Mitochondrial Metabolic Reprogramming and DNA Damage in Arsenic Carcinogenesis

砷致癌过程中的线粒体代谢重编程和 DNA 损伤

基本信息

批准号：
8927921
负责人：
Teresa Whei-Mei Fan
金额：
$ 18.81万
依托单位：
UNIVERSITY OF KENTUCKY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-06-15 至 2017-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8927921
关键词：
Acetylcysteine Address Adopted Arsenic Arsenites Biochemical Biochemical Pathway Biological Assay Biological Markers Biological Models Cancer Patient Cells Citric Acid Cycle Complex Coupled DNA DNA Damage DNA Repair Data Data Correlations Dependence Development Electron Transport Environmental Carcinogens Enzymes Epidemiologic Studies Epigenetic Process Epithelial Cells Equilibrium Etiology Event Fumarate Hydratase Future Gene Expression Gene Mutation Generations Genes Genetic Genomic DNA Genomics Genus Hippocampus Glucose Glutamine Goals Health Homeostasis Human In Vitro Kidney Knowledge Lead Lesion Link Literature Lung Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Mammalian Cell Maps Measures Mediating Metabolic Metabolism Mitochondria Mitochondrial DNA Modeling Molecular Mus Mutation Nuclear Oxidation-Reduction Oxidative Stress Pathway interactions Pattern Phase Poly(ADP-ribose) Polymerases Populations at Risk Preventive Process Production Protocols documentation RNA RNA Sequences Research Personnel Respiration Risk Role Succinate Dehydrogenase System Testing Therapeutic Time Tracer Translating Translations Xenograft procedure arsenic-induced carcinogenesis base carcinogenesis cell transformation environmental carcinogenesis enzyme activity epigenomics genetic regulatory protein in vivo insight mRNA Expression metabolomics mitochondrial dysfunction next generation next generation sequencing oxidative DNA damage public health relevance repaired respiratory response stable isotope transcriptome sequencing transcriptomics tumor validation studies

项目摘要

DESCRIPTION (provided by applicant): Although epidemiological studies have associated arsenic (As) exposures with increased risk of lung cancer, the molecular mechanisms involved in lung cancer development by arsenite exposure remain poorly defined. Recent literature has implicated altered mitochondrial (mt) metabolism, oxidative stress, and DNA damage in environmental As carcinogenesis, but the biochemical dysregulations that underlie these processes and their interactions are unknown. It is also unclear if benchside understanding of As carcinogenesis in model systems can be translated into bedside understanding of human cancer. In this R21/R33 proposal, this team of investigators will address these issues by adopting an integrated `omics approach that uses stable isotope-resolved metabolomics (SIRM) to define mt and cellular metabolic networks central to proliferation, redox homeostasis and oxidative DNA damage/repair response in lung cells, as perturbed by As transformation and carcinogenesis. This approach will reveal pathway changes for further testing on their dysregulations (e.g. key enzymes and related regulatory proteins) at the gene mutational, epigenetic, and transcriptional level by querying into genomic, epigenomic, and transcriptomic sequencing data. The investigators will fulfill their goal with two specific aims (SA1 & 2) in the R21 phase followed by SA3 & 4 in the R33 phase and SA1 to determine if and how mt and cellular metabolic networks are altered in As-transformed lung cells using SIRM. Transformed lung cells will be studied in vitro and in mouse xenografts using SIRM. The reprogrammed metabolic networks obtained will be related to those separately acquired in vivo from human lung cancer patients. SA2) to link ROS production, mt, and nuclear DNA damages/repair processes to metabolic reprogramming in As-transformed lung cells. Correlations of SA1&2 data will enable hypothesis generation for further testing of cause-and-effect relationships in SA3&4. SA3) to reveal cause-and-effect relations among altered Mt/cellular metabolic networks, ROS production, and DNA damage during As-induced transformation by their time-dependence. SA4) to map corresponding genetic, epigenetic and gene expression changes in As-transformed lung cells for hypothesis testing on As action. Next-generation sequence data will be acquired and queried for mutational signatures, epigenetic status, and mRNA expression of key genes involved in the reprogrammed metabolic networks, redox balance, and DNA repair. These data will help delineate how As-induced genetic or epigenetic lesions lead to metabolic reprogramming or vice versa. Such integration of genomic, epigenomic, transcriptomic, and metabolomics analysis of As action in model lung cell systems should provide unprecedentedly detailed insights into metabolic dysregulation and DNA damage in the mitochondria and how they may be linked to nuclear DNA damage and altered gene expression. The integrated `omics information will facilitate full-scale validation studies on the molecular mechanism for As-induced carcinogenesis and translation of such basic insights into human lung cancer resulting from As exposure (e.g. discovery of mechanistic and robust biomarker patterns).

描述（由申请人提供）：虽然流行病学研究表明砷（As）暴露与肺癌风险增加有关，但亚砷酸盐暴露引起肺癌发展的分子机制仍然不明确，最近的文献表明线粒体（mt）代谢、氧化。砷致癌过程中的应激和 DNA 损伤，但这些过程背后的生化失调及其相互作用尚不清楚。模型系统可以转化为对人类癌症的临床理解，在这个 R21/R33 提案中，该研究小组将通过采用综合“组学”方法来解决这些问题，该方法使用稳定同位素解析代谢组学 (SIRM) 来定义 mt 和细胞代谢。肺细胞中的增殖、氧化还原稳态和氧化 DNA 损伤/修复反应的核心网络，受 As 转化和致癌作用的干扰，这种方法将揭示途径的变化，以进一步测试它们。通过查询基因组、表观基因组和转录组测序数据，研究人员将在 R21 中通过两个特定目标（SA1 和 2）来实现基因突变、表观遗传和转录水平的失调（例如关键酶和相关调节蛋白）。阶段，然后是 R33 阶段的 SA3 和 4，以及使用 SIRM 确定 As 转化肺细胞中 mt 和细胞代谢网络是否以及如何改变的 SA1。使用 SIRM 在体外和小鼠异种移植物中获得的重编程代谢网络将分别与从人类肺癌患者体内获得的代谢网络相关，以将 ROS 产生、mt 和核 DNA 损伤/修复过程与 As- 中的代谢重编程联系起来。 SA1 和 2 数据的相关性将能够生成假设，以进一步测试 SA3 和 4 中的因果关系，以揭示改变的 Mt/细胞代谢之间的因果关系。相应的网络、ROS 产生和 As 诱导转化过程中的 DNA 损伤（其时间依赖性），以绘制 As 转化肺细胞的遗传、表观遗传和基因表达变化，以对 As 作用进行假设检验。获取并查询参与重编程代谢网络、氧化还原平衡和 DNA 修复的关键基因的突变特征、表观遗传状态和 mRNA 表达。这些数据将有助于描述 As 诱导的遗传或表观遗传损伤如何导致代谢重编程或代谢重编程。反之亦然。这种对模型肺细胞系统中As作用的基因组、表观基因组、转录组和代谢组学分析的整合应该为线粒体中的代谢失调和DNA损伤以及它们如何与核DNA损伤和基因表达相关提供前所未有的详细见解。综合的组学信息将有助于对 As 诱导的分子机制进行全面验证研究。致癌作用以及对砷暴露引起的人类肺癌的基本认识的转化（例如发现机制和强大的生物标志物模式）。