A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort.

To map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1000 patients undergoing Roux-en-Y gastric bypass (RYGB) and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than 100,000 gene expression traits representing four metabolically relevant tissues: liver, omental adipose, subcutaneous adipose, and stomach. We successfully identified 24,531 eSNPs corresponding to about 10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high-quality disease map for each tissue in morbidly obese patients to not only inform genetic associations identified in this cohort, but in previously published genome-wide association studies as well. These data can aid in elucidating the key networks associated with morbid obesity, response to RYGB, and disease as a whole.

Variations in DNA elucidate molecular networks that cause disease.

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.

Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits.

Forward genetic approaches to identify genes involved in complex traits such as common human diseases have met with limited success. Fine mapping of linkage regions and validation of positional candidates are time-consuming and not always successful. Here we detail a hybrid procedure to map loci involved in complex traits that leverages the strengths of forward and reverse genetic approaches. By integrating genotypic and expression data in a segregating mouse population, we show how clusters of expression quantitative trait loci linking to regions of the genome accurately reflect the underlying perturbation to the transcriptional network induced by DNA variations in genes that control the complex traits. By matching patterns of gene expression in a segregating population with expression responses induced by single-gene perturbation experiments, we show how genes controlling clusters of expression and clinical quantitative trait loci can be mapped directly. We demonstrate the utility of this approach by identifying 5-lipoxygenase as underlying previously identified quantitative trait loci in an F(2) cross between strains C57BL/6J and DBA/2J and showing that it has pleiotropic effects on body fat, lipid levels and bone density.

An integrative genomics approach to infer causal associations between gene expression and disease.

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.

Serotonin-2 Receptor Agonists Produce Anti-inflammatory Effects through Functionally Selective Mechanisms That Involve the Suppression of Disease-Induced Arginase 1 Expression.

Functional selectivity in the context of serotonin 2A (5-HT2A) receptor agonists is often described as differences psychedelic compounds have in the activation of Gq vs ß-arrestin signaling in the brain and how that may relate to inducing psychoactive and hallucinatory properties with respect to each other. However, the presence of 5-HT2A receptors throughout the body in several cell types, including endothelial, endocrine, and immune-related tissues, suggests that functional selectivity may exist in the periphery as well. Here, we examine functional selectivity between two 5-HT2A receptor agonists of the phenylalkylamine class: (R)-2,5-dimethoxy-4-iodoamphetamine [(R)-DOI] and (R)-2,5-dimethoxy-4-trifluoromethylamphetamine [(R)-DOTFM]. Despite comparable in vitro activity at the 5-HT2A receptor as well as similar behavioral potency, (R)-DOTFM does not exhibit an ability to prevent inflammation or elevated airway hyperresponsiveness (AHR) in an acute murine ovalbumin-induced asthma model as does (R)-DOI. Furthermore, there are distinct differences between protein expression and inflammatory-related gene expression in pulmonary tissues between the two compounds. Using (R)-DOI and (R)-DOTFM as tools, we further elucidated the anti-inflammatory mechanisms underlying the powerful anti-inflammatory effects of certain psychedelics and identified key mechanistic components of the anti-inflammatory effects of psychedelics, including suppression of arginase 1 expression.

11ß-HSD1 inhibition reduces atherosclerosis in mice by altering proinflammatory gene expression in the vasculature.

11ß-Hydroxysteroid dehydrogenase type 1 (11ß-HSD1) is implicated in the etiology of metabolic syndrome. We previously showed that pharmacological inhibition of 11ß-HSD1 ameliorated multiple facets of metabolic syndrome and attenuated atherosclerosis in ApoE-/- mice. However, the molecular mechanism underlying the atheroprotective effect was not clear. In this study, we tested whether and how 11ß-HSD1 inhibition affects vascular inflammation, a major culprit for atherosclerosis and its associated complications. ApoE-/- mice were treated with an 11ß-HSD1 inhibitor for various periods of time. Plasma lipids and aortic cholesterol accumulation were quantified. Several microarray studies were carried out to examine the effect of 11ß-HSD1 inhibition on gene expression in atherosclerotic tissues. Our data suggest 11ß-HSD1 inhibition can directly modulate atherosclerotic plaques and attenuate atherosclerosis independently of lipid lowering effects. We identified immune response genes as the category of mRNA most significantly suppressed by 11ß-HSD1 inhibition. This anti-inflammatory effect was further confirmed in plaque macrophages and smooth muscle cells procured by laser capture microdissection. These findings in the vascular wall were corroborated by reduction in circulating MCP1 levels after 11ß-HSD1 inhibition. Taken together, our data suggest 11ß-HSD1 inhibition regulates proinflammatory gene expression in atherosclerotic tissues of ApoE-/- mice, and this effect may contribute to the attenuation of atherosclerosis in these animals.

Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver.

Liver cytochrome P450s (P450s) play critical roles in drug metabolism, toxicology, and metabolic processes. Despite rapid progress in the understanding of these enzymes, a systematic investigation of the full spectrum of functionality of individual P450s, the interrelationship or networks connecting them, and the genetic control of each gene/enzyme is lacking. To this end, we genotyped, expression-profiled, and measured P450 activities of 466 human liver samples and applied a systems biology approach via the integration of genetics, gene expression, and enzyme activity measurements. We found that most P450s were positively correlated among themselves and were highly correlated with known regulators as well as thousands of other genes enriched for pathways relevant to the metabolism of drugs, fatty acids, amino acids, and steroids. Genome-wide association analyses between genetic polymorphisms and P450 expression or enzyme activities revealed sets of SNPs associated with P450 traits, and suggested the existence of both cis-regulation of P450 expression (especially for CYP2D6) and more complex trans-regulation of P450 activity. Several novel SNPs associated with CYP2D6 expression and enzyme activity were validated in an independent human cohort. By constructing a weighted coexpression network and a Bayesian regulatory network, we defined the human liver transcriptional network structure, uncovered subnetworks representative of the P450 regulatory system, and identified novel candidate regulatory genes, namely, EHHADH, SLC10A1, and AKR1D1. The P450 subnetworks were then validated using gene signatures responsive to ligands of known P450 regulators in mouse and rat. This systematic survey provides a comprehensive view of the functionality, genetic control, and interactions of P450s.

Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers.

Common inflammatome gene signatures as well as disease-specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co-expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes in this signature are tightly connected in tissue-specific Bayesian networks (BNs) built from multiple independent mouse and human cohorts. Both the inflammatome signature and the corresponding consensus BNs are highly enriched for immune response-related genes supported as causal for adiposity, adipokine, diabetes, aortic lesion, bone, muscle, and cholesterol traits, suggesting the causal nature of the inflammatome for a variety of diseases. Integration of this inflammatome signature with the BNs uncovered 151 key drivers that appeared to be more biologically important than the non-drivers in terms of their impact on disease phenotypes. The identification of this inflammatome signature, its network architecture, and key drivers not only highlights the shared etiology but also pinpoints potential targets for intervention of various common diseases.

Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes.

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

An eQTL biological data visualization challenge and approaches from the visualization community.

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains.

Novel transcriptome profiling analyses demonstrate that selective peroxisome proliferator-activated receptor γ (PPARγ) modulators display attenuated and selective gene regulatory activity in comparison with PPARγ full agonists.

Selective peroxisome proliferator-activated receptor γ (PPARγ) modulators (SPPARγMs) have been actively pursued as the next generation of insulin-sensitizing antidiabetic drugs, because the currently marketed PPARγ full agonists, pioglitazone and rosiglitazone, have been reported to produce serious adverse effects among patients with type 2 diabetes mellitus. We conducted extensive transcriptome profiling studies to characterize and to contrast the activities of 70 SPPARγMs and seven PPARγ full agonists. In both 3T3-L1 adipocytes and adipose tissue from db/db mice, the SPPARγMs generated attenuated and selective gene-regulatory responses, in comparison with full agonists. More importantly, SPPARγMs regulated the expression of antidiabetic efficacy-associated genes to a greater extent than that of adverse effect-associated genes, whereas PPARγ full agonists regulated both gene sets proportionally. Such SPPARγM selectivity demonstrates that PPARγ ligand regulation of gene expression can be fine-tuned, and not just turned on and off, to achieve precise control of complex cellular and physiological functions. It also provides a potential molecular basis for the superior therapeutic window previously observed with SPPARγMs versus full agonists. On the basis of our profiling results, we introduce two novel, gene expression-based scores, the γ activation index and the selectivity index, to aid in the detection and characterization of novel SPPARγMs. These studies provide new insights into the gene-regulatory activity of SPPARγMs as well as novel quantitative indices to facilitate the identification of PPARγ ligands with robust insulin-sensitizing activity and improved tolerance among patients with type 2 diabetes, compared with presently available PPARγ agonist drugs.

Elucidating host cell response pathways and repurposing therapeutics for SARS-CoV-2 and other coronaviruses.

COVID-19, first reported in late 2019, is an ongoing pandemic that has been causing devastation across the globe. Although there are multiple vaccines that can prevent severe symptoms, effective COVID-19 therapeutics are still of importance. Using our proprietary in silico engine, we screened more than 22,000 unique compounds represented by over half a million gene expression profiles to uncover compounds that can be repurposed for SARS-CoV-2 and other coronaviruses in a timely and cost-efficient manner. We then tested 13 compounds in vitro and found three with potency against SARS-CoV-2 with reasonable cytotoxicity. Bortezomib and homoharringtonine are some of the most promising hits with IC50 of 1.39 µM and 0.16 µM, respectively for SARS-CoV-2. Tanespimycin and homoharringtonine were effective against the common cold coronaviruses. In-depth analysis highlighted proteasome, ribosome, and heat shock pathways as key targets in modulating host responses during viral infection. Further studies of these pathways and compounds have provided novel and impactful insights into SARS-CoV-2 biology and host responses that could be further leveraged for COVID-19 therapeutics development.

Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.

Type 2 diabetes results from severe insulin resistance coupled with a failure of b cells to compensate by secreting sufficient insulin. Multiple genetic loci are involved in the development of diabetes, although the effect of each gene on diabetes susceptibility is thought to be small. MicroRNAs (miRNAs) are noncoding 19-22-nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand the relationship between miRNA regulation and obesity-induced diabetes, we quantitatively profiled approximately 220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs profiled were expressed in all three tissues, with many miRNAs in each tissue showing significant changes in response to genetic obesity. Furthermore, several miRNAs in each tissue were differentially responsive to obesity in B6 versus BTBR mice, suggesting that they may be involved in the pathogenesis of diabetes. In liver there were approximately 40 miRNAs that were downregulated in response to obesity in B6 but not BTBR mice, indicating that genetic differences between the mouse strains play a critical role in miRNA regulation. In order to elucidate the genetic architecture of hepatic miRNA expression, we measured the expression of miRNAs in genetically obese F2 mice. Approximately 10% of the miRNAs measured showed significant linkage (miR-eQTLs), identifying loci that control miRNA abundance. Understanding the influence that obesity and genetics exert on the regulation of miRNA expression will reveal the role miRNAs play in the context of obesity-induced type 2 diabetes.

Development of the Digital Arthritis Index, a Novel Metric to Measure Disease Parameters in a Rat Model of Rheumatoid Arthritis.

Despite a broad spectrum of anti-arthritic drugs currently on the market, there is a constant demand to develop improved therapeutic agents. Efficient compound screening and rapid evaluation of treatment efficacy in animal models of rheumatoid arthritis (RA) can accelerate the development of clinical candidates. Compound screening by evaluation of disease phenotypes in animal models facilitates preclinical research by enhancing understanding of human pathophysiology; however, there is still a continuous need to improve methods for evaluating disease. Current clinical assessment methods are challenged by the subjective nature of scoring-based methods, time-consuming longitudinal experiments, and the requirement for better functional readouts with relevance to human disease. To address these needs, we developed a low-touch, digital platform for phenotyping preclinical rodent models of disease. As a proof-of-concept, we utilized the rat collagen-induced arthritis (CIA) model of RA and developed the Digital Arthritis Index (DAI), an objective and automated behavioral metric that does not require human-animal interaction during the measurement and calculation of disease parameters. The DAI detected the development of arthritis similar to standard in vivo methods, including ankle joint measurements and arthritis scores, as well as demonstrated a positive correlation to ankle joint histopathology. The DAI also determined responses to multiple standard-of-care (SOC) treatments and nine repurposed compounds predicted by the SMarTRTM Engine to have varying degrees of impact on RA. The disease profiles generated by the DAI complemented those generated by standard methods. The DAI is a highly reproducible and automated approach that can be used in-conjunction with standard methods for detecting RA disease progression and conducting phenotypic drug screens.

From drug to protein: using yeast genetics for high-throughput target discovery.

The budding yeast Saccharomyces cerevisiae has long been an effective eukaryotic model system for understanding basic cellular processes. The genetic tractability and ease of manipulation in the laboratory make yeast well suited for large-scale chemical and genetic screens. Several recent studies describing the use of yeast genetics for high-throughput drug target identification are discussed in this review.

Identification and validation of genes affecting aortic lesions in mice.

Atherosclerosis represents the most significant risk factor for coronary artery disease (CAD), the leading cause of death in developed countries. To better understand the pathogenesis of atherosclerosis, we applied a likeli-hood-based model selection method to infer gene-disease causality relationships for the aortic lesion trait in a segregating mouse population demonstrating a spectrum of susceptibility to developing atherosclerotic lesions. We identified 292 genes that tested causal for aortic lesions from liver and adipose tissues of these mice, and we experimentally validated one of these candidate causal genes, complement component 3a receptor 1 (C3ar1), using a knockout mouse model. We also found that genes identified by this method overlapped with genes progressively regulated in the aortic arches of 2 mouse models of atherosclerosis during atherosclerotic lesion development. By comparing our gene set with findings from public human genome-wide association studies (GWAS) of CAD and related traits, we found that 5 genes identified by our study overlapped with published studies in humans in which they were identified as risk factors for multiple atherosclerosis-related pathologies, including myocardial infarction, serum uric acid levels, mean platelet volume, aortic root size, and heart failure. Candidate causal genes were also found to be enriched with CAD risk polymorphisms identified by the Wellcome Trust Case Control Consortium (WTCCC). Our findings therefore validate the ability of causality testing procedures to provide insights into the mechanisms underlying atherosclerosis development.

Genomics and cardiovascular drug development.

In the last half century, phenomenal advances have been made in understanding the pathophysiology of cardiovascular disease and in developing therapies to reduce cardiovascular risk. Nevertheless, cardiovascular disease remains the leading cause of death and morbidity in the industrialized world, with rapidly rising prevalence in developing countries, accounting for approximately 30% of all deaths worldwide. Since the initial availability of statin drugs in 1987, few novel cardiovascular therapies have emerged. Whereas statins reduce the mortality and morbidity from atherosclerotic heart disease by approximately 30%, the staggering 70% residual cardiovascular risk underscores the persistent need for novel therapies. Substantial advances in genomic research offer promise to greatly facilitate cardiovascular drug development. Over the past decade, often termed "the genomics revolution," such advancements as the emergence of genome-wide genotyping in humans, the industrialization of messenger ribonucleic acid expression profiling, and the maturation of proteomic and metabolomic methodologies have been made. In addition, the advancement of informatics to allow the intersection of multiple complex datasets has led to the field of systems biology. Genomic approaches are already being utilized to drive novel compound pipelines by helping with the identification and validation of novel targets. In the future, the study of genomics is expected to support biomarker discovery and development and the identification of responder patient segments. The focus of the present review is the application of genomics to the development of novel atherosclerosis therapies.

Diurnal variation of the human adipose transcriptome and the link to metabolic disease.

BACKGROUND: Circadian (diurnal) rhythm is an integral part of the physiology of the body; specifically, sleep, feeding behavior and metabolism are tightly linked to the light-dark cycle dictated by earth's rotation. METHODS: The present study examines the effect of diurnal rhythm on gene expression in the subcutaneous adipose tissue of overweight to mildly obese, healthy individuals. In this well-controlled clinical study, adipose biopsies were taken in the morning, afternoon and evening from individuals in three study arms: treatment with the weight loss drug sibutramine/fasted, placebo/fed and placebo/fasted. RESULTS: The results indicated that diurnal rhythm was the most significant driver of gene expression variation in the human adipose tissue, with at least 25% of the genes having had significant changes in their expression levels during the course of the day. The mRNA expression levels of core clock genes at a specific time of day were consistent across multiple subjects on different days in all three arms, indicating robust diurnal regulation irrespective of potential confounding factors. The genes essential for energy metabolism and tissue physiology were part of the diurnal signature. We hypothesize that the diurnal transition of the expression of energy metabolism genes reflects the shift in the adipose tissue from an energy-expending state in the morning to an energy-storing state in the evening. Consistent with this hypothesis, the diurnal transition was delayed by fasting and treatment with sibutramine. Finally, an in silico comparison of the diurnal signature with data from the publicly-available Connectivity Map demonstrated a significant association with transcripts that were repressed by mTOR inhibitors, suggesting a possible link between mTOR signaling, diurnal gene expression and metabolic regulation. CONCLUSION: Diurnal rhythm plays an important role in the physiology and regulation of energy metabolism in the adipose tissue and should be considered in the selection of novel targets for the treatment of obesity and other metabolic disorders.

Gene expression profiling of rat liver reveals a mechanistic basis for ritonavir-induced hyperlipidemia.

The molecular mechanisms of action of a HIV protease inhibitor, ritonavir, on hepatic function were explored on a genomic scale using microarrays comprising genes expressed in the liver of Sprague-Dawley rats (Rattus norvegicus). Analyses of hepatic transcriptional fingerprints led to the identification of several key cellular pathways affected by ritonavir treatment. These effects were compared to a compendium of gene expression responses for 52 unrelated compounds and to other protease inhibitors, including atazanavir and two experimental compounds. We identified genes involved in cholesterol and fatty acid biosynthesis, as well as genes involved in fatty acid and cholesterol breakdown, whose expressions were regulated in opposite manners by ritonavir and bezafibrate, a hypolipidemic agonist of the peroxisome proliferator-activated receptor alpha. Ritonavir also upregulated multiple proteasomal subunit transcripts as well as genes involved in ubiquitination, consistent with its known inhibitory effect on proteasomal activity. We also tested three other protease inhibitors in addition to ritonavir. Atazanavir did not impact ubiquitin or proteasomal gene expression, although the two other experimental protease inhibitors impacted both proteasomal gene expression and sterol regulatory element-binding protein-activated genes, similar to ritonavir. Identification of key metabolic pathways that are affected by ritonavir and other protease inhibitors will enable us to understand better the downstream effects of protease inhibitors, thus leading to better drug design and an effective method to mitigate the side effects of this important class of HIV therapeutics.

Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes.

Complex biological systems are best modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of changing conditions. Here we highlight several novel network-based approaches to elucidate genetic networks underlying complex traits. These integrative genomic approaches combine large-scale genotypic and gene expression results in segregating mouse populations to reconstruct reliable genetic networks underlying complex traits such as disease or drug response. We apply these novel approaches to one of the most extensive surveys of gene expression studies ever undertaken in whole brain in a segregating mouse population. More than 23,000 genes were monitored in whole brain samples from more than 300 mice derived from an F2 intercross population and genotyped at over 1200 SNP markers uniformly spread over the entire genome. We explore the topological properties of the brain transcriptional network and highlight different approaches to inferring causal associations among genes by integrating genotypic and expression data. We demonstrate the utility of these approaches by identifying and experimentally validating brain gene expression traits predicted to respond to a strong expression quantitative trait locus (eQTL) for the pituitary tumor-transforming 1 gene (Pttg1) that coincides with the physical location of this gene (a cis eQTL). We identify core functional modules making up the brain transcriptional network in mice that are coherent for core biological processes associated with metabolic disease traits including obesity and diabetes.