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

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

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

A novel 3D imaging approach for quantification of GLUT4 levels across the intact myocardium

Cellular heterogeneity is a well-accepted feature of tissues, and both transcriptional and metabolic diversity have been revealed by numerous approaches, including optical imaging. However, the high magnification objective lenses needed for high-resolution imaging provides information from only small layers of tissue, which can result in poor cell statistics. There is therefore an unmet need for an imaging modality that can provide detailed molecular and cellular insight within intact tissue samples in 3D. Using GFP-tagged GLUT4 as proof of concept, we present here a novel optical mesoscopy approach that allows precise measurement of the spatial location of GLUT4 within specific anatomical structures across the myocardium in ultrathick sections (5 mm x 5 mm x 3 mm) of intact mouse heart. We reveal distinct GLUT4 distribution patterns across cardiac walls and highlight specific changes in GLUT4 expression levels in response to high fat diet-feeding, and we identify sex-dependent differences in expression patterns. This method is applicable to any target that can be labelled for light microscopy, and to other complex tissues when organ structure needs to be considered simultaneously with cellular detail

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

Isoform-specific AMPK association with TBC1D1 is reduced by a mutation associated with severe obesity

AMP-activated protein kinase (AMPK) is a key regulator of cellular and systemic energy homeostasis which achieves this through the phosphorylation of a myriad of downstream targets. One target is TBC1D1 a Rab-GTPase-activating protein that regulates glucose uptake in muscle cells by integrating insulin signalling with that promoted by muscle contraction. Ser237 in TBC1D1 is a target for phosphorylation by AMPK, an event which may be important in regulating glucose uptake. Here, we show AMPK heterotrimers containing the α1, but not the α2, isoform of the catalytic subunit form an unusual and stable association with TBC1D1, but not its paralogue AS160. The interaction between the two proteins is direct, involves a dual interaction mechanism employing both phosphotyrosinebinding (PTB) domains of TBC1D1 and is increased by two different pharmacological activators of AMPK (AICAR and A769962). The interaction enhances the efficiency by which AMPK phosphorylates TBC1D1 on its key regulatory site, Ser237. Furthermore, the interaction is reduced by a naturally occurring R125W mutation in the PTB1 domain of TBC1D1, previously found to be associated with severe familial obesity in females, with a concomitant reduction in Ser237 phosphorylation. Our observations provide evidence for a functional difference between AMPK α-subunits and extend the repertoire of protein kinases that interact with substrates via stabilisation mechanisms that modify the efficacy of substrate phosphorylation

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