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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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The cell cycle + cytokinesis + polarity = core interaction network of fission yeast proteins.(A) Venn diagram showing the overlap among the different Gene Ontology functional groups for the proteins belonging to the core network. Proteins with multiple functional annotations have colours that are the sum of the colours of the individual functional annotations; proteins belonging to all three functional groups are in white. (B) Protein-protein interactions inside the fission yeast core network (from the STRING database at cutoff 0.7). Node colours are the same as in panel A. Node size is proportional to the degree of each protein, and node order within a category (clockwise) is also determined by degree. 165 black edges link proteins that do not share functional annotations, while 1869 grey edges link proteins that have at least one common GO annotation (thus white nodes have only grey links). White nodes (nodes belonging to all categories) are shown in the inner circle in the middle of the network.
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pcbi-1002732-g002: The cell cycle + cytokinesis + polarity = core interaction network of fission yeast proteins.(A) Venn diagram showing the overlap among the different Gene Ontology functional groups for the proteins belonging to the core network. Proteins with multiple functional annotations have colours that are the sum of the colours of the individual functional annotations; proteins belonging to all three functional groups are in white. (B) Protein-protein interactions inside the fission yeast core network (from the STRING database at cutoff 0.7). Node colours are the same as in panel A. Node size is proportional to the degree of each protein, and node order within a category (clockwise) is also determined by degree. 165 black edges link proteins that do not share functional annotations, while 1869 grey edges link proteins that have at least one common GO annotation (thus white nodes have only grey links). White nodes (nodes belonging to all categories) are shown in the inner circle in the middle of the network.

Mentions: We then examined the effects of increasing the cutoff in STRING confidence scores in both the genome-wide interaction dataset of fission yeast and that of the better characterized budding yeast Saccharomyces cerevisiae on the network topology. Increasing the cutoff decreased the amount of nodes (Figure 1A) and the edge density (Figure 1B) in the largest component (the connected component in the network containing the highest number of edges and nodes) of both the fission and budding yeast networks (Tables S1, S2). This decrease was less sharp in budding yeast compared to fission yeast due to the extensive amount of genome-wide interaction experiments carried out in the former, increasing the amount of high-confidence interactions. Interestingly, in the ‘core’ sub-network consisting of proteins involved in cell cycle regulation, polarity and cytokinesis (Figure 2 for fission yeast and Figure S1 for budding yeast), the drop off in the number of nodes and edges was far less significant in both yeasts, suggesting that interaction data for the core fission yeast network tends to be more reliable than interaction data for the rest of the network (Figure 1, red stars versus red dots, also Tables S1, S2, S3, S4). As a more stringent test, we constructed networks for both organisms using only data from BioGRID [47]. BioGRID is a database that only contains data from manually annotated experiments (distinguishing between experiments that show direct physical interaction and genetic interactions). Networks built using the BioGRID physical interaction data also show that the core networks of fission yeast and budding yeast are relatively dense, while the fission yeast organism-wide network is rather sparse (Figure 1). Even with the relatively high coverage of the core (regulation of cell cycle, cytokinesis, polarity) network in fission yeast, it is important to note that fission yeast lacks any genome-wide protein-protein interaction experiments, and as such, several of the interactions predicted by STRING are based on indirect evidence such as genetic interactions, inference from homology, or literature mining [35], [36].


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)

The cell cycle + cytokinesis + polarity = core interaction network of fission yeast proteins.(A) Venn diagram showing the overlap among the different Gene Ontology functional groups for the proteins belonging to the core network. Proteins with multiple functional annotations have colours that are the sum of the colours of the individual functional annotations; proteins belonging to all three functional groups are in white. (B) Protein-protein interactions inside the fission yeast core network (from the STRING database at cutoff 0.7). Node colours are the same as in panel A. Node size is proportional to the degree of each protein, and node order within a category (clockwise) is also determined by degree. 165 black edges link proteins that do not share functional annotations, while 1869 grey edges link proteins that have at least one common GO annotation (thus white nodes have only grey links). White nodes (nodes belonging to all categories) are shown in the inner circle in the middle of the network.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002732-g002: The cell cycle + cytokinesis + polarity = core interaction network of fission yeast proteins.(A) Venn diagram showing the overlap among the different Gene Ontology functional groups for the proteins belonging to the core network. Proteins with multiple functional annotations have colours that are the sum of the colours of the individual functional annotations; proteins belonging to all three functional groups are in white. (B) Protein-protein interactions inside the fission yeast core network (from the STRING database at cutoff 0.7). Node colours are the same as in panel A. Node size is proportional to the degree of each protein, and node order within a category (clockwise) is also determined by degree. 165 black edges link proteins that do not share functional annotations, while 1869 grey edges link proteins that have at least one common GO annotation (thus white nodes have only grey links). White nodes (nodes belonging to all categories) are shown in the inner circle in the middle of the network.
Mentions: We then examined the effects of increasing the cutoff in STRING confidence scores in both the genome-wide interaction dataset of fission yeast and that of the better characterized budding yeast Saccharomyces cerevisiae on the network topology. Increasing the cutoff decreased the amount of nodes (Figure 1A) and the edge density (Figure 1B) in the largest component (the connected component in the network containing the highest number of edges and nodes) of both the fission and budding yeast networks (Tables S1, S2). This decrease was less sharp in budding yeast compared to fission yeast due to the extensive amount of genome-wide interaction experiments carried out in the former, increasing the amount of high-confidence interactions. Interestingly, in the ‘core’ sub-network consisting of proteins involved in cell cycle regulation, polarity and cytokinesis (Figure 2 for fission yeast and Figure S1 for budding yeast), the drop off in the number of nodes and edges was far less significant in both yeasts, suggesting that interaction data for the core fission yeast network tends to be more reliable than interaction data for the rest of the network (Figure 1, red stars versus red dots, also Tables S1, S2, S3, S4). As a more stringent test, we constructed networks for both organisms using only data from BioGRID [47]. BioGRID is a database that only contains data from manually annotated experiments (distinguishing between experiments that show direct physical interaction and genetic interactions). Networks built using the BioGRID physical interaction data also show that the core networks of fission yeast and budding yeast are relatively dense, while the fission yeast organism-wide network is rather sparse (Figure 1). Even with the relatively high coverage of the core (regulation of cell cycle, cytokinesis, polarity) network in fission yeast, it is important to note that fission yeast lacks any genome-wide protein-protein interaction experiments, and as such, several of the interactions predicted by STRING are based on indirect evidence such as genetic interactions, inference from homology, or literature mining [35], [36].

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