Systematic Molecular Analysis of Antagonistic Pleiotropy

描述（由申请人提供）：慢性感染和肿瘤发生都是进化过程，其中适应受到有益突变发生率的限制，以及这些突变在不断变化的环境中是否仍然有益。许多有益的突变表现出多效性，引起改变许多表型的“涟漪效应”，其中一些可能在一个环境中增加适应性，但在另一个环境中降低适应性。这种现象，拮抗性多效性（AP），被认为是衰老，进化权衡，以及有害等位基因在人类群体中以高频率持续存在的基础。最近的全基因组关联研究表明，许多常见的人类SNP通过AP与某些癌症，代谢综合征和免疫介导的疾病相关。理论上，AP也被证明会严重降低有益突变的平均选择值，降低其固定的可能性。尽管它们在医学和进化生物学中的重要性，但有益突变率，其适应性效应的分布以及有益突变显示AP的程度尚未以系统，前瞻性和无偏见的方式进行经验测量。这项提议将填补这些知识空白，利用分子条形码的创新，这将提供进化动力学最详细的视图。使用模型真核生物，酿酒酵母，我们的团队已经取得了关于适应，上位性和渐渗的分子基础，以及AP如何导致生理权衡的基本发现。我们在这里提出扩展这些发现。我们的具体目标是：1）在一个环境中进化和测量有益突变的适应性效应，然后测量它们在其他环境中的适应性; 2）确定适应性和拮抗多效性的分子基础; 3）在分子水平上确定谱系如何适应性地逃避拮抗多效性的有害影响。对于目标1，我们已经开发了一个基于分子条形码的谱系跟踪系统，我们可以用它来量化，以高分辨率，在不断发展的人口中的数千个适应性克隆的出现和建立。我们将使用谱系追踪来估计这些新突变体在多种环境中的有益突变率和选择系数的分布，这将揭示AP的程度。在目标2中，我们将对数百个克隆进行测序，这些克隆要么表现出AP，要么不表现出AP，以确定负责突变的潜在分子性质，并比以往任何时候都更详细地定义进化条件下的适应性突变谱。最后，在目标3中， 再次使用我们的谱系跟踪系统来发现新的有益突变体自适应地从AP“逃脱”的突变途径，消除其成本，同时保留原始利益。这些次级突变的身份将提供有关代谢网络的潜在布线和弱点或优点的新信息，并揭示上位相互作用及其在适应性进化中的作用。

DESCRIPTION (provided by applicant): Chronic infection and tumorigenesis are both evolutionary processes, in which adaptation is constrained by the rate at which beneficial mutations occur, and by whether those mutations are still beneficial in a changing environment. Many beneficial mutations exhibit pleiotropy, causing a "ripple effect" that alters many phenotypes, some of which may increase fitness in one environment, but decrease fitness in another. This phenomenon, antagonistic pleiotropy (AP), is thought to underlie senescence, evolutionary trade-offs, and the persistence of deleterious alleles at high frequency in human populations. Recent genome-wide association studies have shown that many common human SNPs are linked via AP to certain cancers, metabolic syndromes, and immune mediated disorders. AP has also been shown theoretically to severely reduce the mean selective value of beneficial mutations, reducing their likelihood of fixation. Despite their importance in medicine and evolutionary biology, the beneficial mutation rate, the distribution of their fitness effects, nd the extent to which beneficial mutations show AP have yet to be measured empirically in a systematic, prospective and unbiased manner. This proposal will fill these knowledge gaps, using innovations in molecular barcoding, which will provide the most detailed view yet of evolutionary dynamics. Using the model eukaryote, Saccharomyces cerevisiae, our groups have made fundamental discoveries concerning the molecular bases of adaptation, epistasis and introgression, and how AP causes physiological trade-offs. We propose here to extend these discoveries. Our Specific Aims are: 1) to evolve and measure the fitness effects of beneficial mutations in one environment, then measure their fitness in other environments; 2) to determine the molecular basis of adaptation and antagonistic pleiotropy; and 3) to determine at the molecular level how lineages can adaptively escape from the harmful effects of antagonistic pleiotropy. For Aim 1, we have developed a molecular barcode-based lineage tracking system with which we can quantify, to high-resolution, the emergence and establishment of thousands of adaptive clones in an evolving population. We will use lineage tracking to estimate the beneficial mutation rate and the distribution of selection coefficients for these new mutants in multiple environments, which will reveal the extent of AP. In Aim 2, we will sequence hundreds of clones that either do or do not demonstrate AP, in order to determine the underlying molecular nature of the responsible mutations, and define the adaptive mutational spectrum for the evolutionary condition in greater detail than has ever been possible. Finally, in Aim 3 we will again use our lineage tracking system to discover the mutational routes by which novel beneficial mutants adaptively "escape" from AP, eliminating its cost, while preserving the original benefit. The identity of these secondary mutations will provide qualitatively new information about the underlying wiring of, and weak or strong points in metabolic networks, and shed light upon epistatic interactions and the role they play in adaptive evolution.