Comparative Functional Genomics of Yeast

项目概要/摘要 生物学的一个中心目标是了解DNA序列（基因型）和基因型之间的关系。 表型（phenotype）。大约有世纪的时间，遗传学家鉴定出了 表型感兴趣的通过遗传筛选，并自20世纪70年代以来，可以分离突变基因 对这种变化负责。最近，对于少数模式生物，系统方法已经 被设计用来询问基因敲除或敲低的表型后果， 全基因组的收集，这需要数年的时间来构建。最近的技术革新使之成为可能 为了系统地测量基因敲除/敲低的后果， 生物，但比较的方法，以确定基因功能，不仅在大规模上，但在多个， 缺乏相关的生物。这样的研究对于更好地理解变化之间的关系至关重要 以及表型和适应性的结果。此外，没有任何研究表明， 在一组相关的生物体中表征遗传相互作用网络，以确定下一个层次的 职能组织随着时间的推移而发展。为了解决这些知识差距，我们提出了3个综合目标： 五个密切相关的酵母属物种，我们将1）确定破坏每个物种的适应性后果 基因，在多个实验条件下，2）在基因的子集下生成遗传相互作用网络， 对于基因的重要子集，相同的多个实验条件，以及3）对于基因和遗传 这些相互作用显示出物种间的明显差异，进一步研究这些差异的潜在性质。 差异在第一个目标中，我们将利用SATAY，一种饱和转座子诱变方法 这将使我们能够测量数十万个转座子插入事件的适应性，在许多情况下， 实验条件这不仅使我们能够确定5个物种中每个物种的必需基因， 在某些情况下，它还允许鉴定基因亚结构。因为我们将测量 适当的表型，达尔文适应性，我们也将能够进行定量比较， 不同的物种在特定条件下破坏任何给定的直系同源物的后果。在 第二个目标，我们将创建遗传相互作用网络，通过测量一组合理选择的基因， 使用CRISPRiSeq（一种合并的成对相互作用适应方法）， 我们最近开发的。这些数据将填补基因网络如何改变的知识空白 进化时间，这可能会导致更好的预测遗传相互作用。最后，在第三个目标中， 进一步研究目标1和2中观察到的种间差异。我们的初步数据显示， 会有许多基因在某些物种中是必不可少的，但在其他物种中不是，我们预测我们也会有许多基因在某些物种中是必不可少的，但在其他物种中不是，我们预测我们也会有许多基因在某些物种中是必不可少的。 观察遗传相互作用网络的变化（定性和定量）;我们将研究 这些差异的根本原因。

Project Summary/Abstract A central goal of biology is to understand the relationship between DNA sequence (genotype) and the characteristics of the resulting organism (phenotype). For a century or so, geneticists identified mutants with phenotypes of interest through genetic screens, and, since the ~1970s, could isolate the mutated genes responsible for such changes. More recently, for a handful of model organisms, systematic approaches have been devised to interrogate the phenotypic consequences of gene knockouts or knockdowns using specialized genome wide collections, which took years to construct. Recent technological innovations have made it possible to systematically measure the consequences of gene knockout/knockdown in high throughput in many organisms, yet comparative approaches, to determine gene function not only on a large scale, but in multiple, related organisms, are lacking. Such studies are crucial to better understand the relationship between changes in DNA sequence, and the phenotypic and fitness consequences. Furthermore, there are no studies that have characterized genetic interaction networks in a group of related organisms, to determine how this next level of functional organization evolves over time. To address these knowledge gaps, we propose 3 integrated aims: for five closely related Saccharomyces species, we will 1) determine the fitness consequences of disrupting each gene, under multiple experimental conditions, 2) generate genetic interaction networks under a subset of the same multiple experimental conditions for an important subset of the genes, and 3) for genes and genetic interactions that show clear differences across species, further investigate the underlying nature of those differences. In the first Aim, we will take advantage of SATAY, a saturation transposon mutagenesis approach that will allow us to measure the fitness of hundreds of thousands of transposon insertion events, under many experimental conditions. Not only will this allow us to determine the essential genes in each of the 5 species, but in some cases, it also allows the identification of gene substructure. Because we will measure the most appropriate phenotype, Darwinian fitness, we will also be able to make quantitative comparisons between different species as to the consequences of disrupting any given ortholog under a particular condition. In the second Aim, we will create genetic interaction networks, by measuring for a rationally chosen set of genes, tens of thousands of genetic interaction scores, using CRISPRiSeq, a pooled pairwise interaction fitness approach we recently developed. These data will bridge a crucial gap in knowledge on how genetic networks change over evolutionary time, which could result in better prediction of genetic interactions . Finally, in the third Aim, we will further investigate the observed inter-species differences from Aims 1 and 2. Our preliminary data suggest that there will be many genes that are essential in some species, but not others, and we predict that we will also observe changes (both qualitative and quantitative) in genetic interaction networks; we will investigate the underlying causes of these differences.