Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets.

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C.

Environmental robustness of the global yeast genetic interaction network.

Phenotypes associated with genetic variants can be altered by interactions with other genetic variants (GxG), with the environment (GxE), or both (GxGxE). Yeast genetic interactions have been mapped on a global scale, but the environmental influence on the plasticity of genetic networks has not been examined systematically. To assess environmental rewiring of genetic networks, we examined 14 diverse conditions and scored 30,000 functionally representative yeast gene pairs for dynamic, differential interactions.

τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast.

Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens.

Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies.

Precise discrimination of tumor from normal tissues remains a major roadblock for therapeutic efficacy of chimeric antigen receptor (CAR) T cells. Here, we perform a comprehensive in silico screen to identify multi-antigen signatures that improve tumor discrimination by CAR T cells engineered to integrate multiple antigen inputs via Boolean logic, e.g.

Exploring whole-genome duplicate gene retention with complex genetic interaction analysis.

Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event.

Discovering genetic interactions bridging pathways in genome-wide association studies.

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner.

Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell.

Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved.

Systematic analysis of complex genetic interactions.

To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network.

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network.

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast In particular, TheCellMap.

Exploring genetic suppression interactions on a global scale.

Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insights into the functional wiring diagram of the cell.

Unraveling the biology of a fungal meningitis pathogen using chemical genetics.

The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound.

Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection.

Background: Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions.

Global linkage map connects meiotic centromere function to chromosome size in budding yeast.

Synthetic genetic array (SGA) analysis automates yeast genetics, enabling high-throughput construction of ordered arrays of double mutants. Quantitative colony sizes derived from SGA analysis can be used to measure cellular fitness and score for genetic interactions, such as synthetic lethality. Here we show that SGA colony sizes also can be used to obtain global maps of meiotic recombination because recombination frequency affects double-mutant formation for gene pairs located on the same chromosome and therefore influences the size of the resultant double-mutant colony.

Comparison of profile similarity measures for genetic interaction networks.

Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data.

SGAtools: one-stop analysis and visualization of array-based genetic interaction screens.

Screening genome-wide sets of mutants for fitness defects provides a simple but powerful approach for exploring gene function, mapping genetic networks and probing mechanisms of drug action. For yeast and other microorganisms with global mutant collections, genetic or chemical-genetic interactions can be effectively quantified by growing an ordered array of strains on agar plates as individual colonies, and then scoring the colony size changes in response to a genetic or environmental perturbation. To do so, requires efficient tools for the extraction and analysis of quantitative data.

A Whole Genome Screen for Minisatellite Stability Genes in Stationary-Phase Yeast Cells.

Repetitive elements comprise a significant portion of most eukaryotic genomes. Minisatellites, a type of repetitive element composed of repeat units 15-100 bp in length, are stable in actively dividing cells but change in composition during meiosis and in stationary-phase cells. Alterations within minisatellite tracts have been correlated with the onset of a variety of diseases, including diabetes mellitus, myoclonus epilepsy, and several types of cancer.

Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs.

A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions.

Putting genetic interactions in context through a global modular decomposition.

Genetic interactions provide a powerful perspective into gene function, but our knowledge of the specific mechanisms that give rise to these interactions is still relatively limited. The availability of a global genetic interaction map in Saccharomyces cerevisiae, covering ∼30% of all possible double mutant combinations, provides an unprecedented opportunity for an unbiased assessment of the native structure within genetic interaction networks and how it relates to gene function and modular organization. Toward this end, we developed a data mining approach to exhaustively discover all block structures within this network, which allowed for its complete modular decomposition.

Systematic exploration of essential yeast gene function with temperature-sensitive mutants.

Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae. We constructed 787 ts strains, covering 497 (∼45%) of the 1,101 essential yeast genes, with ∼30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)-based, high-throughput methods.

Genetic interactions reveal the evolutionary trajectories of duplicate genes.

The characterization of functional redundancy and divergence between duplicate genes is an important step in understanding the evolution of genetic systems. Large-scale genetic network analysis in Saccharomyces cerevisiae provides a powerful perspective for addressing these questions through quantitative measurements of genetic interactions between pairs of duplicated genes, and more generally, through the study of genome-wide genetic interaction profiles associated with duplicated genes. We show that duplicate genes exhibit fewer genetic interactions than other genes because they tend to buffer one another functionally, whereas observed interactions are non-overlapping and reflect their divergent roles.

The genetic landscape of a cell.

A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function.