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Metabolic profiling of virus-infected transgenic wheat with resistance to wheat yellow mosaic virus
Physiological and Molecular Plant Pathology 96 (2016) 60e68
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Physiological and Molecular Plant Pathology journal homepage: www.elsevier.com/locate/pmpp
Metabolic profiling of virus-infected transgenic wheat with resistance to wheat yellow mosaic virus Wei Fu a, Zhixin Du b, Yan He c, Wenjie Zheng c, Chenggui Han d, Baofeng Liu e, **, Shuifang Zhu a, * a
Institute of Plant Quarantine, Chinese Academy of Inspection and Quarantine, Beijing 100029, China Guangxi Entry-Exit Inspection and Quarantine Bureau, Guangxi 530028, China Tianjin Entry-Exit Inspection and Quantity Bureau, Tianjin 300201, China d Faculty of Plant Pathology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China e Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 April 2016 Received in revised form 26 July 2016 Accepted 2 August 2016 Available online 3 August 2016
Wheat (Triticum aestivum L.) is an important crop, and wheat yellow mosaic virus (WYMV) can cause a severe loss in wheat yield. A genetically modified (GM) wheat carrying a WYMV 72kD coding gene (Wheat 72kD) with resistance to WYMV has been constructed in a previous study. However, neither the influence of genetic modification on wild-type wheat (WT) metabolism nor the effect of WYMV-infection on the metabolic profiling in 72kD is clear. Gas chromatography-mass spectrometry (GC-MS) was used to detect the metabolic profiling in GM, WT, GM with WYMV-inoculation (GMV), WT with WYMVinoculation (WTV) wheat, respectively. As a result, GM and WTV samples were close to each other on the principal component analysis (PCA) plot, indicating genetic modification and WYMV-infection might cause similar changes in wheat metabolism. Only 54 metabolites were annotated, and 16, 12, 17, and 14 metabolites were significantly different between GMV and GM, GMV and WTV, GM and WT, as well as between WTV and WT, respectively. Furthermore, overlapped metabolites were identified, including 3chloro-4-hydroxybenzoic acid (CHBA), 3-sulfocatechol, S-mercaptocysteine, 1-chloro-2-nitrobenzene (2chloronitrobenzene) and melarsoprol. In conclusion, genetic modification or/and WYMV-infection significantly affected the metabolism in wheat. This is the first report investigating the effects of WYMV-infection on metabolic profiling in GM wheat. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Genetic modification Melarsoprol Metabolic profiling Wheat Wheat yellow mosaic virus
1. Introduction Wheat (Triticum aestivum L.) is one of the most important commercial crops and a major source of food worldwide. It is closely related to economic development, food supply, and human nutrition health. To improve the nutrition quality, the capability of drought resistance, insect resistance, disease resistance or antivirus in plant, genetic modification (GM) has been widely utilized in agriculture [1,2]. Following the first transgenic wheat gained in 1992, nearly 200 cases of GM wheat have been reported [3e7]. Wheat yellow mosaic virus (WYMV), which parasitizes in Polymyxa graminis and spreads depending on the spores of P. graminis,
* Corresponding author. No. 11, Ronghuananlu, Yizhuang, Daxing District, Beijing 100176, China. ** Corresponding author. No. 5625, Renmin Rd., Changchun, Jilin 130022, China. E-mail address: [email protected] (S. Zhu). http://dx.doi.org/10.1016/j.pmpp.2016.08.001 0885-5765/© 2016 Elsevier Ltd. All rights reserved.
is a main cause of wheat yellow mosaic disease, and it can cause severe yield losses in wheat [8]. In the past years, GM wheat varieties with strong resistance to WYMV have been developed [9,10], such as GM wheat carrying a WYMV 72kD protein coding gene, which exhibits a strong resistance against WYMV [10]. Analyses have demonstrated that this GM wheat can steadily express WYMV 72kD gene in its offspring, and field trial indicated that they had enhanced resistance to WYMV [10]. However, there is still concern that genetic modification may introduce unintended and unforeseen effects into wheat, which may affect wheat metabolism and increase undesirable metabolites [11]. As wheat is an important crop, the biosafety of GM wheat should be evaluated strictly before large-scale application [12]. New methodologies have been developed to identify alterations in transgenic crops at different biological levels, including transcriptome, proteome, and metabolome. However, previous studies mainly focus on the comparative biosafety assessment of GM crops
W. Fu et al. / Physiological and Molecular Plant Pathology 96 (2016) 60e68
when compared with non-GM crops [13e17]. Little attention has been paid to the metabolic changes in virus-resistant GM wheat after virus infection, which happens frequently in practical. In this study, gas chromatography-mass spectrometry (GC-MS)-based metabolomics strategy was used to detect the metabolic profiling in GM wheat (72kD) and the corresponding wild-type (WT; Yang11) with or without WYMV-inoculation, respectively. The comparison between metabolic profilings of transgenic wheat, non-transgenic wheat, and wheat inoculated with WYMV may contribute to reveal the influences of genetic modification or/and virus infection on wheat metabolism. 2. Materials and methods 2.1. Plant materials Seeds of the GM wheat (WYMV 72kD) and its WT control (Yang11) were provided by the State Key Laboratory for AgroBiotechnology, National Center for Plant Gene Research, College of Agriculture and Biotechnology, China Agricultural University (Beijing, China). Virus source plants and wheat plants with and without WYMV-infection were obtained from Tianjin Entry-Exit Inspection and Quarantine Bureau (Tianjin, China). 2.2. Virus infection Leaves from the virus source plants were grinded with 10 mL 0.2 mol/L phosphate buffer solution (PBS, pH 7.0) in a mortar, and the mixture was filtered through double-layered gauze. Thus, virus solution was generated, which was further diluted with 0.2 mol/L PBS (pH 7.0, dilution rate: 1:10). Seeds of 72kD and Yang11 were sowed in sterilized sand and cultured in an artificial incubator (11e13 C; 16 h light/8 h dark cycle). Afterwards, wheat seedlings with 1e2 flatted true leaves were pulled out from sterilized sand and washed with double-distilled water. Seedlings were classified into 4 groups, namely, 72kD (GM group), 72kD with WYMVinoculation (GMV group), Yang11 (WT group), and Yang11 with WYMV-inoculation (WTV group). After sprayed with carborundum, roots of wheat seedlings classified into the GMV and WTV groups were spread with WYMV solution, whereas roots of wheat seedlings in GM and WT groups were spread with PBS. Thereafter, roots were washed with double-distilled water, and seedlings were re-planted into the sterilized sand. After 20 h incubation in dark, seedlings were grown in a common condition (16 h light/8 h dark cycle; 12 C for day and 11 C for night). When the third leaf appeared at the three-leaf stage, the middle part of the second leaf of each seedling was utilized to prepare the sample for metabolic profiling.
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90 mL N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA, Macherey & Nagel, Düren, Germany) plus 1% trimethylchlorosilane (37 C, 30 min). 2.4. GC-MS analysis GC-MS analysis was performed within 24 h after the sample preparation [16,18]. Briefly, 1 mL sample was injected in splitless injection mode into Aglient-Technologies 7683B series autosampler coupled to a 5975C Agilent mass-selective detector (Aglient-Technologies, CA, USA). The corresponding gas chromatograph was 7890A (Agilent) equipped with capillary column Rtx-5Sil MS (30 m, 0.25 mm id, 0.25 mm film thickness; Restek GmbH, Bad Homburg, Germany). The carrier gas was helium (1.0 mL/min), and the temperatures for injection, ion source, and transfer line were 230 C, 200 C, and 250 C, respectively. The temperature program was: 70 C for 5 min, 5 C/min ramp to 350 C, 330 C for 5 min, fast cooling to 70 C, followed by equilibration at 70 C for 1 min. Mass spectra were recorded (50e400 m/ z, two scans per second). 2.5. Data processing 2.5.1. Data pre-processing and metabolite identification Background of all mass spectral data was firstly subtracted, and the resulting files were submitted to the Mass Profiler Professional (MPP) software (version 2.0, Agilent). The retention time drifts were corrected using the correlation optimized warping (COW) algorithm [19]. To resolve the problem of co-eluting peaks, deconvolution was performed using the Automated Mass Spectrometry Deconvolution and Identification System (AMDIS) software (version 2.66, National Institute of Standards and Technology (NIST); MD, USA), and intensity values of selected ions were utilized to quantify the co-eluting metabolites. Before statistical analysis, peak area of each metabolite was normalized, generating peak area percentage. To identify metabolites, mass spectra and retention index were aligned against the libraries NIST 2008 and Wiley 7 (Wiley, New York, USA) using the NIST Mass Spectral Search software (version 2.0, National Institute of Standards and Technology, Gaithersburg, MD, USA). 2.5.2. Principal component analysis (PCA) PCA is an unsupervised multivariate method to visualize the dataset and display the similarity and difference. The dimensionality of complex data is reduced to what are called Principal Components (PC) that retain the maximal amount of variation within a sample [20]. Here, PCA was conducted using the R software (2011; R Development Core Team) to investigate the differences in metabolites among the samples based on the metabolite data.
2.3. Sample preparation Leaf samples were obtained from wheat in GM (n ¼ 8), GMV (n ¼ 8), WT (n ¼ 4), and WTV (n ¼ 5) groups, and samples for metabolome profiling were prepared according to the methods described by Wu et al. [16] with a slight modification. Briefly, 18 ± 0.2 mg leaf sample was grinded with liquid nitrogen, and 1 mL isopropanol/acetonitrile/water (3:3:2, v:v:v) solution precooled at 20 C was added. The mixture was then shaken at 4 C for 15 min. After centrifugation at 4 C, 150 mL supernatant was generated and then desiccated using a SpeedVac concentrator (SPD111 V-230; Thermo Electron Corporation; MA, USA) at room temperature. Furthermore, derivatization was conducted using 2 mL C8-C40 n-alkanes mixture, 10 mL 40 mg/mL methoxyamine hydrochloride (Sigma-Aldrich, Deisenhofen, Germany) used as the methoxyamination reagent (30 C, 90 min),
2.5.3. Annotation of metabolites Based on the public metabolite databases including Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.kegg.jp/), Human Metabolome Database (HMDB; http://www.hmdb.ca/), and PubChem Compound (http://www.ncbi.nlm.nih.gov/pccompound), all of the detected metabolites were annotated using MetaboSearch software, which is a tool for mass-based metabolite identification using multiple databases (http://omics.georgetown.edu/ metabosearch.html) [21], and their related information were obtained. 2.5.4. Hierarchical clustering of the annotated metabolites Based on the concentration of the annotated metabolites in each sample, hierarchical clustering analysis was performed using pheatmap package in R [22], and metabolites with similar
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concentration were clustered. 2.5.5. Metabolic differences between groups The metabolic profiling data of the four groups were analyzed using Student's t-test to identify the metabolites with significant differences between groups. For each annotated metabolite, fold change (FC) value was calculated to quantify the difference between two groups. jLog2FCj > 1 and p-value < 0.05 were set as cutoff criteria for this analysis. Furthermore, metabolic differences were compared using the online software Venny 2.1.0 [23]. 3. Results 3.1. PCA of all metabolites A total of 1081 metabolite peaks were detected in the leaf samples analyzed. To investigate the difference in metabolites among the GM, GMV, WT, and WTV groups, PCA was performed. The first principal component (PC1) captured the most variation, and the second principal component (PC2) captured the next level of variation. GM samples were clearly separated from GMV, and WT samples were distant from WTV, indicating a difference in metabolites between GM and GMV samples, as well as WT and WTV samples. Furthermore, GM samples were clearly separated from WT samples, and GMV samples were clearly separated from WTV samples, implying a difference in metabolites between GM and WT samples, as well as GMV and WTV samples. These results suggested that WYMV-infection and genetic modification caused remarkable changes in metabolic profiling in wheat. In addition, the GM and WTV samples were positioned closer to each other, suggesting that genetic modification and WYMV-infection caused similar changes in wheat metabolism (Fig. 1A).
groups (Fig. 2). Especially, metabolites were mainly classified into 2 clusters. Metabolites in cluster 1 remained high levels in the four groups (e.g. 1-chloro-2-nitrobenzene), whereas metabolites in cluster 2 significantly changed their levels after WYMV-infection (e.g. S-mercaptocysteine) or genetic modification (e.g. 5mercapto2nitro-benzoic acid). 3.4. Metabolic differences between groups For all the annotated metabolites in wheat, Student's t-test was conducted to identify the metabolites with remarkable differences among the groups. As a result, 16, 12, 17, and 14 metabolites were significantly different between GMV and GM, GMV and WTV, GM and WT, as well as between WTV and WT, respectively (jlog2FCj > 1 and p-value < 0.05, Table 1). Metabolites with log2 FC > 0 were increased, whereas the ones with log2 FC < 0 were decreased. 3Chloro-4-hydroxybenzoic acid (CHBA) and 3-sulfocatechol were increased in GMV (in comparisons with GM and WTV), but decreased in GM (in comparison with WT) and WTV (in comparison with WT); S-mercaptocysteine was increased in GMV (in comparison with GM) and WTV (in comparison with WT); 1chloro-2-nitrobenzene (2-chloronitrobenzene) was increased in GMV (in comparisons with GM and WTV), WTV (in comparison with WT) and GM (in comparison with WT); Melarsoprol was increased in GM (in comparison with WT) and GMV (in comparison with WTV) (Table 1). Additionally, several metabolites were overlapped in the different metabolite-sets. Specially, two metabolites (CHBA and 3-sulfocatechol) were overlapped in all of the four metabolite-sets (Fig. 3). 3-Sulfocatechol was involved in two KEGG pathways (benzoate degradation and microbial metabolism in diverse environments) (Table 1). 4. Discussion
3.2. PCA of the annotated metabolites Among all the detected metabolites, only 54 metabolites were annotated. Based on the concentration of the 54 metabolites in GM, GMV, WT, and WTV samples, PCA was performed (Fig. 1B), and the corresponding results agreed well with that in the above PCA of all metabolites. This result indicated that the 54 annotated metabolites might play crucial roles in wheat metabolism. 3.3. Hierarchical clustering of the 54 annotated metabolites Hierarchical clustering analysis showed that the 54 annotated metabolites possessed remarkably different profiling in different
Wheat is a crucial crop around the world, and GM wheats with resistance to WYMV have been constructed in previous studies [9,10]. However, no report has studied the influence of WYMVinfection on the metabolism in GM wheat. In this work, the metabolic profilings of GM wheat with resistance to WYMV (72kD), WT wheat (Yang 11), 72kD with WYMV-inoculation, and Yang 11 with WYMV-inoculation were detected and compared. In the PCA plots, GM and WTV samples were close to each other, indicating that genetic modification and WYMV-infection might cause similar changes in the metabolism of wheat. The transfection of a heterogenous gene share similarities with virus infection, and the mechanism underlying WYMV-resistance of GM wheat may be
Fig. 1. The PCA plot of GM, GMV, WT, and WTV wheat leaf samples. (A) PCA based on all metabolites. (B) PCA based on the 54 annotated metabolites. PCA: principal component analysis; GM: genetically modified wheat, namely, 72KD; GMV: 72KD with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus; PC1: the first principal component; PC2: the second principal component.
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Fig. 2. Hierarchical clustering of the 54 annotated metabolites. The color bars on the left side represent the clusters of the metabolites, and one color bar represent one cluster. GM: genetically modified wheat, namely, 72KD; GMV: 72KD with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
gene silencing [24]. Multiple studies have reported that gene silencing in plant can be induced by both of transgene [25e27], and virus infection [28e31]. In the GM and WTV wheat plants, transgene and WYMV-infection may lead to gene silencing, which then causes the variation of wheat metabolism. Thus, gene silencing may be a potential cause for the similar metabolic profilings in GM and WTV wheat. In addition, significant metabolic differences were identified between GMV and GM, WTV and WT, GM and WT, as well as between GMV and WTV wheat. Among the metabolites with significant differences between groups, CHBA and 3-sulfocatechol were increased in GMV (in comparisons with GM and WTV), but decreased in GM (in comparison with WT) and WTV (in comparison with WT), indicating only genetic modification plus WYMVinfection caused changes in the metabolism of CHBA and 3sulfocatechol. 3-Sulfocatechol, also named 2,3dihydroxybenzenesulfonic acid, is a metabolite in the degradation of 2-aminobenzene sulfonate, and it can be further degraded into 2hydroxymuconate [32]. 3-Sulfocatechol participates in the pathways of benzoate degradation and microbial metabolism in diverse environments. Benzoate metabolism is associated with plant disease resistance [33]. CHBA is a metabolite in aromatic metabolism. These two metabolites have not yet been found to be correlated with the plant disease resistance or anti-virus, but the results in this study indicated that WYMV-infection and GM changed the
metabolism about CHBA and 3-sulfocatechol in wheat, which might affect the ability of anti-WYMV in wheat. S-mercaptocysteine, also known as thiocysteine, was increased in GMV (in comparison with GM) and WTV (in comparison with WT), indicating that WYMV-infection induced the production of Smercaptocysteine. S-mercaptocysteine is one of the final products in cysteine and methionine metabolism pathway (map00270, http://www.kegg.jp/kegg-bin/show_pathway?map00270þ4.4.1. 1þ4.4.1.8), and it is transformed from L-cystine by cystine desulfhydrase or cystine lyase. A previous study has found that S-allylmercaptocysteine arrests cells in mitosis and induces apoptosis via depolymerizing microtubule and activating c-Jun NH(2)-terminal kinase 1 [34]. Therefore, S-mercaptocysteine may participate in the cell lysis after WYMV-infection. Furthermore, cysteine (Cys) is one of the products of plant sulfur metabolism, and one of its functions is involved in plant defense and stress resistance, via the formation of Cys-rich proteins (CRPs), which are widely expressed in plants as part of their defense arsenals and signal regulators [35]. This study suggested that cysteine metabolism may be influenced by the WYMV-infection, thus affecting wheat defense and stress resistance. Furthermore, 1-chloro-2-nitrobenzene (2-chloronitrobenzene) was increased in GMV (in comparisons with GM and WTV), WTV (in comparison with WT), and GM (in comparison with WT), suggesting that both of genetic modification and WYMV-infection
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Table 1 Metabolites with significant differences between groups. Group
Passed Mass Retention Metabolite time (min) peak number
Log2FC
P-value
GMV vs. GM
1
154
7.5009
S-mercaptocysteine
20.92154082
9.34E-19 C01962
(16)
10
286
23.273342
18.66214880
6.01E-19 e
6 5 2
437 114 173
16.47345 13.285159 15.060551
17.25538310 16.32969125 14.54553681
3.27E-04 e 9.05E-06 e 3.39E-05 e
8
191
17.339651
(5E)-5-[ (2,2-difluoro-1,3benzodioxol-5-yl) methylene]-1,3thiazolidine-2,4-dione Sertaconazole 2-Ketothiazole 3-Chloro-4hydroxybenzoic acid 3-Sulfocatechol
14.23150356
2.59E-04 C06336
6
328
14.447818
14.18493163
3.22E-04 e
2
376
15.8293
13.12931036
3.73E-03 e
6
357
20.149399
11.94112559
3.82E-04 e
5
389
18.03426
9.940982520
1.08E-02 e
5 3
227 449
29.246069 18.534735
9.895131536 8.213952765
4.27E-03 e 1.80E-02 e
24 21
115 158
12.961525 10.138491
4.588192494 2.743427891
9.97E-08 e 8.59E-05 C14407
17 14 10
170 295 173
9.911088 16.296608 15.07547
Pyridoxal-5diphosphate 3-Mercuri-4aminobenzenesulfonamide 2-Carboxyarabinitol-1,5diphosphate {[7-(Difluorophosphono-methyl) -naphthalen-2-yl] -difluoro-methyl} -phosphonic acid 2,2-Dithenyl sulfide 2-Bromo-4-{[ (4-cyanophenyl) (4h-1,2,4-triazol-4-yl) amino]methyl}phenyl sulfamate Trifluoroacetic acid 2-Chloronitrobenzene; 1-chloro-2-nitrobenzene Chlorzoxazone 2-Deoxy-D-ribose 1,5-bisphosphate 3-Chloro-4-hydroxybenzoic acid
6 6 8
437 328 191
16.47345 14.447818 17.339651
Sertaconazole Pyridoxal-5-diphosphate 3-Sulfocatechol
17.25538310 14.18493163 13.59980718
3.64E-03 e 3.60E-03 e 4.50E-04 C06336
2
376
15.8293
13.12931036
2.05E-02 e
10
286
23.273342
12.40374306
1.20E-03 e
16 20
223 399
24.21589 24.89408
3-Mercuri-4aminobenzenesulfonamide (5E)-5-[(2,2-difluoro1,3-benzodioxol-5-yl) methylene]-1,3-thiazolidine-2,4-dione 2,4-Substituted-furan Melarsoprol
12.38390164 9.764359718
1.55E-03 e 3.45E-02 e
GMV vs. WTV (12)
KEGG KEGG pathway entry no.
5.322194549 4.95E-02 C07931 8.389906125 4.45E-02 C04175 19.60369233 1.38E-13 e
map00270: http://www.genome.jp/dbgetbin/show_pathway?map00270þ4.4.1.1 þ4.4.1.8
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11. 2þ1.14.12.14)
e e e
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11. 2þ1.14.12.14)
W. Fu et al. / Physiological and Molecular Plant Pathology 96 (2016) 60e68
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Table 1 (continued ) Group
GM vs. WT (17)
Passed Mass Retention Metabolite time (min) peak number
Log2FC
P-value
21
158
10.138491
1.723569188
5.59E-03 C14407
24 10 6
115 200 394
12.961525 9.95032 18.132032
20
399
24.89408
20 16 4 9
167 223 495 187
7.85185 24.21589 24.51513 15.84517
2-Chloronitrobenzene; 1-cro2-nitrobenzenehlo Trifluoroacetic acid 5-Mercapto-2-nitro-benzoic acid (E)-3-(5((5-(4-chlorophenyl) furan-2-yl)methylene) -4-oxo-2-thioxothiazolidin-3-yl) propanoic acid Melarsoprol Thiodiacetic acid sulfoxide 2,4-Substituted-furan Heparin disaccharide iii-s 2-Phospho-D-glycerate; D-glycerate 2-phosphate
KEGG KEGG pathway entry no. e
2.191135475 3.62E-04 e 15.71182683 5.59E-05 e 18.45291987 7.80E-17 e
17.25550551
8.81E-04 e
15.95963422 13.11197119 10.01679505 9.671226851
5.35E-04 8.70E-03 3.69E-02 2.48E-02
e e e C00631
map00010: http://www.genome.jp/ dbget-bin/show_pathway? map00010þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map00030: http://www.genome.jp/ dbget-bin/show_pathway? map00030þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map00260: http://www.genome.jp/ dbget-bin/show_pathway? map00260þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map00680: http://www.genome.jp/ dbget-bin/show_pathway? map00680þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01060: http://www.genome.jp/ dbget-bin/show_pathway? map01060þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01061: http://www.genome.jp/ dbget-bin/show_pathway? map01061þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01062: http://www.genome.jp/ dbget-bin/show_pathway? map01062þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01063: http://www.genome.jp/ dbget-bin/show_pathway? map01063þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01100: http://www.genome.jp/ dbget-bin/show_pathway? map01100þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01110: http://www.genome.jp/ dbget-bin/show_pathway? map01110þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01120: http://www.genome.jp/ dbget-bin/show_pathway? map01120þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01130: http://www.genome.jp/ dbget-bin/show_pathway? map01130þ2.7.1.165þ2.7.2.-þ3.1.3. (continued on next page)
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Table 1 (continued ) Group
Passed Mass Retention Metabolite time (min) peak number
13 15 21
231 179 158
23.425266 9.594646 10.13849
21
467
27.33467
2
199
13.3496
2
376
15.8293
2 6
341 228
10.5654 9.415833
12
191
10 6 4 WTV vs. WT 13
(14)
Log2FC
P-value
KEGG KEGG pathway entry no.
Diazoxide Allitridin 2-Chloronitrobenzene; 1-chloro-2-nitrobenzene Phosphomethyl phosphonic acid deoxyuridylate ester Dihydroxyacetone phosphate acyl ester
8.761650851 8.188425569 4.203839614
3.63E-02 C06949 1.18E-02 e 1.79E-05 C14407
1.294212179
1.11E-03 e
7.341538585 2.41E-02 e
17.72226
3-Mercuri-4aminobenzenesulfonamide 1,6-Fructose diphosphate (linear form) 2-(Sulfomethyl) thiazolidine-4-carboxylate 3-Sulfocatechol
173 357 332 154
15.07547 20.1494 17.914974 17.62219
3-Chloro-4-hydroxybenzoic acid 2-Carboxyarabinitol-1,5-diphosphate Hydroflumethiazide S-mercaptocysteine
13.34724889 15.98408903 16.07815909 20.41533393
3.38E-03 7.30E-17 1.81E-04 2.44E-12
20 6
167 394
7.85185 18.13203
18.63176991 18.45291987
1.57E-13 e 7.11E-11 e
10 13 24
200 231 115
9.95032 23.425266 12.96153
Thiodiacetic acid sulfoxide (E)-3-(5((5-(4-chlorophenyl)furan-2-yl) methylene) -4-oxo-2-thioxothiazolidin-3-yl) propanoic acid 5-Mercapto-2-nitro-benzoic acid Diazoxide Trifluoroacetic acid
17.66912059 15.41353652 6.181216816
1.14E-07 e 1.41E-09 C06949 7.35E-07 e
7.279426365 2.41E-02 C03372
20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01200: http://www.genome.jp/ dbget-bin/show_pathway? map01200þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01230: http://www.genome.jp/ dbget-bin/show_pathway? map01230þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map04922: http://www.genome.jp/ dbget-bin/show_pathway? map04922þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map05230: http://www.genome.jp/ dbget-bin/show_pathway? map05230þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 e e
map00564: http://www.genome.jp/dbgetbin/show_pathway?map00564þ1.1.1.1 01þ2.3.1.42þ2.5.1.26 map00565: http://www.genome.jp/ dbgetbin/show_pathway?map00565þ1.1.1.1 01þ2.3.1.42þ2.5.1.26 map01100: http://www.genome.jp/dbgetbin/show_pathway?map01100þ1.1.1.1 01þ2.3.1.42þ2.5.1.26
7.717109750 2.41E-02 e 8.883426465 3.13E-02 e 9.695421081 3.94E-02 C06336
e e C07763 C01962
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11 .2þ1.14.12.14)
e map00270: http://www.genome.jp/dbgetbin/show_pathway?map00270þ4.4.1.1 þ4.4.1.8
e
W. Fu et al. / Physiological and Molecular Plant Pathology 96 (2016) 60e68
67
Table 1 (continued ) Group
Passed Mass Retention Metabolite time (min) peak number
Log2FC
P-value
21
158
10.13849
21
467
27.33467
12
191
6 6 4 5 10
228 357 332 149 173
KEGG KEGG pathway entry no.
5.223698318
3.66E-06 C14407
1.262746229
1.95E-02 e
17.72226
2-Chloronitrobenzene;1chloro-2-nitrobenzene Phosphomethyl phosphonic acid deoxyuridylate ester 3-Sulfocatechol
9.415833 20.1494 17.914974 18.05278 15.07547
2-(Sulfomethyl)thiazolidine-4-carboxylate 2-Carboxyarabinitol-1,5-diphosphate Hydroflumethiazide 2-Hydroxy-3-chloropenta-2,4-dienoate 3-Chloro-4-hydroxybenzoic acid
10.79696013 12.40770669 14.89285357 15.28631745 18.40540442
9.063724699 4.50E-02 C06336
1.80E-02 1.49E-02 3.43E-03 4.47E-09 3.07E-14
e e C07763 C11353 e
e
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11 .2þ1.14.12.14)
e e
The metabolites with significant difference were identified with the criteria of jlog2FCj > 1 and p-value < 0.05. Letters in bold represent the overlapped metabolites that are significantly different between groups. The numbers in the column of “Group” represent the number of metabolites that are significantly different between the two groups. GM: genetically modified wheat, namely, 72kD; GMV: 72kD wheat with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus; FC: fold change; KEGG: Kyoto Encyclopedia of Genes and Genomes.
induced the metabolism to produce 2-chloronitrobenzene in wheat. 2-Chloronitrobenzene is an important intermediate in organic synthesis and it is toxic. In a previous study, whole-body inhalation of 18 ppm 2-chloronitrobenzene led to the death of 2 B6C3F1 male mice in 12 weeks, and inhalation of 4.5 ppm or more
2-chloronitrobenzene caused microscopic liver changes, hepatocellular necrosis, hematopoietic cell proliferation, splenic lesions, increase of bone marrow hemosiderin, and hemosiderin deposition in kidney tubule [36]. Additionally, 2-chloronitrobenzene caused methemoglobinemia, responsive anemia, microscopic liver
Fig. 3. Venn diagram showing metabolic differences between wheat groups. GM: genetically modified wheat, namely, 72KD; GMV: 72KD with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus.
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changes, hyperplasia of the epithelium in nasal cavity, alterations of erythrocyte morphology, as well as increase of serum sorbitol dehydrogenase, alanine aminotransferase, and bile acid concentrations in F344/N rats [36]. Therefore, genetic modification and WYMV-infection may induce the production of the toxic substance 2-chloronitrobenzene in wheat at least at the third leaf stage. Additionally, in this study, melarsoprol was found to be increased in GM (in comparison with WT) and GMV (in comparison with WTV), implying that the metabolism to produce melarsoprol was induced by genetic modification. Melarsoprol is an important chemotherapeutic agent utilized to treat late stage trypanosomes, as it can get across the blood/brain barrier [37]. However, it is highly toxic, and approximately 5% of patients die from its toxicity [38]. Therefore, genetic modification may induce the production of toxic melarsoprol in wheat at least at the third leaf stage. In summary, the current study showed that genetic modification or/and WYMV-infection significantly affected the metabolic content in wheat, e.g. CHBA, 3-sulfocatechol, S-mercaptocysteine, 2-chloronitrobenzene and melarsoprol. Specially, 2chloronitrobenzene and melarsoprol may have toxicity, thus it is strongly required to get more safety information of GM and WYMV-infected wheat to see whether the seeds contain these two toxic substances. As far as we know, this is the first report investigating the effects of WYMV-infection on metabolic profiling of WYMV-resistant GM wheat, and our future work will focus on the integrated analysis of proteomics data and metabolomics data. Conflict of interest All authors declare that they have no conflict of interest to state. Funding This work was supported by the National Grand Project of Science and Technology (2016ZX08012-001) and the basic scientific research foundation in the Chinese Academy of Inspection and Quarantine (2015JK005). Acknowledgement The authors thank Dr Yiqiang Zhao (College of Biological Sciences, China Agriculture University) for data processing and analysis, and Dr Jiaolong Sun for useful interpretation of MPP software. References [1] D.W. Lawlor, Genetic engineering to improve plant performance under drought: physiological evaluation of achievements, limitations, and possibilities, J. Exp. Bot. 64 (2013) 83e108. [2] M. Reguera, Z. Peleg, E. Blumwald, Targeting metabolic pathways for genetic engineering abiotic stress-tolerance in crops, Biochim. Biophys. Acta 1819 (2012) 186e194. [3] A. Ganesan, A. Fieberg, B.K. Agan, T. Lalani, M.L. Landrum, G. Wortmann, N.F. Crum-Cianflone, A.R. Lifson, G. Macalino, Results of a 25-year longitudinal analysis of the serologic incidence of syphilis in a cohort of HIV-infected patients with unrestricted access to care, Sex. Transm. Dis. 39 (2012) 440e448. [4] R. Makandar, J.S. Essig, M.A. Schapaugh, H.N. Trick, J. Shah, Genetically engineered resistance to Fusarium head blight in wheat by expression of Arabidopsis NPR1, Mol. Plant Microbe. Interact. 19 (2006) 123e129. [5] A. Regina, A. Bird, D. Topping, S. Bowden, J. Freeman, T. Barsby, B. KosarHashemi, Z. Li, S. Rahman, M. Morell, High-amylose wheat generated by RNA interference improves indices of large-bowel health in rats, Proc. Natl. Acad. Sci. U. S. A. 103 (2006) 3546e3551. [6] C. Saint Pierre, J.L. Crossa, D. Bonnett, K. Yamaguchi-Shinozaki, M.P. Reynolds, Phenotyping transgenic wheat for drought resistance, J. Exp. Bot. 63 (2012) 1799e1808. [7] F. Yan, Y. Zheng, W. Zhang, H. Xiao, S. Li, Z. Cheng, Obtained transgenic wheat expressing pac1 mediated by Agrobacterium is resistant against Barley yellow
dwarf virus-GPV, Chin. Sci. Bull. 51 (2006) 2362e2368. [8] Z.Y. Zhang, X.J. Liu, D.W. Li, J.L. Yu, C.G. Han, Rapid detection of wheat yellow mosaic virus by reverse transcription loop-mediated isothermal amplification, Virol. J. 8 (2011) 550. [9] M. Chen, L. Sun, H. Wu, J. Chen, Y. Ma, X. Zhang, L. Du, S. Cheng, B. Zhang, X. Ye, J. Pang, L. Li, I.B. Andika, H. Xu, Durable field resistance to wheat yellow mosaic virus in transgenic wheat containing the antisense virus polymerase gene, Plant Biotechnol. J. 12 (2014) 447e456. [10] X. Deng, Construction and Identification of Transgenic Wheat Resistant to Wheat Yellow Mosaic Virus, China Agriculture University, Beijing, 2004. [11] E.J. Kok, H.A. Kuiper, Comparative safety assessment for biotech crops, Trends Biotechnol. 21 (2003) 439e444. [12] L. Xia, Y. Ma, Y. He, H.D. Jones, GM wheat development in China: current status and challenges to commercialization, J. Exp. Bot. 63 (2012) 1785e1790. [13] Y. Chang, C. Zhao, Z. Zhu, Z. Wu, J. Zhou, Y. Zhao, X. Lu, G. Xu, Metabolic profiling based on LC/MS to evaluate unintended effects of transgenic rice with cry1Ac and sck genes, Plant Mol. Biol. 78 (2012) 477e487. [14] T. Frank, R.M. Rohlig, H.V. Davies, E. Barros, K.H. Engel, Metabolite profiling of maize kernels-genetic modification versus environmental influence, J. Agric. Food Chem. 60 (2012) 3005e3012. [15] Z. Jiao, X.X. Si, G.K. Li, Z.M. Zhang, X.P. Xu, Unintended compositional changes in transgenic rice seeds ( Oryza sativa L.) studied by spectral and chromatographic analysis coupled with chemometrics methods, J. Agric. Food Chem. 58 (2010) 1746e1754. [16] J. Wu, H. Yu, H. Dai, W. Mei, X. Huang, S. Zhu, M. Peng, Metabolite profiles of rice cultivars containing bacterial blight-resistant genes are distinctive from susceptible rice, Acta Biochim. Biophys. Sin. (Shanghai) 44 (2012) 650e659. [17] J. Zhou, C. Ma, H. Xu, K. Yuan, X. Lu, Z. Zhu, Y. Wu, G. Xu, Metabolic profiling of transgenic rice with cryIAc and sck genes: an evaluation of unintended effects at metabolic level by using GC-FID and GC-MS, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 877 (2009) 725e732. [18] J. Lisec, N. Schauer, J. Kopka, L. Willmitzer, A.R. Fernie, Gas chromatography mass spectrometry-based metabolite profiling in plants, Nat. Protoc. 1 (2006) 387e396. [19] D. Zhang, X. Huang, F.E. Regnier, M. Zhang, Two-dimensional correlation optimized warping algorithm for aligning GC GC-MS data, Anal. Chem. 80 (2008) 2664e2671. [20] R. Bro, A.K. Smilde, Principal component analysis, Anal. Methods UK 6 (2014) 2812e2831. [21] B. Zhou, J. Wang, H.W. Ressom, MetaboSearch: tool for mass-based metabolite identification using multiple databases, PLoS One 7 (2012) e40096. [22] R. Kolde, pheatmap: Pretty Heatmaps. R Package Version 1.0. 2, 2015. [23] J.C. Oliveros, Venny. An Interactive Tool for Comparing Lists with Venn's Diagrams, 2015. http://bioinfogpcnbcsices/tools/venny/indexhtml. [24] A.B. Ibrahim, F.J. Aragao, RNAi-mediated resistance to viruses in genetically engineered plants, Methods Mol. Biol. 1287 (2015) 81e92. clin, S. Boutet, P. Waterhouse, H. Vaucheret, A branched pathway for [25] C. Be transgene-induced RNA silencing in plants, Curr. Biol. 12 (2002) 684e688. [26] S.M. Angell, D.C. Baulcombe, Consistent gene silencing in transgenic plants expressing a replicating potato virus X RNA, EMBO J. 16 (1997) 3675e3684. clin, T. Elmayan, F. Feuerbach, C. Godon, J.B. Morel, [27] H. Vaucheret, C. Be P. Mourrain, J.C. Palauqui, S. Vernhettes, Transgene-induced gene silencing in plants, Plant J. 16 (1998) 651e659. [28] R. Anandalakshmi, G.J. Pruss, X. Ge, R. Marathe, A.C. Mallory, T.H. Smith, V.B. Vance, A viral suppressor of gene silencing in plants, P. Natl. Acad. Sci. U. S. A. 95 (1998) 13079e13084. [29] P.M. Waterhouse, M.-B. Wang, T. Lough, Gene silencing as an adaptive defence against viruses, Nature 411 (2001) 834e842. [30] R. Lu, A.M. Martin-Hernandez, J.R. Peart, I. Malcuit, D.C. Baulcombe, Virusinduced gene silencing in plants, Methods 30 (2003) 296e303. [31] T.M. Burch-Smith, J.C. Anderson, G.B. Martin, S.P. Dinesh-Kumar, Applications and advantages of virus-induced gene silencing for gene function studies in plants, Plant J. 39 (2004) 734e746. [32] J. Mampel, J. Ruff, F. Junker, A.M. Cook, The oxygenase component of the2aminobenzenesulfonate dioxygenase system from Alcaligenes sp. strain O-1, Microbiology 145 (1999) 3255e3264. [33] M.C. Mehdy, Y.K. Sharma, K. Sathasivan, N.W. Bays, The role of activated oxygen species in plant disease resistance, Physiol. Plant. 98 (1996) 365e374. [34] D. Xiao, J.T. Pinto, J.W. Soh, A. Deguchi, G.G. Gundersen, A.F. Palazzo, J.T. Yoon, H. Shirin, I.B. 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Contents lists available at ScienceDirect
Physiological and Molecular Plant Pathology journal homepage: www.elsevier.com/locate/pmpp
Metabolic profiling of virus-infected transgenic wheat with resistance to wheat yellow mosaic virus Wei Fu a, Zhixin Du b, Yan He c, Wenjie Zheng c, Chenggui Han d, Baofeng Liu e, **, Shuifang Zhu a, * a
Institute of Plant Quarantine, Chinese Academy of Inspection and Quarantine, Beijing 100029, China Guangxi Entry-Exit Inspection and Quarantine Bureau, Guangxi 530028, China Tianjin Entry-Exit Inspection and Quantity Bureau, Tianjin 300201, China d Faculty of Plant Pathology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China e Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 April 2016 Received in revised form 26 July 2016 Accepted 2 August 2016 Available online 3 August 2016
Wheat (Triticum aestivum L.) is an important crop, and wheat yellow mosaic virus (WYMV) can cause a severe loss in wheat yield. A genetically modified (GM) wheat carrying a WYMV 72kD coding gene (Wheat 72kD) with resistance to WYMV has been constructed in a previous study. However, neither the influence of genetic modification on wild-type wheat (WT) metabolism nor the effect of WYMV-infection on the metabolic profiling in 72kD is clear. Gas chromatography-mass spectrometry (GC-MS) was used to detect the metabolic profiling in GM, WT, GM with WYMV-inoculation (GMV), WT with WYMVinoculation (WTV) wheat, respectively. As a result, GM and WTV samples were close to each other on the principal component analysis (PCA) plot, indicating genetic modification and WYMV-infection might cause similar changes in wheat metabolism. Only 54 metabolites were annotated, and 16, 12, 17, and 14 metabolites were significantly different between GMV and GM, GMV and WTV, GM and WT, as well as between WTV and WT, respectively. Furthermore, overlapped metabolites were identified, including 3chloro-4-hydroxybenzoic acid (CHBA), 3-sulfocatechol, S-mercaptocysteine, 1-chloro-2-nitrobenzene (2chloronitrobenzene) and melarsoprol. In conclusion, genetic modification or/and WYMV-infection significantly affected the metabolism in wheat. This is the first report investigating the effects of WYMV-infection on metabolic profiling in GM wheat. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Genetic modification Melarsoprol Metabolic profiling Wheat Wheat yellow mosaic virus
1. Introduction Wheat (Triticum aestivum L.) is one of the most important commercial crops and a major source of food worldwide. It is closely related to economic development, food supply, and human nutrition health. To improve the nutrition quality, the capability of drought resistance, insect resistance, disease resistance or antivirus in plant, genetic modification (GM) has been widely utilized in agriculture [1,2]. Following the first transgenic wheat gained in 1992, nearly 200 cases of GM wheat have been reported [3e7]. Wheat yellow mosaic virus (WYMV), which parasitizes in Polymyxa graminis and spreads depending on the spores of P. graminis,
* Corresponding author. No. 11, Ronghuananlu, Yizhuang, Daxing District, Beijing 100176, China. ** Corresponding author. No. 5625, Renmin Rd., Changchun, Jilin 130022, China. E-mail address: [email protected] (S. Zhu). http://dx.doi.org/10.1016/j.pmpp.2016.08.001 0885-5765/© 2016 Elsevier Ltd. All rights reserved.
is a main cause of wheat yellow mosaic disease, and it can cause severe yield losses in wheat [8]. In the past years, GM wheat varieties with strong resistance to WYMV have been developed [9,10], such as GM wheat carrying a WYMV 72kD protein coding gene, which exhibits a strong resistance against WYMV [10]. Analyses have demonstrated that this GM wheat can steadily express WYMV 72kD gene in its offspring, and field trial indicated that they had enhanced resistance to WYMV [10]. However, there is still concern that genetic modification may introduce unintended and unforeseen effects into wheat, which may affect wheat metabolism and increase undesirable metabolites [11]. As wheat is an important crop, the biosafety of GM wheat should be evaluated strictly before large-scale application [12]. New methodologies have been developed to identify alterations in transgenic crops at different biological levels, including transcriptome, proteome, and metabolome. However, previous studies mainly focus on the comparative biosafety assessment of GM crops
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when compared with non-GM crops [13e17]. Little attention has been paid to the metabolic changes in virus-resistant GM wheat after virus infection, which happens frequently in practical. In this study, gas chromatography-mass spectrometry (GC-MS)-based metabolomics strategy was used to detect the metabolic profiling in GM wheat (72kD) and the corresponding wild-type (WT; Yang11) with or without WYMV-inoculation, respectively. The comparison between metabolic profilings of transgenic wheat, non-transgenic wheat, and wheat inoculated with WYMV may contribute to reveal the influences of genetic modification or/and virus infection on wheat metabolism. 2. Materials and methods 2.1. Plant materials Seeds of the GM wheat (WYMV 72kD) and its WT control (Yang11) were provided by the State Key Laboratory for AgroBiotechnology, National Center for Plant Gene Research, College of Agriculture and Biotechnology, China Agricultural University (Beijing, China). Virus source plants and wheat plants with and without WYMV-infection were obtained from Tianjin Entry-Exit Inspection and Quarantine Bureau (Tianjin, China). 2.2. Virus infection Leaves from the virus source plants were grinded with 10 mL 0.2 mol/L phosphate buffer solution (PBS, pH 7.0) in a mortar, and the mixture was filtered through double-layered gauze. Thus, virus solution was generated, which was further diluted with 0.2 mol/L PBS (pH 7.0, dilution rate: 1:10). Seeds of 72kD and Yang11 were sowed in sterilized sand and cultured in an artificial incubator (11e13 C; 16 h light/8 h dark cycle). Afterwards, wheat seedlings with 1e2 flatted true leaves were pulled out from sterilized sand and washed with double-distilled water. Seedlings were classified into 4 groups, namely, 72kD (GM group), 72kD with WYMVinoculation (GMV group), Yang11 (WT group), and Yang11 with WYMV-inoculation (WTV group). After sprayed with carborundum, roots of wheat seedlings classified into the GMV and WTV groups were spread with WYMV solution, whereas roots of wheat seedlings in GM and WT groups were spread with PBS. Thereafter, roots were washed with double-distilled water, and seedlings were re-planted into the sterilized sand. After 20 h incubation in dark, seedlings were grown in a common condition (16 h light/8 h dark cycle; 12 C for day and 11 C for night). When the third leaf appeared at the three-leaf stage, the middle part of the second leaf of each seedling was utilized to prepare the sample for metabolic profiling.
61
90 mL N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA, Macherey & Nagel, Düren, Germany) plus 1% trimethylchlorosilane (37 C, 30 min). 2.4. GC-MS analysis GC-MS analysis was performed within 24 h after the sample preparation [16,18]. Briefly, 1 mL sample was injected in splitless injection mode into Aglient-Technologies 7683B series autosampler coupled to a 5975C Agilent mass-selective detector (Aglient-Technologies, CA, USA). The corresponding gas chromatograph was 7890A (Agilent) equipped with capillary column Rtx-5Sil MS (30 m, 0.25 mm id, 0.25 mm film thickness; Restek GmbH, Bad Homburg, Germany). The carrier gas was helium (1.0 mL/min), and the temperatures for injection, ion source, and transfer line were 230 C, 200 C, and 250 C, respectively. The temperature program was: 70 C for 5 min, 5 C/min ramp to 350 C, 330 C for 5 min, fast cooling to 70 C, followed by equilibration at 70 C for 1 min. Mass spectra were recorded (50e400 m/ z, two scans per second). 2.5. Data processing 2.5.1. Data pre-processing and metabolite identification Background of all mass spectral data was firstly subtracted, and the resulting files were submitted to the Mass Profiler Professional (MPP) software (version 2.0, Agilent). The retention time drifts were corrected using the correlation optimized warping (COW) algorithm [19]. To resolve the problem of co-eluting peaks, deconvolution was performed using the Automated Mass Spectrometry Deconvolution and Identification System (AMDIS) software (version 2.66, National Institute of Standards and Technology (NIST); MD, USA), and intensity values of selected ions were utilized to quantify the co-eluting metabolites. Before statistical analysis, peak area of each metabolite was normalized, generating peak area percentage. To identify metabolites, mass spectra and retention index were aligned against the libraries NIST 2008 and Wiley 7 (Wiley, New York, USA) using the NIST Mass Spectral Search software (version 2.0, National Institute of Standards and Technology, Gaithersburg, MD, USA). 2.5.2. Principal component analysis (PCA) PCA is an unsupervised multivariate method to visualize the dataset and display the similarity and difference. The dimensionality of complex data is reduced to what are called Principal Components (PC) that retain the maximal amount of variation within a sample [20]. Here, PCA was conducted using the R software (2011; R Development Core Team) to investigate the differences in metabolites among the samples based on the metabolite data.
2.3. Sample preparation Leaf samples were obtained from wheat in GM (n ¼ 8), GMV (n ¼ 8), WT (n ¼ 4), and WTV (n ¼ 5) groups, and samples for metabolome profiling were prepared according to the methods described by Wu et al. [16] with a slight modification. Briefly, 18 ± 0.2 mg leaf sample was grinded with liquid nitrogen, and 1 mL isopropanol/acetonitrile/water (3:3:2, v:v:v) solution precooled at 20 C was added. The mixture was then shaken at 4 C for 15 min. After centrifugation at 4 C, 150 mL supernatant was generated and then desiccated using a SpeedVac concentrator (SPD111 V-230; Thermo Electron Corporation; MA, USA) at room temperature. Furthermore, derivatization was conducted using 2 mL C8-C40 n-alkanes mixture, 10 mL 40 mg/mL methoxyamine hydrochloride (Sigma-Aldrich, Deisenhofen, Germany) used as the methoxyamination reagent (30 C, 90 min),
2.5.3. Annotation of metabolites Based on the public metabolite databases including Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.kegg.jp/), Human Metabolome Database (HMDB; http://www.hmdb.ca/), and PubChem Compound (http://www.ncbi.nlm.nih.gov/pccompound), all of the detected metabolites were annotated using MetaboSearch software, which is a tool for mass-based metabolite identification using multiple databases (http://omics.georgetown.edu/ metabosearch.html) [21], and their related information were obtained. 2.5.4. Hierarchical clustering of the annotated metabolites Based on the concentration of the annotated metabolites in each sample, hierarchical clustering analysis was performed using pheatmap package in R [22], and metabolites with similar
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concentration were clustered. 2.5.5. Metabolic differences between groups The metabolic profiling data of the four groups were analyzed using Student's t-test to identify the metabolites with significant differences between groups. For each annotated metabolite, fold change (FC) value was calculated to quantify the difference between two groups. jLog2FCj > 1 and p-value < 0.05 were set as cutoff criteria for this analysis. Furthermore, metabolic differences were compared using the online software Venny 2.1.0 [23]. 3. Results 3.1. PCA of all metabolites A total of 1081 metabolite peaks were detected in the leaf samples analyzed. To investigate the difference in metabolites among the GM, GMV, WT, and WTV groups, PCA was performed. The first principal component (PC1) captured the most variation, and the second principal component (PC2) captured the next level of variation. GM samples were clearly separated from GMV, and WT samples were distant from WTV, indicating a difference in metabolites between GM and GMV samples, as well as WT and WTV samples. Furthermore, GM samples were clearly separated from WT samples, and GMV samples were clearly separated from WTV samples, implying a difference in metabolites between GM and WT samples, as well as GMV and WTV samples. These results suggested that WYMV-infection and genetic modification caused remarkable changes in metabolic profiling in wheat. In addition, the GM and WTV samples were positioned closer to each other, suggesting that genetic modification and WYMV-infection caused similar changes in wheat metabolism (Fig. 1A).
groups (Fig. 2). Especially, metabolites were mainly classified into 2 clusters. Metabolites in cluster 1 remained high levels in the four groups (e.g. 1-chloro-2-nitrobenzene), whereas metabolites in cluster 2 significantly changed their levels after WYMV-infection (e.g. S-mercaptocysteine) or genetic modification (e.g. 5mercapto2nitro-benzoic acid). 3.4. Metabolic differences between groups For all the annotated metabolites in wheat, Student's t-test was conducted to identify the metabolites with remarkable differences among the groups. As a result, 16, 12, 17, and 14 metabolites were significantly different between GMV and GM, GMV and WTV, GM and WT, as well as between WTV and WT, respectively (jlog2FCj > 1 and p-value < 0.05, Table 1). Metabolites with log2 FC > 0 were increased, whereas the ones with log2 FC < 0 were decreased. 3Chloro-4-hydroxybenzoic acid (CHBA) and 3-sulfocatechol were increased in GMV (in comparisons with GM and WTV), but decreased in GM (in comparison with WT) and WTV (in comparison with WT); S-mercaptocysteine was increased in GMV (in comparison with GM) and WTV (in comparison with WT); 1chloro-2-nitrobenzene (2-chloronitrobenzene) was increased in GMV (in comparisons with GM and WTV), WTV (in comparison with WT) and GM (in comparison with WT); Melarsoprol was increased in GM (in comparison with WT) and GMV (in comparison with WTV) (Table 1). Additionally, several metabolites were overlapped in the different metabolite-sets. Specially, two metabolites (CHBA and 3-sulfocatechol) were overlapped in all of the four metabolite-sets (Fig. 3). 3-Sulfocatechol was involved in two KEGG pathways (benzoate degradation and microbial metabolism in diverse environments) (Table 1). 4. Discussion
3.2. PCA of the annotated metabolites Among all the detected metabolites, only 54 metabolites were annotated. Based on the concentration of the 54 metabolites in GM, GMV, WT, and WTV samples, PCA was performed (Fig. 1B), and the corresponding results agreed well with that in the above PCA of all metabolites. This result indicated that the 54 annotated metabolites might play crucial roles in wheat metabolism. 3.3. Hierarchical clustering of the 54 annotated metabolites Hierarchical clustering analysis showed that the 54 annotated metabolites possessed remarkably different profiling in different
Wheat is a crucial crop around the world, and GM wheats with resistance to WYMV have been constructed in previous studies [9,10]. However, no report has studied the influence of WYMVinfection on the metabolism in GM wheat. In this work, the metabolic profilings of GM wheat with resistance to WYMV (72kD), WT wheat (Yang 11), 72kD with WYMV-inoculation, and Yang 11 with WYMV-inoculation were detected and compared. In the PCA plots, GM and WTV samples were close to each other, indicating that genetic modification and WYMV-infection might cause similar changes in the metabolism of wheat. The transfection of a heterogenous gene share similarities with virus infection, and the mechanism underlying WYMV-resistance of GM wheat may be
Fig. 1. The PCA plot of GM, GMV, WT, and WTV wheat leaf samples. (A) PCA based on all metabolites. (B) PCA based on the 54 annotated metabolites. PCA: principal component analysis; GM: genetically modified wheat, namely, 72KD; GMV: 72KD with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus; PC1: the first principal component; PC2: the second principal component.
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Fig. 2. Hierarchical clustering of the 54 annotated metabolites. The color bars on the left side represent the clusters of the metabolites, and one color bar represent one cluster. GM: genetically modified wheat, namely, 72KD; GMV: 72KD with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
gene silencing [24]. Multiple studies have reported that gene silencing in plant can be induced by both of transgene [25e27], and virus infection [28e31]. In the GM and WTV wheat plants, transgene and WYMV-infection may lead to gene silencing, which then causes the variation of wheat metabolism. Thus, gene silencing may be a potential cause for the similar metabolic profilings in GM and WTV wheat. In addition, significant metabolic differences were identified between GMV and GM, WTV and WT, GM and WT, as well as between GMV and WTV wheat. Among the metabolites with significant differences between groups, CHBA and 3-sulfocatechol were increased in GMV (in comparisons with GM and WTV), but decreased in GM (in comparison with WT) and WTV (in comparison with WT), indicating only genetic modification plus WYMVinfection caused changes in the metabolism of CHBA and 3sulfocatechol. 3-Sulfocatechol, also named 2,3dihydroxybenzenesulfonic acid, is a metabolite in the degradation of 2-aminobenzene sulfonate, and it can be further degraded into 2hydroxymuconate [32]. 3-Sulfocatechol participates in the pathways of benzoate degradation and microbial metabolism in diverse environments. Benzoate metabolism is associated with plant disease resistance [33]. CHBA is a metabolite in aromatic metabolism. These two metabolites have not yet been found to be correlated with the plant disease resistance or anti-virus, but the results in this study indicated that WYMV-infection and GM changed the
metabolism about CHBA and 3-sulfocatechol in wheat, which might affect the ability of anti-WYMV in wheat. S-mercaptocysteine, also known as thiocysteine, was increased in GMV (in comparison with GM) and WTV (in comparison with WT), indicating that WYMV-infection induced the production of Smercaptocysteine. S-mercaptocysteine is one of the final products in cysteine and methionine metabolism pathway (map00270, http://www.kegg.jp/kegg-bin/show_pathway?map00270þ4.4.1. 1þ4.4.1.8), and it is transformed from L-cystine by cystine desulfhydrase or cystine lyase. A previous study has found that S-allylmercaptocysteine arrests cells in mitosis and induces apoptosis via depolymerizing microtubule and activating c-Jun NH(2)-terminal kinase 1 [34]. Therefore, S-mercaptocysteine may participate in the cell lysis after WYMV-infection. Furthermore, cysteine (Cys) is one of the products of plant sulfur metabolism, and one of its functions is involved in plant defense and stress resistance, via the formation of Cys-rich proteins (CRPs), which are widely expressed in plants as part of their defense arsenals and signal regulators [35]. This study suggested that cysteine metabolism may be influenced by the WYMV-infection, thus affecting wheat defense and stress resistance. Furthermore, 1-chloro-2-nitrobenzene (2-chloronitrobenzene) was increased in GMV (in comparisons with GM and WTV), WTV (in comparison with WT), and GM (in comparison with WT), suggesting that both of genetic modification and WYMV-infection
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Table 1 Metabolites with significant differences between groups. Group
Passed Mass Retention Metabolite time (min) peak number
Log2FC
P-value
GMV vs. GM
1
154
7.5009
S-mercaptocysteine
20.92154082
9.34E-19 C01962
(16)
10
286
23.273342
18.66214880
6.01E-19 e
6 5 2
437 114 173
16.47345 13.285159 15.060551
17.25538310 16.32969125 14.54553681
3.27E-04 e 9.05E-06 e 3.39E-05 e
8
191
17.339651
(5E)-5-[ (2,2-difluoro-1,3benzodioxol-5-yl) methylene]-1,3thiazolidine-2,4-dione Sertaconazole 2-Ketothiazole 3-Chloro-4hydroxybenzoic acid 3-Sulfocatechol
14.23150356
2.59E-04 C06336
6
328
14.447818
14.18493163
3.22E-04 e
2
376
15.8293
13.12931036
3.73E-03 e
6
357
20.149399
11.94112559
3.82E-04 e
5
389
18.03426
9.940982520
1.08E-02 e
5 3
227 449
29.246069 18.534735
9.895131536 8.213952765
4.27E-03 e 1.80E-02 e
24 21
115 158
12.961525 10.138491
4.588192494 2.743427891
9.97E-08 e 8.59E-05 C14407
17 14 10
170 295 173
9.911088 16.296608 15.07547
Pyridoxal-5diphosphate 3-Mercuri-4aminobenzenesulfonamide 2-Carboxyarabinitol-1,5diphosphate {[7-(Difluorophosphono-methyl) -naphthalen-2-yl] -difluoro-methyl} -phosphonic acid 2,2-Dithenyl sulfide 2-Bromo-4-{[ (4-cyanophenyl) (4h-1,2,4-triazol-4-yl) amino]methyl}phenyl sulfamate Trifluoroacetic acid 2-Chloronitrobenzene; 1-chloro-2-nitrobenzene Chlorzoxazone 2-Deoxy-D-ribose 1,5-bisphosphate 3-Chloro-4-hydroxybenzoic acid
6 6 8
437 328 191
16.47345 14.447818 17.339651
Sertaconazole Pyridoxal-5-diphosphate 3-Sulfocatechol
17.25538310 14.18493163 13.59980718
3.64E-03 e 3.60E-03 e 4.50E-04 C06336
2
376
15.8293
13.12931036
2.05E-02 e
10
286
23.273342
12.40374306
1.20E-03 e
16 20
223 399
24.21589 24.89408
3-Mercuri-4aminobenzenesulfonamide (5E)-5-[(2,2-difluoro1,3-benzodioxol-5-yl) methylene]-1,3-thiazolidine-2,4-dione 2,4-Substituted-furan Melarsoprol
12.38390164 9.764359718
1.55E-03 e 3.45E-02 e
GMV vs. WTV (12)
KEGG KEGG pathway entry no.
5.322194549 4.95E-02 C07931 8.389906125 4.45E-02 C04175 19.60369233 1.38E-13 e
map00270: http://www.genome.jp/dbgetbin/show_pathway?map00270þ4.4.1.1 þ4.4.1.8
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11. 2þ1.14.12.14)
e e e
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11. 2þ1.14.12.14)
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65
Table 1 (continued ) Group
GM vs. WT (17)
Passed Mass Retention Metabolite time (min) peak number
Log2FC
P-value
21
158
10.138491
1.723569188
5.59E-03 C14407
24 10 6
115 200 394
12.961525 9.95032 18.132032
20
399
24.89408
20 16 4 9
167 223 495 187
7.85185 24.21589 24.51513 15.84517
2-Chloronitrobenzene; 1-cro2-nitrobenzenehlo Trifluoroacetic acid 5-Mercapto-2-nitro-benzoic acid (E)-3-(5((5-(4-chlorophenyl) furan-2-yl)methylene) -4-oxo-2-thioxothiazolidin-3-yl) propanoic acid Melarsoprol Thiodiacetic acid sulfoxide 2,4-Substituted-furan Heparin disaccharide iii-s 2-Phospho-D-glycerate; D-glycerate 2-phosphate
KEGG KEGG pathway entry no. e
2.191135475 3.62E-04 e 15.71182683 5.59E-05 e 18.45291987 7.80E-17 e
17.25550551
8.81E-04 e
15.95963422 13.11197119 10.01679505 9.671226851
5.35E-04 8.70E-03 3.69E-02 2.48E-02
e e e C00631
map00010: http://www.genome.jp/ dbget-bin/show_pathway? map00010þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map00030: http://www.genome.jp/ dbget-bin/show_pathway? map00030þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map00260: http://www.genome.jp/ dbget-bin/show_pathway? map00260þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map00680: http://www.genome.jp/ dbget-bin/show_pathway? map00680þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01060: http://www.genome.jp/ dbget-bin/show_pathway? map01060þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01061: http://www.genome.jp/ dbget-bin/show_pathway? map01061þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01062: http://www.genome.jp/ dbget-bin/show_pathway? map01062þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01063: http://www.genome.jp/ dbget-bin/show_pathway? map01063þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01100: http://www.genome.jp/ dbget-bin/show_pathway? map01100þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01110: http://www.genome.jp/ dbget-bin/show_pathway? map01110þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01120: http://www.genome.jp/ dbget-bin/show_pathway? map01120þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01130: http://www.genome.jp/ dbget-bin/show_pathway? map01130þ2.7.1.165þ2.7.2.-þ3.1.3. (continued on next page)
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Table 1 (continued ) Group
Passed Mass Retention Metabolite time (min) peak number
13 15 21
231 179 158
23.425266 9.594646 10.13849
21
467
27.33467
2
199
13.3496
2
376
15.8293
2 6
341 228
10.5654 9.415833
12
191
10 6 4 WTV vs. WT 13
(14)
Log2FC
P-value
KEGG KEGG pathway entry no.
Diazoxide Allitridin 2-Chloronitrobenzene; 1-chloro-2-nitrobenzene Phosphomethyl phosphonic acid deoxyuridylate ester Dihydroxyacetone phosphate acyl ester
8.761650851 8.188425569 4.203839614
3.63E-02 C06949 1.18E-02 e 1.79E-05 C14407
1.294212179
1.11E-03 e
7.341538585 2.41E-02 e
17.72226
3-Mercuri-4aminobenzenesulfonamide 1,6-Fructose diphosphate (linear form) 2-(Sulfomethyl) thiazolidine-4-carboxylate 3-Sulfocatechol
173 357 332 154
15.07547 20.1494 17.914974 17.62219
3-Chloro-4-hydroxybenzoic acid 2-Carboxyarabinitol-1,5-diphosphate Hydroflumethiazide S-mercaptocysteine
13.34724889 15.98408903 16.07815909 20.41533393
3.38E-03 7.30E-17 1.81E-04 2.44E-12
20 6
167 394
7.85185 18.13203
18.63176991 18.45291987
1.57E-13 e 7.11E-11 e
10 13 24
200 231 115
9.95032 23.425266 12.96153
Thiodiacetic acid sulfoxide (E)-3-(5((5-(4-chlorophenyl)furan-2-yl) methylene) -4-oxo-2-thioxothiazolidin-3-yl) propanoic acid 5-Mercapto-2-nitro-benzoic acid Diazoxide Trifluoroacetic acid
17.66912059 15.41353652 6.181216816
1.14E-07 e 1.41E-09 C06949 7.35E-07 e
7.279426365 2.41E-02 C03372
20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01200: http://www.genome.jp/ dbget-bin/show_pathway? map01200þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map01230: http://www.genome.jp/ dbget-bin/show_pathway? map01230þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map04922: http://www.genome.jp/ dbget-bin/show_pathway? map04922þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 map05230: http://www.genome.jp/ dbget-bin/show_pathway? map05230þ2.7.1.165þ2.7.2.-þ3.1.3. 20þ3.1.3.80þ 4.2.1.11þ5.4.2.11þ5.4.2. 12 e e
map00564: http://www.genome.jp/dbgetbin/show_pathway?map00564þ1.1.1.1 01þ2.3.1.42þ2.5.1.26 map00565: http://www.genome.jp/ dbgetbin/show_pathway?map00565þ1.1.1.1 01þ2.3.1.42þ2.5.1.26 map01100: http://www.genome.jp/dbgetbin/show_pathway?map01100þ1.1.1.1 01þ2.3.1.42þ2.5.1.26
7.717109750 2.41E-02 e 8.883426465 3.13E-02 e 9.695421081 3.94E-02 C06336
e e C07763 C01962
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11 .2þ1.14.12.14)
e map00270: http://www.genome.jp/dbgetbin/show_pathway?map00270þ4.4.1.1 þ4.4.1.8
e
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67
Table 1 (continued ) Group
Passed Mass Retention Metabolite time (min) peak number
Log2FC
P-value
21
158
10.13849
21
467
27.33467
12
191
6 6 4 5 10
228 357 332 149 173
KEGG KEGG pathway entry no.
5.223698318
3.66E-06 C14407
1.262746229
1.95E-02 e
17.72226
2-Chloronitrobenzene;1chloro-2-nitrobenzene Phosphomethyl phosphonic acid deoxyuridylate ester 3-Sulfocatechol
9.415833 20.1494 17.914974 18.05278 15.07547
2-(Sulfomethyl)thiazolidine-4-carboxylate 2-Carboxyarabinitol-1,5-diphosphate Hydroflumethiazide 2-Hydroxy-3-chloropenta-2,4-dienoate 3-Chloro-4-hydroxybenzoic acid
10.79696013 12.40770669 14.89285357 15.28631745 18.40540442
9.063724699 4.50E-02 C06336
1.80E-02 1.49E-02 3.43E-03 4.47E-09 3.07E-14
e e C07763 C11353 e
e
map00362: Benzoate degradation (http://www.genome.jp/dbgetbin/show_pathway?map00362þ1.13. 11 .2þ1.14.12.14) map01120: Microbial metabolism in diverse environments (http://www.genome.jp/dbgetbin/show_pathway?map01120þ1.13. 11 .2þ1.14.12.14)
e e
The metabolites with significant difference were identified with the criteria of jlog2FCj > 1 and p-value < 0.05. Letters in bold represent the overlapped metabolites that are significantly different between groups. The numbers in the column of “Group” represent the number of metabolites that are significantly different between the two groups. GM: genetically modified wheat, namely, 72kD; GMV: 72kD wheat with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus; FC: fold change; KEGG: Kyoto Encyclopedia of Genes and Genomes.
induced the metabolism to produce 2-chloronitrobenzene in wheat. 2-Chloronitrobenzene is an important intermediate in organic synthesis and it is toxic. In a previous study, whole-body inhalation of 18 ppm 2-chloronitrobenzene led to the death of 2 B6C3F1 male mice in 12 weeks, and inhalation of 4.5 ppm or more
2-chloronitrobenzene caused microscopic liver changes, hepatocellular necrosis, hematopoietic cell proliferation, splenic lesions, increase of bone marrow hemosiderin, and hemosiderin deposition in kidney tubule [36]. Additionally, 2-chloronitrobenzene caused methemoglobinemia, responsive anemia, microscopic liver
Fig. 3. Venn diagram showing metabolic differences between wheat groups. GM: genetically modified wheat, namely, 72KD; GMV: 72KD with WYMV-infection; WT: wild-type wheat, namely, Yang11; WTV: Yang11 with WYMV-infection; WYMV: wheat yellow mosaic virus.
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changes, hyperplasia of the epithelium in nasal cavity, alterations of erythrocyte morphology, as well as increase of serum sorbitol dehydrogenase, alanine aminotransferase, and bile acid concentrations in F344/N rats [36]. Therefore, genetic modification and WYMV-infection may induce the production of the toxic substance 2-chloronitrobenzene in wheat at least at the third leaf stage. Additionally, in this study, melarsoprol was found to be increased in GM (in comparison with WT) and GMV (in comparison with WTV), implying that the metabolism to produce melarsoprol was induced by genetic modification. Melarsoprol is an important chemotherapeutic agent utilized to treat late stage trypanosomes, as it can get across the blood/brain barrier [37]. However, it is highly toxic, and approximately 5% of patients die from its toxicity [38]. Therefore, genetic modification may induce the production of toxic melarsoprol in wheat at least at the third leaf stage. In summary, the current study showed that genetic modification or/and WYMV-infection significantly affected the metabolic content in wheat, e.g. CHBA, 3-sulfocatechol, S-mercaptocysteine, 2-chloronitrobenzene and melarsoprol. Specially, 2chloronitrobenzene and melarsoprol may have toxicity, thus it is strongly required to get more safety information of GM and WYMV-infected wheat to see whether the seeds contain these two toxic substances. As far as we know, this is the first report investigating the effects of WYMV-infection on metabolic profiling of WYMV-resistant GM wheat, and our future work will focus on the integrated analysis of proteomics data and metabolomics data. Conflict of interest All authors declare that they have no conflict of interest to state. Funding This work was supported by the National Grand Project of Science and Technology (2016ZX08012-001) and the basic scientific research foundation in the Chinese Academy of Inspection and Quarantine (2015JK005). Acknowledgement The authors thank Dr Yiqiang Zhao (College of Biological Sciences, China Agriculture University) for data processing and analysis, and Dr Jiaolong Sun for useful interpretation of MPP software. References [1] D.W. Lawlor, Genetic engineering to improve plant performance under drought: physiological evaluation of achievements, limitations, and possibilities, J. Exp. Bot. 64 (2013) 83e108. [2] M. Reguera, Z. Peleg, E. Blumwald, Targeting metabolic pathways for genetic engineering abiotic stress-tolerance in crops, Biochim. Biophys. Acta 1819 (2012) 186e194. [3] A. Ganesan, A. Fieberg, B.K. Agan, T. Lalani, M.L. Landrum, G. Wortmann, N.F. Crum-Cianflone, A.R. Lifson, G. Macalino, Results of a 25-year longitudinal analysis of the serologic incidence of syphilis in a cohort of HIV-infected patients with unrestricted access to care, Sex. Transm. Dis. 39 (2012) 440e448. [4] R. Makandar, J.S. Essig, M.A. Schapaugh, H.N. Trick, J. Shah, Genetically engineered resistance to Fusarium head blight in wheat by expression of Arabidopsis NPR1, Mol. Plant Microbe. Interact. 19 (2006) 123e129. [5] A. Regina, A. Bird, D. Topping, S. Bowden, J. Freeman, T. Barsby, B. KosarHashemi, Z. Li, S. Rahman, M. Morell, High-amylose wheat generated by RNA interference improves indices of large-bowel health in rats, Proc. Natl. Acad. Sci. U. S. A. 103 (2006) 3546e3551. [6] C. Saint Pierre, J.L. Crossa, D. Bonnett, K. Yamaguchi-Shinozaki, M.P. Reynolds, Phenotyping transgenic wheat for drought resistance, J. Exp. Bot. 63 (2012) 1799e1808. [7] F. Yan, Y. Zheng, W. Zhang, H. Xiao, S. Li, Z. Cheng, Obtained transgenic wheat expressing pac1 mediated by Agrobacterium is resistant against Barley yellow
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