Comparative Functional Genomics of Yeast

项目摘要/摘要 生物学的一个中心目标是了解DNA序列(基因)和基因之间的关系 所产生的生物体的特征(表型)。在一个世纪左右的时间里，遗传学家将突变体与 通过基因筛选发现感兴趣的表型，自20世纪70年代以来，可以分离出突变的基因 对这些变化负有责任。最近，对于少数几个模型生物来说，系统的方法已经 被设计用来询问基因敲除或敲除的表型后果 全基因组的收集，这花了几年的时间来构建。最近的技术创新使之成为可能 系统地测量基因敲除/敲除在许多情况下的高吞吐量的后果 生物体，但比较的方法，以确定基因功能不仅在大范围，而且在多个， 相关的生物体，是缺乏的。这类研究对于更好地理解变化之间的关系至关重要 在DNA序列中，以及表型和适合度的后果。此外，没有研究表明 描述了一组相关生物体中的遗传相互作用网络，以确定这一下一水平的 职能组织随着时间的推移而演变。为了解决这些知识差距，我们提出了三个综合目标： 五个密切相关的酵母菌物种，我们将1)确定扰乱每一个物种的适应性后果 基因，在多个实验条件下，2)在基因的子集下产生遗传相互作用网络 对于重要的基因子集，相同的多个实验条件；3)对于基因和基因 显示出不同物种之间明显差异的相互作用，进一步研究这些 不同之处。在第一个目标中，我们将利用饱和转座子诱变的SATY方法 这将使我们能够测量数十万个转座子插入事件的适合性，在许多情况下 实验条件。这不仅将使我们能够确定这5个物种中每一个物种的必要基因，而且 在某些情况下，它还允许识别基因的亚结构。因为我们将测量最多的 适当的表型，达尔文的适合度，我们也能做出定量的比较 不同的物种在特定条件下破坏任何给定的直系同源基因的后果。在 第二个目标是，我们将通过测量一组合理选择的基因来创建遗传相互作用网络， 使用CRISPRiSeq，一种汇集成对交互适应度方法，对数千个遗传交互得分进行了分析 我们最近开发了。这些数据将弥补关于遗传网络如何变化的知识的一个关键差距 进化时间，这可能导致对遗传相互作用的更好预测。最后，在第三个目标中，我们将 进一步研究从目标1和目标2观察到的物种间差异。我们的初步数据表明 将会有许多基因在一些物种中是必不可少的，但在另一些物种中却不是，我们预测我们也会 观察遗传相互作用网络的变化(定性和定量)；我们将调查 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.