https://orcid.org/0000-0002-9489-9445
Figures
Abstract
Rice blast, caused by rice blast fungus (Magnaporthe oryzae), is a global threat to food security, with up to 50% yield losses. Panicle blast is a severe form of rice blast, and disease responses vary between cultivars with different genotypes. Reactive oxygen species (ROS)-mediated signaling reactions and the phenylpropanoid pathway are important defense mechanisms involved in recognizing and resisting against fungal infection. To understand rice-M. oryzae interactions in resistant and susceptible cultivars, we determined dynamic changes in the activities of five defense-related enzymes in resistant cultivar jingsui 18 and susceptible cultivar jinyuan 899 infected with M. oryzae from 4 to 25 days after infection. We then performed untargeted metabolomics analyses to profile the metabolomes of the cultivars under infected and non-infected conditions. Dynamic changes in the activities of five defense-related enzymes were closely related to panicle blast resistance in rice. Metabolome data analysis identified 634 differentially accumulated metabolites (DAMs) between resistant and susceptible cultivars following infection, potentially explaining differences in disease response between varieties. The most enriched DAMs were associated with lipids and lipid-like molecules, phenylpropanoids and polyketides, organoheterocyclic compounds, organic acids and derivatives, and lignans, neolignans, and related compounds. Multiple metabolic pathways are involved in resistance to panicle blast in rice, including biosynthesis of other secondary metabolites, amino acid metabolism, lipid metabolism, phenylpropanoid biosynthesis, arachidonic acid metabolism, arginine biosynthesis, tyrosine metabolism, tryptophan metabolism, tyrosine and tryptophan biosynthesis, lysine biosynthesis, and oxidative phosphorylation.
Citation: Yang X, Yan S, Li G, Li Y, Li J, Cui Z, et al. (2024) Rice-Magnaporthe oryzae interactions in resistant and susceptible rice cultivars under panicle blast infection based on defense-related enzyme activities and metabolomics. PLoS ONE 19(3): e0299999. https://doi.org/10.1371/journal.pone.0299999
Editor: Kandasamy Ulaganathan, Osmania University, INDIA
Received: October 19, 2023; Accepted: February 19, 2024; Published: March 7, 2024
Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The raw metabolomics data generated in this study have been submitted to the Open Archive for Miscellaneous Data in China National Center for Bioinformation (https://ngdc.cncb.ac.cn/omix/) under accession number OMIX002708. Raw data from metabolome sequencing can be accessed through this login accession number when the article is published. Raw data can currently be viewed for manuscript validation through this link https://ngdc.cncb.ac.cn/omix/preview/X6tO9etJ.
Funding: This work was supported by Tianjin Rice Research System_the Green Prevention and Control Project (ITTRRS2021100014) and National Rice Variety Research Project (2022) to Xiurong Yang, the Innovative Research for Young Scientists of Tianjin Academy of Agricultural Sciences (2022010) to Yuejiao Li and Tianjin Science and Technology Program Project (22ZYCGSN00330) to Zhongqiu Cui. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist
Introduction
Rice blast disease, caused by the fungus (Magnaporthe oryzae), has become one of the most destructive diseases in rice-growing regions globally, as it can lead to up to 50% yield losses if ideal conditions are attained [1,2]. Panicle blast is the most damaging type of blast, and responses to infection depend on genotype [3]. Therefore, the identification of disease-resistant genetic resources and selection of varieties with high resistance is an economical and effective approach for disease management. In addition, due to the lack of research surrounding panicle blast in rice, it is key to understand the resistance mechanisms in resistant and susceptible cultivars, in terms of their interaction with M. oryzae.
A number of studies have uncovered that plants can establish effective defense responses to recognize and resist against fungal infection through the activation of complex defense processes [4,5]. One of these is the generation of reactive oxygen species (ROS) and establishing a balance between ROS production and scavenging during pathogen-plant interactions [6]. ROS scavenging enzymes including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) are key components for regulating ROS levels and are also pertinent to plant disease resistance [7]. Furthermore, the phenylpropanoid pathway has been acknowledged to be important in higher plants [8]. Phenylalanine ammonia-lyase (PAL), the initial enzyme in the pathway, is involved in the synthesis of a range of defensive lignin and phenolic compounds and thereby plays a critical role in plant defense systems [9,10]. Polyphenol oxidase (PPO) is also an essential enzyme in producing phenolic compounds that shield against plant pathogens [7]. In addition, ROS and ROS scavenging enzymes have been reported to mediate resistance against M. oryzae [11]. Rice phenylalanine ammonia-lyase gene OsPAL4 is linked to broader-spectrum disease resistance, while OsPAL2 and OsPAL6 are associated with disease resistance to a variety of pathogens [12]. The activities of defense-related enzymes may be very important for evaluating resistance to panicle blast in rice cultivars with different genotypes. However, information regarding the mechanisms and progression of resistance to panicle blast via antioxidative defense systems and the phenylpropanoid pathway in rice remains scarce.
Metabolites produced by plants are known to play important roles in plant defense responses against both biotic and abiotic stresses [13]. Recent progress in metabolomics has enabled detailed information about biochemical changes occurring at cellular and tissue levels in plants to be obtained and applied in the study of the interactions between plants and pathogens [14–17]. For example, certain metabolites have been identified and employed as resistance biomarkers in a range of plants, including tomatoes [18], soybean [19], grapes [20] and rice [13], infected by various species of phytopathogens. Furthermore, metabolomic data can be leveraged to develop treatments that employ key metabolites to promote plant growth and disease resistance, thus offering an effective approach for protecting farmland from potential hazards [21].
The molecular mechanisms underlying rice blast resistance have been investigated, and many blast resistance genes have been cloned [22,23]. However, the biochemical mechanisms involved in the interaction between rice and the pathogen M. oryzae remain elusive, limiting the development of improved panicle blast resistance in rice. In order to gain insight into the biochemical mechanisms of panicle blast resistance, this study profiled the metabolome of resistant and susceptible varieties of rice and identified compounds and associated metabolic pathways related to panicle resistance, particularly antioxidative defense systems and the phenylpropanoid pathway. This study is the first to report in detail the biochemical mechanisms associated with resistance to panicle blast in rice, and the findings provide valuable data that can be used in the prevention of blast disease.
Materials and methods
Plant materials and growth conditions
In this study, two japonica rice cultivars, panicle blast-resistant cultivar jinsui18 and susceptible cultivar jinyuan899, were utilized. These two cultivars exhibited considerable variations in their resistance to panicle blast, with the same heading date and growth period. Moreover, the plants were cultivated in the netting house at the WuQing Experimental Base in Tianjin, with a row pitch of 20 cm and a row space of 20 cm. Disease resistance tests involving M. oryzae inoculation were conducted for two years, from 2020 to 2021.
M. oryzae strains, culture conditions and inoculation
In this experiment, Rice blast fungus M. oryzae strain ZD1, the predominant strain in Northern China, was utilized. The strain was cultivated on half-strength Murashige & Skoog (1/2 MS) solid medium, with a 16 h light / 8 h dark photoperiod. For panicle blast resistance recognition, a 2 mL spore suspension at a concentration of 1×105/mL was used to inject each panicle at the booting stage of rice. In total, fifty panicles of each cultivar were injected, with three replicates measured. Panicle blast resistance was measured using the 0–9 scale of the Standard Evaluation System for Rice (International Rice Research Institute, 2013).
Sampling and phenotyping
Samples were collected and recorded at 4, 7, 11, 15, and 25 days after inoculation (DAI), each including five replicates. Two leaves were collected from three plants in infected and non-infected resistant and susceptible cultivar groups per treatment at each time point, placed in liquid nitrogen, and stored at -80°C.
Phenylpropanoid pathway enzyme activity determination
For assessment of PAL and PPO activity, 100 mg leaf tissues were homogenized with 6 mL of ice-cold borate buffer (5 mM, pH 8.8) using a pre-chilled mortar and pestle. The resulting homogeneity was then centrifuged at 8,000 g for 20 min at 4°C. The supernatant was mixed with 0.02 M phenylalanine and distilled water and used as crude extract. After incubating at 30°C for 30 min, PAL activity in the extract were measured at a wavelength of 490 nm. And PPO activity was assessed following oxidation of catechol at 420 nm.
ROS scavenging enzyme activity determination
For determination of ROS scavenging enzyme activity, leaf samples (100 mg) were homogenized in ice-cold buffer (pH 7.8) with 10 mL of 25 mM potassium phosphate containing 0.2 mM EDTA and 2% polyvinylpyrrolidone. Then, the resulting homogeneity was then centrifuged at 10,000 g for 25 min at 4°C, and the supernatant was used as enzyme extract to determine the activity of ROS scavenging enzymes (SOD, POD and CAT). All spectrophotometry analyses were then conducted on an SP-756P spectrophotometer (Shanghai Spectrum Instruments Co., Ltd, Shanghai, China). SOD activity was measured by a SOD-1-W detection kit (Suzhou Comin Biotechnology Co. Ltd., Suzhou, China). POD activity was assayed using a POD-1-Y detection kit (Suzhou Comin Biotechnology Co. Ltd.), while CAT activity was determined using a CAT-1-Y detection kit (Suzhou Comin Biotechnology Co. Ltd.) by measuring H2O2 decomposition as the decrease in absorbance at 240 nm.
Detection of H2O2
Each leaf samples (100 mg) were collected and assessed the H2O2 accumulation using H2O2 detection kit (Beijing Applygen Technologies Inc.) at a wavelength of 415 nm.
Untargeted metabolomic profiling
Rice tissue were harvested and flash-frozen in liquid N2 immediately after collection. Samples were lyophilized and then stored at -80°C. The extraction solvent containing isopropanol/acetonitrile/water (3:3:2, v/v/v) was added to each freeze-dried sample, which using 10 g/mL umbelliferone as an internal standard. After sonicated for 1 h, the mixture was centrifuged at 14,000 rpm for 10 min at 4°C. The collected supernatant was used for liquid chromatography-mass spectrometry (LC-MS) analysis. The LC-MS system employed was an Agilent UHPLC-Q Exactive HF-X 1290 instrument coupled to an Agilent 6540 ultra-high-definition quadrupole time-of-flight mass spectrometer with an electrospray ionization source. Separation of components was achieved by use of an ACQUITY rapid resolution high definition (RRHD) UPLC HSS T3 column (1.8 μm, 2.1 100 mm), with mobile phases A (95% water/5% acetonitrile plus 0.1% formic acid), and B (5% water/95% acetonitrile plus 0.1% formic acid). The gradient for separation was 1% mobile phase B for 2 min, 35% for 3 min, 60% for 8 min, 70% for 10 min, 83% for 20 min, 90% for 22 min, 100% for 26 min, and then again for 28 min. The flow rate was 0.3 mL/min and 5 μL liquors of sample were loaded for each individual analysis. The capillary voltage and spray shield were set to 3500V and the sheath gas and nebulizer gas were set to 10 L/min and 12 L/min, respectively, both at a temperature of 350°C. Spectra were acquired between m/z 50 and 1200, with collision energy in MS/MS mode set to 20V. All chemicals used in the experiment, such as LC-MS-grade acetonitrile, isopropanol, distilled water and formic acid, were of the highest purity, purchased from Fisher Scientific (Waltham, MA, USA), and standards were obtained from Sigma-Aldrich (Beijing, China).
Data analysis
In this study, all data are presented as the mean ± SE of three biological replicates. The significance of differences was analyzed by using a one-way analysis of variance (ANOVA) with Duncan’s multiple range test at a 5% significance level.
Metabolomics data was pre-processed with ProgenesisQI software (Waters Corp., Milford, USA). LC-MS analysis was conducted with m/z values from ionization products in positive and negative ion modes and matched with METLIN database (http://metlin.scripps.edu/), HMDB database (http://www.hmdb.ca/) and KEGG Compound database (https://www.kegg.jp/kegg/compound/). Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were performed with R package ropls. Differentially accumulated metabolites (DAMs) were assessed with the KEGG database (http://www.kegg.jp/). Significance was determined by assessing Variable Importance in Projection (VIP) values and conducting One-Way Analysis of Variance (ANOVA) or Two-tailed t-tests with Student’s t-test p-values less than 0.05, with VIP values that exceed 1.0.
Results
Dynamic changes in phenotypes of resistant and susceptible rice cultivars following M. oryzae panicle infection
Resistant (jinsui18) and susceptible (jinyuan899) cultivars were simultaneously inoculated in panicles with rice blast fungus M. oryzae. We then observed and compared phenotypic changes between resistant and susceptible cultivars from 4 to 25 DAI, and the results are shown by Fig 1. From DAI4−DAI11, there were no obvious phenotypic differences between resistant and susceptible cultivars. There were no phenotypic changes in their appearance compared with plants inoculated at DAI4. Panicles appeared slightly broken but not pulled out, and leaf sheaths and panicles had turned slightly brown at DAI7. Some grains had turned brown, and some panicles were pulled out at DAI11. However, from DAI15 to DAI25, the performance of resistant and susceptible cultivars differed markedly. Grains of the resistant cultivar became browner, and some disappeared, while grains of the susceptible cultivar dried up, and there were obvious disease spots on branches.
Phenotype changes in resistant cultivar jinsui18 and susceptible cultivar jinyuan899 from 4–25 days after inoculation are shown.
Panicle blast resistance was investigated in mature rice which at 40 days after inoculation, and the performance of both cultivars is shown in Fig 2. There were significant differences in resistance between resistant and susceptible cultivars (Table 1).
Dynamic changes in defense enzyme activities and H2O2 accumulation in resistant and susceptible rice cultivars following M. oryzae infection
We measured changes in the activities of five defense enzymes in rice leaves, namely SOD, PPO, CAT, POD, and PAL, related to plant resistance from DAI4 to DAI25. The differences in the activities of these enzymes between the resistant and the susceptible cultivars were evident (Fig 3). For the resistant cultivar jinsui18, SOD, PPO, and CAT activities increased from DAI4 to their peak at DAI11, after which the activities decreased. On the other hand, the susceptible cultivar jinyuan899 had the opposite trend, with its activities first decreasing to the minimum at DAI7, then gradually increasing (Fig 3A–3C). The content of H2O2 gradually decreased DAI4 to their low peak at DAI11, and then gradually increasing in both resistant and susceptible cultivar. However, the rate of reduction in H2O2 content was greater in susceptible varieties from DAI4 to DAI11 (Fig 3D).
Dynamic changes in the activities of defense enzymes SOD (A), POD (B), CAT (C), PAL (E), PPO (F) and H2O2 accumulation (D) for resistant and susceptible cultivars at 4–25 DAI with M. oryzae. (*p <0.05).
Moreover, PPO activity increased in the resistant cultivar until it reached its peak at DAI11, while in the susceptible cultivar it decreased to its lowest value at DAI7 and was then maintained at a low level (Fig 3E). PAL activity in both the resistant and the susceptible cultivars increased continuously after inoculation, but the rate of increase was significantly higher in the resistant cultivar. PAL activity reached its peak in the resistant cultivar at DAI11, and then decreased. In the susceptible cultivar, it reached the peak at DAI15, and then slowly decreased (Fig 3F). Furthermore, the differences in the activities of PPO, CAT, POD and PAL between the resistant and susceptible cultivars were the largest at DAI11, while SOD activity showed the greatest disparity at DAI7.
Metabolic profiling by Ultra-performance LC-MS analysis at DAI11
Metabolomic analysis was conducted on resistant and susceptible cultivars of rice at DAI11, where the differences in defense enzymes activities were most pronounced. As listed in Table 2, the number of metabolites from each sample was determined. The masses of the analyte peaks were compared with the commercial reference standard compounds, resulting in the identification of 3461 metabolites (Fig 4 and S1 Table). Of these, 3052 metabolites were detected in all four samples. 3453 metabolites were detected in infected samples, 3422 in non-infected control samples and 3300 metabolites were detected in both resistant and susceptible cultivar samples. This data provides valuable evidence regarding the differences between resistant cultivar jinsui18 and susceptible jinyuan899.
Jy899_DW1 is the metabolome of susceptible cultivar jinyuan 899 infected with M. oryzae sampled at DAI11. jy899_DW2 is the metabolome of susceptible cultivar jinyuan 899 without infection, sampled at the same time as jy899_DW1. js18_DW1 is the metabolome of resistant cultivar jinsui18 infected with M. oryzae sampled at DAI11. js18_DW2 is the metabolome of resistant cultivar jinsui18 without infection sampled at the same time as js18_DW2.
Metabolome differences between resistant and susceptible rice cultivars in infected and non-infected samples
Our PCA of metabolites in four samples revealed a distinct separation between resistant and susceptible rice cultivars, with the first two principal components accounting for 54.9% of the total variance of the data. This revealed a larger discrepancy in metabolic activity between resistant and susceptible cultivars when compared to infected and non-infected samples (Fig 5A). The results of PCA indicated that the majority of the variance in the metabolites data originated from the cultivars.
Jy899_DW1 is the metabolome of susceptible cultivar jinyuan899 infected with M. oryzae sampled at DAI11. jy899_DW2 is the metabolome of susceptible cultivar jinyuan899 without infection, sampled at the same time as jy899_DW1. js18_DW1 is the metabolome of resistant cultivar jinsui18 infected with M. oryzae sampled at DAI11. js18_DW2 is the metabolome of resistant cultivar jinsui18 without infection sampled at the same time as js18_DW1. (A) PCA scores of metabolomes in rice panicles for infected and non-infected resistant and susceptible cultivars. OPLS-DA scores plots (B) and permutation tests (C) for metabolite profiling of resistant cultivar jinsui18 and susceptible cultivar jinyuan899 with M. oryzae infection. OPLS-DA scores plots (D) and permutation tests (E) for metabolite profiling of resistant cultivar jinsui18 and susceptible cultivar jinyuan899 without M. oryzae infection.
After constructing the OPLS-DA models to investigate the differences between resistant and susceptible cultivars, we observed a distinct clustering of cultivars in both infected and non-infected conditions (Fig 5B–5E). The OPLS-DA models of fungi-challenged panicles at DAI11 demonstrated an impressive variance of 94.6% and 93.2% for the first two components, respectively, compared to 81.9% and 79.8% for infected and non-infected samples, respectively. Furthermore, our permutation tests demonstrated that the OPLS-DA models were valid, with the plots indicating a clear distinction between resistant and susceptible cultivars in both infected and non-infected samples.
Furthermore, clusters generated by the heatmap analysis of representative differential metabolites in the four samples exhibited obvious segregation between resistant and susceptible cultivars, indicating distinct differences in their accumulated metabolomes (Fig 6). Differential metabolites could be delineated into two groups based on their accumulation patterns: Group 1 metabolites manifested high accumulation in the susceptible cultivars jinyuan899 and low accumulation in the resistant variety jinsui18, whereas Group 2 metabolites presented high levels in the resistant cultivar but low levels in the susceptible cultivar. The Group 2 metabolites could be further divided into three subcategories. Subclass 1 metabolites had high accumulation in infected samples, Subclass 2 metabolites with low indications in infected samples of the susceptible variety, and all other metabolites falling into Subclass 3. Group2 metabolites did not attribute significantly to any differential patterns between infected and non-infected samples in the resistant variety. These findings further corroborate the significant disparities between resistant and susceptible varieties of their metabolomes, with the susceptible cultivar being more prone to changes inflicted by the rice blast fungus.
Jy899_1 and jy899_2, jy899_3 are the metabolome of susceptible cultivar jinyuan899 infected with M. oryzae sampled at DAI11 with three repeats; jy899_4, jy899_5, and jy899_6 are the metabolome of susceptible cultivar jinyuan 899 without infection sampled at the same time as jy899_1 to jy899_3; js18_1 to js18_3 are the metabolome of resistant cultivar jinsui18 infected with M. oryzae sampled at DAI11 with three repeats. js18_4, js18_5, and js18_6 are the metabolome of resistant cultivar jinsui 18 without infection sampled at the same time as js18_1 to js18_3. group1. group1, metabolites with low abundance in resistant cultivar jinsui18 and high abundance in susceptible cultivar jinyuan899; group2, metabolites with high abundance in resistant cultivar jinsui18 and low abundance in susceptible cultivar jinyuan899. group2 can be divided into three subtypes (subclass1, subclass2, subclass3) according to patterns in abundance between infected and non-infected samples for susceptible cultivar jinyuan899.
Subsequently, the OPLS-DA analysis of the four sample comparisons revealed 1083 differentially accumulated metabolites (DAMs) with corresponding variable importance in projection (VIP) and Student’s t-test values higher than 1 and 0.05 respectively (Fig 7 and S2 Table). In the js18_DW1 vs. js18_DW2 comparison, which reflected differences in metabolomes between infected and non-infected samples in the resistant cultivar jinsui18, 95 of 137 DAMs were unique in this comparison. In the jy899_DW1 vs. jy899_DW2 comparison, 143 out of 231 DAMs were unique, which reflected differences in metabolomes between infected and non-infected samples in the susceptible cultivar jinyuan899. Eight DAMs were present at high levels in infected samples in both resistant and susceptible cultivars when comparing infected and non-infected samples. The detected DAMs may be involved in the defense system of both susceptible and resistant rice cultivars. These metabolites encompassed a variety of compounds, including lipids/lipid-like molecules (e.g. 2-oxo-4-methylthiobutanoic acid, dihydroroseoside, unshuoside A), organoheterocyclic compounds (limonene-1,2-epoxide), organic oxygen compound (6-hydroxypseudooxynicotine), phenylpropanoid/polyketide compound (dhghab), benzenoid compound (methyl n-acetylanthranilate) and organic acid/derivative compound (leucylleucine) (S2 Table).
Js18_DW1_vs. jy899_DW1 represents differential metabolites between resistant cultivar jinsui18 and susceptible cultivar jinyuan899 infected with M. oryzae; js18_DW2_vs. jy899_DW2 represents differential metabolites between resistant cultivar jinsui18 and susceptible cultivar jinyuan899 without infection; js18_DW1_vs. js18_DW2 represents differential metabolites between infected and non-infected samples of resistant cultivar jinsui18; jy899_DW1_vs. jy899_DW2 represents differential metabolites between infected and non-infected samples of susceptible cultivar jinyuan899.
In the js18_DW2 vs. jy899_DW2 comparison without infection at DAI11, 547 DAMs were identified, of which 146 were unique in this comparison. This represents a clear difference in metabolites between jinsui18 and jinyuan899, not related to infection by M. oryzae. In the js18_DW1 vs. jy899_DW1 comparison, which reflects differences in metabolites between resistant and susceptible cultivars infected by M. oryzae, we identified 634 DAMs, of which 269 were unique in this comparison; 148 were accumulated at low levels, and 466 were present at high levels in the resistant cultivar (Fig 7 and S3 Table). The top 30 compounds are shown in Fig 8. The major DAMs observed among the studied cultivars were ten lipids/lipid-like molecules, six phenylpropanoids/polyketides, five organoheterocyclic compounds, three organic oxygen compounds, two organic acids/derivatives and one lignan/neolignan and related compound (S2 Table).
Relative abundances of the top 30 differential metabolites between resistant and susceptible cultivars with M. oryzae infection. Data are normalized against unit variance. Comparisons were generated via hierarchical cluster analysis using an average linkage method based on Euclidian distance. Shades from blue to red represent increasing metabolite levels (*p <0.05, **p <0.01, ***p <0.001 for groups compared using one-way ANOVA).
Categorization of DAMs between panicle blast resistant and susceptible cultivars challenged with M. oryzae
In total, 634 DAMs were observed in resistant and susceptible cultivars infected at DAI11, with 604 of these being classified based on super class on the HMDB 4.0 database (Fig 9 and S3 Table). Lipids and lipid-like molecules, such as phenol lipids (66), fatty acyl (45) and steroids/steroid derivatives (20) were the most frequent DAMs, accounting for 23.18% (140). This was followed by organic oxygen compounds (16.56%, 100), phenylpropanoids and polyketides (16.06%, 97), organoheterocyclic compounds (13.25%, 80), organic acids and derivatives (12.91%, 78), benzenoids (10.10%, 61) and few nucleosides, nucleotides and analogues (16), lignans, neolignans and related compounds (15), alkaloids and derivatives (3), and hydrocarbons (2).
Metabolic pathways of DAMs between panicle blast resistant and susceptible cultivars challenged with M. oryzae
Further analysis of the 634 DAMs related to metabolic pathways showed that multiple metabolic pathways may be responsible for changes in rice cultivars challenged with M. oryzae. As shown in Fig 10A, metabolic pathways with more than 10 DAMs were biosynthesis of other secondary metabolites (28 DAMs), amino acid metabolism (12 of DAMs), lipid metabolism (11), and carbohydrate metabolism (10) (Fig 10A). These metabolic pathways may be related to resistance to rice panicle blast. Among them, metabolites related to the biosynthesis of other secondary metabolism and amino acid metabolism were also the metabolites showing major differences between infected and non-infected samples in the resistant cultivar jinsui18 (Fig 10B).
(A) Pathway classification of differential metabolites between resistant cultivar jinsui18 and susceptible cultivar jinyuan899 with M. oryzae infection. (B) Pathway classification of differential metabolites between infected and non-infected samples for resistant cultivar jinsui18.
KEGG pathway enrichment analysis results indicated that the identified DAMs were enriched in eight metabolic pathways with p-values less than 0.05. These pathways included phenylpropanoid biosynthesis (8 DAMs), arachidonic acid metabolism (7), arginine biosynthesis (3), tyrosine metabolism (5), tryptophan metabolism (6), phenylalanine, tyrosine, and tryptophan biosynthesis (3), lysine biosynthesis (3), and oxidative phosphorylation (1). The results of the analysis showed that each pathway was significantly enriched in DAMs associated with the metabolism of specific metabolites (Fig 11).
Discussion
Resistance to rice panicle blast is related to dynamic activities of ROS-scavenging defense enzymes
ROS are involved in many important processes, including signal transduction during pathogen and host plant interactions [24]. ROS production is coupled with ROS degradation by various cellular antioxidants to protect cells from ROS-induced damage to DNA, proteins, and cell membranes, and ultimately cell death [24]. An important part of the antioxidative system is the balance between the generation and scavenging of reactive oxygen species (ROS), which is tightly controlled during plant-M. oryzae interactions [24]. ROS scavenging defense enzymes are involved in adjusting ROS levels and maintaining resistance to pathogens [25]. El-Komy et al. [26] demonstrated that a resistant faba bean (Vicia faba) cultivar had a higher enzymatic ROS scavenging capacity compared to a susceptible one during Botrytis fabae infection.Wang et al. [27] reported ROS bursts at 1–3 DAI, and ROS scavenging started at 5 DAI with M. oryzae. In our current study, we measured the activities of ROS-scavenging defense enzymes at 4–25 DAI, shortly after ROS scavenging began. We demonstrated that panicle blast resistance rice cultivar jinsui18 was far superior at ROS scavenging than susceptible cultivar jinyuan899. The activities of ROS-scavenging defense enzymes (SOD, CAT, POD) increased at first, then decreased in the resistant variety; by contrast, it decreased then increased in the susceptible variety. The activities of ROS-scavenging defense enzymes were higher in the resistant cultivar than in the susceptible cultivar throughout most of the M. oryzae infection.
We also examined the H2O2 levels in the resistant varieties after inoculation, and the trend of H2O2 changes in the resistant varieties could be explained by changes in antioxidant enzyme activity, i.e., as the activity of antioxidant enzymes increased, the content of H2O2 gradually decreased, and as the activity of antioxidant enzymes decreased the content of H2O2 gradually increased. However, in susceptible varieties, the changes in H2O2 enzyme content could not be explained by the changes in antioxidant enzyme activity. The changes in H2O2 content in susceptible varieties may be dominated by M. oryzae. Fungi also need effective antioxidant measures so that they can better invade the host cells [24].
Therefore, we conclude that resistance to rice panicle blast is related to dynamic activities of ROS-scavenging defense enzymes.
Resistance to rice panicle blast is related to the phenylpropanoid pathway
Phenylpropanoid pathway defense enzymes are crucial components of plant disease resistance [28–30]. For example, GmPAL2.1 has been identified as a major component of soybean resistance to Phytophthora sojae [31]. OsPAL4 and OsPAL6 are also important contributors to rice resistance; they could be used as targets in breeding plans to develop enhanced broad-spectrum disease resistance in this crop [12]. In our current study, it was observed that PAL enzyme activity increased in both resistant and susceptible varieties after pathogen inoculation, although this increase was much more pronounced in the resistant one. Thus, PAL enzymes appeared to be involved in protecting against M. oryzae in panicles in both resistant and susceptible varieties, and PAL activity as higher in the resistant cultivar. Meanwhile, PPO activity first increased, then decreased in the resistant cultivar; by contrast, it decreased then increased in the susceptible variety after pathogen infection. In addition, our KEGG pathway enrichment analysis revealed that DAMs between resistant and susceptible cultivars were enriched in the phenylpropanoid biosynthesis pathway. Therefore, resistance-associated differences in both enzyme activity and defense-associated metabolites (DAMs) related to the phenylpropanoid pathway were observed between the resistant and susceptible cultivars. This indicates that the phenylpropanoid pathway is associated with resistance in rice panicle blast. Further investigation is needed to uncover the underlying molecular and biochemical mechanisms behind this.
Diverse compounds mediate resistance to rice panicle blast
Previous research has shown that pathogen-induced metabolites are essential contributors to enhanced plant resistance [32]. In this study, 634 distinct defense-associated metabolites were identified between resistant and susceptible cultivars after infection. Organic oxygen compounds, organic acids and derivatives, lipids and lipid-like molecules, phenylpropanoids, and polyketides were among the abundant metabolites. These findings are in line with those of a preceding study characterizing the dominant metabolites in MoSDT1-transgenic rice plants, which demonstrated substantially increased tolerance to M. oryzae [13].
Organic oxygen compounds and organic acids are key metabolites in infected plants [2]. The accumulation of organic oxygen compounds is often linked to the synthesis of ascorbic acid, an antioxidant known for its role in plant resistance-related properties [33]. Duan et al. [13] reported that organozcbic compounds were uniquely present in M. oryzae-infected MoSDT1 transgenic rice plants. Organic acids, a product of incomplete oxidation of photosynthetic products, represent a pool of stored carbon generated during metabolic conversions and enable increased resistance to abiotic and biotic stresses through respiration [34].
One of the main components of biological membranes is lipids, forming an essential boundary between a cell and its external environment [35]. Recent studies point to the critical role of lipids in signal transduction pathways, with the plasma membrane forming an important source for signalling molecules [35]. Lipids are divided into eight categories based on the characteristic hydrophobic and hydrophilic parts of their chemical backbones, namely fatty acyl, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, phenol lipids, saccharolipids, and polyketides [35]. In the present study, we observed that 66 phenol lipids, 45 fatty acyl, and 20 sterol lipids were the predominant differentially accumulated lipids in panicles of rice cultivars exhibiting either resistance or susceptibility to M. oryzae infection. However, further investigation is necessary to elucidate the precise mechanisms behind the defence-promoting properties of these metabolites.
Plants contain high levels of conflicting phenylpropanoid molecules that have various physiological activities, such as attraction of pollinators, secondary cell wall growth and defense against plant diseases [36]. The antioxidant property of phenylpropanoids is often attributed to the hydroxyl groups and unsaturated double bonds that react with radical and oxidized ions [36]. Polyketides are one type of phenylpropanoid, known for the range of their chemical structures, which are indispensable for the production of pharmaceuticals. In addition to participating in phenylpropanoid metabolism in plants, some polyketides are capable of producing interesting biological effects [36].
Multiple metabolic pathways mediate resistance to rice panicle blast
Critical metabolic pathways are activated when plants are infected by pathogens [37]. Therefore, understanding these pathways could provide insight and a specific strategy for strengthening plant resistance systems. Our current study revealed that multiple metabolic pathways with many DAMs between resistant and susceptible rice cultivars maybe responsible for resistance following challenge with M. oryzae. Analyzing the KEGG pathways uncovered that a multitude of processes within biosynthesis and metabolism, such as biosynthesis of secondary metabolites, amino acid metabolism, lipid metabolism, carbohydrate metabolism, phenylpropanoid biosynthesis, arginine biosynthesis, tyrosine and tryptophan biosynthesis, lysine biosynthesis, arachidonic acid metabolism, tyrosine metabolism, and tryptophan metabolism, are correlated with resistance to panicle blast in rice.
Secondary metabolites produced by plant pathogenic fungi are complex and have unique structures. M. oryzae, the cause of rice blast disease, is known to produce four types of secondary metabolites, namely melanin, tenuazonic acid, nectriapyrones, and pyriculols [38]. Many of these metabolites are involved in the interaction between M. oryzae and rice; however, in most cases, the exact metabolites produced in that interaction remain unknown [38]. An interesting example of this is the virulence gene ACE1, which is found in M. oryzae isolates that are specifically recognized by rice cultivars carrying the Pi33 resistance gene [39]. While it is known that ACE1 is a secondary metabolism gene, the exact product that is produced has yet to be determined. This highlights the importance of secondary metabolites in the rice-M. oryzae interaction, and our research aims to uncover additional information in this regard.
Lipid metabolism has an important role in modulating the plant response to biotic and abiotic stresses by altering the structure and signaling of lipids [35,40]. A key mechanism of this process is the hydrolysis of membrane phospholipids by phospholipase D (PLD), which produces the signaling compound phosphatidic acid (PA). PLD-PA is a potentially important signaling complex for regulating lipid metabolism, hormone signaling, and plant defense responses [41]. It has the capability to interact with various proteins and other lipid molecules to modulate action in cytoskeleton dynamics, alter microtubule cytoskeleton, and stimulate hormone and reactive oxygen species (ROS) production [41]. A distinct feature of the PLD-PA complex is that PA can bind to and regulate a broad range of proteins by changing the physical and chemical properties of the lipids [41]. Findings from our study suggest that lipid metabolism was the most significantly changed metabolic process in response to M. oryzae infection, and it is plausible that a variety of lipid signaling molecules can be produced by alterations in lipid metabolism.
Successful infection by pathogens is heavily contingent on their capacity to utilize available nutrient sources provided by plants, in addition to their ability to penetrate and overpower host plants’ defensive mechanisms [42]. Primary metabolites, particularly certain specialized metabolites, are crucial components of plant development, growth, reproduction processes, in addition to protecting against biotic and abiotic stresses [43,44]. Nutrient metabolism, including amino acid metabolism, arginine biosynthesis, lysine biosynthesis, tyrosine metabolism, tryptophan metabolism, and arachidonic acid metabolism, has been identified to have a substantial impact on the growth, asexuality, differentiation, and virulence of M. oryzae and other pathogens [6,45–47]. Amino acids participate in the process of nitrogen assimilation and serve as precursors for multiple defense-related secondary metabolites [48]. The gene MoCPA2 of M. oryzae encodes a large subunit of arginine-specific carbamoyl phosphate synthase and has been found to be involved in arginine biosynthesis and induction of plant defense responses as well as scavenging ROS during pathogen-plant interactions [6]. The transgenic lines expressing MoSDT1, which elicited improved blast resistance in rice, contained significantly more tyrosine and tryptophan, two aromatic amino acids relative to production of a number of defense-related secondary metabolites [49]. Additionally, MoLys2 in M. oryzae was determined to be essential for lysine biosynthesis, thus constituting a potential target for development of novel fungicides against M. oryzae by targeting lysine biosynthesis [50]. Arachidonic acids have been reported to trigger programmed cell death, defense responses in plants, as well as inducing resistance to viruses in potato [51] and tobacco (Nicotiana tabacum) [52].
Conclusions
Our exploration has revealed that dynamic ROS levels and the phenylpropanoid pathway are both associated with resistance to rice panicle blast. Multiple molecules, encompassing organic acids and derivatives, oxygen-containing organic compounds, lipids and lipid-like compounds, as well as phenylpropanoids and polyketides, were reported to be connected to resistance against the disease. In addition, various metabolic pathways such as secondary metabolite biosynthesis, amino acid metabolism, lipid metabolism, phenylpropanoid biosynthesis, arachidonic acid metabolism, arginine biosynthesis, tyrosine metabolism, tryptophan metabolism, phenylalanine, tyrosine, tryptophan biosynthesis, lysine biosynthesis, and oxidative phosphorylation are further linked to resistance to rice panicle blast. Such research outcomes are likely to be beneficial in the optimization of resistance to panicle blast in rice, in addition to providing elementary data relevant to future plant-disease prevention.
Supporting information
S1 Table. Information of 3461 metabolites accumulated between panicle blast resistant and susceptible cultivars challenged with M.oryzae at DAI11.
https://doi.org/10.1371/journal.pone.0299999.s001
(XLS)
S2 Table. Information of all accumulated metabolites accumulated between panicle blast resistant and susceptible cultivars challenged with M.oryzae at DAI11.
https://doi.org/10.1371/journal.pone.0299999.s002
(XLS)
S3 Table. Information of 634 differentially accumulated metabolites between panicle blast resistant and susceptible cultivars challenged with M.oryzae at DAI11.
https://doi.org/10.1371/journal.pone.0299999.s003
(XLS)
References
- 1. Jain P, Dubey H, Singh PK, Solanke AU, Singh AK, and Sharma TR. Deciphering signalling network in broad spectrum Near Isogenic Lines of rice resistant to Magnaporthe oryzae. Sci Rep. 2019; 9: 16939. pmid:31729398
- 2. Kumar G, and Nanda S. Molecular perspectives of plant-pathogen interactions: An overview on plant immunity. Biological Forum-An International Journal 2021; 13: 48–53.
- 3. Dangl JL, Horvath DM, and Staskawicz BJ. Pivoting the plant immune system from dissection to deployment. Science. 2013; 341: 746–751. pmid:23950531
- 4. Zhang S, Liu J, Xu B, and Zhou J. Differential responses of Cucurbita pepo to Podosphaera xanthii reveal the mechanism of powdery mildew disease resistance in Ppumpkin. Front Plant Sci 2021; 12: 633221. pmid:33912204
- 5. Dangl JL, and Jones JD. Plant pathogens and integrated defence responses to infection. Nature. 2001; 411: 826–833. pmid:11459065
- 6. Liu X, Cai Y, Zhang X, Zhang H, Zheng X, and Zhang Z. Carbamoyl phosphate synthetase subunit MoCpa2 affects development and pathogenicity by modulating arginine biosynthesis in Magnaporthe oryzae. Front Microbiol. 2016; 7: 2023. pmid:28066349
- 7. Niu J, Cao Y, Lin X, Leng Q, Chen Y, and Yin J. Field and laboratory screening of anthurium cultivars for resistance to foliar bacterial blight and the induced activities of defence-related enzymes. Folia Horticulturae. 2018; 30: 129–137.
- 8. Hou L, Wang L, Wu X, Gao W, Zhang J, and Huang C. Expression patterns of two pal genes of Pleurotus ostreatus across developmental stages and under heat stress. BMC Microbiol. 2019; 19: 231. pmid:31655558
- 9. Wang XH, Gong M, Tang L, Zheng S, Lou JD, Ou L, et al. Cloning, bioinformatics and the enzyme activity analyses of a phenylalanine ammonia-lyase gene involved in dragon’s blood biosynthesis in Dracaena cambodiana. Mol Biol Rep. 2013; 40: 97–107. pmid:23108992
- 10. Kamalipourazad M, Sharifi M, Maivan HZ, Behmanesh M, and Chashmi NA. Induction of aromatic amino acids and phenylpropanoid compounds in Scrophularia striata Boiss. cell culture in response to chitosan-induced oxidative stress. Plant Physiol and Bioch. 2016; 107: 374–384. pmid:27392152
- 11. Liu X, Zhou Q, Guo Z, Liu P, Shen L, Chai N, et al. A self-balancing circuit centered on MoOsm1 kinase governs adaptive responses to host-derived ROS in Magnaporthe oryzae. Elife. 2020; 9: e61605. pmid:33275098
- 12. Tonnessen BW, Manosalva P, Lang JM, Baraoidan M, Bordeos A, Mauleon R, et al. Rice phenylalanine ammonia-lyase gene OsPAL4 is associated with broad spectrum disease resistance. Plant Mol Biol. 2015; 87: 273–286. pmid:25515696
- 13. Duan G, Li C, Liu Y, Ma X, Luo Q, and Yang J. Magnaporthe oryzae systemic defense trigger 1 (MoSDT1)-mediated metabolites regulate defense response in Rice. BMC Plant Biol. 2021; 21: 40. pmid:33430779
- 14. Jabran M, Chen D, Muhae-Ud-Din G, Liu T, Chen W, Liu C, et al. Metabolomic analysis of wheat grains after Tilletia laevis Kuhn infection by using ultrahigh-performance liquid chromatography-Q-exactive mass spectrometry. Metabolites. 2022; 12: 805. pmid:36144210
- 15. Zhang J, Li Y, Guo J, Du B, He G, Zhang Y, et al. Lipid profiles reveal different responses to brown planthopper infestation for pest susceptible and resistant rice plants. Metabolomics. 2018; 14: 120. pmid:30830454
- 16. Xu XH, Wang C, Li SX, Su ZZ, Zhou HN, Mao LJ, et al. Friend or foe: differential responses of rice to invasion by mutualistic or pathogenic fungi revealed by RNAseq and metabolite profiling. Sci Rep. 2015; 5: 13624. pmid:26346313
- 17. Wu Z, Wang G, Zhang B, Dai T, Gu A, Li X, et al. Metabolic mechanism of plant defense against rice blast induced by probenazole. Metabolites. 2021; 11: 246. pmid:33923492
- 18. Galeano Garcia P, Neves Dos Santos F, Zanotta S, Eberlin MN, and Carazzone C. Metabolomics of Solanum lycopersicum infected with Phytophthora infestans leads to early detection of late blight in asymptomatic plants. Molecules. 2018; 23: E3330. pmid:30558273
- 19. Silva E, da Graca JP, Porto C, Martin do Prado R, Hoffmann-Campo CB, Meyer MC., et al. Unraveling asian soybean rust metabolomics using mass spectrometry and molecular networking approach. Sci Rep. 2020;10: 138. pmid:31924833
- 20. Chitarrini G, Soini E, Riccadonna S, Franceschi P, Zulini L, Masuero D, et al. Identification of biomarkers for defense response to Plasmopara viticola in a resistant grape variety. Front Plant Sci. 2017; 8: 1524. pmid:28928759
- 21. Wang Y, Wang Z, Jibril SM, Wei M, Pu X, Yang C, et al. Transcriptome and quasi-targeted metabolome analyze overexpression of 4-Hydroxyphenylpyruvate dioxygenase alleviates fungal toxicity of 9-Phenanthrol in Magnaporthe oryzae. Int J Mol Sci. 2022; 23: 7116. pmid:35806121
- 22. Hong Y, Yang Y, Zhang H, Huang L, Li D, and Song F. Overexpression of MoSM1, encoding for an immunity-inducing protein from Magnaporthe oryzae, in rice confers broad-spectrum resistance against fungal and bacterial diseases. Sci Rep. 2017; 7, 41037. pmid:28106116
- 23. Chowdhury S, Basu A, and Kundu S. Biotrophy-necrotrophy switch in pathogen evoke differential response in resistant and susceptible sesame involving multiple signaling pathways at different phases. Sci Rep. 2017; 7: 17251. pmid:29222513
- 24. Liu X, and Zhang Z. A double-edged sword: reactive oxygen species (ROS) during the rice blast fungus and host interaction. FEBS J. 2022; 289: 5505–5515. pmid:34453409
- 25. Sang Y, and Macho AP. Analysis of PAMP-triggered ROS burst in plant immunity. Methods Mol Bio. 2017; 1578: 143–153. pmid:28220421
- 26. El-Komy MH. Comparative analysis of defense responses in chocolate spot-resistant and -susceptible Faba Bean (Vicia faba) cultivars following infection by the necrotrophic fungus Botrytis fabae. Plant Pathol J. 2014;.30: 355–366. pmid:25506300
- 27. Wang C, Li C, Duan G, Wang Y, Zhang Y, and Yang J. Overexpression of Magnaporthe Oryzae systemic defense trigger 1 (MoSDT1) confers improved rice blast resistance in rice. Int J Mol Sci. 2019; 20: E4762. pmid:31557947
- 28. Paparu P, Dubois T, Coyne D, and Viljoen A. Effect of Fusarium oxysporum endophyte inoculation on the activities of phenylpropanoid pathway enzymes and Radopholus similis numbers in susceptible and tolerant east African highland bananas. Nematology. 2010; 12: 469–480.
- 29. Narendra Babu A, Jogaiah S, Ito SI, Kestur Nagaraj A, and Tran LS. Improvement of growth, fruit weight and early blight disease protection of tomato plants by rhizosphere bacteria is correlated with their beneficial traits and induced biosynthesis of antioxidant peroxidase and polyphenol oxidase. Plant Sci. 2015; 231: 62–73. pmid:25575992
- 30. Jia H, Zhao P, Wang B, Pervaiz T, Zhao F, Zhao M, et al. Overexpression of polyphenol oxidase gene in strawberry fruit delays the fungus infection process. Plant Mol Biol Rep. 2016; 34: 592–606.
- 31. Zhang C, Wang X, Zhang F, Dong L, Wu J, Cheng Q, et al. Phenylalanine ammonia-lyase2.1 contributes to the soybean response towards Phytophthora sojae infection. Sci Rep. 2017; 7: 7242. pmid:28775360
- 32. Pott DM, Osorio S, and Vallarino JG. From central to specialized metabolism: an overview of some secondary compounds derived from the primary metabolism for their role in conferring nutritional and organoleptic characteristics to fruit. Front Plant Sci. 2019;. 10: 835. pmid:31316537
- 33. Armijo G, Schlechter R, Agurto M, Muñoz D, Nuñez C, and Arce-Johnson P. Grapevine pathogenic microorganisms: understanding infection strategies and host response scenarios. Front. Plant Sci. 2016;.30:382. pmid:27066032
- 34. Igamberdiev AU, and Eprintsev AT. Organic Acids: The pools of fixed carbon involved in redox regulation and energy balance in higher plants. Front Plant Sci. 2016; 7: 1042. pmid:27471516
- 35. Hou Q, Ufer G, and Bartels D. Lipid signalling in plant responses to abiotic stress. Plant Cell Environ. 2016; 39: 1029–1048. pmid:26510494
- 36. Yu O, and Jez JM. Nature’s assembly line: biosynthesis of simple phenylpropanoids and polyketides. Plant J. 2008; 54: 750–762. pmid:18476876
- 37. Morkunas I, and Ratajczak B. The role of sugar signaling in plant defense responses against fungal pathogens. Acta Physiologiae Plantarum. 2014; 36: 1607–1619.
- 38. Motoyama T, Yun CS, and Osada H. Biosynthesis and biological function of secondary metabolites of the rice blast fungus Pyricularia oryzae. J Ind Microbiol Biotechnol. 2021; 48: kuab058. pmid:34379774
- 39. Bohnert HU, Fudal I, Dioh W, Tharreau D, Notteghem JL, and Lebrun MH. A putative polyketide synthase/peptide synthetase from Magnaporthe grisea signals pathogen attack to resistant rice. Plant Cell. 2004; 16: 2499–2513. pmid:15319478
- 40. Zhao J, Sun P, Sun Q, Li R, Qin Z, Sha G, et al. The MoPah1 phosphatidate phosphatase is involved in lipid metabolism, development, and pathogenesis in Magnaporthe oryzae. Mol Plant Pathol. 2022;. 23: 720–732. pmid:35191164
- 41. Zhao J. Phospholipase D and phosphatidic acid in plant defence response: from protein-protein and lipid-protein interactions to hormone signalling. J Exp Bot. 2015; 66: 1721–1736. pmid:25680793
- 42. Solomon PS, Tan KC, and Oliver RP. The nutrient supply of pathogenic fungi; a fertile field for study. Mol Plant Pathol. 2003; 4: 203–210. pmid:20569380
- 43. Fang C, Fernie A R, and Luo J. Exploring the diversity of plant metabolism. Trends Plant Sci. 2019; 24: 83–98. pmid:30297176
- 44. Sulpice R, and McKeown PC. Moving toward a comprehensive map of central plant metabolism. Annu Rev Plant Biol. 2015; 66: 187–210. pmid:25621519
- 45. Wilson RA, Fernandez J, Quispe CF, Gradnigo J, Seng A, Moriyama E, et al. Towards defining nutrient conditions encountered by the rice blast fungus during host infection. PLoS One, 2012; 7: e47392. pmid:23071797
- 46. Du Y, Zhang H, Hong L, Wang J, Zheng X, and Zhang Z. Acetolactate synthases MoIlv2 and MoIlv6 are required for infection- related morphogenesis in Magnaporthe oryzae. Mol Plant Pathol. 2013, 14: 870–884. pmid:23782532
- 47. Yan X, Que Y, Wang H, Wang C, Li Y, Yue X, et al. The MET13 methylenetetrahydrofolate reductase gene is essential for infection-related morphogenesis in the rice blast fungus Magnaporthe oryzae. PLoS One 2013; 8: e76914. pmid:24116181
- 48. Castro-Moretti FR, Gentzel IN, Mackey D, and Alonso A P. Metabolomics as an emerging tool for the study of plant-pathogen interactions. metabolites. 2020; 10: E52. pmid:32013104
- 49. Schenck CA, and Maeda HA. Tyrosine biosynthesis, metabolism, and catabolism in plants. Phytochemistry. 2018; 149: 82–102. pmid:29477627
- 50. Chen Y, Zuo R, Zhu Q, Sun Y, Li M, Dong Y, et al. MoLys2 is necessary for growth, conidiogenesis, lysine biosynthesis, and pathogenicity in Magnaporthe oryzae. Fungal Genet Biol. 2014; 67: 51–57. pmid:24731804
- 51. Ozeretskovskaya O, Varlamov V, Vasyukova N, Chalenko G, Gerasimova N, and Panina Y. Influence of systemic signal molecules on the rate of spread of the immunizing effect of elicitors over potato tissues. Appl Biochemi Micro. 2004; 40: 213–216.
- 52. Rozhnova N, Gerashchenkov G, and Babosha A. The effect of arachidonic acid and viral infection on the phytohemagglutinin activity during the development of tobacco acquired resistance. Russ J Plant Physi. 2003: 50: 661–665.