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  • 1
    Publication Date: 2010-04-24
    Description: We generated a high-resolution whole-genome sequence and individually deleted 5100 genes in Sigma1278b, a Saccharomyces cerevisiae strain closely related to reference strain S288c. Similar to the variation between human individuals, Sigma1278b and S288c average 3.2 single-nucleotide polymorphisms per kilobase. A genome-wide comparison of deletion mutant phenotypes identified a subset of genes that were conditionally essential by strain, including 44 essential genes unique to Sigma1278b and 13 unique to S288c. Genetic analysis indicates the conditional phenotype was most often governed by complex genetic interactions, depending on multiple background-specific modifiers. Our comprehensive analysis suggests that the presence of a complex set of modifiers will often underlie the phenotypic differences between individuals.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4412269/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4412269/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Dowell, Robin D -- Ryan, Owen -- Jansen, An -- Cheung, Doris -- Agarwala, Sudeep -- Danford, Timothy -- Bernstein, Douglas A -- Rolfe, P Alexander -- Heisler, Lawrence E -- Chin, Brian -- Nislow, Corey -- Giaever, Guri -- Phillips, Patrick C -- Fink, Gerald R -- Gifford, David K -- Boone, Charles -- DK076284/DK/NIDDK NIH HHS/ -- GM035010/GM/NIGMS NIH HHS/ -- GM069676/GM/NIGMS NIH HHS/ -- P01 NS055923/NS/NINDS NIH HHS/ -- R01 GM035010/GM/NIGMS NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2010 Apr 23;328(5977):469. doi: 10.1126/science.1189015.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/20413493" target="_blank"〉PubMed〈/a〉
    Keywords: Crosses, Genetic ; Gene Deletion ; *Gene Expression Regulation, Fungal ; Gene Regulatory Networks ; *Genes, Essential ; *Genes, Fungal ; Genetic Variation ; Genome, Fungal ; Genotype ; Mutation ; Phenotype ; Saccharomyces cerevisiae/*genetics ; Sequence Analysis, DNA
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2014-04-12
    Description: Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254748/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254748/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lee, Anna Y -- St Onge, Robert P -- Proctor, Michael J -- Wallace, Iain M -- Nile, Aaron H -- Spagnuolo, Paul A -- Jitkova, Yulia -- Gronda, Marcela -- Wu, Yan -- Kim, Moshe K -- Cheung-Ong, Kahlin -- Torres, Nikko P -- Spear, Eric D -- Han, Mitchell K L -- Schlecht, Ulrich -- Suresh, Sundari -- Duby, Geoffrey -- Heisler, Lawrence E -- Surendra, Anuradha -- Fung, Eula -- Urbanus, Malene L -- Gebbia, Marinella -- Lissina, Elena -- Miranda, Molly -- Chiang, Jennifer H -- Aparicio, Ana Maria -- Zeghouf, Mahel -- Davis, Ronald W -- Cherfils, Jacqueline -- Boutry, Marc -- Kaiser, Chris A -- Cummins, Carolyn L -- Trimble, William S -- Brown, Grant W -- Schimmer, Aaron D -- Bankaitis, Vytas A -- Nislow, Corey -- Bader, Gary D -- Giaever, Guri -- GM103504/GM/NIGMS NIH HHS/ -- GM44530/GM/NIGMS NIH HHS/ -- MOP-700724/Canadian Institutes of Health Research/Canada -- MOP-79368/Canadian Institutes of Health Research/Canada -- MOP-81340/Canadian Institutes of Health Research/Canada -- P01 HG000205/HG/NHGRI NIH HHS/ -- P41 GM103504/GM/NIGMS NIH HHS/ -- R01 003317-07/PHS HHS/ -- R01 CA157456/CA/NCI NIH HHS/ -- R01 GM044530/GM/NIGMS NIH HHS/ -- R01 HG003317/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2014 Apr 11;344(6180):208-11. doi: 10.1126/science.1250217.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24723613" target="_blank"〉PubMed〈/a〉
    Keywords: Cell Line, Tumor ; Cells/*drug effects ; Drug Evaluation, Preclinical/*methods ; Drug Resistance/*genetics ; *Gene Regulatory Networks ; Genome-Wide Association Study/*methods ; Haploinsufficiency ; Humans ; Pharmacogenetics ; Saccharomyces cerevisiae/drug effects/genetics ; Small Molecule Libraries/*pharmacology
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2012-10-10
    Description: Systematic analysis of gene overexpression phenotypes provides an insight into gene function, enzyme targets, and biological pathways. Here, we describe a novel functional genomics platform that enables a highly parallel and systematic assessment of overexpression phenotypes in pooled cultures. First, we constructed a genome-level collection of ~5100 yeast barcoder strains, each of which carries a unique barcode, enabling pooled fitness assays with a barcode microarray or sequencing readout. Second, we constructed a yeast open reading frame (ORF) galactose-induced overexpression array by generating a genome-wide set of yeast transformants, each of which carries an individual plasmid-born and sequence-verified ORF derived from the Saccharomyces cerevisiae full-length EXpression-ready (FLEX) collection. We combined these collections genetically using synthetic genetic array methodology, generating ~5100 strains, each of which is barcoded and overexpresses a specific ORF, a set we termed "barFLEX." Additional synthetic genetic array allows the barFLEX collection to be moved into different genetic backgrounds. As a proof-of-principle, we describe the properties of the barFLEX overexpression collection and its application in synthetic dosage lethality studies under different environmental conditions.
    Electronic ISSN: 2160-1836
    Topics: Biology
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  • 4
    Publication Date: 2013-08-08
    Description: The application of new proteomics and genomics technologies support a view in which few drugs act solely by inhibiting a single cellular target. Indeed, drug activity is modulated by complex, often incompletely understood cellular mechanisms. Therefore, efforts to decipher mode of action through genetic perturbation such as RNAi typically yields "hits" that fall into several categories. Of particular interest to the present study, we aimed to characterize secondary activities of drugs on cells. Inhibiting a known target can result in clinically relevant synthetic phenotypes. In one scenario, drug perturbation could, for example, improperly activate a protein that normally inhibits a particular kinase. In other cases, additional, lower affinity targets can be inhibited as in the example of inhibition of c-Kit observed in Bcr-Abl–positive cells treated with Gleevec. Drug transport and metabolism also play an important role in the way any chemicals act within the cells. Finally, RNAi per se can also affect cell fitness by more general off-target effects, e.g. , via the modulation of apoptosis or DNA damage repair. Regardless of the root cause of these unwanted effects, understanding the scope of a drug’s activity and polypharmacology is essential for better understanding its mechanism(s) of action, and such information can guide development of improved therapies. We describe a rapid, cost-effective approach to characterize primary and secondary effects of small-molecules by using small-scale libraries of virally integrated short hairpin RNAs. We demonstrate this principle using a "minipool" composed of shRNAs that target the genes encoding the reported protein targets of approved drugs. Among the 28 known reported drug–target pairs, we successfully identify 40% of the targets described in the literature and uncover several unanticipated drug–target interactions based on drug-induced synthetic lethality. We provide a detailed protocol for performing such screens and for analyzing the data. This cost-effective approach to mammalian knockdown screens, combined with the increasing maturation of RNAi technology will expand the accessibility of similar approaches in academic settings.
    Electronic ISSN: 2160-1836
    Topics: Biology
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  • 5
  • 6
    Publication Date: 2005-05-16
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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