Adiponectin is an adipocyte-derived hormone. Recent genome-wide scans have mapped a susceptibility locus for type 2 diabetes and metabolic syndrome to chromosome 3q27, where the gene encoding adiponectin is located. Here we show that decreased expression of adiponectin correlates with insulin resistance in mouse models of altered insulin sensitivity. Adiponectin decreases insulin resistance by decreasing triglyceride content in muscle and liver in obese mice. This effect results from increased expression of molecules involved in both fatty-acid combustion and energy dissipation in muscle. Moreover, insulin resistance in lipoatrophic mice was completely reversed by the combination of physiological doses of adiponectin and leptin, but only partially by either adiponectin or leptin alone. We conclude that decreased adiponectin is implicated in the development of insulin resistance in mouse models of both obesity and lipoatrophy. These data also indicate that the replenishment of adiponectin might provide a novel treatment modality for insulin resistance and type 2 diabetes.

Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.In the past few years, gene-expression microarrays and other general molecular profiling technologies have been applied to a wide range of biological problems and have contributed to discoveries about the complex network of biochemical processes underlying living Correspondence should be addressed to E.E.S. (eric_schadt@merck.com). Note: Supplementary information is available on the Nature Genetics website. COMPETING INTERESTS STATEMENTThe authors declare that they have no competing financial interests. NIH Public Access Author ManuscriptNat Genet. Author manuscript; available in PMC 2010 March 18. Published in final edited form as:Nat Genet. 2005 July ; 37(7): 710-717. doi:10.1038/ng1589. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript systems 1 , common human diseases 2,3 and gene discovery and structure determination [4][5][6] . Microarrays have also helped to identify biomarkers 7 , disease subtypes 3,8,9 and mechanisms of toxicity 10 and, more recently, to elucidate the genetics of gene expression in human populations 11,12 and to reconstruct gene networks by integrating gene-expression and genetic data 13 . The use of molecular profiling technologies as tools to identify genes underlying common, polygenic diseases has been less successful. Hundreds or even thousands of genes whose expression changes are associated with disease traits have been identified, but determining which of the genes cause disease rather than respond to the disease state has proven difficult.Microarray data have recently been combined with other experimental approaches to facilitate identification of key mechanistic drivers of complex traits 3,[13][14][15][16][17] . One such technique involves treating relative transcript abundances as quantitative traits in segregating populations. In this method, chromosomal regions that control the level of expression of a particular gene are mapped as expression quantitative trait loci (eQTLs). Gene-expression QTLs that contain the gene encoding t...

Leptin-replacement therapy improved glycemic control and decreased triglyceride levels in patients with lipodystrophy and leptin deficiency. Leptin deficiency contributes to the insulin resistance and other metabolic abnormalities associated with severe lipodystrophy.

Mitochondrial uncoupling proteins (UCPs) are transporters that are important for thermogenesis. The net result of their activity is the exothermic movement of protons through the inner mitochondrial membrane, uncoupled from ATP synthesis. We have cloned a third member of the UCP family, UCP3. UCP3 is expressed at high levels in muscle and rodent brown adipose tissue. Overexpression in yeast reduced the mitochondrial membrane potential, showing that UCP3 is a functional uncoupling protein. UCP3 RNA levels are regulated by hormonal and dietary manipulations. In contrast, levels of UCP2, a widely expressed UCP family member, showed little hormonal regulation. In particular, muscle UCP3 levels were decreased 3-fold in hypothyroid rats and increased 6-fold in hyperthyroid rats. Thus UCP3 is a strong candidate to explain the effects of thyroid hormone on thermogenesis. White adipose UCP3 levels were greatly increased by treatment with the ␤3-adrenergic agonist, CL214613, suggesting another pathway for increasing thermogenesis. UCP3 mRNA levels were also regulated by dexamethasone, leptin, and starvation, albeit differently in muscle and brown adipose tissue. Starvation caused increased muscle and decreased BAT UCP3, suggesting that muscle assumes a larger role in thermoregulation during starvation. The UCP3 gene is located close to that encoding UCP2, in a chromosomal region implicated in previous linkage studies as contributing to obesity.Changes in body weight result from the difference between energy intake and energy expenditure. It is presumed that each individual has a target weight that the body tries to maintain. Since weight remains relatively constant despite large variations in energy intake, energy expenditure must be regulated. For example, people who gain weight become metabolically less efficient, whereas those who lose weight become more efficient (1).The biochemical mechanisms responsible for the regulation of energy expenditure and the efficiency of energy usage are poorly understood. Possible ways to increase energy expenditure include increasing physical activity (2) and energy dissipation as heat by futile metabolic cycles (3-5).