Mouse models of type 1 and type 2 diabetes derived from the same closed colony: genetic susceptibility shared between two types of diabetes

Except for rare subtypes of diabetes, both type 1 and type 2 diabetes are multifactorial diseases in which genetic factors consisting of multiple susceptibility genes and environmental factors contribute to the disease development. Due to complex interaction among multiple susceptibility genes and between genetic and environmental factors, genetic analysis of multifactorial diseases is difficult in humans. Inbred animal models, in which the genetic background is homogeneous and environmental factors can be controlled, are therefore valuable in genetic dissection of multifactorial diseases. We are fortunate to have excellent animal models for both type 1 and type 2 diabetes--the nonobese diabetic (NOD) mouse and the Nagoya-Shibata-Yasuda (NSY) mouse, respectively. Congenic mapping of susceptibility genes for type 1 diabetes in the NOD mouse has revealed that susceptibility initially mapped as a single locus often consists of multiple components on the same chromosome, indicating the importance of congenic mapping in defining genes responsible for polygenic diseases. The NSY mouse is an inbred animal model of type 2 diabetes established from Jcl:ICR, from which the NOD mouse was also derived. We have recently mapped three major loci contributing to type 2 diabetes in the NSY mouse. Interestingly, support intervals where type 2 diabetes susceptibility genes were mapped in the NSY mouse overlapped the regions where type 1 diabetes susceptibility genes have been mapped in the NOD mouse. Although additional evidence is needed, it may be possible that some of the genes predisposing to diabetes are derived from a common ancestor contained in the original closed colony, contributing to type 1 diabetes in the NOD mouse and type 2 diabetes in the NSY mouse. Such genes, if they exist, will provide valuable information on etiological pathways common to both forms of diabetes, for the establishment of effective methods for prediction, prevention, and intervention in both type 1 and type 2 diabetes.     

Genome-wide co-expression based prediction of differential expressions

MOTIVATION: Microarrays have been widely used for medical studies to detect novel disease-related genes. They enable us to study differential gene expressions at a genomic level. They also provide us with informative genome-wide co-expressions. Although many statistical methods have been proposed for identifying differentially expressed genes, genome-wide co-expressions have not been well considered for this issue. Incorporating genome-wide co-expression information in the differential expression analysis may improve the detection of disease-related genes. RESULTS: In this study, we proposed a statistical method for predicting differential expressions through the local regression between differential expression and co-expression measures. The smoother span parameter was determined by optimizing the rank correlation between the observed and predicted differential expression measures. A mixture normal quantile-based method was used to transform data. We used the gene-specific permutation procedure to evaluate the significance of a prediction. Two published microarray data sets were analyzed for applications. For the data set collected for a prostate cancer study, the proposed method identified many genes with weak differential expressions. Several of these genes have been shown in literature to be associated with the disease. For the data set collected for a type 2 diabetes study, no significant genes could be identified by the traditional methods. However, the proposed method identified many genes with significantly low false discovery rates. AVAILABILITY: The R codes are freely available at http://home.gwu.edu/~ylai/research/CoDiff, where the gene lists ranked by our method are also provided as the Supplementary Material.     

Functional genomic screen identifies novel mediators of collagen uptake

Tissue fibrosis occurs when matrix production outpaces matrix degradation. Degradation of collagen, the main component of fibrotic tissue, is mediated through an extracellular proteolytic pathway and intracellular pathway of cellular uptake and lysosomal digestion. Recent studies demonstrate that disruption of the intracellular pathways can exacerbate fibrosis. These pathways are poorly characterized. Here we identify novel mediators of the intracellular pathway of collagen turnover through a genome-wide RNA interference screen in Drosophila S2 cells. Screening of 7505 Drosophila genes conserved among metazoans identified 22 genes that were required for efficient internalization of type I collagen. These included proteins involved in vesicle transport, the actin cytoskeleton, and signal transduction. We show further that the flotillin genes have a conserved and central role in collagen uptake in Drosophila and human cells. Short hairpin RNA–mediated silencing of flotillins in human monocyte and fibroblasts impaired collagen uptake by promoting lysosomal degradation of the endocytic collagen receptors uPARAP/Endo180 and mannose receptor. These data provide an initial characterization of intracellular pathways of collagen turnover and identify the flotillin genes as critical regulators of this process. A better understanding of these pathways may lead to novel therapies that reduce fibrosis by increasing collagen turnover.     

The New Perspectives on Genetic Studies of Type 2 Diabetes and Thyroid Diseases

Recently, genome-wide association studies (GWAS) have led to the discovery of hundreds of susceptibility loci that are associated with complex metabolic diseases, such as type 2 diabetes and hyperthyroidism. The majority of the susceptibility loci are common across different races or populations; while some of them show ethnicity-specific distribution. Though the abundant novel susceptibility loci identified by GWAS have provided insight into biology through the discovery of new genes or pathways that were previously not known, most of them are in introns and the associated variants cumulatively explain only a small fraction of total heritability. Here we reviewed the genetic studies on the metabolic disorders, mainly type 2 diabetes and hyperthyroidism, including candidate genes-based findings and more recently the GWAS discovery; we also included the clinical relevance of these novel loci and the gene-environmental interactions. Finally, we discussed the future direction about the genetic study on the exploring of the pathogenesis of the metabolic diseases.     

Type 2 Diabetes Mellitus: New Genetic Insights will Lead to New Therapeutics

Type 2 diabetes is a disorder of dysregulated glucose homeostasis. Normal glucose homeostasis is a complex process involving several interacting mechanisms, such as insulin secretion, insulin sensitivity, glucose production, and glucose uptake. The dysregulation of one or more of these mechanisms due to environmental and/or genetic factors, can lead to a defective glucose homeostasis. Hyperglycemia is managed by augmenting insulin secretion and/or interaction with hepatic glucose production, as well as by decreasing dietary caloric intake and raising glucose metabolism through exercise. Although these interventions can delay disease progression and correct blood glucose levels, they are not able to cure the disease or stop its progression entirely. Better management of type 2 diabetes is sorely needed. Advances in genotyping techniques and the availability of large patient cohorts have made it possible to identify common genetic variants associated with type 2 diabetes through genome-wide association studies (GWAS). So far, genetic variants on 19 loci have been identified. Most of these loci contain or lie close to genes that were not previously linked to diabetes and they may thus harbor targets for new drugs. It is also hoped that further genetic studies will pave the way for predictive genetic screening. The newly discovered type 2 diabetes genes can be classified based on their presumed molecular function, and we discuss the relation between these gene classes and current treatments. We go on to consider whether the new genes provide opportunities for developing alternative drug therapies.Key Words: Type 2 diabetes, drug targets, genetics, personalized medicine.     

Genetics of type 2 diabetes

Identification and characterization of genetic variants that either cause or predispose to diabetes are a major focus of biomedical research. As of early 2007, the molecular basis of most forms of monogenic diabetes resulting from beta-cell dysfunction is known and, in particular, there has been recent success in delineating the genetic aetiology of neonatal diabetes. Finding genes predisposing to more common, multifactorial forms of type 2 diabetes represents a far greater challenge, and only a handful of robust, well-replicated examples have been established. Nevertheless, 2006 heralded identification of the most important type 2 diabetes susceptibility gene known so far, TCF7L2, and in 2007 large-scale genome-wide association studies are destined to provide novel insights into the genetic architecture and biology of type 2 diabetes.     

Prevention of Type 1A Diabetes Mellitus

ObjectiveTo review prediction of type 1 diabetes mellitus in light of current trials for prevention and novel preclinical therapies.MethodsThe stages in the development of type 1A diabetes are reviewed and strategies for prevention are discussed.ResultsFrom islet autoantibody testing of random cadaveric donors, it is apparent that approximately one-half million persons in the United States express multiple islet autoantibodies and are in the process of developing type 1A (immune-mediated) diabetes. It is now possible to predict not only risk for type 1A diabetes but also the approximate age of diabetes onset in children followed up from birth. In animal models, diabetes can be prevented. Some of the immunologic therapies effective in animal models are able to delay loss of insulin secretion in humans.ConclusionsNone of the therapies studied to date in humans can completely arrest progressive loss of insulin secretion resulting from destruction of islet b cells. Nevertheless, current knowledge of pathogenesis (targeting trimolecular recognition complex: major histocompatibility complex, peptide, T-cell receptor) and natural history combined with newer diagnostic methods allows accurate diagnosis and has stimulated the search for novel safe and effective preventive therapies. (Endocr Pract. 2012;18:745-749)     

Genes, environment, and interactions in prevention of type 2 diabetes: a focus on physical activity and lifestyle changes

Type 2 diabetes is one of the fastest growing public health problems worldwide. Both environmental (e.g. physical activity, obesity, and diet) and genetic factors are involved in the development of type 2 diabetes. The associations between physical activity and diabetes risk have been assessed by a number of prospective studies and clinical trials. The results from these studies consistently indicate that the regular physical activity during occupation, commuting, leisure time or daily life reduces the risk of type 2 diabetes by 15-60%; and lifestyle intervention, including counselling for physical activity, nutrition, and body weight, can reduce the risk of type 2 diabetes by 40-60% among adults with impaired glucose tolerance and by about 20% among general individuals. In the past decade, studies using traditional linkage analysis and candidate-gene association approach have found dozens of genes harboring common variants that were related to the common-form type 2 diabetes. However, most reported associations are lack of reproducibility, except TCF7L2, PPARG, CAPN10, and KCNJ11. Since 2007, seven genome-wide association (GWA) studies emerged to generate a list of new diabetes genes. The genetic effects are largely of moderate size. These findings provide novel insight into the diabetes etiology and pave new avenue for predicting the disease risk using genetic information. In addition, data especially those from intervention trials display preliminary but promising evidence that the genetic variants might interact with physical activity in predisposing to type 2 diabetes. The gene-environment interactions merit extensive exploration in large, prospective studies.     

Genetic determinants of plasma triglycerides

Plasma triglyceride (TG) concentration is reemerging as an important cardiovascular disease risk factor. More complete understanding of the genes and variants that modulate plasma TG should enable development of markers for risk prediction, diagnosis, prognosis, and response to therapies and might help specify new directions for therapeutic interventions. Recent genome-wide association studies (GWAS) have identified both known and novel loci associated with plasma TG concentration. However, genetic variation at these loci explains only ～10% of overall TG variation within the population. As the GWAS approach may be reaching its limit for discovering genetic determinants of TG, alternative genetic strategies, such as rare variant sequencing studies and evaluation of animal models, may provide complementary information to flesh out knowledge of clinically and biologically important pathways in TG metabolism. Herein, we review genes recently implicated in TG metabolism and describe how some of these genes likely modulate plasma TG concentration. We also discuss lessons regarding plasma TG metabolism learned from various genomic and genetic experimental approaches. Treatment of patients with moderate to severe hypertriglyceridemia with existing therapies is often challenging; thus, gene products and pathways found in recent genetic research studies provide hope for development of more effective clinical strategies.     

Strategies and issues in the detection of pathway enrichment in genome-wide association studies

A fundamental question in human genetics is the degree to which the polygenic character of complex traits derives from polymorphism in genes with similar or with dissimilar functions. The many genome-wide association studies now being performed offer an opportunity to investigate this, and although early attempts are emerging, new tools and modeling strategies still need to be developed and deployed. Towards this goal, we implemented a new algorithm to facilitate the transition from genetic marker lists (principally those generated by PLINK) to pathway analyses of representational gene sets in either threshold or threshold-free downstream applications (e.g. DAVID, GSEA-P, and Ingenuity Pathway Analysis). This was applied to several large genome-wide association studies covering diverse human traits that included type 2 diabetes, Crohn’s disease, and plasma lipid levels. Validation of this approach was obtained for plasma HDL levels, where functional categories related to lipid metabolism emerged as the most significant in two independent studies. From analyses of these samples, we highlight and address numerous issues related to this strategy, including appropriate gene based correction statistics, the utility of imputed versus non-imputed marker sets, and the apparent enrichment of pathways due solely to the positional clustering of functionally related genes. The latter in particular emphasizes the importance of studies that directly tie genetic variation to functional characteristics of specific genes. The software freely provided that we have called ProxyGeneLD may resolve an important bottleneck in pathway-based analyses of genome-wide association data. This has allowed us to identify at least one replicable case of pathway enrichment but also to highlight functional gene clustering as a potentially serious problem that may lead to spurious pathway findings if not corrected. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Molecular mechanism of insulin resistance and obesity

Obesity and insulin resistance have been recognized as leading causes of major health issues. We have endeavored to depict the molecular mechanism of insulin resistance, focusing on the function of adipocyte. We have investigated a role of PPARgamma on the pathogenesis of Type II diabetes. Heterozygous PPARgamma-deficient mice were protected from the development of insulin resistance due to adipocyte hypertrophy under a high-fat diet. Moreover, a Pro12Ala polymorphism in the human PPARgamma2 gene was associated with decreased risk of Type II diabetes in Japanese. Taken together with these results, PPARgamma is proved to be a thrifty gene mediating Type II diabetes. Pharmacological inhibitors of PPARgamma/RXR ameliorate high-fat diet-induced insulin resistance in animal models of Type II diabetes. We have performed a genome-wide scan of Japanese Type 2 diabetic families using affected sib pair analysis. Our genome scan reveals at least 9 chromosomal regions potentially harbor susceptibility genes of Type II diabetes in Japanese. Among these regions, 3q26-q28 appeared to be very attractive one, because of the gene encoding adiponectin, the expression of which we had found enhanced in insulin-sensitive PPARgamma-deficient mice. Indeed, the subjects with the G/G genotype of SNP276 in the adiponectin gene were at increased risk for Type II diabetes compared with those having the T/T genotype. The plasma adiponectin levels were lower in the subjects with the G allele, suggesting that genetically inherited decrease in adiponectin levels predispose subjects to insulin resistance and Type II diabetes. Our work also confirmed that replenishment of adiponectin represents a novel treatment strategy for insulin resistance and Type II diabetes using animal models. Further investigation will be needed to clarify how adiponectin exerts its effect and to discover the molecular target of therapies.     

Integrated analysis of genome-wide genetic and epigenetic association data for identification of disease mechanisms

Many human diseases are multifactorial, involving multiple genetic and environmental factors impacting on one or more biological pathways. Much of the environmental effect is believed to be mediated through epigenetic changes. Although many genome-wide genetic and epigenetic association studies have been conducted for different diseases and traits, it is still far from clear to what extent the genomic loci and biological pathways identified in the genetic and epigenetic studies are shared. There is also a lack of statistical tools to assess these important aspects of disease mechanisms. In the present study, we describe a protocol for the integrated analysis of genome-wide genetic and epigenetic data based on permutation of a sum statistic for the combined effects in a locus or pathway. The method was then applied to published type 1 diabetes (T1D) genome-wide- and epigenome-wide-association studies data to identify genomic loci and biological pathways that are associated with T1D genetically and epigenetically. Through combined analysis, novel loci and pathways were also identified, which could add to our understanding of disease mechanisms of T1D as well as complex diseases in general.     

Gene Network Analysis of Candidate Loci for Human Anorectal Malformations

Anorectal malformations (ARMs) are birth defects that require surgery and carry significant chronic morbidity. Our earlier genome-wide copy number variation (CNV) study had provided a wealth of candidate loci. To find out whether these candidate loci are related to important developmental pathways, we have performed an extensive literature search coupled with the currently available bioinformatics tools. This has allowed us to assign both genic and non-genic CNVs to interrelated pathways known to govern the development of the anorectal region. We have linked 11 candidate genes to the WNT signalling pathway and 17 genes to the cytoskeletal network. Interestingly, candidate genes with similar functions are disrupted by the same type of CNV. The gene network we discovered provides evidence that rare mutations in different interrelated genes may lead to similar phenotypes, accounting for genetic heterogeneity in ARMs. Classification of patients according to the affected pathway and lesion type should eventually improve the diagnosis and the identification of common genes/molecules as therapeutic targets.     

Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes,and Cytokine Signaling Genes in Crohn's Disease

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn''s disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.     

Metabolic syndrome: from epidemiology to systems biology

Metabolic syndrome (MetSyn) is a group of metabolic conditions that occur together and promote the development of cardiovascular disease (CVD) and diabetes. Recent genome-wide association studies have identified several novel susceptibility genes for MetSyn traits, and studies in rodent models have provided important molecular insights. However, as yet, only a small fraction of the genetic component is known. Systems-based approaches that integrate genomic, molecular and physiological data are complementing traditional genetic and biochemical approaches to more fully address the complexity of MetSyn.     

Huntington's Disease and its therapeutic target genes: a global functional profile based on the HD Research Crossroads database

ABSTRACT: BACKGROUND: Huntington's disease (HD) is a fatal progressive neurodegenerative disorder caused by the expansion of the polyglutamine repeat region in the huntingtin gene. Although the disease is triggered by the mutation of a single gene, intensive research has linked numerous other genes to its pathogenesis. To obtain a systematic overview of these genes, which may serve therapeutic targets, the Cure Huntington's Disease Initiative (CHDI) has recently established the HD Research Crossroads database. With currently over 800 cataloged genes, this web-based resource constitutes the most extensive curation of genes relevant to HD. It provides us with an unprecedented opportunity to survey molecular mechanisms involved in HD in a holistic manner. METHODS: To gain a synoptic view of therapeutic targets for HD, we have carried out a variety of bioinformatical and statistical analyses to scrutinize the functional association of genes curated in the HD Research Crossroads database. In particular, enrichment analyses were performed with respect to Gene Ontology categories, KEGG signaling pathways, and Pfam protein families. For selected processes, we also analyzed differential expression, using published microarray data. Additionally, we generated a candidate set of novel genetic modifiers of HD by combining information from the HD Research Crossroads database with previous genome-wide linkage studies. RESULTS: Our analyses led to a comprehensive identification of molecular mechanisms associated with HD. Remarkably, we not only recovered processes and pathways, which have frequently been linked to HD (such as cytotoxicity, apoptosis, and calcium signaling), but also found strong indications for other potentially disease-relevant mechanisms that have been less intensively studied in the context of HD (such as the cell cycle and RNA splicing, as well as Wnt and ErbB signaling). For follow-up studies, we provide a compendium of molecular mechanisms that are associated with HD. Additionally, we derived a candidate set of 24 novel genetic modifiers, including histone deacetylase 3 (HDAC3), metabotropic glutamate receptor 1 (GRM1), CDK5 regulatory subunit 2 (CDK5R2), and coactivator 1beta of the peroxisome proliferator-activated receptor gamma (PPARGC1B). CONCLUSIONS: The results of our study give us an intriguing picture of the molecular complexity of HD. Our analyses can be seen as a first step towards a comprehensive list of biological processes, molecular functions, and pathways involved in HD, and may provide a basis for the development of more holistic disease models and new therapeutics.     

Genome-wide searching of rare genetic variants in WTCCC data

Although they have demonstrated success in searching for common variants for complex diseases, genome-wide association (GWA) studies are less successful in detecting rare genetic variants because of the poor statistical power of most of current methods. We developed a two-stage method that can apply to GWA studies for detecting rare variants. Here we report the results of applying this two-stage method to the Wellcome Trust Case Control Consortium (WTCCC) dataset that include seven complex diseases: bipolar disorder, cardiovascular disease, hypertension (HT), rheumatoid arthritis, Crohn’s disease, type 1 diabetes and type 2 diabetes (T2D). We identified 24 genes or regions that reach genome wide significance. Eight of them are novel and were not reported in the WTCCC study. The cumulative risk (or protective) haplotype frequency for each of the 8 genes or regions is small, being at most 11%. For each of the novel genes, the risk (or protective) haplotype set cannot be tagged by the common SNPs available in chips (r ² < 0.32). The gene identified in HT was further replicated in the Framingham Heart Study, and is also significantly associated with T2D. Our analysis suggests that searching for rare genetic variants is feasible in current GWA studies and candidate gene studies, and the results can severe as guides to future resequencing studies to identify the underlying rare functional variants.     

Convergence and divergence of genetic and modular networks between diabetes and breast cancer

Diabetes mellitus (DM) and breast cancer (BC) can simultaneously occur in the same patient populations, but the molecular relationship between them remains unknown. In this study, we constructed genetic networks and used modularized analysis approaches to investigate the multi‐dimensional characteristics of two diseases and one disease subtype. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to validate potential subnetworks and to divide the modules, respectively. A total of 793 DM‐related genes, 386 type 2 diabetes (T2DM) genes and 873 BC‐related genes were identified from the Online Mendelian Inheritance in Man database. For DM and BC, a total of 99 overlapping genes, 9 modules, 29 biological processes and 7 pathways were identified. Meanwhile, for T2DM and BC, 56 overlapping genes, 5 modules, 20 biological processes and 12 pathways were identified. Based on the Gene Ontology functional enrichment analysis of the top 10 non‐overlapping modules of the two diseases, 10 biological functions and 5 pathways overlapped between them. The glycosphingolipid and lysosome pathways verified molecular mechanisms of cell death related to both DM and BC. We also identified new biological functions of dopamine receptors and four signalling pathways (Parkinson's disease, Alzheimer's disease, Huntington's disease and long‐term depression) related to both diseases; these warrant further investigation. Our results illustrate the landscape of the novel molecular substructures between DM and BC, which may support a new model for complex disease classification and rational therapies for multiple diseases.