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Different sets of QTLs influence fitness variation in yeast.

ABSTRACT: 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.

SUBMITTER: Romano GH

PROVIDER: S-EPMC2835564 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Different sets of QTLs influence fitness variation in yeast.

Romano Gal Hagit GH Gurvich Yonat Y Lavi Ofer O Ulitsky Igor I Shamir Ron R Kupiec Martin M

Molecular systems biology 20100216

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 a ...[more]

PMID: 20160707

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Project description:Variation in gene copy number can alter gene expression and influence downstream phenotypes; thus copy-number variation provides a route for rapid evolution if the benefits outweigh the cost. We recently showed that genetic background significantly influences how yeast cells respond to gene overexpression, revealing that the fitness costs of copy-number variation can vary substantially with genetic background in a common-garden environment. But the interplay between copy-number variation tolerance and environment remains unexplored on a genomic scale. Here, we measured the tolerance to gene overexpression in four genetically distinct Saccharomyces cerevisiae strains grown under sodium chloride stress. Overexpressed genes that are commonly deleterious during sodium chloride stress recapitulated those commonly deleterious under standard conditions. However, sodium chloride stress uncovered novel differences in strain responses to gene overexpression. West African strain NCYC3290 and North American oak isolate YPS128 are more sensitive to sodium chloride stress than vineyard BC187 and laboratory strain BY4743. Consistently, NCYC3290 and YPS128 showed the greatest sensitivities to overexpression of specific genes. Although most genes were deleterious, hundreds were beneficial when overexpressed-remarkably, most of these effects were strain specific. Few beneficial genes were shared between the sodium chloride-sensitive isolates, implicating mechanistic differences behind their sodium chloride sensitivity. Transcriptomic analysis suggested underlying vulnerabilities and tolerances across strains, and pointed to natural copy-number variation of a sodium export pump that likely contributes to strain-specific responses to overexpression of other genes. Our results reveal extensive strain-by-environment interactions in the response to gene copy-number variation, raising important implications for the accessibility of copy-number variation-dependent evolutionary routes under times of stress.

Project description:ObjectivesQuantitative multidetector computed tomography (MDCT) as a potential biomarker is increasingly used for severity assessment of emphysema in chronic obstructive pulmonary disease (COPD). Aim of this study was to evaluate the user-independent measurement variability between five different fully-automatic densitometry software tools.Material and methodsMDCT and full-body plethysmography incl. forced expiratory volume in 1s and total lung capacity were available for 49 patients with advanced COPD (age = 64±9 years, forced expiratory volume in 1 s = 31±6% predicted). Measurement variation regarding lung volume, emphysema volume, emphysema index, and mean lung density was evaluated for two scientific and three commercially available lung densitometry software tools designed to analyze MDCT from different scanner types.ResultsOne scientific tool and one commercial tool failed to process most or all datasets, respectively, and were excluded. One scientific and another commercial tool analyzed 49, the remaining commercial tool 30 datasets. Lung volume, emphysema volume, emphysema index and mean lung density were significantly different amongst these three tools (p<0.001). Limits of agreement for lung volume were [-0.195, -0.052 l], [-0.305, -0.131 l], and [-0.123, -0.052 l] with correlation coefficients of r = 1.00 each. Limits of agreement for emphysema index were [-6.2, 2.9%], [-27.0, 16.9%], and [-25.5, 18.8%], with r = 0.79 to 0.98. Correlation of lung volume with total lung capacity was good to excellent (r = 0.77 to 0.91, p<0.001), but segmented lung volume (6.7±1.3-6.8±1.3 l) were significantly lower than total lung capacity (7.7±1.7 l, p<0.001).ConclusionsTechnical incompatibilities hindered evaluation of two of five tools. The remaining three showed significant measurement variation for emphysema, hampering quantitative MDCT as a biomarker in COPD. Follow-up studies should currently use identical software, and standardization efforts should encompass software as well.

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Different sets of QTLs influence fitness variation in yeast.

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Different sets of QTLs influence fitness variation in yeast.

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