Volume 15 Supplement 11
Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches
© Sahu et al.; licensee BioMed Central Ltd. 2014
Published: 21 October 2014
Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets.
In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. The interolog-based method predicts ~0.79M PPIs involving around 7700 Arabidopsis and 1068 Pseudomonas proteins in the full genome. The domain-based method predicts 85650 PPIs comprising 11432 Arabidopsis and 887 Pseudomonas proteins. Further, around 11000 PPIs have been identified as interacting from both the methods as a consensus.
The present work predicts the protein-protein interaction network between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 in a genome wide scale with a high confidence. Although the predicted PPIs may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. This can be a useful resource to the plant community to characterize the host-pathogen interaction in Arabidopsis and Pseudomonas system. Further, these prediction models can be applied to the agriculturally relevant crops.
KeywordsPlant-pathogen interactions Bioinformatics Unsupervised learning Arabidopsis Pseudomonas syringae Interactome Computational prediction
Pseudomonas syringae is a Gram-negative bacterium causing economically important diseases in a wide range of plant species leading to severe agricultural losses worldwide. Each strain of Pseudomonas shows a high degree of host specificity and infects only a limited number of plant species or even a few cultivars of a single plant species [1, 2]. Among them, pathovar tomato strain DC3000 (Pst DC3000) has been asserted to infect the plant host Arabidopsis thaliana and tomato causing bacterial spec and brown spot. Thus, Arabidopsis-Pseudomonas has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions in both resistance and susceptible interactions [1, 3, 4]. The whole genome sequence of Pst DC3000 revealed that it has ~300 virulence-related genes . One of the major classes of virulence factors includes effector proteins that are delivered into the host through a type III protein secretion system (TTSS) to suppress plant immune responses, and also to facilitate disease development . Basically, Pseudomonas syringae pathogenesis is dependent on effector proteins and to date, nearly 60 different type III effector proteins encoded by hop genes have been identified [http://www.pseudomonas-syringae.org/]. In addition, Pst DC3000 also produces non-proteinaceous virulence effectors, including coronatine (COR), which are crucial for pathogenesis. However, the virulence function of a large number of potential effectors encoded by the Pst DC3000 genome and their mode of action is still unknown. Similarly, in Arabidopsis it has been seen that approximately 3000 proteins are directly related to plant defense . Many of these proteins interact directly with the pathogen proteins and some of them initiate plant defense responses to the infection. Recently, Mukhtar et al.  reported an experimental protein interaction network (PPIN-1) containing 843 Arabidopsis proteins and 83 pathogen effectors including very few interactions with Pst DC3000. Till now, only nearly 10 % of the full genome of Arabidopsis has been evidenced for interaction. Therefore, to functionally characterize the dynamic interactions of plants with bacterial pathogens, there is a need for genome-wide study of the host-pathogen interactions. Knowledge of such novel resistance interactions provides the backbone of the understanding of plant resistance mechanisms and will aid in the further analysis of plant immunity .
Generally, pathogen attacks host tissues, secreting degradation enzymes and toxin release. Many of such mechanisms involve the protein-protein interactions (PPIs). PPIs are essential process in all living cells and play a crucial role in the infection process, and initiating a defense response. In this context, understanding the PPI network (interactome) between plant proteins and pathogen proteins is a critical step for studying the molecular basis of pathogenesis [10, 11]. In particular, computational approaches ameliorate the study of host-pathogen protein interactions in a genome-wide range.
In the past decade, a series of PPI prediction methods have been elegantly developed and are playing an increasingly important role in complementing experimental approaches. Diverse data types or properties, such as gene ontology (GO) annotations , protein sequence similarity , protein domain interactions , and protein structural information [15, 16] have been frequently utilized to construct PPI prediction methods. Among these computational methods, the interolog and the domain-based methods [17–23] are widely used approaches for PPIs prediction.
In this work, we used the interolog and the domain-based methods to jointly predict the protein-protein interactions between Pseudomonas syringae and Arabidopsis thaliana. The domain-based approach infers inter-species protein-protein interactions by known domain-domain interactions from various databases and the interolog approach identifies protein-protein interactions based on homologous pairs of protein interactions across different organisms. We present the prediction pipeline in detail and the functional analysis of the predicted results.
Materials and methods
The whole proteome of Pseudomonas syringae pv. tomato DC3000 is downloaded from Pseudomonas genome database (http://www.pseudomonas.com/download.jsp) which contains 5619 protein sequences. Similarly, the full genome of Arabidopsis thaliana containing 35386 protein sequences is extracted from the TAIR10 database (http://www.arabidopsis.org/). To infer the prediction from the interolog, we have used two types of datasets: the HPIDB dataset and DIP dataset. Database of Interacting Proteins (DIP) is a collection of experimental determined interactions between proteins in intra-species . As of Jan 2014, DIP database contains 25749 sequences of 72380 protein-protein interactions. Host Pathogen Interaction Database (HPIDB) is a database of experimental determined interactions between 62 host and 529 pathogens . As of Jan 2014, HPIDB database contains 29922 sequences of 23735 unique protein-protein interactions. To implement the domain based model, the domain-domain interaction databases, iPfam and 3DID are used. The iPfam database is a catalog of protein family interactions, including domain and ligand interactions, calculated from known structures in protein data bank (PDB). As of Jan 2014, the iPfam1.0 database contains 5442 domain-domain interactions. The database of three-dimensional interacting domains (3DID) is a collection of high-resolution three-dimensional structural templates for domain-domain interactions. It contains templates for interactions between two globular domains as well as novel domain-peptide interactions. As of Jan. 2014, the 3DID database contains 8323 domain-domain interactions.
Identification of secreted proteins in Pseudomonas syringae
All proteins of Pseudomonas are processed through the Psortb3.0 (widely used tool for protein localization in bacteria ) and those predicted as cytoplasmic or cytoplasmic membrane are discarded as these proteins have less chance of involvement in interaction. The rest proteins annotated with extracellular, outer membrane and unknown are considered to be positive candidates for interaction. Again we search the whole proteome of Pseudomonas through the effector database (http://www.effectors.org/) , which is an integrated database for secreted type proteins for bacteria. Those identified as secreted are considered as positive candidates for interaction. Combining these two steps, 2744 potential candidate proteins of PstDC3000 are filtered for interaction prediction.
Prediction of PPIs between Arabidopsis and Pseudomonas
Domain based protein-protein interaction prediction
The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If two proteins contain an interacting domain pair, it is expected that these two proteins may interact with each other. To get the domains in Arabidopsis and Pseudomonas, HMMPfam is used in interproscan5 . In total, 49073 domains are extracted for all the Arabidopsis proteins and 7253 domains are collected for PstDC3000. If a protein pair between Pseudomonas and Arabidopsis contains an interacting domain pair from iPfam and 3DID, then the pair is expected to interact with each other.
Interolog based protein-protein interaction prediction
Results and discussion
Prediction of interactions
Prediction results of Arabidopsis and Pseudomonas syringae interactions using domain and Interolog approache s.
# of PPIs
# Arabidopsis protein involved
# of Pseudomonas proteins involved
Domain based method
From Both Methods
Predicted effector hubs
The effectors of Pseudomonas with the highest number of edges (hubs) are PSPTO_0135, PSPTO_0400, PSPTO_0540, PSPTO_0808, PSPTO_1510, PSPTO_2303, PSPTO_2529, PSPTO_2632, PSPTO_3161, PSPTO_3583, PSPTO_3890, PSPTO_3912 and PSPTO_4001 with more than 400 PPIs in the Arabidopsis-Pseudomonas interactome. There are also several effectors with more than 40 predicted PPIs. These are PSPTO_4497, PSPTO_1482, PSPTO_4868, PSPTO_4602, PSPTO_3882, PSPTO_0405, PSPTO_1492, PSPTO_4093, PSPTO_1949, PSPTO_4776, PSPTO_3130, PSPTO_3900, PSPTO_5014 and PSPTO_4090. In contrast to these hub proteins, several effectors are predicted to interact with very few proteins. These hub proteins play important role in pathogenesis, hence can be further investigated for deciphering virulence mechanism.
Functional enrichment analysis of proteins involved in the Interaction
Enriched GO biological process terms involved in predicted Arabidopsis protein s.
protein amino acid phosphorylation
phosphate metabolic process
transmembrane receptor protein tyrosine kinase signaling pathway
cell surface receptor linked signal transduction
reproductive cellular process
small GTPase mediated signal transduction
intracellular signaling cascade
abscisic acid mediated signaling
regulation of ion transport
regulation of ion transmembrane transporter activity
negative regulation of abscisic acid mediated signaling
cell redox homeostasis
protein amino acid dephosphorylation
regulation of stomatal movement
PSII associated light-harvesting complex II catabolic process
protein kinase cascade
response to cadmium ion
response to metal ion
response to salt stress
response to abiotic stimulus
toxin catabolic process
cell redox homeostasis
cell wall macromolecule catabolic process
negative regulation of protein kinase activity
brassinosteroid mediated signaling
cytokinin mediated signaling
Enriched GO biological process terms involved in predicted Pseudomonas syringae protein s.
protein amino acid phosphorylation
phosphate metabolic process
two-component signal transduction system
regulation of transcription, DNA-dependent
regulation of RNA metabolic process
aminoglycan catabolic process
cell wall macromolecule catabolic process
negative regulation by symbiont of pathogen-associated molecular pattern-induced host innate immunity
polysaccharide catabolic process
aminoglycan metabolic process
regulation of gene expression, epigenetic
negative regulation by symbiont of defense-related host callose deposition
Subcellular localization of Arabidopsis proteins targeted by the predicted Pseudomonas proteins
Pathogens suppress host immunity by directing a range of secreted proteins or effectors, to the cytoplasm of host cells. Once these effector proteins traversed the host plasma-membrane, are transported to many subcellular locations where they subvert the host immune system to enable pathogen growth and reproduction. The knowledge of cellular compartments of the Arabidopsis proteins targeted by the predicted Pseudomonas will be helpful in deciphering the mechanism of host-pathogen interactions. If the targeted Arabidopsis proteins are located in cellular compartments that are very relevant to the pathogen's infection or very likely to be involved in interactions with the pathogen, then the prediction result supports the host-pathogen predictions.
Distribution of subcellular localization for predicted interacting proteins in Arabidopsis thaliana from both the domain and interolog-based approache s.
Arabidopsis protein location
# of proteins
In this study, we have demonstrated that the sequence and domain similarity to known interactions are valuable information in predicting the host-pathogen interactions. We identified ~11000 PPIs between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 based on the domain-based and interolog approaches. The functional annotations of both Arabidopsis and Pseudomonas proteins involved in the predicted PPI are analyzed and it shows the relevance of the proteins for host defense and pathogen infections. The present work may provide some useful information and resource to the plant community to understand the molecular mechanism of the plant immunity system against pathogen virulence. The quality of the predicted interactome could further be improved by combining these methods with other computational approaches and biological data sources. The reliability of the predicted interactions can be further assessed through experimental validations.
List of abbreviations used
Database of Interacting Proteins
Host Pathogen Interaction Database
type III protein secretion system
three-dimensional interacting domains
Protein family interactions
Database for Annotation, Visualization and Integrated Discovery.
The authors duly acknowledge the funding support to this study from Oklahoma Center for Advancement of Science and Technology (OCAST), grant number PS12-062. We also thank the anonymous referees for critical review of the manuscript that helped improving the research article.
Funding for the publication of this article has come from OCAST funds account AB-5-42820, OSU.
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 11, 2014: Proceedings of the 11th Annual MCBIOS Conference. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S11.
- Katagiri F, Thilmony R, SY H: The Arabidopsis thaliana-Pseudomonas syringae Interaction. The Arabidopsis Book, Rockville, MD, USA: American Society of Plant Biologists. 2002, 11-35.Google Scholar
- Barah P, Winge P, Kusnierczyk A, Tran DH, Am B: Molecular signature of Arabidopsis thaliana in response to Insect attack and bacterial attack. PLOS One. 2013, 8 (3): e58987-10.1371/journal.pone.0058987.PubMed CentralView ArticlePubMedGoogle Scholar
- Abramovitch RB, Gb M: Strategies used by bacterial pathogens to suppress plant defenses. Curr Opin Plant Biol. 2004, 7 (4): 356-364. 10.1016/j.pbi.2004.05.002.View ArticlePubMedGoogle Scholar
- Quirino BF, Af B: Deciphering host resistance and pathogen virulence: the Arabidopsis/Pseudomonas interaction as a model. Molecular Plant Pathology. 2003, 4 (6): 517-530. 10.1046/j.1364-3703.2003.00198.x.View ArticlePubMedGoogle Scholar
- Buell CR, Joardar V, Lindeberg M, Selengut J, Paulsen IT, Gwinn ML, Dodson RJ, Deboy RT, Durkin AS, Jf K: The complete genome sequence of the Arabidopsis and tomato pathogen Pseudomonas syringae pv. tomato DC3000. Proc Natl Acad Sci. 2003, 100 (18): 10181-10186. 10.1073/pnas.1731982100.PubMed CentralView ArticlePubMedGoogle Scholar
- Nomura K, Melotto M, Sy H: Suppression of host defense in compatible plant-Pseudomonas syringae interactions. Curr Opin Plant Biol. 2005, 8 (4): 361-368. 10.1016/j.pbi.2005.05.005.View ArticlePubMedGoogle Scholar
- Bishop JG, Dean AM, T M-O: Rapid evolution in plant chitinases: molecular targets of selection in plant-pathogen coevolution. Proc Natl Acad Sci USA. 2000, 97 (10): 5322-5327. 10.1073/pnas.97.10.5322.PubMed CentralView ArticlePubMedGoogle Scholar
- Mukhtar MS, Carvunis AR, Dreze M, Epple P, Steinbrenner J, Moore J, Tasan M, Galli M, Hao T, Nishimura MT: Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science. 2011, 333 (6042): 596-601. 10.1126/science.1203659.PubMed CentralView ArticlePubMedGoogle Scholar
- Goritschnig S, Krasileva KV, Dahlbeck D, Bj S: Computational Prediction and Molecular Characterization of an Oomycete Effector and the Cognate Arabidopsis Resistance Gene. PLOS genetics. 2012, 8 (2): e1002502-10.1371/journal.pgen.1002502.PubMed CentralView ArticlePubMedGoogle Scholar
- Pinzon A, Rodriguez RL, Gonzalez A, Bernal A, S R: Targeted metabolic reconstruction: a novel approach for the characterization of plant-pathogen interactions. Brief Bioinform. 2011, 12 (2): 151-162. 10.1093/bib/bbq009.View ArticlePubMedGoogle Scholar
- Kim JG, Park D, Kim BC, Cho SW, Kim YT, Park YJ, Cho HJ, Park H, Kim KB, Yoon KO: Predicting the interactome of Xanthomonas oryzae pathovar oryzae for target selection and db service. BMC Bioinformatics. 2008, 9: 41-10.1186/1471-2105-9-41.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu X, Zhu L, Guo J, Zhang DY, K L: Prediction of yeast protein-protein interaction network: insights from the gene ontology and annotations. Nucleic Acids Research. 2006, 34 (7): 2137-2150. 10.1093/nar/gkl219.PubMed CentralView ArticlePubMedGoogle Scholar
- Matthews LR, Vaglio P, Reboul J, Ge H, Davis BP, Garrels J, Vincent S, M V: Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or ''interologs''. Genome Research. 2001, 11 (12): 2120-2126. 10.1101/gr.205301.PubMed CentralView ArticlePubMedGoogle Scholar
- Ng SK, Zhang Z, SH T: Integrative approach for computationally inferring protein domain interactions. Bioinformatics. 2003, 19 (8): 923-929. 10.1093/bioinformatics/btg118.View ArticlePubMedGoogle Scholar
- Ogmen U, Keskin O, Aytuna AS, Nussinov R, A G: Prism: protein interactions by structural matching. Nucleic Acids Research. 2005, 33: W331-W336. 10.1093/nar/gki585.PubMed CentralView ArticlePubMedGoogle Scholar
- Davis FP, Barkan DT, Eswar N, Mckerrow JH, A S: Host-Pathogen protein interactions predicted by comparative modeling, Protein Science. Protein Science. 2007, 16 (12): 2585-2596. 10.1110/ps.073228407.PubMed CentralView ArticlePubMedGoogle Scholar
- Shoemaker BA, Ar P: Deciphering protein-protein interactions. Part ii. Computational methods to predict protein and domain interaction partners. PLoS Comput Bio. 2007, 3 (4): e43-10.1371/journal.pcbi.0030043.View ArticleGoogle Scholar
- Li ZG, He F, Zhang Z, YL P: Prediction of protein-protein interactions between Ralstonia solanacearum and Arabidopsis thaliana. Amino Acids. 2012, 42: 2363-2371. 10.1007/s00726-011-0978-z.View ArticlePubMedGoogle Scholar
- Kurubanjerdjit N, Tsai JJP, Sheu CY, Kl N: The prediction of protein-protein interaction of A. thaliana and X. campestris pv. campestris based on protein domain and interolog approaches. Plant Omics Journal. 2013, 6 (6): 388-398.Google Scholar
- Schlekera S, Garcia-Garciab J, Seetharamana JK, B O: Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes. Chem Biodivers. 2012, 9 (5): 991-1018. 10.1002/cbdv.201100392.View ArticleGoogle Scholar
- Zhou H, Rezaei J, Hugo W, Gao S, Jin J, Fan M, Yong CH, Wozniak M, L W: Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions. BMC Systems Biology. 2013, 7 (6): S6-PubMed CentralView ArticlePubMedGoogle Scholar
- S W: Computational Prediction of Host-Parasite Protein Interactions between P. falciparum and H. sapiens. PLoS ONE. 2011, 6 (11): e26960-10.1371/journal.pone.0026960.View ArticleGoogle Scholar
- Dyer M: Computational prediction of host-pathogen protein-protein interactions. Bioinformatics. 2007, 23: i159-i166. 10.1093/bioinformatics/btm208.View ArticlePubMedGoogle Scholar
- Xenarios I, Salwinski L, Duan XJ, Higney P, Kim S, D E: DIP: The Database of Interacting Proteins. A research tool for studying cellular networks of protein interactions. Nucleic acids research. 2002, 30: 303-305. 10.1093/nar/30.1.303.PubMed CentralView ArticlePubMedGoogle Scholar
- Kumar R, B N: HPIDB-a unified resource for host-pathogen interactions. BMC Bioinformatics. 2010, 11: S16-PubMed CentralView ArticlePubMedGoogle Scholar
- Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ: PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010, 26 (13): 1608-1615. 10.1093/bioinformatics/btq249.PubMed CentralView ArticlePubMedGoogle Scholar
- Jehl MA, Arnold R, T R: Effective - a database of predicted secreted bacterial proteins. Nucleic Acids Research. 2010, 1-5.Google Scholar
- Quevillon E: InterProScan: protein domains identifier. Nucleic Acids Research. 2005, 33: W116-W120. 10.1093/nar/gki442.PubMed CentralView ArticlePubMedGoogle Scholar
- Yu H, Luscombe NM, L H: Annotation Transfer Between Genomes: Protein-Protein Interologs and Protein-DNA Regulogs. Genome Research. 2004, 14: 1107-1118. 10.1101/gr.1774904.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang DW, Sherman BT, RA L: Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protocol. 2009, 4 (1): 44-57.View ArticleGoogle Scholar
- Fones H: Metal hyperaccumulation armors plants against disease. PLoS Pathogen. 2010, 6 (9): p1-View ArticleGoogle Scholar
- Franza T, Mahe B, D E: Erwinia chrysanthemi requires a second iron transport route dependent of the siderophore achrophore achromobactin for extracellular growth and plant infection. Mol Microbiol. 2005, 55: 261-275.View ArticlePubMedGoogle Scholar
- Block A, Jr A: Plant targets for Pseudomonas syringae type III effectors: virulence targets or guarded decoys? Current Opinion in Microbiology. Current Opinion in Microbiology. 2011, 14: 39-46. 10.1016/j.mib.2010.12.011.PubMed CentralView ArticlePubMedGoogle Scholar
- Qiu JL, Fiil BK, Petersen K, Nielsen HB, Botanga CJ, Thorgrimsen S, Palma K, Suarez Rodriguez MC, Sandbech Clausen S, Lichota J: Arabidopsis MAP kinase 4 regulates gene expression through transcription factor release in the nucleus. European Molecular Biology Organization Journal. 2008, 27: 2214-2221. 10.1038/emboj.2008.147.View ArticleGoogle Scholar
- Ligterink W, Kroj T, zurNieden U, Hirt H, D S: Receptor-mediated activation of a MAP kinase in pathogen defense of plants. Science. 1997, 276 (5321): 2054-2057. 10.1126/science.276.5321.2054.View ArticlePubMedGoogle Scholar
- Kaundal R, Saini R, PX Z: Combining Machine Learning and Homology-Based Approaches to Accurately Predict Subcellular Localization in Arabidopsis. Plant Physiology. 2010, 154: 36-54. 10.1104/pp.110.156851.PubMed CentralView ArticlePubMedGoogle Scholar
- Citovsky V, Kapelnikov A, Oliel S, Zakai N, Rojas MR, Gilbertson RL, Tzfira T, A L: Protein interactions involved in nuclear import of the Agrobacterium VirE2 Protein in vivo and in vitro. Journal of Biological Chemistry. 2004, 279: 29528-29533. 10.1074/jbc.M403159200.View ArticlePubMedGoogle Scholar
- Tao R, Qu F, TJ M: The Nuclear Localization of the Arabidopsis Transcription Factor TIP Is Blocked by Its Interaction with the Coat Protein of Turnip Crinkle Virus. Virology. 2005, 331 (2): 316-324. 10.1016/j.virol.2004.10.039.View ArticleGoogle Scholar
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