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Ainslie B. Parsons

Bio: Ainslie B. Parsons is an academic researcher from University of Toronto. The author has contributed to research in topics: Synthetic genetic array & Saccharomyces cerevisiae. The author has an hindex of 14, co-authored 18 publications receiving 8881 citations.

Papers
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Journal ArticleDOI
14 Dec 2001-Science
TL;DR: A method for systematic construction of double mutants, termed synthetic genetic array (SGA) analysis, in which a query mutation is crossed to an array of ∼4700 deletion mutants is developed, which should produce a global map of gene function.
Abstract: In Saccharomyces cerevisiae, more than 80% of the ∼6200 predicted genes are nonessential, implying that the genome is buffered from the phenotypic consequences of genetic perturbation. To evaluate function, we developed a method for systematic construction of double mutants, termed synthetic genetic array (SGA) analysis, in which a query mutation is crossed to an array of ∼4700 deletion mutants. Inviable double-mutant meiotic progeny identify functional relationships between genes. SGA analysis of genes with roles in cytoskeletal organization (BNI1,ARP2, ARC40, BIM1), DNA synthesis and repair (SGS1, RAD27), or uncharacterized functions (BBC1, NBP2) generated a network of 291 interactions among 204 genes. Systematic application of this approach should produce a global map of gene function.

2,164 citations

Journal ArticleDOI
06 Feb 2004-Science
TL;DR: Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.
Abstract: A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

2,037 citations

01 Jan 2003
TL;DR: A method called Synthetic Genetic Array (SGA) analysis was developed in this paper, which automates yeast genetics and enables a systematic and high- throughput construction of double mutants from an ordered array of ~4700 viable gene deletion mutants.
Abstract: budding yeast Saccharomyces cerevisiae, ~80% of the ~6000 genes are nonessential, indi- cating that many biological processes are buffered from the phenotypic consequences of genetic per- turbation. To examine these functional relation- ships we developed a method called Synthetic Genetic Array (SGA) analysis, which automates yeast genetics and enables a systematic and high- throughput construction of double mutants from an ordered array of ~4700 viable gene deletion mutants. In particular, double mutants showing reduced fitness (a synthetic sick phenotype) or lethality (a synthetic lethal phenotype) define functional relationships between genes and their corresponding pathways. We have undertaken a project to generate a synthetic genetic interaction network for the yeast cell with the expectation that it will represent a global map of functional relationships amongst most genes. We found that synthetic genetic interactions are more common than anticipated previously, with an average query gene displaying ~30 different interactions. Cluster analysis of a compendium of ~132 SGA screens revealed that genes displaying similar patterns of genetic interactions often encode proteins within the same pathway or complex; therefore, the yeast genetic interaction network predicts precise molec- ular roles of previously uncharacterized genes. Moreover, because a gene deletion mutation pro- vides a model for the effect of a compound that inhibits its corresponding gene product, our com- pendium of synthetic genetic profiles provides a key for determining the cellular targets of small molecules and drugs. Finally, the surprisingly large number of synthetic genetic interactions observed for defined mutations of inbred laboratory yeast strains suggests that digenic interactions of this type may also occur frequently amongst different alleles of genes found within the individuals of an outbred population and thus similar genetic inter- actions may underlie many of the inherited pheno- types in other organisms.

1,927 citations

Journal ArticleDOI
11 Mar 2005-Cell
TL;DR: An extended network, consisting of 198 putative physical interactions and 451 putative genetic and chemical-genetic interactions, was found to connect Hsp90 to cofactors and substrates involved in a wide range of cellular functions.

804 citations

Journal ArticleDOI
TL;DR: By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, this method provides a powerful means for inferring mechanism of action.
Abstract: Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H(+)-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.

654 citations


Cited by
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Journal ArticleDOI
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

32,980 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
10 Feb 2006-Cell
TL;DR: The physiological consequences of mammalianTORC1 dysregulation suggest that inhibitors of mammalian TOR may be useful in the treatment of cancer, cardiovascular disease, autoimmunity, and metabolic disorders.

5,553 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations

Journal ArticleDOI
16 Oct 2003-Nature
TL;DR: The construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.
Abstract: A fundamental goal of cell biology is to define the functions of proteins in the context of compartments that organize them in the cellular environment. Here we describe the construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins. We classify these proteins, representing 75% of the yeast proteome, into 22 distinct subcellular localization categories, and provide localization information for 70% of previously unlocalized proteins. Analysis of this high-resolution, high-coverage localization data set in the context of transcriptional, genetic, and protein-protein interaction data helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.

4,310 citations