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

Let-7b Inhibits Human Cancer Phenotype by Targeting Cytochrome P450 Epoxygenase 2J2

BACKGROUND: MicroRNAs (miRNAs) are small, noncoding RNA molecules of 20 to 22 nucleotides that regulate gene expression by binding to their 3' untranslated region (3'UTR). Increasing data implicate altered miRNA participation in the progress of cancer. We previously reported that CYP2J2 epoxygenase promotes human cancer phenotypes. But whether and how CYP2J2 is regulated by miRNA is not understood. METHODS AND RESULTS: Using bioinformatics analysis, we found potential target sites for miRNA let-7b in 3'UTR of human CYP2J2. Luciferase and western blot assays revealed that CYP2J2 was regulated by let-7b. In addition, let-7b decreased the enzymatic activity of endogenous CYP2J2. Furthermore, let-7b may diminish cell proliferation and promote cell apoptosis of tumor cells via posttranscriptional repression of CYP2J2. Tumor xenografts were induced in nude mice by subcutaneous injection of MDA-MB-435 cells. The let-7b expression vector, pSilencer-let-7b, was injected through tail vein every 3 weeks. Let-7b significantly inhibited the tumor phenotype by targeting CYP2J2. Moreover, quantitative real-time polymerase chain reaction and western blotting were used to determine the expression levels of let-7b and CYP2J2 protein from 18 matched lung squamous cell cancer and adjacent normal lung tissues; the expression level of CYP2J2 was inversely proportional to that of let-7b. CONCLUSIONS: Our results demonstrated that the decreased expression of let-7b could lead to the high expression of CYP2J2 protein in cancerous tissues. These findings suggest that miRNA let-7b reduces CYP2J2 expression, which may contribute to inhibiting tumor phenotypes

Cytochrome P450-derived eicosanoids: the neglected pathway in cancer

Endogenously produced lipid autacoids are locally acting small molecule mediators that play a central role in the regulation of inflammation and tissue homeostasis. A well-studied group of autacoids are the products of arachidonic acid metabolism, among which the prostaglandins and leukotrienes are the best known. They are generated by two pathways controlled by the enzyme systems cyclooxygenase and lipoxygenase, respectively. However, arachidonic acid is also substrate for a third enzymatic pathway, the cytochrome P450 (CYP) system. This third eicosanoid pathway consists of two main branches: ω-hydroxylases convert arachidonic acid to hydroxyeicosatetraenoic acids (HETEs) and epoxygenases convert it to epoxyeicosatrienoic acids (EETs). This third CYP pathway was originally studied in conjunction with inflammatory and cardiovascular disease. Arachidonic acid and its metabolites have recently stimulated great interest in cancer biology; but, unlike prostaglandins and leukotrienes the link between cytochome P450 metabolites and cancer has received little attention. In this review, the emerging role in cancer of cytochrome P450 metabolites, notably 20-HETE and EETs, are discussed

Soluble epoxide hydrolase in human tissues: Distribution, subcellular localization and novel endogeneous substrates

Soluble epoxide hydrolase (sEH) is a phase I xenobiotic metabolizing enzyme. Endogenous substrates include arachidonic acid epoxides (EETs) which are generated by cytochrome P450 epoxygenases. sEH has been suggested to be involved in various biological processes such as cell signaling, regulation of blood pressure and inflammation. Most of these effects have been attributed to regulation of the relatively well studied EET substrates. Recently, sEH was found to possess an N-terminal phosphatase domain in addition to the C-terminal epoxide hydrolase domain; however, data regarding potential endogenous substrates for the N-terminal domain is limited. sEH is known to exist predominantly in the liver and kidney. However, previous studies of its subcellular localization in these tissues have been controversial. In this study, we tested the hypothesis that sEH has a broad distribution in human tissues and that its subcellular localization may vary from one tissue to another. Indeed, our results show that sEH and CYP450 epoxygenases are widely expressed in various human tissues such as liver, kidney, intestine, adrenal gland, pituitary gland, prostate, vascular wall, lymphoid follicles and pancreas. In addition, previous evidence suggestive of a potential role of these CYPs and their EET products in carcinogenesis led us to evaluate the expression of these enzymes in neoplastic human tissue samples. The subcellular localization results interestingly showed cell/tissue-specific compartmentalization of sEH. In human liver and kidney sEH was found to be both cytosolic and peroxisomal, whereas it was found to be exclusively cytosolic in other sEH-expressing tissues. The peroxisomal subcellular localization of sEH in human liver led us to evaluate the peroxisomal isoprenoid phosphate intermediates as potential substrates of the N-terminal phosphatase domain. Several isoprenoid phosphates have been found to be substrates for the N-terminal domain such as farnesyl monophosphate that had a Km of 24 μM and Vmax of 684 nmol/min/mg. Subsequently, we were able to identify several isoprenoid derived compounds as novel inhibitors of the N-terminal domain with IC50 values from 0.84 μM to 55 μM. Our results are suggestive of a potential role for sEH in the regulation of isoprenoid dependent pathways such as cholesterol biosynthesis and G-protein isoprenylation.

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.National Science Foundation (U.S.) (Graduate Research Fellowship)National Institutes of Health (U.S.) (grant GM081871

Insights into the catalytic mechanism of human sEH phosphatase by site-directed mutagenesis and LC-MS/MS analysis

We have recently reported that human soluble epoxide hydrolase (sEH) is a bifunctional enzyme with a novel phosphatase enzymatic activity. Based on a structural relationship with other members of the haloacid dehalogenase superfamily, the sEH N-terminal phosphatase domain revealed four conserved sequence motifs, including the proposed catalytic nucleophile D9, and several other residues potentially implicated in substrate turnover and/or Mg(2+) binding. To enlighten the catalytic mechanism of dephosphorylation, we constructed sEH phosphatase active-site mutants by site-directed mutagenesis. A total of 18 mutants were constructed and recombinantly expressed in Escherichia coli as soluble proteins, purified to homogeneity and subsequently analysed for their kinetic parameters. A replacement of residues D9, K160, D184 or N189 resulted in a complete loss of phosphatase activity, consistent with an essential function for catalysis. In contrast, a substitution of D11, T123, N124 and D185 leads to sEH mutant proteins with altered kinetic properties. We further provide evidence of the formation of an acylphosphate intermediate on D9 by liquid chromatography-tandem mass spectrometry based on the detection of homoserine after NaBH(4) reduction of the phosphorylated enzyme, which identifies D9 as the catalytic nucleophile. Surprisingly, we could only show such homoserine formation using the D11N mutant, which strongly suggests D11 to be involved in the acylphosphate hydrolysis. In the D11 mutant, the second catalytic step becomes rate limiting, which then allows trapping of the labile intermediate. Substrate turnover in the presence of (18)H(2)O revealed that the nucleophilic attack during the second reaction step occurs at the acylphosphate phosphorous. Based on these findings, we propose a two-step catalytic mechanism of dephosphorylation that involves the phosphate substrate hydrolysis by nucleophilic attack by the catalytic nucleophile D9 followed by hydrolysis of the acylphosphate enzyme intermediate supported by D11