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...
The enzyme 11β–hydroxysteroid dehydrogenase (HSD) type 1 converts inactive cortisone into active cortisol in cells, thereby raising the effective glucocorticoid (GC) tone above serum levels. We report that pharmacologic inhibition of 11β-HSD1 has a therapeutic effect in mouse models of metabolic syndrome. Administration of a selective, potent 11β-HSD1 inhibitor lowered body weight, insulin, fasting glucose, triglycerides, and cholesterol in diet-induced obese mice and lowered fasting glucose, insulin, glucagon, triglycerides, and free fatty acids, as well as improved glucose tolerance, in a mouse model of type 2 diabetes. Most importantly, inhibition of 11β-HSD1 slowed plaque progression in a murine model of atherosclerosis, the key clinical sequela of metabolic syndrome. Mice with a targeted deletion of apolipoprotein E exhibited 84% less accumulation of aortic total cholesterol, as well as lower serum cholesterol and triglycerides, when treated with an 11β-HSD1 inhibitor. These data provide the first evidence that pharmacologic inhibition of intracellular GC activation can effectively treat atherosclerosis, the key clinical consequence of metabolic syndrome, in addition to its salutary effect on multiple aspects of the metabolic syndrome itself.
The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods, that utilizes naturally occurring genetic variations as a source of perturbations to elucidate the network. This algorithm incorporates relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of this novel algorithm is demonstrated via application to liver gene expression data from a segregating mouse population. We demonstrate that the network derived from these data using our novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.
11β-hydroxysteroid dehydrogenases (11β-HSD) perform prereceptor metabolism of glucocorticoids through interconversion of the active glucocorticoid, cortisol, with inactive cortisone. Although the immunosuppressive and anti-inflammatory activities of glucocorticoids are well documented, the expression of 11β-HSD enzymes in immune cells is not well understood. Here we demonstrate that 11β-HSD1, which converts cortisone to cortisol, is expressed only upon differentiation of human monocytes to macrophages. 11β-HSD1 expression is concomitant with the emergence of peroxisome proliferator activating receptor γ, which was used as a surrogate marker of monocyte differentiation. The type 2 enzyme, 11β-HSD2, which converts cortisol to cortisone, was not detectable in either monocytes or cultured macrophages. Incubation of monocytes with IL-4 or IL-13 induced 11β-HSD1 activity by up to 10-fold. IFN-γ, a known functional antagonist of IL-4 and IL-13, suppressed the induction of 11β-HSD1 by these cytokines. THP-1 cells, a human macrophage-like cell line, expressed 11β-HSD1 and low levels of 11β-HSD2. The expression of 11β-HSD1 in these cells is up-regulated 4-fold by LPS. In summary, we have shown strong expression of 11β-HSD1 in cultured human macrophages and THP-1 cells. The presence of the enzyme in these cells suggests that it may play a role in regulating the immune function of these cells.
A common inflammatome signature, as well as disease-specific expression patterns, was identified from 11 different rodent inflammatory disease models. Causal regulatory networks and the drivers of the inflammatome signature were uncovered and validated.
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