Phenotypic implications of genetic interaction networks in Saccharomyces cerevisiae

Gene functions were studied as extensive networks comprising synergistic functional interactions between overlapping pairs of genes. Elucidation of such networks related to drug phenotypes (statins in this thesis) provides additional information to classical genetics as to what genes and metabolic pathways might be involved in phenotypes and, importantly, where side-effects might arise in drug effects. A key question is whether there are genetic interaction networks that vary with individuals and with phenotypes. To answer this question a panel of twenty-six fully sequenced yeast strains from the Saccharomyces Genome Resequencing Project (SGRP; Sanger Institute) was screened for statin resistance to approximate a model for individuals in a human population. Three strains (Y55, SK1 and YPS606) were shown to be 500% more resistant to atorvastatin than the S288C laboratory control strain and were selected for further analysis. Synthetic genetic array analysis (SGA) and chemical genetic profiling were utilised to elucidate genetic interaction networks in the four different strains. SGA analysis depends on the availability of a genome-wide deletion mutant array (DMA) which already exists for S288C and the current studies constructed equivalent Y55-, SK1-, and YS606-strain specific deletion mutant arrays called here “ssDMA’s”. Creating the new ssDMAs involved six back-crossings (1-1/2⁶) of Y55, SK1 and YPS606 with S288C to place the genome-wide deletion mutations of S288C on the genetic background of the strains using appropriate selection markers between each backcrossing. The four DMAs were then subjected to chemical genetic profiling with two statin drugs and also subjected to SGA analysis utilising five query genes chosen for their involvement with the cellular response to statins. The query genes HMG1, HMG2, ARV1, BTS1 and OPI3 were constructed to be strain specific and generated a total 25 genetic interaction networks. The chemical genetic profiles in the ssDMAs identified off-target interactions genes associated with the resistance phenotype in Y55, SK1, and YPS606 that were not observed to show genetic interactions in the more sensitive S288c strain. There was little conservation of the genetic interaction networks elicited by the specific query genes between the strains with the exception of OPI3. There was, however, conservation of fundamental cellular processes, as might be expected, but the genes encoding these processes in the SGAs of the different strains were for the most part different. Therefore, we conclude that the genetic interaction networks concerning statins are different between individuals

Phenotypic implications of genetic interaction networks in Saccharomyces cerevisiae

Gene functions were studied as extensive networks comprising synergistic functional interactions between overlapping pairs of genes. Elucidation of such networks related to drug phenotypes (statins in this thesis) provides additional information to classical genetics as to what genes and metabolic pathways might be involved in phenotypes and, importantly, where side-effects might arise in drug effects. A key question is whether there are genetic interaction networks that vary with individuals and with phenotypes. To answer this question a panel of twenty-six fully sequenced yeast strains from the Saccharomyces Genome Resequencing Project (SGRP; Sanger Institute) was screened for statin resistance to approximate a model for individuals in a human population. Three strains (Y55, SK1 and YPS606) were shown to be 500% more resistant to atorvastatin than the S288C laboratory control strain and were selected for further analysis. Synthetic genetic array analysis (SGA) and chemical genetic profiling were utilised to elucidate genetic interaction networks in the four different strains. SGA analysis depends on the availability of a genome-wide deletion mutant array (DMA) which already exists for S288C and the current studies constructed equivalent Y55-, SK1-, and YS606-strain specific deletion mutant arrays called here “ssDMA’s”. Creating the new ssDMAs involved six back-crossings (1-1/2⁶) of Y55, SK1 and YPS606 with S288C to place the genome-wide deletion mutations of S288C on the genetic background of the strains using appropriate selection markers between each backcrossing. The four DMAs were then subjected to chemical genetic profiling with two statin drugs and also subjected to SGA analysis utilising five query genes chosen for their involvement with the cellular response to statins. The query genes HMG1, HMG2, ARV1, BTS1 and OPI3 were constructed to be strain specific and generated a total 25 genetic interaction networks. The chemical genetic profiles in the ssDMAs identified off-target interactions genes associated with the resistance phenotype in Y55, SK1, and YPS606 that were not observed to show genetic interactions in the more sensitive S288c strain. There was little conservation of the genetic interaction networks elicited by the specific query genes between the strains with the exception of OPI3. There was, however, conservation of fundamental cellular processes, as might be expected, but the genes encoding these processes in the SGAs of the different strains were for the most part different. Therefore, we conclude that the genetic interaction networks concerning statins are different between individuals

Phenotypic implications of genetic interaction networks in Saccharomyces cerevisiae

Gene functions were studied as extensive networks comprising synergistic functional interactions between overlapping pairs of genes. Elucidation of such networks related to drug phenotypes (statins in this thesis) provides additional information to classical genetics as to what genes and metabolic pathways might be involved in phenotypes and, importantly, where side-effects might arise in drug effects. A key question is whether there are genetic interaction networks that vary with individuals and with phenotypes. To answer this question a panel of twenty-six fully sequenced yeast strains from the Saccharomyces Genome Resequencing Project (SGRP; Sanger Institute) was screened for statin resistance to approximate a model for individuals in a human population. Three strains (Y55, SK1 and YPS606) were shown to be 500% more resistant to atorvastatin than the S288C laboratory control strain and were selected for further analysis. Synthetic genetic array analysis (SGA) and chemical genetic profiling were utilised to elucidate genetic interaction networks in the four different strains. SGA analysis depends on the availability of a genome-wide deletion mutant array (DMA) which already exists for S288C and the current studies constructed equivalent Y55-, SK1-, and YS606-strain specific deletion mutant arrays called here “ssDMA’s”. Creating the new ssDMAs involved six back-crossings (1-1/2⁶) of Y55, SK1 and YPS606 with S288C to place the genome-wide deletion mutations of S288C on the genetic background of the strains using appropriate selection markers between each backcrossing. The four DMAs were then subjected to chemical genetic profiling with two statin drugs and also subjected to SGA analysis utilising five query genes chosen for their involvement with the cellular response to statins. The query genes HMG1, HMG2, ARV1, BTS1 and OPI3 were constructed to be strain specific and generated a total 25 genetic interaction networks. The chemical genetic profiles in the ssDMAs identified off-target interactions genes associated with the resistance phenotype in Y55, SK1, and YPS606 that were not observed to show genetic interactions in the more sensitive S288c strain. There was little conservation of the genetic interaction networks elicited by the specific query genes between the strains with the exception of OPI3. There was, however, conservation of fundamental cellular processes, as might be expected, but the genes encoding these processes in the SGAs of the different strains were for the most part different. Therefore, we conclude that the genetic interaction networks concerning statins are different between individuals.</p

Evolthon: A community endeavor to evolve lab evolution.

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress-low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation