Saccharomyces bayanus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA116169 | ENA |
REPOSITORIES: ENA
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Project description:Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms.
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Project description:Hybrid progeny can enjoy increased fitness and stress tolerance relative to their ancestral species, a phenomenon known as hybrid vigor. Though this phenomenon has been documented throughout the Eukarya, evolution of hybrid populations has yet to be explored experimentally in the lab. To fill this knowledge gap we created a pool of Saccharomyces cerevisiae and S. bayanus homoploid and aneuploid hybrids, and then investigated how selection in the form of incrementally increased temperature or ethanol impacted hybrid genome structure and adaptation. During 500 generations of continuous ammonia-limited, glucose-sufficient culture, temperature was raised from 25C to 46??C. This selection invariably resulted in nearly-complete loss of the S. bayanus genome, although the dynamics of genome loss differed among independent replicates. Temperature-evolved isolates were significantly more thermal tolerant and exhibited greater phenotypic plasticity than parental species and founding hybrids. By contrast, when the same hybrid pool was subjected to increases in exogenous ethanol from 0% to 14%, selection favored euploid S. cerevisiae x S. bayanus hybrids. Ethanol-evolved isolates exhibited significantly greater ethanol tolerance relative only to S. bayanus and one of the founding hybrids tested. Adaptation to thermal and ethanol stress manifested as heritable changes in cell wall structure demonstrated by resistance to zymolyase or micafungin treatment. This is the first study to show experimentally that the fate of interspecific hybrids critically depends on the type of selection they encounter during the course of evolution.
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Project description:Saccharomyces pastorianus is the yeast used to make lager beer; it is known to be an interspecific hybrid formed by the fusion between S. cerevisiae and S. bayanus genomes. This data set queries 17 S. pastorianus strains, collected at various times over the last 125 years from various breweries located in different geographical locations, which were obtained from CBS and DBVPG culture collections. The data in this set represent array-CGH experiments performed with these strains, using "2-species" custom Agilent arrays (the "2-species" arrays contain probes spaced every ~2 kb across the whole genomes of both S. cerevisiae and S. bayanus; the probes are unique and specific for each genome). The data set also contains 3 self-self hybridizations (S. cerevisiae + S. bayanus DNA mixed together in equimolar amounts, then labeled green or red in separate reactions, then hybridized to the "2-species" arrays) used for normalization in CGH-Miner analysis. A strain or line experiment design type assays differences between multiple strains, cultivars, serovars, isolates, lines from organisms of a single species.
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