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Genetic screens in Saccharomyces cerevisiae have allowed for the identification of many genes as sensors or effectors of DNA damage, typically by comparing the fitness of genetic mutants in the presence or absence of DNA-damaging treatments. However, these static screens overlook the dynamic nature of DNA damage response pathways, missing time-dependent or transient effects. Here, we examine gene dependencies in the dynamic response to ultraviolet radiation-induced DNA damage by integrating ultra-high-density arrays of 6144 diploid gene deletion mutants with high-frequency time-lapse imaging. We identify 494 ultraviolet radiation response genes which, in addition to recovering molecular pathways and protein complexes previously annotated to DNA damage repair, include components of the CCR4-NOT complex, tRNA wobble modification, autophagy, and, most unexpectedly, 153 nuclear-encoded mitochondrial genes. Notably, mitochondria-deficient strains present time-dependent insensitivity to ultraviolet radiation, posing impaired mitochondrial function as a protective factor in the ultraviolet radiation response.

Eukaryotic chromosomes are replicated in interphase and the two newly duplicated sister chromatids are held together by the cohesin complex and several cohesin auxiliary factors. Sister chromatid cohesion is essential for accurate chromosome segregation during mitosis, yet has also been implicated in other processes, including DNA damage repair, transcription and DNA replication. To assess how cohesin and associated factors functionally interconnect and coordinate with other cellular processes, we systematically mapped genetic interactions of 17 cohesin genes centered on quantitative growth measurements of >52,000 gene pairs in budding yeast. Integration of synthetic genetic interactions unveiled a cohesin functional map that constitutes 373 genetic interactions, revealing novel functional connections with post-replication repair, microtubule organization and protein folding. Accordingly, we show that the microtubule-associated protein Irc15 and the prefoldin complex members Gim3, Gim4 and Yke2 are new factors involved in sister chromatid cohesion. Our genetic interaction map thus provides a unique resource for further identification and functional interrogation of cohesin proteins. Since mutations in cohesin proteins have been associated with cohesinopathies and cancer, it may also identify cohesin interactions relevant in disease etiology.

We propose a mixture model framework for estimating positive false discovery rates in multiple-testing problems. The density of a transformed p -value is modeled by a finite mixture of skewed distributions. We argue that a mixture of skewed distributions like the skew-normal one is better for addressing some features in modeling than the more commonly used mixture of normal distributions. Using the fitted distributions, we estimate the proportion of true null hypotheses, the positive false discovery rate and other important functionals in multiple-testing problems. We investigate the performance of our methodology via simulation and illustrate the effectiveness of the proposed procedure using real data examples. We also discuss the role of an empirical null in place of the theoretical null distributions in the context of common biomedical applications.

High-throughput genetic screens in model microbial organisms are a primary means of interrogating biological systems. In numerous cases, such screens have identified the genes that underlie a particular phenotype or a set of gene-gene, gene-environment or protein-protein interactions, which are then used to construct highly informative network maps for biological research. However, the potential test space of genes, proteins, or interactions is typically much larger than current screening systems can address. To push the limits of screening technology, we developed an ultra-high-density, 6144-colony arraying system and analysis toolbox. Using budding yeast as a benchmark, we find that these tools boost genetic screening throughput 4-fold and yield significant cost and time reductions at quality levels equal to or better than current methods. Thus, the new ultra-high-density screening tools enable researchers to significantly increase the size and scope of their genetic screens.

Loss of tumor suppressor activity is a near universal feature of cancer. Synthetic lethality, in which combined perturbation of two genes results in cell death, has been proposed as a strategy to identify drug targets that specifically attack cancers deficient in a tumor suppressor (TS) gene. To develop a catalog of such combinations, we screened for synthetic-lethal interactions among all yeast orthologs of known TS and druggable genes, resulting in quantitative growth rates for 130,000 pairwise genetic mutants. Similar interaction profiles were highly predictive of TS genes in the same pathway, identifying a novel role for NDNL2/NSE3 in the G1/S checkpoint. TS/drug combinations likely to induce cancer cell death were prioritized using multiple criteria, including conservation across divergent species. Among the top hits were putative interactions involving either BRCA1 or XRCC3 with histone deacetylases, as well as Rad17 with CHK1/2. These were confirmed using small molecule inhibitors and short-pin RNA silencing in human cancer cell lines. This work provides a resource of >300 deeply conserved interactions among tumor suppressors and matched drug targets. Citation Format: Rohith Srivas, John Paul Shen, Jian Feng Li, Katherine Licon, Ze Zhong Wang, Ana Bojoquez-Gomez, Lucy Xu, Andrew Gross, Gordon Bean, Robert Sobol, Trey Ideker. High-throughput synthetic lethal interaction screening in model organisms as a strategy for the identification of novel therapeutic targets in cancer. [abstract]. In: Proceedings of the AACR Special Conference: The Translational Impact of Model Organisms in Cancer; Nov 5-8, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(11 Suppl):Abstract nr B34.

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.