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.

Binding of the lipid A portion of bacterial lipopolysaccharide (LPS) to leukocyte CD14 activates phagocytes and initiates the septic shock syndrome. Two lipid A analogs, lipid IVA and Rhodobacter sphaeroides lipid A (RSLA), have been described as LPS-receptor antagonists when tested with human phagocytes. In contrast, lipid IVA activated murine phagocytes, whereas RSLA was an LPS antagonist. Thus, these compounds displayed a species-specific pharmacology. To determine whether the species specificity of these LPS antagonists occurred as a result of interactions with CD14, the effects of lipid IVA and RSLA were examined by using human, mouse, and hamster cell lines transfected with murine or human CD14 cDNA expression vectors. Several precursors and analogs of the toxic lipid A moiety from Escherichia coli LPS have been shown to inhibit LPS activation of lymphocytes, neutrophils, monocytes, and macrophages. By increasing the concentration of LPS relative to the concentration of antagonist, inhibition by these agents was overcome, suggesting that they competed with LPS for binding to a specific component of the LPS-signaling system. For example, Rhodobacter sphaeroides lipid A (RSLA) exhibited LPS antagonist properties in both murine (15-17) and human LPS-responsive cells (15,18). In contrast to RSLA, the tetraacyldisaccharide lipid A precursor, designated lipid IVA, inhibited LPS-induced activation of human cells (15,18,19) but acted as an LPS mimetic in murine cells (15,18), demonstrating a species-specific effect of these LPS-receptor antagonists.Kitchens et al. (20,21) reported that lipid IVA, when used in nanomolar concentrations under physiologic conditions, effectively blocked LPS-induced activation of human monocytes, whereas micromolar concentrations of lipid IVA were required to block specific binding of LPS to surface CD14. The difference between the concentration of LPS antagonists required to inhibit signal transduction compared to concentrations required to block specific binding of LPS to CD14 suggested that CD14 was not the cellular target for antagonists such as lipid IVA. Collectively, these findings are consistent with a model of signal transduction in which LPS-bound CD14 interacts with an as-yet-unidentified protein(s) present in limiting quantities on endotoxin-responsive cells that then induces a signal-transduction event across the plasma membrane. However, a definitive interpretation of these cellbinding studies (20,21) is complicated by the problems inherent with lipophilic and amphipathic ligands such as LPS. For Abbreviations: LPS, lipopolysaccharide; FBS, fetal bovine serum; NF-KB, nuclear factor KB; IFN-y, interferon -y; RSLA, Rhodobacter sphaeroides lipid A; GPI, glycosyl-phosphatidylinositol; EMSA, electrophoretic mobility-shift assay.

Peroxisome proliferator-activated receptor-␥ (PPAR␥) has been shown to play an important role in the regulation of expression of a subclass of adipocyte genes and to serve as the molecular target of the thiazolidinedione (TZD) and certain non-TZD antidiabetic agents. Hypercorticosteroidism leads to insulin resistance, a variety of metabolic dysfunctions typically seen in diabetes, and hypertrophy of visceral adipose tissue. In adipocytes, the enzyme 11␤-hydroxysteroid dehydrogenase type 1 (11␤-HSD-1) converts inactive cortisone into the active glucocorticoid cortisol and thereby plays an important role in regulating the actions of corticosteroids in adipose tissue. Here, we show that both TZD and non-TZD PPAR␥ agonists markedly reduced 11␤-HSD-1 gene expression in 3T3-L1 adipocytes. This diminution correlated with a significant decrease in the ability of the adipocytes to convert cortisone to cortisol. The half-maximal inhibition of 11␤-HSD-1 mRNA expression by the TZD, rosiglitazone, occurred at a concentration that was similar to its K d for binding PPAR␥ and EC 50 for inducing adipocyte differentiation thereby indicating that this action was PPAR␥-dependent. The time required for the inhibitory action of the TZD was markedly greater for 11␤-HSD-1 gene expression than for leptin, suggesting that these genes may be down-regulated by different molecular mechanisms. Furthermore, whereas regulation of PPAR␥-inducible genes such as phosphoenolpyruvate carboxykinase was maintained when cellular protein synthesis was abrogated, PPAR␥ agonist inhibition of 11␤-HSD-1 and leptin gene expression was ablated, thereby supporting the conclusion that PPAR␥ affects the down-regulation of 11␤-HSD-1 indirectly. Finally, treatment of diabetic db/db mice with rosiglitazone inhibited expression of 11␤-HSD-1 in adipose tissue. This decrease in enzyme expression correlated with a significant decline in plasma corticosterone levels. In sum, these data indicate that some of the beneficial effects of PPAR␥ antidiabetic agents may result, at least in part, from the down-regulation of 11␤-HSD-1 expression in adipose tissue.

We have investigated the potential use of peroxisome proliferator-activated receptor γ (PPARγ) agonists as anti-inflammatory agents in cell-based assays and in a mouse model of endotoxemia. Human peripheral blood monocytes were treated with LPS or PMA and a variety of PPARγ agonists. Although 15-deoxy-Δ12,14-prostaglandin J2 (15d-PGJ2) at micromolar concentrations significantly inhibited the production of TNF-α and IL-6, four other high affinity PPARγ ligands failed to affect cytokine production. Similar results were obtained when the monocytes were allowed to differentiate in culture into macrophages that expressed significantly higher levels of PPARγ or when the murine macrophage cell line RAW 264.7 was used. Furthermore, saturating concentrations of a potent PPARγ ligand not only failed to block cytokine production, but also were unable to block the inhibitory activity of 15d-PGJ2. Thus, activation of PPARγ does not appear to inhibit the production of cytokines by either monocytes or macrophages, and the inhibitory effect observed with 15d-PGJ2 is most likely mediated by a PPARγ-independent mechanism. To examine the anti-inflammatory activity of PPARγ agonists in vivo, db/db mice were treated with a potent thiazolidinedione that lowered their elevated blood glucose and triglyceride levels as expected. When thiazolidinedione-treated mice were challenged with LPS, they displayed no suppression of cytokine production. Rather, their blood levels of TNF-α and IL-6 were elevated beyond the levels observed in control db/db mice challenged with LPS. Comparable results were obtained with the corresponding lean mice. Our data suggest that compounds capable of activating PPARγ in leukocytes will not be useful for the treatment of acute inflammation.