Pleiotropy: patterns, mechanisms, and evolutionary consequences

多效性：模式、机制和进化后果

• 批准号: 9757489
• 负责人: JIANZHI ZHANG
• 金额: $ 30.63万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9757489
• 关键词: Address; Affect; Aging; Antibiotic Resistance; Antibiotics; Biological Models; Biological Process; Carrying Capacities; Conflict (Psychology); Data; Detection; Disease; Dissection; Environment; Environmental Impact; Evolution; Exhibits; Fertility; Funding; Gene Deletion; Gene Proteins; Genes; Genetic; Genetic Diseases; Genetic Drift; Genetic Epistasis; Genotype; Grant; Growth; Individual; Knowledge; Malignant Neoplasms; Maps; Measures; Methods; Modeling; Molecular; Molecular Evolution; Molecular Genetics; Mutation; Nature; Nucleotides; Organism; Pattern; Phenotype; Population; Population Growth; Probability; Quantitative Trait Loci; RNA; Research; Resolution; Role; Study models; Testing; Variant; Work; Yeasts; cancer genetics; causal variant; combat; density; driving force; environmental adaptation; fitness; genetic variant; in vivo; life history; mathematical model; null mutation; pathogen; pleiotropism; programs; public health relevance; senescence; theories; trait; tumor

PROJECT SUMMARY The long-term objective of the PI's research program is to understand the molecular genetic mechanisms and driving forces of phenotypic variation and evolution. Pleiotropy is one of the most common yet least understood phenomena in genetics. It refers to the observation that one mutation impacts multiple phenotypic traits. Pleiotropy may be concordant or antagonistic, depending on whether the mutational effects on multiple traits are in the same or opposite directions (when the directions are alignable). Pleiotropy, especially antagonistic pleiotropy, is widely invoked in explanations and models of senescence, cancer, genetic disease, sexual conflict, cooperation, evolutionary constraint, adaptation, neofunctionalization, and speciation, among other things. This project addresses three key gaps in our understanding of pleiotropy: patterns, mechanisms, and evolutionary consequences. First, while the environmental pleiotropy of null mutations has been extensively studied, the same is not true for non-null mutations. This project will use a high-throughput method to determine the in vivo fitness landscapes of one yeast RNA gene and four protein genes in 12 environments. Each landscape will include >20,000 genotypes, providing unprecedentedly large data for inducing general principles of environmental pleiotropy. More importantly, these data will allow inferring fitness effects of mutations in one environment from those in another, which will be instrumental in explaining and predicting evolution in nature. Second, while pleiotropy is typically studied from the perspective of mutations, the other side of the coin is the relationship between phenotypic traits that are often impacted by the same mutations. Maximum growth rate r and carrying capacity K of density-dependent population growth are key life-history traits fundamental to many ecological and evolutionary theories and are directly relevant to combating pathogens and tumors. Although r and K are generally thought to be negatively correlated, both r-K tradeoffs and "tradeups" have been observed. However, neither the conditions under which each of these relationships occur nor the causes of these relationships are well understood. These questions will be addressed in yeast by mapping quantitative trait loci influencing r and K and estimating the r and K of 500 single-gene deletion strains in multiple environments, followed by modeling of biological processes impacting r and K. Third, if mutations with large benefits in one environment are generally deleterious in other environments, a population adapting to a changing environment may have few adaptive substitutions, despite continuous and strong selections. This project will test the above hypothesis using experimental evolution of yeast in constant vs. changing environments. If supported, this hypothesis will profoundly alter our interpretation of the nonsynonymous/synonymous substitution rate ratio estimated from intra and interspecific comparisons, impacting the assessment of the relative roles of genetic drift and positive selection in molecular evolution.

项目摘要 PI研究计划的长期目标是了解分子遗传机制， 表型变异和进化的驱动力。基因多效性是最常见但最不为人所知的 遗传学现象。它是指观察到一个突变影响多个表型性状。 多效性可能是一致的或拮抗的，这取决于突变是否对多个性状产生影响 在相同或相反的方向上（当方向可互换时）。多效性，尤指拮抗性 多效性，被广泛用于解释和模型的衰老，癌症，遗传疾病，性 冲突、合作、进化约束、适应、新功能化和物种形成等 东西该项目解决了我们对多效性理解的三个关键差距：模式，机制和 进化的后果。首先，虽然无效突变的环境多效性已被广泛研究， 然而，对于非无效突变来说，情况并非如此。该项目将使用高通量方法， 确定一个酵母RNA基因和四个蛋白质基因在12个环境中的体内适应性景观。 每个景观将包括> 20，000个基因型，为诱导一般的基因型提供前所未有的大数据。 环境多效性原理更重要的是，这些数据将允许推断健身效果 一种环境中的突变与另一种环境中的突变，这将有助于解释和预测 自然界的进化第二，虽然通常从突变的角度研究多效性，但另一个 硬币的另一面是表型性状之间的关系，这些性状往往受到相同突变的影响。 密度依赖型种群增长的最大增长率r和承载力K是关键的生活史 许多生态和进化理论的基础特征，并直接关系到打击 病原体和肿瘤。虽然r和K通常被认为是负相关的，但r-K权衡 和"贸易升级"。然而，这些关系中的每一种关系所处的条件 这些关系的发生也没有很好的理解。这些问题将在酵母中解决， 对影响r和K的数量性状位点进行定位，并估算500个单基因缺失菌株的r和K 在多种环境中，其次是影响r和K的生物过程的建模。第三，如果突变 在一个环境中有很大的好处，在其他环境中通常是有害的， 尽管有连续的和强的选择，但对变化的环境的适应性替代可能很少。 这个项目将测试上述假设使用实验进化的酵母在恒定与变化 环境.如果得到支持，这一假设将深刻改变我们对 通过种内和种间比较估计的非同义/同义替换率比， 影响了对遗传漂变和正选择在分子进化中的相对作用的评估。