Regulating insulin signaling and beta-cell function through IRS proteins

Diabetes mellitus is a complex disorder that arises from various causes, including dysregulated glucose sensing and impaired insulin secretion (maturity onset diabetes of youth, MODY), autoimmune-mediated beta-cell destruction (type 1), or insufficient compensation for peripheral insulin resistance (type 2). Type 2 diabetes is the most prevalent form that usually occurs at middle age; it afflicts more than 30 million people over the age of 65, but is appearing with greater frequency in children and adolescents. Dysregulated insulin signaling exacerbated by chronic hyperglycemia promotes a cohort of systemic disorders--including dyslipidemia, hypertension, cardiovascular disease, and female infertility. Understanding the molecular basis of insulin resistance can prevent these disorders and their inevitable progression to type 2 diabetes.     

Glycosphingolipids and insulin resistance

Obesity is associated with an increased risk for insulin resistance, a state characterized by impaired responsiveness of liver, muscle and adipose tissue to insulin. One class of lipids involved in the development of insulin resistance are the (glyco)sphingolipids. Ceramide, the most simple sphingolipid, directly inhibits phosphorylation of the insulin signaling mediator Akt/Protein Kinase B. More complex glycosphingolipids, so-called gangliosides, block phosphorylation of the insulin receptor and down-stream signaling, possibly by exclusion of the insulin receptor from specific membrane domains. Pharmacological inhibition of glycosphingolipid synthesis is found to markedly improve insulin sensitivity in rodent models of insulin resistance. Partial glycosphingolipid reduction is well tolerated and may thus offer an attractive new treatment modality for obesity-induced insulin resistance and type II diabetes.     

Bioinformatics for the NuGO proof of principle study: analysis of gene expression in muscle of ApoE3*Leiden mice on a high-fat diet using PathVisio

Insulin resistance is a characteristic of type-2 diabetes and its development is associated with an increased fat consumption. Muscle is one of the tissues that becomes insulin resistant after high fat (HF) feeding. The aim of the present study is to identify processes involved in the development of HF-induced insulin resistance in muscle of ApOE3*Leiden mice by using microarrays. These mice are known to become insulin resistant on a HF diet. Differential gene expression was measured in muscle using the Affymetrix mouse plus 2.0 array. To get more insight in the processes, affected pathway analysis was performed with a new tool, PathVisio. PathVisio is a pathway editor customized with plug-ins (1) to visualize microarray data on pathways and (2) to perform statistical analysis to select pathways of interest. The present study demonstrated that with pathway analysis, using PathVisio, a large variety of processes can be investigated. The significantly regulated genes in muscle of ApOE3*Leiden mice after 12 weeks of HF feeding were involved in several biological pathways including fatty acid beta oxidation, fatty acid biosynthesis, insulin signaling, oxidative stress and inflammation.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

Targeted proteomics reveals strain‐specific changes in the mouse insulin and central metabolic pathways after a sustained high‐fat diet

The metabolic syndrome is a collection of risk factors including obesity, insulin resistance and hepatic steatosis, which occur together and increase the risk of diseases such as diabetes, cardiovascular disease and cancer. In spite of intense research, the complex etiology of insulin resistance and its association with the accumulation of triacylglycerides in the liver and with hepatic steatosis remains not completely understood. Here, we performed quantitative measurements of 144 proteins involved in the insulin‐signaling pathway and central metabolism in liver homogenates of two genetically well‐defined mouse strains C57BL/6J and 129Sv that were subjected to a sustained high‐fat diet. We used targeted mass spectrometry by selected reaction monitoring (SRM) to generate accurate and reproducible quantitation of the targeted proteins across 36 different samples (12 conditions and 3 biological replicates), generating one of the largest quantitative targeted proteomics data sets in mammalian tissues. Our results revealed rapid response to high‐fat diet that diverged early in the feeding regimen, and evidenced a response to high‐fat diet dominated by the activation of peroxisomal β‐oxidation in C57BL/6J and by lipogenesis in 129Sv mice.     

Fatty aldehyde dehydrogenase: potential role in oxidative stress protection and regulation of its gene expression by insulin

Phosphatidylinositol 3-kinase signaling regulates the expression of several genes involved in lipid and glucose homeostasis; deregulation of these genes may contribute to insulin resistance and progression toward type 2 diabetes. By employing RNA arbitrarily primed-PCR to search for novel phosphatidylinositol 3-kinase-regulated genes in response to insulin in isolated rat adipocytes, we identified fatty aldehyde dehydrogenase (FALDH), a key component of the detoxification pathway of aldehydes arising from lipid peroxidation events. Among these latter events are oxidative stresses associated with insulin resistance and diabetes. Upon insulin injection, FALDH mRNA expression increased in rat liver and white adipose tissue and was impaired in two models of insulin-resistant mice, db/db and high fat diet mice. FALDH mRNA levels were 4-fold decreased in streptozotocin-treated rats, suggesting that FALDH deregulation occurs both in hyperinsulinemic insulin-resistant state and hypoinsulinemic type 1 diabetes models. Moreover, insulin treatment increases FALDH activity in hepatocytes, and expression of FALDH was augmented during adipocyte differentiation. Considering the detoxifying role of FALDH, its deregulation in insulin-resistant and type 1 diabetic models may contribute to the lipid-derived oxidative stress. To assess the role of FALDH in the detoxification of oxidized lipid species, we evaluated the production of reactive oxygen species in normal versus FALDH-overexpressing adipocytes. Ectopic expression of FALDH significantly decreased reactive oxygen species production in cells treated by 4-hydroxynonenal, the major lipid peroxidation product, suggesting that FALDH protects against oxidative stress associated with lipid peroxidation. Taken together, our observations illustrate the importance of FALDH in insulin action and its deregulation in states associated with altered insulin signaling.     

Landscape mapping of functional proteins in insulin signal transduction and insulin resistance: a network-based protein-protein interaction analysis

The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins.     

Construction of a polycystic ovarian syndrome (PCOS) pathway based on the interactions of PCOS-related proteins retrieved from bibliomic data

Polycystic ovary syndrome (PCOS) is a complex but frequently occurring endocrine abnormality. PCOS has become one of the leading causes of oligo-ovulatory infertility among premenopausal women. The definition of PCOS remains unclear because of the heterogeneity of this abnormality, but it is associated with insulin resistance, hyperandrogenism, obesity and dyslipidaemia. The main purpose of this study was to identify possible candidate genes involved in PCOS. Several genomic approaches, including linkage analysis and microarray analysis, have been used to look for candidate PCOS genes. To obtain a clearer view of the mechanism of PCOS, we have compiled data from microarray analyses. An extensive literature search identified seven published microarray analyses that utilized PCOS samples. These were published between the year of 2003 and 2007 and included analyses of ovary tissues as well as whole ovaries and theca cells. Although somewhat different methods were used, all the studies employed cDNA microarrays to compare the gene expression patterns of PCOS patients with those of healthy controls. These analyses identified more than a thousand genes whose expression was altered in PCOS patients. Most of the genes were found to be involved in gene and protein expression, cell signaling and metabolism. We have classified all of the 1081 identified genes as coding for either known or unknown proteins. Cytoscape 2.6.1 was used to build a network of protein and then to analyze it. This protein network consists of 504 protein nodes and 1408 interactions among those proteins. One hypothetical protein in the PCOS network was postulated to be involved in the cell cycle. BiNGO was used to identify the three main ontologies in the protein network: molecular functions, biological processes and cellular components. This gene ontology analysis identified a number of ontologies and genes likely to be involved in the complex mechanism of PCOS. These include the insulin receptor signaling pathway, steroid biosynthesis, and the regulation of gonadotropin secretion among others.     

内脂素与2型糖尿病关系的研究进展

内脂素是新近被发现的主要由内脏脂肪合成的一种脂肪细胞因子,它具有类胰岛素样作用,能降低血糖和促进脂肪组织的分化与合成。内脂素还可以调节血管平滑肌的成熟和影响胰岛细胞的胰岛素的分泌,亦具有调节炎症反应和免疫功能的作用。随着研究的发展,人们对内脂素的结构特性、分布、表达调控及其生物学功能有了更加深入的认识。2型糖尿病是以胰岛素抵抗和糖代谢紊乱为特征的代谢性疾病,研究发现内脂素与2型糖尿病密切相关,其中与肥胖、胰岛素抵抗及胰岛素分泌方面的关系尤为显著,深入研究内脂素的生理和病理生理作用将会有力地促进对2型糖尿病的进一步认识、治疗与预防。     

Intergenerational transmission of insulin resistance and type 2 diabetes

Studies in women with type 1 or type 2 diabetes mellitus (DM) and their children suggest that the in utero ‘diabetic’ environment in which the fetus develops can increase the risk of diabetes in the child, in a non-genetic but heritable fashion. Studies in rodents provide strong evidence for maternal transmission of diabetes, but are based primarily on a model type 1 DM and there is no standard animal model of type 2 DM in pregnancy or of gestational diabetes mellitus (GDM), although those reported uniformly show glucose intolerance in the offspring. Rodent models of diet-induced obesity have relevance to current upward trends in maternal obesity and GDM, although maternal glucose homeostasis is not always assessed and elements of the diet may have an independent influence. The mechanisms by which maternal type 2DM evokes a higher risk of the disorder in the offspring are likely to result from epigenetic modification in early life of pathways of pancreatic β cells and of liver and muscle insulin signalling pathways. Also, epigenetic processes associated with hormonal imbalance may lead to irreversible ‘reordering’ of hypothalamic neural networks in fetal/neonatal life, permanently alter energy balance and lead to obesity with associated insulin resistance.     

Mitochondrial dysfunction in diabetes and the regulatory roles of antidiabetic agents on the mitochondrial function

The prevalence of type 2 diabetes mellitus (T2DM) is increasing rapidly with its associated morbidity and mortality. Many pathophysiological pathways such as oxidative stress, inflammatory responses, adipokines, obesity-induced insulin resistance, improper insulin signaling, and beta cell apoptosis are associated with the development of T2DM. There is increasing evidence of the role of mitochondrial dysfunction in the onset of T2DM, particularly in relation to the development of diabetic complications. Here, the role of mitochondrial dysfunction in T2DM is reviewed together with its modulation by antidiabetic therapeutic agents, an effect that may be independent of their hypoglycemic effect.     

Insulin Signaling in Type 2 Diabetes: EXPERIMENTAL AND MODELING ANALYSES REVEAL MECHANISMS OF INSULIN RESISTANCE IN HUMAN ADIPOCYTES*

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.     

Identifying candidate genes for wood formation in poplar based on microarray network analysis and graph theory

In poplar, genetic research on wood properties is very important for the improvement of wood quality. Studies of wood formation genes at each developmental stage using modern biotechnology have often been limited to several genes or gene families. Because of the complex regulatory network involved in the co-expression and interactions of thousands of genes, however, the genetic mechanisms of wood formation must be surveyed on a genome-wide scale. In this study, we identified wood formation-related genes using a differentially co-expressed (DCE) gene subset approach based on biological networks inferred from microarray data. Gene co-expression networks in leaf, root, and wood tissues were first constructed and topologically analyzed using microarray data collected from the Gene Expression Omnibus. The DCE gene modules in wood-forming tissue were then detected based on graph theory, which was followed by gene ontology (GO) enrichment analysis and GO annotation of probe sets. Finally, 72 probe sets were identified in the largest cohesive subgroup of the DCE gene network in wood tissue, with most of the probe sets associated with wood formation-related biological processes and GO cellular component categories. The approach described in this paper provides an effective strategy to identify wood formation genes in poplar and should contribute to the better understanding of the genetic and molecular mechanisms underlying wood properties in trees.     

Influence of obesity and insulin sensitivity on insulin signaling genes in human omental and subcutaneous adipose tissue

Obesity and insulin resistance are independent risk factors for metabolic syndrome, diabetes, and cardiovascular disease. Adipose tissue samples from nonobese (NO), insulin-sensitive obese (ISO), and insulin-resistant obese (IRO) subjects from subcutaneous (SC) and omental (OM) adipose tissue (n = 28) were analyzed by microarray and confirmed by real-time PCR. Insulin signaling gene expression changes were greater in OM than in SC tissue and were related to insulin resistance rather than to obesity; few genes correlated with body mass index. Insulin receptor and insulin receptor substrate 1 (IRS-1) increased in the IRO versus pooled insulin-sensitive (NO+ISO) subjects. In glucose transport, PI3Kalpha and PDK2 decreased in IRO subjects, whereas PI3Kgamma, Akt2, GLUT4, and GLUT1 increased. IRS-1 regulators Jnk and IKK increased in IRO (P < 0.01 and P < 0.001 respectively). In protein synthesis, most genes examined were downregulated in IRO subjects, including mTor, Rheb, and 4EBP and eIF members (all P < 0.05). In proliferation, SHC, SOS, and Raf1 (P < 0.05) were increased, whereas Ras and MEK1/2 kinase 1 (P < 0.05) were decreased, in IRO subjects. Finally, in differentiation, PPARgamma, CEBPalpha, and CEBPbeta decreased, whereas PPARdelta, CEBPgamma, and CEBPepsilon increased, in IRO subjects (P < 0.05). Together, microarray and real-time PCR data demonstrate that insulin resistance rather than obesity is associated with altered gene expression of insulin signaling genes, especially in OM adipose tissue.     

Tumor necrosis factor-alpha-induced insulin resistance in adipocytes

Recent studies examining the link between insulin resistance and the development of obesity and noninsulin-dependent diabetes mellitus are consistent with the involvement of tumor necrosis factor-alpha (TNF-alpha) as a central mediator. In insulin resistant obese mouse models, neutralization of TNF-alpha in circulation has been demonstrated to restore insulin-mediated glucose uptake. Adipose tissue has been shown to be a site for synthesis of TNF-alpha, with the degree of adiposity directly correlated with the level of synthesis. Studies conducted on obese human patients have demonstrated a correlation between levels of TNF-alpha, the extent of obesity, as well as the level of hyperinsulinemia observed. Mechanistic studies in cell culture have suggested that TNF-alpha functions to render cells insulin resistant through regulation of the synthesis of the insulin responsive glucose transporter as well as through interference with insulin signaling. This review will address these issues and additionally introduce the reader to the molecular aspects of TNF-alpha, its receptors as well as TNF-alpha-initiated signaling cascades, that are necessary to understand the function of this cytokine in the regulation of adipose tissue metabolism.     

Transcriptomic Analysis of Insulin-Sensitive Tissues from Anti-Diabetic Drug Treated ZDF Rats,a T2DM Animal Model

Gene expression changes have been associated with type 2 diabetes mellitus (T2DM); however, the alterations are not fully understood. We investigated the effects of anti-diabetic drugs on gene expression in Zucker diabetic fatty (ZDF) rats using oligonucleotide microarray technology to identify gene expression changes occurring in T2DM. Global gene expression in the pancreas, adipose tissue, skeletal muscle, and liver was profiled from Zucker lean control (ZLC) and anti-diabetic drug treated ZDF rats compared with those in ZDF rats. We showed that anti-diabetic drugs regulate the expression of a large number of genes. We provided a more integrated view of the diabetic changes by examining the gene expression networks. The resulting sub-networks allowed us to identify several biological processes that were significantly enriched by the anti-diabetic drug treatment, including oxidative phosphorylation (OXPHOS), systemic lupus erythematous, and the chemokine signaling pathway. Among them, we found that white adipose tissue from ZDF rats showed decreased expression of a set of OXPHOS genes that were normalized by rosiglitazone treatment accompanied by rescued blood glucose levels. In conclusion, we suggest that alterations in OXPHOS gene expression in white adipose tissue may play a role in the pathogenesis and drug mediated recovery of T2DM through a comprehensive gene expression network study after multi-drug treatment of ZDF rats.