Identification of the Amino Acids 300–600 of IRS-2 as 14-3-3 Binding Region with the Importance of IGF-1/Insulin-Regulated Phosphorylation of Ser-573

Phosphorylation of insulin receptor substrate (IRS)-2 on tyrosine residues is a key event in IGF-1/insulin signaling and leads to activation of the PI 3-kinase and the Ras/MAPK pathway. Furthermore, phosphorylated serine/threonine residues on IRS-2 can induce 14-3-3 binding. In this study we searched IRS-2 for novel phosphorylation sites and investigated the interaction between IRS-2 and 14-3-3. Mass spectrometry identified a total of 24 serine/threonine residues on IRS-2 with 12 sites unique for IRS-2 while the other residues are conserved in IRS-1 and IRS-2. IGF-1 stimulation led to increased binding of 14-3-3 to IRS-2 in transfected HEK293 cells and this binding was prevented by inhibition of the PI 3-kinase pathway and an Akt/PKB inhibitor. Insulin-stimulated interaction between endogenous IRS-2 and 14-3-3 was observed in rat hepatoma cells and in mice liver after an acute insulin stimulus and refeeding. Using different IRS-2 fragments enabled localization of the IGF-1-dependent 14-3-3 binding region spanning amino acids 300–600. The 24 identified residues on IRS-2 included several 14-3-3 binding candidates in the region 300–600. Single alanine mutants of these candidates led to the identification of serine 573 as 14-3-3 binding site. A phospho-site specific antibody was generated to further characterize serine 573. IGF-1-dependent phosphorylation of serine 573 was reduced by inhibition of PI 3-kinase and Akt/PKB. A negative role of this phosphorylation site was implicated by the alanine mutant of serine 573 which led to enhanced phosphorylation of Akt/PKB in an IGF-1 time course experiment. To conclude, our data suggest a physiologically relevant role for IGF-1/insulin-dependent 14-3-3 binding to IRS-2 involving serine 573

Nonsuppressed Glucagon After Glucose Challenge as a Potential Predictor for Glucose Tolerance

Glucagon levels are classically suppressed after glucose challenge. It is still not clear as to whether a lack of suppression contributes to hyperglycemia and thus to the development of diabetes. We investigated the association of postchallenge change in glucagon during oral glucose tolerance tests (OGTTs), hypothesizing that higher postchallenge glucagon levels are observed in subjects with impaired glucose tolerance (IGT). Glucagon levels were measured during OGTT in a total of 4,194 individuals without diabetes in three large European cohorts. Longitudinal changes in glucagon suppression were investigated in 50 participants undergoing a lifestyle intervention. Only 66-79% of participants showed suppression of glucagon at 120 min (fold change glucagon(120/0)Peer reviewe

Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: Rs13422522 (NYAP2; P = 8.87 × 10-11), rs12454712 (BCL2; P = 2.7 × 10-8), and rs10506418 (FAM19A2; P = 1.9 × 10-8). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci

Peroxisome proliferator-activated receptor gamma (PPARG) modulates free fatty acid receptor 1 (FFAR1) dependent insulin secretion in humans

Genetic variation in FFAR1 modulates insulin secretion dependent on non-esterified fatty acid (NEFA) concentrations. We previously demonstrated lower insulin secretion in minor allele carriers of PPARG Pro12Ala in high-NEFA environment, but the mode of action could not been revealed. We tested if this effect is mediated by FFAR1 in humans. Subjects with increased risk of diabetes who underwent oral glucose tolerance tests were genotyped for 7 tagging SNPs in FFAR1 and PPARG Pro12Ala. The FFAR1 SNPs rs12462800 and rs10422744 demonstrated interactions with PPARG on insulin secretion. FFAR1 rs12462800 (p = 0.0006) and rs10422744 (p = 0.001) were associated with reduced insulin secretion in participants concomitantly carrying the PPARG minor allele and having high fasting FFA. These results suggest that the minor allele of the PPARG SNP exposes its carriers to modulatory effects of FFAR1 on insulin secretion. This subphenotype may define altered responsiveness to FFAR1-agonists, and should be investigated in further studies. (C) 2014 The Authors. Published by Elsevier GmbH

Allele Summation of Diabetes Risk Genes Predicts Impaired Glucose Tolerance in Female and Obese Individuals

Introduction: Single nucleotide polymorphisms (SNPs) in approximately 40 genes have been associated with an increased risk for type 2 diabetes (T2D) in genome-wide association studies. It is not known whether a similar genetic impact on the risk of prediabetes (impaired glucose tolerance [IGT] or impaired fasting glycemia [IFG]) exists. Methods: In our cohort of 1442 non-diabetic subjects of European origin (normal glucose tolerance [NGT] n = 1046, isolated IFG n = 142, isolated IGT n = 140, IFG+IGT n = 114), an impact on glucose homeostasis has been shown for 9 SNPs in previous studies in this specific cohort. We analyzed these SNPs (within or in the vicinity of the genes TCF7L2, KCNJ11, HHEX, SLC30A8, WFS1, KCNQ1, MTNR1B, FTO, PPARG) for association with prediabetes. Results: The genetic risk load was significantly associated with the risk for IGT (p = 0.0006) in a model including gender, age, BMI and insulin sensitivity. To further evaluate potential confounding effects, we stratified the population on gender, BMI and insulin sensitivity. The association of the risk score with IGT was present in female participants (p = 0.008), but not in male participants. The risk score was significantly associated with IGT (p = 0.008) in subjects with a body mass index higher than 30 kg/m(2) but not in non-obese individuals. Furthermore, only in insulin resistant subjects a significant association between the genetic load and the risk for IGT (p = 0.01) was found. Discussion: We found that T2D genetic risk alleles cause an increased risk for IGT. This effect was not present in male, lean and insulin sensitive subjects, suggesting a protective role of beneficial environmental factors on the genetic risk

Simultaneous extraction of metabolome and lipidome with methyl tert-butyl ether from a single small tissue sample for ultra-high performance liquid chromatography/mass spectrometry

A common challenge for scientists working with animal tissue or human biopsy samples is the limitation of material and consequently, the difficulty to perform comprehensive metabolic profiling within one experiment. Here, we present a novel approach to simultaneously perform targeted and non-targeted metabolomics as well as lipidomics from one small piece of liver or muscle tissue by ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS) following a methyl tert-butyl ether (MTBE)-based extraction. Equal relative amounts of the resulting polar and non-polar fractions were pooled, evaporated and reconstituted in the appropriate solvent for UHPLC/MS analysis. This mix was comparable or superior in yield and reproducibility to a standard 80% methanol extraction for the profiling of polar and lipophilic metabolites (free carnitine, acylcarnitines and FFA). The mix was also suitable for non-targeted metabolomics, an easy measure to increase the metabolite coverage by 30% relative to using the polar fraction alone. Lipidomics was performed from an aliquot of the non-polar fraction. This novel strategy could successfully be applied to one mouse soleus muscle with a dry weight of merely 2.5 mg. By enabling a simultaneous profiling of lipids and metabolites with mixed polarity while saving material for molecular, biochemical or histological analyses, our approach may open up new perspectives toward a comprehensive investigation of small, valuable tissue samples. (C) 2013 Elsevier B.V. All rights reserved

Independent component analysis in non-hypothesis driven metabolomics: Improvement of pattern discovery and simplification of biological data interpretation demonstrated with plasma samples of exercising humans.

In a non-hypothesis driven metabolomics approach plasma samples collected at six different time points (before, during and after an exercise bout) were analyzed by gas chromatography-time of flight mass spectrometry (GC-TOF MS). Since independent component analysis (ICA) does not need a priori information on the investigated process and moreover can separate statistically independent source signals with non-Gaussian distribution, we aimed to elucidate the analytical power of ICA for the metabolic pattern analysis and the identification of key metabolites in this exercise study. A novel approach based on descriptive statistics was established to optimize ICA model. In the GC-TOF MS data set the number of principal components after whitening and the number of independent components of ICA were optimized and systematically selected by descriptive statistics. The elucidated dominating independent components were involved in fuel metabolism, representing one of the most affected metabolic changes occurring in exercising humans. Conclusive time dependent physiological changes of the metabolic pattern under exercise conditions were detected. We conclude that after optimization ICA can successfully elucidate key metabolite pattern as well as characteristic metabolites in metabolic processes thereby simplifying the explanation of complex biological processes. Moreover. ICA is capable to study time series in complex experiments with multi-levels and multi-factors

Preanalytical aspects and sample quality assessment in metabolomics studies of human blood.

BACKGROUND: Metabolomics is a powerful tool that is increasingly used in clinical research. Although excellent sample quality is essential, it can easily be compromised by undetected preanalytical errors. We set out to identify critical preanalytical steps and biomarkers that reflect preanalytical inaccuracies. METHODS: We systematically investigated the effects of preanalytical variables (blood collection tubes, hemolysis, temperature and time before further processing, and number of freeze-thaw cycles) on metabolomics studies of clinical blood and plasma samples using a nontargeted LC-MS approach. RESULTS: Serum and heparinate blood collection tubes led to chemical noise in the mass spectra. Distinct, significant changes of 64 features in the EDTA-plasma metabolome were detected when blood was exposed to room temperature for 2, 4, 8, and 24 h. The resulting pattern was characterized by increases in hypoxanthine and sphingosine 1-phosphate (800% and 380%, respectively, at 2 h). In contrast, the plasma metabolome was stable for up to 4 h when EDTA blood samples were immediately placed in iced water. Hemolysis also caused numerous changes in the metabolic profile. Unexpectedly, up to 4 freeze-thaw cycles only slightly changed the EDTA-plasma metabolome, but increased the individual variability. CONCLUSIONS: Nontargeted metabolomics investigations led to the following recommendations for the preanalytical phase: test the blood collection tubes, avoid hemolysis, place whole blood immediately in ice water, use EDTA plasma, and preferably use nonrefrozen biobank samples. To exclude outliers due to preanalytical errors, inspect the biomarker signal intensities reflecting systematic as well as accidental and preanalytical inaccuracies before processing the bioinformatics data