Systems understanding of plant–pathogen interactions through genome-wide protein–protein interaction networks

doi:10.15302/J-FASE-2016100

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 Author(s): 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.

URL:

https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2016100
https://academic.hep.com.cn/fase/EN/Y2016/V3/I2/102

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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