Summary of Research: Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis.

This article provides a summary of a previously published paper: Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis. The paper reported the results of the CHAMPION-MG trial which investigated the drug ravulizumab in the rare disease, myasthenia gravis. Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis (MP4 594600 KB).

Terminal Complement Inhibitor Ravulizumab in Generalized Myasthenia Gravis.

Ravulizumab in Generalized Myasthenia GravisIn this randomized controlled trial, ravulizumab provided rapid and efficacious treatment of adult patients with anti-acetylcholine reception antibody-positive generalized myasthenia gravis, as determined by both patient- and clinician-rated outcomes, with few adverse events.

Associating Cognition With Amyloid Status Using Partially Ordered Set Analysis.

Background: The presence of brain amyloid-beta positivity is associated with cognitive impairment and dementia, but whether there are specific aspects of cognition that are most linked to amyloid-beta is unclear. Analysis of neuropsychological test data presents challenges since a single test often requires drawing upon multiple cognitive functions to perform well. It can thus be imprecise to link performance on a given test to a specific cognitive function. Our objective was to provide insight into how cognitive functions are associated with brain amyloid-beta positivity among samples consisting of cognitively normal and mild cognitively impaired (MCI) subjects, by using partially ordered set models (POSETs). Methods: We used POSET classification models of neuropsychological test data to classify samples to detailed cognitive profiles using ADNI2 and AIBL data. We considered 3 gradations of episodic memory, cognitive flexibility, verbal fluency, attention and perceptual motor speed, and performed group comparisons of cognitive functioning stratified by amyloid positivity (yes/no) and age (<70, 70-80, 81-90 years). We also employed random forest methods stratified by age to assess the effectiveness of cognitive testing in predicting amyloid positivity, in addition to demographic variables, and APOE4 allele count. Results: In ADNI2, differences in episodic memory and attention by amyloid were found for <70, and 70-80 years groups. In AIBL, episodic memory differences were found in the 70-80 years age group. In both studies, no cognitive differences were found in the 81-90 years group. The random forest analysis indicates that variable importance in classification depends on age. Cognitive testing that targets an intermediate level of episodic memory and delayed recall, in addition to APOE4 allele count, are the most important variables in both studies. Conclusions: In the ADNI2 and AIBL samples, the associations between specific cognitive abilities and brain amyloid-beta positivity depended on age, but in general episodic memory was most consistently predictive of brain amyloid-beta positivity. Random forest methods and OOB error rates establish the feasibility of predicting the presence of brain beta-amyloid using cognitive testing, APOE4 genotyping and demographic variables.

Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks.

MOTIVATION: Understanding and predicting an individual's response in a clinical trial is the key to better treatments and cost-: effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets. METHODS: We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response. RESULTS: We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates.

Analysis of changes in hepatic gene expression in a murine model of tolerance to acetaminophen hepatotoxicity (autoprotection).

Pretreatment of mice with a low hepatotoxic dose of acetaminophen (APAP) results in resistance to a subsequent, higher dose of APAP. This mouse model, termed APAP autoprotection was used here to identify differentially expressed genes and cellular pathways that could contribute to this development of resistance to hepatotoxicity. Male C57BL/6J mice were pretreated with APAP (400mg/kg) and then challenged 48h later with 600mg APAP/kg. Livers were obtained 4 or 24h later and total hepatic RNA was isolated and hybridized to Affymetrix Mouse Genome MU430_2 GeneChip. Statistically significant genes were determined and gene expression changes were also interrogated using the Causal Reasoning Engine (CRE). Extensive literature review narrowed our focus to methionine adenosyl transferase-1 alpha (MAT1A), nuclear factor (erythroid-derived 2)-like 2 (Nrf2), flavin-containing monooxygenase 3 (Fmo3) and galectin-3 (Lgals3). Down-regulation of MAT1A could lead to decreases in S-adenosylmethionine (SAMe), which is known to protect against APAP toxicity. Nrf2 activation is expected to play a role in protective adaptation. Up-regulation of Lgals3, one of the genes supporting the Nrf2 hypothesis, can lead to suppression of apoptosis and reduced mitochondrial dysfunction. Fmo3 induction suggests the involvement of an enzyme not known to metabolize APAP in the development of tolerance to APAP toxicity. Subsequent quantitative RT-PCR and immunochemical analysis confirmed the differential expression of some of these genes in the APAP autoprotection model. In conclusion, our genomics strategy identified cellular pathways that might further explain the molecular basis for APAP autoprotection.

Comparison of hepatic transcription profiles of locked ribonucleic acid antisense oligonucleotides: evidence of distinct pathways contributing to non-target mediated toxicity in mice.

Development of LNA gapmers, antisense oligonucleotides used for efficient inhibition of target RNA expression, is limited by non-target mediated hepatotoxicity issues. In the present study, we investigated hepatic transcription profiles of mice administered non-toxic and toxic LNA gapmers. After repeated administration, a toxic LNA gapmer (TS-2), but not a non-toxic LNA gapmer (NTS-1), caused hepatocyte necrosis and increased serum alanine aminotransferase levels. Microarray data revealed that, in addition to gene expression patterns consistent with hepatotoxicity, 17 genes in the clathrin-mediated endocytosis (CME) pathway were altered in the TS-2 group. TS-2 significantly down-regulated myosin 1E (Myo1E), which is involved in release of clathrin-coated pits from plasma membranes. To map the earliest transcription changes associated with LNA gapmer-induced hepatotoxicity, a second microarray analysis was performed using NTS-1, TS-2, and a severely toxic LNA gapmer (HTS-3) at 8, 16, and 72 h following a single administration in mice. The only histopathological change observed was minor hepatic hypertrophy in all LNA groups across time points. NTS-1, but not 2 toxic LNA gapmers, increased immune response genes at 8 and 16 h but not at 72 h. TS-2 significantly perturbed the CME pathway only at 72 h, while Myo1E levels were decreased at all time points. In contrast, HTS-3 modulated DNA damage pathway genes at 8 and 16 h and also modulated the CME pathway genes (but not Myo1E) at 16 h. Our results may suggest that different LNAs modulate distinct transcriptional genes and pathways contributing to non-target mediated hepatotoxicity in mice.

A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions.

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88,629 articles relating over 1,200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 254,173 toxicogenomic interactions (152,173 chemical-disease, 58,572 chemical-gene, 5,345 gene-disease and 38,083 phenotype interactions). All chemical-gene-disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer's text-mining process to collate the articles, and CTD's curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug-disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades' worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/

Molecular causes of transcriptional response: a Bayesian prior knowledge approach.

MOTIVATION: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes. METHODS: We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile. RESULTS: Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated. AVAILABILITY AND IMPLEMENTATION: Source code for the method is available upon request.

Assessing the translatability of in vivo cardiotoxicity mechanisms to in vitro models using causal reasoning.

Drug-induced cardiac toxicity has been implicated in 31% of drug withdrawals in the USA. The fact that the risk for cardiac-related adverse events goes undetected in preclinical studies for so many drugs underscores the need for better, more predictive in vitro safety screens to be deployed early in the drug discovery process. Unfortunately, many questions remain about the ability to accurately translate findings from simple cellular systems to the mechanisms that drive toxicity in the complex in vivo environment. In this study, we analyzed translatability of cardiotoxic effects for a diverse set of drugs from rodents to two different cell systems (rat heart tissue-derived cells (H9C2) and primary rat cardiomyocytes (RCM)) based on their transcriptional response. To unravel the altered pathway, we applied a novel computational systems biology approach, the Causal Reasoning Engine (CRE), to infer upstream molecular events causing the observed gene expression changes. By cross-referencing the cardiotoxicity annotations with the pathway analysis, we found evidence of mechanistic convergence towards common molecular mechanisms regardless of the cardiotoxic phenotype. We also experimentally verified two specific molecular hypotheses that translated well from in vivo to in vitro (Kruppel-like factor 4, KLF4 and Transforming growth factor beta 1, TGFB1) supporting the validity of the predictions of the computational pathway analysis. In conclusion, this work demonstrates the use of a novel systems biology approach to predict mechanisms of toxicity such as KLF4 and TGFB1 that translate from in vivo to in vitro. We also show that more complex in vitro models such as primary rat cardiomyocytes may not offer any advantage over simpler models such as immortalized H9C2 cells in terms of translatability to in vivo effects if we consider the right endpoints for the model. Further assessment and validation of the generated molecular hypotheses would greatly enhance our ability to design predictive in vitro cardiotoxicity assays.

GeneTopics--interpretation of gene sets via literature-driven topic models.

BACKGROUND: Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set. METHODS: Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research. RESULTS: We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner. CONCLUSIONS: Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly tied to relevant articles in the literature. Extending a general topic model method, the approach introduced here establishes a workflow for the interpretation of gene sets generated from diverse experimental scenarios that can complement the classical approach of comparison to reference gene sets.

Causal reasoning on biological networks: interpreting transcriptional changes.

MOTIVATION: The interpretation of high-throughput datasets has remained one of the central challenges of computational biology over the past decade. Furthermore, as the amount of biological knowledge increases, it becomes more and more difficult to integrate this large body of knowledge in a meaningful manner. In this article, we propose a particular solution to both of these challenges. METHODS: We integrate available biological knowledge by constructing a network of molecular interactions of a specific kind: causal interactions. The resulting causal graph can be queried to suggest molecular hypotheses that explain the variations observed in a high-throughput gene expression experiment. We show that a simple scoring function can discriminate between a large number of competing molecular hypotheses about the upstream cause of the changes observed in a gene expression profile. We then develop an analytical method for computing the statistical significance of each score. This analytical method also helps assess the effects of random or adversarial noise on the predictive power of our model. RESULTS: Our results show that the causal graph we constructed from known biological literature is extremely robust to random noise and to missing or spurious information. We demonstrate the power of our causal reasoning model on two specific examples, one from a cancer dataset and the other from a cardiac hypertrophy experiment. We conclude that causal reasoning models provide a valuable addition to the biologist's toolkit for the interpretation of gene expression data. AVAILABILITY AND IMPLEMENTATION: R source code for the method is available upon request.

Assessing statistical significance in causal graphs.

BACKGROUND: Causal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing. RESULTS: First, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs; previous work on graph randomization has studied undirected graphs and directed but unsigned graphs. CONCLUSION: We present algorithmic solutions to two statistical significance questions necessary to apply the causal graph methodology, a powerful tool for biological network analysis. The algorithms we present are both fast and provably correct. Our work may be of independent interest in non-biological contexts as well, as it generalizes mathematical results that have been studied extensively in other fields.

Modeling the mechanism of action of a DGAT1 inhibitor using a causal reasoning platform.

Triglyceride accumulation is associated with obesity and type 2 diabetes. Genetic disruption of diacylglycerol acyltransferase 1 (DGAT1), which catalyzes the final reaction of triglyceride synthesis, confers dramatic resistance to high-fat diet induced obesity. Hence, DGAT1 is considered a potential therapeutic target for treating obesity and related metabolic disorders. However, the molecular events shaping the mechanism of action of DGAT1 pharmacological inhibition have not been fully explored yet. Here, we investigate the metabolic molecular mechanisms induced in response to pharmacological inhibition of DGAT1 using a recently developed computational systems biology approach, the Causal Reasoning Engine (CRE). The CRE algorithm utilizes microarray transcriptomic data and causal statements derived from the biomedical literature to infer upstream molecular events driving these transcriptional changes. The inferred upstream events (also called hypotheses) are aggregated into biological models using a set of analytical tools that allow for evaluation and integration of the hypotheses in context of their supporting evidence. In comparison to gene ontology enrichment analysis which pointed to high-level changes in metabolic processes, the CRE results provide detailed molecular hypotheses to explain the measured transcriptional changes. CRE analysis of gene expression changes in high fat habituated rats treated with a potent and selective DGAT1 inhibitor demonstrate that the majority of transcriptomic changes support a metabolic network indicative of reversal of high fat diet effects that includes a number of molecular hypotheses such as PPARG, HNF4A and SREBPs. Finally, the CRE-generated molecular hypotheses from DGAT1 inhibitor treated rats were found to capture the major molecular characteristics of DGAT1 deficient mice, supporting a phenotype of decreased lipid and increased insulin sensitivity.

Opposite regulation of cholesterol levels by the phosphatase and hydrolase domains of soluble epoxide hydrolase.

Soluble epoxide hydrolase (sEH) is a bifunctional enzyme with two catalytic domains: a C-terminal epoxide hydrolase domain and an N-terminal phosphatase domain. Epidemiology and animal studies have attributed a variety of cardiovascular and anti-inflammatory effects to the C-terminal epoxide hydrolase domain. The recent association of sEH with cholesterol-related disorders, peroxisome proliferator-activated receptor activity, and the isoprenoid/cholesterol biosynthesis pathway additionally suggest a role of sEH in regulating cholesterol metabolism. Here we used sEH knock-out (sEH-KO) mice and transfected HepG2 cells to evaluate the phosphatase and hydrolase domains in regulating cholesterol levels. In sEH-KO male mice we found a approximately 25% decrease in plasma total cholesterol as compared with wild type (sEH-WT) male mice. Consistent with plasma cholesterol levels, liver expression of HMG-CoA reductase was found to be approximately 2-fold lower in sEH-KO male mice. Additionally, HepG2 cells stably expressing human sEH with phosphatase only or hydrolase only activity demonstrate independent and opposite roles of the two sEH domains. Whereas the phosphatase domain elevated cholesterol levels, the hydrolase domain lowered cholesterol levels. Hydrolase inhibitor treatment in sEH-WT male and female mice as well as HepG2 cells expressing human sEH resulted in higher cholesterol levels, thus mimicking the effect of expressing the phosphatase domain in HepG2 cells. In conclusion, we show that sEH regulates cholesterol levels in vivo and in vitro, and we propose the phosphatase domain as a potential therapeutic target in hypercholesterolemia-related disorders.

Distribution and expression of soluble epoxide hydrolase in human brain.

Epoxyeicosatrienoic acids (EETs) are cytochrome P450 metabolites of arachidonic acid, which function in the brain to regulate cerebral blood flow and protect against ischemic brain injury. EETs are converted by soluble epoxide hydrolase (sEH) to the corresponding inactive diol metabolites. Previous animal studies have indicated that sEH gene deletion or treatment with sEH inhibitors results in increased levels of EETs and protection against stroke-induced brain damage. To begin elucidating the underlying mechanism for these effects, we sought to determine the distribution, expression, and activity of sEH in human brain samples obtained from patients with no neurological changes/pathologies. Immunohistochemical analyses showed the distribution of sEH mainly in the neuronal cell bodies, oligodendrocytes, and scattered astrocytes. Surprisingly, in the choroid plexus, sEH was found to be highly expressed in ependymal cells. Vascular localization of sEH was evident in several regions, where it was highly expressed in the smooth muscles of the arterioles. Western blot analysis and enzyme assays confirmed the presence of sEH in the normal brain. Our results indicate differential localization of sEH in the human brain, thus suggestive of an essential role for this enzyme in the central nervous system. This manuscript contains online supplemental material at http://www.jhc.org. Please visit this article online to view these materials.

Novel Role of Soluble Epoxide Hydrolase in Regulating Cholesterol in Mammalian Cells.

Soluble epoxide hydrolase (sEH) is becoming an attractive therapeutic target in cardiovascular disease. Recently, known human sEH polymorphisms were associated with elevated plasma cholesterol and atherosclerosis. In this study we evaluated the potential role of sEH in regulating cholesterol metabolism through modulating the levels of fatty acid epoxide substrates and/or their corresponding diol products known to activate peroxisome proliferator activated receptors (PPARs). We measured changes in cholesterol levels induced by expressing sEH proteins in mammalian cell lines and in response to treatment with various sEH-related compounds. Our results indicate that sEH has a cholesterol lowering effect that is mediated at least in part through its C-terminal hydrolase activity. In addition, several fatty acid epoxides and their corresponding diols showed cholesterol lowering effects in the current study. In conclusion, this study provides evidence that fatty acid epoxides and diols are endogenous cholesterol lowering molecules and that sEH may be involved in cholesterol regulation by modulating their levels.

Distribution of soluble epoxide hydrolase, cytochrome P450 2C8, 2C9 and 2J2 in human malignant neoplasms.

Soluble epoxide hydrolase (sEH) is a bifunctional enzyme with a C-terminal epoxide hydrolase activity and an N-terminal phosphatase activity. Arachidonic acid epoxides, previously suggested to be involved in apoptosis, oncogenesis and cell proliferation, are generated by cytochrome P450 epoxygenases and are good substrates of the sEH C-terminal domain. In addition, the N-terminal phosphatase domain hydrolyzes isoprenoid mono- and pyrophosphates, which are involved in cell signaling and apoptosis. Here we provide a comprehensive analysis of the distribution of sEH, CYP2C8, 2C9 and 2J2 in human neoplastic tissues using tissue micro-arrays. The human neoplastic tissue micro-arrays provide a well-controlled side by side analysis of a wide array of neoplastic tissues and their surrounding normal tissue controls. Many of the neoplastic tissues showed altered expression of these enzymes as compared to normal tissues. Altered expression was not limited to the neoplastic tissues but also found in the surrounding non-neoplastic tissues. For example, sEH expression in renal and hepatic malignant neoplasms and surrounding non-neoplastic tissues was found to be significantly decreased, whereas expression was found to be increased in seminoma as compared to normal tissues. Our study warrants further investigation of the role of altered expression of these enzymes in neoplastic tissues.

Effects of human soluble epoxide hydrolase polymorphisms on isoprenoid phosphate hydrolysis.

Soluble epoxide hydrolase (sEH) is highly expressed in human liver and contains a C-terminal epoxide hydrolase activity and an N-terminal phosphatase activity. Endogenous C-terminal hydrolase substrates include arachidonic acid epoxides, however, data are limited regarding possible endogenous substrates for the N-terminal phosphatase. Possible sEH N-terminal substrates include isoprenoid phosphate precursors of cholesterol biosynthesis and protein isoprenylation. Here, we report the kinetic analysis for a range of sEH isoprenoid substrates. We also provide an analysis of the effects of human sEH polymorphisms on isoprenoid hydrolysis. Interestingly, the Arg287Gln polymorphism recently suggested to be involved in hypercholesterolemia was found to possess a higher isoprenoid phosphatase activity than the wild type sEH. Consistent with the finding of isoprenoid phosphates as substrates for sEH, we identified isoprenoid-derived N-terminal inhibitors with IC50 values ranging from 0.84 (+/-0.9) to 55.1 (+/-30.7) microM. Finally, we evaluated the effects of the different isoprenoid compounds on the C-terminal hydrolase activity.

Cell-specific subcellular localization of soluble epoxide hydrolase in human tissues.

Soluble epoxide hydrolase (sEH) is a phase-I xenobiotic metabolizing enzyme having both an N-terminal phosphatase activity and a C-terminal epoxide hydrolase activity. Endogenous hydrolase substrates include arachidonic acid epoxides, which have been involved in regulating blood pressure and inflammation. The subcellular localization of sEH has been controversial. Earlier studies using mouse and rat liver suggested that sEH may be cytosolic and/or peroxisomal. In this study we applied immunofluorescence and confocal microscopy using markers for different subcellular compartments to evaluate sEH colocalization in an array of human tissues. Results showed that sEH is both cytosolic and peroxisomal in human hepatocytes and renal proximal tubules and exclusively cytosolic in other sEH-containing tissues such as pancreatic islet cells, intestinal epithelium, anterior pituitary cells, adrenal gland, endometrium, lymphoid follicles, prostate ductal epithelium, alveolar wall, and blood vessels. sEH was not exclusively peroxisomal in any of the tissues evaluated. Our data suggest that human sEH subcellular localization is tissue dependent, and that sEH may have tissue- or cell-type-specific functionality. To our knowledge, this is the first report showing the subcellular localization of sEH in a wide array of human tissues.

Distribution of soluble epoxide hydrolase and of cytochrome P450 2C8, 2C9, and 2J2 in human tissues.

Soluble epoxide hydrolase (sEH) hydrolyzes a wide variety of endogenous and exogenous epoxides. Many of these epoxides are believed to be formed by cytochrome P450 epoxygenases. Here we report the distribution of sEH and cytochrome P450 epoxygenases 2C8, 2C9, and 2J2 by immunohistochemistry. A large number of different tissues from different organs were evaluated using high-throughput tissue microarrays. sEH was found in the liver, kidney, and in many other organs, including adrenals, pancreatic islets, pituitary gland, lymphoid tissues, muscles, certain vascular smooth muscles, and epithelial cells in the skin, prostatic ducts, and the gastrointestinal tract. Immunolabeling for sEH was highly specific for particular tissues and individual cell types. CYP2C9 was also found in almost all of these organs and tissues, suggesting that 2C9 and sEH are very similar in their tissue-specific patterns of expression. CYP2C8 and 2J2 were also widely distributed in human tissues but were less frequently associated with sEH. The results suggest potentially distinct pathways of endogenous fatty acid epoxide production and hydrolysis in a variety of human tissues.