肝细胞内源性过氧化氢代谢的计算模型,GSH 在其中发挥关键作用

IF 3.1 Q2 TOXICOLOGY
L.M. Bilinsky
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引用次数: 0

摘要

本文介绍了肝细胞内源性 H2O2 代谢的常微分方程 (ODE) 模型,该模型在撰写本文时是独一无二的,因为它能够在氧化应激事件中准确计算细胞内 H2O2 浓度,并可用于构建产生 ROS 的异种生物的 PBPK/PD 模型。本文介绍了该模型在体外大鼠肝细胞和体内小鼠肝脏中的不同版本。给出了使用该模型创建 PBPK/PD 模型的通用方法,该模型可预测细胞内 H2O2 浓度和氧化应激诱导的肝细胞死亡;这些可从体外数据集中识别,这些数据集报告了暴露于不同水平的异种生物后的细胞死亡率。该程序针对三价砷化物二甲基砷酸(DMAIII)进行了演示,该物质在肝脏中产生,是砷消除途径的一部分。这是第一个肝细胞中 H2O2 代谢模型,其特点是线粒体和其他细胞器产生 H2O2 的内源性速率值是根据生理学文献推断出来的,并且对 GSH 代谢进行了详细、现实的处理;后者是通过结合 Reed 和同事的肝脏 GSH 代谢先驱模型的最小版本来实现的。模型模拟表明,临界 GSH 消耗是细胞内 H2O2 上升到与细胞凋亡相关浓度(>;1μM),这可能只发生在异种生物浓度达到峰值数小时之后("延迟效应"),当临界 GSH 耗尽发生时,H2O2 浓度会在两个边界层的序列中迅速上升,这两个边界层的特征是谷胱甘肽过氧化物酶(第一边界层)和过氧化氢酶(第二边界层)的动力学,细胞内 H2O2 浓度为 1μM 意味着临界 GSH 耗尽。有人推测,与细胞凋亡相关范围内的 ROS 水平只是表明而不是导致细胞凋亡的环境。模型模拟与这一观点一致。更广泛的生理学界感兴趣的结果是,延迟效应提供了一种以 GSH 为基础的机制,通过这种机制,细胞可以区分 H2O2 浓度的瞬时升高(用于细胞内信号传导)和持续升高(表明病理或毒素的存在),第二种状态最终会引发细胞凋亡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A computational model of endogenous hydrogen peroxide metabolism in hepatocytes, featuring a critical role for GSH

This paper presents an ordinary differential equation (ODE) model of endogenous H2O2 metabolism in hepatocytes that is unique, at the time of writing, in its ability to accurately compute intracellular H2O2 concentration during incidents of oxidative stress and in its usefulness for constructing PBPK/PD models for ROS-generating xenobiotics. Versions of the model are presented for rat hepatocytes in vitro and mouse liver in vivo. A generic method is given for using the model to create PBPK/PD models which predict intracellular H2O2 concentration and oxidative-stress-induced hepatocyte death; these are identifiable from in vitro data sets reporting cell mortality following xenobiotic exposure at various levels. The procedure is demonstrated for the trivalent arsenical dimethylarsinous acid (DMAIII), which is produced in liver as part of the arsenic elimination pathway. This is the first model of H2O2 metabolism in hepatocytes to feature values for the endogenous rates of H2O2 production by mitochondria and other organelles which are inferred from the physiology literature, and to feature a detailed, realistic treatment of GSH metabolism; the latter is achieved by incorporating a minimal version of Reed and coworkers’ pioneering model of GSH metabolism in liver. Model simulations indicate that critical GSH depletion is the immediate trigger for intracellular H2O2 rising to concentrations associated with apoptosis (>1μM), that this may only occur hours after the xenobiotic concentration peaks (“delay effect”), that when critical GSH depletion does occur, H2O2 concentration rises rapidly in a sequence of two boundary layers, characterized by the kinetics of glutathione peroxidase (first boundary layer) and catalase (second boundary layer), and that intracellular H2O2 concentration >1μM implies critical GSH depletion. There has been speculation that ROS levels in the range associated with apoptosis simply indicate, rather than cause, an apoptotic milieu. Model simulations are consistent with this view. In a result of interest to the wider physiology community, the delay effect is shown to provide a GSH-based mechanism by which cells can distinguish transient elevations in H2O2 concentration, of use in intracellular signaling, from persistent ones indicative of either pathology or the presence of toxins, the second state of affairs eventually triggering apoptosis.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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