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      Estimation of the Mechanism of Adrenal Action of Endocrine-Disrupting Compounds Using a Computational Model of Adrenal Steroidogenesis in NCI-H295R Cells

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          Abstract

          Adrenal toxicity is one of the major concerns in drug development. To quantitatively understand the effect of endocrine-active compounds on adrenal steroidogenesis and to assess the human adrenal toxicity of novel pharmaceutical drugs, we developed a mathematical model of steroidogenesis in human adrenocortical carcinoma NCI-H295R cells. The model includes cellular proliferation, intracellular cholesterol translocation, diffusional transport of steroids, and metabolic pathways of adrenal steroidogenesis, which serially involve steroidogenic proteins and enzymes such as StAR, CYP11A1, CYP17A1, HSD3B2, CYP21A2, CYP11B1, CYP11B2, HSD17B3, and CYP19A1. It was reconstructed in an experimental dynamics of cholesterol and 14 steroids from an in vitro steroidogenesis assay using NCI-H295R cells. Results of dynamic sensitivity analysis suggested that HSD3B2 plays the most important role in the metabolic balance of adrenal steroidogenesis. Based on differential metabolic profiling of 12 steroid hormones and 11 adrenal toxic compounds, we could estimate which steroidogenic enzymes were affected in this mathematical model. In terms of adrenal steroidogenic inhibitors, the predicted action sites were approximately matched to reported target enzymes. Thus, our computer-aided system based on systems biological approach may be useful to understand the mechanism of action of endocrine-active compounds and to assess the human adrenal toxicity of novel pharmaceutical drugs.

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          Steroidogenic acute regulatory protein (StAR), a novel mitochondrial cholesterol transporter.

          Cholesterol is a vital component of cellular membranes, and is the substrate for biosynthesis of steroids, oxysterols and bile acids. The mechanisms directing the intracellular trafficking of this nearly insoluble molecule have received increased attention through the discovery of the steroidogenic acute regulatory protein (StAR) and similar proteins containing StAR-related lipid transfer (START) domains. StAR can transfer cholesterol between synthetic liposomes in vitro, an activity which appears to correspond to the trans-cytoplasmic transport of cholesterol to mitochondria. However, trans-cytoplasmic cholesterol transport in vivo appears to involve the recently-described protein StarD4, which is expressed in most cells. Steroidogenic cells must also move large amounts of cholesterol from the outer mitochondrial membrane to the first steroidogenic enzyme, which lies on the matrix side of the inner membrane; this action requires StAR. Congenital lipoid adrenal hyperplasia, a rare and severe disorder of human steroidogenesis, results from mutations in StAR, providing a StAR knockout of nature that has provided key insights into its activity. Cell biology experiments show that StAR moves large amounts of cholesterol from the outer to inner mitochondrial membrane, but acts exclusively on the outer membrane. Biophysical data show that only the carboxyl-terminal alpha-helix of StAR interacts with the outer membrane. Spectroscopic data and molecular dynamics simulations show that StAR's interactions with protonated phospholipid head groups on the outer mitochondrial membrane induce a conformational change (molten globule transition) needed for StAR's activity. StAR appears to act in concert with the peripheral benzodiazepine receptor, but the precise itinerary of a cholesterol molecule entering the mitochondrion remains unclear.
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            Dynamic modeling of genetic networks using genetic algorithm and S-system.

            The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.
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              Algorithms for the Solution of the Nonlinear Least-Squares Problem

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                Author and article information

                Journal
                J Toxicol
                J Toxicol
                JT
                Journal of Toxicology
                Hindawi Publishing Corporation
                1687-8191
                1687-8205
                2016
                17 February 2016
                : 2016
                : 4041827
                Affiliations
                1Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Higashi-ku, Fukuoka 812-8582, Japan
                2Biology Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama 335-8505, Japan
                3DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama 335-8505, Japan
                4Safety Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Kisarazu-shi, Chiba 292-0818, Japan
                Author notes

                Academic Editor: Steven J. Bursian

                Article
                10.1155/2016/4041827
                4773560
                27057163
                1f32bd4e-f4d5-4b53-b8fa-3ab9376f9a71
                Copyright © 2016 Ryuta Saito et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 November 2015
                : 20 January 2016
                : 21 January 2016
                Categories
                Research Article

                Toxicology
                Toxicology

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