Large-scale Genetic Interaction Screen Research Articles

Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.

Genetic Interaction Networks: Toward an Understanding of Heritability

Understanding the relationship between the genotypes and phenotypes of individuals is key for identifying genetic variants responsible for disease and developing successful therapeutic strategies. Mapping the phenotypic effects of individual genetic variants and their combinations in human populations presents numerous practical and statistical challenges. However, model organisms, such as the budding yeast Saccharomyces cerevisiae, provide an incredible set of molecular tools and advanced technologies that should be able to efficiently perform this task. In particular, large-scale genetic interaction screens in yeast and other model systems have revealed common properties of genetic interaction networks, many of which appear to be maintained over extensive evolutionary distances. Indeed, despite relatively low conservation of individual genes and their pairwise interactions, the overall topology of genetic interaction networks and the connections between broad biological processes may be similar in most organisms. Taking advantage of these general principles should provide a fundamental basis for mapping and predicting genetic interaction networks in humans.

Systematic genetic interaction screens uncover cell polarity regulators and functional redundancy

Although single gene loss of function analyses can identify components of particular processes, important molecules are missed due to the robustness of biological systems. Here we show that large scale RNAi screening for suppression interactions with functionally related mutants greatly expands the repertoire of genes known to act in a shared process and reveals a new layer of functional relationships. We performed RNAi screens for 17 C. elegans cell polarity mutants, generating the most comprehensive polarity network in a metazoan, connecting 184 genes. Of these, 72% were not previously linked to cell polarity and 80% have human homologs. We experimentally confirmed functional roles predicted by the network and characterised through biophysical analyses eight myosin regulators. In addition, we discovered functional redundancy between two unknown polarity genes. Similar systematic genetic interaction screens for other biological processes will help uncover the inventory of relevant genes and their patterns of interactions.

Exploring the Yeast Acetylome Using Functional Genomics

Lysine acetylation is a dynamic posttranslational modification with a well-defined role in regulating histones. The impact of acetylation on other cellular functions remains relatively uncharacterized. We explored the budding yeast acetylome with a functional genomics approach, assessing the effects of gene overexpression in the absence of lysine deacetylases (KDACs). We generated a network of 463 synthetic dosage lethal (SDL) interactions involving class I and II KDACs, revealing many cellular pathways regulated by different KDACs. A biochemical survey of genes interacting with the KDAC RPD3 identified 72 proteins acetylated in vivo. In-depth analysis of one of these proteins, Swi4, revealed a role for acetylation in G1-specific gene expression. Acetylation of Swi4 regulates interaction with its partner Swi6, both components of the SBF transcription factor. This study expands our view of the yeast acetylome, demonstrates the utility of functional genomic screens for exploring enzymatic pathways, and provides functional information that can be mined for future studies.

A Large-Scale Complex Haploinsufficiency-Based Genetic Interaction Screen in Candida albicans: Analysis of the RAM Network during Morphogenesis

The morphogenetic transition between yeast and filamentous forms of the human fungal pathogen Candida albicans is regulated by a variety of signaling pathways. How these pathways interact to orchestrate morphogenesis, however, has not been as well characterized. To address this question and to identify genes that interact with the Regulation of Ace2 and Morphogenesis (RAM) pathway during filamentation, we report the first large-scale genetic interaction screen in C. albicans. Our strategy for this screen was based on the concept of complex haploinsufficiency (CHI). A heterozygous mutant of CBK1 (cbk1Δ/CBK1), a key RAM pathway protein kinase, was subjected to transposon-mediated, insertional mutagenesis. The resulting double heterozygous mutants (6,528 independent strains) were screened for decreased filamentation on Spider Medium (SM). From the 441 mutants showing altered filamentation, 139 transposon insertion sites were sequenced, yielding 41 unique CBK1-interacting genes. This gene set was enriched in transcriptional targets of Ace2 and, strikingly, the cAMP-dependent protein kinase A (PKA) pathway, suggesting an interaction between these two pathways. Further analysis indicates that the RAM and PKA pathways co-regulate a common set of genes during morphogenesis and that hyper-activation of the PKA pathway may compensate for loss of RAM pathway function. Our data also indicate that the PKA–regulated transcription factor Efg1 primarily localizes to yeast phase cells while the RAM–pathway regulated transcription factor Ace2 localizes to daughter nuclei of filamentous cells, suggesting that Efg1 and Ace2 regulate a common set of genes at separate stages of morphogenesis. Taken together, our observations indicate that CHI–based screening is a useful approach to genetic interaction analysis in C. albicans and support a model in which these two pathways regulate a common set of genes at different stages of filamentation.