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Search Results: 1 - 10 of 78 matches for " Guri Giaever "
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Off-Target Effects of Psychoactive Drugs Revealed by Genome-Wide Assays in Yeast
Elke Ericson,Marinella Gebbia,Lawrence E. Heisler,Jan Wildenhain,Mike Tyers,Guri Giaever,Corey Nislow
PLOS Genetics , 2008, DOI: 10.1371/journal.pgen.1000151
Abstract: To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac) interfered with establishment of cell polarity, cyproheptadine (Periactin) targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil) interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol) and pimozide (Orap). Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.
Noise Minimization in Eukaryotic Gene Expression
Hunter B. Fraser,Aaron E. Hirsh,Guri Giaever,Jochen Kumm,Michael B. Eisen
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0020137
Abstract: All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or “noise.” Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.
Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action
Maureen E Hillenmeyer, Elke Ericson, Ronald W Davis, Corey Nislow, Daphne Koller, Guri Giaever
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-3-r30
Abstract: Yeast competitive fitness data constitute a unique, genome-wide assay of the cellular response to environmental and chemical perturbations [1-8]. Here, we systematically analyzed the largest fitness dataset available, comprising measurements of the growth rates of barcoded, pooled deletion strains in the presence of over 400 unique perturbations [1] and show that the dataset reveals novel aspects of cellular physiology and provides a valuable resource for systems biology. In the haploinsufficiency profiling (HIP) assay consisting of all 6,000 heterozygous deletions (where one copy of each gene is deleted), most strains (97%) grow at the rate of wild type [9] when assayed in parallel. In the presence of a drug, the strain deleted for the drug target is specifically sensitized (as measured by a decrease in growth rate) as a result of a further decrease in 'functional' gene dosage by the drug binding to the target protein. In this way, fitness data allow identification of the potential drug target [3,4,10]. In the homozygous profiling (HOP) assay (applied to non-essential genes), both copies of the gene are deleted in a diploid strain to produce a complete loss-of-function allele. This assay identifies genes required for growth in the presence of compound, often identifying functions that buffer the drug target pathway [5-8].The field of functional genomics aims to predict gene functions using high-throughput datasets that interrogate functional genetic relationships. To address the value of fitness data as a resource for functional genomics, we asked how well co-fitness (correlated growth of gene deletion strains in compounds) predicts gene function compared to other large-scale datasets, including co-expression, protein-protein interactions, and synthetic lethality [11-13]. Interestingly, co-fitness predicts cellular functions not evident in these other datasets. We also investigated the theory that genes are essential because they belong to essential complexes [14,1
A comparative analysis of DNA barcode microarray feature size
Ron Ammar, Andrew M Smith, Lawrence E Heisler, Guri Giaever, Corey Nislow
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-471
Abstract: We demonstrate that barcode microarrays with smaller features are equally capable of detecting variation in DNA barcode intensity when compared to larger feature sizes within a specific microarray platform. The barcodes used in this study are the well-characterized set derived from the Yeast KnockOut (YKO) collection used for screens of pooled yeast (Saccharomyces cerevisiae) deletion mutants. We treated these pools with the glycosylation inhibitor tunicamycin as a test compound. Three generations of barcode microarrays at 30, 8 and 5 μm features sizes independently identified the primary target of tunicamycin to be ALG7.We show that the data obtained with 5 μm feature size is of comparable quality to the 30 μm size and propose that further shrinking of features could yield barcode microarrays with equal or greater resolving power and, more importantly, higher density.Genome-wide studies often measure changes in the abundance of all gene products over a period of time or under varying conditions. Microarrays have made these studies possible by enabling researchers to monitor all known genes of an organism simultaneously to detect patterns of gene activity [1], alternative splicing variants [2] the presence of single nucleotide polymorphisms [3], the presence of copy number variants and [4] DNA binding sites of diverse proteins [5], among others. One application of microarrays that our laboratory has focused on is the parallel identification of individual molecular barcoded gene deletion mutants grown competitively in pools [6,7]. Through the efforts of the Yeast Deletion Consortium, a Yeast KnockOut (YKO) collection was constructed consisting of approximately 6,000 heterozygous gene deletions (>96% of all annotated open reading frames), of which over 1,100 are known to be essential for growth [7]. The remaining ~5,000 genes are nonessential, created as homozygous deletions and MATαand MATα deletion collections. These collections were made by systematic replacement o
Noise Minimization in Eukaryotic Gene Expression
Hunter B Fraser ,Aaron E Hirsh,Guri Giaever,Jochen Kumm,Michael B Eisen
PLOS Biology , 2004, DOI: 10.1371/journal.pbio.0020137
Abstract: All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or “noise.” Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.
Gene Annotation and Drug Target Discovery in Candida albicans with a Tagged Transposon Mutant Collection
Julia Oh,Eula Fung,Ulrich Schlecht,Ronald W. Davis,Guri Giaever,Robert P. St. Onge,Adam Deutschbauer,Corey Nislow
PLOS Pathogens , 2010, DOI: 10.1371/journal.ppat.1001140
Abstract: Candida albicans is the most common human fungal pathogen, causing infections that can be lethal in immunocompromised patients. Although Saccharomyces cerevisiae has been used as a model for C. albicans, it lacks C. albicans' diverse morphogenic forms and is primarily non-pathogenic. Comprehensive genetic analyses that have been instrumental for determining gene function in S. cerevisiae are hampered in C. albicans, due in part to limited resources to systematically assay phenotypes of loss-of-function alleles. Here, we constructed and screened a library of 3633 tagged heterozygous transposon disruption mutants, using them in a competitive growth assay to examine nutrient- and drug-dependent haploinsufficiency. We identified 269 genes that were haploinsufficient in four growth conditions, the majority of which were condition-specific. These screens identified two new genes necessary for filamentous growth as well as ten genes that function in essential processes. We also screened 57 chemically diverse compounds that more potently inhibited growth of C. albicans versus S. cerevisiae. For four of these compounds, we examined the genetic basis of this differential inhibition. Notably, Sec7p was identified as the target of brefeldin A in C. albicans screens, while S. cerevisiae screens with this compound failed to identify this target. We also uncovered a new C. albicans-specific target, Tfp1p, for the synthetic compound 0136-0228. These results highlight the value of haploinsufficiency screens directly in this pathogen for gene annotation and drug target identification.
Genome-wide analysis of intracellular pH reveals quantitative control of cell division rate by pHc in Saccharomyces cerevisiae
Rick Orij, Malene L Urbanus, Franco J Vizeacoumar, Guri Giaever, Charles Boone, Corey Nislow, Stanley Brul, Gertien J Smits
Genome Biology , 2012, DOI: 10.1186/gb-2012-13-9-r80
Abstract: Introducing a pH-sensitive reporter protein into the yeast deletion collection allowed quantitative genome-wide analysis of pHc in live, growing yeast cultures. pHc is robust towards gene deletion; no single gene mutation led to a pHc of more than 0.3 units lower than that of wild type. Correct pHc control required not only vacuolar proton pumps, but also strongly relied on mitochondrial function. Additionally, we identified a striking relationship between pHc and growth rate. Careful dissection of cause and consequence revealed that pHc quantitatively controls growth rate. Detailed analysis of the genetic basis of this control revealed that the adequate signaling of pHc depended on inositol polyphosphates, a set of relatively unknown signaling molecules with exquisitely pH sensitive properties.While pHc is a very dynamic parameter in the normal life of yeast, genetically it is a tightly controlled cellular parameter. The coupling of pHc to growth rate is even more robust to genetic alteration. Changes in pHc control cell division rate in yeast, possibly as a signal. Such a signaling role of pHc is probable, and may be central in development and tumorigenesis.Cytosolic pH (pHc) determines the relative protonation state of all weak acid compounds of the cytosol and affects many, if not all, processes in the cell. It is known to affect redox equilibria [1], metabolic rates and energy storing or generating gradients [2,3], protein interactions [4-7], as well as signal transduction [8-10], and it is an important thermodynamic constraint on metabolic reactions [11]. Furthermore, pHc homeostasis is intricately connected with that of other cations, with membrane potentials, and therefore with cellular energy homeostasis [12,13].Only relatively recently, with the development of green fluorescent protein (GFP)-based pH sensors, has it become possible to study pH in live, unperturbed cells, in an organelle specific fashion [14-18]. The use of this technology is generating inc
Combining chemical genomics screens in yeast to reveal spectrum of effects of chemical inhibition of sphingolipid biosynthesis
Danielle Kemmer, Lianne M McHardy, Shawn Hoon, Delphine Rebérioux, Guri Giaever, Corey Nislow, Calvin D Roskelley, Michel Roberge
BMC Microbiology , 2009, DOI: 10.1186/1471-2180-9-9
Abstract: Using the example of the tumor cell invasion inhibitor dihydromotuporamine C, we show that a more complete picture of drug action can be obtained by combining different chemical genomics approaches – analysis of the sensitivity of ρ0 cells lacking mitochondrial DNA, drug-induced haploinsufficiency, suppression of drug sensitivity by gene overexpression and chemical-genetic synthetic lethality screening using strains deleted of nonessential genes. Killing of yeast by this chemical requires a functional mitochondrial electron-transport chain and cytochrome c heme lyase function. However, we find that it does not require genes associated with programmed cell death in yeast. The chemical also inhibits endocytosis and intracellular vesicle trafficking and interferes with vacuolar acidification in yeast and in human cancer cells. These effects can all be ascribed to inhibition of sphingolipid biosynthesis by dihydromotuporamine C.Despite their similar conceptual basis, namely altering drug sensitivity by modifying gene dosage, each of the screening approaches provided a distinct set of information that, when integrated, revealed a more complete picture of the mechanism of action of a drug on cells.Knowledge of the different proteins and cellular processes affected by chemicals is necessary to rationally guide drug discovery and development. This is a difficult challenge because unbiased techniques to sample all possible target proteins and pathways are currently lacking. The observation that modifying the amount or activity of a gene product via mutation, overexpression, downregulation or deletion can change the response of a cell to a chemical [1,2] raises hope that systematic genome-wide screens of drug sensitivity can help uncover direct and indirect drug targets as well as modifiers of cellular responses to chemicals.The yeast Saccharomyces cerevisiae is currently the eukaryotic model organism with the most comprehensive genome-wide collections of mutant strains avail
Multiple Means to the Same End: The Genetic Basis of Acquired Stress Resistance in Yeast
David B. Berry,Qiaoning Guan,James Hose,Suraiya Haroon,Marinella Gebbia,Lawrence E. Heisler,Corey Nislow,Guri Giaever,Audrey P. Gasch
PLOS Genetics , 2011, DOI: 10.1371/journal.pgen.1002353
Abstract: In nature, stressful environments often occur in combination or close succession, and thus the ability to prepare for impending stress likely provides a significant fitness advantage. Organisms exposed to a mild dose of stress can become tolerant to what would otherwise be a lethal dose of subsequent stress; however, the mechanism of this acquired stress tolerance is poorly understood. To explore this, we exposed the yeast gene-deletion libraries, which interrogate all essential and non-essential genes, to successive stress treatments and identified genes necessary for acquiring subsequent stress resistance. Cells were exposed to one of three different mild stress pretreatments (salt, DTT, or heat shock) and then challenged with a severe dose of hydrogen peroxide (H2O2). Surprisingly, there was little overlap in the genes required for acquisition of H2O2 tolerance after different mild-stress pretreatments, revealing distinct mechanisms of surviving H2O2 in each case. Integrative network analysis of these results with respect to protein–protein interactions, synthetic–genetic interactions, and functional annotations identified many processes not previously linked to H2O2 tolerance. We tested and present several models that explain the lack of overlap in genes required for H2O2 tolerance after each of the three pretreatments. Together, this work shows that acquired tolerance to the same severe stress occurs by different mechanisms depending on prior cellular experiences, underscoring the context-dependent nature of stress tolerance.
Genome-Wide Screen in Saccharomyces cerevisiae Identifies Vacuolar Protein Sorting, Autophagy, Biosynthetic, and tRNA Methylation Genes Involved in Life Span Regulation
Paola Fabrizio equal contributor,Shawn Hoon equal contributor,Mehrnaz Shamalnasab,Abdulaye Galbani,Min Wei,Guri Giaever,Corey Nislow ,Valter D. Longo
PLOS Genetics , 2010, DOI: 10.1371/journal.pgen.1001024
Abstract: The study of the chronological life span of Saccharomyces cerevisiae, which measures the survival of populations of non-dividing yeast, has resulted in the identification of homologous genes and pathways that promote aging in organisms ranging from yeast to mammals. Using a competitive genome-wide approach, we performed a screen of a complete set of approximately 4,800 viable deletion mutants to identify genes that either increase or decrease chronological life span. Half of the putative short-/long-lived mutants retested from the primary screen were confirmed, demonstrating the utility of our approach. Deletion of genes involved in vacuolar protein sorting, autophagy, and mitochondrial function shortened life span, confirming that respiration and degradation processes are essential for long-term survival. Among the genes whose deletion significantly extended life span are ACB1, CKA2, and TRM9, implicated in fatty acid transport and biosynthesis, cell signaling, and tRNA methylation, respectively. Deletion of these genes conferred heat-shock resistance, supporting the link between life span extension and cellular protection observed in several model organisms. The high degree of conservation of these novel yeast longevity determinants in other species raises the possibility that their role in senescence might be conserved.
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