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Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression.

De Bodt S, Proost S, Vandepoele K, Rouzé P, Van de Peer Y - BMC Genomics (2009)

Bottom Line: We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions.Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered.Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, B-9052 Gent, Belgium. stefanie.debodt@psb.vib-ugent.be

ABSTRACT

Background: Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome.

Results: In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively.

Conclusion: We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

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Protein-protein interactions involved in 'DNA replication'. Green edges represent PCC values > 0.3 (see Figure 5 for more details). For grey edges, no co-expression information is available.
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Figure 4: Protein-protein interactions involved in 'DNA replication'. Green edges represent PCC values > 0.3 (see Figure 5 for more details). For grey edges, no co-expression information is available.

Mentions: As could be expected, well-conserved proteins and functions, such as those involved in transcription, translation, and proteolysis, are overrepresented. Typically, proteasome and ribosomal proteins are identified as highly connected (CAST clusters 1 and 3; see Supplementary data site). A number of protein-protein interaction networks involved in particular processes such as organelle organization and biogenesis, lipid metabolism, and ATP binding as well as the biological processes mentioned below can be viewed through our Supplementary data site. In addition, a considerable number of protein-protein interactions with a role in transmembrane transport, membrane receptor activity and vesicle trafficking were detected as previously reported by Geisler-Lee et al. [45] (see Supplementary data-Transmembrane activity). For example, a link was found between protein clusters of interacting VAMPs (Vesicle associated membrane proteins) and SNAREs (CAST clusters 10 and 70) with vacuolar H+ pumping ATPases (CAST clusters 9, 34 and 120) and cation/H+ exchangers (CAST clusters 100, 145 and 185). Although not connected to the above-mentioned clusters, a protein cluster containing components of the translocase inner membrane complex (CAST cluster 13) associated with carrier proteins (CAST cluster 94), was retrieved as well. Several links between cell cycle control, protein degradation and related processes are captured in the protein clusters enriched for GO categories containing 'cell cycle' (CAST clusters 3, 12, 18 and 61; see Supplementary data). Whereas ubiquitin-mediated proteolysis regulates the activity of cyclins during the progression through the different phases of the cell cycle, B-type cyclins interact with several microtubule-related components during cytokinesis. Similarly, A-type cyclins, DNA replication proteins (CDC, MCM and ORC subunits) and RBR/WEE proteins are associated during the G1/S transition [48] (see Supplementary data - Cell cycle + DNA repair + DNA replication). Overall, we observe a high similarity in expression patterns for the genes encoding proteins in the 'cell cycle' clusters most probably due to the tight regulation of proteins involved in DNA replication (green edges, see Fig. 4, Supplementary data). Moreover, the interactions in these clusters are often supported by the GO biological process measure (solid edges, see Supplementary data). These two properties occur mainly within protein clusters. In contrast, genes encoding interacting proteins involved in transmembrane activity, described above, show an overall lower similarity in expression patterns, even within protein clusters (yellow edges, see Supplementary data). This difference in the degree of co-expression is probably due to the more specific expression patterns of transmembrane activity genes resulting in transient interactions that cannot be identified using a global co-expression measure. However, most of the interactions in the transmembrane activity network are supported through the GO biological process and/or GO cellular component feature and thereby were identified through our approach (solid and thick edges, see Supplementary data). Reasonably, localization is very important in transmembrane activity-related processes such as ion transmembrane transport and protein excretion through vesicular exocytosis.


Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression.

De Bodt S, Proost S, Vandepoele K, Rouzé P, Van de Peer Y - BMC Genomics (2009)

Protein-protein interactions involved in 'DNA replication'. Green edges represent PCC values > 0.3 (see Figure 5 for more details). For grey edges, no co-expression information is available.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2719670&req=5

Figure 4: Protein-protein interactions involved in 'DNA replication'. Green edges represent PCC values > 0.3 (see Figure 5 for more details). For grey edges, no co-expression information is available.
Mentions: As could be expected, well-conserved proteins and functions, such as those involved in transcription, translation, and proteolysis, are overrepresented. Typically, proteasome and ribosomal proteins are identified as highly connected (CAST clusters 1 and 3; see Supplementary data site). A number of protein-protein interaction networks involved in particular processes such as organelle organization and biogenesis, lipid metabolism, and ATP binding as well as the biological processes mentioned below can be viewed through our Supplementary data site. In addition, a considerable number of protein-protein interactions with a role in transmembrane transport, membrane receptor activity and vesicle trafficking were detected as previously reported by Geisler-Lee et al. [45] (see Supplementary data-Transmembrane activity). For example, a link was found between protein clusters of interacting VAMPs (Vesicle associated membrane proteins) and SNAREs (CAST clusters 10 and 70) with vacuolar H+ pumping ATPases (CAST clusters 9, 34 and 120) and cation/H+ exchangers (CAST clusters 100, 145 and 185). Although not connected to the above-mentioned clusters, a protein cluster containing components of the translocase inner membrane complex (CAST cluster 13) associated with carrier proteins (CAST cluster 94), was retrieved as well. Several links between cell cycle control, protein degradation and related processes are captured in the protein clusters enriched for GO categories containing 'cell cycle' (CAST clusters 3, 12, 18 and 61; see Supplementary data). Whereas ubiquitin-mediated proteolysis regulates the activity of cyclins during the progression through the different phases of the cell cycle, B-type cyclins interact with several microtubule-related components during cytokinesis. Similarly, A-type cyclins, DNA replication proteins (CDC, MCM and ORC subunits) and RBR/WEE proteins are associated during the G1/S transition [48] (see Supplementary data - Cell cycle + DNA repair + DNA replication). Overall, we observe a high similarity in expression patterns for the genes encoding proteins in the 'cell cycle' clusters most probably due to the tight regulation of proteins involved in DNA replication (green edges, see Fig. 4, Supplementary data). Moreover, the interactions in these clusters are often supported by the GO biological process measure (solid edges, see Supplementary data). These two properties occur mainly within protein clusters. In contrast, genes encoding interacting proteins involved in transmembrane activity, described above, show an overall lower similarity in expression patterns, even within protein clusters (yellow edges, see Supplementary data). This difference in the degree of co-expression is probably due to the more specific expression patterns of transmembrane activity genes resulting in transient interactions that cannot be identified using a global co-expression measure. However, most of the interactions in the transmembrane activity network are supported through the GO biological process and/or GO cellular component feature and thereby were identified through our approach (solid and thick edges, see Supplementary data). Reasonably, localization is very important in transmembrane activity-related processes such as ion transmembrane transport and protein excretion through vesicular exocytosis.

Bottom Line: We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions.Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered.Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, B-9052 Gent, Belgium. stefanie.debodt@psb.vib-ugent.be

ABSTRACT

Background: Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome.

Results: In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively.

Conclusion: We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

Show MeSH
Related in: MedlinePlus