Professor Marc-Emmanuel Dumas

Prof. Dumas’ research interests in systems medicine include metabolomics, quantitative genetics, metagenomics and network biology, more specifically integration of metabolomic data with other "omics" (transcriptomics, proteomics, genome polymorphism and metagenomics) to identify metabolites and genes associated with metabolic and cardiorespiratory pathologies, ie insulin resistance,  metabolic syndrome, type 2 diabetes, obesity, asthma, dyslipidemia, atherosclerosis, hypertension, non-alcoholic fatty liver disease, but also ageing and cancer. This functional integration of "omics" sciences identifies candidate drug targets for mechanistic validation.

Prof. Dumas’ contribution to insulin resistance research was the identification of a gut microbiota  – related metabolic phenotype (or “metabotype”) in non-alcoholic fatty liver disease. Methylamines (monomethylamine, dimethylamine, trimethylamine and trimethylamine-N-oxide), a class of microbial metabolites derived from dietary choline-containing compounds, are associated with the development of insulin resistance, and more specifically non-alcoholic fatty liver disease. This observation on gut microbial metabolism brought an independent confirmation of several previous findings in metagenomic projects from a metabolic point of view. Methylamines are now thought to be involved in other insulin resistance related pathologies such as atherosclerosis. 

Prof. Dumas is one of the pioneers in the field of Metabotype Quantitative Trait Locus (mQTL) Mapping and Metabolome-wide Genome-Wide Association Studies (i.e., metabolomic GWAS), by integrating genome-wide polymorphisms with metabolome-wide quantitative variation. Dr. Dumas now develops the concept of microbiome-wide metabolome-wide association studies (MW²AS), combining High-Throughput Sequencing and Metabolic Profiling technologies. Prof. Dumas has also developed a keen interest in knowledge visualization and integration of metabolic signatures with protein-protein interaction networks (interactome), and recently introduced a new approach to understand the regulation of metabolic patterns, by mapping metabolic networks onto protein-protein interaction networks: integrated metabolome and interactome mapping (iMIM).

Prof. Dumas introduced the concept of "Metabolomics-on-a-chip", i.e. the hyphenation of bioartificial organs based on microfluidic biochips with metabolic profiling technologies, and pioneered applications for in vitro predictive systems toxicology and pharmacology. He now pursues Organ-on-a-Chip approaches to study bioactivities of microbial metabolites.

Metabolic phenotypes, or metabotypes, are regulated by both host genetics and the environment.

Prof. Dumas pioneered the genetics of metabolism and introduced the first quantitative trait locus mapping of metabotypes (mQTL) in animal models using genetic intercrosses. He then worked towards applying mQTL to human cohorts. 

Professor Dumas also studies the role of mutations on molecular phenotypes, typically for neurodevelopmental disorders.

Key references

Brial F, Le Lay A, Hedjazi L, Tsang T, Fearnside JF, Otto GW, Alzaid F, Wilder SP, Venteclef N, Cazier JB, Nicholson JK, Day C, Burt AD, Gut IG, Lathrop M, Dumas ME, Gauguier D.Systems Genetics of Hepatic Metabolome Reveals Octopamine as a Target for Non-Alcoholic Fatty Liver Disease Treatment. Sci Rep. 2019;9(1):3656.

Davidovic L, Navratil V, Bonaccorso CM, Catania MV, Bardoni B, Dumas ME*.A Metabolomic and Systems Biology Perspective of Fragile X Syndrome in The Mouse. Genome Res,2011;21(12):2190-2202.

Nicholson G, Rantalainen M, Li JV, Maher AD, Malmodin D, Ahmadi KR, Faber JH, Min JL, William Rayner N, Toft H, Krestyaninova M, Viksna J, Guha Neogi S, Dumas ME, Sarkans U, Donnelly P, Allen M, Zondervan KT, Spector TD, Nicholson JK, Lindon JC, Baunsgaard D, Holmes EC, McCarthy MI, Holmes CC and MolPAGE consortium. A genome-wide metabolic QTL analysis in Europeans identifies functional effects of two loci shaped by recent positive selection. PLoS Genetics2011;7(9):e1002270.

Nicholson G, Rantalainen M, Maher AD, Li JV, Malmodin D, Ahmadi KR, Faber JH, Hallgrímsdóttir IB, Barrett A, Toft H, Krestyaninova M, Viksna J, Neogi SG, Dumas ME, Sarkans U, The Molpage Consortium, Silverman BW, Donnelly P, Nicholson JK, Allen M, Zondervan KT, Lindon JC, Spector TD, McCarthy MI, Holmes E, Baunsgaard D, Holmes CC. Human metabolic profiles are stably controlled by genetic and environmental variation. Molecular Systems Biology.2011; 7:525.

Blaise BJ, Giacomotto, J, Elena B, Dumas ME, Toulhoat P, Segalat L, Emsley L. Metabotyping of Caenorhabditis elegans reveals latent phenotypes. Proc. Natl. Acad. Sci. USA.2007; 104:19808-12.

Dumas ME, et al., Direct quantitative trait locus mapping of mammalian phenotypes in diabetic and normoglycemic rat models. Nature Genetics. 2007; 39: 666-672.

Professor Dumas models the cellular events regulating the formation of metabotype patterns using protein interaction and signaling networks. The kinome (i.e. the 513 kinases in the human genome) commonly regulates catalytic activities of proteins through >500,000 phosphorylation sites, integrating multiple transduction pathways from receptor to target gene expression into complex signaling networks regulating obesity mechanisms. 

To elucidate the complexity of these signalling events, Prof. Dumas' group has developed  “integrated Metabolome and Interactome mapping" (iMIM), a biological navigation software which compiles>150,000 protein interactions and 34,000 protein-metabolite interactions and analyzes the topology of the resulting network in terms of shortest paths and busiest signaling nodes.

More recently, the group published two R packages in Bioconductor dedicated to Metabolome-Wide Association Study Tools (R/MWASTools) and navigating directional metabolic and signalling networks (R/MetaboSignal).

Key references

Rodriguez-Martinez A*, Ayala R, Posma JM, Dumas ME*.Exploring the genetic landscape of metabolic phenotypes with MetaboSignal. Current Protocols in Bioinformatics, 2018; 61(1): 14.14.1-14.14.13.

Rodriguez-Martinez A, Ayala R, Posma JM, Neves AL, Gauguier D, Nicholson JK, Dumas ME*. MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways. Bioinformatics 2017; 33(5);773-5.

Dumas ME*, Domange C, Calderari S, Rodríguez Martínez A, Ayala R, Wilder SP, Suarez Zamorano N, Collins SC, Wallis RH, Gu Q, Wang Y, Hue C, Otto GW, Argoud K, Navratil V, Mitchell S, Lindon J, Holmes E, Cazier JB, Nicholson JK, Gauguier D*. Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series. Genome Med 2016;8:101.

Ouattara DA, Prot JM, Bunescu A, Dumas ME, Elena-Hermann B, Leclerc E, Brochot C. Metabolomics-on-a-chip and metabolic flux analysis for label-free modeling of the internal metabolism of HepG2/C3A cells. Mol BioSyst. 2012;8(7):1908-1920.

Davidovic L, Navratil V, Bonaccorso CM, Catania MV, Bardoni B, Dumas ME*.A Metabolomic and Systems Biology Perspective of Fragile X Syndrome in The Mouse. Genome Res 2011;21(12):2190-2202.

Martin FP, Dumas ME, Wang Y, Legido-Quigley C, Yap IKS, Tang H, Zirah S, Cloarec O, Murphy GM, Lindon JC, Sprenger N, Fay LB, Kochhar S, Holmes E, Nicholson JK. A molecular systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Molecular Systems Biology. 2007; 3: 112.

Professor Dumas investigates the microbiome-host crosstalk by unraveling the fundamental mechanisms by which the microbiome interacts with its host through bioactive metabolites. 

Through a range of integrative modelling and mechanistic approaches, his group focusses on how the microbiome impacts host signalling and metabolism in health and disease.

With Dr. Lesley Hoyles, Professor Dumas has developed a range of bioinformatic tools in collaboration with the Bioinformatic Data Science Group to set up the College's shotgun metagenomics data analysis workflow.

Professor Dumas and his group screen microbial-host co-metabolites against host pharmacological targets such as G-protein coupled receptors, nuclear receptors or kinases in order to identify novel mechanisms actioned by the microbiome.

Key references

Hoyles L, Fernández-Real JM, Federici M, Serino M, Abbott J, Charpentier J, Heymes C, Latorre-Luque J, Anthony E, Rayah F, Chilloux J, Myridakis A, Martinez-Gili L, Moreno-Navarrete JM, Azalbert V, Blasco-Baque V, Barton RH, Puig J, Xifra G, Ricart W, Tomlinson C, Cardellini M, Davato F, Cardolini I, Porzio O, Gentilieschi P, Lopez F, Foufelle F, Butcher SA, Holmes E, Nicholson JK, Postic C, Burcelin R*, Dumas ME*. Molecular Phenomics and Metagenomics of Hepatic Steatosis in Non-Diabetic Obese Women. Nature Medicine, 2018;24(7):1070-1080.

Hoyles L, Jimenez Pranteda ML, Chilloux J, Brial F, Myridakis A, Aranias T, Magnan C, Gibson GR, Sanderson JD, Nicholson JK, Gauguier D, McCartney AL*, Dumas ME*. Metabolic retroconversion of trimethylamine N-oxide and the gut microbiota. Microbiome, 2018;6(1):73.

Dumas ME*, Rothwell AR, Hoyles L, Calderari S, Chilloux J, Noll EM, Boulangé CL, Blancher C, Barton RH, Gu Q, Deshayes C, Hue C, Fearnside JF, Scott J, Nicholson JK, Gauguier D. Microbial-Host Co-metabolites Are Prodromal Markers Predicting Phenotypic Heterogeneity in Behavior, Obesity, and Impaired Glucose Tolerance.Cell Reports2017;20(1):136-148.

Plovier H, Everard A, Druart C, Depommier C, Van Hul M, Geurts L, Chilloux J, Ottman N, Duparc T, Lichtenstein L, Myridakis A, Delzenne NM, Klievink J, Bhattacharjee A, van der Ark KC, Aalvink S, Martinez LO, Dumas ME, Maiter D, Loumaye A, Hermans MP, Thissen JP, Belzer C, de Vos WM, Cani PD. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nature Medicine. 2017; 23(1):107-113.

Dao MC, Everard A, Aron-Wisnewsky J, Sokolovska N, Prifti E, Chilloux J, Hoyles L, Verger EO, MICRO-Obes Consortium, Dumas ME, Rizkalla SW, Doré J, Cani PD, Clément K. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 2016;65(3):426-36.

Shoaie S, Ghaffari P, Kovatcheva-Datchary P, Mardinoglu A, Sen P, Pujos-Guillot E, de Wouters T, Juste C, Rizkalla S, Chilloux J, Hoyles L, Nicholson JK; MICRO-Obes Consortium, Doré J, Dumas ME, Clément K, Bäckhed F, Nielsen J. Quantifying diet induced changes in amino acid metabolism of the human gut microbiome. Cell Metab. 2015; 22(2):320-331.

Venkatesh M, Mukherjee S Wang H, Li H, Sun K, Benechet AP, Qiu Z, Maher L, Redinbo MR, Phillips RS, Fleet JC, Kortagere S, Mukherjee P, Fasano A, Le Ven J, Nicholson JK, Dumas ME, Khanna KM, Mani S. Symbiotic Bacterial Metabolites Regulate GI Barrier Function via PXR and TLR4. Immunity, 2014; 41(2):296–310.

Merrifeld CA, Lewis MC, Claus SP, Pearce JTM, Cloarec O, Duncker S, Heinzmann SS, Dumas ME, Kochhar S, Rezzi S, Mercenier A, Nicholson JK, Bailey M, Holmes E. Weaning Diet Induces Sustained Metabolic Phenotype Shift in the Pig and Influences Host Response to Bifidobacterium LactisNCC2818. Gut, 2013; 62(6):842-51.

Martin FP, Dumas ME, Wang Y, Legido-Quigley C, Yap IKS, Tang H, Zirah S, Cloarec O, Murphy GM, Lindon JC, Sprenger N, Fay LB, Kochhar S, Holmes E, Nicholson JK. A molecular systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Molecular Systems Biology.2007; 3: 112.

Dumas ME*, Barton R, Toye A, Cloarec O, Blancher C, Rothwell A, Fearnside J, Tatoud R, Blanc V, Lindon J, Mitchell SV, Holmes E, McCarthy MI, Scott J, Gauguier D, and Nicholson J*Metabolic profiling reveals a contribution of gut microbiota to dietary-induced fatty liver phenotype in mice. Proc. Natl. Acad. Sci. USA,2006; 103 :12511-12516.

Professor Dumas is one of the pioneers in the development of metabolic phenotyping for lab-on-a-chip applications, by combining microfluidic cell culture with ¹H NMR spectroscopy.

Key references

Snouber LC, Bunescu A, Legallais C, Brochot C, Dumas ME, Elena-Herrmann B, Leclerc E. Metabolomics-on-a-chip of hepatotoxicity induced by anticancer drug flutamide and its active metabolite hydroxyflutamide using HepG2/C3a microfluidic biochips. Tox Sci, 2013;132(1):8-20.

Ouattara DA, Prot JM, Bunescu A, Dumas ME, Elena-Hermann B, Leclerc E, Brochot C. Metabolomics-on-a-chip and metabolic flux analysis for label-free modeling of the internal metabolism of HepG2/C3A cells. Mol BioSyst.2012;8(7):1908-1920.

Prot JM, Bunescu A, Brochot C, Elena B, Corlu A, Aninat C, Griscom L, Shintu L, Bois F, Legallais C, Dumas ME, Leclerc E. Alternative method in predictive toxicology using systemic biology on chip: application to acetaminophen injury. Toxicol Appl Pharmacol, 2012;259(3):270-280.

Shintu L, Baudoin R, Navratil V, Prot JM, Pontoizeau C, Defernez M, Blaise BJ, Domange C, Péry ARR, Toulhoat P, Legallais C, Brochot C, Leclerc E*, Dumas ME*. Metabolomics-on-a-Chip and Predictive Systems Toxicology of Small Molecules in Microfluidic Bioartificial Organs. Analytical Chemistry2012;84(4):1840-48.