Revealing Different Systems Responses to Brown Planthopper

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Revealing Different Systems Responses to Brown Planthopper Infestation for Pest Susceptible and Resistant Rice Plants with the Combined Metabonomic and Gene-Expression Analysis Caixiang Liu,†,# Fuhua Hao,‡,# Jing Hu,† Weilin Zhang,† Linglin Wan,† Lili Zhu,† Huiru Tang,*,‡ and Guangcun He*,† Key Laboratory of Ministry of Education for Plant Development Biology, College of Life Sciences, Wuhan University, Wuhan 430072, China, and State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan 430071, China Received September 22, 2010

Brown planthopper (BPH) is a notorious pest of rice plants attacking leaf sheaths and seriously affecting global rice production. However, how rice plants respond against BPH remains to be fully understood. To understand systems metabolic responses of rice plants to BPH infestation, we analyzed BPH-induced metabolic changes in leaf sheaths of both BPH-susceptible and resistant rice varieties using NMRbased metabonomics and measured expression changes of 10 relevant genes using quantitative realtime PCR. Our results showed that rice metabonome was dominated by more than 30 metabolites including sugars, organic acids, amino acids, and choline metabolites. BPH infestation caused profound metabolic changes for both BPH-susceptible and resistant rice plants involving transamination, GABA shunt, TCA cycle, gluconeogenesis/glycolysis, pentose phosphate pathway, and secondary metabolisms. BPH infestation caused more drastic overall metabolic changes for BPH-susceptible variety and more marked up-regulations for key genes regulating GABA shunt and biosynthesis of secondary metabolites for BPH-resistant variety. Such observations indicated that activation of GABA shunt and shikimatemediated secondary metabolisms was vital for rice plants to resist BPH infestation. These findings filled the gap of our understandings in the mechanistic aspects of BPH resistance for rice plants and demonstrated the combined metabonomic and qRT-PCR analysis as an effective approach for understanding plant-herbivore interactions. Keywords: brown planthopper attack • rice plants • systems metabolic responses • metabonomics • quantitative real-time PCR

1. Introduction The brown planthopper (Nilaparvata lugens Stål, BPH) is a notorious pest of rice (Oryza sativa L.) regularly occurring throughout the important rice production regions and seriously affecting the global rice production. In 1991, for instance, the affected rice field in China alone accounted for about 2 million hm2. Prolonged BPH infestation often reduces photosynthetic rates, and subsequently causes reductions in protein, sugars, and dry weight of susceptible rice plants. In contrast, resistant rice plants carrying BPH-resistance genes incur little damage and grow normally under BPH attack.1-3 Therefore, breeding * To whom correspondence should be addressed. Guangcun He, Key Laboratory of Ministry of Education for Plant Development Biology, College of Life Sciences, Wuhan University, Wuhan 430072, China. Tel, +86-2787641314; fax, +86-27-68752327; e-mail, [email protected]. Huiru Tang, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan 430071, China. Tel, +86-27-87198430; fax, +86-27-87199291; e-mail, [email protected]. † Wuhan University. # These authors contributed equally to this work. ‡ Wuhan Institute of Physics and Mathematics.

6774 Journal of Proteome Research 2010, 9, 6774–6785 Published on Web 10/12/2010

and growing resistant rice plants is widely accepted to be the most effective and environmentally friendly way to avoid BPHcaused production losses. Understanding the systematic responses to BPH stress in the susceptible and resistant rice plants is the essential part for comprehension of the defensive mechanism of rice plants against BPH, which ought to be helpful for effective development of BPH-resistance rice plants. Previous transcriptomic and proteomic analyses have already indicated that BPH infestation induced complex biological changes in multiple gene expressions and protein regulations for rice plants. Such gene expression changes were associated with alterations in oxidative stress, wound-responses, and signaling pathways.4 For example, genes involved in reactive oxygen species (ROS) production, protein degradation, and stress responses were up-regulated, whereas those involved in photosynthesis were down-regulated under BPH stress in the susceptible rice plants.5 More recently, a transcriptomic study showed that 160 genes had significant expression alterations upon BPH infestation for BPH-susceptible and resistant rice plants, among which 38 genes showed similar responses to BPH for both plants.3 Gene function analysis revealed that BPH 10.1021/pr100970q

 2010 American Chemical Society

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Revealing Different Systems Responses to BPH Infestation infestation significantly activated aspartic protease, ubiquitin conjugating enzyme, and pyrophosphate fructose-6-phosphate1-phosphotransferase (PPi-PFP).3 This further confirmed that BPH treatment led to protein degradation and changes in carbohydrate metabolism. Furthermore, proteomic results indicated that proteins involved in oxidative stress responses, photosynthesis, and jasmonic acid synthesis showed significant changes in rice plants upon BPH infestation.6 These studies also showed that BPH attack induced reprogramming of genes and proteins involved in metabolisms. For example, biosynthesis of glucosinolates, secondary metabolites which play important roles in defense against herbivores, has been elevated in the BPH-challenged plants.7 Primary metabolism is also thought to be relevant to the plant defense against insect attacks8 since plant primary metabolism is closely linked to the secondary metabolism through shikimate mediation, for instance. It is well-known that plants often respond to insect attacks by reducing allocation of metabolites to growth, reproduction, and storage while increasing allocation to defense responses. Plants can also change their primary metabolisms to promote tolerances to herbivores. For instance, compatible plant-herbivore interactions could cause more than 30% level reduction for the carbon-containing metabolites and more than 40% level elevation for the nitrogen-containing compounds.9 For rice plants, previous works already showed that BPH-infestation caused level changes for glutamate, asparagine, oxalate, and trans-aconitate.10,11 However, these works were only focused on some limited metabolites. For the time being, systems metabolic responses of rice plants to BPH attack remain to be thoroughly investigated to ascertain whether other metabolic processes are involved or not and which ones are important to plant resistance to BPH infestation. Information on such responses ought to be obtainable with metabonomics approach since metabonomics is the branch of science concerned with effects of both endogenous and exogenous factors on the holistic metabolic networks of biological systems.12-14 In fact, metabonomic analysis has become an established powerful methodology for providing comprehensive understandings of systems biological responses to stresses12,15-17 and for molecular phenotyping.18 Such approaches have already been successfully applied to understand the systems responses to toxin stresses,19-22 pathogenesis and progression,23-25 pathogen infection,26,27 and herbivore infestation on host plants.28 For example, chlorogenic acid was reported as a resistance factor in Chrysanthemum against thrips injury.29 However, to the best of our knowledge, there have been no reports on the metabonomic responses of rice plants to BPH infestation so far. In this work, we systematically analyzed the BPH-infestation induced metabolic changes for a BPH susceptible and a BPH resistant rice variety as a function of infestation time using NMR spectroscopy in conjunction with multivariate statistical analysis. We also analyzed the BPH-induced changes in the expression levels of 10 relevant genes using quantitative RTPCR. Our objectives are to obtain the differences of the BPHsusceptible and resistant varieties in term of their detailed metabolic responses to BPH attack and to enhance our understandings on the metabolic aspects of the interactions between rice plants and BPH. This will provide useful information for ultimate understandings of the defense mechanisms of rice plants against BPH infestation.

2. Materials and Methods 2.1. Plant Materials and Insects. Two rice varieties were employed, including B5, which is BPH-resistant,30 and TN1 which is the BPH-susceptible variety Taichung Native 1. All plants were grown in pots (8 cm in diameter and 14 cm in height) with 10 plants per pot in a greenhouse which was controlled to have 28 °C/14 h light (06:00-20:00) and 25 °C/10 h dark (20:00-06:00) cycles. After growing to the fourth leaf stage (for about 3 weeks), plants were divided into four groups for each variety (i.e., four TN1 groups and four B5 groups) with six pots per group. For both varieties, one group was used as controls and the other three groups treated with BPH for 12, 48, and 96 h, respectively. Such design will provide six replicates (each consists of 10 plants) for every group. The detailed experimental design and groupings are shown in Supporting Information Figure S1. For BPH treatment, eight BPH nymphae (three- to four-instar) were introduced to each plant of the treated groups at the selected time points (12, 48, 96 h to the end of experiments). Control groups were maintained in parallel but without BPH introduction. At the end of treatments, the two outmost layers of leaf sheaths were collected (at 17:00 pm) from the rice plants, snap-frozen in liquid nitrogen, and kept at -80 °C until further analysis. The leaf sheaths of 10 plants in each pot were collected as one combined sample to give sufficient sample quantity, and thus, each group had six independent samples and each sample represented an average of 10 plants. Samples so prepared also represented four groups for each rice variety, namely, controls, BPH-treated groups for 12, 48, and 96 h, respectively. 2.2. Sample Metabolite Extraction. Every sample of the leaf sheaths harvested above was individually ground to fine power in liquid nitrogen with a mortar and pestle. About 150 ( 5 mg such powder was transferred into a 2 mL Eppendorf tube and added with 1 mL of acetonitrile/H2O (1:1, v/v). After vortexmixing for 30 s, the mixture was intermittently sonicated in an ice bath for 10 min in the manner of 30 s sonication and 30 s break. Supernatant was then collected following 10 min centrifugation (16 099g, 4 °C). The insoluble residues were further extracted twice using the same procedure and the supernatants from three extractions were combined. After removal of organic solvent with a Speed-Vac Concentrator (Thermo SAVANT, SC110A-230) under vacuum, the supernatants were lyophilized in a freeze-drier which took at least 24 h. The dried extracts were redissolved in 600 µL of Na-K phosphate buffer (0.1 M K2HPO4-NaH2PO4, pH 7.42) containing 10% D2O (v/v) (as a field lock) and TSP-d4 (0.3 mM) since this buffer had good phosphate solubility and low-temperature stability.31 A total of 550 µL supernatant from each sample was then transferred into a 5 mm NMR tube following centrifugation at 16 099g for 10 min at 4 °C. 2.3. NMR Spectroscopy. All one-dimensional NMR spectra were recorded at 298 K on a Bruker AV III 600 spectrometer (Bruker Biospin, Germany) equipped with an inverse detection cryogenic probe operating at 600.13 MHz for 1H. Onedimensional 1H NMR spectra were acquired using the standard noesypr1d pulse sequence (RD-90°-t1-90°-tm-90°-acquisition) where the water peak was irradiated during recycle delay (RD, 2s) and mixing time (tm, 100 ms). The 90° pulse length was set to approximately 10 µs for each sample and t1 was set to 4 µs. A total of 64 scans were collected with 32 k data points over a spectral width of 20 ppm. Journal of Proteome Research • Vol. 9, No. 12, 2010 6775

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Table 1. Primers for Quantitative Real-Time PCR Measurements of Expression Levels of Selected Genes accession

gene name

LOC_Os09g24910

phosphofructokinase

LOC_Os12g05110

pyruvate kinase

LOC_Os07g22350

glucose-6-phosphate 1-dehydrogenase

LOC_Os11g33240

citrate synthase

LOC_Os08g27840

phosphoenolpyruvate carboxylase

LOC_Os09g09470

isocitrate dehydrogenase

LOC_Os03g51080

glutamate decarboxylase

LOC_Os04g37460

glutamate decarboxylase

LOC_Os08g41990

γ-aminobutyric acid aminotransferase

LOC_Os02g41630

phenylalanine ammonia-lyase

LOC_Os03g50890

actin-1

For resonance assignment purposes, a series of twodimensional NMR spectra (2D NMR) were recorded for selected samples on a Bruker AVIII 800 MHz spectrometer (Bruker Biospin, Germany) equipped with a cryogenic probe operating at 800.20 MHz for 1H. These included 1H-1H correlation spectroscopy (COSY), 1H-1H total correlation spectroscopy (TOCSY), 1H J-resolved spectroscopy (JRES), 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC), and 1 H-13C heteronuclear multiple-bond correlation (HMBC) spectra. In both COSY and TOCSY experiments, 48 transients were collected into 2048 data points for each of 160 increments with spectral widths of 8417 Hz in both dimensions. COSY experiment was acquired with the gradient selected pulse sequence; TOCSY experiment was acquired with DIPSI2 as spin-lock scheme and mixing time of 100 ms. In JRES spectra, 48 transients were collected into 4096 data points for each of 64 increments with spectral width of 8417 Hz in acquisition and 64 Hz in evolution dimensions, respectively. Both HSQC and HMBC spectra were acquired using the gradient-selected sequences. For HSQC experiment, 320 transients were collected into 2048 data points for each of 128 increments with spectral width of 8417 Hz in the 1H dimension and 35214 Hz in the 13C dimension. In HMBC experiment, 400 transients were collected into 2048 data points for each of 128 increments with spectral width of 8417 Hz in the 1H dimension and 44270 Hz in the 13C dimension. 2.4. Spectral Processing and Date Analysis. The free induction decays (FIDs) for one-dimensional data were zero-filled to 128 k data points and multiplied by an exponential function with a line-broadening factor of 1 Hz prior to Fourier transformation (FT). After phase- and baseline-corrections and referencing to TSP (δ0.00), the NMR spectral region δ 0.5s8.5 was divided into segments with width of 0.003 ppm (1.8 Hz) using AMIX package (v3.8.3, Bruker Biospin). The regions at δ 2.07s2.09 and δ 4.61s5.22 were excluded to eliminate residual acetonitrile and water signals, respectively. The NMR data were then normalized to the total sum of the spectral integrals to compensate for intersample concentration differences. Multivariate data analysis was carried out on the normalized NMR 6776

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primers (5′-3′)

product length

F: GCTGTTCATGGAGCCTTTGC R: GTCAGTGGAAGTCCGGTTGG F: TGCTTGACGGGAGTGATGCC R: TCGCACCGCAGAGGAAGC F: CGTTCTGATGAGTTGGATGC R: CATGCCTTCTGTTCTGTGGTC F: TGAAGTTGTGCCTCCAATCCTC R: TGGTGACACTCTTCGGTCTTTC F: AGGTTGCTGGACACAAGG R: ATACTCACTGGTCGGGTTC F: GCCTCTTCATCTGCCAACCTTG R: CGCCCGACATTTCCATCTCTTG F: GCTCATCTTCCACATCAAC R: GTTCTCCTGGCAGTTCTC F: GGTCATCTGGCGCAACAAGG R: TTGGAGAAGTTGAGCGTGAAGG F: AATCATCGGTGGCGGTCTTCC R: CGTAGGTTCCAGGCTCCATCAG F: GGTGTTCCTCGGCATCAG R: GTGTATGGCAATGGCAATGG F: GATCACTGCCTTGGCTCCTA R: GTACTCAGCCTTGGCAATCC

172 bp 200 bp 180 bp 218 bp 227 bp 232 bp 154 bp 99 bp 170 bp 126 bp 139 bp

data sets using SIMCA-P+ software (v11.0, Umetrics, Umea˚, Sweden) including principal component analysis (PCA) and orthogonal projection to latent structure-discriminant analysis (OPLS-DA).32 In OPLS-DA models, one orthogonal and one predictive component were calculated using the unit-variance (UV) scaled NMR data as X-matrix and the class information as Y-matrix with 6-fold cross-validation. The results were displayed in the forms of scores plots showing group clustering and loadings plots showing variables (metabolites) contributing to intergroup differences. In the OPLS-DA results, loadings were back-transformed and the variables were color-coded according to the absolute values of the correlation coefficients (|r|)33 with hot colors (e.g., red) indicating metabolites having more significant contributions to the group classification than cold ones (e.g., blue). These color-coded loadings plots were produced with an in-house developed Matlab script (V7.0, The Math-works, MA). Quality of the cross-validated OPLS-DA models was described by R2X which represented the explained variances for X matrix and Q2 which indicated model predictability. In this study (n ) 6), the absolute values of correlation coefficient 0.75 was used as the cutoff value for the statistical significance based on the discrimination significance at the level of p < 0.05. The relative metabolite changes during BPH treatment process were also calculated against the controls, that is, [Ct - Cc]/Cc, where Ct and Cc stand for the metabolite concentration in the BPH-treated samples and in the BPH-free controls, respectively. 2.5. RNA Extraction and cDNA Synthesis. Total RNA was extracted with TRIzol reagent (Invitrogen) from approximately 100 mg samples of leaf sheaths. First strand cDNA synthesis was performed using a RevertAid First Strand cDNA Synthesis Kit (MBI Fermentas) and using 3 µg samples of total RNA (treated with RNase-free DNase kit; Qiagen) with oligo(dT)18 primers following the manufacturer’s instructions. 2.6. Quantitative Real-Time PCR Analysis. The primers used for real-time PCR were listed in Table 1. Portions (0.5 µL) of the synthesized first-strand cDNA were amplified by PCR in 10 µL reaction mixtures using a Rotogene 6000 real-time PCR system (Corbett Research) with the following procedure: 94 °C

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Figure 1. Typical 600 MHz 1H NMR spectra of TN1 plants treated with BPH for 0 h (A), 48 h (B), and those of B5 plants treated for 0 h (C) and 48 h (D). The region δ 5.6-8.5 was vertically expanded eight times compared with the region δ 0.7-4.7 and δ 5.1-5.5. Keys: 1, isoleucine (Ile); 2, leucine (Leu); 3, valine (Val); 4, lactate (LA); 5, alanine (Ala); 6, γ-aminobutyric acid (GABA); 7, lipid; 8, glutamate (Glu); 9, succinate (Succ); 10, glutamine (Gln); 11, lipid; 12, methylamine; 13, malate (Mal); 14, dimethylamine; 15, aspartate (Asp); 16, asparagine (Asn); 17, ethanolamine (EA); 18, choline (Cho); 19, betaine (Bet); 20, β-glucose (Glc); 21, R-glucose; 22, sucrose (Suc); 23, uridine; 24, adenosine; 25, fumarate (Fum); 26, tyrosine (Tyr); 27, phenylalanine (Phe); 28, ATP; 29, formate; 30, lysine (Lys), 31, lipid.

for 2 min, followed by 35-45 cycles of 94 °C for 5 s, 55 °C for 10 s, and 72 °C for 15 s; actin-1 gene was used as an internal standard. After amplifications, a melting curve analysis was performed to ensure that the products were specific. The reactions were performed in triplicate and the results were averaged. The values were calculated using three independent biological samples and the well-known 2 Ct method34 was employed for the analysis of relative gene expression.

3. Results 3.1. NMR Resonance Assignments and Metabolite Identifications. Figure 1 showed four typical 1H NMR spectra of extracts from TN1 plants without BPH infestation (Figure 1A), TN1 plants with BPH-infestation for 48 h (Figure 1B), B5 plants without BPH-infestation (Figure 1C), and B5 plants with BPH-infestation for 48 h (Figure 1D). The signals were assigned to individual metabolites based on literature data,35,36 and inhouse databases. The assignments were further confirmed with data in a series of 2D-NMR spectra including COSY, TOCSY, JRES, HSQC, and HMBC. More than 30 metabolites were identified with a few resonances remaining unidentified. Metabolites detected in extracts of both rice varieties consisted of 2 sugars (sucrose and glucose), 5 organic acids (malate, succinate, fumarate, lactate, and formate), 12 amino acids (Ala, Val, Ile, Leu, Glu, Gln, Asp, Asn, Phe, Tyr, GABA, and Lys), 5 choline metabolites (choline, ethanolamine, methylamine, betaine, and dimethylamine), 2 RNA associated nucleosides (uridine and adenosine), ATP, and lipids. Detailed information including 1H and 13C chemical shifts and signal multiplicities was tabulated in Table S1. Visual inspection showed that BPHinfestation caused obvious changes in the levels of Gln and Glu (Figure 1C,D). To extract detailed information on the BPHinduced metabonomic alterations in both susceptible and resistant rice plants and variety dependence of such changes, multivariate data analyses were performed on the NMR data. 3.2. BPH-Induced Metabolic Changes for Both TN1 and B5 Rice Varieties. Figure 2 showed the PCA scores plots for the BPH-susceptible (TN1) and BPH-resistant (B5) rice plants infested with BPHs for 0, 12, 48, and 96 h, where the first two principal components (PC1 and PC2) explained 83% and 70%

Figure 2. PCA scores plots of plant extracts of TN1 (A) and B5 (B) infested by BPH for different length of time and respective controls. The numbers in parentheses indicate the overall variance explained in the first two principal components (PCs). Symbols: 1, control TN1 without BPH infestation (T0h); 3, TN1 infested by the BPH for 12 h (T12h); 9, TN1 infested by BPH for 48 h (T48h); 0, TN1 infested by BPH for 96 h (T96h); •, control B5 without BPH infestation (B0h); O, B5 infested by BPH for 12 h (B12h); 2, B5 infested by BPH for 48 h (B48h); 4, B5 infested by BPH for 96 h (B96h). TN1 and B5 are BPH-susceptible and resistant rice plants, respectively.

of the variances for TN1 (Figure 2A) and B5 (Figure 2B), respectively. In both cases, each point represented the rice metabolite composition (i.e., metabonome) of each sample. Therefore, BPH infestation induced obvious and dynamic metabonomic changes in both varieties. OPLS-DA of these NMR data (metabonome) was further performed for both varieties to extract these metabolites which showed significant changes upon BPH infestation (Table 2). For the BPH-susceptible rice plants (TN1), the scores plots from OPLS-DA showed clear separations between controls and BPHtreated plants for all three treatment periods with good model qualities judged from the values of R2X and Q2 (Figure 3, left). Permutation tests with 200 permutations further confirmed that all OPLS-DA models were robust (see Figure S2A-C). The coefficient-coded loadings plots indicated that BPH-treatments on TN1 for 12 h led to significant elevation of sucrose, glucose, and succinate together with alleviation of lactate, some amino acids (e.g., Ala, Val, Ile, Glu, Gln and Asn), choline, and ethanolamine compared with controls (Figure 3A, Table 2). Compared with controls, BPH-treatments for 48 h also caused Journal of Proteome Research • Vol. 9, No. 12, 2010 6777

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Table 2. BPH Infestation Induced Changes of Metabolites for Rice Plants susceptible rice plants (TN1) metabolites

Sugars sucrose glucose Organic acids malate succinate fumarate lactate Amino acids Ala Val Ile Glu Gln Asp Asn Phe GABA Others betaine choline ethanolamine adenosine a

resistant rice plants (B5)

changes in T12h (vs T0h)b

changes in T48h (vs T0h)b

changes in T96h (vs T0h)b

changes in B12h (vs B0h)b

changes in B48h (vs B0h)c

changes in B96h (vs B0h)c

v v

s v

V s

s v

s v

s v

s v s V

V v V s

V v V s

s v V s

s s V s

s s V s

V V V V V s V s s

s s v s s s V v s

v v v s s v s v s

V V s V s V s v s

s s s V V V s s v

s s s V V V s s s

s V V s

s V s s

v V s V

s s V s

s s s s

s V V s

v, significant increase (p < 0.05); V significant decrease (p < 0.05); s no significant changes (p > 0.05).

elevation of glucose and succinate accompanied with alleviation of Asn and choline. However, elevation of Ile and Phe was also observable together with alleviation of malate and fumarate (Figure 3B, Table 2). Furthermore, BPH-treatments for 96 h resulted in the level increases for succinate, Ile, and Phe accompanied with level decreases for malate, fumarate, and choline, which was broadly similar to the results from BPHtreatments for 48 h. In addition, such prolonged infestation also elevated the levels of some other amino acids (Ala, Val, and Asp) and betaine but alleviated the levels of sucrose and adenosine (Figure 3C, Table 2). For the BPH-resistant rice plants (B5), significant metabonomic differences were evident between the BPH-treated samples for 12 h and controls, which is highlighted with clear separations between these two groups in the scores plot (Figure 4, left). Quality of this model can be judged from the R2X and Q2 values with cross-validation and validity of this model was further confirmed with permutation tests (200 permutations, Figure S2D). The coefficient-coded loadings plot indicated that BPH-treatments for 12 h led to elevation of glucose, succinate, and Phe accompanied with alleviation of fumarate, Ala, Val, Glu, Asp, and ethanolamine (Figure 4, Table 2). Although crossvalidated OPLS-DA models for control versus B5 treated with BPH for 48 and 96 h showed reasonable Q2 values (48 h, Q2 ) 0.632; 96 h, Q2 ) 0.713), permutation tests with 200 permutations showed that these models were marginal (see Figure S2E,F). This is probably due to limited statistical numbers of samples (n ) 6). Nevertheless, such observations are also suggestive that BPH-induced metabonomic changes for this BPH-resistant rice plants are less drastic with 48-96 h infestation. To reveal the metabolic changes induced by BPH treatments for 48 and 96 h, we analyzed each variable using classical t tests with the results included in Table 2. The results showed that, compared to controls, BPH treatment for 48 h led to significant elevation of glucose and γ-aminobutyric acid (GABA) 6778

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b

Results from OPLS-DA. c Results from t test.

but alleviation of fumarate, Glu, Gln, and Asp for this pestresistant rice variety (Table 2). Such BPH treatment for 96 h also caused glucose elevation and alleviation of fumarate, Glu, Gln and Asp. In addition, the levels of choline and ethanolamine were also significantly declined compared with controls (Table 2). However, GABA did not show significant changes. We further calculated the ratios of concentration changes against controls for some selective metabolites in both BPHsusceptible and resistant rice plants in order to reveal the BPHinduced metabolic changes in the rice leaf sheaths as a function of treatment durations. The results (Figure 5A) showed that, in BPH-susceptible rice plants, sucrose level was increased 2.7fold while Asn level showed 60% decrease with 12 h BPH treatment. Such treatment for 96 h increased more than 2-fold for Ala, 3-fold for Phe, and 4-fold for Ile and for Val. The levels for succinate and betaine were increased more than 4- and 5-fold, respectively, compared with controls. In contrast, the scale of concentration changes for metabolites was within 60% for all treatments being much smaller for the BPH-resistant rice variety (Figure 5B). It is particularly interesting to note that when this rice plants were treated with BPH for even 96 h the greatest changes were only less than 30% (e.g., succinate, Ala, and Phe). The results showed that BPH infestation comprehensively affected plant primary metabolism including GABA shunt, TCA cycle, gluconeogenesis/glycolysis, choline metabolism, and amino acid metabolism for both pest-susceptible and resistant rice plants. The alterations in metabolite levels were much dramatic in the susceptible plants compared with those in the resistant plants, probably attributed to the absence of resistance gene in the susceptible plants. The changes of phenylalanine (Phe) indicate that BPH-infestation may also induce changes in plant secondary metabolism since Phe is a precursor for shikimate-mediated biosynthesis of phenylpropanoids and polyphenols.

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Figure 3. OPLS-DA scores plots (left) and corresponding coefficient-coded loadings plots (right) obtained from metabolic profiles of TN1 plants with BPH treatments for several hours. (A) 12 h (T12h) vs 0 h (T0h); (B) 48 h (T48h) vs 0 h (T0h); (C) 96 h (T96h) vs 0 h (T0h). |r|: absolute values of correlation coefficients indicating the contributions of variables (metabolites) to the group classifications.

Figure 4. OPLS-DA scores plots (left) and corresponding coefficient-coded loadings plots (right) obtained from metabolic profiles of B5 rice plants treated with for 12 h (B12h) and 0 h (B0h); |r|: absolute values of correlation coefficients indicating the contributions of variables (metabolites) to the group classifications.

3.3. Quantitative Real-Time PCR Analysis. To obtain complementary information related to the above metabolic changes induced by BPH infestation, we also examined expression levels of some key genes regulating corresponding metabolic pathways, including glycolysis, TCA cycle, pentose phosphate pathway (PPP), GABA shunt, and phenylpropanoid pathways, using quantitative real-time PCR measurements; the transcriptional data were summarized in Figure 6. Quantitative RT-PCR results showed that, upon BPH treatments, the gene encoding phenylalanine ammonia-lyase (PAL) had significant up-regulation in the case of both BPHsusceptible and resistant rice plants (Figure 6); such remarkable activation of PAL gene suggests strong promotion of biosysnthesis of phenylpropanoids, salicylic acid, and polyphenols

since PAL is the key enzyme catalyzing the biotransformation of Phe into cinnamic acid. Furthermore, since the BPH-induced alterations in the Phe levels (Table 2) can be related to glycolysis and PPP, in which glucose-6-phosphate can be oxidized to yield ribulose-5-phosphate for reductive biosynthesis reactions within cells,37 we measured the expression levels of gene encoding glucose-6-phosphate dehydrogenase (G6PD) acting as the ratelimiting enzyme of the PPP. For the BPH-susceptible rice plants, the level of G6PD gene expression showed significant increases after BPH infestation for 48 h (Figure 6), while for the resistant plants, it showed some decreases only after infestation for 96 h with no significant changes when infested for less than 48 h. Furthermore, Phosphofructokinase (PFK) and pyruvate kinase (PK) are two key regulatory enzymes in glycolysis which Journal of Proteome Research • Vol. 9, No. 12, 2010 6779

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Figure 5. Relative changes for metabolite levels against theses in controls as a function of the BPH treatment duration (0, 12, 48, 96 h). (A) BPH-susceptible rice plants TN1; (B) BPH-resistant rice plants B5. The relative changes for metabolite levels were calculated as, [Ct - Cc]/Cc, where Ct and Cc stand for metabolite concentrations in the BPH-treated samples and in the BPH-free control samples, respectively.

Figure 6. Quantitative real-time PCR data for the mRNA expression levels of PFK, PK, G6PD, CS, PEPC, IDH, GAD5, GAD2, GABA-T, and PAL genes in leaf sheaths of control and BPH-treated rice plants. The values represented relative mRNA levels against control groups, values of which were all set to 1 unit. Asterisks (*) indicated statistically significant differences (p < 0.05). TN1 and B5 are BPH-susceptible and resistant rice plants, respectively.

is responsible for the breakdown of sugars to produce ATP and NADH for cells. Therefore, the expression patterns of genes encoding PFK and PK in the susceptible and resistant rice plants were examined. The results showed that PFK and PK genes had similar BPH-infestation responses similar to G6PD gene. Upon BPH infestation, the BPH-susceptible rice plants had significant changes in PFK and PK gene expression (upregulation) only after treatment for 48 h. In contrast, BPHresistant variety showed significant down-regulation of PFK and PK genes only after treatment for 96 h (Figure 6). The above results also indicated that BPH infestation affected TCA cycle taking place in mitochondria, which plays a central role in plant metabolism and is responsible for the later steps in the breakdown of carbohydrates, fatty acids, and amino acids to generate energy and intermediates required for metabolite synthesis.38 Therefore, we further measured the expression 6780

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levels of genes encoding three important enzymes engaged in TCA cycle, namely, citrate synthase (CS), phosphoenolpyruvate carboxylase (PEPC), and isocitrate dehydrogenase (IDH). The results showed that for both rice varieties these three genes were only up-regulated after BPH-treatment for longer than 48 h with the exception that CS transcripts started to show significant accumulation after BPH treatment for 12 h in the case of BPH-susceptible rice plants (Figure 6). These three genes responded to BPH infestation much more vigorously for the BPH-susceptible rice than for the pest-resistant variety (Figure 6). Among them, IDH gene showed the least responses to treatment periods as well as the scales of changes. Moreover, since our metabonomic data showed BPHinduced significant changes in γ-aminobutyric acid (GABA) shunt, we then measured the BPH-induced expressional changes for genes encoding key enzymes in this pathway. These genes

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Figure 7. BPH-induced metabolic changes in BPH-susceptible (TN1) and resistant (B5) rice plants described by changes in metabolite and gene expression levels. Metabolites identified and genes whose transcription profiles were examined were shown in bold letters. The changes of gene expressional levels were obtained from qRT-PCR. Red symbols denoted significant increases (p < 0.05) and green ones denoted significant decreases (p < 0.05). T12h vs T0h meant comparison between BPH-susceptible rice variety, TN1, treated with BPH for 12 h and for 0 h (control); B12h meant BPH-resistant rice variety, B5, treated with BPH for 12 h.

include glutamate decarboxylase genes (GAD5 and GAD2) regulating the glutamate-to-GABA conversion, 4-aminobutyrate aminotransferase gene (GABA-T) responsible for transformation of GABA into succinate semialdehyde (SSA) which can further be converted into succinate by SSA dehydrogenase (SSADH) in the mitochondria. The results indicated that BPH infestation induced up-regulation of about 10-fold in GAD5 expression level for the BPH-resistant rice plants, whereas such upregulation was about 3-fold for the BPH-susceptible ones (Figure 6). GAD2 showed more than 3-fold up-regulation for the resistant plants with much less up-regulation for the BPHsusceptible plants (Figure 6), which only started to show marked GAD2 up-regulation after BPH infestation for 96 h. GABA-T expression for the BPH-resistant plants showed significant up-regulation after BPH treatment for 48 and 96 h (Figure 6), whereas its expression level in the BPH-susceptible plants did not change significantly during the whole treatment period.

4. Discussion The above results from both metabonomic and gene expression analyses indicated that BPH infestation caused profound complex responses from multiple interconnected metabolic pathways for both BPH-susceptible and resistant rice varieties (Figure 7). These responses were observed as the BPH-induced alterations in widespread metabolic networks including transamination, GABA shunt, TCA cycle, pentose phosphate pathway (PPP), gluconeogenesis/glycolysis, choline metabolism, and shikimate-mediated secondary metabolisms in rice plants (Figure 7). Such responses are time-dependent for both varieties but in different manners. The BPH-susceptible rice plants showed much more vigorous reactions to BPH infestation than the BPH-resistant variety in terms of metabolites involved and the scale of responses. On the other hand, the BPH-resistant rice plants showed much stronger responses in the regulations of GABA shunt and secondary metabolisms (Figures 6 and 7). Journal of Proteome Research • Vol. 9, No. 12, 2010 6781

research articles This probably implies that these two metabolic aspects play important roles in BPH resistance in rice plants. 4.1. BPH-Induced Changes in Gluconeogenesis and GABA Shunt for Rice Plants. It is well-known that insect infestation on plants often leads to generation of reactive oxygen species (ROS) for most of higher plants,39 which further causes significant up-regulation of genes encoding proteases.3,5 This subsequently results in protease activation and protein degradation leading to intracellular hyperammonia. To prevent hyperammonia-induced cytotoxicity, plant cells often convert ammonium ions into Gln and Asn through transaminations. These transamination products can then be evacuated by entering into citrate cycle via 2-oxoglutarate, oxaloacetate, and succinate (i.e., GABA shunt) or feeding into proline biosynthesis.40 Our present results showed that short-time BPH infestation caused accumulation of glucose and succinate accompanied with alleviation of Glu in both BPH-susceptible and resistant rice plants (Table 2); the former also showed alleviation of Gln and Asn and the latter showed level decline for Asp. This together with the level decreases of the glucogenic amino acids (e.g., Ala, Val, and Ile) suggests that BPH-invasion has promoted gluconeogenesis probably through GABA shunt and TCA cycle with the consumption of the transamination products (Gln and Asn). GABA shunt is well-known for its responses to both biotic and abiotic stresses on plants in effectively relieving the ROS-induced cytotoxicities.41,42 The decrease of Glu levels (Table 2) and activation of GAD5 and GAD2 genes (Figure 6) regulating Glu-to-GABA transformation for both rice varieties further supported the BPH-induced activation of GABA shunt with succinate elevation. When such infestation prolonged (to 48 and 96 h), the BPHresistant rice variety (B5) responded with continued activation of GABA shunt by showing activation of GABA-T gene regulating conversion of GABA into succinate semialdehyde in addition to up-regulation of GAD5 and GAD2 genes (Figures 6 and 7). Such notion is also supported with alleviation of Glu, Gln, and Asp (Figure 7, Table 2). Promotion of gluconeogenesis seemed to continue as well by showing elevation of glucose and alleviation of TCA cycle intermediate (fumarate). The expressions of PFK and PK genes showed mostly no changes or sometimes down-regulations. In contrast, gluconeogenesis continued with 48 h infestation to the BPH-susceptible variety (TN1) without obvious changes for GAD5, GAD2, and GABA-T gene expressions which was reflected with continued elevation of glucose although the alleviation of glucogenic amino acids (Ala and Val) ceased. When BPH infestation was prolonged to 96 h, elevation of glucose stopped and glucogenic amino acids (Ala, Val, and Ile) were significantly elevated. This together with the activation of PFK, PK, and PEPC genes (Figures 6 and 7) from 48-96 h infestation seems to suggest a switch from gluconeogenesis to glycolysis for this rice variety. In this BPHsusceptible variety, GABA shunt was not responding actively during BPH-treatments for 48-96 h since no significant changes were observed for GAD5 and GABA-T gene expressions. These results indicated that there were clear response differences for BPH-resistant and susceptible rice varieties upon 48 and 96 h BPH infestations. The short-term changes with 12 h treatment, such as transamination and gluconeogenesis, are therefore probably stress-related responses rather than defensive responses. Sucrose content increased considerably in the early stages (12 h), but decreased in the late stages (96 h) of BPH attack in the susceptible plants compared with the untreated controls 6782

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Liu et al. whereas the sucrose levels did not alter under BPH stress in the resistant plants (Figure 5, Table 2). The early stage increase in sucrose content has been observed in Catharanthus roseus leaves after phytoplasma infection26 and in Lolium perenne following exposure to a fungal endophyte.43 Herbivore attack has been found to increase sink strength by altering sourcesink relationships, as found in Trifolium repens following Mycorrhizal infection.44 In rice plants, leaves and stems are source and sink, respectively, with leaf sheaths used in our study as part of stems. The rise of sucrose level during acute BPH infestation (for 12 h) may result from a shift of sucrose from source (leaves) to sink (leaf sheaths) with promoted starch hydrolysis45 as another possibility. Further BPH infestation (e.g., for 96 h) caused significant sucrose level decline in the susceptible plants probably due to phloem sap deprivation by BPH,45 reduced photosynthetic rate,3 and accelerated glycolysis (Figure 6). The sucrose level exhibited little changes for the BPH-resistant rice plants even after infestation for 96 h which is consistent with previous observation of normal photosynthetic activity in the plants3 and/or unchanged glycolysis (Figure 6). 4.2. BPH-Induced Changes in TCA Cycle. Differences are clearly observable for BPH-caused TCA cycle changes between the BPH-susceptible and resistant rice varieties. Short-term BPH-treatments (12 h) caused elevation of succinate, whereas longer term treatment (48-96 h) led to alleviation of fumarate in both varieties (Figure 7, Table 2). For BPH-susceptible plants, infested by BPH for 12-96 h, activation of CS gene was noticeable but IDH gene was activated only with 96 h infestation (Figure 6). This and the up-regulations of PFK, PK, and PEPC genes for longer term infestation (48-96 h) suggested activation of TCA cycle and glycolysis for the BPH-susceptible variety but not for the resistant one. This implies that BPH infestation disturbs energy management for the susceptible variety probably due to its susceptibility. In fact, accelerated glycolysis has also been found in wheat when infested by the Hessian fly9 probably for similar reasons. Therefore, the higher levels of succinate in both susceptible and resistant plants are probably due to enhancement of GABA shunt (Figure 6). The expression level of G6PD gene was increased after BPH infestation for 48-96 h in the susceptible plants whereas its upregulation was not observed for the BPH-resistant variety (Figure 6) probably indicating involvement of PPP and shikimate-mediated biosynthesis of secondary metabolites in BPH response. 4.3. BPH-Induced Promotion of Secondary Metabolism. Plant secondary metabolism is well-known for its roles in plant defense against insect infestations. BPH-induced up-regulation of PAL gene (Figures 6 and 7) for both BPH-susceptible and resistant rice varieties suggests that shikimate-mediated secondary metabolisms are vitally important for the rice plants to defend against the BPH invasion since PAL is a key enzyme for biosynthesis of secondary metabolites such as phenylpropanoids and polyphenols. The level increases of Phe for both rice varieties upon BPH infestation is probably also related to the promotion of biosynthesis of secondary metabolites since Phe is an essential precursor for biosynthesis of secondary metabolites (e.g., phenylpropanoids and polyphenols), which are involved in defense against wounding and herbivore/or pathogen attacks in many plants.46,47 While the BPH-susceptible rice plants showed significant Phe level increases with prolonged infestation, the resistant variety showed significant increase with short-term infestation but little changes with

Revealing Different Systems Responses to BPH Infestation prolonged infestation (Table 2). Such response differences are probably related to the different efficiency in transforming Phe into phenylpropanoids and polyphenols functioning as insect repellents. The changes in PAL gene expression and Phe levels may also be related to stress-induced signaling since Phe is a precursor for biosynthesis of salicylic acid which is an important signal molecule involved in the activation of defensive responses against abiotic and biotic stress.48 This is in broad agreement with our previous findings that activation of Bph14 gene, which conferred resistance to BPH in rice plants, was related to activation of salicylic acid signaling pathway.49 Furthermore, BPH-induced changes for PAL gene expression was much greater in the resistant plants than in the susceptible plants (Figure 6) indicating the importance of secondary metabolism probably including both phenylpropanoid pathways and salicylic acid signaling pathway in plant resistance to BPH infestation. It is obvious that such promotion of secondary metabolism will be assisted with diversion of primary metabolites toward shikimate pathways. In fact, such collaborative efforts of primary and secondary metabolisms have been reported before27,50-53 in plant responses to both biotic and abiotic stresses although not for BPH infestation on rice plants. 4.4. BPH-Induced Changes in Choline Metabolism and Other Metabolic Processes. BPH infestation caused level reduction for ethanolamine and choline in both the BPHsusceptible and resistant rice plants (Table 2) indicating alterations in choline metabolism. The BPH-susceptible plants also showed level increase in betaine with 96 h infestation whereas the BPH-resistant ones did not (Figure 5, Table 2). Betaine is synthesized from choline and known to be an effective osmoprotectant which regulates the cellular osmotic pressure. Accumulation of this osmolyte is a typical response to abiotic stress for many plants.54 Therefore, BPH infestation may also lead to osmotic stresses to the susceptible rice variety probably due to altered cell osmosis which results from BPH ingestion of a lot of sap from rice phloem cells. Such betaine level changes might not be directly related to BPH resistance since the BPH-resistant variety did not show such responses to BPH infestation. The BPH infestation caused depletion of adenosine in the susceptible rice plants but not in the resistant variety (Table 2). This indicates that BPH infestation can probably induce alterations in purine metabolism or biosynthesis of RNA when such effects reach certain severity.

5. Conclusions The combination of comprehensive metabonomic analysis with qRT-PCR measurements revealed that BPH infestation caused complex metabolic changes for both BPH-susceptible and resistant rice plants. Such changes involved transamination, GABA shunt, TCA cycle, gluconeogenesis/glycolysis, PPP, choline metabolism, and secondary metabolism (Figure 7). The BPH-resistant and susceptible rice plants showed substantial differences in metabolic responses; the latter had overall more drastic reactions to BPH invasion in terms of the number of metabolites involved and scale of their changes (Figure 7). For both varieties, the common responses to BPH infestation are highlighted with activation of GABA shunt and sustained promotion of shikimate-mediated secondary metabolism; the resistant variety showed much more drastic activations of genes regulating GABA shunt and transformation of phenylalanine toward secondary metabolites. The activation of GABA shunt appears to be a rapid and effective way to balance the levels

research articles of transamination products to prevent intracellular hyperammonia which results from BPH-invasion caused ROS generation; activation of the shikimate-mediated secondary metabolisms probably promotes biosynthesis of phenylpropanoids and polyphenols, which act as natural antioxidants for ROS suppression, insect repellents, and cell wall cross-linkers for strengthening cell wall defenses. Salicylic acid produced through such secondary metabolism can also be important for response signaling pathways. These results have demonstrated that the combination of metabonomic analysis with quantitative RTPCR measurements of gene expressions is an effective approach to investigate the plant systems responses to insect infestation and to understand plant-herbivore interactions. Abbreviations: BPH, brown planthopper; NMR, nuclear magnetic resonance; FID, free induction decay; FT, Fourier transformation; PCA, principal components analysis; OPLS-DA, orthogonal partial least-squares-discriminant analysis; TCA cycle, tricarboxylic acid cycle; PPP, pentose phosphate pathway; qRT-PCR, quantitative real-time PCR; ROS, reactive oxygen species; Suc, sucrose; Glc, glucose; Frc, fructose; G-6-P, glucose6-phosphate; F-6-P, fructose-6-phosphate; F-1,6-2P, fructose1, 6-biphosphate; 3-PGA, 3-phosphoglycerate; EA, ethanolamine; Cho, choline; Bet, betaine; PEP, phosphoenolpyruvate; Shik, shikimate; 6PGL, 6-phosphogluconate; E-4-P, erythrose4-phosphate; Pyr, pyruvate; Cit, citrate; Ici, isocitrate; R-KG, R-ketoglutarate; Succ, succinate; Fum, fumarate; Mal, malate; OAA, oxalacetic acid; SSA, succinate semialdehyde; LA, lactate; GABA, γ-aminobutyric acid; PFK, Phosphofructokinase; PK, pyruvate kinase; CS, citrate synthase; PEPC, phosphoenolpyruvate carboxylase; IDH, isocitrate dehydrogenase; G6PD, glucose6-phosphate dehydrogenase; PAL, phenylalanine ammonialyase;GAD,glutamatedecarboxylase;GABA-T,GABAaminotransferase.

Acknowledgment. We acknowledge financial supports from the National Natural Science Foundation of China (30730062, 20825520 and 20921004), the National Special Key Project on Functional Genomics and Biochips (2006AA10A103) and the Ministry of Agriculture of China (2009ZX08012-023B). The authors are grateful to Laixing Liu for his help with preparing the figures. The authors thank Zhe Wei and Wei Hu for helpful discussions. We also thank Dr. Hang Zhu, Wuhan Institute of Physics and Mathematics, for developing the Matlab scripts for color-coding coefficients based on a script originally downloaded from http:// www.mathworks.com/matlabcentral/fileexchange Supporting Information Available: Table S1, NMR data for metabolites of two different rice varieties (TN1 and B5). Figure S1. schematic description of BPH treatments of leaf sheaths of the BPH-susceptible (TN1) and resistant (B5) rice plants. Figure S2, permutation test results for OPLS-DA models with 2 components and 200 permutations. T0h, T12h, T48h and T96h denote four groups of BPH-susceptible rice plants (TN1) treated with BPH for 0, 12, 48, and 96 h, respectively; B0h, B12h, B48h and B96h denote four groups of BPH-resistant rice plants (B5) treated with BPH for 0, 12, 48, and 96 h, respectively. (A) T12h vs T0h (intercepts: R2 ) 0.0, 0.763; Q2 ) 0.0, 0.063); (B) T48h vs T0h (intercepts: R2 ) 0.0, 0.783; Q2 ) 0.0, -0.117); (C) T96h vs T0h groups (intercepts: R2 ) 0.0, 0.734; Q2 ) 0.0, -0.177); (D) B12h vs B0h (intercepts: R2 ) 0.0, 0.748; Q2 ) 0.0, -0.291); (E) B48h vs B0h (intercepts: R2 ) 0.0, 0.829, Q2 ) 0.0, -0.107); (F) B96h vs B0h (intercepts: R2 ) 0.0, 0.791; Q2 ) 0.0, -0.206). This material is available free of charge via the Internet at http://pubs.acs.org. Journal of Proteome Research • Vol. 9, No. 12, 2010 6783

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research articles

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(50) Broeckling, C. D.; Huhman, D. V.; Farag, M. A.; Smith, J. T.; May, G. D.; Mendes, P.; Dixon, R. A.; Sumner, L. W. Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism. J. Exp. Bot. 2005, 56, 323– 336. (51) Choi, Y. H.; Kim, H. K.; Linthorst, H. J. M.; Hollander, J. G.; Lefeber, A. W. M.; Erkelens, C.; Nuzillard, J. M.; Verpoort, R. NMR metabolomics to revisit the tobacco mosaic virus infection in Nicotiana tabacum leaves. J. Nat. Prod. 2006, 69, 742–748. (52) Kim, J. K.; Bamba, T.; Harada, K.; Fukusaki, E.; Kobayashi, A. Timecourse metabolic profiling in Arabidopsis thaliana cell cultures after salt stress treatment. J. Exp. Bot. 2007, 58, 415–424. (53) Zulak, K. G.; Weljie, A. M.; Vogel, H. J.; Facchini, P. J. Quantitative 1 H NMR metabolomics reveals extensive metabolic reprogramming of primary and secondary metabolism in elicitor-treated opium poppy cell cultures. BMC Plant Biol. 2008, 8, 5. (54) Allard, F.; Houde, M.; Kro¨l, M.; Ivanov, A.; Huner, N. P. A.; Sarhan, F. Betaine improves freezing tolerance in wheat. Plant Cell Physiol. 1998, 39, 1194–1202.

PR100970Q

Journal of Proteome Research • Vol. 9, No. 12, 2010 6785