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Marc-Emmanuel Dumas

Researcher at Imperial College London

Publications -  116
Citations -  12085

Marc-Emmanuel Dumas is an academic researcher from Imperial College London. The author has contributed to research in topics: Microbiome & Gut flora. The author has an hindex of 41, co-authored 98 publications receiving 9420 citations. Previous affiliations of Marc-Emmanuel Dumas include McGill University & Chinese Academy of Sciences.

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A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice

TL;DR: It is shown that A. muciniphila retains its efficacy when grown on a synthetic medium compatible with human administration and enhanced its capacity to reduce fat mass development, insulin resistance and dyslipidemia in mice, and Amuc_1100, a specific protein isolated from the outer membrane of A. Sydneyi, interacts with Toll-like receptor 2, is stable at temperatures used for pasteurization and partly recapitulates the beneficial effects of the bacterium.
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Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice

TL;DR: 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, and indicates that gut microbiota may play an active role in the development of insulin resistance.
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Impact of the gut microbiota on inflammation, obesity, and metabolic disease.

TL;DR: 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 is discussed, with a focus on the importance of the axis that links gut microbes and host metabolic inflammation.
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Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets.

TL;DR: The implementation of the statistical total correlation spectroscopy (STOCSY) analysis method 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.