Prediction of protein–protein interactions between Ralstonia solanacearum and Arabidopsis thaliana

Li, Zhigang; He, Fei; Zhang, Ziding; Peng, You-Liang

doi:10.1007/s00726-011-0978-z

Cited by 37 publications

(33 citation statements)

References 42 publications

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“…Generally, each of the Psy and Hpa targets interacted with only one Psy effector and one Hpa effector, respectively (the median values are shown). On the contrary, each Psy or Hpa effector interacted with two Arabidopsis proteins (the median values are shown), which may be one of factors that enable pathogen infection through a handful of effectors (Li et al, 2012). …”

Section: Resultsmentioning

confidence: 99%

“…The other is important for global diffusion of information throughout the network, featured by higher betweenness centrality. It has been reported that host proteins targeted by pathogen proteins display higher degree (Li et al, 2012; Weßling et al, 2014; Halehalli and Nagarajaram, 2015; Memisevic et al, 2015), including those host proteins targeted by the Psy or Hpa effectors (Mukhtar et al, 2011). We also validated this tendency in our comprehensive PPI network (Supplementary Figure S6), indicating that the effector targets are indeed important for local network organization.…”

Section: Resultsmentioning

confidence: 99%

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Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

Zhou

Zhang

2017

Front. Plant Sci.

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Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein–protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant–pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

Zhou

Zhang

2017

Front. Plant Sci.

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“…Interolog information has been used previously to predict PPIs, both for intraspecies and interspecies predictions [16][45]. With particular relevance to this work, Krishnadev et al [13] obtained a list of predicted interactions between human host and Salmonella using a conceptually similar approach.…”

Section: Discussionmentioning

confidence: 99%

Prediction and Comparison of SalmonellaHuman and SalmonellaArabidopsis Interactomes

Schleker

Garcı́a-Garcı́a

Klein‐Seetharaman

et al. 2012

Chemistry & Biodiversity

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Salmonellosis caused by Salmonella bacteria is a food-borne disease and worldwide health threat causing millions of infections and thousands of deaths every year. This pathogen infects an usually broad range of host organisms including human and plants. A better understanding of the mechanisms of communication between Salmonella and its hosts requires identifying the interactions between Salmonella and host proteins. Protein-protein interactions (PPIs) are the fundamental building blocks of communication. Here we utilize the prediction platform BIANA to obtain the putative Salmonella-human and Salmonella-Arabidopsis interactomes based on sequence and domain similarity to known PPIs. A gold standard list of Salmonella-host PPIs served to validate the quality of the human model. 24,726 and 10,926 PPIs comprising interactions between 38 and 33 Salmonella effectors and virulence factors with 9,740 human and 4,676 Arabidopsis proteins, respectively, were predicted. Putative hub proteins could be identified and parallels between the two interactomes were discovered. This approach can provide insight into possible biological functions of so far uncharacterized proteins. The predicted interactions are available via a web interface which allows filtering of the database according to parameters provided by the user to narrow down the list of suspected interactions. The interactions are available via a webinterface at http://sbi.imim.es/web/SHIPREC.php

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“…In the past decade, a series of computational approaches for PPI prediction have been developed [16, 17], and these now play important roles in complementing the various experimental approaches. The existing computational approaches for PPI prediction have exploited diverse data features, which include domain and motif information [18–21], network topology [21, 22], gene ontology (GO) [18–20], gene expression [18, 19], protein sequence similarity [14, 23], and pathway analysis [24]. At present, the interolog and domain-based approaches [25–27] are widely used [14,15,28].…”

Section: Introductionmentioning

confidence: 99%

“…The existing computational approaches for PPI prediction have exploited diverse data features, which include domain and motif information [18–21], network topology [21, 22], gene ontology (GO) [18–20], gene expression [18, 19], protein sequence similarity [14, 23], and pathway analysis [24]. At present, the interolog and domain-based approaches [25–27] are widely used [14,15,28]. The interolog method is based on protein sequence similarity to conduct the PPI prediction, which maps interactions in the source organism onto the target organism to find possible interactions in the target organism [25, 26].…”

Section: Introductionmentioning

confidence: 99%

Bradyrhizobium diazoefficiens USDA 110-Glycine max interactome provides candidate proteins associated with symbiosis

Zhang

Liu

et al. 2018

Preprint

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Although the legume-rhizobium symbiosis is a most important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog and domain-based approaches, we constructed an inter-species protein interactome with 5115 protein-protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was validated by expression pattern analysis in nodules, GO term semantic similarity, and co-expression analysis. One sub-network was further confirmed using luciferase complementation image assay. In the G. max-B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top ten highly interacting proteins (hubs) for each of the two species. KEGG pathway analysis for each hub showed that two 14-3-3 proteins (SGF14g and SGF14k) and five heat shock proteins in G. max are possibly involved in symbiosis, and ten hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related SNARE proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome and symbiosis proteins provide a valuable basis for understanding the molecular mechanism of root nodule symbiosis in soybean.

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Prediction of protein–protein interactions between Ralstonia solanacearum and Arabidopsis thaliana

Cited by 37 publications

References 42 publications

Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

Prediction and Comparison of SalmonellaHuman and SalmonellaArabidopsis Interactomes

Bradyrhizobium diazoefficiens USDA 110-Glycine max interactome provides candidate proteins associated with symbiosis

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