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Frontiers of Agricultural Science and Engineering

Front. Agr. Sci. Eng.    2016, Vol. 3 Issue (2) : 102-112     https://doi.org/10.15302/J-FASE-2016100
REVIEW |
Systems understanding of plant–pathogen interactions through genome-wide protein–protein interaction networks
Hong LI,Ziding ZHANG()
State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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Abstract

Plants are frequently affected by pathogen infections. To effectively defend against such infections, two major modes of innate immunity have evolved in plants; pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity. Although the molecular components as well as the corresponding pathways involved in these two processes have been identified, many aspects of the molecular mechanisms of the plant immune system remain elusive. Recently, the rapid development of omics techniques (e.g., genomics, proteomics and transcriptomics) has provided a great opportunity to explore plant–pathogen interactions from a systems perspective and studies on protein–protein interactions (PPIs) between plants and pathogens have been carried out and characterized at the network level. In this review, we introduce experimental and computational identification methods of PPIs, popular PPI network analysis approaches, and existing bioinformatics resources/tools related to PPIs. Then, we focus on reviewing the progress in genome-wide PPI networks related to plant–pathogen interactions, including pathogen-centric PPI networks, plant-centric PPI networks and interspecies PPI networks between plants and pathogens. We anticipate genome-wide PPI network analysis will provide a clearer understanding of plant–pathogen interactions and will offer some new opportunities for crop protection and improvement.

Keywords plant–pathogen interactions      systems biology      omics      plant immunity      protein–protein interaction      network     
Corresponding Authors: Ziding ZHANG   
Just Accepted Date: 11 May 2016   Online First Date: 13 June 2016    Issue Date: 05 July 2016
 Cite this article:   
Hong LI,Ziding ZHANG. Systems understanding of plant–pathogen interactions through genome-wide protein–protein interaction networks[J]. Front. Agr. Sci. Eng. , 2016, 3(2): 102-112.
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http://journal.hep.com.cn/fase/EN/10.15302/J-FASE-2016100
http://journal.hep.com.cn/fase/EN/Y2016/V3/I2/102
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Hong LI
Ziding ZHANG
Fig.1  Two major plant innate immune modes; pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI). Pattern recognition receptors (PRRs) are plant proteins present on plasma membranes. Once PRRs sense PAMPs, PTI will be activated. To interfere with PTI, pathogens secrete effectors into plant cells. Nucleotide binding/leucine-rich-repeat (NB-LRR) proteins can recognize effectors to initiate ETI.
Fig.2  Introduction to pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) network. (a) Each node represents a protein and each edge represents an interaction between two proteins; (b) date hubs exhibit low co-expression with their interacting partners (i.e., interact with partners at different time and/or space), while party hubs tend to highly co-express with their partners (i.e., co-expression at the same time and space); (c) a PPI network is divided into five modules, which are marked in different colors; (d) plant PPIs are shown using green edges, pathogen PPIs are shown using red edges, while plant–pathogen PPIs are shown using yellow edges.
Name Description URL
Keyword: network clustering algorithm
MCL Fast and scalable unsupervised clustering algorithm based on simulation of flow http://micans.org/mcl
CFinder Fast and efficient clustering algorithm based on the Clique Percolation Method http://cfinder.org
MCODE Well-known automated clustering algorithm to find highly interconnected subgraphs http://baderlab.org/Software/MCODE
ClusterONE A graph clustering algorithm that readily generates overlapping clusters http://paccanarolab.org/clusterone
Keyword: cytoscape and its plugin
Cytoscape An open source software tool for integrating, visualizing, and analyzing data in the context of networks http://www.cytoscape.org
BiNGO GO enrichment analysis plugin http://apps.cytoscape.org
ClueGO GO enrichment analysis plugin http://apps.cytoscape.org
GeneMANIA Gene function prediction plugin http://apps.cytoscape.org
ReactomeFIPlugIn Pathway analysis plugin http://apps.cytoscape.org
KEGGscape Pathway analysis plugin http://apps.cytoscape.org
clusterMaker An integrative cluster plugin http://apps.cytoscape.org
Keyword: database
BioGRID A comprehensive database containing plant PPI http://www.thebiogrid.org
IntAct A comprehensive database containing plant and plant–pathogen PPI http://www.ebi.ac.uk/intact
TAIR An integrated Arabidopsis database containing PPI http://www.arabidopsis.org
Arabidopsis Interactome Network Map A proteome-wide binary protein–protein interaction map for Arabidopsis http://interactome.dfci.harvard.edu/A_thaliana
AtPID Arabidopsis protein interactome database http://www.megabionet.org/atpid/webfile
ANAP An integrated PPI database for Arabidopsis http://gmdd.shgmo.org/Computational-Biology/ANAP
PHI-base plant–pathogen PPI database http://www.phi-base.org
PRIN A predicted rice PPI network http://bis.zju.edu.cn/prin
AraNet Arabidopsis functional gene network http://www.functionalnet.org/aranet
AraONE A genome-wide Arabidopsis gene network http://systbio.cau.edu.cn/pinet/home.php
PPIN-1 A plant–pathogen immune network http://signal.salk.edu/interactome/PPIN1.html
PPIRA R. solanacearum–Arabidopsis PPI network http://protein.cau.edu.cn/ppira
Tab.1  Bioinformatics tools and resources related to plant–pathogen interactions
Fig.3  Methodological overview of the integration of AraONE and plant immunity-related transcriptomics data. Using the PTI/ETI gene expression profiles and the integrated gene network (i.e., AraONE) as input, the NGF algorithm is used to train classification models for distinguishing different microarray data. Based on the trained classification models, key genes/interactions involved in the plant immune response can be inferred, which are further used to provide insights into the gene network organizations of PTI and ETI.
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