The protein–protein interaction map of Helicobacter pylori

Rain, Jean‐Christophe; Selig, Luc; Reuse, Hilde De; Battaglia, Véronique; Reverdy, Céline; Simon, Stéphane; Lenzen, Gerlinde; Petel, Fabien; Wojcik, Jérôme; Schächter, Vincent; Chemama, Y.; Labigne, Agnès; Legrain, Pierre

doi:10.1038/35051615

Cited by 1,041 publications

(789 citation statements)

References 23 publications

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“…Helicobacter pylori (Rain et al, 2001), Sacchoromyces cereviciae (von Mering et al, 2002;Bu et al, 2003), and Homo sapiens (Rual et al, 2005). Here we consider the main connected component of these networks in which interactions between proteins are taken to be undirected and no selfinteractions are considered.…”

Section: Study Of Protein-protein Interaction Networkmentioning

confidence: 99%

Generalized walks-based centrality measures for complex biological networks

Estrada

2010

Journal of Theoretical Biology

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A strategy for zooming in and out the topological environment of a node in a complex network is developed. This approach is applied here to generalize the subgraph centrality of nodes in complex networks. In this case the zooming in strategy is based on the use of some known matrix functions which allow focusing locally on the environment of a node. When a zooming out strategy is applied new matrix functions are introduced, which give a more global picture of the topological surrounds of a node. These indices permit a modulation of the scales at which the environment of a node influences its centrality. We apply them to the study of 10 protein-protein interaction (PPI) networks. We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures. We show here that the use of centrality indices based on the zooming in strategy identifies a larger number of essential proteins in the yeast PPI network than any of the other centrality measures studied.

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Section: Study Of Protein-protein Interaction Networkmentioning

confidence: 99%

Generalized walks-based centrality measures for complex biological networks

Estrada

2010

Journal of Theoretical Biology

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“…For instance, direct physical interactions among proteins are being mapped by systematic two-hybrid [11][12][13][14][15] or immunoprecipitation studies 16,17 , whereas physical interactions between transcription factors and promoter sites are determined using chromatinimmunoprecipitation in conjunction with DNA microarrays 18,19 . 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.…”

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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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“…To search for new Atox1 partners, we performed a yeast twohybrid screen (Hybrigenics; similar to (Rain et al 2001)) using Atox1 as a LexA fusion bait toward a human placenta library of protein fragments (Wittung-Stafshede, unpublished); among 98 million possible interactions, we found 25 confident target proteins that interacted with Atox1 (Table 1). Of these interactions partners, at least six are known DNA/RNA-binding proteins.…”

Section: Interactions With Other Proteins and New Functionsmentioning

confidence: 99%

Unresolved questions in human copper pump mechanisms

Wittung-Stafshede

2015

Quart. Rev. Biophys.

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Abstract. Copper (Cu) is an essential transition metal providing activity to key enzymes in the human body. To regulate the levels and avoid toxicity, cells have developed elaborate systems for loading these enzymes with Cu. Most Cu-dependent enzymes obtain the metal from the membrane-bound Cu pumps ATP7A/B in the Golgi network. ATP7A/B receives Cu from the cytoplasmic Cu chaperone Atox1 that acts as the cytoplasmic shuttle between the cell membrane Cu importer, Ctr1 and ATP7A/B. Biological, genetic and structural efforts have provided a tremendous amount of information for how the proteins in this pathway work. Nonetheless, basic mechanistic-biophysical questions (such as how and where ATP7A/B receives Cu, how ATP7A/B conformational changes and domain-domain interactions facilitate Cu movement through the membrane, and, finally, how target polypeptides are loaded with Cu in the Golgi) remain elusive. In this perspective, unresolved inquiries regarding ATP7A/B mechanism will be highlighted. The answers are important from a fundamental view, since mechanistic aspects may be common to other metal transport systems, and for medical purposes, since many diseases appear related to Cu transport dysregulation.

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The protein–protein interaction map of Helicobacter pylori

Cited by 1,041 publications

References 23 publications

Generalized walks-based centrality measures for complex biological networks

Generalized walks-based centrality measures for complex biological networks

Systematic interpretation of genetic interactions using protein networks

Unresolved questions in human copper pump mechanisms

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