Guri Giaever

Functional profiling of the Saccharomyces cerevisiae genome

Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed ‘molecular bar codes’ uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

The Genetic Landscape of a Cell

Making Connections Genetic interaction profiles highlight cross-connections between bioprocesses, providing a global view of cellular pleiotropy, and enable the prediction of genetic network hubs. Costanzo et al. (p. 425) performed a pairwise fitness screen covering approximately one-third of all potential genetic interactions in yeast, examining 5.4 million gene-gene pairs and generating quantitative profiles for ∼75% of the genome. Of the pairwise interactions tested, about 3% of the genes investigated interact under the conditions tested. On the basis of these data, a reference map for the yeast genetic network was created. A genome-wide interaction map of yeast identifies genetic interactions, networks, and function. 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 ~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. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes

Genetics aims to understand the relation between genotype and phenotype. However, because complete deletion of most yeast genes (∼80%) has no obvious phenotypic consequence in rich medium, it is difficult to study their functions. To uncover phenotypes for this nonessential fraction of the genome, we performed 1144 chemical genomic assays on the yeast whole-genome heterozygous and homozygous deletion collections and quantified the growth fitness of each deletion strain in the presence of chemical or environmental stress conditions. We found that 97% of gene deletions exhibited a measurable growth phenotype, suggesting that nearly all genes are essential for optimal growth in at least one condition.

Genomic profiling of drug sensitivities via induced haploinsufficiency

Lowering the dosage of a single gene from two copies to one copy in diploid yeast results in a heterozygote that is sensitized to any drug that acts on the product of this gene. This haploinsufficient phenotype thereby identifies the gene product of the heterozygous locus as the likely drug target. We exploited this finding in a genomic approach to drug-target identification. Genome sequence information was used to generate molecularly tagged heterozygous yeast strains that were pooled, grown competitively in drug and analysed for drug sensitivity using high-density oligonucleotide arrays. Individual heterozygous strain analysis verified six known drug targets. Parallel analysis identified the known target and two hypersensitive loci in a mixed culture of 233 strains in the presence of the drug tunicamycin. Our discovery that both drug target and hypersensitive loci exhibit drug-induced haploinsufficiency may have important consequences in pharmacogenomics and variable drug toxicity observed in human populations.

Systematic screen for human disease genes in yeast

High similarity between yeast and human mitochondria allows functional genomic study of Saccharomyces cerevisiae to be used to identify human genes involved in disease. So far, 102 heritable disorders have been attributed to defects in a quarter of the known nuclear-encoded mitochondrial proteins in humans. Many mitochondrial diseases remain unexplained, however, in part because only 40–60% of the presumed 700–1,000 proteins involved in mitochondrial function and biogenesis have been identified. Here we apply a systematic functional screen using the pre-existing whole-genome pool of yeast deletion mutants to identify mitochondrial proteins. Three million measurements of strain fitness identified 466 genes whose deletions impaired mitochondrial respiration, of which 265 were new. Our approach gave higher selection than other systematic approaches, including fivefold greater selection than gene expression analysis. To apply these advantages to human disorders involving mitochondria, human orthologs were identified and linked to heritable diseases using genomic map positions.

Noise minimization in eukaryotic gene expression.

All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or “noise.” Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.

Defining genetic interaction

Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutations individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, Additive, Log, and Min). Whether this choice holds practical consequences has not been clear, because the definitions yield identical results under some conditions. Here, we show that the choice among alternative definitions can have profound consequences. Although 52% of known synergistic genetic interactions in Saccharomyces cerevisiae were inferred according to the Min definition, we find that both Product and Log definitions (shown here to be practically equivalent) are better than Min for identifying functional relationships. Additionally, we show that the Additive and Log definitions, each commonly used in population genetics, lead to differing conclusions related to the selective advantages of sexual reproduction.

Genotype to Phenotype: A Complex Problem

In yeast, the impact of gene knockouts depends on genetic background. We generated a high-resolution whole-genome sequence and individually deleted 5100 genes in Σ1278b, a Saccharomyces cerevisiae strain closely related to reference strain S288c. Similar to the variation between human individuals, Σ1278b and S288c average 3.2 single-nucleotide polymorphisms per kilobase. A genome-wide comparison of deletion mutant phenotypes identified a subset of genes that were conditionally essential by strain, including 44 essential genes unique to Σ1278b and 13 unique to S288c. Genetic analysis indicates the conditional phenotype was most often governed by complex genetic interactions, depending on multiple background-specific modifiers. Our comprehensive analysis suggests that the presence of a complex set of modifiers will often underlie the phenotypic differences between individuals.

Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions

Systematic genetic interaction studies have illuminated many cellular processes. Here we quantitatively examine genetic interactions among 26 Saccharomyces cerevisiae genes conferring resistance to the DNA-damaging agent methyl methanesulfonate (MMS), as determined by chemogenomic fitness profiling of pooled deletion strains. We constructed 650 double-deletion strains, corresponding to all pairings of these 26 deletions. The fitness of single- and double-deletion strains were measured in the presence and absence of MMS. Genetic interactions were defined by combining principles from both statistical and classical genetics. The resulting network predicts that the Mph1 helicase has a role in resolving homologous recombination–derived DNA intermediates that is similar to (but distinct from) that of the Sgs1 helicase. Our results emphasize the utility of small molecules and multifactorial deletion mutants in uncovering functional relationships and pathway order.

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. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes.