Global ProteinâProtein Interaction Network of Rice Sheath Blight

Article pubs.acs.org/jpr

Global Protein−Protein Interaction Network of Rice Sheath Blight Pathogen Ding Lei,†,∥ Runmao Lin,†,∥ Chuanchun Yin,†,∥ Ping Li,*,†,‡,§ and Aiping Zheng*,†,‡,§ †

Rice Research Institute of Sichuan Agricultural University, Chengdu 611130, China State Key Laboratory of Hybrid Rice, Sichuan Agricultural University, Chengdu 611130, China § Key Laboratory of Southwest Crop Gene Resource and Genetic Improvement of Ministry of Education, Sichuan Agricultural University, Ya’an 625014, China ‡

S Supporting Information *

ABSTRACT: Rhizoctonia solani is the major pathogenic fungi of rice sheath blight. It is responsible for the most serious disease of rice (Oryza sativa L.) and causes significant yield losses in ricegrowing countries. Identifying the protein−protein interaction (PPI) maps of R. solani can provide insights into the potential pathogenic mechanisms and assign putative functions to unknown genes. Here, we exploited a PPI map of R. solani anastomosis group 1 IA (AG-1 IA) based on the interolog and domain−domain interaction methods. We constructed a core subset of high-confidence protein networks consisting of 6705 interactions among 1773 proteins. The high quality of the network was revealed by comprehensive methods, including yeast two-hybrid experiments. Pathogenic interaction subnetwork, secreted proteins subnetwork, and mitogen-activated protein kinase (MAPK) cascade subnetwork and their interacting partners were constructed and analyzed. Moreover, to exactly predict the pathogenic factors, the expression levels of the interaction proteins were investigated by analyzing RNA sequences that consisted of samples from the entire infection progress. The PPIs offer an exceptionally rich source of data that can be used to understand the gene functions and biological processes of this serious disease at the system level. KEYWORDS: protein−protein interaction, Rhizoctonia solani, interolog, domain−domain interaction, pathogenic genes, secreted proteins, MAPK signaling cascade

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INTRODUCTION Rhizoctonia solani is a common soil-borne pathogenic filamentous fungus that causes serious disease in many economically important plant species.1 R. solani and other Rhizoctonia fungi do not produce conidiospores and only rarely produce basidiospores under certain conditions, making it difficult to classify these fungi. On the basis of their hyphal anastomosis and physiological-biochemical characteristics, R. solani can be subdivided into 14 anastomosis groups (AG1 to AG13 and AGBI). These various groups were further classified into several subgroups.2,3 Certain AGs of R. solani are associated with specific crops, whereas others have a wide range of host plants, including rice,4 wheat,5 maize,6 cotton,7 potato,8−10 soybean,11 and tomato.12 As the agent of rice sheath blight disease, R. solani anastomosis group 1 IA causes the most serious economic losses in the field of agriculture. Many genes and quantitative trait locis (QTLs) for R. solani resistance have been identified in rice.13−15 Unfortunately, only a few pathogenic genes, such as the α subunit of G protein,16 have been identified in this pathogen. The mechanisms for overcoming the plant immune system remain poorly understood. Studies of the genetics and pathology of this pathogen have been experimentally hindered by the lack of an effective transformation system. To date, limited information about the pathogenic factors was gathered from the PHI Database. © 2014 American Chemical Society

Traditional research is ineffective at producing a holistic understanding of a complete biological process because it relies on a single gene or protein.17 Therefore, an effective way to complement the experimental approaches is needed to advance our understanding of the pathogenic processes of this filamentous fungus. Within cells, the proteins interact with each other and function as a complex network. Therefore, every protein can be regarded as part of a complex PPI network. The generation of PPI networks provides us with a valuable framework to better analyze the vast majority of cellular events and biological processes, including cellular pathways, regulation, and packaging. Recently, data gathered from the use of high-throughput methods, such as yeast two-hybrid,18 mass spectrometry,19 and protein chip,20 have been implemented to generate proteomescale interactomes and to identify PPIs in many model organisms, including Saccharomyces cerevisiae,21 Caenorhabditis elegans,22 Drosophila melanogaster,23 Helicobacter pylori,24 and Homo sapiens.25 These interactome data provide resources that allow us to understand the function of the proteins at the level of system biological processes, regulatory pathway signaling, and entire organisms despite some drawbacks to the Received: January 29, 2014 Published: June 4, 2014 3277

dx.doi.org/10.1021/pr500069r | J. Proteome Res. 2014, 13, 3277−3293

Journal of Proteome Research

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experimentally determined PPIs.26 Unfortunately, none of these high-throughput methods have been applied to R. solani even though such applications are urgently required. Aside from the large-scale experimental determination of PPIs, many computational methods have been widely used. The prediction methods can be classified into interaction− ortholog (interolog), 27 gene expression profile,28 gene annotation,29 domain composition,30,31 coevolution,32 and structural information33 methods based on the various attributes, data types, and concepts used. Many PPIs in organisms were identified using these approaches, including Arabidopsis thaliana,34 Plasmodium falciparum,35 Magnaporthe oryzae,36 Fusarium graminearum,37 and others.38,39 Among these methods, the interolog and domain interaction-based methods, which are two sequence-based approaches, have been widely implemented to predict interactions from experimentally determined interactomes of model organisms.26,40 The interolog approach was first proposed several years ago, and this method can be described as the transfer of known interactions from model organisms to other species based on comparative genomics.26 Genes in different species that originated from a single gene in their last common ancestor are conserved.41,42 These genes often have shared identical biological roles in the different species. Thus, it is possible to transfer the functional information between the two genomes with a high degree of reliability, depending on the identification of orthologous genes in two genomes.27,43,44 However, there may be large numbers of potential orthologs within protein families due to gene duplications in the second species. Additionally, some paralogs, which are referred to as outparalogs, predating the species split can be confused with the presence of bona fide orthologs. A stringent algorithm must be used to distinguish the true gene orthologs from the outparalogs.45 Moreover, interactions predicted using the interolog method are generally complemented by domain−domain interactions (DDIs).30 The DDIs can predict interacting proteins even without interacting orthologs and only contain interacting domains that were determined experimentally. To date, these two methods have both been widely used to construct PPI networks for many important organisms.36,37,46,47 In this work, we construct a predicted PPI network of R. solani AG1 IA using interolog and domain interaction methods. To the best of our knowledge, this is the first PPI map for this destructive fungal pathogen, although it is complicated by some missing interactions and false positives. Despite these issues, our map provides new insights into the functional genomics of R. solani AG1 IA. Computational assessment and yeast twohybrid experiments verified the high quality of the predicted interactions. The predicted network provides a framework to better understand the gene functions of this pathogen at the systems level, and providing a guide for future study.

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3864 interactions from the Worm interactome database (http://interactome.dfci.harvard.edu/C_elegans/; version: 8), 1768 genes and 3227 interactions from the Human interactome database (http://interactome.dfci.harvard.edu/H_sapiens/ index.php), and 5176 genes and 14925 interactions from the Yeast interactome database (http://interactome.dfci.harvard. edu/S_cerevisiae/index.php), which was derived from three interactome databases (LC-multiple of the Yeast Interactome database, Y2H of the Yeast Interactome database, and the Combined-AP/MS of the Yeast Interactome database). Finally, 3017 genes and 11674 interactions were obtained from the MPID Interactome database (http://bioinformatics.cau.edu. cn/cgi-bin/zzd-cgi/ppi/mpid.pl). Supplementary Table S1 (Supporting Information) describes the details of these interactome databases. Orthologous genes between R. solani AG1 IA and these species were detected using the Inparanoid algorithm.53 Briefly, if there are four proteins (A and B in R. solani AG1 IA and interacting proteins C and D in another organism) and the evolutionary relationships among these genes are known (A and C are orthologs and B and D are orthologs), then the two R. solani AG1 IA proteins (A, B) can be used to predict the interaction. That is, any two proteins in R. solani AG1 IA were predicted to interact if their orthologs had at least one experimentally verified interaction in the five different species. Additionally, the DDIs of R. solani AG1 IA proteins were identified according the DDIs in DOMINE.54 The DOMINE database v2.0 contains 5410 domains (26219 DDIs) gathered from 15 different sources, including iPfam, 3did, ME, RCDP, Pvalue, Interdom, DPEA, PE, GPE, DIPD, RDFF, K-GIDDI, Insite, DomainGA, and DIMA. In this work, the interacting Pfam domain pairs in the DOMINE database were used to predict the PPIs. Domain annotations of R. solani AG1 IA proteins were predicted by searching the amino acid sequences against the Pfam databases (http://pfam.sanger.ac.uk/) using HMMER (http://hmmer.janelia.org). Every domain pair in the R. solani AG1 IA proteins was used to predict potential interactions. If two domains were found to interact with each other in the DOMINE database, the pair was considered a candidate for a possible domain interaction. PPIs were then predicted following a previously described method30 based on two assumptions: (1) the DDIs are independent, and (2) a pair of proteins interacts with each other only if they have at least one pair of interacting domains. DOMINE provided a rich set of DDIs to understand the interaction interfaces, although it also generated many false positives and negatives. We only used DDIs to complement the results of the interolog method. Finally, the integrated predictions from both the interologs and DDIs, along with a confidence score of at least 0.5 in the interologs, were used to generate a core PPI map with highly confident interactions (Supplementary Figure S1, Supporting Information).

METHODS

Assessment of the Reliability of Predicted PPIs

Predicting PPI Maps

Because few R. solani PPIs have been established to date, the validation of the R. solani AG1 IA PPI network was difficult from experiment. Some computational experiments were designed to validate the quality of the predicted PPI networks. We used two computational approaches and one experimental approach (the yeast two-hybrid assay) to test these predicted interactions. In the first computational approach, gene ontology (GO) annotation55 was used to verify the predicted R. solani AG1 IA

A total of 10489 protein sequences of R. solani AG1 IA were downloaded from NCBI (accession no.: AFRT01000000).48 In this study, the interaction data sets of five well-studied species, namely D. melanogaster,23 C. elegans,22 H. sapiens,49 S. cerevisiae,50−52 and M. oryzae,36 were used to identify R. solani AG1 IA protein interactions. These data included 7718 genes and 26239 interactions from the DroID interactome database (http://www.droidb.org/; version: 2010_10), 2528 genes and 3278



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extracted from the R. solani AG1 IA strain and was fused to the C-terminal part of GAL4 that contains the AD (GAL4-AD) in the prey vector pGADT7 (Clontech Laboratories, Takara Bio Group). Other proteins were amplified and were fused to the N-terminal part of GAL4 that contains the DBD (GAL4-DBD) in the bait vector pGBKT7 (Clontech Laboratories, Takara Bio Group). The primers used are listed in Supplementary Table S2 (Supporting Information). All assays were performed according to the protocols described in the Yeastmaker Yeast Transformation System 2 User Manual (PT1172-1). The pGBKT7 and pGADT7 plasmid pairs were co-transformed into yeast AH109 cells, and the cells were plated on the selective SD/Leu-Trp (DDO) screening medium plate to identify the transformants. Positive growth cotransformants were selected on plates containing the selective medium SD/-Leu-His-AdeTrp (QDO) with X-α-Gal to detect the initiation of reporter gene (ADE2, HIS3, MEL1, and LacZ) transcription. The colonies were then incubated at 30 °C for approximately 5 days, and the colonies that turned blue were selected as positive clones. A transformant containing pGBKT-53 and pGADT7-T (Stratagene) was used as a positive control for the expected growth on the selective screening medium. A cotransformant containing the empty (bait) pGBKT7 and (prey) pGADT7 vector, pGBKT-Lam, and pGADT7-T (Stratagene) was used as the negative control.

PPIs. GO annotation is a useful and popular taxonomy method that can be used to assess the functional relationship between different gene products. The GO annotations of R. solani AG1 IA were included in our previous work.48 The GO terms were established according to three independent hierarchies: biological process, molecular function, and cellular component. The GO organization structure is displayed as a directed acyclic graph, and we defined the shortest path length. Because the interacting protein pairs generally have similar, but not identical functions, they are predicted to share some, but not all, of their GO annotations. Therefore, high-quality PPI networks tend to have a greater proportion of predicted PPIs that share GO terms. To validate the accuracy of the interaction data, we calculated the GO terms of the interactions that shared at least one GO term in any of the three GO categories, and we compared the percentages between the predicted PPIs and same-sized randomized networks that were formed from nonself-interaction protein pairs of our predicted proteins. To ensure that this result was not merely the product of very general GO annotations, we calculated the proportion of interacting proteins that shared a GO term at depths of 3−8 and greater than 8 in the GO hierarchy. During the analyses, we discarded proteins with the GO term “unknown molecular function, biological process, and cellular compartment” as well as self-interactions. In the second set of computational experiments, we examined the coexpression of the predicted PPIs, which was wildly used to assess the predicted protein interactions, such as Fusarium graminearum,37 Neurospora crassa,38 and Magnaporthe oryzae.36 A large amount of research has shown that the interacting proteins generally tend to have correlated gene expression profiles in the same organism.56,57 In particular, some subunits in the permanent protein complex or some proteins associated with a common functional module involved in a same biological process showed significant coexpression, such as the ribosome and proteasome.57 In addition, in order to interact with each other, the expression profiles of two proteins are likely to be similar.58 Therefore, we reasoned that the predicted interaction proteins may show similar behavior, and coexpressed genes of the networks can provide a framework to assess the network.59 We utilized genome-wide expression data from Aiping Zheng et al. The level of coexpression of an interacting protein pair was identified by using the Pearson correlation coefficient (PCC) as a gene similarity metric between the interacting protein pairs. The PCC value for each pair of non-self-interaction proteins was calculated using the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) value of mRNA expression at six infection stages after inoculation (the 10-, 18-, 24-, 32-, 48-, and 72-h stage). We compared the PCC values between the expression data of each interacting pair of proteins and the randomized networks of the same size that were formed from the non-self-interaction proteins of the predicted proteins to validate our interaction data. The yeast two-hybrid assay was also used to detect interactions in living yeast cells; this method represents a straightforward way to verify the predicted interactions in a laboratory setting. Many published interactions have been detected using an Y2H screen.21,25,49,60,61 In our work, a hub, AG1IA_05961, and its 11 partners were randomly selected to test the interactions in vivo using a yeast two-hybrid assay. The full-length coding sequence (CDS) of AG1IA _05961 was amplified by polymerase chain reaction (PCR) from total RNA

PPI Subnetworks

To identify the meaningful genes or their satellite proteins, we extracted these genes from the PPI network and generated three PPI subnetworks: the one-core network, the multiplecore network, and the crossover network.62 The one-core network consisted of one core protein with its interacting proteins. An example of this type of network is AG1IA_05961 and its satellite proteins, which were selected to test the reliability of the predicted PPIs in our experiment. The multiple-core network contained several core proteins in a pathway that interact with other proteins, such as the cell wall integrity (CWI) pathway. The CWI pathway was selected to compare the differences between R. solani AG1 IA and M. oryzae. Finally, the crossover network contained several multiple-core networks and/or one-core networks, many of which have central roles in most biological processes (data not shown). In addition, the putative functions can be assigned for the unknown function genes according to the PPI subnetworks. Following the method describing in previous research,63 which is based on the principle that interacting proteins may share at least one common functional class, the function prediction is identified by the global connectivity pattern of PPIs. Briefly, if a hypothetical protein P1 interacted with at least two annotated proteins (P2, P3, et al.) which have common functional classes, then protein P1 is assigned to these common classes. In a subnetwork, GO annotation of some proteins could lead to the function prediction for unannotated proteins. Moreover, because the interacted proteins have similar or related functions, we also can predict the annotation proteins may take part in a certain biological processes which were unrevealed previously, such as pathogenicity, CWI, or osmostress adaptation. Subnetwork of Predicted Pathogenic Genes

R. solani is a necrotrophic, plant pathogenic fungus that has many genes involved in pathogenicity. Similar to the pathogenesis of Fusarium graminearum,64 the pathogenesis of R. 3279



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Table 1. Number of Potential PPIs and Core PPIs in R. solani AG1 IA Based on the Orthologous Proteins (Interologs) In the Different Model Organism PPI Data Sets, Including D. melanogaster, C. elegans, H. sapiens, S. cerevisiae, and M. oryzae, as Well as Domain Interactions (in Parentheses) PPIs

core PPIs

organism

interactions

proteins

result

interactions

proteins

result

D. melanogaster C. elegans H. sapiens S. cerevisiae M. oryzae

1393 275 233 3140 4457

784 218 124 926 1510

8312 (3995)

812 157 86 2721 4005

634 179 89 867 1425

6705 (3166)

Figure 1. Predicted R. solani PPIs. (A) The area-proportional Venn diagram shows the predicted PPIs of the different data sets from each organism and their overlapping areas. This diagram shows that the data from the different organism sources only overlap to a small extent. (B) The number of the core higher confidence R. solani AG1 IA PPIs. This Venn diagram shows the core PPIs identified by the overlap of the two methods and a confidence score of at least 0.5 as provided by the interolog analysis.

number of secreted virulence proteins termed “effectors”, many of which are directly delivered into the host cell to trigger defense responses. Most fungal effector proteins are secreted through secretory organelles, such as the fungal endoplasmic reticulum (ER) and Golgi apparatus of haustoria and infection hyphae.68,69 The targets of effectors in plants are the apoplast (the space outside plant cell membranes) or different host cellular compartments such as the nucleus (cytoplasmic effectors).70 Although the effectors are important, so far the definitive answer of the effector delivery from pathogens into the plant cells remains unclear. Recently, some research indicated that either exosomes or secreted vesicles,71 biotrophic interfacial complex (BIC),72 or pathogen-independent delivery73 was used to deliver effectors into host cells. Similar to the pathogenic genes, which were integrated with the differentially expressed genes and the PPI networks, the secreted network was predicted.

solani AG1 IA is thought to involve a complex network of proteins and other molecules. In our recent work, many pathogenic factors and some genes of R. solani AG1 IA were assigned to the PHI database. These genes and their interacting partners were extracted from the PPI network for further analysis, including the heterotrimeric G proteins and the glycoside hydrolase (GH) protein family. Additionally, the transcriptomes of R. solani AG1 IA collected at six stages after infection were used to detect the pathogenic genes exactly. We generated a pathogenic network by integrating the differentially expressed pathogenic genes and the PPI networks. The genes in the PHI database and pathogenic factors were used as the core genes, and the differentially expressed genes were used as the interacting partners. All maps were visualized by Cytoscape.65 Subnetwork of Predicted Secreted Genes

Traditionally, biochemical purification followed by genetic analysis has been used to identify secreted proteins. Currently, biochemical, genetic, and bioinformatics strategies are often used in combination to identify candidate secreted proteins. When used individually, these approaches often predict that nonsecreted proteins are secreted, but their accuracy can be increased when the methods are used in combination with each other. Most secreted proteins are exported with the help of short, N-terminal amino acid sequences known as signal peptides.66 However, such signal peptides are highly degenerate and are difficult to identify using DNA hybridization and PCRbased techniques. Various bioinformatics tools were used to help predict the fungal secretomes. Several algorithms were used to analyze the secreted proteins of R. solani AG1 IA, and WolfPSort67 was used to predict the eventual cellular location of the secreted proteins in this work. Some fungi deploy a large

MAPK Cascades in the Network

In fungi, a family of serine/threonine specific protein kinases known as the MAPKs serves a central role in the intracellular signal transduction pathways and regulates fundamental aspects of cell biology, including infection and virulence.74 Saccharomyces cerevisiae has multiple MAPK pathways. Some R. solani AG1 IA genes and M. oryzae genes display highly similarity to the sequences of S. cerevisiae genes involved in the MAPK signaling pathway. Homologous genes of the MAPK signaling pathway of R. solani and M. oryzae were identified in previous works.48,75 To further compare the MAPK signaling differences between these two rice-destroying pathogenic fungi, the CWI pathway was chosen for detailed analysis. Its core components and their interacting partners were extracted from the PPI 3280



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overlap between the PPIs predicted using the distinct approaches, suggesting that the two approaches can complement each other. Supplementary Figure S2 (Supporting Information) presents the core interactions as visualized in Cytoscape.65 In the core PPI networks, the proteins are the nodes, and the interactions that connect the two nodes are the edges. The degree of a node represents the number of interactions it has with the neighboring molecules. Highly connected proteins (hubs) with central roles in network architecture are more essential than proteins with only a small number of interactions.76 These proteins also provide a valuable resource for studying this pathogen. In our predicted PPI network, the average degree was 7.56, and 72 of the 1773 proteins have degrees higher than 30 (Supplementary Table S7, Supporting Information), some ribosomal proteins and key regulators are included, such as AG1IA_04285 (60S ribosomal protein L3) and AG1IA_01860 (Cdc2 cyclin-dependent kinase). The highest degree identified was 68, which is AG1IA_04285 (Supplementary Figure S3A, Supporting Information). The 60S ribosomal protein L3 is a component of the large 60S subunit of cytoplasmic ribosomes and is co-transcribed with several small nucleolar RNA genes, which are located in several of its introns. This gene has been characterized as an alternate transcriptional splice variant that is encoded in different isoforms. In addition, the degree of AG1IA_01860 (Cdc2 cyclin-dependent kinase) is 52, which is a key regulator of the cell cycle (Supplementary Figure S3B, Supporting Information). The cell division cycle 2 (Cdc2) cyclin-dependent kinase is a highly conserved protein member of the serine/threonine kinase family, and the phosphorylation and dephosphorylation of Cdc2 regulate the cell cycle control.77 The Cdc2 interacts with cyclin B (CCNB1, CCNB2, and CCNB3) to form a protein kinase complex maturation promoting factor (MPF), which plays a key role in the control of the G1/S and G2/M phase transitions of eukaryotic cell cycle.78 The Cdc2 is activated through the cyclin accumulation and destructed through the cell cycle. This gene also has been found as an alternatively spliced transcript variant that is encoded in different isoforms. Thus, it is not strange that these proteins interact with so many other proteins.

network, and these genes were annotated by integrating the EuKaryotic Orthologous Groups (KOG) database and the PHI database, according to our previous methods.48

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RESULTS

Predicted PPI Network of R. solani AG1 IA

A considerable number of identifiable evolutionarily conserved protein interactions of R. solani AG1 IA could be generated using the protein-interaction mapping data from model organisms by identifying of the orthologs. Through this approach, we used the reported data of the PPIs from five model organisms, D. melanogaster,23 C. elegans,22 H. sapiens,49 S. cerevisiae,50−52 and M. oryzae,36 and identified the orthologs between R. solani AG1 IA and these species using the Inparanoid algorithm.53 In total, 8312 interactions were inferred among the 1991 R. solani AG1 IA proteins. The analysis of the PPIs showed 3140 interactions between the 926 S. cerevisiae proteins that have R. solani AG1 IA orthologs. Similarly, 4457 interactions were found between the 1510 proteins in M. oryzae, 233 interactions were found between the 124 proteins in H. sapiens, 275 interactions were found between the 218 proteins in C. elegans, and 1393 interactions were found between the 784 proteins in D. melanogaster that have orthologs in R. solani AG1 IA. Table 1 and Figure 1A summarize the sources of these predicted interaction details from the different species. The results show that most of the predicted interactions are from S. cerevisiae and M. oryzae, which may be because S. cerevisiae and M. oryzae are evolutionarily closer to R. solani. The numbers of predicted interactions from H. sapiens and C. elegans were considerably smaller than those from S. cerevisiae, M. oryzae, and D. melanogaster, possibly because there were fewer orthologs found in these species are less than others. We believe that the quality and number of R. solani AG1 IA protein interactions will be greatly enhanced by using these data in a comprehensive and complementary manner. Additionally, we utilized the DDI information available in the DOMINE database54 and used the interacting Pfam domain pairs from DOMINE to predict PPIs. In our work, 1336786 interactions were annotated between the 4780 proteins associated with the DDIs in the DOMINE database. Although various successful computation methods were used to predict the DDIs, a significant number of false positives and negatives were also obtained. Indeed, this method was less accurate than the interolog method. To reduce the number of false positives, we only use the DDIs complementation with the interolog method. Finally, 3995 of the 1336786 interactions were predicted using the interolog method. Furthermore, we selected interactions with a confidence score of at least 0.5, as provided by Inparanoid, and interactions predicted using both the interolog and DDI methods to obtain our core interactions network. This network contained a total of 6705 interactions among 1773 proteins, and 3166 (47.22%) of these interactions were supported by DDIs. From the core PPIs, 2721 of the interactions among 867 proteins were predicted by the protein-interaction data sets of S. cerevisiae and 4005 interactions among 1425 proteins were predicted by the PPIs of M. oryzae (Table 1). Our results indicated that approximately two-thirds of the interactions were from the PPI data of M. oryzae and that considerably fewer predictions were obtained from H. sapiens than from the other species. Figure 1B presents a Venn diagram of the core PPI network. There was an

Assessment of the Predicted PPI Network

Because there was a high false positive rate in the current largescale experimental PPI data,79 the PPIs based on the interolog method unavoidably contained a large number of false positives. To overcome this drawback, the entire interaction network was assessed by GO55 annotations. In the network, a pair of physiologically interacting proteins would be expected to have related, but not identical, functions. Therefore, they should share some, but not all, of their GO annotations.44 High-confidence interaction data sets should predict a greater proportion of interactions between functionally related proteins than low-confidence interaction data sets. The GO annotation data set for R. solani AG1 IA proteins was generated in our previous work,48 and 1146 genes included in the R. solani AG1 IA PPIs were annotated using GO terms, which accounted for 6070 interactions (5665 non-self-interactions). We count the interactions for which the connected proteins share common GO terms to evaluate the interaction data set.23 We consider two proteins to interact with each other if they shared at least one GO term in any of the three GO categories (cellular component, biological processes, and molecular functions). To 3281



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Figure 2. Validation of the predicted PPI network based on GO and PCC analysis. (A) The percentages of the core higher confidence PPIs sharing GO terms at different levels in the GO hierarchy were compared with a randomly generated network containing the same size of proteins. The x-axis indicates the depth of the GO terms, and the y-axis indicates the percentage of non-self-interaction partners that share the associated GO term at the indicated depth or deeper in the GO hierarchy. (B) The distributions of PCCs for the PPIs in the core higher confidence and randomized networks. The x-axis indicates the PCCs, and the y-axis indicates the count of non-self-interactions.

Figure 3. Interactions of the one-core network for AG1IA_05961 and its yeast two-hybrid assessment. The prefix “AG1IA_” was omitted from the gene names in the figure for clarity. (A) The one-core protein AG1IA_05961 radially interacts with its partners. The DDIs are displayed with different color line segment, and the interacting domains are shown, such as G-gamma (AG1IA_00962) and WD40 (AG1IA_05961). (B) Yeast twohybrid assessment of the one-core network for AG1IA_05961. The full length coding sequence (CDS) of AG1IA _05961 was amplified by polymerase chain reaction (PCR) from total RNA extracted from the R. solani AG1 IA strain and was fused to the C-terminal part of GAL4 that contains the AD (GAL4-AD) in the prey vector pGADT7. Other proteins were amplified and were fused to the N-terminal part of GAL4 containing the DBD (GAL4-DBD) in the bait vector pGBKT7. The above panel shows the growth of the transformants with the indicated plasmids on the selective screening medium plate SD/-Leu-Trp (DDO) with different concentration (104, 103, and 102 yeasts per microliter, respectively). The below panel showed that the same transformants grown on a selective screening medium plate with SD/-Leu-His-Ade-Trp (QDO) and X-α-Gal. If the tested protein pairs have an interaction, the transformants in the interaction pair can only grow on the selective screening medium plate (QDO) and will turn blue. A cotransformant containing the pGBKT-53 and pGADT7-T plasmids was used as the positive control. A cotransformant containing the empty (bait) pGBKT7 and (prey) pGADT7 vectors, as well as pGBKT-Lam and pGADT7-T, was used as the negative control.

evaluate the predicted interaction network, we compare the proportion of the interactions that shared at least one GO term

in the predicted network and a randomly generated network of the same size. Furthermore, we assessed the proportion of 3282



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activity of a diverse set of downstream effector molecules, such as ion channels, MAP kinases, and adenylate cyclases.83,84 This information suggests that Gγ may interact with Gβ to complete some biological processes in R. solani AG1 IA. AG1IA_00847 (RACK1 homologue) is a conserved scaffold protein of tryptophan-aspartate repeat (WD-repeat) family and has homology to the G proteins beta subunit,85 which is involved in cell growth, shape, and protein translation.86 βPropeller WD40 proteins have been shown to interact with WD40 domain proteins, such as the triple WD40 domain protein DDB1,87 which suggests that AG1IA_05961 may interact with AG1IA_00847 in R. solani AG1 IA. Another two interaction pairs identified in our PPI data set were AG1IA_00059 (T-complex protein 1) and AG1IA_05961, AG1IA_05186 (T-complex protein 1 subunit epsilon) and AG1IA_05961. T-complex protein 1 is a molecular chaperone that assists in the folding of various proteins following ATP hydrolysis.88 Recent studies have established that type II Chaperonin Containing TCP-1 (CCT, also known as TCP-1 Ring Complex, TRiC) is involved in the folding of newly synthesized WD40 proteins that are rich in β-sheets, such as the model substrate of the Gβ subunit. With the direct assistance of chaperonin containing T-complex subunit 1 proteins, the nascent G protein β polypeptide is folded, and its orientation is fixed by permanent association with Gγ.89,90 Therefore, it is not surprising that AG1IA_00059 and AG1IA_05186 interacts with AG1IA_05961 in R. solani AG1 IA. To reduce the false positive results from the yeast two-hybrid experiment, we used a stringent selection condition in our experiment, making it inevitable that we missed some weak or transient interactions. In fact, we find another three pairs of interaction showed weakly interactions, which are AG1IA_05961 and AG1IA_04857 (phosphoglucomutase), AG1IA_05961 and AG1IA_01360 (ribosomal protein subunit S2), AG1IA_05961 and AG1IA_02195 (glutamate 5-kinase). The yeast transformants harboring the indicated plasmids can grow well on the less stringent selection synthetic medium without Leu, Trp, Ade. Moreover, we also observed that the yeast transformants containing the plasmids, AG1IA_05961 and AG1IA_04857, AG1IA_05961 and AG1IA_01360 can grow on the selective medium SD/-Leu-His-Ade-Trp (QDO) weakly, but the colonies cannot turn blue with X-α-Gal. The details are shown in Supplementary Table S5 (Supporting Information). Taken together, the results of the yeast twohybrid experiments on the selected prediction pairs demonstrated that our predictions were reliable, and our predicted PPI data can provide useful guideline for future research.

interaction partners at the different levels of the GO annotations at depths 3−8 and greater than 8 in the GO hierarchy. The analysis showed that 1197 (approximately 19%) of the non-self-interactions shared at least one GO term annotation. Considering mandatory only cellular location annotation, 745 (approximately 12%) non-self-interactions shared common GO terms. However, in the randomized networks, only 174 interactions shared cellular location category annotations. Furthermore, we can see that the proportion of interactions sharing the GO annotations at any level of the GO hierarchy is higher than the largest percentage in the randomized networks (Figure 2A, Supplementary Table S3, Supporting Information). The results suggest that the predicted PPI network indeed preferentially connects the functionally related proteins sharing GO terms at any level of the GO hierarchy. To further validate the predicted interactions, we investigated whether the interacting proteins tend to have correlated gene expression profiles.57 The lack of coexpression would suggest that the predicted PPIs do not exist in practice.37 Moreover, we analyzed the transcriptome of R. solani AG1 IA at six stages during the infection process and found that 10103 genes were expressed.48 Among those genes, many have correlated gene expression profiles, which were up-regulated or down-regulated together. The level of coexpression of an interacting protein pair can be assessed using PCCs. In this network, we found that the PCCs of 2085 interactions were larger than 0.6 (Supplementary Table S4, Supporting Information). This result indicates that these PPIs exhibit high PCCs, meaning that the corresponding gene pairs are coexpressed and have a higher reliability. We calculated the PCCs for the predicted interactomes and compared them to those of a same size random network. Figure 2B depicts the distribution of PCCs for the predicted PPIs against those for a random interactome. As shown, the number of PPIs with higher PCCs in the predicted network was significantly larger than that of the random protein pairs. This result demonstrates that the protein interaction pairs in the predicted PPI network prefer to be coexpressed, implying that the predicted PPI network was more reliable than random protein pairs. Interaction validation of the Predicted PPI Network in the Laboratory

To validate the predicted PPI network accuracy in laboratory, we selected a hub, AG1IA_05961, and randomly selected its 11 partners using a yeast two-hybrid assay, which is a wellestablished genetic approach for testing PPIs in vivo. AG1IA_05961 is a candidate G protein beta subunit (Gβ), which is a very important basic unit of the heterotrimeric G protein-mediated signaling pathway. Heterotrimeric G proteinmediated signaling plays a central role in the virulence, mycelial growth, and development of filamentous fungi.80−82 However, the role of the Gβ protein in R. solani has not been elucidated to date. Among the selected predictions, two candidate proteins encoded by the AG1IA_03263 and AG1IA_06033 genes could active the yeast reporter genes showed autoactivated. Four pairs clearly displayed interaction: AG1IA_05961 and AG1IA_00962, AG1IA_05961 and AG1IA_00847, and AG1IA_05961 and AG1IA_00059, AG1IA_05961 and AG1IA_05186 (Figure 3AB). AG1IA_00962 (G-gamma domain-containing protein) is also a component of the heterotrimeric G proteins. In organism, the Gα and Gβγ heterodimer independently or synergistically regulate the

Pathogenic Proteins in the Network

The expression of pathogenic genes during infection is crucial for overcoming the innate immunity of rice and for the maintenance of its parasitic lifestyle. The identification of a pathogenic network can provide an alternative way to predict potential pathogenic factors, based on the released pathogenic factor data and the Pathogen−Host Interaction (PHI) genes. Moreover, the validated pathogenic model networks of Fusarium graminearum was constructed.64 We deduced that there was a complex network of pathogenic proteins and their partners involved in R. solani AG1 IA pathogenicity. In our network, 724 interactions were inferred among 124 pathogenic proteins that were assigned from the PHI database (Supplementary Table S8, Supporting Information). We identified some pathogenic factors, PHI genes and their interacting 3283



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Figure 4. Multiple-core networks containing pathogenic genes. The nodes shown in yellow represent the core pathogenic genes. (A) The GH31 genes (AG1IA_06593 and AG1IA_03930) and their interacting partners. (B) The core components of the G protein genes, AG1IA_05961, AG1IA_00847, and AG1IA_03263, and their interaction partners.

glucosidase II, which is required for the pathogenicity of Ustilago maydis.92 Alpha glucosidase acts upon 1,4-alpha bonds and breaks down starch and disaccharides into glucose. Four additional interacting proteins, endoplasmic reticulum protein (AG1IA_02797), EF-hand domain-containing protein (AG1IA_09314), and two 60S ribosomal proteins (AG1IA_00596 and AG1IA_09800), were tightly connected to the two core proteins. Endoplasmic reticulum protein may form a protein-degrading complex called the proteasome to degrade many proteins of the endoplasmic reticulum for ubiquitination. EF-hand domain-containing protein may be involved in the binding of intracellular calcium and plays a role in buffering the intracellular calcium levels. The calcium− calcineurin signaling pathway is required for virulence in many fungal pathogens.83,93 Thus, we can infer that the two partners may be required for the pathogenesis of R. solani AG1 IA. Additionally, the 60S ribosomal protein encodes a component of the 60S ribosomal subunit and binds 5S rRNA to form a stable complex called the 5S ribonucleoprotein particle. This particle transports nonribosome-associated cytoplasmic 5S rRNA to the nucleolus for ribosome assembly. Ribosomes are the organelles that catalyze protein synthesis, and many pathogenic proteins are also included. In total, using this network, we can predict that these four proteins may be involved in the pathogenesis of R. solani during the infection process. Moreover, we also generated a complex multiple-core network containing the core components of the G proteins,

partners, and used these genes to generate subnetworks for further analysis. Because proteins involved in the network that interact with the core pathogenic ones may also be pathogenic proteins within the same pathways or work in concert as a functional module.91 Thus, we can predict the functions of previously uncharacterized members of the network. In the pathogenic subnetwork, based on GO annotation, the protein encoded by AG1IA_05623 (one of 17 hypothetical proteins) which was assigned to functional classes of nucleic acid binding, nucleoside binding, metal ion binding, transition metal ion binding, nucleotidyl transferase activity, and RNA polymerase activity was predicted to involve in pathogenicity (Supplementary Table S6, Supporting Information). The protein encoded by AG1IA_05623 interacted with E3 ubiquitin ligase protein (AG1IA_01341) and secreted small cysteine-rich effector protein (AG1IA_06970). Because the E3 ubiquitin ligase protein and secreted small cysteine rich protein were usually related to pathogenicity of plant pathogenic microbes, AG1IA_05623 may also involve in the pathogenicity in R. solani AG1 IA. In this study, we detected many types of one-core networks and multiple-core networks62 in the pathogenic proteins. We selected some multiple-core networks for further analysis (Figure 4AB). Figure 4A shows the two glycoside hydrolases 31 (GH31) genes and their interaction partners. The two GH31 family members, which encode by AG1IA_06593 and AG1IA_03930, were confirmed as pathogenic proteins in the PHI database. They have the same putative function as alpha 3284



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Figure 5. One-core and multiple-core networks of different extracellular secreted proteins and their interacting partners. (A) Some extracellular secreted proteins have fewer interaction partners. (B) Some extracellular secreted proteins have many interacting partners, such as AG1IA_06970 and AG1IA_09161. (C) A novel potential secreted effector, which can produce a cell death phenotype after inoculation for 48 h, was validated from our previous work.

secreted proteins were inferred (Supplementary Table S9, Supporting Information). Some secreted proteins have functions beyond the cells in which they reside. Many of these secreted proteins are from the cells into the extracellular media, while some secreted proteins are translocated into the rice cells through specialized structures, such as infection vesicles and haustoria. Some proteins that are included in this group of secreted proteins have fewer interacting partners in the predicted R. solani AG1 IA secretome network, such as AG1IA_07704 (CAP domaincontaining protein) (Figure 5A). CAP domain containing proteins are often secreted and have an extracellular endocrine or paracrine functions. These proteins potentially act as proteases or protease inhibitors and often are involved in the regulation of the extracellular matrix. In contrast, some extracellular secreted proteins have many interacting partners, such as AG1IA_06970 (TFIIS_C domain-containing protein) (Figure 5B), which is a transcription elongation factor involved in protein synthesis, and a new potential secreted effector AG1IA_09161 (glycosyltransferase GT family 2 domain) (Figure 5C), which can cause cell death phenotypes after inoculation for 48 h.48 We noticed that AG1IA_09161 interacts with another extracellular secreted protein, AG1IA_04033 (ER membrane glycoprotein subunit of the glycosylphosphatidylinositol transamidase complex). AG1IA_09161 can transfer sugars from nucleotide sugars to a wide range of small molecule acceptors to establish natural glycosidic linkages. Many eukaryotic proteins are synthesized by membrane-bound ribosomes that are tethered to the plasma membrane via glycosylphosphatidylinositol (GPI).101 The glycosylphosphatidylinositol transamidase (GPIT) complex is retained in the endoplasmic reticulum and involved in the addition of GPIanchors to newly synthesized proteins.102 This result indicates that AG1IA_09161 and AG1IA_04033 may interact with each other and may be related to GPI-anchored protein synthesis. GPI has been reported to play an important role in cellular processes, host−pathogen interactions, and virulence.103,104

namely AG1IA_05961, AG1IA_00847, AG1IA_03263, and their interaction partners (Figure 4B). Similarly, the two proteins in the complex multiple-core network, protein SCP160 (AG1IA_03438) and candidate G-protein beta subunit (AG1IA_05961), may also be related to pathogenesis because they interact with RACK1 homologue (AG1IA_00847), and heterotrimeric G protein alpha subunit B (AG1IA_03263). Moreover, the AG1IA_05961 is also a PHI protein that regulates hyphal growth, development, and virulence through multiple signaling pathways in Fusarium oxysporum.94,95 AG1IA_03263 was found to control the growth, development, and pathogenicity of R. solani.16 Thus, AG1IA_03438 may also be involved in pathogenicity. Additionally, we showed that AG1IA_05961 and AG1IA_00847 interact with each other using a yeast two-hybrid system. Secretory Proteins in the Network

Secretory proteins are common in organisms and regulate a multitude of biochemical and physiological processes, such as the immune system,96 nutrient acquisition,97 signaling and environmental sensing,98 and competition with other organisms.99 In pathogens, the secretome provides clues for understanding the complex relationship between plants and their pathogens. Among filamentous fungi, secretory proteins mainly include carbohydrate-active enzymes that degrade glycosidic bonds.100 Experimentally, some secretory proteins, termed effectors, can modulate the interaction between the pathogenic microbes and their hosts, manipulating and/or destroying the host cells with special signatures. The generation of the secretome subnetwork is therefore urgently needed to decipher the biochemical activities of these effectors, which will be helpful in understanding how the pathogens successfully colonize and reproduce on their host plants. With the genome sequencing technologies coupled with several algorithms, secreted proteins of R. solani AG1 IA were annotated in our previous work.48 In our study, 841 interactions among 132 3285



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Figure 6. Transcriptome analysis of the pathogenic genes. By integrating the PPI maps and the gene expression data, we generated a subnetwork that demonstrated some significant expression changes in the pathogenic genes and their interacting partners. The FPKM values of mRNA expression at the previous four time-points (10, 18, 24, and 32 h) were calculated and are shown in different colors. The higher expression genes are shown in darker colors. A, B, C, and D represent the expression of genes after pathogen invasion into rice at 10, 18, 24, and 32 h, respectively.

These results may suggest that AG1IA_04033 maybe also be related to virulence. Additionally, we used WolfPSort67 to predict the eventual cellular location of the mature protein to further analyze the 132 predicted secreted proteins. We found that 40 of these proteins were located outside of the cell (Supplementary Table S9, Supporting Information). This result suggests that these proteins may interact with rice proteins. Furthermore, many secreted proteins are expressed in only specialized cell types, specific stages of development, or in response to specific cellular stimuli.105 Therefore, predicting all of the secreted proteins in a given environment is an incredible challenge. With the increased identification of additional secreted proteins, the R. solani AG1 IA secretome network will be more complete.

pathogenicity either increased or decreased their expression during the infection process (Figure 6). From Figure 6, such proteins include AG1IA_07839, AG1IA_00217, AG1IA_07353, and AG1IA_09717. Meanwhile, some proteins displayed subtle expression changes, such as AG1IA_02095 in Figure 6. The expression of AG1IA_07839 (ste7-like protein) tended to increase during the four infection stages. Ste7-like protein is a member of the mitogen-activated protein kinase (MAPK) signaling pathway and is multiply phosphorylated in response to either pheromones or coexpression of the dominant Ste11 protein. Ste7 involved in many biological processes in Botrytis cinerea106 and Ustilago maydis,107 including vegetative growth and pathogenicity. The expression of AG1IA_00217 (heat shock protein 82) peaked at the 18-h stage during the first four infection processes. Heat shock protein 82 is a molecular chaperone involved in cell cycle control and signal transduction. Heat shock proteins (Hsps) are linked to the virulence of several pathogenic microbes.108−110 We therefore deduced that AG1IA_00217 in R. solani AG1 IA may be associated with pathogenicity. Another protein, AG1IA_02095 (a hypothetical kinase), exhibited a slight change in expression during the first four infection stages. Because both AG1IA_07839 and AG1IA_00217 are annotated in the PHI database and are significantly differentially expressed after the invasion of the pathogenic fungus, we predicted that AG1IA_02095 may be a pathogenic factor involved in the transfer of phosphate groups during the infection process. Actually, in Candida glabrata, this gene is predicted as MAPK Ste11, which plays a role in virulence.111 Similarly, AG1IA_07353 (ribosomal protein S15a) and AG1IA_09717 (peptidyl-prolyl cis−trans isomerase)

Transcriptome Analysis of the Pathogenic Genes

To detect the pathogenic genes in a precise way, the gene expression profiles of R. solani AG1 IA were determined 10, 18, 24, 32, 48, and 72 h after its invasion into rice. The expression of some pathogenic genes was generally altered following the invasion of the pathogen into the rice. By integrating the protein interaction map with the gene expression data, we can predict the pathogenic genes of R. solani AG1 IA in a precise manner. The genes that tightly interact with known pathogenic genes, such as those from the PHI database and other pathogenic factors, and the genes that are differentially expressed after the invasion of rice are candidate pathogenic genes. Using this approach, we choose genes with significant expression changes and their interacting partners to generate a subnetwork. The network demonstrates that many proteins that interact with the core proteins associated with 3286



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Figure 7. Core components and their interacting partners from the CWI pathway in R. solani AG1 IA and M. oryzae. The different colors represent the different KOG annotations.

catalyzing protein synthesis to maintain the normal physiological processes, including pathopoiesis.

displayed differential expression and interacted with the PHI p r o t e i n A G 1 I A _ 0 0 2 1 7 . Ri b o s o m a l p r o t e i n S1 5 a (AG1IA_07353) may take part in the biosynthesis of some pathogenic proteins. Peptidyl-prolyl cis−trans isomerases (AG1IA_09717) may act together with other folding enzymes, such as the chaperones and protein disulfide isomerases that are involved in protein folding, signal transduction, trafficking, assembly, and cell cycle regulation.112 These findings suggest that AG1IA_07353 and AG1IA_09717 may be required for the pathogenicity of R. solani AG1 IA. Additionally, because of the complexity of the pathogenicity of R. solani AG1 IA, we should consider the pathogenic genes as a whole network. Indeed, many secreted proteins are the products of pathogen genes. We also constructed some multiple-core networks that contain the core secreted proteins (Supplementary Figure S4, Supporting Information). As shown in the Supplementary Figure S4, the expression of AG1IA_03051 (60S ribosomal protein L31) tended to decrease, whereas the expression of AG1IA_03704 (60S ribosomal protein L17/L23) peaked at the 18-h stage. These proteins are both members of the 60S ribosomal protein family and components of the 60S subunit. Many of their interacting partners exhibited decreased expression as well, such as AG1IA_02452 (40S ribosomal protein S15), AG1IA_000503 (60S ribosomal protein L9), AG1IA_07353 (ribosomal protein S15a), and AG1IA_02425 (phosphomannomutase). Furthermore, most of the genes that were found to interact with both AG1IA_03051 and AG1IA_03704 encoded ribosomal proteins. This result suggests that these genes play pivotal roles in

Mitogen-Activated Protein Kinase (MAPK) Cascade in the Network

Recently, with the increasing number of pathogenic fungi sequenced genomes, it offered an approach of valuable insight into the functional analysis of proteins in the signal transduction pathways. In our work, we predicted 158 interactions among 20 genes from the MAPK pathways of R. solani AG1 IA and compared it with another rice destructive fungus, M. oryzae, in which 437 interactions were observed between 40 genes (Supplementary Figure S5, Supporting Information). As shown in Supplementary Figure S5 (Supporting Information), the pathways of the two fungi share some components of the network, such as Fks1, Rho1, and Pbs2. This similarity shows that there are common components between the CWI and osmostress adaptation pathways. However, the pathways also have many different elements, such as Gpe1, Ste4, Ste18, and Ssk2, which are only found in R. solani AG1 IA, and Cdc42, Bck1, Slt2, and Hog1, which are only found in M. oryzae. The different compositions of these pathways may be due to the differences in the lifestyle and infection processes of the two pathogens. Furthermore, a detailed comparison analysis of the CWI pathway, also called the PKC1-MAPK cell integrity pathway, was performed (Figure 7A,B). The PKC1−MPK pathway is highly conserved and plays a crucial role in the maintenance of CWI to retain fungal growth, survival, and pathogenesis in 3287



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response to stress. Figure 7A and B show that R. solani AG1 IA and M. oryzae have two common core components, Rho1 and Pkc1. A variety of proteins interact with Rho1 in both R. solani AG1 IA and M. oryzae. Most of the proteins that interact with Rho1 have similar functions and are involved in similar biological processes between the two rice destructive funguses. For example, the Rho GDP dissociation inhibitor is involved in signal transduction mechanisms in both species (AG1IA_02791 in R. solani AG1 IA, MGG_01689 in M. oryzae). Subunit PSMD9 of the 26S proteasome regulatory complex is involved in posttranslational modification, protein turnover, and chaperones functions (AG1IA_09766 and AG1IA_07708 in R. solani AG1 IA, MGG_09953 in M. oryzae). Additionally, 1,3β-glucan synthase/callose synthase catalytic subunit is involved in the biogenesis of the cell wall, membrane, and envelope (AG1IA_01405 in R. solani AG1 IA, MGG_00865 in M. oryzae). The AG1IA_01405 in R. solani AG1 IA and MGG_00865 in M. oryzae encode Fksl, which produces alternative catalytic subunits of the GS complex.113 Rho1 is an essential regulatory subunit of the GS complex and stimulates GS complex activity in a GTP-dependent manner.114,115 Fks1 colocalizes with Rho1 in the plasma membrane, and both of these proteins participate in the cell wall remodeling,116 suggesting that Fks1 may interact with Rho1. However, the two fungi were also shown to have some different proteins. For example, the AG1IA_00200 heat shock transcription factor was predicted in R. solani AG1 IA but not in M. oryzae. Similarly, MGG_03282 (Lipoate-protein ligase A), which is involved in coenzyme transport and metabolism, was predicted in M. oryzae but not in R. solani AG1 IA. Two other genes, Pkc1 and Mkk1, and their interacting partners had similar characteristics. However, they also had significant differences, particularly in terms of the proteins that interact with Pkc1. Pkc1 is the target of GTP-bound Rho1 and has several substrates.117 Pkc1 serves as a central regulator with multiple outputs and may connect several different signaling pathways with each other.118 Thus, Pkc1 may interact with proteins that exist in other signaling pathways. A similar conclusion can also be reached for the predicted subnetwork of R. solani AG1 IA and M. oryzae; for example, in the R. solani AG1 IA subnetwork, AG1IA_06982 (Pkc1) interacts with AG1IA_04099 (Kss1), which is a core element of the filamentous growth response. Similarly, in the M. oryzae subnetwork, MGG_08689 (Pkc1) interacts with MGG_01362 (Fus3), MGG_09565 (Fus3), and MGG_09125 (Sho1), which are involved in the pheromone response and osmostress adaptation. Interestingly, the observations from these two subnetworks suggested that Pkc1 directly interacts with Mkk1. In the network of R. solani AG1 IA, AG1IA_04099 (Kss1) connects AG1IA_06982 (Pkc1) and AG1IA_07839 (Mkk1). In the network of M. oryzae, MGG_00883 (Bck1) interacts with MGG_06482 (Mkk1) but lacks the interaction between MGG_08689 (Pck1) and MGG_00883 (Bck1). Together, these findings highlight the diversity and complexity of signal regulation in fungi, which can contain both the highly conserved MAPK phosphorylation cascades as well as a variety of regulatory genes. These differences may be due to the differences in lifestyle and infection process between the two fungi.

Article

DISCUSSION

In the current work, we comprehensively analyzed a draft map of the PPI network in R. solani AG1 IA based on the wellrecognized interolog approach and DDI information available from the DOMINE database. Moreover, we generated the core interaction network from the overlap of the two methods and a confidence score of at least 0.5 from the interolog analysis. GO annotations and gene coexpression profiles were developed to assess the overall reliability of the predicted PPIs. Compared with the randomized networks, the result clearly demonstrated that the predicted interacting proteins were more reliable. Additionally, the infection transcriptome analysis also illustrated that many interacting proteins were either up-regulated or down-regulated together, and this conclusion was particularly true for pathogenic genes. Furthermore, a hub AG1IA_05961 and its interacting partners were randomly selected from our predictions and were assessed in vivo using a yeast two-hybrid experiment. We discarded two genes that could independently activate the yeast reporter genes and continued to test the remaining interactions. Finally, four pairs of interactions from our randomly selected interactions could grew well on the QDO agar plates and activated the yeast reporter genes, demonstrating a real interaction. Three pairs of interactions showed weakly interaction. The identification of interaction in the yeast system greatly enhanced the possibility that the interactions might also exist in R. solani AG1 IA under natural conditions. Because the two-hybrid system does not suitable for detecting all protein−protein interactions, including translation initiation factor, membrane proteins, and proteins contain strong targeting signals.119−121 Aditionally, we use a stringent condition in yeast two-hybrid. So some really interactions may be missed. Taken together, we only tested a small set of randomly selected interactions using the yeast two-hybrid experiments, but our results still indicated the reliability of the predicted interactome, to some extent. With the assistance of Cytoscape,65 the protein interaction networks were visualized and analyzed. The analysis of the predicted networks can provide the framework for describing the PPI network in a format that could allow for quantitative insights into the characterization of the PPI network structure. Based on an analysis of the PPI networks, we can better understand the web of interactions that takes place inside a cell, as well as the basic internal components and organization at the network level. One method to better understand the entire network is to partition it into a more manageable series of subnetworks.62 We identified three PPI subnetworks in the entire PPI network: the one-core network, the multiple-core network, and the crossover network. In the subnetwork, one or many proteins often radially interact with other proteins, and many subnetworks often overlap with each other to form a network of their own in the cell.122 Many subnetworks play essential roles in basal cellular mechanisms, especially in the pathogenicity and signal transmission pathways. We selected some multiple-core networks with core genes related to pathogenic and secreted proteins and their interacting partners for further analysis. The results indicated that many of their interacting partners shared similar or related functions with the core genes. In particular, the secreted proteins acted as ribosomal proteins, and many of their interacting partners were ribosomal proteins. Moreover, we compared the differences of a multiple-core network using the MAPK signaling cascades of R. solani AG1 IA and M. oryzae. The core 3288



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interactions identified, Gene Ontology annotation of interacted proteins, Pearson correlation coefficient of interactions, KOG annotation of R. solani AG1 IA and M. oryzae proteins involved in Cell Wall remodeling in MAPK pathways, comparison of KOG annotation of proteins involved in Cell Wall remodeling pathways between R. solani AG1 IA and M. oryzae. This material is available free of charge via the Internet at http:// pubs.acs.org.

components and the interacting partners of the CWI signaling pathway between R. solani AG1 IA and M. oryzae were compared. Our results indicated that the two fungi have many similarities but there were also many differences, which may relate to the differences in their environments and lifestyles. By integrating the differentially expressed genes and the PPI networks, we generated a more exact pathogenic subnetwork and secreted subnetwork. This approach was a very effective at predicting the pathogenic genes of R. solani AG1 IA. In R. solani AG1 IA, many genes are important for pathogenicity, which should be considered a complex network. Indeed, many secreted proteins also play a role in pathogenicity, and many of these proteins function at the interface with rice or even inside the rice cells. The potential secreted effector AG1IA_09161 was identified in the secretome network generated by our previous work.48 This result provided a clue for the further identification of the interaction partners of these effectors. Through the subnetwork analysis, AG1IA_09161 was shown to interact with AG1IA_04033, which may be related to the synthesis of glycosylphosphatidylinositol (GPI)-anchored proteins. In short, although the pathogenic proteins shown here is far from complete and many of them should be further exposed using experimental methods, the predicted network in our study offers further clues to understanding the pathogenic proteins of R. solani AG1 IA and provide helpful guidelines for future experiments in the lab. Finally, we also noticed that we cannot construct the complete PPI network of R. solani AG1 IA. The accuracy and coverage of predicted PPI network largely depend on the quality of interaction data sets and the ability to identify the orthologs from the model organisms. Because of the incomplete coverage original interaction data sets of the model organisms, especially the lack PPIs of the closely related species, we will miss some PPIs of R. solani AG1 IA. In addition, to adapt to the diverse environments, some genes in the pathogenic organism, especially some effectors, are always highly evolving and perturbing. The interactions of these proteins will be different or the interaction pairs of the same protein maybe change between various environments. Moreover, some protein interactions in the cell are transient, unstable, or limited in their location. So we cannot generate the whole interactions in one time. Thus, with the continuation of the model organism PPI mapping projects, the coverage of model organism PPI maps will be greatly enhanced. Accordingly, the PPI of R. solani AG1 IA will become more complete with the increasing amounts of protein data. In addition, the different databases of the same model organism can also generate higher coverage interaction maps, such as the LC multiple, Y2H and the Combined AP/MS of the Yeast Interactome database in our work. The new technologies may also infer more interactions among proteins in R. solani AG1 IA. Regardless, the PPI network of R. solani AG1 IA described here provides a rich source of information for the better understanding of the functional organization of the cell.

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AUTHOR INFORMATION

Corresponding Authors

*Tel: +86-2882650965. Fax: +86-2882650965. E-mail: [email protected]. *Tel: +86-2882650965. Fax: +86-2882650965. E-mail: [email protected]. Author Contributions ∥

D.L., R.L., and C.Y. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge Prof. Fengling Fu at the Maize Research Institute of Sichuan Agricultural University (Chengdu, China) for providing the vectors pGADT7 and pGBKT7 and the AH109 yeast strain. We thank Mingan Sun at The Chinese University of Hong Kong for helpful discussions.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Supporting Information file 1: Supplementary Figures S1−S5. Supplementary Tables S1−S6. Supporting Information file 2: Supplemenary Tables S7−S9. Supporting Information file 3: core protein−protein interactions of R. solani AG1 IA, orthologs identified in other model species, domain−domain 3289



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