DISSERTATION RESEARCH: The fitness landscape of a yeast tRNA gene

突变为所有遗传变异和进化提供了原料。这些突变如何影响个体的适应性以及这些适应性效应的分布，即适应性景观，对于理解几乎所有的进化过程都是至关重要的。这个项目将探索酵母基因的适应性景观。这些数据将为突变的适应度效应提供前所未有的细节，包括突变之间的遗传相互作用的特性，并为测试许多理论模型提供宝贵的机会。这些模型适用于许多重要现象，如遗传病的基础、病原体多药耐药性的起源以及动物和植物的驯化。本研究探索了tRNA-ARG基因在酿酒酵母中的适应度景观，并研究了该基因点突变之间的上位性。这项研究将产生大约10万个酵母菌株，每个菌株在其原生基因组位置携带tRNA基因的一种变体，与野生型相比，这种变体平均有两个单核苷酸变化。通过灵敏的高通量方法，将对大约20,000个基因型的适应度值进行大规模测量，并对大量突变对进行上位性估计。这项研究为了解突变上位性和适应度效应的生物物理基础提供了难得的机会，因为tRNA的功能在很大程度上取决于其二级结构，而二级结构可以通过计算得到高精度的预测。此外，适应度景观和上位性将在多种条件下量化，以了解环境相互作用的基因型。此外，我们还将研究不同tRNA变体的表达情况，以更好地了解tRNA内部启动子的工作原理，以及tRNA表达水平对适应度的影响。

Mutation provides the raw material for all genetic variation and evolution. How those mutations affect the fitness of an individual and the distribution of those fitness effects, known as the fitness landscape, is of fundamental importance for understanding almost all evolutionary processes. This project will explore the fitness landscapes of a yeast gene. The data will offer unprecedented details of the fitness effects of mutations, including properties of genetic interaction among mutations, and provide an invaluable opportunity to test many theoretical models. These models are applied to many important phenomena such as the basis of genetic diseases, the origins of multidrug resistance in pathogens, and the domestication of animals and plants. This research explores the fitness landscape of the tRNA-ARG gene in the yeast Saccharomyces cerevisiae and studies epistasis between point mutations in the gene. The study will generate approximately 100,000 yeast strains, each carrying, at its native genomic location, a variant of the tRNA gene that has on average two single nucleotide changes compared to the wild-type. The fitness values of approximately 20,000 genotypes will be measured en masse by a sensitive high-throughput method, and epistasis will be estimated for a large number of mutation pairs. This study offers a rare opportunity to understand the biophysical basis of epistasis and fitness effects of mutations, because tRNA function is largely determined by its secondary structure, which can be computationally predicted with high accuracy. Further, fitness landscapes and epistasis will be quantified under multiple conditions to understand genotype by environment interactions. In addition, the expressions of different tRNA variants will be probed to better understand how the internal promoter of the tRNA works and how fitness is affected by the expression level of the tRNA.