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抗应激基因调控网络中的剂量反应关系。

Dose response relationship in anti-stress gene regulatory networks.

作者信息

Zhang Qiang, Andersen Melvin E

机构信息

Division of Computational Biology, CIIT Centers for Health Research, Research Triangle Park, North Carolina, United States of America.

出版信息

PLoS Comput Biol. 2007 Mar 2;3(3):e24. doi: 10.1371/journal.pcbi.0030024. Epub 2006 Dec 22.

DOI:10.1371/journal.pcbi.0030024
PMID:17335342
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1808489/
Abstract

To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on the level of local gains, presence of gain-changing events, and degree of feedforward gene activation, this region can appear as superlinear, sublinear, or even J-shaped. The general dose response transition proposed here was further examined in a complex anti-electrophilic stress pathway, which involves multiple genes, enzymes, and metabolic reactions. This work would help biologists and especially toxicologists to better assess and predict the cellular impact brought about by biological stressors.

摘要

为维持稳定的细胞内环境,细胞利用复杂且专门的防御系统来抵御各种外部干扰,如亲电应激、热休克和缺氧等。无论应激类型如何,许多有助于细胞稳态的适应性机制似乎都是通过组织成负反馈回路的基因调控网络来运作的。一般来说,诸如亲电试剂、错误折叠的蛋白质和氧气等受控变量的偏离程度首先由专门的传感器分子检测到,然后信号被转导至特定的转录因子。转录因子可以调节一组抗应激基因的表达,其中许多基因编码的酶可发挥作用以抵消受干扰的变量。本研究的目的是利用控制理论和计算方法,探索这些基因调控网络中细胞应激源与细胞内生化物质(受控变量、转录因子和基因产物)之间稳态剂量反应关系的理论基础。我们的研究表明,剂量反应曲线的形状(线性、超线性或亚线性)取决于反馈回路中分布的局部反应系数(增益)的特定值的变化。抗应激酶和转录因子多聚化为同型二聚体、同型三聚体甚至更高阶的多聚体,在维持稳健的稳态中发挥着重要作用。此外,我们的模拟指出,随着应激源水平的增加,受控变量的剂量反应曲线可以依次经历四个不同阶段:初始超线性且控制较少、超线性且控制程度更高、线性且无控制、亚线性且灾难性。每个阶段都依赖于随着应激源水平增加而发挥作用的特定增益变化事件。低剂量区域本质上是非线性的,并且根据局部增益水平、增益变化事件的存在以及前馈基因激活程度,该区域可能呈现为超线性、亚线性甚至J形。本文提出的一般剂量反应转变在一个复杂的抗亲电应激途径中得到了进一步研究,该途径涉及多个基因、酶和代谢反应。这项工作将有助于生物学家,尤其是毒理学家更好地评估和预测生物应激源对细胞的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/c5db4bffb9d2/pcbi.0030024.g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/00835316b281/pcbi.0030024.g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/727962e396a2/pcbi.0030024.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/cbb2c5e1d6fe/pcbi.0030024.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/77dd32d78797/pcbi.0030024.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/1aed34b29154/pcbi.0030024.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/6660cce56554/pcbi.0030024.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/c5db4bffb9d2/pcbi.0030024.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/7514def4e285/pcbi.0030024.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/283071bdffb4/pcbi.0030024.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/72da4c4209ea/pcbi.0030024.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/a106ec6485aa/pcbi.0030024.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/00835316b281/pcbi.0030024.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/e2bf040c5c37/pcbi.0030024.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/727962e396a2/pcbi.0030024.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/cbb2c5e1d6fe/pcbi.0030024.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/77dd32d78797/pcbi.0030024.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/1aed34b29154/pcbi.0030024.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/6660cce56554/pcbi.0030024.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d9b/1847982/c5db4bffb9d2/pcbi.0030024.g012.jpg

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