SummaryOverweight and obesity affect ~1.5 billion people worldwide, and are major risk factors for type-2 diabetes (T2D), cardiovascular disease and related metabolic and inflammatory disturbances.1,2 Although the mechanisms linking adiposity to its clinical sequelae are poorly understood, recent studies suggest that adiposity may influence DNA methylation,3–6 a key regulator of gene expression and molecular phenotype.7 Here we use epigenome-wide association to show that body mass index (BMI, a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci at P<1x10-7, range P=9.2x10-8 to 6.0x10-46; N=10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find the methylation loci are enriched for functional genomic features in multiple tissues (P<0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P<9.0x10-6, range P=5.5x10-6 to 6.1x10-35, N=1,785 samples). The methylation loci identified highlight genes involved in lipid and lipoprotein metabolism, substrate transport, and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future type-2 diabetes (relative risk per 1SD increase in Methylation Risk Score: 2.3 [2.07-2.56]; P=1.1x10-54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type-2 diabetes and other adverse clinical consequences of obesity.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes.

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.

Here, we study the intricate relationship between gut microbiota and host cometabolic phenotypes associated with dietary-induced impaired glucose homeostasis and nonalcoholic fatty liver disease (NAFLD) in a mouse strain (129S6) known to be susceptible to these disease traits, using plasma and urine metabotyping, achieved by 1 H NMR spectroscopy. 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, i.e., low circulating levels of plasma phosphatidylcholine and high urinary excretion of methylamines (dimethylamine, trimethylamine, and trimethylamine-Noxide), coprocessed by symbiotic gut microbiota and mammalian enzyme systems. Conversion of choline into methylamines by microbiota in strain 129S6 on a high-fat diet reduces the bioavailability of choline and mimics the effect of choline-deficient diets, causing NAFLD. These data also indicate that gut microbiota may play an active role in the development of insulin resistance.metabonomics ͉ NMR ͉ nonalcoholic fatty liver disease ͉ nutritional genomics ͉ metabolic syndrome H ighly complex animals such as mammals can be considered as ''superorganisms'' with a karyome, a chondriome, and a microbiome (1), resulting from a coevolutionary symbiotic ecosystem of diverse intestinal microbiota interacting metabolically with the host (2). Recent molecular analyses of human microbiota 16s ribosomal DNA sequences revealed a majority of uncultivated or unknown species with a strong degree of interindividual diversity (3, 4). Also, some of the molecular foundations of beneficial symbiotic host-bacteria relationships in the gut were revealed by colonization of germ-free mice with known microbes and by comparisons of the genomes of members of the intestinal microbiota (5). For instance, Bacteroides thetaiotaomicron, a dominant member of normal distal intestinal microbiota, hydrolyzes otherwise indigestible dietary polysaccharides, thus supplying the host with 10-15% of calorific requirement (6). Gut Lactobacillus spp. are also responsible for a significant proportion of bile acid deconjugation, a process that efficiently reduces lipid absorption in the gut (7). Such symbiotic relationships are the result of coevolution and operate at the genome, proteome, and metabolome levels (6,8).Insulin resistance (IR) is central to a cluster of frequent and increasingly prevalent pathologies, including type 2 diabetes mellitus, central obesity, hypertension hepatic steatosis, and dyslipidemia (9). IR contributes to major causes of morbidity and mortality worldwide (10). Epidemiological and genetic studies in human and animal models have demonstrated the importance of both genetic and environmental factors in the etiology of IR (9): Dietary variation and intervention, in particular, have a strong influence on the development of IR. Nonalcoholic fatty liver disease (NAFLD), is the most frequent liver condition associated with IR (11). It is associa...

Hypoxia-inducible factor (HIF) controls an extensive range of adaptive responses to hypoxia. To better understand this transcriptional cascade we performed genome-wide chromatin immunoprecipitation using antibodies to two major HIF-α subunits, and correlated the results with genome-wide transcript profiling. Within a tiled promoter array we identified 546 and 143 sequences that bound, respectively, to HIF-1α or HIF-2α at high stringency. Analysis of these sequences confirmed an identical core binding motif for HIF-1α and HIF-2α (RCGTG) but demonstrated that binding to this motif was highly selective, with binding enriched at distinct regions both upstream and downstream of the transcriptional start. Comparison of HIF-promoter binding data with bidirectional HIF-dependent changes in transcript expression indicated that whereas a substantial proportion of positive responses (>20% across all significantly regulated genes) are direct, HIF-dependent gene suppression is almost entirely indirect. Comparison of HIF-1α- versus HIF-2α-binding sites revealed that whereas some loci bound HIF-1α in isolation, many bound both isoforms with similar affinity. Despite high-affinity binding to multiple promoters, HIF-2α contributed to few, if any, of the transcriptional responses to acute hypoxia at these loci. Given emerging evidence for biologically distinct functions of HIF-1α versus HIF-2α understanding the mechanisms restricting HIF-2α activity will be of interest.

The European Union, the UK National Institute for Health Research, the Wellcome Trust, the UK Medical Research Council, Action on Hearing Loss, the UK Biotechnology and Biological Sciences Research Council, the Oak Foundation, the Economic and Social Research Council, Helmholtz Zentrum Munchen, the German Research Center for Environmental Health, the German Federal Ministry of Education and Research, the German Center for Diabetes Research, the Munich Center for Health Sciences, the Ministry of Science and Research of the State of North Rhine-Westphalia, and the German Federal Ministry of Health.

BackgroundHypoxia in cancers results in the upregulation of hypoxia inducible factor 1 (HIF-1) and a microRNA, hsa-miR-210 (miR-210) which is associated with a poor prognosis.Methods and FindingsIn human cancer cell lines and tumours, we found that miR-210 targets the mitochondrial iron sulfur scaffold protein ISCU, required for assembly of iron-sulfur clusters, cofactors for key enzymes involved in the Krebs cycle, electron transport, and iron metabolism. Down regulation of ISCU was the major cause of induction of reactive oxygen species (ROS) in hypoxia. ISCU suppression reduced mitochondrial complex 1 activity and aconitase activity, caused a shift to glycolysis in normoxia and enhanced cell survival. Cancers with low ISCU had a worse prognosis.ConclusionsInduction of these major hallmarks of cancer show that a single microRNA, miR-210, mediates a new mechanism of adaptation to hypoxia, by regulating mitochondrial function via iron-sulfur cluster metabolism and free radical generation.

Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological, proteomic and transcriptomic profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants, are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome (microbiome) perturbations that affect disease processes through transgenomic effects may influence QTL detection.