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...

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.

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Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages—acute coronary syndrome, chronic IHD and IHD with heart failure—and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

This paper comprises an updated version of the 2014 review which reported 1846 volatile organic compounds (VOCs) identified from healthy humans. In total over 900 additional VOCs have been reported since the 2014 review and the VOCs from semen have been added. The numbers of VOCs found in breath and the other bodily fluids are: blood 379, breath 1488, faeces 443, milk 290, saliva 549, semen 196, skin 623 and urine 444. Compounds were assigned CAS registry numbers and named according to a common convention where possible. The compounds have been included in a single table with the source reference(s) for each VOC, an update on our 2014 paper. VOCs have also been grouped into tables according to their chemical class or functionality to permit easy comparison. Careful use of the database is needed, as a number of the identified VOCs only have level 2—putative assignment, and only a small fraction of the reported VOCs have been validated by standards. Some clear differences are observed, for instance, a lack of esters in urine with a high number in faeces and breath. However, the lack of compounds from matrices such a semen and milk compared to breath for example could be due to the techniques used or reflect the intensity of effort e.g. there are few publications on VOCs from milk and semen compared to a large number for breath. The large number of volatiles reported from skin is partly due to the methodologies used, e.g. by collecting skin sebum (with dissolved VOCs and semi VOCs) onto glass beads or cotton pads and then heating to a high temperature to desorb VOCs. All compounds have been included as reported (unless there was a clear discrepancy between name and chemical structure), but there may be some mistaken assignations arising from the original publications, particularly for isomers. It is the authors’ intention that this work will not only be a useful database of VOCs listed in the literature but will stimulate further study of VOCs from healthy individuals; for example more work is required to confirm the identification of these VOCs adhering to the principles outlined in the metabolomics standards initiative. Establishing a list of volatiles emanating from healthy individuals and increased understanding of VOC metabolic pathways is an important step for differentiating between diseases using VOCs.