The Chemical Genetic Interactions of Statin Drugs with Their Target Genes in Saccharomyces cerevisiae

Statins, competitive inhibitors of the rate limiting cholesterol/ergosterol enzymes HMG-CoA reductase (HMG1 and HMG2), are the most widely prescribed human therapeutic drugs. They are effective in lowering cholesterol levels in atherosclerosis and related syndromes. However, statins exhibit a range of pleiotropic side effects whose mechanisms are poorly understood. This study investigates statin pleiotropy by analysis of genetic interaction networks in yeast, Saccharomyces cerevisiae, which shows high homology to mammalian pathways affected by statins. Synthetic genetic array (SGA) analysis allows elucidation of functional genetic networks of genes of interest ("query genes") by measurement of genetic epistasis in double mutants of the query gene with the genome - wide deletion mutant array of ~4800 non-essential strains. Chemicalgenetic profiling is similar where a SMP may effectively replace the query gene in genome wide epistatic analysis. The genetic interaction networks resulting from use of HMG1 and HMG2 as query genes for SGA analysis were compared to the chemical-genetic profiles of atorvastatin, cerivastatin and lovastatin. The genes ARV1, BTS1, OPI3 displaying phenotypic enhancements (i.e. their deletion caused major growth inhibition) with statins became essential in the presence of all the statins. Two mitochondrial genes, COX17 and MMM1, showed phenotypic suppressions (i.e. their deletion allowed better growth) in common to all three statin drugs. An attractive hypothesis is that major pleiotropic effects of statins could be due to variation in function or expression of these enhancing or suppressing genes. Other processes compensating statin use were also elucidated. For example, when HMG1 and its epistatically interacting genes are shut down by deletion coupled with inhibition of HMG2 with statin, there is strong evidence that the cell attempts to maintain membrane/lipid homeostasis via anterograde and retrograde transport mechanisms, including the mobilisation of lipid storage droplets. To aid refinement of genetic analysis in this and future studies, a more direct phenotypic assay was developed for quantifying ergosterol. Such an assay may be used as a phenotype to map the effect of up - and downstream - genes, or network genes affecting ergosterol levels. This assay was used to quantify ergosterol in a drug - resistant mutant developed by others aiding confirmation of the drug target

The Chemical Genetic Interactions of Statin Drugs with Their Target Genes in Saccharomyces cerevisiae

Statins, competitive inhibitors of the rate limiting cholesterol/ergosterol enzymes HMG-CoA reductase (HMG1 and HMG2), are the most widely prescribed human therapeutic drugs. They are effective in lowering cholesterol levels in atherosclerosis and related syndromes. However, statins exhibit a range of pleiotropic side effects whose mechanisms are poorly understood. This study investigates statin pleiotropy by analysis of genetic interaction networks in yeast, Saccharomyces cerevisiae, which shows high homology to mammalian pathways affected by statins. Synthetic genetic array (SGA) analysis allows elucidation of functional genetic networks of genes of interest ("query genes") by  measurement of genetic epistasis in double mutants of the query gene with the genome - wide deletion mutant array of ~4800 non-essential strains. Chemicalgenetic profiling is similar where a SMP may effectively replace the query gene in genome wide epistatic analysis. The genetic interaction networks resulting from use of HMG1 and HMG2 as query genes for SGA analysis were compared to the chemical-genetic profiles of atorvastatin, cerivastatin and lovastatin. The genes ARV1, BTS1, OPI3 displaying phenotypic enhancements (i.e. their deletion caused major growth inhibition) with statins became essential in the presence of all the statins. Two mitochondrial genes, COX17 and MMM1, showed phenotypic suppressions (i.e. their deletion allowed better growth) in common to all three statin drugs. An attractive hypothesis is that major pleiotropic effects of statins could be due to variation in function or expression of these enhancing or suppressing genes. Other processes compensating statin use were also elucidated. For example, when HMG1 and its epistatically interacting genes are shut down by deletion coupled with inhibition of HMG2 with statin, there is strong evidence that the cell attempts to maintain membrane/lipid homeostasis via anterograde and retrograde transport mechanisms, including the mobilisation of lipid storage droplets. To aid refinement of genetic analysis in this and future studies, a more direct phenotypic assay was developed for quantifying ergosterol. Such an assay may be used as a phenotype to map the effect of up - and downstream - genes, or network genes affecting ergosterol levels. This assay was used to quantify ergosterol in a drug - resistant mutant developed by others aiding confirmation of the drug target.</p

A resource of variant effect predictions of single nucleotide variants in model organisms

The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure-based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli. Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.ISSN:1744-429

The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae

Abstract Loss‐of‐function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild‐type growth differences and the deletion of pleiotropic genes showed above‐average changes in phenotypes. In addition, we performed a genome‐wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene–condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes

Conserved phosphorylation hotspots in eukaryotic protein domain families

Protein phosphorylation is the best characterized post-translational modification that regulates almost all cellular processes through diverse mechanisms such as changing protein conformations, interactions, and localization. While the inventory for phosphorylation sites across different species has rapidly expanded, their functional role remains poorly investigated. Here, we combine 537,321 phosphosites from 40 eukaryotic species to identify highly conserved phosphorylation hotspot regions within domain families. Mapping these regions onto structural data reveals that they are often found at interfaces, near catalytic residues and tend to harbor functionally important phosphosites. Notably, functional studies of a phospho-deficient mutant in the C-terminal hotspot region within the ribosomal S11 domain in the yeast ribosomal protein uS11 shows impaired growth and defective cytoplasmic 20S pre-rRNA processing at 16 °C and 20 °C. Altogether, our study identifies phosphorylation hotspots for 162 protein domains suggestive of an ancient role for the control of diverse eukaryotic domain families

Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae

Eukaryotic genetic interaction networks (GINs) are extensively described in the Saccharomyces cerevisiae S288C model using deletion libraries, yet being limited to this one genetic background, not informative to individual drug response. Here we created deletion libraries in three additional genetic backgrounds. Statin response was probed with five queries against four genetic backgrounds. The 20 resultant GINs representing drug-gene and gene-gene interactions were not conserved by functional enrichment, hierarchical clustering, and topology-based community partitioning. An unfolded protein response (UPR) community exhibited genetic background variation including different betweenness genes that were network bottlenecks, and we experimentally validated this UPR community via measurements of the UPR that were differentially activated and regulated in statin-resistant strains relative to the statin-sensitive S288C background. These network analyses by topology and function provide insight into the complexity of drug response influenced by genetic background.status: publishe

Conserved phosphorylation hotspots in eukaryotic protein domain families

Protein phosphorylation has various regulatory functions. Here, the authors map 241 phosphorylation hotspot regions across 40 eukaryotic species, showing that they are enriched at interfaces and near catalytic residues, and enable the discovery of functionally important phospho-sites

High-throughput functional characterization of protein phosphorylation sites in yeast

Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, fewer than 5% of thousands of recently discovered phosphosites have been functionally annotated. In this study, we devised a chemical genetic approach to study the functional relevance of phosphosites in Saccharomyces cerevisiae. We generated 474 yeast strains with mutations in specific phosphosites that were screened for fitness in 102 conditions, along with a gene deletion library. Of these phosphosites, 42% exhibited growth phenotypes, suggesting that these are more likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions and validated a subset by thermal proteome profiling and lipidomics. A high fraction exhibited phenotypes not seen in the corresponding gene deletion, suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This high-throughput approach allows for functionally characterizing individual phosphosites at scale

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