Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants

Tong, Amy; Evangelista, Marie; Parsons, Ainslie B.; Xu, Hong; Bader, Gary D.; Page, Nicholas F.; Robinson, Mark D.; Raghibizadeh, Sasan; Hogue, Christopher W. V.; Bussey, Howard; Andrews, Brenda; Tyers, Mike; Boone, Charles

doi:10.1126/science.1065810

Cited by 1,930 publications

(1,996 citation statements)

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“…These interactions comprise a physical network, which correlates with the network of genetic interactions and provides potential clues as to the mechanisms behind particular synthetic-lethal effects. Previous studies have demonstrated this correlated structure in yeast, by showing that two proteins in the same region of the genetic network are likely to also physically interact 8,10 , that genes with similar patterns of genetic interactions often occur within the same protein complex 10 and that a protein with many interactions in the physical network typically has many interactions in the genetic network also 20 .…”

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confidence: 90%

“…1 online and Tong et al 8,10 ), but demonstrate a need for structured models to efficiently separate signal from noise. Towards this goal, we implemented a probabilistic modeling procedure to capture the between-pathway interpretation of genetic interactions.…”

Section: Between-pathway Interpretations For Genetic Interactionsmentioning

confidence: 99%

“…In humans, genetic interactions are critical in linkage analysis of complex diseases 5 and in discovery of new pharmaceuticals 6 . Although genetic interactions are classically identified by mutant screens 7 , recent studies have applied systematic 'reverse' methods such as synthetic genetic arrays (SGA) 8 or synthetic lethal analysis by microarrays (SLAM) 9 to catalog ~4,000 synthetic-lethal and synthetic-sick interactions in Saccharomyces cerevisiae.…”

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Systematic interpretation of genetic interactions using protein networks

2005

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Genetic interaction analysis, in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between-or within-pathway explanations encoded in the physical networks, covering ~40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than withinpathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.A major biological challenge is to interpret observed genetic interactions in a physical cellular context 1-3 . There are several major types of genetic interactions: synthetic-lethal interactions, in which mutations in two nonessential genes are lethal when combined; suppressor interactions, in which one mutation is lethal but when combined with a second, cell viability is restored; and an array of other effects such as enhancement and epistasis. Genetic interactions have been used extensively to shed light on pathway organization in model organisms [1][2][3][4] . In humans, genetic interactions are critical in linkage analysis of complex diseases 5 and in discovery of new pharmaceuticals 6 . Although genetic interactions are classically identified by mutant screens 7 , recent studies have applied systematic 'reverse' methods such as synthetic genetic arrays (SGA) 8 or synthetic lethal analysis by microarrays (SLAM) 9 to catalog ~4,000 synthetic-lethal and synthetic-sick interactions in Saccharomyces cerevisiae.Because of the high-throughput nature of SGA, discovery of new genetic interactions is largely automated. However, interpreting the functional significance of each result remains a relatively slow process. The problem is compounded by the large number of genetic interactions measured when screening one gene versus all others (~34 on average 10 ) as well as possible false positives if the interactions are not confirmed by tetrad or random spore analysis. Thus,

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Section: Between-pathway Interpretations For Genetic Interactionsmentioning

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Systematic interpretation of genetic interactions using protein networks

2005

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“…Similar profiles were observed for almost all such complexes as described in either ref 70 or ref 69 (see Table 1 of Supporting Information and Discussion). Intriguingly, in addition to such physical interactions, these proteins also interact with each other genetically [SGD, Saccharomyces Genome Database, http://www.yeastgenome.org/ (58,71,72)], indicating that the observed physical interactions also have functional significance in vivo (Table 2 of Supporting Information). In addition, several of the proteins have been found to interact genetically with genes encoding transcription elongation-related functions, such as CDC73 (58,71,72).…”

Section: Functional Organization Of Pcaps and Associated Proteinsmentioning

confidence: 99%

“…Intriguingly, in addition to such physical interactions, these proteins also interact with each other genetically [SGD, Saccharomyces Genome Database, http://www.yeastgenome.org/ (58,71,72)], indicating that the observed physical interactions also have functional significance in vivo (Table 2 of Supporting Information). In addition, several of the proteins have been found to interact genetically with genes encoding transcription elongation-related functions, such as CDC73 (58,71,72). Moreover, such genes encoding transcription elongation-related functions (e.g., CDC73) in their turn interact genetically with CTK1 (71)(72)(73).…”

Section: Functional Organization Of Pcaps and Associated Proteinsmentioning

confidence: 99%

Expanding the Functional Repertoire of CTD Kinase I and RNA Polymerase II: Novel PhosphoCTD-Associating Proteins in the Yeast Proteome

2004

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CTD kinase I (CTDK-I) of Saccharomyces cerevisiae is required for normal phosphorylation of the C-terminal repeat domain (CTD) on elongating RNA polymerase II. To elucidate cellular roles played by this kinase and the hyperphosphorylated CTD (phosphoCTD) it generates, we systematically searched yeast extracts for proteins that bound to the phosphoCTD made by CTDK-I in vitro. Initially, using a combination of far-western blotting and phosphoCTD affinity chromatography, we discovered a set of novel phosphoCTD-associating proteins (PCAPs) implicated in a variety of nuclear functions. We identified the phosphoCTD-interacting domains of a number of these PCAPs, and in several test cases (namely, Set2, Ssd1, and Hrr25) adduced evidence that phosphoCTD binding is functionally important in vivo. Employing surface plasmon resonance (BIACORE) analysis, we found that recombinant versions of these and other PCAPs bind preferentially to CTD repeat peptides carrying SerPO 4 residues at positions 2 and 5 of each seven amino acid repeat, consistent with the positional specificity of CTDK-I in vitro [Jones, J. C., et al. (2004) J. Biol. Chem. 279, 24957-24964]. Subsequently, we used a synthetic CTD peptide with three doubly phosphorylated repeats (2,5P) as an affinity matrix, greatly expanding our search for PCAPs. This resulted in identification of approximately 100 PCAPs and associated proteins representing a wide range of functions (e.g., transcription, RNA processing, chromatin structure, DNA metabolism, protein synthesis and turnover, RNA degradation, snRNA modification, and snoRNP biogenesis). The varied nature of these PCAPs and associated proteins points to an unexpectedly diverse set of connections between Pol II elongation and other processes, conceptually expanding the role played by CTD phosphorylation in functional organization of the nucleus.The carboxyl-terminal repeat domain (CTD) 1 of the largest subunit of eukaryotic RNA polymerase II (Pol II) comprises tandem repeats of the consensus heptapeptide YSPTSPS. This highly conserved sequence, which is repeated 26 times in yeast and 52 times in mammals, is essential for viability. Phosphorylation of the CTD regulates the activities of the polymerase: only Pol II with an unphosphorylated CTD (Pol IIA) is thought to be able to assemble into preinitiation complexes at the promoter, whereas elongating polymerase has a hyperphosphorylated CTD (Pol IIO) (1,2). The serines at positions 2 and 5 within the heptad repeat represent major sites of phosphorylation during transcription. † Supported by National Institutes of Health Grant GM40505.© 2004 American Chemical Society * To whom correspondence should be addressed. . E-mail: arno@biochem.duke.edu. 1 Abbreviations: CTDK-I, CTD kinase I; Pol II, RNA polymerase II; CTD, C-terminal repeat domain; phosphoCTD, hyperphosphorylated CTD; PCTD, phosphoCTD; PCAP, phosphoCTD-associating protein; PCID, phosphoCTD-interacting domain; SGD, Saccharomyces Genome Database; SPR, surface plasmon resonance; RU, response units. NIH Publi...

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Functional genomics in S accharomyces cerevisiae

Dolinski

Troyanskaya

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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The availability of the Saccharomyces cerevisiae genome has fueled an explosion of high‐throughput methods aimed at the functional characterization of all proteins in yeast. These diverse methods include expression and protein microarray experiments, protein–protein and protein–DNA interaction studies, proteomics data, and phenotype analyses. Integrated bioinformatics analysis is necessary to utilize the full potential of these data. Such analysis can be facilitated by public distribution of these data sets in standardized form and by development of accurate and efficient analysis algorithms. In this chapter, we outline available data sources and their distribution, data sharing standards, and data integration methods.

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Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants

Cited by 1,930 publications

References 22 publications

Systematic interpretation of genetic interactions using protein networks

Systematic interpretation of genetic interactions using protein networks

Expanding the Functional Repertoire of CTD Kinase I and RNA Polymerase II: Novel PhosphoCTD-Associating Proteins in the Yeast Proteome

Functional genomics in S accharomyces cerevisiae

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