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.
While it is known that susceptibility to certain diseases is heritable, the underlying genetic causes are often difficult to identify. One possible explanation for this is that some disease alleles are only relevant in the context of some genetic backgrounds. We will use easy-to-manipulate yeast species to understand how the phenotypic effect of a gene disruption depends on the genetic background and which changes in the genetic network might underlie these differences; these data will provide crucial information for understanding the genetics underlying disease.
