Genetic Complexity and Quantitative Trait Loci Mapping of Yeast Morphological Traits

Yale University, United States of America
PLoS Genetics (Impact Factor: 7.53). 03/2007; 3(2):e31. DOI: 10.1371/journal.pgen.0030031
Source: PubMed


Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.

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    • "Strain-to-strain morphological variation has been investigated, revealing essential genes in a natural environment (Yang et al., 2014). Quantitative trait loci (Nogami et al., 2007) and the structure of phenotypic diversity (Skelly et al., 2013) in natural isolates have also been assessed. A high-dimensional data set alone does not intuitively provide compelling biological information; it is ambiguous as to which traits should be of focus. "
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    ABSTRACT: The demand for phenomics, a high-dimensional and high-throughput phenotyping method, has been increasing in many fields of biology. The budding yeast Saccharomyces cerevisiae, a unicellular model organism, provides an invaluable system for dissecting complex cellular processes using high-resolution phenotyping. Moreover, the addition of spatial and temporal attributes to subcellular structures based on microscopic images has rendered this cell phenotyping system more reliable and amenable to analysis. A well-designed experiment followed by appropriate multivariate analysis can yield a wealth of biological knowledge. Here we review recent advances in cell imaging and illustrate their broad applicability to eukaryotic cells by showing how these techniques have advanced our understanding of budding yeast.
    Molecular Biology of the Cell 11/2015; 26(22):3920-3925. DOI:10.1091/mbc.E15-07-0466 · 4.47 Impact Factor
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    • "We used budding yeast, Saccharomyces cerevisiae, as a system to study such trade-offs in growth, in favorable, oxidizing, and DNAdamaging environments. It is well known that different genetic loci regulate traits such as growth and sporulation for strains adapted to laboratory conditions compared with natural strains (Nogami et al. 2007; Fraser et al. 2012). In addition, different genetic variants can regulate variation in the same phenotype in different strains (Cubillos et al. 2011), emphasizing the importance of the evolutionary history of a strain. "
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    ABSTRACT: Antagonistic pleiotropy (AP), the ability of a gene to show opposing effects in different phenotypes, has been identified in various life history traits and complex disorders, indicating its fundamental role in balancing fitness over the course of evolution. It is intuitive that natural selection might maintain AP to allow organisms phenotypic flexibility in different environments. However, despite several attempts, little evidence exists for its role in adaptation. We performed a meta-analysis in yeast to identify the genetic basis of AP in bi-parental segregants, natural isolates and a laboratory strain genome-wide deletion collection, by comparing growth in favorable and stress conditions. We found that whereas AP was abundant in the synthetic populations, it was absent in natural isolates. This indicated resolution of trade-offs i.e. mitigation of trade-offs over evolutionary history probably through accumulation of compensatory mutations. In the deletion collection, organizational genes showed AP, suggesting ancient resolutions of trade-offs in the basic cellular pathways. We find abundant AP in the segregants, higher than estimated in the deletion collection or observed in previous studies, with IRA2, a negative regulator of the Ras/PKA signaling pathway, showing trade-offs across diverse environments. Additionally, IRA2 and several other Ras/PKA pathway genes showed balancing selection in isolates of Saccharomyces cerevisiae and Saccharomyces paradoxus, indicating that multiple alleles maintain AP in this pathway in natural populations. We propose that during AP resolution, retaining the ability to vary signaling pathways such as Ras/PKA, may provide organisms with phenotypic flexibility. However, with increasing organismal complexity AP resolution may become difficult. A partial resolution of AP could manifest as complex human diseases, and the inability to resolve AP may play a role in speciation. Our findings suggest that testing a universal phenomenon like AP across multiple experimental systems may elucidate mechanisms underlying its regulation and evolution. Copyright © 2015 Author et al.
    G3-Genes Genomes Genetics 02/2015; 5(5). DOI:10.1534/g3.115.017020 · 3.20 Impact Factor
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    • "QTL approaches have led to the detection of many genes, typically unlinked to each other [16]. In Saccharomyces cerevisiae, several QTL analyses have already been carried out, to identify genes involved in growth at high temperature [17, 18], sporulation [19–22], cell morphology [23], drug sensitivity [24], ethanol tolerance and growth [22, 23, 25–27], flocculation [28], wine aroma production [29], amino acids consumption [30], and to decipher regulatory network variations [31, 32]. These studies have shown some phenotypes to be highly complex. "
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    ABSTRACT: Background In conditions of nitrogen limitation, Saccharomyces cerevisiae strains differ in their fermentation capacities, due to differences in their nitrogen requirements. The mechanisms ensuring the maintenance of glycolytic flux in these conditions are unknown. We investigated the genetic basis of these differences, by studying quantitative trait loci (QTL) in a population of 133 individuals from the F2 segregant population generated from a cross between two strains with different nitrogen requirements for efficient fermentation. Results By comparing two bulks of segregants with low and high nitrogen requirements, we detected four regions making a quantitative contribution to these traits. We identified four polymorphic genes, in three of these four regions, for which involvement in the phenotype was validated by hemizygote comparison. The functions of the four validated genes, GCN1, MDS3, ARG81 and BIO3, relate to key roles in nitrogen metabolism and signaling, helping to maintain fermentation performance. Conclusions This study reveals that differences in nitrogen requirement between yeast strains results from a complex allelic combination. The identification of three genes involved in sensing and signaling nitrogen and specially one from the TOR pathway as affecting nitrogen requirements suggests a role for this pathway in regulating the fermentation rate in starvation through unknown mechanisms linking nitrogen signaling to glycolytic flux. Electronic supplementary material The online version of this article (doi: 10.1186/1471-2164-15-495) contains supplementary material, which is available to authorized users.
    BMC Genomics 06/2014; 15(1):495. DOI:10.1186/1471-2164-15-495 · 3.99 Impact Factor
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