Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus

Background Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide. The main goal of these studies is the detailed analysis of a particular aspect of the disease. Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches. Results Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM). Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes. The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study. Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction (128) of yet barely characterized novel candidate genes. Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks. Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any – yet non-associated – gene. Conclusion In our paper we have identified a core set of 213 T2DM candidate genes by a meta-analysis of existing data sources. We have explored the relation of these genes to disease relevant information and – using enrichment analysis – we have identified biological networks on different layers of cellular information such as signaling and metabolic pathways, gene regulatory networks and protein-protein interactions. The web interface is accessible via http://t2dm-geneminer.molgen.mpg.de webcite

The adipokine sFRP4 induces insulin resistance and lipogenesis in the liver

Secreted frizzled-related protein (sFRP) 4 is an adipokine with increased expression in white adipose tissue from obese subjects with type 2 diabetes and non-alcoholic fatty liver disease (NAFLD). Yet, it is unknown whether sFRP4 action contributes to the development of these pathologies. Here, we determined whether sFRP4 expression in visceral fat associates with NAFLD and whether it directly interferes with insulin action and lipid and glucose metabolism in primary hepatocytes and myotubes. The association of sFRP4 with clinical measures was investigated in obese men with or without type 2 diabetes and with or without biopsy-proven NAFLD. To determine the impact of sFRP4 on metabolic parameters, primary human myotubes (hSkMC), or primary hepatocytes from metabolic healthy C57B16 and from systemic insulin-resistant mice, i.e. aP2-SREBP-1c, were used. Gene expression of sFRP4 in visceral fat from obese men associated with insulin sensitivity, triglycerides and NAFLD. In C57B16 hepatocytes, sFRP4 disturbed insulin action. Specifically, sFRP4 decreased the abundance of IRS1 and FoxO1 together with impaired insulin-mediated activation of Akt-signalling and glycogen synthesis and a reduced suppression of gluconeogenesis by insulin. Moreover, sFRP4 enhanced insulin-stimulated hepatic de novo lipogenesis (DNL). In hSkMC, sFRP4 induced glycolysis rather than inhibiting insulin signalling. Finally, in hepatocytes from aP2-SREBP-1c mice, sFRP4 potentiates existing insulin resistance. Collectively, we show that sFRP4 interferes with hepatocyte insulin action. Physiologically, sFRP4 promotes DNL in hepatocytes and glycolysis in myotubes. These sFRP4-mediated responses may result in a vicious cycle, in which enhanced rates of DNL and glycolysis aggravate hepatic lipid accumulation and insulin resistance

PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios

<p>Abstract</p> <p>Background</p> <p>Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins.</p> <p>Results</p> <p>We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates.</p> <p>Conclusions</p> <p>Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.</p

Identification of Ser-1275 and Ser-1309 as autophosphorylation sites of the insulin receptor 1This paper is dedicated to Prof. Günter Legler on the occasion of his 70th birthday.1

AbstractWe have identified Ser-1275 and Ser-1309 as novel serine autophosphorylation sites by direct sequencing of HPLC-purified tryptic phosphopeptides of the histidine-tagged insulin receptor kinase IRKD-HIS. The corresponding peptides (Ser-1275, amino acids 1272–1292; Ser-1309, amino acids 1305–1313) have been detected in the HPLC profiles of both the soluble kinase IRKD, which contains the entire cytoplasmic domain of the insulin receptor β-subunit, and the insulin receptor purified from human placenta. In contrast, a kinase negative mutant, IRKD-K1018A, did not undergo phosphorylation at either the tyrosine or serine residues, strongly suggesting that insulin receptor kinase has an intrinsic activity to autophosphorylate serine residues

Use of Multiple Metabolic and Genetic Markers to Improve the Prediction of Type 2 Diabetes: the EPIC-Potsdam Study

OBJECTIVE — We investigated whether metabolic biomarkers and single nucleotide poly-morphisms (SNPs) improve diabetes prediction beyond age, anthropometry, and lifestyle risk factors. RESEARCH DESIGN AND METHODS — A case-cohort study within a prospective study was designed. We randomly selected a subcohort (n 2,500) from 26,444 participants, of whom 1,962 were diabetes free at baseline. Of the 801 incident type 2 diabetes cases identified in the cohort during 7 years of follow-up, 579 remained for analyses after exclusions. Prediction models were compared by receiver operatoring characteristic (ROC) curve and integrated dis-crimination improvement. RESULTS — Case-control discrimination by the lifestyle characteristics (ROC-AUC: 0.8465) im-proved with plasma glucose (ROC-AUC: 0.8672, P 0.001) and A1C (ROC-AUC: 0.8859, P 0.001). ROC-AUC further improved with HDL cholesterol, triglycerides, -glutamyltransferase, and alanine aminotransferase (0.9000, P 0.002). Twenty SNPs did not improve discrimination beyond these characteristics (P 0.69). CONCLUSIONS — Metabolic markers, but not genotyping for 20 diabetogenic SNPs, im-prove discrimination of incident type 2 diabetes beyond lifestyle risk factors. Diabetes Care 32:2116–2119, 2009 A ccurate identification of individualswho are at increased risk for type 2diabetes is a requirement for a tar-geted prevention. We therefore tested whether metabolic and genetic markers add substantial prognostic information to age, anthropometry, and lifestyle characteristics

Overexpressing high levels of human vaspin limits high fat diet-induced obesity and enhances energy expenditure in a transgenic mouse

Adipose tissue inflammation and insulin resistance are hallmarks in the development of metabolic diseases resulting from overweight and obesity, such as type 2 diabetes and non-alcoholic fatty liver disease. In obesity, adipocytes predominantly secrete proinflammatory adipokines that further promote adipose tissue dysfunction with negative effects on local and systemic insulin sensitivity. Expression of the serpin vaspin (SERPINA12) is also increased in obesity and type 2 diabetes, but exhibits compensatory roles in inflammation and insulin resistance. This has in part been demonstrated using vaspin-transgenic mice. We here report a new mouse line (h-vaspinTG) with transgenic expression of human vaspin in adipose tissue that reaches vaspin concentrations three orders of magnitude higher than wild type controls (&gt;200 ng/ml). Phenotyping under chow and high-fat diet conditions included glucose-tolerance tests, measurements of energy expenditure and circulating parameters, adipose tissue and liver histology. Also, ex vivo glucose uptake in isolated adipocytes and skeletal muscle was analyzed in h-vaspinTG and littermate controls. The results confirmed previous findings, revealing a strong reduction in diet-induced weight gain, fat mass, hyperinsulinemia, -glycemia and -cholesterolemia as well as fatty liver. Insulin sensitivity in adipose tissue and muscle was not altered. The h-vaspinTG mice showed increased energy expenditure under high fat diet conditions, that may explain reduced weight gain and overall metabolic improvements. In conclusion, this novel human vaspin-transgenic mouse line will be a valuable research tool to delineate whole-body, tissue- and cell-specific effects of vaspin in health and disease

Opposing effects of reduced kidney mass on liver and skeletal muscle insulin sensitivity in obese mice

Reduced kidney mass and/or function may result in multiple metabolic derangements, including insulin resistance. However, underlying mechanisms are poorly understood. Herein, we aimed to determine the impact of reduced kidney mass on glucose metabolism in lean and obese mice. To that end, seven-week-old C57BL6/J mice underwent uninephrectomy (UniNx) or sham operation. After surgery, animals were fed either a chow (standard) or a high fat diet (HFD) and glucose homeostasis was assessed 20 weeks after surgery. Intraperitoneal glucose tolerance was similar in sham-operated and UniNx mice. However, insulin-stimulated glucose disposal in vivo was significantly diminished in UniNx mice, whereas insulin-stimulated glucose uptake into isolated skeletal muscle was similar in sham-operated and UniNx mice. Of note, capillary density was significantly reduced in skeletal muscle of HFD-fed UniNx mice. In contrast, hepatic insulin sensitivity was improved in UniNx mice. Furthermore, adipose tissue HIF1α-expression and inflammation was reduced in HFD-fed UniNx mice. Treatment with the angiotensin II receptor blocker telmisartan improved glucose tolerance and hepatic insulin sensitivity in HFD-fed sham-operated but not UniNx mice. In conclusion, UniNx protects from obesity-induced adipose tissue inflammation and hepatic insulin resistance but it reduces muscle capillary density and, thus, deteriorates HFD-induced skeletal muscle glucose disposal

4-Methylumbelliferone improves the thermogenic capacity of brown adipose tissue.

Therapeutic increase of brown adipose tissue (BAT) thermogenesis is of great interest as BAT activation counteracts obesity and insulin resistance. Hyaluronan (HA) is a glycosaminoglycan, found in the extracellular matrix, which is synthesized by HA synthases (Has1/Has2/Has3) from sugar precursors and accumulates in diabetic conditions. Its synthesis can be inhibited by the small molecule 4-methylumbelliferone (4-MU). Here, we show that the inhibition of HA-synthesis by 4-MU or genetic deletion of Has2/Has3 improves BAT`s thermogenic capacity, reduces body weight gain, and improves glucose homeostasis independently from adrenergic stimulation in mice on diabetogenic diet, as shown by a magnetic resonance T2 mapping approach. Inhibition of HA synthesis increases glycolysis, BAT respiration and uncoupling protein 1 expression. In addition, we show that 4-MU increases BAT capacity without inducing chronic stimulation and propose that 4-MU, a clinically approved prescription-free drug, could be repurposed to treat obesity and diabetes