Mapping deep evolutionary divergences in cellular models of stress response

摘要 了解大自然如何创造新的特征是进化遗传学的一个基本目标。不偏不倚 到目前为止，对野生性状变异的实验解剖一直使用连锁或关联作图，这 仅适用于给定物种的相容个体之间的杂交。此对象的父R01 补充剂使用了我们小组的一种名为RH-SEQ的方法，该方法能够对天然的 繁殖隔离物种间的性状变异。母公司R01的一个目标是关注进化和 酵母高温生长这一典型性状的生物物理机制。第二个目标是 亲本R01将RH-seq应用于一种称为细胞衰老的细胞自主小鼠表型。在这 作为补充，我们建议为微生物学研究生项目的博士生Abel Duarte提供培训 在加州大学伯克利分校，将我们的分析扩展到萌芽酵母克鲁维酵母。在这些遥远的亲戚中 酵母菌，我们发现了一套抗逆性特征，区分了一个物种，K.marxanus，从 支派的其他成员。我们建议使用RH-seq和其他组学方法来剖析这一特征综合征。 自从两千万年前马氏克利乌斯与其最近的近亲分道扬镳以来，该支系为我们提供了机会。 在比我们或其他人在任何数量遗传学中所取得的更深的分歧上研究性状进化 到目前为止的测绘工作。该项目将涉及计算数据分析、应用统计学和分子 进化测试以及实验基因组学和分子生物学。它代表着一种极好的训练 Abel掌握计算/量化技能的机会，他在个人中列出了这一点 将发展计划作为主要的培训目标。因此，资助这一补充将支持亚伯的发展 作为一名数量基因组科学家；由于亚伯是墨西哥血统，也是他家族中第一个去世的人 随着他利用我们的培训完成他的博士学位，并进入生物医学研究领域， 他将为劳动力的多样性做出贡献。

SUMMARY Understanding how nature builds new traits is a fundamental goal of evolutionary genetics. Unbiased experimental dissection of trait variation from the wild has to date used linkage or association mapping, which are suitable only for crosses between compatible individuals of a given species. The parent R01 for this supplement makes use of a method from our group called RH-seq, which enables the genetic mapping of natural trait variation between reproductively isolated species. One aim of the parent R01 focuses on evolutionary and biophysical mechanisms of a model trait, high-temperature growth in Saccharomyces yeast. A second aim of the parent R01 applies RH-seq to a cell-autonomous mouse phenotype called cellular senescence. In this supplement, we propose to provide training to Abel Duarte, a Ph.D. student in the Microbiology graduate program at UC Berkeley, to extend our analyses to the budding yeast Kluyveromyces. In these distant relatives of Saccharomyces, we have found a suite of stress-resistance traits distinguishing one species, K. marxianus, from the rest of the clade. We propose to use RH-seq and other omics approaches to dissect this trait syndrome. Since K. marxianus diverged 20 million years ago from its closest relative, the clade affords us the opportunity to study trait evolution over a far deeper divergence than we or others have achieved in any quantitative-genetic mapping work to date. This project will involve computational data analysis, applied statistics, and molecular evolution tests as well as experimental genomics and molecular biology. It represents an excellent training opportunity for Abel to master computational/quantitative skills, which he has set out in his Individual Development Plan as a major training goal. As such, funding this supplement will support Abel’s development as a quantitative genomic scientist; and since Abel is of Mexican descent and the first in his family to have gone to college, as he harnesses our training to complete his Ph.D. and move on to a career in biomedical research, he will contribute to the diversity of the workforce.