Functional Characterization of the
            <i>S. cerevisiae</i>
            Genome by Gene Deletion and Parallel Analysis

Winzeler, Elizabeth A.; Shoemaker, Daniel; Astromoff, Anna; Liang, Hong; Anderson, Keith M.; André, Brunó; Bangham, Rhonda; Benito, Rocí­o; Boeke, Jef D.; Bussey, Howard; Chu, Angela M.; Connelly, Carla; Davis, Karen; Dietrich, Fred S.; Dow, Sally; Bakkoury, Mohamed El; Foury, Françoise; Friend, Stephen H.; Gentalen, Erik; Giaever, Guri; Hegemann, Johannes H.; Jones, Ted; Laub, Michael T.; Liao, Hong; Liebundguth, Nicole; Lockhart, David J.; Lucau-Danila, Anca; Lussier, Marc; M’Rabet, Nasiha; Ménard, Patrice; Mittmann, Michael; Pai, Chai; Rebischung, Corinne; Revuelta, José Luis; Riles, Linda; Roberts, Christopher J.; Ross‐Macdonald, Petra; Scherens, Bart; Snyder, M; Sookhai-Mahadeo, Sharon; Storms, Reginald; Véronneau, Steeve; Voet, Marleen; Volckaert, Guido; Ward, Teresa R.; Wysocki, Robert; Yen, Grace; Yu, Kexin; Zimmermann, Katja; Philippsen, Peter; Johnston, Mark; Davis, Ronald W.

doi:10.1126/science.285.5429.901

Cited by 3,720 publications

(2,853 citation statements)

References 36 publications

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“…This simulated tolerance against random mutation is in agreement with systematic mutagenesis studies, which identified a striking capacity of yeast to tolerate the deletion of a substantial number of individual proteins from its proteome 9,10 . Yet, if indeed this is due to a topological component to error tolerance, on average less connected proteins should prove less essential than highly connected ones.…”

supporting

confidence: 85%

Lethality and centrality in protein networks

Jeong

Mason

Barabási

et al. 2001

Nature

4,679

153

3,566

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Lethality and centrality in protein networksCell biology traditionally identifies proteins based on their individual actions as catalysts, signaling molecules, or building blocks of cells and microorganisms. Currently, we witness the emergence of a post-genomic view that expands the protein's role, regarding it as an element in a network of proteinprotein interactions as well, with a 'contextual' or 'cellular' function within functional modules 1, 2 . Here we provide quantitative support for this paradigm shift by demonstrating that the phenotypic consequence of a single gene deletion in the yeast, S. cerevisiae, is affected, to a high degree, by the topologic position of its protein product in the complex, hierarchical web of molecular interactions.The S. cerevisiae protein-protein interaction network we investigate has 1870 proteins as nodes, connected by 2240 identified direct physical interactions, and is derived from combined, nonoverlapping data 3, 4 obtained mostly by systematic two-hybrid analyses 3 . Due to its size, a complete map of the network (Fig. 1a), while informative, in itself offers little insight into its large-scale characteristics. Thus, our first goal was to identify the architecture of this network, determining if it is best described by an inherently uniform exponential topology with proteins on average possessing the same number of links, or by a highly heterogeneous scale-free topology with proteins having widely different connectivities 5 . As we show in Fig. 1b, the probability that a given yeast protein interacts with k other yeast proteins follows a power-law 5 with an exponential cutoff 6 at k c ≅ 20, a topology that is also shared by the protein-protein interaction network of the bacterium, H. pylori 7 . This indicates that the network of protein interactions in two separate organisms forms a highly inhomogeneous scale-free network in which a few highly connected proteins play a central role in mediating interactions among numerous, less connected proteins.An important known consequence of the inhomogeneous structure is the network's simultaneous tolerance against random errors coupled with fragility against the removal of the most connected nodes 8 . Indeed, we find that random mutations in the genome of S. cerevisiae, -modeled by the removal of randomly selected yeast proteins-, do not affect the overall topology of the network. In contrast, when

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supporting

confidence: 85%

Lethality and centrality in protein networks

Jeong

Mason

Barabási

et al. 2001

Nature

4,679

153

3,566

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“…Our vectors can integrate into commonly used auxotrophic yeast strains, including the designer deletion strains (Brachmann et al, 1998), making them compatible with the diverse strain collections (Winzeler et al, 1999;Huh et al, 2003;Ghaemmaghami et al, 2003;Mnaimneh et al, 2004). Efficient integration into the various alleles (carrying point mutations, insertions, partial or complete deletions) is facilitated by using marker genes on the plasmids without any overlap with the host genome (Wach et al, 1997;Goldstein et al, 1999;Gueldener et al, 2002 For the extrachromosomal maintenance of genes at low or high copy number, the series contains centromeric and episomal shuttle vectors, respectively.…”

Section: Discussionmentioning

confidence: 99%

A shuttle vector series for precise genetic engineering of Saccharomyces cerevisiae

Gnügge

Liphardt

Rudolf

2016

Yeast

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Shuttle vectors allow for an efficient transfer of recombinant DNA into yeast cells and are widely used in fundamental research and biotechnology. While available shuttle vectors are applicable in many experimental settings, their use in quantitative biology is hampered by insufficient copy number control. Moreover, they often have practical constraints, such as limited modularity and few unique restriction sites. We constructed the pRG shuttle vector series, consisting of single-and multi-copy integrative, centromeric and episomal plasmids with marker genes for the selection in all commonly used auxotrophic yeast strains. The vectors feature a modular design and a large number of unique restriction sites, enabling an efficient exchange of every vector part and expansion of the series. Integration into the host genome is achieved using a double-crossover recombination mechanism, resulting in stable single-and multi-copy modifications. As centromeric and episomal plasmids give rise to a heterogeneous cell population, an analysis of their copy number distribution and loss behaviour was performed. Overall, the shuttle vector series supports the efficient cloning of genes and their maintenance in yeast cells with improved copy number control.

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“…1). The first was generated by SGA, a large-scale screen 10 crossing 132 yeast gene deletion strains versus each of the ~4,700 available deletion strains 22 and resulting in 2,012 observed synthetic-lethal interactions and 2,113 synthetic-sick interactions. The second data source consisted of an additional 687 synthetic-lethal interactions culled from the literature and catalogued at the Munich Information Center for Protein Sequences (MIPS) 23 .…”

Section: Construction Of Genetic and Physical Networkmentioning

confidence: 99%

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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Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis

Cited by 3,720 publications

References 36 publications

Lethality and centrality in protein networks

Lethality and centrality in protein networks

A shuttle vector series for precise genetic engineering of Saccharomyces cerevisiae

Systematic interpretation of genetic interactions using protein networks

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