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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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Concepts of ‘linker’ protein detection and robustness of the method.(A) ‘Linker’ proteins are found at the edge of a sub-network, but are central in the context of a larger network. Such proteins have low betweenness centrality (BC) score when considered in the context of their sub-network, but have a high BC score in the core network even though they do not have a functional annotation to the other category making up the core network. Black edges indicate edges between proteins that do not share functional annotations, while the other edges are gray. Table on right gives ranks and linkerity measures for all nodes in network ‘A’ in the same style as Table 2 does. (B) Analysis of the robustness of linkerity scores for the polarity network of fission yeast cells. We added 10% extra edges randomly to the network, and computed the linkerity score of all proteins after each iteration. Bars show mean ranking with standard deviation. Blue dashed line indicates cutoff for top 10% and red line marks the top 20% (results of other type of network perturbations are reported in Figure S5).
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pcbi-1002732-g004: Concepts of ‘linker’ protein detection and robustness of the method.(A) ‘Linker’ proteins are found at the edge of a sub-network, but are central in the context of a larger network. Such proteins have low betweenness centrality (BC) score when considered in the context of their sub-network, but have a high BC score in the core network even though they do not have a functional annotation to the other category making up the core network. Black edges indicate edges between proteins that do not share functional annotations, while the other edges are gray. Table on right gives ranks and linkerity measures for all nodes in network ‘A’ in the same style as Table 2 does. (B) Analysis of the robustness of linkerity scores for the polarity network of fission yeast cells. We added 10% extra edges randomly to the network, and computed the linkerity score of all proteins after each iteration. Bars show mean ranking with standard deviation. Blue dashed line indicates cutoff for top 10% and red line marks the top 20% (results of other type of network perturbations are reported in Figure S5).

Mentions: To systematically study proteins linking different cellular processes, we next used a network-based approach aiming to identify proteins that function as ‘linkers’ between different functional categories (Figure 4A). To do so, we constructed protein-protein interaction networks consisting only of proteins with one of the investigated functional annotations (cell cycle, cytokinesis or polarity regulation). We then calculated the betweenness centrality score for every node in each of these networks and in the merged core network. Betweenness Centrality (BC) measures how often a node is found in the shortest path between pairs of other nodes in the network; intuitively, it can be thought of as a measure of how central a node is in a network. If a node has a low centrality score it is localized at the fringe of a network, while if it has a high score it is localized near the centre. Next we ranked the proteins based on their BC score (in case of a tie, these proteins got their average rank). To ensure that this ranking method is robust even in the presence of imperfect interaction data certainly missing important links, we randomly added 10% extra edges to all the networks 1000 times, and recalculated the ranking of all proteins at each iteration (Figures S4). While the exact ranking of proteins is not very robust to addition of extra edges, if we examine all the proteins in the top 20%, we can observe that most fluctuate out of the top 20% only very rarely, and that we nearly never observe a protein in the top 10% drop out of the top 20%. It is also reassuring that the top of the rankings starts with expected key regulators of each function: the polarity landmark Tea1 [57]–[59], the actin-regulating Rho GTPase Cdc42 [60], [61] and actin (Act1) all came on the top of the polarity list. At the same time Cdc2, Wee1 and Cdc25 [62] are on the top of the cell cycle list (and also on the top of the core list) and the SIN scaffold Cdc11 [63] and the CDK counteracting, SIN activator phosphatase Clp1 [64], [65] are leading the cytokinesis ranking (Figure S4 and Table S3).


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)

Concepts of ‘linker’ protein detection and robustness of the method.(A) ‘Linker’ proteins are found at the edge of a sub-network, but are central in the context of a larger network. Such proteins have low betweenness centrality (BC) score when considered in the context of their sub-network, but have a high BC score in the core network even though they do not have a functional annotation to the other category making up the core network. Black edges indicate edges between proteins that do not share functional annotations, while the other edges are gray. Table on right gives ranks and linkerity measures for all nodes in network ‘A’ in the same style as Table 2 does. (B) Analysis of the robustness of linkerity scores for the polarity network of fission yeast cells. We added 10% extra edges randomly to the network, and computed the linkerity score of all proteins after each iteration. Bars show mean ranking with standard deviation. Blue dashed line indicates cutoff for top 10% and red line marks the top 20% (results of other type of network perturbations are reported in Figure S5).
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Related In: Results  -  Collection

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

pcbi-1002732-g004: Concepts of ‘linker’ protein detection and robustness of the method.(A) ‘Linker’ proteins are found at the edge of a sub-network, but are central in the context of a larger network. Such proteins have low betweenness centrality (BC) score when considered in the context of their sub-network, but have a high BC score in the core network even though they do not have a functional annotation to the other category making up the core network. Black edges indicate edges between proteins that do not share functional annotations, while the other edges are gray. Table on right gives ranks and linkerity measures for all nodes in network ‘A’ in the same style as Table 2 does. (B) Analysis of the robustness of linkerity scores for the polarity network of fission yeast cells. We added 10% extra edges randomly to the network, and computed the linkerity score of all proteins after each iteration. Bars show mean ranking with standard deviation. Blue dashed line indicates cutoff for top 10% and red line marks the top 20% (results of other type of network perturbations are reported in Figure S5).
Mentions: To systematically study proteins linking different cellular processes, we next used a network-based approach aiming to identify proteins that function as ‘linkers’ between different functional categories (Figure 4A). To do so, we constructed protein-protein interaction networks consisting only of proteins with one of the investigated functional annotations (cell cycle, cytokinesis or polarity regulation). We then calculated the betweenness centrality score for every node in each of these networks and in the merged core network. Betweenness Centrality (BC) measures how often a node is found in the shortest path between pairs of other nodes in the network; intuitively, it can be thought of as a measure of how central a node is in a network. If a node has a low centrality score it is localized at the fringe of a network, while if it has a high score it is localized near the centre. Next we ranked the proteins based on their BC score (in case of a tie, these proteins got their average rank). To ensure that this ranking method is robust even in the presence of imperfect interaction data certainly missing important links, we randomly added 10% extra edges to all the networks 1000 times, and recalculated the ranking of all proteins at each iteration (Figures S4). While the exact ranking of proteins is not very robust to addition of extra edges, if we examine all the proteins in the top 20%, we can observe that most fluctuate out of the top 20% only very rarely, and that we nearly never observe a protein in the top 10% drop out of the top 20%. It is also reassuring that the top of the rankings starts with expected key regulators of each function: the polarity landmark Tea1 [57]–[59], the actin-regulating Rho GTPase Cdc42 [60], [61] and actin (Act1) all came on the top of the polarity list. At the same time Cdc2, Wee1 and Cdc25 [62] are on the top of the cell cycle list (and also on the top of the core list) and the SIN scaffold Cdc11 [63] and the CDK counteracting, SIN activator phosphatase Clp1 [64], [65] are leading the cytokinesis ranking (Figure S4 and Table S3).

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