Equipment Supplement: Genomic and Systemic Approaches of Evolutionary Mechanisms

设备补充：进化机制的基因组和系统方法

• 批准号: 10793042
• 负责人: JIANZHI ZHANG
• 金额: $ 4.24万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10793042
• 关键词: Aging; Area; Biological Models; Biology; Computer Simulation; Data Analyses; Dependence; Development; Disease; Environment; Equipment; Evolution; Genes; Genetic; Genetic Epistasis; Genetic study; Genome; Genomic approach; Genomics; Human; Individual; Joints; Light; Mammals; Maps; Molecular; Molecular Biology; Molecular Genetics; Mutation; Pattern; Phenotype; Prevalence; Process; RNA Editing; Research; Role; Technology; Testing; Theoretical model; Yeasts; biological systems; disease-causing mutation; driving force; environmental adaptation; fitness; functional genomics; gene interaction; genomic data; human disease; laboratory experiment; pleiotropism; programs

Project Summary The long-term objective of the PI's research program is to discover and understand general principles of evolution in terms of molecular genetic mechanisms and driving forces. This is a rapidly progressing area, thanks to the technological revolution in molecular biology and genomics, rapid accumulation of genome sequences and functional genomic data, and most importantly, conceptual developments in various fields of biology. Genomic and systemic approaches to evolution are especially valuable, because the genomic approach allows assessing the generalities and relative contributions of individual mechanisms discovered and the systemic approach helps refocus the study of evolutionary processes from roles of individual genes/mutations to those of interactions among genes/mutations (and the environment), which are key to uncovering the inner workings and evolution of biological systems. The PI's lab studies evolutionary mechanisms from genomic and systemic perspectives using multiple model systems (mostly yeast and mammals) and a combination of theoretical modeling, computer simulation, integrative data analysis, laboratory experimental evolution, and molecular and genomic experimentation. This proposal centers on two related themes that are of fundamental importance to evolution: epistasis and pleiotropy. On the topic of epistasis, the PI and his team will characterize patterns of intragenic and intergenic epistasis by mapping fitness landscapes of representative genes and joint fitness landscapes of pairs of interacting genes, study the genetic background dependency of gene essentiality at the genomic scale, experimentally test the hypothesis that the existence and persistence of RNA editing is explained by evolutionary entrenchment, and attempt to resolve the apparently contradictory inferences of epistasis patterns from adaptive evolution and mutation accumulation. On the topic of pleiotropy, the PI and his team will map multi-environment fitness landscapes of representative genes to study general patterns of environmental pleiotropy of mutations, identify common loci of environmental adaptations, quantify the influence of pleiotropy on molecular adaptations in changing environments, and assess the impact of pleiotropy on the rate of phenotypic evolution and human aging and disease. Together, these projects promise to deepen the understanding of the genetic mechanisms and driving forces of evolution and shed light on disease mechanisms and prevalence.

项目概要 PI 研究计划的长期目标是发现和理解以下领域的一般原理： 分子遗传机制和驱动力方面的进化。这是一个快速发展的领域， 得益于分子生物学和基因组学的技术革命，基因组的快速积累 序列和功能基因组数据，最重要的是，各个领域的概念发展 生物学。进化的基因组学和系统学方法尤其有价值，因为基因组学 方法允许评估已发现和的单个机制的一般性和相对贡献 系统方法有助于将进化过程的研究重新集中在个体的角色上 基因/突变与基因/突变（和环境）之间的相互作用，这是关键 揭示生物系统的内部运作和进化。 PI 的实验室研究进化论 使用多个模型系统（主要是酵母和 哺乳动物）以及理论建模、计算机模拟、综合数据分析的结合， 实验室实验进化以及分子和基因组实验。该提案主要围绕两点展开 对进化至关重要的相关主题：上位性和多效性。关于主题 上位性，PI 和他的团队将通过绘图来表征基因内和基因间上位性的模式 代表性基因的适应度景观和相互作用基因对的联合适应度景观，研究 在基因组规模上基因重要性的遗传背景依赖性，通过实验检验假设 RNA 编辑的存在和持续性可以通过进化巩固来解释，并试图 解决适应性进化和突变中上位模式明显矛盾的推论 积累。在多效性这一主题上，PI 和他的团队将绘制多环境适应性景观图 代表性基因，用于研究突变的环境多效性的一般模式，识别常见基因座 环境适应的影响，量化多效性对分子适应变化的影响 环境，并评估多效性对表型进化速率和人类衰老的影响 疾病。这些项目共同致力于加深对遗传机制和驱动力的理解。 进化的力量并揭示疾病机制和患病率。