Introduction
Rice sheath blight caused by the soil-borne necrotrophic fungus Rhizoctonia solani, is considered as one of the major diseases that leads to significant yield and quality losses. The yield loss in rice due to this disease is estimated to the tune of 6 to 70% depending upon diverse environmental factors and cropping practices1,2. The disease spread is favoured by intensive farming practices, the adoption of high-yielding dwarf cultivars, and the high planting density. The pathogen Rhizoctonia solani Kuhn (Teleomorph: Thanatephorus cucumeris (Frank) Donk) AG1-IA survives as dormant mycelium within crop debris and sclerotia in the absence of the host3.
Due to its long survival ability, wide host range and low inherent resistance level in rice cultivars, management of this disease is quite problematic. Currently, disease management in farmers’ fields heavily relies on chemical interventions, which unfortunately carry adverse effects on the environment, beneficial microbes, and human health. The best approach to combat ShB lies in the development of resistant cultivars, either through conventional or molecular breeding techniques. However, the complex polygenic nature of resistance coupled with the absence of major resistance genes in the primary gene pool poses significant challenges in the breeding program4. However, germplasm sources with varied level of resistance and quantitative trait loci governing sheath blight resistance are reported. Rice lines Tadukan, Tetep, Teqing and Jasmine 85 used in breeding programmes are reported to exhibit high level of quantitative resistance. To date, limited resources of genetic resistance are available worldwide and hence, searching for germplasm with high resistance has become an international effort5.
Although sheath blight disease is one of the most destructive diseases worldwide, the molecular mechanisms governing the R. solani-rice interactions are not fully clear and very limited information is available6. Proteins participate directly in the formation of new plant phenotypes and they are the key players in plant-pathogen interaction., Proteomics emerges as a valuable tool, shedding light on the crop’s adaptability to the extreme pressure of the pathogen which in turn aids to identify the resistant proteins formed before and after infection and develop the resistant cultivars6. Proteomic analysis stands as a beneficial tool for unravelling the intricate interactions between plants and pathogens, owing to the direct implication of proteins in molecular processes and biological functions7. Unlike other omics branches, proteomics offers distinct advantages; the proteome is closer to the phenotype, than the genome or transcriptome. Moreover, evidences suggest a low correlation between protein and transcript expression levels across various organisms6,8.
The era of proteomics dawned with the conventional gel-based approaches and advanced further with gel-free, labelled and label-free approaches as a result of advancements in mass spectroscopy (MS). Conventional techniques such as two dimensional gel electrophoresis (2DGE) and difference gel electrophoresis (DIGE) were used to identify defense proteins such as chitinase and β-1, 3-glucanase in resistant varieties challenged with R. solani9. Gel based techniques are effective, but suffer from limitations such as restricted coverage, low throughput and less sensitivity and hence, labelled and label-free proteomics have emerged as the preferred methods for studying the entire proteome. The rapid progress in MS-based proteomics, coupled with advancements in bioinformatics, has empowered the analysis of a larger proportion of proteins, offering a comprehensive understanding of the host system9. Gas chromatography–mass spectrometry (GC–MS) and RNA sequencing analyses showed that alteration of host photosynthesis, respiration, phytohormone signalling, and secondary metabolism is crucial in rice during R. solani infection10. Differentially expressed proteins (DEPs) associated with ShB across various genotypes were characterized using MALDI-TOF/MS as photosynthesis and stress responsive proteins11. Ma et al. employed iTRAQ for comparative proteomic analysis of ShB-resistant Teqing and susceptible Lemont, and identified proteins involved in diverse metabolic pathways such as glyoxylate and dicarboxylate metabolism; glycine, serine and threonine metabolism, as well as unsaturated fatty acid biosynthesis and gluconeogenesis regulation pathways12. Leaf proteome of rice variety ASD16 infected with R. Solani was analysed using 2DE and upregulated proteins such as acidic endochitinase and peroxiredoxin were identified13.
Feng et al. investigated DEPs in resistant rice cultivar YSBR1 using iTRAQ, classifying them into functions like cell redox homeostasis, carbohydrate metabolism, phenylpropanoid biosynthesis, photosynthesis and chlorophyll biosynthesis in response to R. solani14. In recent years, the integration of linear ion trap with an Orbitrap analyzer has emerged as a gold standard approach in MS, particularly for characterizing the whole proteome15which affords high-resolution MS spectra acquisition at rapid sequencing speeds, without compromising on depth of analysis.
At present, most of the donors reported or used for ShB resistance breeding are moderately resistant. In spite of introduction of high yielding varieties and hybrids, some traditional landraces are grown by the farmers over the years due to their unique qualities16. Landraces are the natural reservoirs of valuable traits like disease/pest resistance, drought tolerance, nutrition qualities created by diverse allelic combination occurred over several generations. Exploitation of such diversity in rice appears promising alternative to look for resistance source against sheath blight. Due to the scarcity of highly resistant germplasm sources, we made an effort to screen a panel of 450 rice landraces of Karnataka, India against R. solani and identified few highly resistant landraces (Grade 1 with 1–20% Relative Lesion Height ) as per Standard evaluation system17. Subsequent independent investigations employing association mapping and bulk segregant approaches revealed the presence of novel Quantitative Trait Loci (QTLs) associated with ShB resistance in these landraces[,18,19. In this background, present study was aimed to decipher the mechanisms underlying resistance in the novel landraces utilizing a comparative proteomic approach and to pinpoint the key proteins responsible for ShB resistance using Orbitrap-MS.
Materials and methods
Plant material and pathogen inoculation
The landrace Nizam Shait, exhibited high resistance consistantly in the screening trials from 2017 to 2022 with the lowest relative lesion height (RLH) of 9 to 9.86% (Grade 1) among the 450 entries screened as per Standard evaluation system17. We reported about the presence of novel QTL region in this18,19. This landrace is maintained in the repository of the University of Agricultural Sciences (UAS), Dharwad, Karnataka, India. Permission was obtained from the University of Agricultural Sciences, Dharwad to use Nizam Shait for research purpose. The variety BPT-5204 is a popular variety of India but highly susceptible to disease (RLH 98.6%). The seeds of Nizam Shait and BPT-5204 were sown in earthen pots filled with autoclaved soil and fertilized as recommended. Each pot contained three robust and healthy plants, two of which were deliberately inoculated with the pathogen, while the third served as a control18. The experiment was replicated thrice to minimize potential errors. The ten-days-old culture of highly virulent Rhizoctonia solani AG1-IA isolate RS4 (GenBank Accession No MK213724), maintained on potato dextrose agar (PDA) was utilised for this study20. Inoculation was conducted when the plants reached 50 days after sowing, employing the agar block method21. This involved delicately opening the leaf sheath and placing agar blocks containing sclerotia and mycelia using forceps. The inoculated area was covered with wet cotton and aluminum foil to prevent moisture loss, and the pots were enclosed in polythene bags to uphold humidity levels conducive to disease development. For negative control, the plants were left uninoculated. The experiment was meticulously replicated thrice to minimize potential errors.
Sample collection
The leaf sheath samples were collected from infected Nizam Shait, BPT-5204 plants and their respective controls in triplicates at five days post inoculation (dpi) for protein isolation (Fig. 1). The polythene cover, aluminium foil and moist cotton were removed from the inoculated plants and the inoculation site was gently wiped with cotton soaked in 70 per cent alcohol in order to eliminate any pathogen structures. Using sterilized scissors, the leaf sheath portion above the lesion was carefully excised, collected in aluminum foil, promptly wrapped, and immersed in liquid nitrogen to halt enzymatic activity before being stored at -80 °C for further analysis.
Fig. 1 [Images not available. See PDF.]
Sheath blight reaction in susceptible variety, BPT-5204 (RLH %- 98.06) (a) and resistant landrace, Nizam Shait (RLH %-9.86) (c) along with their respective healthy un-inoculated controls (b,d).
Protein isolation and quantification
Protein isolation was conducted using the phenol extraction method as outlined by Amalraj et al. with slight modification22. The protein content in the test samples was quantified using the Bradford protocol23. Bovine serum albumin (BSA) stock solution (1.0 mg/ml) was utilized to prepare protein standards. Later, final volume was adjusted to 30 µl by adding 10 µl sample buffer. Dilutions of different protein samples were prepared by dissolving 2.5 µl of protein samples with 7.5 µl of sample buffer, followed by the addition of 20 µl of deionized water to each dilution. Subsequently, 1 ml Bradford’s reagent was added to both protein standards and samples and allowed to incubate for 20 min in dark conditions. Absorbance readings were recorded at 595 nm using the Eppendorf BioSpectrometer (Eppendorf, Germany). A linear standard curve and regression equation was generated in Microsoft excel (Microsoft, USA) based on the absorbance values of BSA standards. The protein sample’s concentration was determined by comparing its absorbance value to the standards and using the regression equation.
Trypsin digestion
The samples were diluted with solubilization buffer to achieve a final protein concentration of 50 µg. Then, 10 µl of the diluted sample was mixed with double-distilled water to a total volume of 20 µl. To this mixture, Tris(2-carboxylethyl)phosphine (TCEP) (0.8 µl, 20 mM) (Sisco Research Laboratories, India) was added and incubated at 37 °C for 60 min, followed by addition of Iodoacetamide (IAA) (1.6 µl, 40 mM) (Bio-Rad Laboratories, USA) and further incubation in darkness for 30 min. Subsequently, 179.2 µl of 50 mM ammonium bicarbonate (pH 8.0) (HiMedia, India) in a 1:8 ratio was added to dilute urea to a final concentration of 1 M. Afterwards, pierce trypsin protease (ThermoFisher Scientific, USA) was introduced in a 1:50 ratio (4 µl of trypsin for 200 µl of total mixture) and incubated at 37 °C for 12 h. The digested samples were lyophilized and samples were subjected to orbitrap-MS analysis at the Mass Spectrometry Facility at IIT Bombay (MASSFIITB), India.
Protein identification and data analysis
The proteome database for Oryza sativa was accessed from UniProt, followed by protein identification using Proteome Discoverer Software 2.4. The analysis entailed treatments comprising pathogen-treated resistant germplasm (Nizam Shait) and pathogen-treated susceptible check (BPT-5204). Subsequently, the mean of protein abundances was calculated, and relative abundance ratios were determined by dividing the two treatments of interest. These ratios were then converted into log2-fold change values. Values below − 1.5 indicated protein downregulation in the treatment compared to the control, while positive values exceeding 1.5 folds indicated upregulation. The p-value was computed using the standard t-test formula. The proteins with log2-fold change values surpassing 1.5 and the p-value below 0.05 were designated as significantly upregulated proteins, whereas those with log2-fold change values below − 1.5 and a p-value below 0.05 were deemed significantly downregulated24. Principal component analysis (PCA) was conducted on the average abundance values of each protein for every treatment using the R package to confirm the findings.
In silico analysis
The gene ontology, interms of biological processes, molecular functions and cellular components, was elucidated using db2db (bioDBnet - Biological Database Network (ncifcrf.gov). The enrichment analysis was conducted using ShinyGO 0.76.125. The functional classification of significantly upregulated and downregulated proteins was accomplished using Panther database (pantherdb.org) in conjunction with a thorough literature search. Additionally, the interplay among defense-related proteins was scrutinized using String version 11.5 at string-db.org.
Validation of proteomic data through quantitative real time polymerase chain reaction (RT-qPCR)
Validation commenced with the isolation of RNA from samples collected at 5 dpi. Following this, total RNA from both inoculated and uninoculated leaf sheath samples was extracted using the TRIzol method, with subsequent quantification performed using the Eppendorf BioSpectrometer (Eppendorf, Germany). The cDNA synthesis was executed utilizing PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). For the assessment of selected gene transcript levels, qRT-PCR was employed in a total voume of 10 µl containing 0.2 µl of each primer (100 µM), 1 µl of the cDNA, 5 µl of TB greenPremix Ex Taq™ II (Takara, Japan), ROX reference dye 0.4 µl (Takara, Japan) and 3.2 µl of nuclease free water in QuantStudio™ 5 Real-TimePCR system (Thermo Fisher Scientific, USA). Cycling conditions comprised an initial denaturation step at 95 °C for 5 min, followed by 45 cycles of amplification (denaturation at 95 °C for 15 s, annealing as per primer specifications for 30 s, and extension at 72 °C for 45 s).
Key defense related proteins, including 14-3-3-like protein GF14-E, Acetylserotonin O-methyltransferase 2 (ASMT), Probable glutathione S-transferase GSTF2 (GST), Chitinase 8, Non-expressor of pathogenesis related gene (NPR4) and photosynthesis related protein Chlorophyll a-b binding protein 2 (CBP) were selected (Supplementary Table S1). The data were normalized against rice OsEF-Iα gene specific primer OSEF-1 F/OSEF-1R. For each treatment, three technical replicates were used. The relative expression of different expressed genes was determined using ΔΔCT method26.
Results
Proteomic analysis
The proteomes of highly resistant landrace Nizam Shait and susceptible variety BPT-5204 under R. solani inoculated and un inoculated condtions were analysed in our study. The MS datasets detailing rice-R. solani interaction yielded 5,133 proteins in the present analysis. Subsequent statistical analysis revealed that in pathogen-inoculated Nizam Shait, 373 proteins were upregulated, among which 118 proteins were significantly upregulated in comparision to the challenge inoculated BPT-5204 sample. Similarly, 393 proteins were found to be downregulated in pathogen-inoculated resistant Nizam Shait compared to susceptible BPT-5204, among which 172 proteins were significantly downregulated. The protein abundance varied significantly among pathogen-inoculated Nizam Shait, BPT-5204 and un-inoculated Nizam Shait, however there was no uniquely expressed protein present in any of the treatments. PCA distinguished between pathogen-inoculated BPT-5204, Nizam Shait, and control Nizam Shait, supporting the sequencing results. The first two dimensions/PCs accounted for 53.6% of the genetic variation among the 5133 DEPs (Fig. 2). These findings indicate that the treatments used in this study had substantial genetic variation in the selected DEPs despite some minor variations due to other biological factors (Fig. 2). The mass spectrometry proteomics data was deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier “PXD040634”.
Fig. 2 [Images not available. See PDF.]
Principle component analysis for identified proteins among resistant and susceptible entries using R-Studio (NP: Inoculated Nizam Shait, NC: Uninoculated Nizam Shait and BP: Inoculated BPT-5204).
GO enrichment analysis of DEPs
Significantly upregulated proteins demonstrated more than 30-fold increase in expression of proteins associated with biological processes involving monocarboxylic acid, carboxylic acid, carbohydrate, and oxoacid metabolism. Chloroplasts and ribosomes were the most enriched cellular components, exhibiting greater than 30-fold increase. Furthermore, proteins related to redox homeostasis and structural functions were predominantly represented in the molecular functions category with a fold increase exceeding 40% (Fig. 3a). In case of significantly downregulated proteins, the predominant biological processes were translation and peptide synthesis with over 60-fold enrichment. Downregulated proteins were notably associated with ribosomal components while proteins involved in translation and nucleic acid binding exhibited strong enrichment (Fig. 3b).
Fig. 3 [Images not available. See PDF.]
GO enrichment analysis of significantly upregulated (a) and downregulated proteins (b) from Nizam Shait in terms of molecular functions, biological process and cellular component.
Functional annotation of DEPs
The significantly upregulated and downregulated proteins were segregated into different functional categories based on biological process using Panther database (pantherdb.org) and literature search. Significantly upregulated 118 proteins were classified into seven functional categories: interspecies interaction between organisms, biological regulation, cellular process, localization, metabolic process, response to stimulus and defense related proteins (Supplementary Fig. S1). Some proteins exhibited multiple functions. Total 41 defense related proteins were identified. Among these, 28 proteins were found to be directly engaged in defense mechanisms, encompassing well known plant defense proteins such as chitinase 8 (Q7XCK6) (2.63 folds), phenylalanine ammonia lyase (PAL) (P14717) (2.19 folds), peroxiredoxin Q (P0C5D5) (2.10 folds), peroxiredoxin-2E-2 (Q7F8S5) (1.69 folds), alongside some less familiar yet pivotal defense proteins like 14-3-3-like protein GF14-E (Q6EUP4) (5.80 folds), cysteine synthase (Q9XEA6) (4.43 folds), sucrose synthase 7 (Q7XNX6) (3.93 folds), pyruvate dehydrogenase E1 component subunit beta-2 (Q0J0H4) (3.13 folds), and others. The defense proteins along with their gene ontology and chromosome number have been mentioned in Supplementary Table S2. Notably, the 14-3-3 like protein GF 14 E (Q6K8J4), located on chromosome 2, showed highest fold change increase (5.80 folds). During the interaction, 172 proteins were significantly downregulated which were grouped into 7 biological functions: biological regulation, cellular process, developmental process, localization, metabolic process, response to stimulus, and defense related proteins (Supplementary Fig. S1, Supplementary Table S3).
Protein-protein interaction studies
The analysis yielded a comprehensive diagram elucidating the interrelation of proteins concerning biological processes, molecular functions, cellular components, and KEGG pathways. The strıng analysis successfully retrieved data for 36 out of 41 proteins.Within this framework, 8 proteins crucial to jasmonic acid-based induced systemic resistance (JA-ISR) and 5 proteins contributing to cell wall fortification were pinpointed. Specifically, the JA-ISR ensemble comprised allene oxide cyclase (A2XID3), allene oxide synthase 2 (Q7XYS3), allene oxide synthase 1 (Q7Y0C8), probable linoleate 9 S-lipoxygenase 4 (Q53RB0), probable lipoxygenase 8 (Q84YK8), putative 12-oxophytodienoate reductase 4 (Q69TH8), putative 12-oxophytodienoate reductase 5 (Q69TI0) and putative 12-oxophytodienoate reductase 11 (B9FSC8).
During the protein-protein interaction among these 36 protein, several networks were identified. A network of proteins orchestrating cellular biosynthesis processes emerged, featuring Probable lipoxygenase 8 (Q84YK8), Allene oxide synthase 2 (Q7XYS3), Allene oxide synthase 1 (Q7Y0C8), Putative 12-oxophytodienoate reductase 4 (Q69TH8), Putative 12-oxophytodienoate reductase 5 (Q69TI0), Putative 12-oxophytodienoate reductase 11 (B9FSC8), Probable linoleate 9 S-lipoxygenase 4 (Q53RB0) (Supplementary Fig. S2a). This assembly, along with 9-cis-epoxycarotenoid dioxygenase NCED1 (Q6YVJ0), Glycolate oxidase 1 (Q10CE4) and 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (Q6K8J4) contributed to the monocarboxylic acid metabolic process (Supplementary Fig. S2b). The same proteins in addition to Pyruvate dehydrogenase E1 subunit beta-1 (Q6Z1G7), Pyruvate dehydrogenase E1 subunit beta-2 (Q0J0H4), Succinate dehydrogenase (Q6ZDY8), Malate dehydrogenase (Q7XDC8) and ATP-citrate synthase beta chain protein 1 (Q93VT8) support an increased influx of carbon for secondary metabolite production during the defense process (Fig. 4). Additionally, a protein network comprising Peroxiredoxin Q (P0C5D5), Peroxiredoxin-2E-2 (Q7F8S5), Catalase isozyme A (Q0E4K1) and Monodehydroascrobate reductase 3 (Q652L6) formed a part of cellular oxidant detoxification mechanism (Supplementary Fig. S2c).
Fig. 4 [Images not available. See PDF.]
Protein network involved in the synthesis of defense related secondary metabolites during rice sheath blight infection, prepared using string version 11.5 at string-db.org (CM-LOX2-Probable lipoxygenase 8, CYP74A1-Allene oxide synthase 1, CYP74A2- Allene oxide synthase 2, OPR4- Putative 12-oxophytodienoate reductase 4, OPR5- Putative 12-oxophytodienoate reductase 5, OPR11- Putative 12-oxophytodienoate reductase 11, NCED1- 9-cis-epoxycarotenoid deoxygenase NCED1, ISPG- 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase, CATA-Catalase isozyme A, GLO1-Glycolate oxidase 1, ACLB-1-ATP-citrate synthase beta chain protein 1, Q7XDC8-Malate dehydrogenase, SDH1-Succinate dehydrogenase, Q6Z1G7-Pyruvate dehydrogenase E1 subunit beta 1 and Q0J0H4-Pyruvate dehydrogenase E1 subunit beta 2).
Validation of key proteins at transcript level by transcriptomic/ qRT-PCR approach
Validation of DEPs at transcript level was performed to confirm the results of proteomic findings., All genes, except for NPR4, exhibited upregulation at the transcript level in Nizam Shait relative to BPT-5204 post ShB inoculation. Remarkably, the most pronounced upregulation was observed in case of 14-3-3-like protein GF14-E (26.61 folds), followed by CBP (22.47 folds), Chitinase 8 (5.00 folds), GST (1.88 folds) and ASMT (1.36 folds). Conversely, the NPR4 gene displayed a downregulation of 0.49 folds in pathogen-inoculated Nizam Shait compared to BPT-5204. Encouragingly, the RT-qPCR data aligned closely with the outcomes derived from proteomic analyses, affirming the robustness and coherence of the findings across methodologies (Fig. 5).
Fig. 5 [Images not available. See PDF.]
Fold change values of genes in ShB inoculated Nizam Shait and BPT-5204 normalized against housekeeping gene OSEF-α (The values in bracket indicate Gene ID and * indicate the fold change values of proteins associated with the genes in ShB inoculated Nizam Shait with respect to ShB inoculated BPT-5204).
Discussion
Sheath blight of rice caused by R. solani has become a devastating disease world wide. Majority of the cultivated rice varieties are susceptible and the germplasm utilized in breeding programmes are either moderately resistant or tolerant. Identification of stable high resistance sources is of paramount importance to develop durable resistant rice varieties. Rice landraces serve as novel sources of resistance to many diseases and pests. It is essential to decipher the interaction between resistant landrace and pathogen which will further guide us to design management strategies. In the present study, by utilizing the landrace Nizam Shait as a resistance source, we aimed to decipher the proteins pivotal to the resistance response and establish a comprehensive proteome database for the Rice- R. solani interaction. Sample collection on the 5th day post-inoculation (DPI) was chosen, aligning with the onset of visible symptoms on Nizam Shait. Leaf sheath samples were specifically targeted, recognizing their role as the primary barrier against the R. solani invasion11. In proteomics, meticulous sample preparation is paramount as it significantly impacts the quality of data generated by mass spectrometry (MS)27. The efficacy of a protein extraction method depends upon tissues type, age of plant species and presence or absence of non-proteinaceous interfering compounds28. Therefore, standardizing protein extraction protocols across different plant samples is imperative. Moreover, the abundance of dietary fiber in cereal crops poses challenges in cell wall disruption and contaminant removal during protein extraction. Successful data retrival hinges on the purity of sample extracted with the best isolation protocol. Among the array of protein isolation methods, phenol and TCA (Trichloro acetic acid)- acetone precipitation methods are regarded as the most reliable. In our investigation, proteins were extracted from the rice leaf sheath sample using phenol extraction method, ensuring robust data acquisition22. Rice research is challenged by the complex nature of the proteome, which necessitates specialized analytical approaches for data preprocessing, method selection, and result validation. Recently, a rice proteome database named RiceProteomeDB (RPDB) has been established to streamline the analysis of rice proteome data29.
In our investigation, 26 proteins associated with cell redox homeostasis were identified, revealing that, apart from Peroxiredoxin Q and Peroxiredoxin-2E-2, the majority of peroxiredoxins, glutaredoxins, and thioredoxins, were downregulated. Recent research report associated the resistance mechanism in ShB resistant varieties with the suppression of ROS burst induced by R solani14. Cell redox homeostasis is pivotal for regulating the levels of reactive oxygen species (ROS) within a cell, wherein ROS serves a dual role in plant biology. It acts as a signaling molecule during plant defense, while excessive concentration of ROS intermediates can induce cell death through direct oxidation30. The identification of these cell redox and antioxidant-related proteins suggests that Nizam Shait effectively maintains ROS levels during its fight against R. solani.
The hypersensitive response (HR) or programmed cell death is a crucial defense mechanism against biotrophic pathogens, yet it paradoxically benefits necrotrophic pathogens by facilitating their survival and proliferation. The two key proteins involved in the gamma-aminobutyric acid (GABA) shunt mediated defense, Succinate-semialdehyde dehydrogenase and Gamma-aminobutyrate transaminase were upregulated in Nizam Shait versus BPT-5204 after inoculation. This shunt prevents the accumulation of ROS intermediates, thereby averting cell death31. In our study, the key enzymes associated with the cell death, viz., superoxide dismutases [Cu-Zn] (SOD) and its copper chaperone, fumarylacetoacetase, DEAD-box ATP-dependent RNA helicase 38 and aspartyl protease, were observed to be downregulated following R. solani infection in resistant Nizam Shait compared to the susceptible BPT-5024.
The pathways tricarboxylic acid cycle (TCA), glycolysis and pentose phosphate pathway (PPP) are prominent for oxidizing carbohydrate molecules and generate energy in the plant to fight against the pathogen32.In our experiment, the key proteins involved in the glycolysis, TCA and PPP such as fructose-1, 6-bisphosphatase, fructose-bisphosphate aldolases, ATP-citrate synthase, malate dehydrogenase, succinate dehydrogenase, pyruvate dehydrogenases, glyceraldehyde-3-phosphate and 6-phosphogluconate dehydrogenase, were significantly upregulated as a result of pathogen attack. These pathways contribute to the lignin production via phenylpropanoid pathways in R. solani-infected rice plants, which are integral components of plant defense mechanisms5,33The findings sync with the resulst demonstrated by Mutuku and Nose34and Ghosh et al.10 at mRNA level. Importantly, the production of these enzymes in response to pathogen inoculation was more pronounced in Nizam Shait compared to BPT-5204, indicating a faster utilization of carbohydrate reserves in Nizam Shait to impede pathogen invasion.
In our investigation, the proteins crucial for remodeling and fortifying the cell wall displayed upregulation in pathogen-inoculated Nizam Shait compared to BPT-5204. The cell wall strengthening proteins identified in our study can be broadly categorized into four groups, viz., UDP-arabinopyranose mutase, expansins, sucrose synthase and cinnamyl alcohol dehydrogenase. Among these, the cytosolic enzyme UDP-arabinopyranose mutase plays a pivotal role, converting UDP-arabinopyranose to UDP-arabinofuranose, a principal component of both primary and secondary cell walls34.
The 14-3-3- protein, a member of regulatory protein family is well characterized in yeast, human and Arabidopsis thaliana. In rice, total eight isoforms (GF 14-A to GF 14-H) have been identified35. İn our study, the 14-3-3-like protein GF14-E was highly upregulated in pathogen-inoculated Nizam Shait, is paramount in the brassinosteroid signaling pathway and acts as an adapter for phosphorylated proteins. It catalyzes the activation of other defense-related proteins and serves as a transcriptional factor, regulating signaling pathways based on environmental cues. Apart from this, it interacts with transcriptional factors such as BAD ( Bcl-2-antagonist of cell death) and the FKHRL-1 to inhibit the cell death35,36. The protein was reported to be associated with effector triggered immunity (ETI) as well as pathogen associated molecular pattern (PAMP)-triggered immunity (PTI)37,38. Durgadevi et al. have previously noted its upregulation in response to R. solani during tripartite interactions39. Furthermore, it was reported to bolster ShB resistance in transgenic rice inoculated with R. solani by activating the MAPK cascade, highlighting its role in fortifying plant defense mechanisms40.
Defense proteins, including chalcone synthase and PAL, essential for the synthesis of flavonoids, isoflavonoids, and phytoalexins, were upregulated in this study. Similar upregulation of these genes was observed in the transcriptomic analysis of rice germplasm, CR 1014 inoculated with ShB, where the increased expression of these genes correlated with the production of secondary metabolites contributing to ShB tolerance41. Chitinase, also recognized as PR3 protein, breaks down fungal chitin into simpler subunits. There are several reports available in which overexpression of chitinase gene has led to the enhanced plant resistance in case of ShB26,42, 43–44. Recently, Oschib1 gene encoding a GH18 chitinase was observed to confer resistance against ShB of rice in a RNA seq study on six diverse rice genotypes viz.,TN1, BPT5204, Vandana, N22, Tetep, and Pankaj44.
The RT-qPCR validation results aligned with the proteomic data, confirming its accuracy. All selected genes, except NPR, exhibited upregulation. Notably, the 14-3-3-like protein GF14-E, which displayed the highest significant upregulation in the proteomic data, also demonstrated the highest fold change of 26.61 in RT-qPCR validation, indicating robust gene expression at both transcript and protein levels. Feng et al. observed several glutathione S-transferases (1.62–2.90 folds), chitinases (1.52–3.86 folds) and Chlorophyll a-b binding proteins (0.08–0.36 folds) to be differentially expressed in R. solani inoculated ShB resistant variety, YSBR1 compared to the control. However, transcriptome-level validation for these genes was not conducted26. In our validation study, the expression of the Chlorophyll a-b binding protein was highly upregulated in ShB inoculated Nizam Shait. Similarly, Cao et al. reported that preventing chlorophyll degradation could confer resistance against ShB45 while previous reports highlighted the upregulation of 14-3-3 protein in R. solani- BPT-5204-Bacillus subtilis tripartite interaction, qRT-PCR validation was lacking39. Therefore, this study marks the first instance where proteomic findings were validated at the transcriptome level in the rice-sheath blight interaction.
Based on our results, probable interaction model for R. solani-Nizam Shait is depicted in Fig. 6. When Rhizoctonia solani isolate RS4 encounters Nizam Shait, the non-specific lipid transfer proteins 2B acting as pattern recognition receptor (PRR) on the rice plant’s surface detect the PAMP, initiating PTI. This triggers four defense processes, first, activation of cell wall strengthening enzymes such as expansions, UDP-arabinopyranose mutase, and cinnamyl alcohol dehydrogenase and second, JA-ISR signaling pathway, third, ABA defense signaling and fourth, the most important, Brassinosteroid (BR) signaling pathway. In JA-ISR, the main enzymes such as, lipogenase (9 S-LOX4), allene oxide synthase 1,2 (AOS 1, AOS 2) and allene oxide cylase (AOC) were upregulated. The pathway commences with conversion of α-linolenic acid to 13-hydroperoxyoctadecadienoic acid (HPOD) using lipoxygenase enzyme (9 S-LOX). Subsequently, allene oxide synthase 1 (AOS 1) and allene oxide synthase 2 (AOS 2) convert it to 12–13 epoxy octadecatrienoic acid (12–13 EOT), which further forms oxyphtodienoic acid (OPDA) and jasmonic acid (JA) via allene oxide cyclase (AOC) and oxophytodienoate reductase 11 (OPR 11), respectively. Jasmonic acid then functions as a signaling molecule, relaying information about the pathogen infection to the distant cells. In conjuction with regulatory elements (other than NPR-4) and the transcriptional factor, 14-3-3 protein, it triggers the secretion of defense molecules such as Chitinase, PAL etc. The JA-ISR signaling pathway was expressed in low levels in BPT-5204. The induction of 14-3-3 proteinGF-E serves as a transcription factor for activation of other defense-related proteins in Nizam Shait compared to BPT-5204 following ShB inoculation (Fig. 6).
Fig. 6 [Images not available. See PDF.]
A putative model of defense response to Rhizoctonia solani infection.
In conclusion, current study elucidates the proteome of resistance in Nizam Shait against sheath blight compared to the highly susceptible BPT-5204. The substantial upregulation of 14-3-3 proteins suggests its pivotal role in the defense mechanism, highlighting the contribution of the brassinosteroid signaling pathway, JA-ISR, PTI, and cell wall strengthening. This information on key proteins could be utilized to develop a peptide-based marker panel for breeding sheath blight-resistant rice varieties.
Acknowledgements
The first author acknowledges financial support in the form of Senior Research Fellowship awarded by ICAR, New Delhi. Government of India.
Author contributions
PSK conceptualized the idea and supervised all the experiments; AM conducted the experiments and AM, AYP and RA did statistical analysis: PSK, AM interpreted the results and formulated the manuscript. SN, YH and PUK critically read the manuscript and provided valuable suggestions.
Data availability
“The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier “PXD040634”. “The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request”.
Declarations
Competing interests
The authors declare no competing interests.
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Abstract
Sheath blight (ShB) disease, caused by Rhizoctonia solani Kuhn, poses a significant economic threat to rice production world wide. Acknowledging the limited understanding of ShB resistance proteomics in highly resistant germplasm, our study aimed to unravel the proteomic intricacies underlying the interaction between resistant landrace Nizam Shait and R. solani. Utilizing Nizam Shait and BPT-5204 as representatives of ShB resistance and susceptibility, a comparative proteome analysis was performed using Orbitrap-Fusion mass spectrometry. The analysis unveiled 5133 differentially expressed proteins, with 118 significantly upregulated and 172 significantly downregulated at 0.05 p-value. Functional annotation revealed that the proteins associated with jasmonic acid-induced systemic resistance (JA-ISR), brassinosteroid (BR) signaling pathway, terpenoid biosynthesis, cell wall remodeling and carbohydrate metabolism showed significant upregulation in Nizam Shait upon ShB infection. The proteins associated with systemic acquired resistance (SAR), pathogenesis related proteins, cell redox homeostasis and cell death were downregulated, Notably, the 14-3-3 like protein GF-E exhibited highest upregulation, indicating its pivotal role in defense modulation through the brassinosteroid signaling pathway. The two key proteins of gamma-aminobutyric acid (GABA) shunt mediated defense, Succinate-semialdehyde dehydrogenase and Gamma-aminobutyrate transaminase were upregulated in Nizam Shait versus BPT-5204 and many other defense proteins were upregulated. Key signaling pathways involved in ShB resistance in Nizam Shait encompassed PTI via JA-ISR, cell wall strengthening, and brassinosteroid mediated resistance. Validation of the proteome data through RT-qPCR corroborated the findings, highlighting the significance of this research for future proteome assisted breeding efforts aimed at developing ShB resistant rice varieties.In conclusion, the current study deciphers pathways responsible for high resistance in landrace Nizam Shait against R. solani and identifies key proteins in Rice-R. solani interaction.
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Details
1 Department of Plant Pathology, University of Agricultural Sciences, 580005, Dharwad, Karnataka, India (ROR: https://ror.org/02qn0hf26) (GRID: grid.464716.6) (ISNI: 0000 0004 1765 6428); Department of Plant Pathology, Sri Karan Narendra Agriculture University, 303329, Jobner, Jaipur, Rajasthan, India (ROR: https://ror.org/03ag2mf63) (GRID: grid.506059.f)
2 Department of Plant Pathology, University of Agricultural Sciences, 580005, Dharwad, Karnataka, India (ROR: https://ror.org/02qn0hf26) (GRID: grid.464716.6) (ISNI: 0000 0004 1765 6428); Department of Biotechnology, University of Agricultural Sciences, 580005, Dharwad, Karnataka, India (ROR: https://ror.org/03js6zg56) (GRID: grid.413008.e) (ISNI: 0000 0004 1765 8271)
3 Department of Biotechnology, University of Agricultural Sciences, 580005, Dharwad, Karnataka, India (ROR: https://ror.org/03js6zg56) (GRID: grid.413008.e) (ISNI: 0000 0004 1765 8271)
4 Department of Plant Pathology, University of Agricultural Sciences, 580005, Dharwad, Karnataka, India (ROR: https://ror.org/02qn0hf26) (GRID: grid.464716.6) (ISNI: 0000 0004 1765 6428)
5 Department of Agricultural Microbiology, University of Agricultural Sciences, 580005, Dharwad, Karnataka, India (ROR: https://ror.org/02qn0hf26) (GRID: grid.464716.6) (ISNI: 0000 0004 1765 6428)