Four linked genes participate in controlling sporulation efficiency in budding yeast

Assessing the complex architecture of polygenic traits in diverged yeast populations

Phenotypic variation arising from populations adapting to different niches has a complex underlying genetic architecture. A major challenge in modern biology is to identify the causative variants driving phenotypic variation. Recently, the baker's yeast, Saccharomyces cerevisiae has emerged as a powerful model for dissecting complex traits. However, past studies using a laboratory strain were unable to reveal the complete architecture of polygenic traits. Here, we present a linkage study using 576 recombinant strains obtained from crosses of isolates representative of the major lineages. The meiotic recombinational landscape appears largely conserved between populations; however, strain-specific hotspots were also detected. Quantitative measurements of growth in 23 distinct ecologically relevant environments show that our recombinant population recapitulates most of the standing phenotypic variation described in the species. Linkage analysis detected an average of 6.3 distinct QTLs for each condition tested in all crosses, explaining on average 39% of the phenotypic variation. The QTLs detected are not constrained to a small number of loci, and the majority are specific to a single cross-combination and to a specific environment. Moreover, crosses between strains of similar phenotypes generate greater variation in the offspring, suggesting the presence of many antagonistic alleles and epistatic interactions. We found that subtelomeric regions play a key role in defining individual quantitative variation, emphasizing the importance of the adaptive nature of these regions in natural populations. This set of recombinant strains is a powerful tool for investigating the complex architecture of polygenic traits.

Flo11p adhesin required for meiotic differentiation in Saccharomyces cerevisiae minicolonies grown on plastic surfaces

Saccharomyces cerevisiae grown on plastic surfaces formed organized structures, termed minicolonies, that consisted of a core of round (yeast-like) cells surrounded by chains of filamentous cells (pseudohyphae). Minicolonies had a much higher affinity for plastic than unstructured yeast communities growing on the same surface. Pseudohyphae at the surface of these colonies developed further into chains of asci. These structures suggest that pseudohyphal differentiation and sporulation are sequential processes in minicolonies. Consistent with this idea, minicolonies grown under conditions that stimulated pseudohyphal differentiation contained higher frequencies of asci. Furthermore, a flo11Δ mutant, which fails to form pseudohyphae, yielded normal sporulation in cultures, but was defective for minicolony sporulation. When minicolonies were dispersed in water and cells were then allowed to settle on the plastic surface, these cells sporulated very efficiently. Taken together, our results suggest that sporulation in minicolonies is stimulated by pseudohyphal differentiation because these pseudohyphae are dispersed from the core of the colony.

Combination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genes

Background

Regulation of meiosis and sporulation in Saccharomyces cerevisiae is a model for a highly regulated developmental process. Meiosis middle phase transcriptional regulation is governed by two transcription factors: the activator Ndt80 and the repressor Sum1. It has been suggested that the competition between Ndt80 and Sum1 determines the temporal expression of their targets during middle meiosis.

Results

Using a combination of ChIP-on-chip and expression profiling, we characterized a middle phase transcriptional network and studied the relationship between Ndt80 and Sum1 during middle and late meiosis. While finding a group of genes regulated by both factors in a feed forward loop regulatory motif, our data also revealed a large group of genes regulated solely by Ndt80. Measuring the expression of all Ndt80 target genes in various genetic backgrounds (WT, sum1Δ and MK-ER-Ndt80 strains), allowed us to dissect the exact transcriptional network regulating each gene, which was frequently different than the one inferred from the binding data alone.

Conclusion

These results highlight the need to perform detailed genetic experiments to determine the relative contribution of interactions in transcriptional regulatory networks.

Sporulation patterning and invasive growth in wild and domesticated yeast colonies

Different cell types can form patterns within fungal communities; for example, colonies of Saccharomyces cerevisiae form two sharply defined layers of sporulating cells separated by an intervening layer of unsporulated cells. Because colony sporulation patterns have only been investigated in a single laboratory strain background (W303), in this report we examined these patterns in other strain backgrounds. Two other laboratory strain backgrounds (SK1 and Sigma1278b) that differ from W303 with respect to colony morphology, invasive growth, and sporulation efficiency nevertheless displayed the same colony sporulation pattern as W303. This pattern was also observed in colonies of wild isolates of S. cerevisiae and Saccharomyces paradoxus. The wild yeast colonies sporulated on a much wider range of carbon sources than did the lab yeast and displayed a similar layered sporulation pattern when grown on either acetate or glucose medium and on either rich or synthetic medium. SK1, Sigma1278b and wild yeast colonies invaded the agar surface. The region of invasion varied between strains with respect to the organization and appearance of cells, but this invasion was always accompanied by sporulation. Thus, sporulation patterns are a general property of S. cerevisiae, and sporulation in colonies can be coordinated with invasive growth.

Different sets of QTLs influence fitness variation in yeast

We have carried out a combination of in-lab-evolution (ILE) and congenic crosses to identify the gene sets that contribute to the ability of yeast cells to survive under alkali stress.

Each selected line acquired a different set of mutations, all resulting in the same phenotype. We identified a total of 15 genes in ILE and 17 candidates in the congenic approach, and studied their individual contribution to the phenotype.

The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.

None of the genes identified encode structural components of the pH machinery. Instead, most encode regulatory functions, such as ubiquitin ligases, chromatin remodelers, GPI anchoring and copper/iron sensing transcription factors.

The majority of phenotypes in nature are complex traits affected by multiple genes [usually called quantitative trait loci (QTLs)], as well as by environmental factors. Many traits with practical importance such as crop yield in plants and susceptibility to various diseases in humans fall under this category. Understanding the architecture of complex traits has become the new frontier of genetic research, and many studies have greatly contributed to this field. However, to date, the genetic basis of only a few of these traits has been identified, and many questions regarding the architecture of complex traits and the accumulation of QTLs during evolution still remain unanswered. Among them are: How many QTLs affect complex phenotypes? What is the effect of each QTL? How do complex traits change during evolution? Is the adaptation process repeatable?, etc. In order to identify the QTLs that affect one of the important components of fitness variability in yeast, and to answer some of the questions above, we combined in-lab evolution (ILE) with the construction of congenic lines to isolate and map several gene sets that contribute to the ability of yeast cells to survive under alkali stress.

We carried out an ILE experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. This process was followed by hybridizations to tiling arrays to identify the mutations acquired during the laboratory selective process. The ILE procedure revealed mutations in 15 genes, thus defining the QTLs and mechanisms that affect, in a quantitative fashion, the ability to cope with alkali stress. Our results indicate that during ILE several populations acquired different sets of QTLs that conferred the same phenotype. We identified each individual mutation in these strains, and validated and estimated their contribution to the phenotype. The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.

In addition to the ILE, we have studied the mechanisms regulating fitness under alkali stress at natural habitats. We used a clinically isolated strain able to grow at high pH and a standard laboratory strain with a limited ability to sustain high pH as the parents of series of backcrosses to construct congenic lines up to the 8th generation. Seventeen genomic intervals that are candidates to contain QTLs were thus identified. In order to detect the contributing QTL in each interval, a predictive algorithm was applied, which scored the candidate genes in each genomic interval based on their interactions and similarity to the ILE genes. The algorithm was validated by testing the effect of the predicted candidate gene's deletions on the phenotype. Twelve out of 29 deletions were found to affect the trait (P-value 0.023).

Interestingly, our results show that almost all beneficial mutations affected regulatory genes, and not structural components of the pH homeostasis machinery (such as proton pumps, which control the cell's pH). The genes identified affect global regulators, such as ubiquitin ligases, proteins involved in GPI anchoring, copper sensing and chromatin remodelers. Thus, we show that adaptive changes tend to occur in genes with wide influence, rather than in genes narrowly affecting the phenotype selected for.

One example of genes identified both in the ILE and in the congenic lines is the copper-sensing transcription factor MAC1, and its downstream targets CTR1 and CTR3, which encode copper transporters. Different mutations at the same residue (Cys 271) were found in four out of five independent ILE lines. These mutations inactivate a copper-sensing region of Mac1 and cause up-regulation of its target genes. The CTR1 and CTR3 genes were identified in the congenic lines. Moreover, we found that a Ty transposable element is responsible for the decreased expression of CTR3 in some strains, and its excision caused transcriptional activation, affecting the ability to thrive at high pH.

This work provides insights on both evolutionary and genetic issues (such as the appearance of adaptive mutations and the architecture of complex traits), while at the same time providing information about the mechanisms that contribute to growth at high pH, a subject with ramifications for cell physiology, pathogenicity, and stress response.

Most of the phenotypes in nature are complex and are determined by many quantitative trait loci (QTLs). In this study we identify gene sets that contribute to one important complex trait: the ability of yeast cells to survive under alkali stress. We carried out an in-lab evolution (ILE) experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. The populations acquired different sets of affecting alleles, showing that evolution can provide alternative solutions to the same challenge. We measured the contribution of each allele to the phenotype. The sum of the effects of the QTLs was larger than the difference between the ancestor phenotype and the evolved strains, suggesting epistatic interactions between the QTLs. In parallel, a clinical isolated strain was used to map natural QTLs affecting growth at high pH. In all, 17 candidate regions were found. Using a predictive algorithm based on the distances in protein-interaction networks, candidate genes were defined and validated by gene disruption. Many of the QTLs found by both methods are not directly implied in pH homeostasis but have more general, and often regulatory, roles.

A combined-cross analysis reveals genes with drug-specific and background-dependent effects on drug sensitivity in Saccharomyces cerevisiae

Effective pharmacological therapy is often inhibited by variable drug responses and adverse drug reactions. Dissecting the molecular basis of different drug responses is difficult due to complex interactions involving multiple genes, pathways, and cellular processes. We previously found a single nucleotide polymorphism within cystathionine beta-synthase (CYS4) that causes multi-drug sensitivity in a vineyard strain of Saccharomyces cerevisiae. However, not all variation was accounted for by CYS4. To identify additional genes influencing drug sensitivity, we used CYS4 as a covariate and conducted both single- and combined-cross linkage mapping. After eliminating numerous false-positive associations, we identified 16 drug-sensitivity loci, only 3 of which had been previously identified. Of 4 drug-sensitivity loci selected for validation, 2 showed replicated associations in independent crosses, and two quantitative trait genes within these regions, AQY1 and MKT1, were found to have drug-specific and background-dependent effects. Our results suggest that drug response may often depend on interactions between genes with multi-drug and drug-specific effects.

Generation of a large set of genetically tractable haploid and diploid Saccharomyces strains

Saccharomyces cerevisiae has proved to be an invaluable model in classical and molecular genetics studies. Despite several hundreds of isolates already available, the scientific community relies on the use of only a handful of unrelated strains. The lack of sequence information, haploid derivatives and genetic markers has prevented novel strains from being used. Here, we release a set of 55 S. cerevisiae and Saccharomyces paradoxus genetically tractable strains, previously sequenced in the Saccharomyces Genome Resequencing Project. These strains are stable haploid derivatives and ura3 auxotrophs tagged with a 6-bp barcode, recognized by a restriction enzyme to allow easy identification. We show that the specific barcode can be used to accurately measure the prevalence of different strains during competition experiments. These strains are now amenable to a wide variety of genetic experiments and can be easily crossed with each other to create hybrids and segregants, providing a valuable resource for breeding programmes and quantitative genetic studies. Three versions of each strain (haploid Mat a and Mat alpha and diploid Mat a/alpha all as ura3::KanMX-Barcode) are available through the National Culture Yeast Collection.

Genetic interactions between transcription factors cause natural variation in yeast

Our understanding of the genetic basis of phenotypic diversity is limited by the paucity of examples in which multiple, interacting loci have been identified. We show that natural variation in the efficiency of sporulation, the program in yeast that initiates the sexual phase of the life cycle, between oak tree and vineyard strains is due to allelic variation between four nucleotide changes in three transcription factors: IME1, RME1, and RSF1. Furthermore, we identified that selection has shaped quantitative variation in yeast sporulation between strains. These results illustrate how genetic interactions between transcription factors are a major source of phenotypic diversity within species.

Interaction of genetic and environmental factors in Saccharomyces cerevisiae meiosis: the devil is in the details

One of the most important principles of scientific endeavour is that the results be reproducible from lab to lab. Although research groups rarely redo the published experiments of their colleagues, research plans almost always rely on the work of someone else. The assumption is that if the same experiment were repeated in another lab, results would be so similar that the same interpretation would be favoured. This notion allows one researcher to compare his/her own results to earlier work from other labs. An essential prerequisite for this is that the experiments are done in identical conditions and therefore the methodology must be clearly stated. While this may be scientific common sense, adherence is difficult because "standard" methods vary from one laboratory to another in subtle ways that are often not reported. More importantly, for many years the field ofyeast meiotic recombination considered typical differences to be innocuous. This chapter will highlight the documented environmental and genetic variables that are known to influence meiotic recombination in Saccharomyces cerevisiae. Other potential methodological sources of variation in meiotic experiments are also discussed. A careful assessment of the effects of these variables, has led to insights into our understanding of the control of recombination and meiosis.

Variations in stress sensitivity and genomic expression in diverse S. cerevisiae isolates

Interactions between an organism and its environment can significantly influence phenotypic evolution. A first step toward understanding this process is to characterize phenotypic diversity within and between populations. We explored the phenotypic variation in stress sensitivity and genomic expression in a large panel of Saccharomyces strains collected from diverse environments. We measured the sensitivity of 52 strains to 14 environmental conditions, compared genomic expression in 18 strains, and identified gene copy-number variations in six of these isolates. Our results demonstrate a large degree of phenotypic variation in stress sensitivity and gene expression. Analysis of these datasets reveals relationships between strains from similar niches, suggests common and unique features of yeast habitats, and implicates genes whose variable expression is linked to stress resistance. Using a simple metric to suggest cases of selection, we found that strains collected from oak exudates are phenotypically more similar than expected based on their genetic diversity, while sake and vineyard isolates display more diverse phenotypes than expected under a neutral model. We also show that the laboratory strain S288c is phenotypically distinct from all of the other strains studied here, in terms of stress sensitivity, gene expression, Ty copy number, mitochondrial content, and gene-dosage control. These results highlight the value of understanding the genetic basis of phenotypic variation and raise caution about using laboratory strains for comparative genomics.

Much attention has been given to the ways in which organisms evolve new phenotypes and the influence of the environment on this process. A major focus of study is defining the genetic basis for phenotypes important for organismal fitness. As a first step toward this goal, we surveyed phenotypic variation in diverse yeast strains collected from different environments by characterizing variations in stress resistance and genomic expression. We uncovered many phenotypic differences across yeast strains, both in stress tolerance and gene expression. The similarities and differences of the strains analyzed uncovered phenotypes shared by strains that live in similar environments, suggesting common features of yeast niches as well as mechanisms that different strains use to thrive in those conditions. We provide evidence that some characteristics of strains isolated from oak tree soil have been selected for, perhaps because of the shared selective pressures imposed by their environment. One theme emerging from our studies is that the laboratory strain of yeast, long used as a model for yeast physiology and basic biology, is aberrant compared to all other strains. This result raises caution about making general conclusions about yeast biology based on a single strain with a specific genetic makeup.

Sequential elimination of major-effect contributors identifies additional quantitative trait loci conditioning high-temperature growth in yeast

Several quantitative trait loci (QTL) mapping strategies can successfully identify major-effect loci, but often have poor success detecting loci with minor effects, potentially due to the confounding effects of major loci, epistasis, and limited sample sizes. To overcome such difficulties, we used a targeted backcross mapping strategy that genetically eliminated the effect of a previously identified major QTL underlying high-temperature growth (Htg) in yeast. This strategy facilitated the mapping of three novel QTL contributing to Htg of a clinically derived yeast strain. One QTL, which is linked to the previously identified major-effect QTL, was dissected, and NCS2 was identified as the causative gene. The interaction of the NCS2 QTL with the first major-effect QTL was background dependent, revealing a complex QTL architecture spanning these two linked loci. Such complex architecture suggests that more genes than can be predicted are likely to contribute to quantitative traits. The targeted backcrossing approach overcomes the difficulties posed by sample size, genetic linkage, and epistatic effects and facilitates identification of additional alleles with smaller contributions to complex traits.

A catalog of neutral and deleterious polymorphism in yeast

The abundance and identity of functional variation segregating in natural populations is paramount to dissecting the molecular basis of quantitative traits as well as human genetic diseases. Genome sequencing of multiple organisms of the same species provides an efficient means of cataloging rearrangements, insertion, or deletion polymorphisms (InDels) and single-nucleotide polymorphisms (SNPs). While inbreeding depression and heterosis imply that a substantial amount of polymorphism is deleterious, distinguishing deleterious from neutral polymorphism remains a significant challenge. To identify deleterious and neutral DNA sequence variation within Saccharomyces cerevisiae, we sequenced the genome of a vineyard and oak tree strain and compared them to a reference genome. Among these three strains, 6% of the genome is variable, mostly attributable to variation in genome content that results from large InDels. Out of the 88,000 polymorphisms identified, 93% are SNPs and a small but significant fraction can be attributed to recent interspecific introgression and ectopic gene conversion. In comparison to the reference genome, there is substantial evidence for functional variation in gene content and structure that results from large InDels, frame-shifts, and polymorphic start and stop codons. Comparison of polymorphism to divergence reveals scant evidence for positive selection but an abundance of evidence for deleterious SNPs. We estimate that 12% of coding and 7% of noncoding SNPs are deleterious. Based on divergence among 11 yeast species, we identified 1,666 nonsynonymous SNPs that disrupt conserved amino acids and 1,863 noncoding SNPs that disrupt conserved noncoding motifs. The deleterious coding SNPs include those known to affect quantitative traits, and a subset of the deleterious noncoding SNPs occurs in the promoters of genes that show allele-specific expression, implying that some cis-regulatory SNPs are deleterious. Our results show that the genome sequences of both closely and distantly related species provide a means of identifying deleterious polymorphisms that disrupt functionally conserved coding and noncoding sequences.

DNA sequence variation makes an important contribution to most traits that vary in natural populations. However, mapping mutations that underlie a trait of interest is a significant challenge. Genome sequencing of multiple organisms provides a complete list of DNA sequence differences responsible for any trait that differs among the organisms. Yet, distinguishing those DNA sequence variants that contribute to a trait from all other variants is not easy. Here, we sequence the genomes of two strains of yeast and, through comparisons with a reference genome, we catalog multiple types of DNA sequence variation among the three strains. Using a variety of comparative genomics methods, we show that a substantial fraction of DNA sequence variations has deleterious effects on fitness. Finally, we show that a subset of deleterious mutations is associated with changes in gene expression levels. Our results imply that comparative genomics methods will be a valuable approach to identifying DNA sequence changes underlying numerous traits of interest.

Septin stability and recycling during dynamic structural transitions in cell division and development

Septins are conserved proteins found in hetero-oligomeric complexes that are incorporated into distinct structures during cell division and differentiation; yeast septins Cdc3, Cdc10, Cdc11, and Cdc12 form hetero-octamers and polymerize into filaments, which form a "collar" at the mother-bud neck [1]. Posttranslational modifications, nucleotide binding, and protein-protein and protein-lipid interactions influence assembly and disassembly of septin structures [2], but whether individual septins are used repeatedly to build higher-order assemblies was not known. We used fluorescence-based pulse-chase methods to visualize the fate of pre-existing (old) and newly synthesized (new) molecules of two septins, Cdc10 and Cdc12. They were recycled through multiple mitotic divisions, and old and new molecules were incorporated indistinguishably into the collar. Likewise, old and new subunits intermixed within hetero-octamers, indicating that exchange occurs at this organizational level. Remarkably, in meiosis, Cdc10 made during vegetative growth was reutilized to build sporulation-specific structures and reused again during spore germination for budding and during subsequent mitotic divisions. Although Cdc12 also persisted during sporulation, it was excluded from septin structures and replaced by another subunit, Spr3; only new Cdc12 populated the collar of germinating spores. Thus, mechanisms governing septin incorporation are specific to each subunit and to the developmental state of the cell.

Identification and dissection of a complex DNA repair sensitivity phenotype in Baker's yeast

Complex traits typically involve the contribution of multiple gene variants. In this study, we took advantage of a high-density genotyping analysis of the BY (S288c) and RM strains of Saccharomyces cerevisiae and of 123 derived spore progeny to identify the genetic loci that underlie a complex DNA repair sensitivity phenotype. This was accomplished by screening hybrid yeast progeny for sensitivity to a variety of DNA damaging agents. Both the BY and RM strains are resistant to the ultraviolet light-mimetic agent 4-nitroquinoline 1-oxide (4-NQO); however, hybrid progeny from a BYxRM cross displayed varying sensitivities to the drug. We mapped a major quantitative trait locus (QTL), RAD5, and identified the exact polymorphism within this locus responsible for 4-NQO sensitivity. By using a backcrossing strategy along with array-assisted bulk segregant analysis, we identified one other locus, MKT1, and a QTL on Chromosome VII that also link to the hybrid 4-NQO-sensitive phenotype but confer more minor effects. This work suggests an additive model for sensitivity to 4-NQO and provides a strategy for mapping both major and minor QTL that confer background-specific phenotypes. It also provides tools for understanding the effect of genetic background on sensitivity to genotoxic agents.

Gene-environment interaction in yeast gene expression

The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol as carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between BY and RM in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth-related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits.

Individuals frequently encounter different environmental conditions, and the physiological and behavioral responses to these conditions can depend on an individual's genetic makeup. This phenomenon is known as gene–environment interaction. For example, individuals who are infected with the Plasmodium falciparum parasite are susceptible to malaria, but not if they carry the sickle-cell allele of hemoglobin. The general properties of gene–environment interaction are poorly understood, and a better understanding is essential if individuals are to make informed health choices guided by their genomic information. We have investigated gene–environment interaction on a genomic level, characterizing its role in over 4,000 traits at once by investigating natural variation in yeast gene expression. We compared lab and vineyard strains of yeast growing in two conditions (glucose and ethanol as carbon sources) in which they adopt two different metabolic states: fermentation and aerobic respiration, respectively. We show that gene–environment interaction is a common phenomenon, describe how different classes of genetic variants affect the nature of the interactions, and provide detailed molecular examples of interactions.

We show that gene-environment interaction is a common phenomenon in the regulation of gene expression, we describe how different classes of genetic variants affect the nature of the interactions, and we provide detailed molecular examples of interactions.

Cascading transcriptional effects of a naturally occurring frameshift mutation in Saccharomyces cerevisiae

Gene-expression variation in natural populations is widespread, and its phenotypic effects can be acted upon by natural selection. Only a few naturally segregating genetic differences associated with expression variation have been identified at the molecular level. We have identified a single nucleotide insertion in a vineyard isolate of Saccharomyces cerevisiae that has cascading effects through the gene-expression network. This allele is responsible for about 45% (103/230) of the genes that show differential gene expression among the homozygous diploid progeny produced by a vineyard isolate. Using isogenic laboratory strains, we confirm that this allele causes dramatic differences in gene-expression levels of key genes involved in amino acid biosynthesis. The mutation is a frameshift mutation in a mononucleotide run of eight consecutive T's in the coding region of the gene SSY1, which encodes a key component of a plasma-membrane sensor of extracellular amino acids. The potentially high rate of replication slippage of this mononucleotide repeat, combined with its relatively mild effects on growth rate in heterozygous genotypes, is sufficient to account for the persistence of this phenotype at low frequencies in natural populations.

Genome sequencing and comparative analysis of Saccharomyces cerevisiae strain YJM789

We sequenced the genome of Saccharomyces cerevisiae strain YJM789, which was derived from a yeast isolated from the lung of an AIDS patient with pneumonia. The strain is used for studies of fungal infections and quantitative genetics because of its extensive phenotypic differences to the laboratory reference strain, including growth at high temperature and deadly virulence in mouse models. Here we show that the approximately 12-Mb genome of YJM789 contains approximately 60,000 SNPs and approximately 6,000 indels with respect to the reference S288c genome, leading to protein polymorphisms with a few known cases of phenotypic changes. Several ORFs are found to be unique to YJM789, some of which might have been acquired through horizontal transfer. Localized regions of high polymorphism density are scattered over the genome, in some cases spanning multiple ORFs and in others concentrated within single genes. The sequence of YJM789 contains clues to pathogenicity and spurs the development of more powerful approaches to dissecting the genetic basis of complex hereditary traits.

Genome-wide analysis of nucleotide-level variation in commonly used Saccharomyces cerevisiae strains

Ten years have passed since the genome of Saccharomyces cerevisiae–more precisely, the S288c strain–was completely sequenced. However, experimental work in yeast is commonly performed using strains that are of unknown genetic relationship to S288c. Here, we characterized the nucleotide-level similarity between S288c and seven commonly used lab strains (A364A, W303, FL100, CEN.PK, ∑1278b, SK1 and BY4716) using 25mer oligonucleotide microarrays that provide complete and redundant coverage of the ∼12 Mb Saccharomyces cerevisiae genome. Using these data, we assessed the frequency and distribution of nucleotide variation in comparison to the sequenced reference genome. These data allow us to infer the relationships between experimentally important strains of yeast and provide insight for experimental designs that are sensitive to sequence variation. We propose a rational approach for near complete sequencing of strains related to the reference using these data and directed re-sequencing. These data and new visualization tools are accessible online in a new resource: the Yeast SNPs Browser (YSB; http://gbrowse.princeton.edu/cgi-bin/gbrowse/yeast_strains_snps) that is available to all researchers.

Genetic basis of individual differences in the response to small-molecule drugs in yeast

Individual response to small-molecule drugs is variable; a drug that provides a cure for some may confer no therapeutic benefit or trigger an adverse reaction in others. To begin to understand such differences systematically, we treated 104 genotyped segregants from a cross between two yeast strains with a collection of 100 diverse small molecules. We used linkage analysis to identify 124 distinct linkages between genetic markers and response to 83 compounds. The linked markers clustered at eight genomic locations, or quantitative-trait locus 'hotspots', that contain one or more polymorphisms that affect response to multiple small molecules. We also experimentally verified that a deficiency in leucine biosynthesis caused by a deletion of LEU2 underlies sensitivity to niguldipine, which is structurally related to therapeutic calcium channel blockers, and that a natural coding-region polymorphism in the inorganic phosphate transporter PHO84 underlies sensitivity to two polychlorinated phenols that uncouple oxidative phosphorylation. Our results provide a step toward a systematic understanding of small-molecule drug action in genetically distinct individuals.