Project description:The experimental evolution of laboratory populations of microbes provides an opportunity to observe the evolutionary dynamics of adaptation in real time.Until very recently, however, such studies have been limited by our inability to systematically find mutations in evolved organisms.We overcome this limitation by using a variety of DNA microarray-based techniques to characterize genetic changes, including point mutations, structural changes, and insertion variation, that resulted from the experimental adaptation of 24 haploid and diploid cultures of Saccharomyces cerevisiae to growth in glucose-, sulfate, or phosphate-limited chemostats for ~ 200 generations.We identified frequent genomic amplifications and rearrangements as well as novel retrotransposition events associated with adaptation.Global mutation detection in 10 clonal isolates identified 32 point mutations. On the basis of mutation frequencies, we infer that these mutations and the subsequent dynamics of adaptation are determined by the batch phase of growth prior to initiation of continuous phase in the chemostat.We relate these genotypic changes to phenotypic outcomes, namely global patterns of gene expression, and to increases in fitness by 5-50%. We found that the spectrum of available mutations in glucose or phosphate-limited environments combined with the batch phase population dynamics early in our experiments to allow several distinct genotypic and phenotypic evolutionary pathways in response to these nutrient limitations. By contrast, sulfate-limited populations were much more constrained in both genotypic and phenotypic outcomes.Thus, the reproducibility of evolution varies with specific selective pressures reflecting the constraints inherent in the system-level organization of metabolic processes in the cell.We were able to relate some of the observed adaptive mutations (e.g. transporter gene amplifications) to known features of the relevant metabolic pathways, but many of the mutations pointed to genes not previously associated with the relevant physiology. Thus, in addition to answering basic mechanistic questions about evolutionary mechanisms our work suggests that experimental evolution can also shed light on the function and regulation of individual metabolic pathways. Keywords: gene expression, CGH, and TSE analysis consult individual records for details of analysis This Dataset consists of several experiments: Gene expression experiments found in Figure 1a of paper: Design: RNA from each evolved clone or population grown in chemostat culture is compared to RNA from matching ancestor strains grown in the same conditions. Samples: GSM339025-GSM339088 CGH experiments found in Figure 2A, Figure 3, and Table S2: Experiment design: DNA from each evolved clone or population is hybridized vs DNA from the matched ancestor strain Samples: GSM339089-GSM339146 CGH experiments found in Table S3: Experiment design: DNA from each segregant derived from an evolved strain is hybridized vs DNA from the matched ancestor strain Samples: GSM339147-GSM339158 Gel band CGH experiments found in Figure S1: Experiment design: Chromosomes from evolved strains were run on a gel and excised. DNA from the bands is hybridized vs matched ancestor genomic DNA Samples: GSM339159-GSM339184 TSE CGH experiments found in Table S4: Experiment design: DNA linked to Ty1/Ty2 sequences was extracted from evolved strains and ancestor strains, and compared to ancestral extracted or genomic DNA as indicated. Samples: GSM339185-GSM339212 CGH experiments for experiments in Figure S7: Experiment design: DNA from 2 strains transformed with a SUL1 plasmid hybridized vs DNA from the matched ancestor strain Samples: GSM339213 and GSM339214

Project description:The experimental evolution of laboratory populations of microbes provides an opportunity to observe the evolutionary dynamics of adaptation in real time.Until very recently, however, such studies have been limited by our inability to systematically find mutations in evolved organisms.We overcome this limitation by using a variety of DNA microarray-based techniques to characterize genetic changes, including point mutations, structural changes, and insertion variation, that resulted from the experimental adaptation of 24 haploid and diploid cultures of Saccharomyces cerevisiae to growth in glucose-, sulfate, or phosphate-limited chemostats for ~ 200 generations.We identified frequent genomic amplifications and rearrangements as well as novel retrotransposition events associated with adaptation.Global mutation detection in 10 clonal isolates identified 32 point mutations. On the basis of mutation frequencies, we infer that these mutations and the subsequent dynamics of adaptation are determined by the batch phase of growth prior to initiation of continuous phase in the chemostat.We relate these genotypic changes to phenotypic outcomes, namely global patterns of gene expression, and to increases in fitness by 5-50%. We found that the spectrum of available mutations in glucose or phosphate-limited environments combined with the batch phase population dynamics early in our experiments to allow several distinct genotypic and phenotypic evolutionary pathways in response to these nutrient limitations. By contrast, sulfate-limited populations were much more constrained in both genotypic and phenotypic outcomes.Thus, the reproducibility of evolution varies with specific selective pressures reflecting the constraints inherent in the system-level organization of metabolic processes in the cell.We were able to relate some of the observed adaptive mutations (e.g. transporter gene amplifications) to known features of the relevant metabolic pathways, but many of the mutations pointed to genes not previously associated with the relevant physiology. Thus, in addition to answering basic mechanistic questions about evolutionary mechanisms our work suggests that experimental evolution can also shed light on the function and regulation of individual metabolic pathways. Keywords: gene expression, CGH, and TSE analysis

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EDEN: evolutionary dynamics within environments.

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EDEN: evolutionary dynamics within environments.

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