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TMT-based quantitative proteomics analysis of defense responses induced by the Bph3 gene following brown planthopper infection in rice

BMC Plant Biology volume 24, Article number: 1092 (2024) Cite this article

Abstract

Background

The brown planthopper (BPH) is an economically significant pest of rice. Bph3 is a key BPH resistance gene. However, the proteomic response of rice to BPH infestation, both in the presence and absence of Bph3, remains largely unexplored.

Results

In this study, we employed tandem mass tag labeling in conjunction with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis to identify differentially expressed proteins (DEPs) in rice samples. We detected 265 and 125 DEPs via comparison of samples infected with BPH for 2 and 4 days with untreated samples of the BPH-sensitive line R582. For the Bph3 introgression line R373, we identified 29 and 94 DEPs in the same comparisons. Bioinformatic analysis revealed that Bph3 significantly influences the abundance of proteins associated with metabolic pathways, secondary metabolite biosynthesis, microbial metabolism in diverse environments, and phenylpropanoid biosynthesis. Moreover, Bph3 regulates the activity of proteins involved in the calcium signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and plant hormone signal transduction.

Conclusions

Our results indicate that Bph3 enhances the resistance of rice to BPH mainly by inhibiting the down-regulation of proteins associated with metabolic pathways; calcium signaling, the MAPK signaling pathway, and plant hormone signal transduction might also be involved in BPH resistance induced by Bph3.

Peer Review reports

Background

Rice (Oryza sativa L.) is one of the world’s staple crops, and it provides sustenance for over 50% of the world’s human population. The brown planthopper (BPH, Nilaparvata lugens Stål) is one of the most destructive insect pests affecting rice production; it feeds on phloem sap and results in reduced yields and significant economic losses annually in rice-producing regions [1]. Additionally, BPH can indirectly damage rice plants by transmitting viruses such as grassy stunt and ragged stunt [2]. Although pesticide application in paddy fields can help control BPHs, this method tends to be effective over short periods; it is also costly, pollutes the environment, and is a source of persistent residues. Consequently, identifying key resistance genes and breeding BPH-resistant cultivars are regarded as the most cost-effective and environmentally sustainable strategies for mitigating BPH-related problems. There is thus an urgent need to identify additional resistance genes and proteins to deepen our understanding of the mechanisms underlying BPH resistance in rice.

To date, over 46 BPH resistance genes and quantitative trait loci (QTLs) have been identified from wild species and cultivars of Oryza, and 17 genes have been successfully cloned using positional cloning methods [1, 3,4,5]. Most of these genes are located on chromosomes 3, 4, 6, and 12; they can be categorized into four groups based on their protein sequences [6]. The first group consists of plasma membrane protein pattern recognition receptors (PRRs), which serve as the initial defense layer in the rice immune system. Notable examples include Bph3 and Bph15, which are activated by conserved herbivore-associated molecular patterns [7, 8]. The second group encompasses intracellular nucleotide-binding domain and leucine-rich repeat proteins, which initiate the defense response by recognizing effectors released into the host cells. Prominent members of this group include Bph14 on chromosome 3 [9]; Bph6, Bph30, and Bph40 on chromosome 4 [5, 10]; Bph37 on chromosome 6 [11]; and Bph9, Bph18, and Bph26 on chromosome 12 [12,13,14]. The third group is characterized by a B3 DNA-binding domain, represented by bph29 on chromosome 6 [15]. Finally, the fourth group encodes proteins containing a short consensus repeat (SCR) domain, such as Bph32, which is also located on chromosome 6 [16]. These studies provide valuable insights into the molecular intricacies of plant–insect interactions and serve as crucial resources for developing molecular approaches to breed BPH-resistant rice.

In recent years, proteomics methods have emerged as powerful techniques for elucidating the proteomic profiles of various plants under different conditions [17,18,19]. Tandem mass tag (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ) labeling technologies have been increasingly used for protein quantification via mass spectrometry (MS) due to recent advances in quantitative proteomics [20]. The gene Bph3, which plays a key role in enhancing the resistance of rice to BPHs, was initially identified in the rice variety Rathu Heenati [21]. Subsequent research has demonstrated that Bph3 confers resistance to four BPH biotypes [16, 22], and it remains effective even after more than 30 years of usage [8, 22,23,24]. Consequently, identifying the Bph3-regulated proteins in rice through a quantitative proteomics approach is essential for comprehensively elucidating the molecular mechanisms underlying plant–insect interactions and for providing potential candidate genes for breeding BPH-resistant rice.

In this study, TMT-based quantitative proteomic analysis and LC-MS/MS were used to identify Bph3-regulated proteins in the BPH-sensitive indica rice variety R582 and its Bph3 introgression line R373, which is extensively utilized for hybrid rice cultivar production in Guangxi Province, China. Our findings demonstrate that Bph3 has the ability to impede protein regulation in response to BPH infection in rice.

Results

Evaluation of BPH resistance in R582 and its Bph3-introgression line R373

We evaluated the BPH resistance of R582 and its Bph3-introgression line, R373, using 10-day-old seedlings with 3 to 4 leaves cultivated under greenhouse conditions (Fig. 1a). Following a 10-day period of BPH infestation, most R582 plants had died, and most of the Bph3-introgression line R373 plants survived (Fig. 1b). The survival rate of R373 was significantly higher than that of R582 (Fig. 1c). Additionally, the BPH resistance score (3.3) for R373 plants during the bulk seedling test was significantly lower than the score (8.3) of R582 plants (Fig. 1d), further confirming the resistance conferred by the Bph3 introgression.

Fig. 1
figure 1

Analysis of BPH resistance was conducted on the Bph3 introgression line R373 and the control line R582. (a) The phenotypes of R373 and R582 plants prior to BPH infection; (b) The Phenotype R373 and R582 plants after a 10-day period of BPH infestation; (c) Survival rates of R373 and R582 plants following BPH infestation (n = 3). Asterisks indicate significant differences as determined by Student’s t test (**, P < 0.01); (d) Resistance scores of R582 and R373 plants in the bulked seedling test (n = 3). Asterisks indicate significant differences compared with R582 plants as determined by Student’s t test (**, P < 0.01)

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Peptide/protein identification and quantification analysis

To investigate the proteins affected by the Bph3 gene in rice, we examined both the susceptible rice variety R582 and the Bph3-introgression line R373 at the 3- to 4-leaf stage. These plants were cultivated in unsterilized soil from a paddy field and exposed to BPH infestation for periods of 0, 2, and 4 days (Fig. 2a). Following the infestation, shoot tissues were harvested for quantitative proteomics analysis. The raw mass spectrometry (MS) data were processed using MaxQuant software (version 1.6.6). Through TMT labeling, we identified a total of 46,979 matched spectra, 20,123 peptides, and 5,621 unique proteins from both R582 and R373 plants (Fig. 2b). Each identified protein was supported by at least one unique peptide, and a total of 5,429 unique proteins were quantified to facilitate the identification of differentially expressed proteins (DEPs) (Table S1). Details of the identified proteins are provided in Table S2, and additional information regarding the peptides is presented in Table S3. Protein quantification analysis revealed a total of 459 DEPs in plants infected with BPH compared with control plants (Table S4). These regulated proteins were organized into four major clusters (Fig. 2c).

Fig. 2
figure 2

Phenotypes of rice seedlings utilized for proteomic analysis, as well as the basic statistical information regarding the proteome and hierarchical clustering of DEPs. (a) The phenotypes of rice seedlings infected with BPH, which were used for proteomic analysis; (b), Basic statistical information regarding the proteome, derived from TMT-based quantitative proteomic analysis. The MS/MS spectra represent secondary mass spectra, with protein is identification conducted using ProteinPilot (version 4.5); (c) Hierarchical clustering of quantified proteins based on LC-MS/MS data

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By comparing the peptide ion intensities of BPH-infected plants to control plants within the BPH-resistant line R373, 29 DEPs were identified at 2 days post-infection, which comprised 17 up-regulated and 12 down-regulated proteins. At 4 days post-infection, this number increased to 94 DEPs, including 63 up-regulated and 31 down-regulated proteins (Fig. 3a). In contrast, the BPH-susceptible line R582 exhibited 265 DEPs at 2 days post-infection, with 46 up-regulated and 219 down-regulated proteins. At 4 days post-infection, this number decreased to 125 DEPs, including 22 up-regulated and 103 down-regulated proteins (Fig. 3b). The expression of proteins, especially those that were down-regulated, was significantly decreased in plants containing the Bph3 gene during BPH infection. Among both the R582 and R373 lines infected with BPHs, only six proteins were up-regulated in both lines, and 15 proteins were down-regulated (Fig. 3c and d). The following DEPs were identified in the various time-point comparisons: 10 up-regulated and 4 down-regulated proteins for R373-(2d/0d); 51 up-regulated and 20 down-regulated proteins for R373-(4d/0d); 38 up-regulated and 188 down-regulated proteins for R582-(2d/0d); and 16 up-regulated and 82 down-regulated proteins for R582-(4d/0d) (Fig. 3c and d). Detailed information on the DEPs identified after BPH infection at each time point for the R373 and R582 plants can be found in Table S5 to S8.

Fig. 3
figure 3

Summarized the number of DEPs in rice at various time points following BPH infection, along with a Venn diagram analysis. The distinct segments of the diagram indicate the numbers of DEPs identified after 2 and 4 days of BPH infection in the R373 and R582 lines. (a) The statistical analysis of DEPs from the R373 line; (b) The statistical analysis of DEPs from the R582 line; (c) The Venn diagram analysis of up-regulated proteins; (d) The Venn diagram analysis of down-regulated proteins

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GO analysis of DEPs

GO analysis was conducted to clarify the enriched functional groups of DEPs; 391 of the 459 unique DEPs were assigned functions in the GO analysis. The GO analysis indicated the significant enrichment of DEPs from both R373 and R582 plants infected with BPH in various categories. In the “biological process” category, DEPs were notably enriched in metabolic processes, cellular processes, and responses to stimuli. The number of DEPs belonging to these three categories was lower in the Bph3-introgression line R373 compared with R582 (Fig. 4). Within the “cellular component” category, proteins involved in cellular anatomical entities, protein-containing complexes, and intracellular components were enriched (Fig. S1). In the “molecular function” category, proteins associated with catalytic activities and binding interactions were significantly regulated (Fig. S1).

Fig. 4
figure 4

Biological process category in GO analysis of DEPs compares samples from BPH-infected plants to control samples

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Enrichment analysis of KEGG pathways for DEPs

To gain deeper insights into the functions of the DEPs, DEPs from R373 and R582 infected with BPH were aligned with the reference pathways in the KEGG database. Metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in various environments, and phenylpropanoid biosynthesis were significantly enriched in DEPs from both R373 and R582 plants infected with BPHs. The expression of most of the DEPs from R373 in these four pathways was up-regulated, and the expression of most of the DEPs from R582 in these same pathways was down-regulated. DEPs were enriched in distinct pathways in the BPH-resistant line R373 and the BPH-sensitive line R582. For example, pathways such as thiamine metabolism, oxidative phosphorylation, steroid biosynthesis, steroid hormone biosynthesis, biosynthesis of unsaturated fatty acids, 2-oxocarboxylic acid metabolism, citrate cycle (TCA cycle), glutathione metabolism, photosynthesis, cysteine and methionine metabolism, and beta-alanine metabolism were specifically enriched in R373. Conversely, pathways including terpenoid backbone biosynthesis, purine metabolism, pantothenate and CoA biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis, alpha-linolenic acid metabolism, ascorbate and aldarate metabolism, porphyrin and chlorophyll metabolism, glycolysis/gluconeogenesis, pyrimidine metabolism, glycine/serine and threonine metabolism, and methane metabolism were specifically enriched in R582 (Fig. 5).

Fig. 5
figure 5

KEGG pathway analysis of DEPs in R373 and R582 plants subjected to BPH infection respectively. (a) The analysis of R373 plants after 2 days of BPH infection; (b) The analysis of R373 plants after 4 days of BPH infection; (c) The analysis of R582 plants after 2 days of BPH infection; (d) The analysis of R582 plants after 4 days of BPH infection

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Functional interaction networks of DEPs

The protein–protein interaction networks of regulated proteins were constructed utilizing the Search Tool for the Retrieval of Interacting Genes/Proteins 11.5 (STRING 11.5) database. The resulting network of regulated proteins in R373-(4d/0d), R582-(2d/0d), and R582-(4d/0d) comprised 19, 163, and 63 proteins, respectively (Fig. 6). However, only 29 regulated proteins were identified in R373 following BPH infection for 2 d, which precluded construction of a network. The regulatory network of the BPH-resistant line R373 predominantly contained up-regulated proteins, whereas the BPH-sensitive line R582 contained mainly down-regulated proteins. In R373-(4d/0d) network, there were 17 up-regulated proteins and only two down-regulated proteins (Fig. 6a). In contrast, the R582-(2d/0d) network comprised 138 down-regulated proteins and only 25 up-regulated proteins, and the R582-(4d/0d) network comprised 54 down-regulated proteins and merely nine up-regulated proteins (Fig. 6b and c). Following BPH infection in plants of the Bph3-introgression line R373, most up-regulated proteins might be activated by Bph3, which potentially suppresses the down-regulation of proteins. Conversely, in R582 plants lacking Bph3, the expression of numerous proteins was down-regulated following BPH infection, which induced plant mortality. Therefore, Bph3 may serve as a trigger for protein regulation in plant cells, which enhances resistance to BPHs.

Fig. 6
figure 6

Volvox graphics representation of the protein-protein interaction networks of regulated proteins. (a) Network from regulated proteins of R373 after 4 days BPH infection; (b) Network from regulated proteins of R582 after 2 days BPH infection; (c) Network from regulated proteins of R582 after 4 days BPH infection. The red and blue node stands for the significantly up- and down-regulated proteins, respectively. The color palette indicates the degree of protein regulation. The squared nodes represent those regulated proteins that were further analyzed by qRT-PCR. The edge represents a probable protein-protein interaction that predicted by STRING database. The thickness represents the confidence score of interactions (≥ 0.4)

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Proteomic data validation by LC-PRM/MS analysis

To validate the TMT-based protein quantitation data, LC-PRM/MS was used to quantify five candidate peptides from DEPs of R373 and five candidate peptides from DEPs of R582, respectively. The expression of most DEPs was up-regulated from R373; consequently, 4 up-regulated proteins (A2 × 8P7, 9-cis-epoxycarotenoid dioxygenase; A2Y8V9, H15 domain-containing protein; A2YAC7, DUF985 domain-containing protein; B8ATA3, 2-oxoacid dehydrogenase acyltransferase catalytic domain-containing protein) and one down-regulated protein (B8AFU0, Glycolipid transfer protein domain-containing protein) were analyzed by LC-PRM/MS analysis. Conversely, since most DEPs were down-regulated from R582, three down-regulated proteins (B8ALU4, Remorin C-terminal domain-containing protein; A2 × 335, Clu domain-containing protein; A2ZEU8, Uncharacterized protein) and two up-regulated proteins (B8BBP8, Protein kinase domain-containing protein; A2YWZ2, AT-hook motif nuclear-localized protein) were used for LC-PRM/MS analysis. Fig. S2 displays chromatographic peak contrast maps of each peptide generated using Skyline software. The peak area of the peptide was determined by analyzing 3–5 sub-ions with high abundance that were as contiguous as possible in the secondary mass spectrometry data from Skyline software (Table S9). The peak areas of the ions and the results of the quantitative analysis of the target peptides are presented in Table S10 and S11, respectively. The results from the PRM quantitative analysis indicated a 100% match in the patterns of the 10 candidate peptides with the proteomic data (Tables 1 and 2). This high level of similarity confirms the reliability of the data obtained through TMT-based quantitative proteomics in this study.

Table 1 Quantitative information of candidate proteins inferred by TMT and PRM from R373 under BPH infection
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Table 2 Quantitative information of candidate proteins by TMT and PRM from R582 with BPH infection
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qRT-PCR and immunoblotting analysis validation of the proteomic data

To validate the relationship between mRNA and protein expression profiles, 27 genes encoding regulated proteins were subjected to qRT-PCR analysis. Actin 2 was used as as the internal control gene to normalize the expression patterns across samples. Following BPH infection of R373 for 2 days, 5 out of 7 genes encoding up-regulated proteins (A2XFY5, B8B674, A2 × 8P7, A2WU38, B8AK41, A2YXK8, and B8ABR1) were up-regulated, and one out of 2 genes encoding down-regulated proteins (A2WRZ6 and B8AQ36) was down-regulated at the mRNA level (Fig. 7a). Following BPH infection of R373 for 4 days, 11 out of 12 genes encoding up-regulated proteins (A2XFY5, B8B674, B8B186, A2WX74, A2WU38, B8BDJ3, A2YXK8, A2 × 8P7, B8AK41, B8ABR1, A2WP38, and A2YVQ8) were up-regulated, and 2 out of 3 genes encoding down-regulated proteins (A2XK95, A2WRZ6, and B8AQ36) were down-regulated at the mRNA level (Fig. 7b). Following BPH infection of R582 for 2 days, 3 out of 4 genes encoding up-regulated proteins (B8APR2, A2YWZ2, B8B9 × 8, and B8AYQ7) were up-regulated, and all 8 genes encoding down-regulated proteins (A2XNK3, B8BET1, B8ARI2, A2XI79, A2ZEU8, B8AY88, A2YVH9, and B8B9T6) were down-regulated at the mRNA level (Fig. 7c). Following BPH infection of R582 for 4 days, all 4 genes encoding up-regulated proteins were up-regulated, and all 8 genes encoding down-regulated proteins were down-regulated at the mRNA level (Fig. 7d). However, some selected genes did not exhibit consistent expression patterns with their corresponding DEPs, which might stem from the effects of posttranscriptional regulatory processes on gene expression, such as translation initiation, mRNA stability, and protein stability [25].

Fig. 7
figure 7

Comparative analysis of the protein and mRNA profiles of selected DEPs. (a) The profiles for R373 plants subjected to BPH infection for 2 days; (b) The profiles for R373 plants after 4 days BPH infection; (c) The profiles for R582 plants infected with BPH for 2 days; (d) The profiles for R582 plants after 4 days of BPH infection. The relative expression levels of proteins and mRNAs were calculated by comparing the samples from BPH-infected plants to the corresponding control samples

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To validate the levels of DEPs identified through the proteomic analysis, one of the up-regulated proteins A2XFY5 and one of the down-regulated proteins B8AQ36 from R373 under BPH infection were chosen for validation using immunoblotting analysis. The specific peptide antibodies targeting the proteins were utilized for confirmation. The immunoblotting analysis indicated that A2XFY5 was up-regulated in R373 plants both after 2 days and 4 days of BPH infection, and B8AQ36 was down-regulated in R373 plants both after 2 days and 4 days of BPH infection (Fig. 8). This result provided additional confirmation of the proteomic data.

Fig. 8
figure 8

Immunoblotting analysis of the up-regulated protein A2XFY5 and the down-regulated protein B8AQ36 in R373 plants after 2 and 4 days BPH infection. Equal amount of 50 µg of total protein from each sample was used for immunoblotting analysis with the enhanced chemiluminescence (ECL) approach. The specific antibodies against A2XFY5 and B8AQ36 protein (dilution of 1:2000) as well as against β-actin (dilution of 1:5000) were used to detect the corresponding protein expressions

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Discussion

The resistance of rice to the BPH is complex and associated with genetically regulated defense mechanisms. Although many BPH resistance genes have been identified in previous studies, the molecular mechanisms underlying the regulation of the genes and proteins involved in this process have not yet been clarified. Bph3 is one of the major genes involved in enhancing the resistance of rice to BPHs, and it plays an important role in enhancing BPH resistance in rice breeding programs [8]. To determine the relationship between changes in protein levels and Bph3 in rice during BPH infection, TMT-based quantitative proteomic analysis was used to identify Bph3-regulated proteins. This led to the identification of 5,429 unique proteins in both the BPH susceptible line R582 and its Bph3-introgression line R373, which shows BPH resistance; 367 DEPs were identified during BPH infection in R582 plants, and only 114 DEPs were obtained in R373 plants. We then annotated DEPs by GO analysis to determine their putative functions. As expected, several GO terms involved in metabolic processes, cellular processes, and responses to stimuli were significantly enriched. Notably, a large number of the down-regulated DEPs from the BPH susceptible line R582 during BPH infection may be related to metabolic processes. These DEPs might be regulated by Bph3 in the protein regulation networks.

BPH infection of rice can induce changes in large amounts of metabolites, including secondary metabolites, primary metabolites, and defense compounds [9, 26, 27]. The content of amino acids of the main metabolites in phloem sap and essential nutrients for BPHs decreased significantly in BPH-resistant rice varieties during BPH feeding [26]. Bph30 regulated the flow of primary and secondary metabolites by the shikimate pathway to control BPH resistance [26]. In our study, the expression of many proteins related to metabolism was down-regulated in the BPH-susceptible line R582 after BPH infection, and the expression of a small number of metabolism-related proteins was up-regulated in the Bph3-introgression line R373 after BPH infection (Table S5–S8). These proteins might play a role in BPH resistance by regulating metabolic pathways.

Ca2+ plays an important role in diverse biological processes as a second messenger in eukaryotes [28]. The earliest response of rice to BPH involves Ca2+ influx [29]. The Ca2+-binding protein NISEF1 decreases the cytosolic Ca2+ content during BPH feeding, and it promotes the survival and feeding of BPHs [30]. In our study, the calcium signaling pathway-related protein B8BET1 was down-regulated after BPH infection in R582 (Table S7), which is susceptible to BPH, whereas the protein B8AMP0 was up-regulated in R373, which is resistant to BPH (Table S6); it might be involved in Ca2+ content regulation in plant cells during BPH infection.

The activation of mitogen-activated protein kinases (MAPKs) is an early reaction to biotic and abiotic stress [31]. Several OsMAPK genes can alter defense gene expression or phytohormone levels to regulate rice BPH resistance. The expression of OsMKK3 was significantly induced following BPH infection; the content of jasmonic acid (JA), JA-Ile, and abscisic acid (ABA) increased, and the salicylic acid (SA) level decreased in plants, which affected BPH feeding and the survival rate [32]. In our study, the MAPK signaling pathway-related proteins (A2ZAA7 and B8B658) were down-regulated in R582 plants after BPH infection (Table S7), whereas the protein B8B7D3 was up-regulated in R373 plants (Table S6), indicating that it might regulate BPH resistance.

Plant hormones play important roles in BPH infection in rice. The expression of SA-related genes (e.g., EDS1, NPR1, ICS1, PAL and PAD4) and the SA content were increased after BPH infection in Bph14-containing plants [9]. Cytokinin (CK), ethylene (ET), gibberellins (GA), ABA, indoleacetic-3-acid (IAA), and brassinosteroids (BR) were also involved in the defense of rice against BPHs [3]. In this study, the plant hormone signal transduction-related protein B8ACK1 was down-regulated after BPH infection in R582 plants (Table S8), whereas A2Y1H2 was up-regulated in R373 plants (Table S5). This means that these proteins might regulate BPH resistance by regulating plant hormone signaling transduction.

Transcription factors (TFs) play important roles in regulating rice BPH defense-related gene expression and defense-associated signaling transduction during BPH infection [33]. OsWRKY53 positively regulated BPH resistance by enhancing the H2O2 content during BPH infection [34]. The ethylene-responsive factor OsERF3 reduces rice BPH resistance by decreasing the BPH-elicited H2O2 content [35]. The RNA sequencing and microarray results demonstrated that both the number and expression levels of TFs significantly differed in resistant and susceptible lines during BPH infection [36]. In our study, three proteins (A2YWL9, A2ZF52 and A2Z6Y8) encoding transcription factors were up-regulated in the resistant line R373 after 4 days of BPH infection (Table S6), but no transcription factor was regulated in the susceptible line R582. These up-regulated TFs might play a role in mediating BPH resistance in R373 plants during BPH infection.

Conclusion

Using a TMT-based proteomic strategy, we identified 265/125 DEPs from the BPH-susceptible R582 plants after 2/4 days of BPH infection, and 29/94 DEPs from the BPH-resistant Bph3-introgression plants after 2/4 days of BPH infection. Most of the regulated proteins were enriched in metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in various environments, and phenylpropanoid biosynthesis, but the changes in their expression differed between BPH-susceptible and BPH-resistant plants. The number of DEPs involved in metabolic pathways significantly decreased in the Bph3-introgression plants after BPH infection, suggesting that BPH3 might control protein regulation during BPH infection to enhance plant survival. BPH3 might also regulate the expression of proteins involved in calcium signaling pathway, MAPK signaling pathway, and plant hormone signal transduction and transcription factors to defend against BPH infection. Collectively, our results identify important BPH3-regulated proteins involved in resistance to BPHs and provide insights with implications for the defense of rice against BPHs.

Materials and methods

Plant materials and BPHs

R582 (Oryza sativa L. subsp. indica cv. Restorer line No.582, R582), R373 (Oryza sativa L. subsp. indica cv. Restorer line No.373, R373), RH (Oryza sativa L. subsp. indica cv. Rathu Heenati, RH), and TN1 (Oryza sativa L. subsp. indica cv. Taichung Native 1, TN1) varieties were used in this study. All rice plants were cultivated under field conditions in Nanning (Guangxi Province, 22° N, 108° E), China.

We used biotype 2 of the BPH population collected from rice fields in Nanning (Guangxi Province, 21° N, 109° E), China, which represents the predominant biotype in most rice cultivation areas in China [37]. BPH insects were maintained on TN1 plants under natural conditions in a greenhouse at the Plant Protection Research Institute, Guangxi Academy of Agricultural Sciences.

BPH resistance measurements

The BPH bioassays were conducted at the seedling stage following previously described methods [38] with some modifications. Rice seeds were germinated in Petri dishes, and approximately 20 well-sprouted seeds were planted in metal trays measuring 52 × 37 cm. Three-leaved old rice seedlings cultivated in trays were infested with second- or third-instar nymphs of BPH, with 10–12 nymphs per seedling. Subsequently, the trays were enclosed with a mesh cage after the infestation. After all TN1 seedlings perished, the plants from R373 and R582 lines were evaluated, and each seedling was assigned a score of 0, 1, 3, 5, 7, or 9 according to predefined criteria [39]. Three replications of the bioassay experiments were performed. Higher scores corresponded to lower BPH resistance. The survival rates were determined by comparing the number of living plants with the total number of plants.

Protein preparation, digestion, and TMT labeling

Total protein was extracted following methods described in previous studies [40, 41]. Tissue powder (0.5 g) was extracted using five volumes (g/mL) of extraction buffer containing 8 M urea, 150 mM Tris-HCl (pH 7.6), 0.5% SDS, 0.5% Triton X-100, 5 mM ascorbic acid, 20 mM EGTA, 20 mM EDTA, 50 mM NaF, 5 mM DTT, 1 mM PMSF, 1% glycerol 2-phosphate, 1× protease inhibitor (complete EDTA-free Roche), and 2% polyvinylpolypyrrolidone. The crude extract was centrifuged at 15,000 rpm for 30 min at 10 ℃ to eliminate debris. Subsequently, three volumes of pre-cooled acetone: methanol (12:1 v/v) at -20 °C were combined with the supernatant and thoroughly mixed to precipitate proteins in a -20 ℃ freezer for at least 2 h. The protein pellet was retrieved through centrifugation at 12,000 rpm for 20 min, and subsequently subjected to two washes with acetone: methanol (12:1 v/v). Next, the protein pellets were re-suspended in RSB solution containing 8 M urea and 100 mM Tris-HCl (pH 8.0). The protein concentration was quantified using the Bradford method, and the samples were utilized in proteomic experiments.

Protein samples (200 µg) underwent trypsin digestion at 37 ℃ for 16 h. The resulting digested peptides were dehydrated through vacuum centrifugation and subsequently reconstituted in 0.1% trifluoroacetic acid for desalting using a Sep-Pak C18 column. Peptide samples from R582 and R373 were individually labeled with TMT16plex labeling reagents (Thermo Fisher Scientific, A44521) following the manufacturer’s guidelines. The labeled peptides were combined, desalted using a Sep-Pak C18 column, and fractionated into 15 portions using high-pH reversed-phase chromatography.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis

Peptides were analyzed using an Orbitrap Fusion Lumos Tribrid mass spectrometer connected to an Easy-nLC 1200 system (Thermo Fisher Scientific). The peptides were injected via an autosampler and subsequently separated on a C18 analytical column (75 μm × 25 cm, 1.9 μm particle size, 100 Å pore size, Thermo). Buffer A (0.1% FA) and buffer B (80% ACN, 0.1% FA) were used to establish a 60 min gradient: 0 min of 8% buffer B, 45 min of 8–25% of buffer B, 12 min of 25–45% buffer B, 1 min of 45–90% buffer B, and 6 min of 90% buffer B; the concentration of buffer B was decreased to 8%. The flow rate was maintained at 300 nL/min, and the isolation window for precursor selection was set to 1.2 Da.

Proteomic data analysis

The MS raw data were analyzed using MaxQuant (version 1.6.6) software and searched using the Andromeda search engine [42]. The rice protein database from UniProt was used to search the spectra files based on the specified parameters: Type (TMT), Fixed modifications [Carbamidomethyl (C)], Variable modifications [Oxidation (M) & Acetyl (Protein N-term)], and Enzyme (Trypsin). Both the MS1 and MS2 match tolerance were configured at 20 ppm. The peptide and protein false discovery rates (FDR) were established at 1%. Up-regulated and down-regulated proteins were filtered based on the following criteria: fold change > 1.5 or < 0.67 and a p-value < 0.05 [41]. The Gene Ontology (GO) database (http://geneontology.org/) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/pathway.html) were used to identify the biological and functional traits of DEPs. The STRING database (version 11.5) (http://string.embl.de) was used to conduct protein interaction analysis with a moderate confidence level of 0.4 and construct a putative protein–protein interaction network. The network was exported as a TSV (Tab-Separated Values) file and visualized using Cytoscape (Version 3.9.1) to create a Volvox graphic [43]. The mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository [44, 45] with the dataset identified PXD051542.

Validation of the proteomic data

Total RNA was extracted from R582 and R373 tissues using the Plant RNA Extraction Kit (Yi Fei Xue Biotech, Nanjing, China). The YfxScript 1st Strand cDNA Synthesis Kit (with gDNA Eraser, Yi Fei Xue Biotech, Nanjing, China) was used to reverse transcribe the extracted RNA, which yielded the first-strand cDNA. qRT-PCR was performed in 20 µL reactions comprising 10 µL of 2× SYBR qPCR Master Mix (Yi Fei Xue Biotech, Nanjing, China), 1 µL of cDNA template, and 0.5 µL of forward and reverse primers (10 µmol/L each), with the remaining volume filled with ddH2O. The thermal cycling conditions were as follows: pre-denaturation at 95 ℃ for 2 min, followed by 95 ℃ for 30 s and at 60 ℃ for 30 s for a total of 39 cycles. The primers for qRT-PCR are listed in Table S12. To validate the TMT results, 15 candidate genes from the DEPs of R373 and 12 from the DEPs of R582 were randomly selected. Three biological replicates were performed for each target gene in the qRT-PCR experiments, and the relative expression levels were determined using the 2-∆∆CT method [41].

Following the LC-PRM/MS analysis as described in prior studies [46], 10 candidate peptides lacking dynamic modification sites from DEPs were chosen for validation using liquid chromatography-parallel reaction monitoring/mass spectrometry (LC-PRM/MS).

The immunoblotting analysis was conducted as previously described [40]. Two rabbit polyclonal antibodies were generated using synthetic oligopeptides 74CQEEDDWKRIEDRIG90 and 319GAGGHQERAVPAK331 corresponding to the specific peptide sequence of the A2XFY5 protein and B8AQ36 protein, which resulted in the production of anti-A2XFY5 and anti- B8AQ36 polyclonal antibodies. The peptide antibodies were commercially made by AtaGenix Laboratories Co., Ltd. in Wuhan, People’s Republic of China. The plant β-actin polyclonal antibody was purchased from Yi Fei Xue Biotech, Nanjing, China. The proteins utilized for immunoblotting analysis were extracted from rice tissues using urea extraction buffer, separated on a 12% SDS-PAGE gel, transferred onto a polyvinylidene fluoride membrane (Millipore, USA), and probed with the anti-A2XFY5 and anti-β-actin polyclonal antibodies.

Statistical analysis

The significance of differences in the plant survival rate and BPH resistance score among groups was analyzed using Student’s t-test; data were presented as mean ± SD.

Data availability

Mass Spectormetric data are available via ProteomeXchange with identified PXD051542. The plant material of the 4 rice varieties used in this study is held at two institutions: Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, People’s Republic of China; Plant Protection Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, People’s Republic of China.

Abbreviations

TMT:

Tandem mass tag

BPH:

Brown planthopper

LC-MS/MS:

Liquid chromatography-tandem mass spectrometry

DEPs:

Differentially expressed proteins

MAPK:

Mitogen-activated protein kinase

qRT-PCR:

Quantitative reverse transcription polymerase chain reaction

QTLs:

Quantitative trait loci

PRRs:

plasma membrane protein pattern recognition receptors

SCR:

Short consensus repeat

iTRAQ:

Isobaric tags for relative and absolute quantitation

MS:

Mass spectrometry

TCA:

Citrate

LC-PRM/MS:

Liquid chromatography-parallel reaction monitoring/mass spectrometry

GO:

Gene ontology

KEGG:

Kyoto encyclopedia of genes and genomes

JA:

Jasmonic acid; ABA: Abscisic acid

SA:

Salicylic acid

CK:

Cytokinin

ET:

Ethylene

GA:

Gibberellins

IAA:

Indoleacetic-3-acid

TFs:

Transcription factors

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (32160645), Guangxi Natural Science Foundation (2023GXNSFDA026060). The authors sincerely acknowledge the services of SpecAlly Life Technology Co., Ltd. (Wuhan, China) to perform proteomic data analysis.

Funding

This study was supported by the National Natural Science Foundation of China (32160645), Guangxi Natural Science Foundation (2023GXNSFDA026060), National Key Research and Development Program of China (2023YFD1902804-2, 2022YFD1100103).

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Author notes

  1. Dongjin Qing and Weiwei Chen contributed equally to this work.

Authors and Affiliations

  1. Rice Research Institute, Guangxi Key Laboratory of Rice Genetics and Breeding, State Key Laboratory for Conservation and Utillzation of Subtropical Agro-bioresources, Guangxi Academy of Agricultural Sciences, Nanning, 530007, People’s Republic of China

    Dongjin Qing, Weiwei Chen, Jingcheng Li, Baiyi Lu, Li Chen, Weiyong Zhou, Yinghua Pan, Juan Huang, Hao Wu, Yujing Peng, De Peng, Lei Chen, Gaoxing Dai & Guofu Deng

  2. School of Chemistry and Chemical Engineering, Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi Minzu University, Nanning, 530006, China

    Yan Zhou

  3. Plant Protection Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, 530007, People’s Republic of China

    Suosheng Huang

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Contributions

D.Q., G.Dai, and G.Deng designed the experiment. D.Q., W.C., J.L., B.L., S.H., Y.Pan, J.H., W.Z., D.Peng, and LiC. conducted the experiments. Y.Peng, LeiC., and H.W. analyzed the data. Y.Z., G.Dai and G.Deng wrote the manuscript. The author(s) read and approved the final manuscript.

Corresponding authors

Correspondence to Yan Zhou, Gaoxing Dai or Guofu Deng.

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Electronic supplementary material

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Supplementary Material 1: Table S1. Proteins quantified for quantitative analysis.

Supplementary Material 2: Table S2. Proteins identified by TMT-based quantitative proteomic analysis.

Supplementary Material 3: Table S3. Peptides identified by TMT-based quantitative proteomic analysis.

Supplementary Material 4: Table S4. DEPs by comparing BPH infection samples to control.

Supplementary Material 5: Table S5. DEPs by comparing BPH infection for 2 days to control of R373 line.

Supplementary Material 6: Table S6. DEPs by comparing BPH infection for 4 days to control of R373 line.

Supplementary Material 7: Table S7. DEPs by comparing BPH infection for 2 days to control of R582 line.

Supplementary Material 8: Table S8. DEPs by comparing BPH infection for 4 days to control of R582 line.

Supplementary Material 9: Table S9. Analysis of target peptide PRM quantitative skyline date-transition results.

Supplementary Material 10: Table S10. Analysis of target peptide PRM quantitative skyline date-normalized AUC.

Supplementary Material 11: Table S11. Analysis of target peptide PRM quantitative skyline data-peptide quantitative.

Supplementary Material 12: Table S12. Primer information used in this study.

Supplementary Material 13

12870_2024_5799_MOESM14_ESM.pdf

Supplementary Material 14: Fig. S1. Cellular component and molecular function categories in GO analysis of DEPs compares samples from BPH-infected plants to control samples.

Supplementary Material 15: Fig. S2. Skyline analysis of candidate peptide fragments of target proteins

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Qing, D., Chen, W., Li, J. et al. TMT-based quantitative proteomics analysis of defense responses induced by the Bph3 gene following brown planthopper infection in rice. BMC Plant Biol 24, 1092 (2024). https://doi.org/10.1186/s12870-024-05799-7

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