Project description:One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increases the predictability of adaptive evolution. DNA from each evolved clone or population is hybridized vs DNA from the ancestral strain

Project description:One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increases the predictability of adaptive evolution.

Project description:One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increases the predictability of adaptive evolution. mRNA from each evolved clone or from the ancestral strain growing in the specificied nitrogen-limited condition was co-hybridized with mRNA from the ancestral strain grown in ammonium limited media

Project description:Advances in genomic technologies are rapidly filling the gap in genomic information available from non-model species that are economically and ecologically important. Reduction in costs and higher throughput of data acquisition will progressively increase the application of genomics in population and quantitative genetics. In molecular breeding, the ability to rapidly develop large numbers of genetic markers and generate high-density genetic maps at low cost is critical because they provide the framework for identification of elements that regulate trait variation, connecting genetics and genomics to phenotype. To generate the first high-density, gene-based genetic map for Eucalyptus, we genotyped a hybrid segregating population with microarray-based markers. The genus Eucalyptus includes the most economically important species used for bioenergy pulp and paper production worldwide. A custom 25-mers oligonucleotide array was designed based on 20,726 expressed sequence tags consensus, and RNA from 28 biologically-replicated individuals allowed clear-cut detection of putative single feature polymorphisms (SFPs) segregating 1:1 and 3:1. A genotyping array confirmed the segregation in a larger portion of the family and a genetic map with 1064 gene-related markers is made available with an average of one marker every 1.2 cM. Interestingly, reanalyzing the data from the 28 genotypes using a mixed-model approach resulted in most of the mapped genes among the significant probesets. We also demonstrate that designing more probes per gene increases the chance of mapping those genes. Finally, in silico validatation shows that 87% of the markers map to expected location on the draft genome, an important characteristic of SFP markers. Simultaneously detecting and genotyping SFP markers in a subset of the mapping population instead of only the genitors was more efficient for mapping genes in outcrossing species than previously reported approaches. Mapping SFP markers require low genomic resources and can be done at relatively low cost. We demonstrate the possibility of mapping hundreds to thousands of genes that can ultimately be used in generating further genomic resources, such as genome assembly, or for more direct applications, such as QTL analysis. Two biological replication of 28 genotypes were hybridized to the custom microarray, labeled with Cy3 and Cy5. Genotypes are fullsib progenies of the cross E. urophylla X E. grandis. A loop design was employed for the hybridization using xylem tissue. In total the microarray comprised 103,000 probes representing 20,726 unigenes with an average of five probes per unigene. Twenty-six negative control probes were also included in the microarray.

Project description:Advances in genomic technologies are rapidly filling the gap in genomic information available from non-model species that are economically and ecologically important. Reduction in costs and higher throughput of data acquisition will progressively increase the application of genomics in population and quantitative genetics. In molecular breeding, the ability to rapidly develop large numbers of genetic markers and generate high-density genetic maps at low cost is critical because they provide the framework for identification of elements that regulate trait variation, connecting genetics and genomics to phenotype. To generate the first high-density, gene-based genetic map for Eucalyptus, we genotyped a hybrid segregating population with microarray-based markers. The genus Eucalyptus includes the most economically important species used for bioenergy pulp and paper production worldwide. A custom 25-mers oligonucleotide array was designed based on 20,726 expressed sequence tags consensus, and RNA from 28 biologically-replicated individuals allowed clear-cut detection of putative single feature polymorphisms (SFPs) segregating 1:1 and 3:1. A genotyping array confirmed the segregation in a larger portion of the family and a genetic map with 1064 gene-related markers is made available with an average of one marker every 1.2 cM. Interestingly, reanalyzing the data from the 28 genotypes using a mixed-model approach resulted in most of the mapped genes among the significant probesets. We also demonstrate that designing more probes per gene increases the chance of mapping those genes. Finally, in silico validatation shows that 87% of the markers map to expected location on the draft genome, an important characteristic of SFP markers. Simultaneously detecting and genotyping SFP markers in a subset of the mapping population instead of only the genitors was more efficient for mapping genes in outcrossing species than previously reported approaches. Mapping SFP markers require low genomic resources and can be done at relatively low cost. We demonstrate the possibility of mapping hundreds to thousands of genes that can ultimately be used in generating further genomic resources, such as genome assembly, or for more direct applications, such as QTL analysis. Two technical replication of 68 individuals were hybridized to the custom microarray, labeled with Cy3 and Cy5. Individuals are fullsib progenies of the cross E. urophylla X E. grandis. A loop design was employed for the hybridization using xylem tissue. In total the microarray comprised 43,803 probes representing 15,698 unigenes. These probes were pre-selected in a screening array based on their mendelian segregation and bimodal signal separation. Twenty-six negative control probes were also included in the microarray.

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