Obesity and type 2 diabetes are associated with low-grade inflammation and specific 34 changes in gut microbiota composition [1][2][3][4][5][6][7] . We previously demonstrated that administration 35 of Akkermansia muciniphila prevents the development of obesity and associated 36 complications 8 . However, its mechanisms of action remain unclear, whilst the sensitivity of 37 A. muciniphila to oxygen and the presence of animal-derived compounds in its growth 38 medium currently limit the development of translational approaches for human medicine 9 . 39Here we addressed these issues by showing that A. muciniphila retains its efficacy when Akkermansia muciniphila is one of the most abundant members of the human gut 53 microbiota, representing between 1 and 5% of our intestinal microbes 10,11 to improve glucose intolerance and insulin resistance regardless of the growth medium used and 71 independently of food intake ( Fig. 1a-g). 72 We previously showed that autoclaving A. muciniphila abolished its beneficial effects 8 . (Fig. 1a-c and Supplemental Fig. 1a-c). In both sets of 81 experiments, we found that mice treated with pasteurized A. muciniphila displayed a much lower 82 glucose intolerance and insulin concentration when compared to the HFD group, resulting in a 83 lower insulin resistance (IR) index (Fig. 1d-g and Supplemental Fig. 1d-g). Treatment with 84 pasteurized A. muciniphila also led to greater goblet cell density in the ileum when compared to 85 ND-fed mice (Fig. 1h), suggesting a higher mucus production, while normalizing the mean 86 adipocyte diameter (Fig. 2a-b) and significantly lowering plasma leptin when compared to HFD-87 fed mice (Fig. 2c). These effects were not observed in mice treated with live A. muciniphila. A 88 similar trend could be observed for plasma resistin (Supplemental Fig. 1h), thereby suggesting 89 improved insulin sensitivity, while plasma adiponectin remained unaffected in all conditions 90 (Supplemental Fig. 1i). We found that mice treated with pasteurized A. muciniphila had a higher 91 fecal caloric content when compared to all other groups (Fig. 2d), suggesting a lower energy (Fig. 2e-g). This resulted in a normalization of the HFD-induced shift of 37% with the 104 pasteurized bacterium, and 17% with the live bacterium ( Fig. 2f). 105By comparing the metabolic profiles of the different groups, we found that the shift 106 induced by pasteurized A. muciniphila was mainly associated with trimethylamine (TMA) and TMA to TMAO, a metabolite associated with atherosclerosis 19,20 . While exposure to a HFD led 114 to a two-fold higher Fmo3 expression when compared to ND-fed mice, treatment with 115 pasteurized A. muciniphila reversed this effect (Fig. 2j) Fmo3 expression were not associated with a modification of plasma TMA and TMAO, as all 121 HFD-fed group displayed similar concentrations for both metabolites (Fig. 2k,l) (Fig. 3a), but not cells expressing TLR5, TLR9 or the NOD2 receptor (Fig. 3b-131 d). 132Genomic and proteomic analyses of A. muciniphila identified p...

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We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.

The human gut harbors more than 100 trillion microbial cells, which have an essential role in human metabolic regulation via their symbiotic interactions with the host. Altered gut microbial ecosystems have been associated with increased metabolic and immune disorders in animals and humans. Molecular interactions linking the gut microbiota with host energy metabolism, lipid accumulation, and immunity have also been identified. However, the exact mechanisms that link specific variations in the composition of the gut microbiota with the development of obesity and metabolic diseases in humans remain obscure owing to the complex etiology of these pathologies. In this review, we discuss current knowledge about the mechanistic interactions between the gut microbiota, host energy metabolism, and the host immune system in the context of obesity and metabolic disease, with a focus on the importance of the axis that links gut microbes and host metabolic inflammation. Finally, we discuss therapeutic approaches aimed at reshaping the gut microbial ecosystem to regulate obesity and related pathologies, as well as the challenges that remain in this area.

Here, we study the intricate relationship between gut microbiota and host cometabolic phenotypes associated with dietary-induced impaired glucose homeostasis and nonalcoholic fatty liver disease (NAFLD) in a mouse strain (129S6) known to be susceptible to these disease traits, using plasma and urine metabotyping, achieved by 1 H NMR spectroscopy. Multivariate statistical modeling of the spectra shows that the genetic predisposition of the 129S6 mouse to impaired glucose homeostasis and NAFLD is associated with disruptions of choline metabolism, i.e., low circulating levels of plasma phosphatidylcholine and high urinary excretion of methylamines (dimethylamine, trimethylamine, and trimethylamine-Noxide), coprocessed by symbiotic gut microbiota and mammalian enzyme systems. Conversion of choline into methylamines by microbiota in strain 129S6 on a high-fat diet reduces the bioavailability of choline and mimics the effect of choline-deficient diets, causing NAFLD. These data also indicate that gut microbiota may play an active role in the development of insulin resistance.metabonomics ͉ NMR ͉ nonalcoholic fatty liver disease ͉ nutritional genomics ͉ metabolic syndrome H ighly complex animals such as mammals can be considered as ''superorganisms'' with a karyome, a chondriome, and a microbiome (1), resulting from a coevolutionary symbiotic ecosystem of diverse intestinal microbiota interacting metabolically with the host (2). Recent molecular analyses of human microbiota 16s ribosomal DNA sequences revealed a majority of uncultivated or unknown species with a strong degree of interindividual diversity (3, 4). Also, some of the molecular foundations of beneficial symbiotic host-bacteria relationships in the gut were revealed by colonization of germ-free mice with known microbes and by comparisons of the genomes of members of the intestinal microbiota (5). For instance, Bacteroides thetaiotaomicron, a dominant member of normal distal intestinal microbiota, hydrolyzes otherwise indigestible dietary polysaccharides, thus supplying the host with 10-15% of calorific requirement (6). Gut Lactobacillus spp. are also responsible for a significant proportion of bile acid deconjugation, a process that efficiently reduces lipid absorption in the gut (7). Such symbiotic relationships are the result of coevolution and operate at the genome, proteome, and metabolome levels (6,8).Insulin resistance (IR) is central to a cluster of frequent and increasingly prevalent pathologies, including type 2 diabetes mellitus, central obesity, hypertension hepatic steatosis, and dyslipidemia (9). IR contributes to major causes of morbidity and mortality worldwide (10). Epidemiological and genetic studies in human and animal models have demonstrated the importance of both genetic and environmental factors in the etiology of IR (9): Dietary variation and intervention, in particular, have a strong influence on the development of IR. Nonalcoholic fatty liver disease (NAFLD), is the most frequent liver condition associated with IR (11). It is associa...

SUMMARY Intestinal microbial metabolites are conjectured to affect mucosal integrity through an incompletely characterized mechanism. Here we showed 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 as a ligand for PXR in vivo, and IPA down-regulated enterocyte TNF–α while up-regulated junctional protein-coding mRNAs. PXR-deficient (Nr1i2−/−) mice showed a distinctly “leaky” gut physiology coupled with up-regulation 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 which involves luminal sensing and signaling by TLR4.

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.

Hepatic steatosis is a multifactorial condition that is often observed in obese patients and is a prelude to non-alcoholic fatty liver disease. Here, we combine shotgun sequencing of fecal metagenomes with molecular phenomics (hepatic transcriptome and plasma and urine metabolomes) in two well-characterized cohorts of morbidly obese women recruited to the FLORINASH study. We reveal molecular networks linking the gut microbiome and the host phenome to hepatic steatosis. Patients with steatosis have low microbial gene richness and increased genetic potential for the processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid metabolism. We demonstrated that fecal microbiota transplants and chronic treatment with phenylacetic acid, a microbial product of aromatic amino acid metabolism, successfully trigger steatosis and branched-chain amino acid metabolism. Molecular phenomic signatures were predictive (area under the curve = 87%) and consistent with the gut microbiome having an effect on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies.