Functional profiling of the Saccharomyces cerevisiae genome

Giaever, Guri; Chu, Angela M.; Ni, Li; Connelly, Carla; Riles, Linda; Véronneau, Steeve; Dow, Sally; Lucau-Danila, Anca; Anderson, Keith M.; André, Brunó; Arkin, Adam P.; Astromoff, Anna; Bakkoury, Mohamed El; Bangham, Rhonda; Benito, Rocí­o; Brachat, Sophie; Campanaro, Stefano; Curtiss, Matt; Davis, Karen; Deutschbauer, Adam M.; Entian, Karl Dieter; Flaherty, Patrick; Foury, Françoise; Garfinkel, David; Gerstein, Mark; Gotte, Deanna; Güldener, Ulrich; Hegemann, Johannes H.; Hempel, Svenja; Herman, Zelek S.; Jaramillo, Daniel; Kelly, Diane; Kelly, Steven L.; Kötter, Peter; LaBonte, Darlene; Lamb, David C.; Lan, Ning; Liang, Hong; Liao, Hong; Liu, Lucy; Luo, C.; Lussier, Marc; Mao, Rong; Ménard, Patrice; Ooi, Siew Loon; Revuelta, José Luis; Roberts, Christopher J.; Rose, M. W. nston; Ross‐Macdonald, Petra; Scherens, Bart; Schimmack, Greg; Shafer, Brenda K.; Shoemaker, Daniel; Sookhai-Mahadeo, Sharon; Storms, Reginald; Strathern, Jeffrey N.; Valle, Giorgio; Voet, Marleen; Volckaert, Guido; Wang, Ching Yun; Ward, Teresa R.; Wilhelmy, Julie; Winzeler, Elizabeth A.; Yang, Yonghong; Yen, Grace; Youngman, Elaine M.; Yu, Kexin; Bussey, Howard; Boeke, Jef D.; Snyder, M; Philippsen, Peter; Davis, Ronald W.; Johnston, Mark

doi:10.1038/nature00935

Cited by 4,015 publications

(3,703 citation statements)

References 18 publications

Supporting

104

Mentioning

3,517

Contrasting

Unclassified

Order By: Relevance

“…For strains that possess the ssd1-d allele, such as W303, deletion of SIT4 is lethal. In contrast, for strain backgrounds bearing the SSD1-v allele, such as S288C and BY4741, sit4∆ cells are viable but grow more slowly than wild-type, due to delayed passage through the G 1 /S cell cycle transition (Giaever et al, 2002;Sutton et al, 1991). On regular YPD medium, sit4∆ cells grew significantly slower than the wild-type control strain, as expected ( Figure 4A, B, left panels).…”

Section: Deletion Of Sit4 or Sap155 Confers Caffeine Resistancesupporting

confidence: 69%

Identification of phosphatase 2A‐like Sit4‐mediated signalling and ubiquitin‐dependent protein sorting as modulators of caffeine sensitivity in S. cerevisiae

Hood-DeGrenier¹

2010

Yeast

View full text Add to dashboard Cite

Caffeine exerts pleiotropic effects on eukaryotic cells via its ability to act as a lowaffinity adenosine analogue. Here we report that the genes HSE1, RTS3, SDS23 and SDS24 confer caffeine resistance when overexpressed in S. cerevisiae. The Hse1 protein functions in ubiquitin-dependent vacuolar protein sorting, whereas the other proteins are poorly characterized. Bioinformatic analysis of genetic and physical interaction data linked Rts3 and Sds23/24 to the phosphatase 2A-like Sit4 pathway. Combinatorial deletions of the identified suppressor genes conferred varying levels of caffeine hypersensitivity. For hse1 and rts3 mutants, caffeine sensitivity was partially rescued by sorbitol osmostabilization, suggesting possible cell wall integrity defects in these strains. Rapamycin sensitivity experiments linked the caffeine sensitivity of rts3 , but not that of sds23/24 or hse1 strains, to inhibition of the TORC1 kinase complex, a central regulator of cell growth and a known caffeine target. Epistasis experiments support a model in which Rts3 and Sds23/24 act in parallel to negatively regulate Sit4, while Hse1 confers caffeine resistance via a separate pathway. In summary, this study identifies the Sit4 phosphatase pathway and membrane protein dynamics as key modulators of caffeine-mediated inhibition of yeast cell growth and proposes novel functions for Rts3 and Sds23/24.

show abstract

Section: Deletion Of Sit4 or Sap155 Confers Caffeine Resistancesupporting

confidence: 69%

Identification of phosphatase 2A‐like Sit4‐mediated signalling and ubiquitin‐dependent protein sorting as modulators of caffeine sensitivity in S. cerevisiae

Hood-DeGrenier¹

2010

Yeast

View full text Add to dashboard Cite

show abstract

“…A revolutionary approach to discover gene function has been to knock out a gene and observe its phenotype. A nearly complete collection of single gene deletions has been performed for Saccharomyces Cerevisiae 22 . Eukaryotes show large amounts of genetic redundancy, however, and single knock outs are no longer informative.…”

Section: Communitiesmentioning

confidence: 99%

The art of community detection

Gulbahce

Lehmann

2008

BioEssays

View full text Add to dashboard Cite

SUMMARYNetworks in nature possess a remarkable amount of structure. Via a series of datadriven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman 1 , introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection. STRUCTURE EVERYWHEREThe view that networks are essentially random was challenged in 1999 when it was discovered that the distribution of number of links per node (degree) of many real networks (internet, metabolic network, sexual contacts, airports, etc) is different from what is expected in random networks 2 . In a large random network node degrees are distributed according to the normal distribution, but in many manmade and biological networks the degree distribution follows a power-law. In the human protein-protein interaction networks 3,4 , for instance, some proteins act as hubs, they are highly connected, and interact with more than 200 other proteins contrary to most proteins that interact with only a few other proteins.Various local to global measures have been introduced to unveil the organizational principles of complex networks 5,6,7,8,9 . Maslov and Sneppen 10 discovered that who links to whom can depend on node degree; in many biological networks, high degree nodes systematically link to nodes of low degree. This disassortativity decreases the likelihood of cross talk between functional modules inside the cell and increases overall robustness. Other networks, for example social networks 11 , are highly assortative -in these networks nodes with similar degree tend to link to each other. The scales of organization of complex networks. The illustrations on the left show how to break down the "hairball" that arises when we plot the entire network. On the smallest scale, the degree provides information about single nodes. The notion of assortativity enters when we discuss pairs of nodes. With three or more nodes, we are in the realm of motifs. Larger groups of nodes are called modules or communities. Hierarchy describes how the various structural elements are combined; how nodes a linked to form motifs, motifs are combined to form communities, and communities are joined into the entire network.Going beyond the properties of single nodes and pairs of nodes, the natural next step is to consider structures that include several nodes. Interestingly, a few select motifs of three to four nodes are ubiquitous in real networks 12 while most others occur only as often as they would at random, or, are actively suppressed....

show abstract

“…Gene duplications are often responsible for such absent effects 11,12 . Genome-scale efforts to eliminate each of thousands of genes in a genome lead to similar results 13,14 , namely that only a fraction of genes have a phenotypic effect in the laboratory.A second line of evidence comes from molecular evolution studies. Duplicate genes experience relaxed selection shortly after their duplication.…”

mentioning

confidence: 99%

Gene duplications, robustness and evolutionary innovations

Wagner

2008

BioEssays

118

View full text Add to dashboard Cite

Gene duplications, robustness and evolutionary innovations Gene duplications, robustness and evolutionary innovations AbstractMutational robustness facilitates evolutionary innovations. Gene duplications are unique kinds of mutations, in that they generally increase such robustness. The frequent association of gene duplications in regulatory networks with evolutionary innovation is thus a special case of a general mechanism linking innovation to robustness. The potential power of this mechanism to promote evolutionary innovations on large time scales is illustrated here with several examples. These include the role of gene duplications in the vertebrate radiation, flowering plant evolution and heart development, which encompass some of the most striking innovations in the evolution of life. Gene duplications, robustness, and evolutionary innovationsAndreas Wagner AbstractMutational robustness facilitates evolutionary innovations in biological systems. Gene duplications are unique kinds of mutations, in that they generally increase such robustness. The frequent association of gene duplications in regulatory networks with evolutionary innovation thus exemplifies a general mechanism linking innovation to robustness. I illustrate the potential power of this mechanism on large time scales with the role of gene duplications in the vertebrate radiation, flowering plant evolution, and heart development, which encompass some of the most striking innovations in the evolution of life. Mutational robustnessMutational robustness is a biological's system ability to withstand mutations. Such robustness exists on multiple levels of biological organization. A case in point are random mutagenesis experiments of various proteins. They suggest that only a small fraction of mutations affect protein function adversely. For instance, a study of the bacteriophage T4 lysozyme generated more than 2000 random amino acid changes in the protein. Only 16% of them affected lysozyme function 1 . Other examples come from regulatory gene networks, such as the molecular network specifying fruit fly segments. Such networks may tolerate much quantitative variation in interactions among network genes 2-5 . Examples at the highest level of organization include macroscopic traits. Even substantial genetic variation -ultimately caused by mutations -may leave such traits unchanged. Take the vulva of the nematode worms Caenorhabditis elegans and Pristionchus pacificus 6 . These organisms shared a common ancestor 200-300 million years ago. Their vulvae are very similar, yet the genetic and cellular networks producing them have diverged greatly. For example, whereas in C. elegans one specific cell -the anchor cell -induces vulva development, multiple gonadal cells are responsible for this induction in P. pacificus 7 . Similarly, the same key signaling molecules, such as Wnt, may play a positive role in the network for vulval induction in C. elegans, but a negative role in P. pacificus 8,9 . In sum, mutational robustness is everywhere, from proteins to or...

show abstract

Functional profiling of the Saccharomyces cerevisiae genome

Cited by 4,015 publications

References 18 publications

Identification of phosphatase 2A‐like Sit4‐mediated signalling and ubiquitin‐dependent protein sorting as modulators of caffeine sensitivity in S. cerevisiae

Identification of phosphatase 2A‐like Sit4‐mediated signalling and ubiquitin‐dependent protein sorting as modulators of caffeine sensitivity in S. cerevisiae

The art of community detection

Gene duplications, robustness and evolutionary innovations

Contact Info

Product

Resources

About