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Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla) is the primary cause of Phomopsis seed decay (PSD) in soybean, Glycine max (L.) Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein–protein interactions (PPI) and to i...
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... ; Stark et al., 2006). The confidence value (CV), gene ontology, and the analysis methods described by Geisler-Lee et al. (2007) were used (Supplementary Tables S1, S2). Additionally, the gene ontology analysis used was the best BLAST hit in F. graminearum because the protein domain information for P. longicolla was not available ( Güldener et al., 2006;Carbon et al., 2009). ...
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... visualize the PPI interactions from the network analysis, the P. longicolla protein data (Supplementary File S1 and Supplemental Table S1) was used as the input file in the Cytoscape (version 3.5.1) analysis ( Shannon et al., 2003;Cline et al., 2007). ...
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... were 215,255 unique PPIs among 3,868 of 16,595 predicted proteins. The relative contribution of each reference species to the predicted interactions is summarized in Supplementary Table S1. The resulting P. longicolla protein interactome (PiPhom) encompassed just 23% of the total proteome because the paralogous and duplicated genes from the FIGURE 1 | Flowchart for developing Phomopsis longicolla interactome. ...
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... were excluded. When duplicated genes were included in the prediction of the interactome, using a many-to-many ortholog matching method that allows the inclusion of paralogs, 50 P. longicolla proteins that were only in the many-to-many set, as well as 189 unique interologs, were added to the uniqueinteractome (Table 1). A premade Cytoscape formatted graphical visualization of P. longicolla interactome for this combined set of proteins was included (Supplementary File S1). ...
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... addition, contributions from each organism were highlighted ( Supplementary Table S1), where S. cerevisiae had the largest contribution of total interactions including both "one to one" and "many to many" for the PPI data set (78%, Figure 2). H. sapiens contributed the second largest number (13%) of interactions to the PiPhom (Supplementary Table S1). ...
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... addition, contributions from each organism were highlighted ( Supplementary Table S1), where S. cerevisiae had the largest contribution of total interactions including both "one to one" and "many to many" for the PPI data set (78%, Figure 2). H. sapiens contributed the second largest number (13%) of interactions to the PiPhom (Supplementary Table S1). ...
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... analysis of conservation indicated the subnetworks with the strongest confidences values which were similar to previously reported interactomes. Confidence values (CVs) for each interaction in the PiPhom interactome are listed (Supplementary Table S1) and added to the network visualization (Supplementary File S1) as an edge feature. Interactions with a CV of 1 were ranked as a low confidence data set. ...
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Citations
... Due to limited studies on specific virulence genes of P. viticola, we carried out qRT-PCR analysis from extracted RNA of fungi, which was concomitantly obtained from liquid culture when bacteria and fungi grow together, and followed the expression level of HOG1, FUS3, and SGE1 genes of P. viticola considering a previous study carried out on P. longicolla which is in the same genus (Li et al., 2018) (Table 1). ...
... Therefore, we could not obtain raw RNA-seq data from fungi as we have requested. Instead of RNA-seq, we selected 3 major genes playing role in virulence of Phomopsis longicolla which has the most genetically close and available corresponding genes of P. viticola (Li et al., 2018). ...
Introduction
Rhizobacteria has the suppression ability to compete with pathogenic microorganisms and help for plant immunity and defense mechanism. Their growth and survival in rhizosphere ensure biological balance in favor of the host plant.
Methods
In our study, with agar diffusion assays, we found a rhizobacterium species belonging to Bacillus safensis , which can significantly suppress Phomopsis viticola . To elucidate the antagonistic mechanism, genome-wide gene expression profiling of B. safensis (strain MM19) was performed in the presence and absence of P. viticola . We used RNA-seq analysis to obtain a comprehensive overview of the responsive B. safensis whole gene expression classified according to biological and metabolic process to P. viticola concomitant growth in liquid culture.
Results
The differential gene expression profiles of B. safensis (MM19) revealed significantly increased expression of prominent genes related to thiamine biosynthesis involving various metabolites and enzymes that play role in the suppression of mycelium growth and pathogen inhibition. Correspondingly, the expression of three major genes (HOG1, FUS3, SGI) involving in the virulence of P. viticola was followed using qPCR analysis. HOG1 was the highest expressed gene in the pathogen when it co-cultivated with MM19. Based on these findings, we performed molecular docking and dynamics analysis to explore the interaction between HOG1 and thiamine, besides expression of network analysis constructed using Cytoscape.
Discussion
The results proved that the functional genomic data related to thiamine biosynthesis and corresponding pathways ensure a priming role in the antagonistic behavior of B. safensis (MM19) against P. viticola as a supporter for plant immunity.
... To date, the pathogenic mechanisms of P. longicolla in crops have not been fully elucidated. Genomic studies, as well as factors related to the pathogenic mechanism, are still being studied as published by Li et al. [11], Li et al. [12], Mena et al. [13]. In addition, P. longicolla is the anamorph type of Diaporthe and they often coexist in nature increasing the complexity of the understanding of this fungus as reported by Mena et al. [13], Udayanga et al. [14], and Hosseini et al. [15]. ...
Rhizobacteria have been proved to have potential for biocontrol of fungal phytopathogens and have been successfully applied in the field to a variety of crops. However, diseases caused by Phomopsis longicolla on soybean have been mainly controlled by agricultural or chemical techniques. The objective of this study was to evaluate the in vitro control of rhizobacteria on P. longicolla through their ability to inhibit the growth of P. longicolla on agar plates and disease caused by P. longicolla on soybean seeds. The results showed that the GTC 5.3.84 rhizobacterial isolate was able to inhibit 31% of the growth of P. longicolla GTC 2.5.1 on the agar plate. The GTC 5.3.84 isolate had no effect on germination rate and was able to completely inhibit the disease caused by P. longicolla GTC 2.5.1 on soybean seeds. This result was very clear evidence for the potential application of indigenous rhizobacteria in the control of diseases caused by P. longicolla in soybean. Further studies need to be continued to exploit the efficiency of the GTC 5.3.84 isolate as well as the potential of other indigenous rhizobacteria in the control of P. longicolla disease in soybean.
... Remmele and co-workers used interolog mapping to identify host-pathogen interactions of A. fumigatus during human and mouse infection, highlighting the roles of the PLB1 virulence factor and APP anti-fungal host protein [284]. Several network studies have also investigated yeast infection in plants [68,91,93,280,[285][286][287]. ...
Interactome analyses have traditionally been applied to yeast, human and other model organisms due to the availability of protein-protein interaction data for these species. Recently, these techniques have been applied to more diverse species using computational interaction prediction from genome sequence and other data types. This review describes the various types of computational interactome networks that can be created and how they have been used in diverse eukaryotic species, highlighting some of the key interactome studies in non-model organisms.
... Remmele and co-workers used interolog mapping to identify host-pathogen interactions of A. fumigatus during human and mouse infection, highlighting the roles of the PLB1 virulence factor and APP anti-fungal host protein [213]. Several network studies have also investigated yeast infection in plants [61,89,91,[214][215][216][217]. ...
Interactome analyses have traditionally been applied to yeast, human and other model organisms due to the availability of protein-protein interactions data for these species. Recently these techniques have been applied to more diverse species using computational interaction prediction from genome sequence and other data types. This review describes the various types of computational interactome networks that can be created and how they have been used in diverse eukaryotic species, highlighting some of the key interactome studies in non-model organisms.
... On the other hand, the domain-based method refers to two proteins that are more likely to interact if they contain interacting domains [24]. The PPI networks of many pathogens, such as Ustilaginoidea virens [25] and Phomopsis longicolla [26], 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 [25,27]. ...
Background
Aeromonas veronii is a bacterial pathogen in aquaculture, which produces virulence factors to enable it colonize and evade host immune defense. Given that experimental verification of virulence factors is time-consuming and laborious, few virulence factors have been characterized. Moreover, most studies have only focused on single virulence factors, resulting in biased interpretation of the pathogenesis of A. veronii .
Results
In this study, a PPI network at genome-wide scale for A. veronii was first constructed followed by prediction and mapping of virulence factors on the network. When topological characteristics were analyzed, the virulence factors had higher degree and betweenness centrality than 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 play important roles in two-component regulatory systems and the synthesis and degradation of cyclic-diGMP. Construction of the interspecies PPI network between A. veronii and its host Oreochromis niloticus revealed that the virulence factors interacted with homologous proteins in the host. Finally, the structures and interacting sites of the virulence factors during interaction with host proteins were predicted.
Conclusions
The findings here indicate that the virulence factors probably regulate the virulence of A. veronii by involving in signal transduction pathway and manipulate host biological processes by mimicking and binding competitively to host proteins. Our results give more insight into the pathogenesis of A. veronii and provides important information for designing targeted antibacterial drugs.
... In a study of peanut stem rot caused by fungi, it was found that the number of GHs is largest in the pathogenic secreted protein [99]. In addition, in research on soybean seed rot, 149 plant cell wall-degrading enzymes were detected, most of which are GHs [100]. Our results are in agreement with these previous reports. ...
Background: Panax notoginseng (Burkill) F. H. Chen is a Chinese medicinal plant of the Araliaceae family commonly used in the treatment of cardiovascular and cerebrovascular diseases in Asia and elsewhere. To meet an increase in Chinese herbal medicine market demand, most P. notoginseng is planted artificially, and is vulnerable to various plant diseases. Root rot disease, in particular, causes substantial P. notoginseng yield reduction and economic losses. High-depth next-generation sequencing technology was used to analyze the rhizosphere and root endophyte microbial communities of P. notoginseng to compare the characteristics of these two communities between healthy and root rot diseased P. notoginseng plants, and to clarify the relationship between these microbial communities and root rot disease.
Results: The P. notoginseng rhizosphere microbial community was more diverse than the root endophyte community, and the difference in functional pathways between healthy and diseased P. notoginseng plants was greater in the root endophyte than in the rhizosphere communities. Multi-database annotation results showed that the highest number of endophytic bacteria occurred in the roots of diseased plants. The number of carbohydrate-active enzymes database families was also higher in diseased roots. The RND antibiotic efflux function was higher in the healthy samples. A high abundance of Variovorax paradoxus and Pseudomonas fluorescens occurred in the healthy and diseased root endophyte communities, respectively. Ilyonectria mors-panacis and Pseudopyrenochaeta lycopersici were most abundant in the diseased samples. In addition, the complete genome of two unknown Flavobacteriaceae species and one unknown Bacteroides species were obtained based on binning analysis.
Conclusions: The rhizosphere and root endophyte microbial communities of healthy and root rot diseased P. notoginseng showed marked differences in diversity and functional pathways. The higher mapping values obtained for the diseased samples reflected the occurrence of root rot disease at the molecular level. Variovorax paradoxus and Pseudomonas fluorescens may be antagonistic bacteria of root rot in P. notoginseng, whereas Ilyonectria mors-panacis and Pseudopyrenochaeta lycopersici appear to be P. notoginseng root rot pathogens. Our study provides a theoretical basis for understanding the occurrence of root rot in P. notoginseng and for further research on potential biological control agents.
... 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]. ...
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.
... Networks exist for Neurospora crassa (Wang et al., 2011) and human-infecting fungi Candida albicans, Aspergillus fumigatus, and Cryptococcus neoformans Remmele et al., 2015). Additional networks are available for a few plant pathogenic species including Magnaporthe grisea (He et al., 2008), Phomopsis longicolla (Li et al., 2018), Rhizoctonia solani (Lei et al., 2014), Fusarium verticillioides (Kim M. et al., 2015), and F. graminearum (Zhao et al., 2009;Liu et al., 2010;Bennett et al., 2012;Lysenko et al., 2013). However, the approaches used differed across studies and do not allow comparative network investigation. ...
... Both B. cinerea and F. graminearum are fungal Ascomycetes and many conserved orthologous genes exist in both species important for virulence on their respective hosts (Van De Wouw and Howlett, 2011). For F. graminearum a rich dataset of genes with phenotypic annotation exists, while for B. cinerea only a comparatively small number of genes have been formally tested in gene modification experiments and phenotypically assayed (Urban et al., 2017;Li et al., 2018). We reasoned that by surveying the predicted interactome of the siRNA target orthologs in F. graminearum additional information could be obtained to pinpoint siRNA targets to more specific protein complexes and metabolic networks, to provide further annotation to the interacting partners and to identify novel candidate genes with a potential function in virulence. ...
Interactions between proteins underlie all aspects of complex biological mechanisms. Therefore, methodologies based on complex network analyses can facilitate identification of promising candidate genes involved in phenotypes of interest and put this information into appropriate contexts. To facilitate discovery and gain additional insights into globally important pathogenic fungi, we have reconstructed computationally inferred interactomes using an interolog and domain-based approach for 15 diverse Ascomycete fungal species, across nine orders, specifically Aspergillus fumigatus, Bipolaris sorokiniana, Blumeria graminis f. sp. hordei, Botrytis cinerea, Colletotrichum gloeosporioides, Colletotrichum graminicola, Fusarium graminearum, Fusarium oxysporum f. sp. lycopersici, Fusarium verticillioides, Leptosphaeria maculans, Magnaporthe oryzae, Saccharomyces cerevisiae, Sclerotinia sclerotiorum, Verticillium dahliae, and Zymoseptoria tritici. Network cartography analysis was associated with functional patterns of annotated genes linked to the disease-causing ability of each pathogen. In addition, for the best annotated organism, namely F. graminearum, the distribution of annotated genes with respect to network structure was profiled using a random walk with restart algorithm, which suggested possible co-location of virulence-related genes in the protein–protein interaction network. In a second ‘use case’ study involving two networks, namely B. cinerea and F. graminearum, previously identified small silencing plant RNAs were mapped to their targets. The F. graminearum phenotypic network analysis implicates eight B. cinerea targets and 35 F. graminearum predicted interacting proteins as prime candidate virulence genes for further testing. All 15 networks have been made accessible for download at www.phi-base.org providing a rich resource for major crop plant pathogens.
... A G-protein interactome revealed a new role of G-proteins in the regulation of cell-wall modification, a process highly relevant for defense (Klopffleisch et al., 2011). Recently, a protein-protein interaction network for the fungus Phomopsis longicolla, causative for Phomopsis seed decay in soybean, was generated by interolog mapping (Yu et al., 2004), i.e., transferring interaction annotations among conserved protein pairs between organisms, and allowed detection of diseaseassociated subnetworks (Li et al., 2018). ...
In natural environments, plants are exposed to diverse microbiota that they interact with in complex ways. While plant–pathogen interactions have been intensely studied to understand defense mechanisms in plants, many microbes and microbial communities can have substantial beneficial effects on their plant host. Such beneficial effects include improved acquisition of nutrients, accelerated growth, resilience against pathogens, and improved resistance against abiotic stress conditions such as heat, drought, and salinity. However, the beneficial effects of bacterial strains or consortia on their host are often cultivar and species specific, posing an obstacle to their general application. Remarkably, many of the signals that trigger plant immune responses are molecularly highly similar and often identical in pathogenic and beneficial microbes. Thus, it is unclear what determines the outcome of a particular microbe–host interaction and which factors enable plants to distinguish beneficials from pathogens. To unravel the complex network of genetic, microbial, and metabolic interactions, including the signaling events mediating microbe–host interactions, comprehensive quantitative systems biology approaches will be needed. Plants need to respond appropriately to surrounding microbes to fend off pathogens and allow colonization by beneficials and symbionts. Challenges are that beneficials and pathogens are often evolutionary close relatives, and productive probiotic interactions can be species and cultivars specific. Systems biology approaches will be vital for understanding the integrated and genetically variable networks mediating host–microbiome interactions.
... Interactome analyses can increase the understanding of the biological interactions and cellular processes within an organism. Recent high-throughput studies have obtained a great amount of PPI data, including model organisms such as Saccharomyces cerevisiae (Lam et al., 2015) and Aspergillus nidulans (Bayram et al., 2012;Jaimes-Arroyo et al., 2015), as well as plant pathogens Fusarium graminearum (Zhao et al., 2009), Magnaporthe grisea (He et al., 2008), and Phomopsis longicolla (Li et al., 2018). Bacteria, plants (Nishiyama et al., 2013), and fungi (Aguirre et al., 2006) use conserved phosphorelay systems to sense different types of environmental stresses. ...
Aspergillus fumigatus, a saprophytic filamentous fungus, is a serious opportunistic pathogen of mammals and it is the primary causal agent of invasive aspergillosis (IA). Mitogen activated protein Kinases (MAPKs) are important components involved in diverse cellular processes in eukaryotes. A. fumigatus MpkC and SakA, the homologs of the Saccharomyces cerevisiae Hog1 are important to adaptations to oxidative and osmotic stresses, heat shock, cell wall damage, macrophage recognition, and full virulence. We performed protein pull-down experiments aiming to identify interaction partners of SakA and MpkC by mass spectrometry analysis. In presence of osmotic stress with sorbitol, 118, and 213 proteins were detected as possible protein interactors of SakA and MpkC, respectively. Under cell wall stress caused by congo red, 420 and 299 proteins were detected interacting with SakA and MpkC, respectively. Interestingly, a group of 78 and 256 proteins were common to both interactome analysis. Co-immunoprecipitation (Co-IP) experiments showed that SakA::GFP is physically associated with MpkC:3xHA upon osmotic and cell wall stresses. We also validated the association between SakA:GFP and the cell wall integrity MAPK MpkA:3xHA and the phosphatase PtcB:3xHA, under cell wall stress. We further characterized A. fumigatus PakA, the homolog of the S. cerevisiae sexual developmental serine/threonine kinase Ste20, as a component of the SakA/MpkC MAPK pathway. The ΔpakA strain is more sensitive to cell wall damaging agents as congo red, calcofluor white, and caspofungin. Together, our data supporting the hypothesis that SakA and MpkC are part of an osmotic and general signal pathways involved in regulation of the response to the cell wall damage, oxidative stress, drug resistance, and establishment of infection. This manuscript describes an important biological resource to understand SakA and MpkC protein interactions. Further investigation of the biological roles played by these protein interactors will provide more opportunities to understand and combat IA.
