Publications
157 results found
Hoyles L, Jiménez-Pranteda MJ, Chilloux J, et al., 2018, Metabolic retroconversion of trimethylamine N-oxide and the gut microbiota, Microbiome, Vol: 6, ISSN: 2049-2618
Background:The dietary methylamines choline, carnitine, and phosphatidylcholine are used by the gut microbiota to produce a range of metabolites, including trimethylamine (TMA). However, little is known about the use of trimethylamine N-oxide (TMAO) by this consortium of microbes.Results:A feeding study using deuterated TMAO in C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Microbial activity necessary to convert TMAO to TMA was suppressed in antibiotic-treated mice, with deuterated TMAO being taken up directly into the bloodstream. In batch-culture fermentation systems inoculated with human faeces, growth of Enterobacteriaceae was stimulated in the presence of TMAO. Human-derived faecal and caecal bacteria (n = 66 isolates) were screened on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by 1H-NMR. As with the in vitro fermentation experiments, TMAO stimulated the growth of Enterobacteriaceae; these bacteria produced most TMA from TMAO. Caecal/small intestinal isolates of Escherichia coli produced more TMA from TMAO than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate when grown in the presence of TMAO but did not produce large amounts of TMA. Clostridia (sensu stricto), bifidobacteria, and coriobacteria were significantly correlated with TMA production in the mixed fermentation system but did not produce notable quantities of TMA from TMAO in pure culture.Conclusions:Reduction of TMAO by the gut microbiota (predominantly Enterobacteriaceae) to TMA followed by host uptake of TMA into the bloodstream from the intestine and its conversion back to TMAO by host hepatic enzymes is an example of metabolic retroconversion. TMAO influences microbial metabolism depending on isolation source and taxon of gut bacterium. Correlation of metabolomic and abundance data from mixed microbiota fermenta
Rodriguez-Martinez A, Ayala R, Posma J, et al., 2018, Exploring the Genetic Landscape of Metabolic Phenotypes with MetaboSignal, Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.], ISSN: 1934-3396
Ebbels TMD, Rodriguez-Martinez A, Dumas M-E, et al., 2018, Advances in Computational Analysis of Metabolomic NMR Data, NMR-based Metabolomics
Moreno-Navarrete JM, Serino M, Blasco-Baque V, et al., 2017, Gut microbiota interacts with markers of adipose tissue Browning, insulin action and plasma acetate in morbid obesity, Molecular Nutrition and Food Research, Vol: 62, ISSN: 1613-4125
SCOPE: To examine the potential relationship among gene expression markers of adipose tissue browning, gut microbiota, and insulin sensitivity in humans. METHODS AND RESULTS: Gut microbiota composition and gene markers of browning are analyzed in subcutaneous (SAT) and visceral (VAT) adipose tissue from morbidly obese subjects (n = 34). Plasma acetate is measured through 1 H NMR and insulin sensitivity using euglycemic hyperinsulinemic clamp. Subjects with insulin resistance show an increase in the relative abundance (RA) of the phyla Bacteroidetes and Proteobacteria while RA of Firmicutes is decreased. In all subjects, Firmicutes RA is negatively correlated with HbA1c and fasting triglycerides, whereas Proteobacteria RA was negatively correlated with insulin sensitivity. Firmicutes RA is positively associated with markers of brown adipocytes (PRDM16, UCP1, and DIO2) in SAT, but not in VAT. Multivariate regression analysis indicates that Firmicutes RA contributes significantly to SAT PRDM16, UCP1, and DIO2 mRNA variance after controlling for age, BMI, HbA1c , or insulin sensitivity. Interestingly, Firmicutes RA, specifically those bacteria belonging to the Ruminococcaceae family, is positively associated with plasma acetate levels, which are also linked to SAT PRDM16 mRNA and insulin sensitivity. CONCLUSION: Gut microbiota composition is linked to adipose tissue browning and insulin action in morbidly obese subjects, possibly through circulating acetate.
Hoyles L, Jiménez-Pranteda ML, Chilloux J, et al., 2017, Metabolic retroconversion of trimethylamine <i>N</i>-oxide and the gut microbiota
<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>BACKGROUND</jats:title><jats:p>The dietary methylamines choline, carnitine and phosphatidylcholine are used by the gut microbiota to produce a range of metabolites, including trimethylamine (TMA). However, little is known about the use of trimethylamine <jats:italic>N</jats:italic>-oxide (TMAO) by this consortium of microbes.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>A feeding study using deuterated TMAO in C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Microbial activity necessary to convert TMAO to TMA was suppressed in antibiotic-treated mice, with deuterated TMAO being taken up directly into the bloodstream. In batch-culture fermentation systems inoculated with human faeces, growth of <jats:italic>Enterobacteriaceae</jats:italic> was stimulated in the presence of TMAO. Human-derived faecal and caecal bacteria (<jats:italic>n</jats:italic> = 66 isolates) were screened on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by <jats:sup>1</jats:sup>H-NMR. As with the <jats:italic>in vitro</jats:italic> fermentation experiments, TMAO stimulated the growth of <jats:italic>Enterobacteriaceae</jats:italic>; these bacteria produced most TMA from TMAO. Caecal/small intestinal isolates of <jats:italic>Escherichia coli</jats:italic> produced more TMA from TMAO than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate when grown in the presence of TMAO, but did not produce large amounts of TMA. Clostridia (<jats:italic>sensu stricto</jats:italic>), bifidobacteria and coriobacteria were significantly correlated with TMA production in the mixed fermentation system but did
Rodriguez-Martinez A, Posma JM, Ayala R, et al., 2017, J-Resolved (1)H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis., Analytical Chemistry, Vol: 89, Pages: 11405-11412, ISSN: 0003-2700
(1)H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D (1)H NMR spectra, we have investigated the use of 2D J-resolved (JRES) (1)H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study the contribution of the gut microbiome to steatosis in obese women, Exploring Human Host-Microbiome Interactions in Health and Disease
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease, increasing in worldwide prevalence as a result of the obesity epidemic. It manifests in hepatic cells as steatosis with or without lobular inflammation and/or ballooning. Animal and human studies have suggested the gut microbiome contributes to steatosis/NAFLD. The aim of this study was to use an integrated approach with various -omics and clinical data to evaluate the contribution of the gut microbiome to the molecular phenome (hepatic transcriptome, metabonome) of steatosis. Metagenomic (faecal microbiome), transcriptomic (liver biopsy), metabonomic (plasma and urine, 1H-NMR) and clinical data were collected for 56 morbidly obese (BMI >35) women from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery. Confounder analyses of clinical data were done using linear modelling. Histological examination of liver biopsies was used to grade steatosis. Faecal metagenomes were generated and analysed using the SCalable Automated Metagenomics Pipeline (SCAMP). Differentially expressed genes were identified in hepatic transcriptomes, and analysed using a range of different bioinformatics tools. 1H-NMR data were generated for plasma and urinary metabonomes. Clinical, metagenomic, transcriptomic and metabonomic data were integrated in the context of steatosis using partial Spearman's correlation, taking confounders (age, body mass index and cohort) into account. Steatosis was anti-correlated with microbial gene richness, and correlated with abundance of Proteobacteria. KEGG analyses of metagenomic data suggested increased microbial processing of dietary lipids and amino acids, as well as endotoxin-related processes related to Proteobacteria. Steatosis-associated hepatic transcriptomes were associated with branched-chain amino acid (BCAA) metabolism, endoplasmic reticulum/phagosome, and immune responses associated with non-specific microbial infections. Metabonom
Bihoreau M-T, Dumas M-E, Lathrop M, et al., 2017, Genomic regulation of type 2 diabetes endophenotypes: Contribution from genetic studies in the Goto-Kakizaki rat., Biochimie, ISSN: 0300-9084
The inbred Goto-Kakizaki (GK) rat strain is a unique model of spontaneous type 2 diabetes mellitus caused by naturally occurring genetic variants that have been selectively isolated from an outbred colony of Wistar rats. Genetic and genomic studies that we designed with Alain Ktorza in experimental crosses and congenic strains of the GK have shed light on the complex etiopathogenesis of diabetes phenotypes in this model. Diabetes-related phenotypes in the GK are under polygenic control and distinct genetic loci regulate glucose tolerance, insulin secretion, β-cell mass and plasma lipids. Metabolome and transcriptome profiling data in GK crosses and congenics, combined with GK genome resequencing, have resulted in a comprehensive landscape of genomic regulations of metabolism that can disentangle causal relationships between GK variants and diabetes phenotypes. Application of systems biology and systems genetics in the GK has contributed to improve our understanding of the fundamental mechanisms regulating metabolism. The wealth of physiological, genetic and genomic information in this strain makes it one of the most powerful model systems to improve our understanding of genetic regulations of metabolism and for testing therapeutic solutions for diabetes.
Rodriguez Martinez A, Posma JM, Ayala R, et al., 2017, MWASTools: an R/Bioconductor package for metabolome-wide association studies, Bioinformatics, Vol: 34, Pages: 890-892, ISSN: 1367-4803
Summary: MWASTools is an R package designed to provide an integrated pipeline to analyze metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of MWAS results.Availability: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/
Dumas M, Rothwell AR, Hoyles L, et al., 2017, Microbial-host co-metabolites are prodromal markers predicting phenotypic heterogeneity in behavior, obesity and impaired glucose tolerance, Cell Reports, Vol: 20, Pages: 136-148, ISSN: 2211-1247
The influence of the gut microbiome on metabolic and behavioral traits is now widely accepted, though the microbiome-derived metabolites involved remain unclear. We carried out untargeted urine 1H NMR spectroscopy-based metabolic phenotyping in an isogenic C57BL/6J mouse population (n=50) and show that microbial-host co-metabolites are prodromal (i.e., early) markers predicting future divergence in metabolic (obesity and glucose homeostasis) and behaviorial (anxiety and activity) outcomes with 94-100% accuracy. Some of these metabolites also modulate disease phenotypes, best illustrated by trimethylamine-N-oxide (TMAO), a product of microbial-host co-metabolism predicting future obesity, impaired glucose tolerance (IGT) and behavior, whilst reducing endoplasmic reticulum stress and lipogenesis in 3T3-L1 adipocytes. Chronic in vivo TMAO treatment limits IGT in HFD-fed mice and isolated pancreatic islets by increasing insulin secretion. We highlight the prodromal potential of microbial metabolites to predict disease outcomes and their potential in shaping mammalian phenotypic heterogeneity.
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, International Scientific Association for Probiotics and Prebiotics
Metagenomic (faecal microbiome), transcriptomic (liver biopsy), metabonomic (plasma and urine, 1H-NMR) and clinical (28 variables) data were collected for 56 morbidly obese (BMI >35) women from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery. Data were integrated to evaluate the contribution of the gut microbiome to the molecular phenome (hepatic transcriptome, plasma and urine metabonome) of NAFLD independent of clinical confounders (age, BMI, cohort) using partial Spearman’s correlation. NAFLD activity score (NAS) was anti-correlated with microbial gene richness, and correlated with abundance of Proteobacteria. KEGG analyses of metagenomic data suggested increased microbial processing of dietary lipids and amino acids, as well as endotoxin-related processes related to Proteobacteria. Metabonomic profiles highlighted imbalances in choline metabolism, branched-chain amino acid (BCAA) metabolism and gut-derived microbial metabolites resulting from metabolism of amino acids. NAFLD-associated hepatic transcriptomes were associated with BCAA metabolism, endoplasmic reticulum/phagosome, and immune responses associated with non-specific microbial infections. Molecular phenomic signatures were stable and predictive regardless of sample size, and consistent with the microbiome making a significant contribution to the NAFLD phenome. There is disruption of the gut– liver axis in NAFLD, which can be seen in the gut microbiome, hepatic transcriptome and urinary and plasma metabonomes. Consistency of phenome signatures strongly supports a relationship between microbial amino acid metabolism and microbial gene richness, hepatic gene expression and biofluid metabonomes, and ultimately NAS.
Hoyles L, Fernandez-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, Tranlsational Bioinformatics
Non-alcoholic fatty liver disease (NAFLD) is a multifactorial condition and one of the most common causes of chronic liver disease, with increasing worldwide prevalence. Microbiome-associated lipopolysaccharides (LPS) are associated with NAFLD in rodent models, but their relevance in human liver disease is not understood. In addition, microbiome-driven degradation of dietary choline – and its subsequent removal from host-associated metabolic processes – is thought to contribute to development of NAFLD. The FLORINASH study set out to determine the contribution of the gut microbiome to the NAFLD-associated molecular phenome (transcriptome, metabonome) independent of clinical confounders.Morbidly obese women [body mass index (BMI) >35] from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery were recruited to the study. Clinical data (28 variables) were recorded. Faecal samples, liver biopsies, blood and urine samples were collected. Faecal metagenomes were analysed using an in-house metagenomics pipeline (SCaleble Automated Metagenomics Pipeline). NAFLD activity score (NAS; 0, 1, 2, 3) was determined by histological examination of liver biopsies. Differentially expressed genes in hepatic transcriptomes were identified, and analysed using several complementary tools. 1H-NMR data were generated for plasma and urinary metabonomes. Clinical, metagenomic, transcriptomic and metabonomic data were integrated using partial Spearman’s correlation, taking identified confounders (age, BMI and cohort) into account.NAS was anti-correlated with microbial gene richness, and correlated with abundance of Gram-negative Proteobacteria. KEGG analyses of metagenomic data suggested increased microbial processing of dietary lipids and amino acids, as well as LPS-related processes associated with Proteobacteria in NAFLD. Activation of immune responses associated with Gram-negative (LPS-associated) microbial infections was correlated with NAS in hepatic tr
Dumas M, 2017, Gordon Research Conference in Lipoprotein Metabolism, Gordon Research Conference in Lipoprotein Metabolism
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, Gut Microbiota for Health World Summit 2017
Objectives: To integrate metagenomic (faecal microbiome), transcriptomic, metabonomic and clinical data to evaluate the contribution of the gut microbiome to the molecular phenome (hepatic transcriptome, plasma and urine metabonome) of non-alcoholic fatty liver disease (NAFLD) independent of clinical confounders in morbidly obese women recruited to the FLORINASH study.Methods: Faecal, liver biopsy, blood and urine samples and data for 28 clinical variables were collected for 56 obese [body mass index (BMI) >35] women from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery. Confounder analyses of clinical data were done using linear modeling. Histological examination of liver biopsies was used to grade NAFLD (NAFLD activity score: 0, 1, 2, 3). Faecal metagenomes were generated and analysed using the Imperial Metagenomics Pipeline. Differentially expressed genes were identified in hepatic transcriptomes, and analysed using Enrichr, network analyses and Signaling Pathway Impact Analysis. 1H-NMR data were generated for plasma and urinary metabonomes. Clinical, metagenomic, transcriptomic and metabonomic data were integrated using partial Spearman’s correlation, taking confounders (age, body mass index and cohort) into account.Results: NAFLD activity score was anti-correlated with microbial gene richness, and correlated with abundance of Proteobacteria. KEGG analyses of metagenomic data suggested increased microbial processing of dietary lipids and amino acids, as well as endotoxin-related processes related to Proteobacteria. Metabonomic profiles highlighted imbalances in choline metabolism, branched-chain amino acid metabolism and gut-derived microbial metabolites resulting from metabolism of amino acids. NAFLD-associated hepatic transcriptomes were associated with branched-chain amino acid metabolism, endoplasmic reticulum/phagosome, and immune responses associated with microbial infections. Molecular phenomic signatures were stable and predic
Dumas M-E, Emanueli C, 2017, Circulating MicroRNAs to Predict the Risk for Metabolic Diseases in the General Population?, Diabetes, Vol: 66, Pages: 565-567, ISSN: 0012-1797
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, MRC-PHE Centre for Environment & Health - Centre Training Programme Annual Meeting
Chilloux J, Dumas ME, 2017, Are gut microbes responsible for post-dieting weight rebound?, Cell Metabolism, Vol: 25, Pages: 6-7, ISSN: 1932-7420
One of the dieting conundrums in the age of the obesity epidemic is the cycle of weight loss and regain known as the "yo-yo effect." Thaiss et al. (2016) demonstrate that the microbiome plays a key role in this phenomenon and that simple dietary supplementations can reset the weight-rebound clock.
Plovier H, Everard A, Depommier C, et al., 2017, A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice, Nature Medicine, Vol: 23, Pages: 107-113, ISSN: 1546-170X
Obesity and type 2 diabetes are associated with low-grade inflammation and specific changes in gut microbiota composition1-7. We previously demonstrated that administration of Akkermansia muciniphila prevents the development of obesity and associated complications8. However, its mechanisms of action remain unclear, whilst the sensitivity of A. muciniphila to oxygen and the presence of animal-derived compounds in its growth medium currently limit the development of translational approaches for human medicine9. Here we addressed these issues by showing that A. muciniphila retains its efficacy when grown on a synthetic medium compatible with human administration. Unexpectedly, we discovered that pasteurization of A. muciniphila enhanced its capacity to reduce fat mass development, insulin resistance and dyslipidemia in mice. These improvements were notably associated with a modulation of the host urinary metabolomics profile and intestinal energy absorption. We demonstrated that Amuc_1100, a specific protein isolated from the outer membrane of A. muciniphila, interacts with Toll-Like Receptor 2, is stable at temperatures used for pasteurization, improves the gut barrier and partly recapitulates the beneficial effects of the bacterium. Finally, we showed that administration of live or pasteurized A. muciniphila grown on the synthetic medium is safe in humansThese findings provide support for the use of different preparations of A. muciniphila as therapeutic options to target human obesity and associated disorders.
Rodriguez Martinez A, Ayala R, Posma JM, et al., 2016, MetaboSignal, a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways, Bioinformatics, Vol: 33, Pages: 773-775, ISSN: 1367-4803
MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes.
Dumas ME, Domange C, Calderari S, et al., 2016, Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series, Genome Medicine, Vol: 8, ISSN: 1756-994X
Background: The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus is caused by complex organ-specific cellular mechanisms contributing to impaired insulin secretion and insulin resistance. Methods: We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualise shortest paths between metabolites and genes significantly associated with each genomic block.Results: Despite strong genomic similarities (95-99%) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific metabotypes (mQTL) and genome-wide expression traits (eQTL). Variation in key metabolites like glucose, succinate, lactate or 3-hydroxybutyrate, and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing shortest path length drove prioritization of biological validations by gene silencing. Conclusions: These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulations and to characterize novel f
Hoyles L, Jimenez-Pranteda ML, Chilloux J, et al., 2016, Reduction of trimethylamine N-oxide to trimethylamine by the human gut microbiota: supporting evidence for 'metabolic retroversion', Exploring Human Host-Microbiome Interactions in Health and Disease
Dietary methylamines [choline, trimethylamine N-oxide (TMAO), phosphatidylcholine, carnitine] are present in meat, fish, nuts and eggs. Gut bacteria are able to use choline and carnitine in a fermentation-like process, with trimethylamine (TMA) among the main end-products. TMA is transported from the intestine via the hepatic vein to hepatocytes, then converted to TMAO by hepatic flavin-containing monooxygenases. TMAO present in urine and plasma is currently considered a biomarker for non-alcoholic fatty liver disease, insulin resistance and cardiovascular disease. However, circulating TMAO may play roles in protection from hyperammonemia, and glutamate neurotoxicity. Little is known about the reduction of TMAO (predominantly from fish) to TMA and other compounds by the gut microbiota. We screened 66 strains of human-associated gut bacteria on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by 1H-NMR. Enterobacteriaceae produced most TMA from TMAO, with caecal/small intestinal isolates of Escherichia coli producing more TMA than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate and biomass when grown in the presence of TMAO, but did not appear to use TMAO as an alternative electron acceptor. Stimulation of the growth of gut Enterobacteriaceae in the presence of TMAO was confirmed in faeces-inoculated, anaerobic, stirred, pH-controlled fermentation systems. Feeding deuterated TMAO to C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Antibiotic-treated mice lacked microbial activity necessary to convert TMAO to TMA, instead taking up TMAO into the bloodstream by an unknown mechanism. This study demonstrates microbial reduction of TMAO to TMA followed by host-mediated oxidation of TMA to regenerate TMAO, i.e. metabolic retroversion.
Chilloux J, Neves AL, Boulangé CL, et al., 2016, The microbial-mammalian metabolic axis: a critical symbiotic relationship, Current Opinion in Clinical Nutrition and Metabolic Care, Vol: 19, Pages: 250-256, ISSN: 1473-6519
Purpose of review: The microbial-mammalian symbiosis plays a critical role in metabolic health. Microbial metabolites emerge as key messengers in the complex communication between the gut microbiota and their host. These chemical signals are mainly derived from nutritional precursors, which in turn are also able to modify gut microbiota population. Recent advances in the characterization of the gut microbiome and the mechanisms involved in this symbiosis allow the development of nutritional interventions. This review covers the latest findings on the microbial-mammalian metabolic axis as a critical symbiotic relationship particularly relevant to clinical nutrition.Recent findings: The modulation of host metabolism by metabolites derived from the gut microbiota highlights the importance of gut microbiota in disease prevention and causation. The composition of microbial populations in our gut ecosystem is a critical pathophysiological factor, mainly regulated by diet, but also by the host's characteristics (e.g. genetics, circadian clock, immune system, age). Tailored interventions, including dietary changes, the use of antibiotics, prebiotic and probiotic supplementation and faecal transplantation are promising strategies to manipulate microbial ecology.Summary: The microbiome is now considered as an easily reachable target to prevent and treat related diseases. Recent findings in both mechanisms of its interactions with host metabolism and in strategies to modify gut microbiota will allow us to develop more effective treatments especially in metabolic diseases.
Boulangé CL, Neves AL, Chilloux J, et al., 2016, Impact of the gut microbiota on inflammation, obesity, and metabolic disease, Genome Medicine, Vol: 8, ISSN: 1756-994X
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.
Kadar H, Dubus J, Dutot J, et al., 2016, A multiplexed targeted assay for high-throughput quantitative analysis of serum methylamines by ultra performance liquid chromatography coupled to high resolution mass spectrometry, Archives of Biochemistry and Biophysics, Vol: 597, Pages: 12-20, ISSN: 1096-0384
Methylamines are biologically-active metabolites present in serum and urine samples, which play complex roles in metabolic diseases. Methylamines can be detected by proton nuclear magnetic resonance (NMR), but specific methods remain to be developed for their routine assay in human serum in clinical settings. Here we developed and validated a novel reliable “methylamine panel” method for simultaneous quantitative analysis of trimethylamine (TMA), its major detoxification metabolite trimethylamine-N-oxide (TMAO), and precursors choline, betaine and l-carnitine in human serum using Ultra Performance Liquid Chromatography (UPLC) coupled to High Resolution Mass Spectrometry (HRMS). Metabolite separation was carried out on a HILIC stationary phase. For all metabolites, the assay was linear in the range of 0.25–12.5 μmol/L and enabled to reach limit of detection of about 0.10 μmol/L. Relative standard deviations were below 16% for the three levels of concentrations. We demonstrated the strong reliability and robustness of the method, which was applied to serum samples from healthy individuals to establish the range of concentrations of the metabolites and their correlation relationships and detect gender differences. Our data provide original information for implementing in a clinical environment a MS-based diagnostic method with potential for targeted metabolic screening of patients at risk of cardiometabolic diseases.
Dao MC, Everard A, Aron-Wisnewsky J, et al., 2016, Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology, Gut, Vol: 65, Pages: 426-436, ISSN: 1468-3288
Objective Individuals with obesity and type 2 diabetes differ from lean and healthy individuals in their abundance of certain gut microbial species and microbial gene richness. Abundance of Akkermansia muciniphila, a mucin-degrading bacterium, has been inversely associated with body fat mass and glucose intolerance in mice, but more evidence is needed in humans. The impact of diet and weight loss on this bacterial species is unknown. Our objective was to evaluate the association between faecal A. muciniphila abundance, faecal microbiome gene richness, diet, host characteristics, and their changes after calorie restriction (CR).Design The intervention consisted of a 6-week CR period followed by a 6-week weight stabilisation diet in overweight and obese adults (N=49, including 41 women). Faecal A. muciniphila abundance, faecal microbial gene richness, diet and bioclinical parameters were measured at baseline and after CR and weight stabilisation.Results At baseline A. muciniphila was inversely related to fasting glucose, waist-to-hip ratio and subcutaneous adipocyte diameter. Subjects with higher gene richness and A. muciniphila abundance exhibited the healthiest metabolic status, particularly in fasting plasma glucose, plasma triglycerides and body fat distribution. Individuals with higher baseline A. muciniphila displayed greater improvement in insulin sensitivity markers and other clinical parameters after CR. These participants also experienced a reduction in A. muciniphila abundance, but it remained significantly higher than in individuals with lower baseline abundance. A. muciniphila was associated with microbial species known to be related to health.Conclusions A. muciniphila is associated with a healthier metabolic status and better clinical outcomes after CR in overweight/obese adults. The interaction between gut microbiota ecology and A. muciniphila warrants further investigation.
Dumas M-E, 2016, Is the way we're dieting wrong?, Genome Medicine, Vol: 8, ISSN: 1756-994X
Progress in personalized medicine is now beingtranslated to personalized nutrition. A recent proofof-conceptstudy shows that the increase in bloodglucose levels after a meal is highly variable betweenindividuals, but can be predicted by using acomputational model that combines information fromgut microbiome profiles and dietary questionnaires.This study raises questions about the usefulness ofuniversal diet recommendations, and suggests wemight need to move on to personalized diets.
Neves AL, Chilloux J, Sarafian MH, et al., 2015, The microbiome and its pharmacological targets: therapeutic avenues in cardiometabolic diseases, Current Opinion in Pharmacology, Vol: 25, Pages: 36-44, ISSN: 1471-4892
Consisting of trillions of non-pathogenic bacteria living in a symbiotic relationship with their mammalian host, the gut microbiota has emerged in the past decades as one of the key drivers for cardiometabolic diseases (CMD). By degrading dietary substrates, the gut microbiota produces several metabolites that bind human pharmacological targets, impact subsequent signalling networks and in fine modulate host's metabolism. In this review, we revisit the pharmacological relevance of four classes of gut microbial metabolites in CMD: short-chain fatty acids (SCFA), bile acids, methylamines and indoles. Unravelling the signalling mechanisms of the microbial–mammalian metabolic axis adds one more layer of complexity to the physiopathology of CMD and opens new avenues for the development of microbiota-based pharmacological therapies.
Dumas M-E, Adamski J, Suhre K, 2015, Guest editorial: special issue on metabolomics, Archives of Biochemistry and Biophysics, Vol: 589, Pages: 1-3, ISSN: 1096-0384
Sarafian MH, Lewis MR, Pechlivanis A, et al., 2015, Bile Acid Profiling and Quantification in Biofluids Using Ultra-Performance Liquid Chromatography Tandem Mass Spectrometry, Analytical Chemistry, Vol: 87, Pages: 9662-9670, ISSN: 1520-6882
Bile acids are important end products of cholesterol metabolism. While they have been identified as key factors in lipid emulsification and absorption due to their detergent properties, bile acids have also been shown to act as signaling molecules and intermediates between the host and the gut microbiota. To further the investigation of bile acid functions in humans, an advanced platform for high throughput analysis is essential. Herein, we describe the development and application of a 15 min UPLC procedure for the separation of bile acid species from human biofluid samples requiring minimal sample preparation. High resolution time-of-flight mass spectrometry was applied for profiling applications, elucidating rich bile acid profiles in both normal and disease state plasma. In parallel, a second mode of detection was developed utilizing tandem mass spectrometry for sensitive and quantitative targeted analysis of 145 bile acid (BA) species including primary, secondary, and tertiary bile acids. The latter system was validated by testing the linearity (lower limit of quantification, LLOQ, 0.25–10 nM and upper limit of quantification, ULOQ, 2.5–5 μM), precision (≈6.5%), and accuracy (81.2–118.9%) on inter- and intraday analysis achieving good recovery of bile acids (serum/plasma 88% and urine 93%). The ultra performance liquid chromatography–mass spectrometry (UPLC-MS)/MS targeted method was successfully applied to plasma, serum, and urine samples in order to compare the bile acid pool compositional difference between preprandial and postprandial states, demonstrating the utility of such analysis on human biofluids.
Shoaie S, Ghaffari P, Kovatcheva-Datchary P, et al., 2015, Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome, Cell Metabolism, Vol: 22, Pages: 320-331, ISSN: 1932-7420
The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.
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