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.

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.

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.

Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis.

BACKGROUND: Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. METHODS AND FINDINGS: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2). CONCLUSIONS: TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT01565551.

Acridine Derivatives as Inhibitors of the IRE1α-XBP1 Pathway Are Cytotoxic to Human Multiple Myeloma.

Using a luciferase reporter-based high-throughput chemical library screen and topological data analysis, we identified N-acridine-9-yl-N',N'-dimethylpropane-1,3-diamine (DAPA) as an inhibitor of the inositol requiring kinase 1α (IRE1α)-X-box binding protein-1 (XBP1) pathway of the unfolded protein response. We designed a collection of analogues based on the structure of DAPA to explore structure-activity relationships and identified N(9)-(3-(dimethylamino)propyl)-N(3),N(3),N(6),N(6)-tetramethylacridine-3,6,9-triamine (3,6-DMAD), with 3,6-dimethylamino substitution on the chromophore, as a potent inhibitor. 3,6-DMAD inhibited both IRE1α oligomerization and in vitro endoribonuclease (RNase) activity, whereas the other analogues only blocked IRE1α oligomerization. Consistent with the inhibition of IRE1α-mediated XBP1 splicing, which is critical for multiple myeloma cell survival, these analogues were cytotoxic to multiple myeloma cell lines. Furthermore, 3,6-DMAD inhibited XBP1 splicing in vivo and the growth of multiple myeloma tumor xenografts. Our study not only confirmed the utilization of topological data analysis in drug discovery but also identified a class of compounds with a unique mechanism of action as potent IRE1α-XBP1 inhibitors in the treatment of multiple myeloma. Mol Cancer Ther; 15(9); 2055-65. ©2016 AACR.

Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury.

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

Multidimensional endotypes of asthma: topological data analysis of cross-sectional clinical, pathological, and immunological data.

BACKGROUND: Incomplete understanding of mechanisms and clinicopathobiological heterogeneity in asthma hinders research progress. Pathogenic roles for T-helper-type 17 (Th17) cells and invariant T cells implied by murine data have yet to be assessed in man. We aimed to investigate the role of Th17 and mucosal associated invariant T (MAIT) cells in airway inflammation; to characterise associations between diverse clinical and immunological features of asthma; and to identify novel multidimensional asthma endotypes. METHODS: In this single-centre, cross-sectional observational study in the UK, we assessed volunteers with mild-to-severe asthma and healthy non-atopic controls using clinical and physiological assessment and immunological sampling of blood, induced sputum, endobronchial biopsy, and bronchoalveolar lavage for flow cytometry and multiplex-electrochemiluminescence assays. Primary outcomes were changes in frequencies of Th17 and MAIT cells between health and asthma using Mann-Whitney U tests and the Jonckheere-Terpstra test (linear trend across ranked groups). The study had 80% power to detect 60% differences in T-cell frequencies at p<0·05. Bayesian Network Analysis (BNA) was used to explore associations between parameters. Topological Data Analysis (TDA) was used to identify multidimensional endotypes. The study had local research ethics approval. All participants provided informed consent. FINDINGS: Participants were 84 male and female volunteers (60 with mild-to-severe asthma and 24 healthy, non-atopic controls) aged 18-70 years recruited from clinics and research cohorts. Th17 cells and γδ17 cells were not associated with asthma, even in severe neutrophilic forms. MAIT-cell frequencies were strikingly reduced in asthma compared with health (median frequency in blood 0·9% of CD3+ cells [IQR 0·3-1·8] in asthma vs 1·6 [1·2-2·6] in health, p=0·005; in sputum 1·1 [0·7-2·0] vs 1·8 [1·6-2·3], p=0·002; and in biopsy samples 1·3 [0·7-2·3] vs 3·9% [1·3-5·3%], p=0·02), especially in severe asthma where BAL regulatory T cells were also reduced compared with those in health (4·4, 3·1-6·1, vs 8·1, 5·6-10; p=0·02). BNA and TDA identified six novel clinicopathobiological clusters of underlying disease mechanisms, with elevated mast cell mediators tryptase (p<0·0001), chymase (p=0·02), and carboxypeptidase A3 (p=0·02) in severe asthma. INTERPRETATION: This study suggests that Th17 cells do not have a major pathogenic role in human asthma. We describe a novel deficiency of MAIT cells in severe asthma. We also provide proof of concept for application of TDA to identification of multidimensional clinicopathobiological endotypes. Endotypes will require validation in further cohorts. FUNDING: Wellcome Trust.

Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene.

Decreased insulin sensitivity, also referred to as insulin resistance (IR), is a fundamental abnormality in patients with type 2 diabetes and a risk factor for cardiovascular disease. While IR predisposition is heritable, the genetic basis remains largely unknown. The GENEticS of Insulin Sensitivity consortium conducted a genome-wide association study (GWAS) for direct measures of insulin sensitivity, such as euglycemic clamp or insulin suppression test, in 2,764 European individuals, with replication in an additional 2,860 individuals. The presence of a nonsynonymous variant of N-acetyltransferase 2 (NAT2) [rs1208 (803A>G, K268R)] was strongly associated with decreased insulin sensitivity that was independent of BMI. The rs1208 "A" allele was nominally associated with IR-related traits, including increased fasting glucose, hemoglobin A1C, total and LDL cholesterol, triglycerides, and coronary artery disease. NAT2 acetylates arylamine and hydrazine drugs and carcinogens, but predicted acetylator NAT2 phenotypes were not associated with insulin sensitivity. In a murine adipocyte cell line, silencing of NAT2 ortholog Nat1 decreased insulin-mediated glucose uptake, increased basal and isoproterenol-stimulated lipolysis, and decreased adipocyte differentiation, while Nat1 overexpression produced opposite effects. Nat1-deficient mice had elevations in fasting blood glucose, insulin, and triglycerides and decreased insulin sensitivity, as measured by glucose and insulin tolerance tests, with intermediate effects in Nat1 heterozygote mice. Our results support a role for NAT2 in insulin sensitivity.

Innate and adaptive T cells in asthmatic patients: Relationship to severity and disease mechanisms.

BACKGROUND: Asthma is a chronic inflammatory disease involving diverse cells and mediators whose interconnectivity and relationships to asthma severity are unclear. OBJECTIVE: We performed a comprehensive assessment of TH17 cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, other T-cell subsets, and granulocyte mediators in asthmatic patients. METHODS: Sixty patients with mild-to-severe asthma and 24 control subjects underwent detailed clinical assessment and provided induced sputum, endobronchial biopsy, bronchoalveolar lavage, and blood samples. Adaptive and invariant T-cell subsets, cytokines, mast cells, and basophil mediators were analyzed. RESULTS: Significant heterogeneity of T-cell phenotypes was observed, with levels of IL-13-secreting T cells and type 2 cytokines increased at some, but not all, asthma severities. TH17 cells and γδ-17 cells, proposed drivers of neutrophilic inflammation, were not strongly associated with asthma, even in severe neutrophilic forms. MAIT cell frequencies were strikingly reduced in both blood and lung tissue in relation to corticosteroid therapy and vitamin D levels, especially in patients with severe asthma in whom bronchoalveolar lavage regulatory T-cell numbers were also reduced. Bayesian network analysis identified complex relationships between pathobiologic and clinical parameters. Topological data analysis identified 6 novel clusters that are associated with diverse underlying disease mechanisms, with increased mast cell mediator levels in patients with severe asthma both in its atopic (type 2 cytokine-high) and nonatopic forms. CONCLUSION: The evidence for a role for TH17 cells in patients with severe asthma is limited. Severe asthma is associated with a striking deficiency of MAIT cells and high mast cell mediator levels. This study provides proof of concept for disease mechanistic networks in asthmatic patients with clusters that could inform the development of new therapies.

Topological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils.

Shiga toxin-producing E. coli O157:H7 and non-O157 have been implicated in many foodborne illnesses caused by the consumption of contaminated fresh produce. However, data on their persistence in soils are limited due to the complexity in datasets generated from different environmental variables and bacterial taxa. There is a continuing need to distinguish the various environmental variables and different bacterial groups to understand the relationships among these factors and the pathogen survival. Using an approach called Topological Data Analysis (TDA); we reconstructed the relationship structure of E. coli O157 and non-O157 survival in 32 soils (16 organic and 16 conventionally managed soils) from California (CA) and Arizona (AZ) with a multi-resolution output. In our study, we took a community approach based on total soil microbiome to study community level survival and examining the network of the community as a whole and the relationship between its topology and biological processes. TDA produces a geometric representation of complex data sets. Network analysis showed that Shiga toxin negative strain E. coli O157:H7 4554 survived significantly longer in comparison to E. coli O157:H7 EDL 933, while the survival time of E. coli O157:NM was comparable to that of E. coli O157:H7 EDL 933 in all of the tested soils. Two non-O157 strains, E. coli O26:H11 and E. coli O103:H2 survived much longer than E. coli O91:H21 and the three strains of E. coli O157. We show that there are complex interactions between E. coli strain survival, microbial community structures, and soil parameters.

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.

Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin(ob/ob) mutation (Lep(ob)). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein-protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

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.

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.

A gene expression signature that classifies human atherosclerotic plaque by relative inflammation status.

BACKGROUND: Atherosclerosis is a complex disease requiring improvements in diagnostic techniques and therapeutic treatments. Both improvements will be facilitated by greater exploration of the biology of atherosclerotic plaque. To this end, we carried out large-scale gene expression analysis of human atherosclerotic lesions. METHODS AND RESULTS: Whole genome expression analysis of 101 plaques from patients with peripheral artery disease identified a robust gene signature (1514 genes) that is dominated by processes related to Toll-like receptor signaling, T-cell activation, cholesterol efflux, oxidative stress response, inflammatory cytokine production, vasoconstriction, and lysosomal activity. Further analysis of gene expression in microdissected carotid plaque samples revealed that this signature is differentially expressed in macrophage-rich and smooth muscle cell-containing regions. A quantitative PCR gene expression panel and inflammatory composite score were developed on the basis of the atherosclerotic plaque gene signature. When applied to serial sections of carotid plaque, the inflammatory composite score was observed to correlate with histological and morphological features related to plaque vulnerability. CONCLUSIONS: The robust mRNA expression signature identified in the present report is associated with pathological features of vulnerable atherosclerotic plaque and may be useful as a source of biomarkers and targets of novel antiatherosclerotic therapies.

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.

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.

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.