Protein-protein Interaction Networks of E. coli and S. cerevisiae are similar

Wuchty, Stefan; Uetz, Peter

doi:10.1038/srep07187

Cited by 25 publications

(21 citation statements)

References 32 publications

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“…We determined that Y. pestis follows the centrality-lethality rule in vitro ( r 2 =0.85, P =0.0030, Fig. 1a), as observed in other prokaryotes7, but not in vivo ( r 2 =0.38, P =0.19, Fig. 1a).…”

Section: Resultsmentioning

confidence: 62%

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Centrality in the host–pathogen interactome is associated with pathogen fitness during infection

et al. 2017

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To perform their functions proteins must interact with each other, but how these interactions influence bacterial infection remains elusive. Here we demonstrate that connectivity in the host–pathogen interactome is directly related to pathogen fitness during infection. Using Y. pestis as a model organism, we show that the centrality-lethality rule holds for pathogen fitness during infection but only when the host–pathogen interactome is considered. Our results suggest that the importance of pathogen proteins during infection is directly related to their number of interactions with the host. We also show that pathogen proteins causing an extensive rewiring of the host interactome have a higher impact in pathogen fitness during infection. Hence, we conclude that hubs in the host–pathogen interactome should be explored as promising targets for antimicrobial drug design.

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Section: Resultsmentioning

confidence: 62%

“…This rule holds for many organisms, both prokaryotes and eukaryotes578. However, the extension of this rule to infectious organisms such as bacteria is not straightforward.…”

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confidence: 99%

Centrality in the host–pathogen interactome is associated with pathogen fitness during infection

et al. 2017

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“…Furthermore, we found that interactions that involve critical proteins are enriched with essential genes. Such a results is a corollary to previous results, indicating that essential proteins tend to cluster together in protein interaction networks of different organisms . Furthermore, critical proteins have already been observed to be essential .…”

Section: Discussionmentioning

confidence: 99%

Links between critical proteins drive the controllability of protein interaction networks

2017

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Focusing on the interactomes of Homo sapiens, Saccharomyces cerevisiae, and Escherichia coli, we investigated interactions between controlling proteins. In particular, we determined critical, intermittent, and redundant proteins based on their tendency to participate in minimum dominating sets. Independently of the organisms considered, we found that interactions that involved critical nodes had the most prominent effects on the topology of their corresponding networks. Furthermore, we observed that phosphorylation and regulatory events were considerably enriched when the corresponding transcription factors and kinases were critical proteins, while such interactions were depleted when they were redundant proteins. Moreover, interactions involving critical proteins were enriched with essential genes, disease genes, and drug targets, suggesting that such characteristics may be key for the detection of novel drug targets as well as assess their efficacy.

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“…The reason is that the transient interactions may not remain conserved in the evolution and genes involved in these kinds of interactions may not exhibit high co-expression. On the other hand, more stable interactions and protein complexes are highly conserved in the evolution [34, 35] and the interacting proteins are highly co-expressed [35]. …”

Section: Discussionmentioning

confidence: 99%

Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources

et al. 2017

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BackgroundAlthough different protein-protein physical interaction (PPI) datasets exist for Escherichia coli, no common methodology exists to integrate these datasets and extract reliable modules reflecting the existing biological process and protein complexes. Naïve Bayesian formula is the highly accepted method to integrate different PPI datasets into a single weighted PPI network, but detecting proper weights in such network is still a major problem.ResultsIn this paper, we proposed a new methodology to integrate various physical PPI datasets into a single weighted PPI network in a way that the detected modules in PPI network exhibit the highest similarity to available functional modules. We used the co-expression modules as functional modules, and we shown that direct functional modules detected from Gene Ontology terms could be used as an alternative dataset. After running this integrating methodology over six different physical PPI datasets, orthologous high-confidence interactions from a related organism and two AP-MS PPI datasets gained high weights in the integrated networks, while the weights for one AP-MS PPI dataset and two other datasets derived from public databases have converged to zero. The majority of detected modules shaped around one or few hub protein(s). Still, a large number of highly interacting protein modules were detected which are functionally relevant and are likely to construct protein complexes.ConclusionsWe provided a new high confidence protein complex prediction method supported by functional studies and literature mining.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1422-x) contains supplementary material, which is available to authorized users.

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Protein-protein Interaction Networks of E. coli and S. cerevisiae are similar

Cited by 25 publications

References 32 publications

Centrality in the host–pathogen interactome is associated with pathogen fitness during infection

Centrality in the host–pathogen interactome is associated with pathogen fitness during infection

Links between critical proteins drive the controllability of protein interaction networks

Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources

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