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Linkers of cell polarity and cell cycle regulation in the fission yeast protein interaction network.

Vaggi F, Dodgson J, Bajpai A, Chessel A, Jordán F, Sato M, Carazo-Salas RE, Csikász-Nagy A - PLoS Comput. Biol. (2012)

Bottom Line: The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms.Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation.As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.

View Article: PubMed Central - PubMed

Affiliation: The Microsoft Research-University of Trento Centre for Computational Systems Biology, Rovereto, Italy.

ABSTRACT
The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify 'linker' proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key role in controlling the temporal changes in the localization of the polarity machinery. Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation. Detailed bibliographic inspection of other predicted 'linkers' also confirms the predictive power of the method. As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.

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Segregation of functional communities in the core network.A clique propagation algorithm was used to identify locally highly connected communities of the core network. The ten cliques generated by the algorithm segregate in the interaction network if laid out by a force-based algorithm that brings closer together the stronger interacting groups (A). Node colour determined by the functional annotation (same as Figure 2, inset on panel B here). Proteins belonging to the same clique share the same border colour. Proteins belonging to the same clique largely share functional annotations. Pie charts show the functional distribution of proteins found in each clique (B). Numbers report the number of proteins with the annotations corresponding to the given colour coded annotation (see inset for colours).
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pcbi-1002732-g003: Segregation of functional communities in the core network.A clique propagation algorithm was used to identify locally highly connected communities of the core network. The ten cliques generated by the algorithm segregate in the interaction network if laid out by a force-based algorithm that brings closer together the stronger interacting groups (A). Node colour determined by the functional annotation (same as Figure 2, inset on panel B here). Proteins belonging to the same clique share the same border colour. Proteins belonging to the same clique largely share functional annotations. Pie charts show the functional distribution of proteins found in each clique (B). Numbers report the number of proteins with the annotations corresponding to the given colour coded annotation (see inset for colours).

Mentions: We further analyzed this effect using a community detection algorithm, which identifies local communities in a network and allows their overlap – as we have nodes with multiple annotations. We applied the k-clique propagation algorithm [53], [54] and examined the communities generated by the method with k = 4. While the communities generated by the algorithm do not exactly match the functional annotations, we find that the cliques generated by the algorithm are primarily formed by proteins that share functional annotations (Figure 3A,B). Upon closer examination, the few proteins that do not share a functional annotation with the other members of a clique seem to have related roles: for example, in the 5th clique on Figure 3B, the lone ‘non-polarity’ protein is Rgf3, which was shown to play an important cell-wall remodeling role downstream of Rho1, one of the key regulators of polarity [55], [56] (consult Table S5 for all clique members).


Linkers of cell polarity and cell cycle regulation in the fission yeast protein interaction network.

Vaggi F, Dodgson J, Bajpai A, Chessel A, Jordán F, Sato M, Carazo-Salas RE, Csikász-Nagy A - PLoS Comput. Biol. (2012)

Segregation of functional communities in the core network.A clique propagation algorithm was used to identify locally highly connected communities of the core network. The ten cliques generated by the algorithm segregate in the interaction network if laid out by a force-based algorithm that brings closer together the stronger interacting groups (A). Node colour determined by the functional annotation (same as Figure 2, inset on panel B here). Proteins belonging to the same clique share the same border colour. Proteins belonging to the same clique largely share functional annotations. Pie charts show the functional distribution of proteins found in each clique (B). Numbers report the number of proteins with the annotations corresponding to the given colour coded annotation (see inset for colours).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475659&req=5

pcbi-1002732-g003: Segregation of functional communities in the core network.A clique propagation algorithm was used to identify locally highly connected communities of the core network. The ten cliques generated by the algorithm segregate in the interaction network if laid out by a force-based algorithm that brings closer together the stronger interacting groups (A). Node colour determined by the functional annotation (same as Figure 2, inset on panel B here). Proteins belonging to the same clique share the same border colour. Proteins belonging to the same clique largely share functional annotations. Pie charts show the functional distribution of proteins found in each clique (B). Numbers report the number of proteins with the annotations corresponding to the given colour coded annotation (see inset for colours).
Mentions: We further analyzed this effect using a community detection algorithm, which identifies local communities in a network and allows their overlap – as we have nodes with multiple annotations. We applied the k-clique propagation algorithm [53], [54] and examined the communities generated by the method with k = 4. While the communities generated by the algorithm do not exactly match the functional annotations, we find that the cliques generated by the algorithm are primarily formed by proteins that share functional annotations (Figure 3A,B). Upon closer examination, the few proteins that do not share a functional annotation with the other members of a clique seem to have related roles: for example, in the 5th clique on Figure 3B, the lone ‘non-polarity’ protein is Rgf3, which was shown to play an important cell-wall remodeling role downstream of Rho1, one of the key regulators of polarity [55], [56] (consult Table S5 for all clique members).

Bottom Line: The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms.Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation.As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.

View Article: PubMed Central - PubMed

Affiliation: The Microsoft Research-University of Trento Centre for Computational Systems Biology, Rovereto, Italy.

ABSTRACT
The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify 'linker' proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key role in controlling the temporal changes in the localization of the polarity machinery. Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation. Detailed bibliographic inspection of other predicted 'linkers' also confirms the predictive power of the method. As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.

Show MeSH