communication between the intestinal symbionts and PXR regulates mucosal integrity through a pathway that involves luminal sensing and signaling by TLR4.

Early metabolic adaptation in C57BL/6 mice resistant to high fat diet induced weight gain involves an activation of mitochondrial oxidative pathways.

We investigated the short-term (7 days) and long-term (60 days) metabolic effect of high fat diet induced obesity (DIO) and weight gain in isogenic C57BL/6 mice and examined the specific metabolic differentiation between mice that were either strong-responders (SR), or non-responders (NR) to weight gain. Mice (n = 80) were fed a standard chow diet for 7 days prior to randomization into a high-fat (HF) (n = 56) or a low-fat (LF) (n = 24) diet group. The (1)H NMR urinary metabolic profiles of LF and HF mice were recorded 7 and 60 days after the diet switch. On the basis of the body weight gain (BWG) distribution of HF group, we identified NR mice (n = 10) and SR mice (n = 14) to DIO. Compared with LF, HF feeding increased urinary excretion of glycine conjugates of ß-oxidation intermediate (hexanoylglycine), branched chain amino acid (BCAA) catabolism intermediates (isovalerylglycine, α-keto-ß-methylvalerate and α-ketoisovalerate) and end-products of nicotinamide adenine dinucleotide (NAD) metabolism (N1-methyl-2-pyridone-5-carboxamide, N1-methyl-4-pyridone-3-carboxamide) suggesting up-regulation of mitochondrial oxidative pathways. In the HF group, NR mice excreted relatively more hexanoylglycine, isovalerylglycine, and fewer tricarboxylic acid (TCA) cycle intermediate (succinate) in comparison to SR mice. Thus, subtle regulation of ketogenic pathways in DIO may alleviate the saturation of the TCA cycle and mitochondrial oxidative metabolism.

Broad-ranging natural metabotype variation drives physiological plasticity in healthy control inbred rat strains.

Maintaining homeostasis in higher organisms involves a complex interplay of multiple ubiquitous and organ-specific molecular mechanisms that can be characterized using functional genomics technologies such as transcriptomics, proteomics, and metabonomics and dissected out through genetic investigations in healthy and diseased individuals. We characterized the genomic, metabolic, and physiological divergence of several inbred rat strains--Brown Norway, Lewis, Wistar Kyoto, Fisher (F344)--frequently used as healthy controls in genetic studies of the cardiometabolic syndrome. Hierarchical clustering of (1)H NMR-based metabolic profiles (n = 20 for urine, n = 16 for plasma) identified metabolic phenotype (metabotype) divergence patterns similar to the phylogenetic variability based on single nucleotide polymorphisms. However, the observed urinary metabotype variation exceeded that explainable by genetic polymorphisms. To understand further this natural variation, we used an integrative, knowledge-based network biology metabolic pathway analysis approach, coined Metabolite-Set Enrichment Analysis (MSEA). MSEA reveals that homeostasis and physiological plasticity can be achieved despite widespread divergences in glucose, lipid, amino acid, and energy metabolism in the host, together with different gut microbiota contributions suggestive of strain-specific transgenomic interactions. This work illustrates the concept of natural metabolomic variation, leading to physiologically stable albeit diverse strategies within the range of normality, all of which are highly relevant to animal model physiology, genetical genomics, and patient stratification in personalized healthcare.

2-hydroxycaproate predicts cardiovascular mortality in patients with atherosclerotic disease.

BACKGROUND AND AIMS: We aimed to identify novel biomarkers for cardiovascular mortality through a non-targeted metabolomics approach in patients with established atherosclerotic disease from the Tor Vergata Atherosclerosis Registry (TVAR). METHODS: We compared the serum baseline metabolome of 19 patients with atherosclerosis suffering from cardiovascular death during follow-up with the baseline serum metabolome of 20 control patients matched for age, gender, body mass index (BMI) and atherosclerotic disease status, who survived during the observation period. RESULTS: Three metabolites were significantly different in the cardiovascular mortality (CVM) group compared to controls: 2-hydroxycaproate, gluconate and sorbitol. 2-hydroxycaproate (otherwise known as alpha hydroxy caproate) was also significantly correlated with time to death. The metabolites performed better when combined together rather than singularly on the identification of CVM status. CONCLUSIONS: Our analysis led to identify few metabolites potentially amenable of translation into the clinical practice as biomarkers for specific metabolic changes in the cardiovascular system in patients with established atherosclerotic disease.

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome.

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations.

Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed.

Metabonomics in diabetes research.

Metabonomics has been defined as "quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification" and can provide information on disease processes, drug toxicity, and gene function. In this approach many samples of biological origin (biofluids such as urine or plasma) are analyzed using techniques that produce simultaneous detection. A variety of analytical metabolic profiling tools are used routinely, are also currently under development, and include proton nuclear magnetic resonance spectroscopy and mass spectrometry with a prior online separation step such as high-performance liquid chromatography, ultra-performance liquid chromatography, or gas chromatography. Data generated by these analytical techniques are often combined with multivariate data analysis, i.e., pattern recognition, for respectively generating and interpreting the metabolic profiles of the investigated samples. Metabonomics has gained great prominence in diabetes research within the last few years and has already been applied to understand the metabolism in a range of animal models and, more recently, attempts have been done to process complex metabolic data sets from clinical studies. A future hope for the metabonomic approach is the identification of biomarkers that are able to highlight individuals likely to suffer from diabetes and enable early diagnosis of the disease or the identification of those at risk. This review summarizes the technologies currently being used in metabonomics, as well as the studies reported related to diabetes prior to a description of the general objective of the research plan of the metabonomics part of the European Union project, Molecular Phenotyping to Accelerate Genomic Epidemiology.

Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies.

In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.