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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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Dependence of network measures on protein-protein interaction data quality.As we increase the minimal accepted confidence (cutoff) for the PPI data of the STRING database, the number of nodes in the largest connected component (A) and the network density (B) both decrease for all networks. This decrease is faster in fission yeast compared to budding yeast, and faster in the full organism network compared to the core network. Triangles overlaid on each curve show the same network measures for the PPI network based on the BioGRID database, the position on the x-axis of BioGRID data is calculated using linear interpolation to estimate the corresponding cutoff in STRING which would give a similarly-sized network, thus the overlay of the BioGRID data gives an indication how this relates to different cutoff STRING data. As can be seen from the figure panels the fission yeast core network is quite robust to cutoff changes and behaves similarly to the core network of budding yeast cells. This is also true for the core networks based on BioGRID data.
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pcbi-1002732-g001: Dependence of network measures on protein-protein interaction data quality.As we increase the minimal accepted confidence (cutoff) for the PPI data of the STRING database, the number of nodes in the largest connected component (A) and the network density (B) both decrease for all networks. This decrease is faster in fission yeast compared to budding yeast, and faster in the full organism network compared to the core network. Triangles overlaid on each curve show the same network measures for the PPI network based on the BioGRID database, the position on the x-axis of BioGRID data is calculated using linear interpolation to estimate the corresponding cutoff in STRING which would give a similarly-sized network, thus the overlay of the BioGRID data gives an indication how this relates to different cutoff STRING data. As can be seen from the figure panels the fission yeast core network is quite robust to cutoff changes and behaves similarly to the core network of budding yeast cells. This is also true for the core networks based on BioGRID data.

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)

Dependence of network measures on protein-protein interaction data quality.As we increase the minimal accepted confidence (cutoff) for the PPI data of the STRING database, the number of nodes in the largest connected component (A) and the network density (B) both decrease for all networks. This decrease is faster in fission yeast compared to budding yeast, and faster in the full organism network compared to the core network. Triangles overlaid on each curve show the same network measures for the PPI network based on the BioGRID database, the position on the x-axis of BioGRID data is calculated using linear interpolation to estimate the corresponding cutoff in STRING which would give a similarly-sized network, thus the overlay of the BioGRID data gives an indication how this relates to different cutoff STRING data. As can be seen from the figure panels the fission yeast core network is quite robust to cutoff changes and behaves similarly to the core network of budding yeast cells. This is also true for the core networks based on BioGRID data.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002732-g001: Dependence of network measures on protein-protein interaction data quality.As we increase the minimal accepted confidence (cutoff) for the PPI data of the STRING database, the number of nodes in the largest connected component (A) and the network density (B) both decrease for all networks. This decrease is faster in fission yeast compared to budding yeast, and faster in the full organism network compared to the core network. Triangles overlaid on each curve show the same network measures for the PPI network based on the BioGRID database, the position on the x-axis of BioGRID data is calculated using linear interpolation to estimate the corresponding cutoff in STRING which would give a similarly-sized network, thus the overlay of the BioGRID data gives an indication how this relates to different cutoff STRING data. As can be seen from the figure panels the fission yeast core network is quite robust to cutoff changes and behaves similarly to the core network of budding yeast cells. This is also true for the core networks based on BioGRID data.
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
Related in: MedlinePlus