Expression genetics of CYP3A drug metabolizing enzymes

CYP3A药物代谢酶的表达遗传学

• 批准号: 9310987
• 负责人: Danxin Wang
• 金额: $ 29.79万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9310987
• 关键词: Accounting; Address; Affect; Architecture; Binding; Biological Markers; CYP2D6 gene; CYP3A4 gene; CYP7A1 gene; Chromatin; Chromatin Loop; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Cytochromes; DNA Binding; Data; Disease; Distal; Down-Regulation; Drug Exposure; Drug usage; Enhancers; Enzymes; Epigenetic Process; Estrogen Receptor alpha; Female; Gene Activation; Gene Cluster; Gene Expression; Gene Expression Regulation; Gene Frequency; Gene Silencing; Genes; Genetic; Genetic Polymorphism; Genetic Transcription; Genetic Variation; Genomic Segment; Genomic approach; Genomics; Goals; Hepatic; Hepatocyte; Human; Individual; Introns; Knowledge; Liver; Mediating; Methodology; Methods; Mission; Modeling; Modification; Molds; Molecular Genetics; Molecular Profiling; Nonlinear Dynamics; Nucleic Acid Regulatory Sequences; Pathway Analysis; Pharmaceutical Preparations; Pharmacogenomics; Pharmacotherapy; Population; Public Health; Publishing; RNA Splicing; Regulation; Regulatory Element; Research; Role; Technology; Transcriptional Regulation; Translating; Treatment outcome; United States National Institutes of Health; Variant; base; biomarker development; biomarker panel; clinical biomarkers; clinical practice; drug metabolism; enzyme activity; functional genomics; genetic approach; genetic variant; genome editing; genomic variation; indexing; innovation; insight; male; mathematical model; network models; non-genetic; novel; personalized therapeutic; predictive modeling; promoter; theories; therapy outcome; transcription factor; transcriptome

Summary/Abstract Drug exposure and therapeutic outcomes are greatly affected by substantial variability in drug metabolism. CYP3A drug metabolizing enzymes, encoded by four genes clustered into one gene locus (most important CYP3A4, and 3A5, 3A7 and 3A43), are abundant and clinically important, metabolizing nearly half of all current drugs. However, CYP3A enzyme expression displays substantial (>10-fold) inter-individual variability, largely of unknown causes and therefore poorly predictable – leading to suboptimal treatment outcomes. Built on novel concepts in functional genomics, this proposal focuses on the genomic architecture and transcription factor (TF) regulation of the CYP3A locus, applying cutting edge methodologies and novel mathematical modeling of regulatory networks. The goal is to identify the combined effect of all main regulatory factors determining highly variable CYP3A enzymes – a critical step toward guiding personalized therapeutics of a substantial portion of current drugs. Our central hypothesis is that long-range interactions between regulatory elements (e.g., enhancers with genetic variants) within the CYP3A locus and perturbation of the relevant TF network, i.e., the CYP3A interactome, account for much of variable CYP3A expression. Applying a comprehensive experimental and computational approach, we have already demonstrated the presence of yet unknown regulatory genetic variants in several new enhancer regions that are spread across the CYP3A locus and interact with each other (via DNA looping), strong support of the need to study the CYP3A locus as an interactome, rather than each gene in isolation. Similarly, we need to study TFs regulating CYP3A expression as a dynamic network – an approach already taken previously. However, using a novel mathematical modeling approach, we identify new key TFs and characterize their dynamic interactions. For example, our modeling predicts that the estrogen receptor alpha (ESR1) (in an unliganded form present in males and females) is a critical determinant of CYP3A4 expression in liver cells. Indeed, our genome-editing methods with CRISPR demonstrate that ESR1 exerts profound influence on CYP3A4 expression, possibly accounting for a substantial portion of variability. Since robust ESR1 genetic variation has been implicated in numerous diseases while the causative variants remain uncertain, this project (aim3) will focus on the molecular genetics of ESR1, with largely unknown functions in the liver. The proposed research is innovative in studying gene regulation of the whole CYP3A interactome rather than single genes, and employing a novel mathematical modeling approach capable of identifying TFs, such as ESR1, as key factors in molding local chromatin architecture. Collectively, the results are expected to advance the field of pharmacogenomics by revealing fundamental mechanisms regulating complex gene loci, broadly applicable to similar gene clusters. The results will account for a substantial portion of CYP3A variability, guiding biomarker development for CYP3A activity.

摘要/摘要 药物暴露和治疗结果受药物代谢的显著变化影响。 CYP 3A药物代谢酶，由聚集在一个基因位点的四个基因编码（最重要的 CYP 3A 4、3A 5、3A 7和3A 43）含量丰富，具有临床重要性，代谢了近一半的当前所有CYP 3A 4、3A 5、3A 7和3A 43。 毒品然而，CYP 3A酶表达显示出显著的（>10倍）个体间变异性，主要是 原因不明，因此难以预测-导致次优治疗结果。建立在小说 功能基因组学的概念，该建议侧重于基因组结构和转录因子 (TF)CYP 3A基因座的调控，应用尖端的方法和新的数学建模， 监管网络。目标是确定所有主要调节因素的综合影响， 高度可变的CYP 3A酶-指导个体化治疗的关键一步 目前的药物。我们的中心假设是调控元件之间的长程相互作用 (e.g.,具有遗传变异的增强子）和相关TF网络的扰动， 也就是说，CYP 3A相互作用物组解释了许多可变CYP 3A表达。应用全面的 通过实验和计算方法，我们已经证明了未知的 分布在CYP 3A基因座上的几个新的增强子区域中的调节遗传变异， 相互作用（通过DNA环），强烈支持需要研究CYP 3A基因座作为一种 相互作用体，而不是孤立的每个基因。同样，我们需要研究调节CYP 3A表达的TF 作为一个动态网络-这是以前已经采用的方法。然而，使用一种新的数学 建模方法，我们确定了新的关键TF和表征他们的动态相互作用。比如我们 模型预测，雌激素受体α（ESR 1）（以非配体形式存在于男性和女性中， 是肝细胞中CYP 3A 4表达的关键决定因素。事实上，我们的基因组编辑方法 CRISPR研究表明，ESR 1对CYP 3A 4表达产生深远影响，可能解释了 for a substantial实质portion部分of variability可变性.由于强大的ESR 1基因变异已经涉及许多 虽然致病变异仍不确定，但本项目（aim 3）将侧重于分子遗传学 ESR 1在肝脏中的功能基本未知。这项研究在研究基因方面具有创新性 整个CYP 3A相互作用组的调控，而不是单个基因，并采用一种新的数学 一种能够识别转录因子（如ESR 1）作为局部染色质成型关键因子的建模方法 架构总的来说，这些结果有望通过揭示 调节复杂基因位点的基本机制，广泛适用于类似的基因簇。结果 将解释CYP 3A变异性的很大一部分，指导CYP 3A活性的生物标志物开发。