The prevalence of type 2 diabetes (T2D) is increasing as obesity increases worldwide and insulin resistance (IR) remains the most important factor in its development. The resulting hyperglycemia leads to complications including retinopathy, stroke and amputations. Life style changes and treatments have not been successful in elevating the progression of IR into diabetes nor preventing co-morbidities with cardiovascular involvement. Despite compelling evidence that susceptibility to obesity and T2D is highly heritable and considerable progress with gene identification, most susceptibility genes continue to elude discovery. We used mouse models of diet-induced metabolic disease to facilitate gene discovery and better characterize the underlying genetic architecture of T2D. Analysis of the C57BL/6JA/J panel of Chromosome substitution strains (CSS) identified 8 chromosomes with at least 1 QTL affecting glucose homeostasis. First, we analyzed global gene expression patterns in 6C1 and 6C2 congenic strains of CSS-6 defining Obrq2, a QTL for body weight, and measures of IR. Pathway analysis of global gene expression patterns in liver identified expression level differences between 6C1 and 6C2 in pathways related to mitochondrial oxidative phosphorylation (OxPhos). The OxPhos expression differences were subtle but evident in each complex of the electron transport chain. This data suggests the importance of hepatic mitochondrial function in the development of obesity and insulin resistance. Next, we developed two consecutive subcongenic panels for Obrq2. Through genetic and phenotypic analysis of congenic, subcongenic and subsubcongenic strains, we uncovered a complex genetic architecture for metabolic traits associated with T2D. Multiple closely linked QTLs demonstrated strong effects with considerable phenotypic heterogeneity. Analysis of one of them, Obrq2a1, identified the solute receptor Slc35b4 as a potential regulator of obesity and insulin resistance. Slc35b4 mRNA expression level differences in liver were associated with the phenotype. Finally, we provided first evidence for a non-imprinting transgenerational paternal effect on body weight and food intake using crosses between obesity resistant congenic strain 6C2d and obesity sensitive strain B6. The phenotype was transmitted through the paternal lineage for at least 3 generations, but was lost if passed through the female lineages.

This book presents the state of the art of type 2 diabetes genetics, from the process of genetic discovery to its interpretation and clinical application, and illustrates a model for other complex human phenotypes.The first section explores genome-wide association studies, the extension of this method to less accessible phenotypes and the arrival of next-generation sequencing. A further section goes beyond genetics to illustrate how other data sources can help interpret genetic data, such as leveraging population diversity, the correlation of genetic associations with physiological measurements, gene expression modulation, environmental factors and our microbial commensals. The third section describes advances in elucidating the complex path from association to function using in-depth sequencing and functional studies of the cellular and molecular effects of genes in the loci identified by genetics. The final section links our current understanding with clinically relevant questions, such as prediction, interactions with drugs or nutrients, and disease prevention, and paints a realistic but hopeful vision of the future. ​

Abstract: Type 2 diabetes mellitus is a metabolic disorder that is believed to be caused by a combination of environmental factors and genetic disposition. Despite an impressive body of research and steady progress in understanding associated risk factors such as obesity and insulin resistance, the underlying disease mechanisms remain unknown. In this project, we described and developed a gene network analysis approach, called Gene Network Enrichment Analysis to study of type 2 diabetes mellitus. Gene networks encode relationships between genes; by accounting for such information in addition to gene expression, we show that Gene Network Enrichment Analysis is able to identify biological processes, signaling pathways, and individual genes associated with disease that are missed by standard analyses. We developed multiple variants of the algorithm and show that it is equally suited to analyzing individual experiments or as a meta-analysis across multiple, biologically related experiments. Application of Gene Network Enrichment Analysis on a large compendium of animal models of disease successfully identifies differential activity in insulin signaling, nuclear receptors, metabolism processes, and inflammatory response. Subsequent network analysis comparing diabetes resistant and diabetes prone mouse models demonstrated that differences in inflammation precedes any measurable metabolic differences, and may stem from differences in the actual number of inflammatory cells. Our results therefore suggest that dysregulation in inflammation, specifically T cell and macrophage activation, may be an early feature of disease. We compare the performance of Gene Network Enrichment Analysis to a number of other tools used in the community including standard hypergeometric enrichment on differentially expressed genes and Gene Set Enrichment Analysis. While we focused on type 2 diabetes mellitus in this project, the Gene Network Enrichment Analysis algorithm that we have developed is generally applicable. To facilitate the adoption of the algorithm by the wider scientific community, we have implemented a publicly available and open-source web server for Gene Network Enrichment Analysis. The server is located at http://soteira.bu.edu.ezproxy.bu.edu .

Due to the resultant health consequences and considerable increase in prevalence, obesity has become a major worldwide health problem. “Obesity and Lipotoxicity” is a comprehensive review of the recent researches to provide a better understanding of the lipotoxicity-related mechanisms of obesity and the potential for the development of new treatment strategies. This book overviews the biochemical pathways leading to obesity-related metabolic disorders that occur subsequent to lipotoxicity. Chapters examine the deleterious effects of nutrient excess at molecular level including the cellular and molecular aspects of breast cancer, resistance to leptin, insulin, adiponectin, and interconnection between the circadian clock and metabolic pathways during high-fat feeding. “Lipotoxicity and Obesity” will be a useful resource for clinicians and basic science researchers, such as biochemists, toxicologists, immunologists, nutritionists, adult and pediatric endocrinologists, cardiologists, as well as students who are thought in this field.

Causes of Diabetes Genetic and Environmental Factors Edited by R. D. G. Leslie St Bartholomew’s Hospital, London, UK There is now firm evidence that a non-genetically determined event plays a major role in the aetiology of insulin-dependent diabetes mellitus (IDDM) and that this event is due to exposure to an environmental agent. While genetic factors are more important in the pathogenesis of non-insulin-dependent diabetes mellitus (NIDDM), environmental factors also play a major role. Causes of Diabetes provides a comprehensive assessment of the causes of NIDDM and IDDM and pays particular attention to the environmental factors involved in disease pathogenesis. Identification of these factors opens the way for therapeutic trials aimed at preventing, even curing, this potentially devastating disease. This book is essential reading for clinicians and researchers working in diabetes, autoimmune and metabolic diseases.