TET2 Drives 5hmc Marking of GATA6 and Epigenetically Defines Pancreatic Ductal Adenocarcinoma Transcriptional Subtypes.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) is characterized by advanced disease stage at presentation, aggressive disease biology, and resistance to therapy, resulting in an extremely poor 5-year survival rate of <10%. PDAC is classified into transcriptional subtypes with distinct survival characteristics, although how these arise is not known. Epigenetic deregulation, rather than genetics, has been proposed to underpin progression, but exactly why is unclear and is hindered by the technical limitations of analyzing clinical samples. METHODS: We performed genome-wide epigenetic mapping of DNA modifications 5-methylcytosine and 5-hydroxymethylcytosine (5hmc) using oxidative bisulfite sequencing from formalin-embedded sections. We identified overlap with transcriptional signatures in formalin-fixed, paraffin-embedded tissue from resected patients, via bioinformatics using iCluster and mutational profiling and confirmed them in vivo. RESULTS: We found that aggressive squamous-like PDAC subtypes result from epigenetic inactivation of loci, including GATA6, which promote differentiated classical pancreatic subtypes. We showed that squamous-like PDAC transcriptional subtypes are associated with greater loss of 5hmc due to reduced expression of the 5-methylcytosine hydroxylase TET2. Furthermore, we found that SMAD4 directly supports TET2 levels in classical pancreatic tumors, and loss of SMAD4 expression was associated with reduced 5hmc, GATA6, and squamous-like tumors. Importantly, enhancing TET2 stability using metformin and vitamin C/ascorbic acid restores 5hmc and GATA6 levels, reverting squamous-like tumor phenotypes and WNT-dependence in vitro and in vivo. CONCLUSIONS: We identified epigenetic deregulation of pancreatic differentiation as an underpinning event behind the emergence of transcriptomic subtypes in PDAC. Our data showed that restoring epigenetic control increases biomarkers of classical pancreatic tumors that are associated with improved therapeutic responses and survival.

Microbial-Host Co-metabolites Are Prodromal Markers Predicting Phenotypic Heterogeneity in Behavior, Obesity, and Impaired Glucose Tolerance.

The influence of the gut microbiome on metabolic and behavioral traits is 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 behavioral (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 while 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.

A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk.

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity.

Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. 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 with P < 1 × 10-7, range P = 9.2 × 10-8 to 6.0 × 10-46; 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 that 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 < 9.0 × 10-6, range P = 5.5 × 10-6 to 6.1 × 10-35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 × 10-54). 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 landscape of copy number variations in Finnish families with autism spectrum disorders.

Rare de novo and inherited copy number variations (CNVs) have been implicated in autism spectrum disorder (ASD) risk. However, the genetic underpinnings of ASD remain unknown in more than 80% of cases. Therefore, identification of novel candidate genes and corroboration of known candidate genes may broaden the horizons of determining genetic risk alleles, and subsequent development of diagnostic testing. Here, using genotyping arrays, we characterized the genetic architecture of rare CNVs (<1% frequency) in a Finnish case-control dataset. Unsurprisingly, ASD cases harbored a significant excess of rare, large (>1 Mb) CNVs and rare, exonic CNVs. The exonic rare de novo CNV rate (∼22.5%) seemed higher compared to previous reports. We identified several CNVs in well-known ASD regions including GSTM1-5, DISC1, FHIT, RBFOX1, CHRNA7, 15q11.2, 15q13.2-q13.3, 17q12, and 22q11.21. Additionally, several novel candidate genes (BDKRB1, BDKRB2, AP2M1, SPTA1, PTH1R, CYP2E1, PLCD3, F2RL1, UQCRC2, LILRB3, RPS9, and COL11A2) were identified through gene prioritization. The majority of these genes belong to neuroactive ligand-receptor interaction pathways, and calcium signaling pathways, thus suggesting that a subset of these novel candidate genes may contribute to ASD risk. Furthermore, several metabolic pathways like caffeine metabolism, drug metabolism, retinol metabolism, and calcium-signaling pathway were found to be affected by the rare exonic ASD CNVs. Additionally, biological processes such as bradykinin receptor activity, endoderm formation and development, and oxidoreductase activity were enriched among the rare exonic ASD CNVs. Overall, our findings may add data about new genes and pathways that contribute to the genetic architecture of ASD.

Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study.

BACKGROUND: Indian Asians, who make up a quarter of the world's population, are at high risk of developing type 2 diabetes. We investigated whether DNA methylation is associated with future type 2 diabetes incidence in Indian Asians and whether differences in methylation patterns between Indian Asians and Europeans are associated with, and could be used to predict, differences in the magnitude of risk of developing type 2 diabetes. METHODS: We did a nested case-control study of DNA methylation in Indian Asians and Europeans with incident type 2 diabetes who were identified from the 8-year follow-up of 25 372 participants in the London Life Sciences Prospective Population (LOLIPOP) study. Patients were recruited between May 1, 2002, and Sept 12, 2008. We did epigenome-wide association analysis using samples from Indian Asians with incident type 2 diabetes and age-matched and sex-matched Indian Asian controls, followed by replication testing of top-ranking signals in Europeans. For both discovery and replication, DNA methylation was measured in the baseline blood sample, which was collected before the onset of type 2 diabetes. Epigenome-wide significance was set at p<1 × 10(-7). We compared methylation levels between Indian Asian and European controls without type 2 diabetes at baseline to estimate the potential contribution of DNA methylation to increased risk of future type 2 diabetes incidence among Indian Asians. FINDINGS: 1608 (11·9%) of 13 535 Indian Asians and 306 (4·3%) of 7066 Europeans developed type 2 diabetes over a mean of 8·5 years (SD 1·8) of follow-up. The age-adjusted and sex-adjusted incidence of type 2 diabetes was 3·1 times (95% CI 2·8-3·6; p<0·0001) higher among Indian Asians than among Europeans, and remained 2·5 times (2·1-2·9; p<0·0001) higher after adjustment for adiposity, physical activity, family history of type 2 diabetes, and baseline glycaemic measures. The mean absolute difference in methylation level between type 2 diabetes cases and controls ranged from 0·5% (SD 0·1) to 1·1% (0·2). Methylation markers at five loci were associated with future type 2 diabetes incidence; the relative risk per 1% increase in methylation was 1·09 (95% CI 1·07-1·11; p=1·3 × 10(-17)) for ABCG1, 0·94 (0·92-0·95; p=4·2 × 10(-11)) for PHOSPHO1, 0·94 (0·92-0·96; p=1·4 × 10(-9)) for SOCS3, 1·07 (1·04-1·09; p=2·1 × 10(-10)) for SREBF1, and 0·92 (0·90-0·94; p=1·2 × 10(-17)) for TXNIP. A methylation score combining results for the five loci was associated with future type 2 diabetes incidence (relative risk quartile 4 vs quartile 1 3·51, 95% CI 2·79-4·42; p=1·3 × 10(-26)), and was independent of established risk factors. Methylation score was higher among Indian Asians than Europeans (p=1 × 10(-34)). INTERPRETATION: DNA methylation might provide new insights into the pathways underlying type 2 diabetes and offer new opportunities for risk stratification and prevention of type 2 diabetes among Indian Asians. FUNDING: 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.

Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus.

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.

The genome-wide landscape of copy number variations in the MUSGEN study provides evidence for a founder effect in the isolated Finnish population.

Here we characterized the genome-wide architecture of copy number variations (CNVs) in 286 healthy, unrelated Finnish individuals belonging to the MUSGEN study, where molecular background underlying musical aptitude and related traits are studied. By using Illumina HumanOmniExpress-12v.1.0 beadchip, we identified 5493 CNVs that were spread across 467 different cytogenetic regions, spanning a total size of 287.83 Mb (∼9.6% of the human genome). Merging the overlapping CNVs across samples resulted in 999 discrete copy number variable regions (CNVRs), of which ∼6.9% were putatively novel. The average number of CNVs per person was 20, whereas the average size of CNV per locus was 52.39 kb. Large CNVs (>1 Mb) were present in 4% of the samples. The proportion of homozygous deletions in this data set (∼12.4%) seemed to be higher when compared with three other populations. Interestingly, several CNVRs were significantly enriched in this sample set, whereas several others were totally depleted. For example, a CNVR at chr2p22.1 intersecting GALM was more common in this population (P=3.3706 × 10(-44)) than in African and other European populations. The enriched CNVRs, however, showed no significant association with music-related phenotypes. Moreover, the most common CNV locations in world's normal population cohorts (6q14.1, 11q11) were overrepresented in this population. Thus, the genome-wide CNV investigation in this Finnish sample set demonstrated features that are characteristic to isolated populations. Novel CNVRs and the functional implications of CNVs revealed in this study elucidate structural variation present in this population isolate, and may also serve as candidate gene loci for music-related traits.

Genome-wide copy number variation analysis in extended families and unrelated individuals characterized for musical aptitude and creativity in music.

Music perception and practice represent complex cognitive functions of the human brain. Recently, evidence for the molecular genetic background of music related phenotypes has been obtained. In order to further elucidate the molecular background of musical phenotypes we analyzed genome wide copy number variations (CNVs) in five extended pedigrees and in 172 unrelated subjects characterized for musical aptitude and creative functions in music. Musical aptitude was defined by combination of the scores of three music tests (COMB scores): auditory structuring ability, Seashores test for pitch and for time. Data on creativity in music (herein composing, improvising and/or arranging music) was surveyed using a web-based questionnaire.Several CNVRs containing genes that affect neurodevelopment, learning and memory were detected. A deletion at 5q31.1 covering the protocadherin-α gene cluster (Pcdha 1-9) was found co-segregating with low music test scores (COMB) in both sample sets. Pcdha is involved in neural migration, differentiation and synaptogenesis. Creativity in music was found to co-segregate with a duplication covering glucose mutarotase gene (GALM) at 2p22. GALM has influence on serotonin release and membrane trafficking of the human serotonin transporter. Interestingly, genes related to serotonergic systems have been shown to associate not only with psychiatric disorders but also with creativity and music perception. Both, Pcdha and GALM, are related to the serotonergic systems influencing cognitive and motor functions, important for music perception and practice. Finally, a 1.3 Mb duplication was identified in a subject with low COMB scores in the region previously linked with absolute pitch (AP) at 8q24. No differences in the CNV burden was detected among the high/low music test scores or creative/non-creative groups. In summary, CNVs and genes found in this study are related to cognitive functions. Our result suggests new candidate genes for music perception related traits and supports the previous results from AP study.

Genome-wide association study identifies a common variant in RAD51B associated with male breast cancer risk.

We conducted a genome-wide association study of male breast cancer comprising 823 cases and 2,795 controls of European ancestry, with validation in independent sample sets totaling 438 cases and 474 controls. A SNP in RAD51B at 14q24.1 was significantly associated with male breast cancer risk (P = 3.02 × 10(-13); odds ratio (OR) = 1.57). We also refine association at 16q12.1 to a SNP within TOX3 (P = 3.87 × 10(-15); OR = 1.50).

Sodium dodecyl sulphate-polyacrylamide denaturing gel electrophoresis and Western blotting techniques.

The study of proteins, their expression and post-translational modification, is a key process in molecular biology. Immunoblotting is a well-established and powerful tool for the study of proteins, which continues to evolve as new reagents and apparatus are developed. This chapter describes in detail the process by which proteins are extracted from cells, quantified, fractionated using poly-acrylamide gel electrophoresis, transferred to a membrane, and assessed by immunoblotting. Variations in experimental technique, and new technologies available to the researcher, are also discussed.

MicroRNA-210 regulates mitochondrial free radical response to hypoxia and krebs cycle in cancer cells by targeting iron sulfur cluster protein ISCU.

BACKGROUND: Hypoxia 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 FINDINGS: In 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. CONCLUSIONS: Induction 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.

Functional annotations of diabetes nephropathy susceptibility loci through analysis of genome-wide renal gene expression in rat models of diabetes mellitus.

BACKGROUND: Hyperglycaemia in diabetes mellitus (DM) alters gene expression regulation in various organs and contributes to long term vascular and renal complications. We aimed to generate novel renal genome-wide gene transcription data in rat models of diabetes in order to test the responsiveness to hyperglycaemia and renal structural changes of positional candidate genes at selected diabetic nephropathy (DN) susceptibility loci. METHODS: Both Affymetrix and Illumina technologies were used to identify significant quantitative changes in the abundance of over 15,000 transcripts in kidney of models of spontaneous (genetically determined) mild hyperglycaemia and insulin resistance (Goto-Kakizaki-GK) and experimentally induced severe hyperglycaemia (Wistar-Kyoto-WKY rats injected with streptozotocin [STZ]). RESULTS: Different patterns of transcription regulation in the two rat models of diabetes likely underlie the roles of genetic variants and hyperglycaemia severity. The impact of prolonged hyperglycaemia on gene expression changes was more profound in STZ-WKY rats than in GK rats and involved largely different sets of genes. These included genes already tested in genetic studies of DN and a large number of protein coding sequences of unknown function which can be considered as functional and, when they map to DN loci, positional candidates for DN. Further expression analysis of rat orthologs of human DN positional candidate genes provided functional annotations of known and novel genes that are responsive to hyperglycaemia and may contribute to renal functional and/or structural alterations. CONCLUSION: Combining transcriptomics in animal models and comparative genomics provides important information to improve functional annotations of disease susceptibility loci in humans and experimental support for testing candidate genes in human genetics.

Genome-wide association of hypoxia-inducible factor (HIF)-1alpha and HIF-2alpha DNA binding with expression profiling of hypoxia-inducible transcripts.

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-alpha 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-1alpha or HIF-2alpha at high stringency. Analysis of these sequences confirmed an identical core binding motif for HIF-1alpha and HIF-2alpha (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-1alpha- versus HIF-2alpha-binding sites revealed that whereas some loci bound HIF-1alpha in isolation, many bound both isoforms with similar affinity. Despite high-affinity binding to multiple promoters, HIF-2alpha contributed to few, if any, of the transcriptional responses to acute hypoxia at these loci. Given emerging evidence for biologically distinct functions of HIF-1alpha versus HIF-2alpha understanding the mechanisms restricting HIF-2alpha activity will be of interest.

Phylometabonomic patterns of adaptation to high fat diet feeding in inbred mice.

Insulin resistance plays a central role in type 2 diabetes and obesity, which develop as a consequence of genetic and environmental factors. Dietary changes including high fat diet (HFD) feeding promotes insulin resistance in rodent models which present useful systems for studying interactions between genetic background and environmental influences contributing to disease susceptibility and progression. We applied a combination of classical physiological, biochemical and hormonal studies and plasma (1)H NMR spectroscopy-based metabonomics to characterize the phenotypic and metabotypic consequences of HFD (40%) feeding in inbred mouse strains (C57BL/6, 129S6, BALB/c, DBA/2, C3H) frequently used in genetic studies. We showed the wide range of phenotypic and metabonomic adaptations to HFD across the five strains and the increased nutrigenomic predisposition of 129S6 and C57BL/6 to insulin resistance and obesity relative to the other strains. In contrast mice of the BALB/c and DBA/2 strains showed relative resistance to HFD-induced glucose intolerance and obesity. Hierarchical metabonomic clustering derived from (1)H NMR spectral data of the strains provided a phylometabonomic classification of strain-specific metabolic features and differential responses to HFD which closely match SNP-based phylogenetic relationships between strains. Our results support the concept of genomic clustering of functionally related genes and provide important information for defining biological markers predicting spontaneous susceptibility to insulin resistance and pathological adaptations to fat feeding.

Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models.

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.

Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice.

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-N-oxide), 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.

Glycomics investigation into insulin action.

Defects in glycosylation are becoming increasingly associated with a range of human diseases. In some cases, the disease is caused by the glycosylation defect, whereas in others, the aberrant glycosylation may be a consequence of the disease. The implementation of highly sensitive and rapid mass spectrometric screening strategies for profiling the glycans present in model biological systems is revealing valuable insights into disease phenotypes. In addition, glycan screening is proving useful in the analysis of knock-out mice where it is possible to assess the role of glycosyltransferases and glycosidases and what function they have at the cellular and whole organism level. In this study, we analysed the effect of insulin on the glycosylation of 3T3-L1 cells and the effect of insulin resistance on glycosylation in a mouse model. Transcription profiling of 3T3-L1 cells treated with and without insulin revealed expression changes of several glycogenes. In contrast, mass spectrometric screening analysis of the glycans from these cells revealed very similar profiles suggesting that any changes in glycosylation were most likely on specific proteins rather than a global phenomenon. A fat-fed versus carbohydrate-fed mouse insulin resistant model was analysed to test the consequences of chronic insulin resistance. Muscle and liver N-glycosylation profiles from these mice are reported.

Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets.

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.