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.