Pathogenicity Genes in <i>Ustilaginoidea virens</i> Revealed by a Predicted Protein–Protein Interaction Network

Zhang, Kang; Li, Yuejiao; Li, Tengjiao; Li, Zhigang; Hsiang, Tom; Zhang, Ziding; Sun, Wenxian

doi:10.1021/acs.jproteome.6b00720

Cited by 24 publications

(18 citation statements)

References 88 publications

(148 reference statements)

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“…Interestingly, the pathogen might hijack rice nutrient supply system to benefit the formation of false smut balls by simulating ovule fertilization (Fan et al 2015;Song et al 2016). Furthermore, U. virens genome and the predicted protein-protein interaction network greatly accelerate progress in identifying pathogenicity factors and understanding effector biology in this pathogen (Zhang et al 2017;Fan et al 2019;Fang et al 2019;Sun et al 2020;Zhang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Mapping quantitative trait loci for disease resistance to false smut of rice

Han¹,

Li²,

Yang³

et al. 2020

Phytopathol Res

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False smut of rice, an important emerging disease caused by Ustilaginoidea virens, greatly reduces grain yield and deteriorates grain quality in many rice-growing regions worldwide. Identification of quantitative trait loci (QTL) conferring false smut resistance will facilitate incorporation of false smut resistance into elite cultivars. In this study, a set of recombinant inbred lines (RILs) derived from a cross of a resistant rice landrace MR183-2 and a high susceptible line 08R2394 were evaluated to map resistance QTLs for false smut. A total of 179 simple sequence repeat (SSR) polymorphic markers were identified for constructing genetic linkage maps. Using a composite interval mapping method, five false smut resistance QTLs were detected on chromosomes 2, 4, 8 and 11 in the RIL population. Among these QTLs, qFsr8-1 within a small region on chromosome 8 represents a major quantitative locus explaining the greatest phenotypic variance. Gene annotation in this major QTL region provides a theoretical basis for cloning of resistance QTLs. The SSR markers genetically linked to qFsr8-1 are valuable for marker-assisted breeding for false smut resistance in rice. In addition, nine QTLs for heading date were detected in this population. Correlation analysis of disease resistance score and heading date indicates that false smut resistance negatively correlates with the growth period length in rice.

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Section: Introductionmentioning

confidence: 99%

Mapping quantitative trait loci for disease resistance to false smut of rice

Han¹,

Li²,

Yang³

et al. 2020

Phytopathol Res

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“…We used subcellular localization data and Gene Ontology (GO) annotations to assess the reliability of F 1 ‐specific PPIs. The methods used for the assessment have been widely used in Interolog‐based PPI predictions (He et al , ; Gu et al , ; Zhang et al , , ). First, we used the proteins in the predicted F 1 ‐specific PPIs to generate 1000 random PPI networks by randomly rewiring the protein pairs.…”

Section: Methodsmentioning

confidence: 99%

The hybrid protein interactome contributes to rice heterosis as epistatic effects

Jiang

et al. 2019

The Plant Journal

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Summary Heterosis is the phenomenon in which hybrid progeny exhibits superior traits in comparison with those of their parents. Genomic variations between the two parental genomes may generate epistasis interactions, which is one of the genetic hypotheses explaining heterosis. We postulate that protein−protein interactions specific to F1 hybrids (F1‐specific PPIs) may occur when two parental genomes combine, as the proteome of each parent may supply novel interacting partners. To test our assumption, an inter‐subspecies hybrid interactome was simulated by in silico PPI prediction between rice japonica (cultivar Nipponbare) and indica (cultivar 9311). Four‐thousand, six‐hundred and twelve F1‐specific PPIs accounting for 20.5% of total PPIs in the hybrid interactome were found. Genes participating in F1‐specific PPIs tend to encode metabolic enzymes and are generally localized in genomic regions harboring metabolic gene clusters. To test the genetic effect of F1‐specific PPIs in heterosis, genomic selection analysis was performed for trait prediction with additive, dominant and epistatic effects separately considered in the model. We found that the removal of single nucleotide polymorphisms associated with F1‐specific PPIs reduced prediction accuracy when epistatic effects were considered in the model, but no significant changes were observed when additive or dominant effects were considered. In summary, genomic divergence widely dispersed between japonica and indica rice may generate F1‐specific PPIs, part of which may accumulatively contribute to heterosis according to our computational analysis. These candidate F1‐specific PPIs, especially for those involved in metabolic biosynthesis pathways, are worthy of experimental validation when large‐scale protein interactome datasets are generated in hybrid rice in the future.

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“…Potential domains of A. veronii proteins were identi ed by PfamScan [54] (e ≤ 1.00×10 −3 ). Three strict standards were adopted to improve the prediction accuracy of A. veronii PPIs [22]. To start with, the protein domains with length coverage < 80% were ltered.…”

Section: Construction Of a Veronii Ppi Networkmentioning

confidence: 99%

“…While the domain-based method refers to that two proteins are more likely to interact if they contain interacting domains [21]. The PPI networks of many pathogens, such as Ustilaginoidea virens [22] and Phomopsis longicolla [23], have been successfully constructed based on these two PPI inference methods. In addition, these two methods have also been successfully applied to predict host-pathogen interspecies PPIs [22,24].…”

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confidence: 99%

Network-Based Analysis of Virulence Factors for Uncovering Aeromonas Veronii Pathogenesis

Tang

et al. 2021

Preprint

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Background: Aeromonas veronii is a pathogen that causes serious harm to aquaculture. Virulence factors are its pathogenic basis, which could promote pathogens to colonize the host, evade host defense and so on. But because experimental verification of virulence factors is time-consuming and laborious, the number of known virulence factors is limited. In this past, most studies only focused on single virulence factor, resulting the biased interpretation for pathogenesis.Results: In this study, a protein-protein interaction (PPI) network at genome-wide scale for A. veronii was first constructed. Then, virulence factors were predicted and mapped on the network. Topological characteristics of the virulence factors were analyzed. The results showed that the virulence factors had higher degree and betweenness centrality than the other proteins in the network. In particular, the virulence factors tended to interact with each other and were enriched in two network modules. One of the modules mainly consisted of histidine kinases, response regulators, diguanylate cyclases and phosphodiesterases, which played important roles in two-component regulatory systems and the synthesis and degradation of cyclic-diGMP. Furthermore, an interspecies PPI network between A. veronii and its host Oreochromis niloticus was also constructed. The structures and interacting sites of the virulence factors and host proteins were added to the interspecies PPI network. By analyzing the interspecies PPI network, we found that the virulence factors could competitively bind host proteins and some of the interacting sites of the virulence factors were shared by different host proteins. Drugs could be designed to target these sites and further prevent pathogen to interfere with host pathways.Conclusions: Our results indicated that the virulence factors regulated the virulence of A. veronii by involving in signal transduction pathway and manipulate host biological processes by mimicking and competitively binding host proteins. Our results deepened the understanding for pathogenesis and had important theoretical significance for designing targeted antibacterial drugs.

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Pathogenicity Genes in Ustilaginoidea virens Revealed by a Predicted Protein–Protein Interaction Network

Cited by 24 publications

References 88 publications

Mapping quantitative trait loci for disease resistance to false smut of rice

Mapping quantitative trait loci for disease resistance to false smut of rice

The hybrid protein interactome contributes to rice heterosis as epistatic effects

Network-Based Analysis of Virulence Factors for Uncovering Aeromonas Veronii Pathogenesis

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