Metabolite profiling of the response to high-nitrogen fertilizer during grain development of bread wheat (Triticum aestivum L.)

Journal of Cereal Science 69 (2016) 85e94

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Metabolite profiling of the response to high-nitrogen fertilizer during grain development of bread wheat (Triticum aestivum L.) Shoumin Zhen a, Jiaxing Zhou a, Xiong Deng a, Gengrui Zhu a, Hui Cao a, Zhimin Wang c, *, Yueming Yan a, b, ** a b c

College of Life Science, Capital Normal University, 100048 Beijing, China Hubei Collaborative Innovation Center for Grain Industry, 434025 Jingzhou, China College of Agricultural and Biotechnology, China Agricultural University, 100091 Beijing, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 September 2015 Received in revised form 17 February 2016 Accepted 23 February 2016 Available online 27 February 2016

Wheat yield and quality are dependent largely on nitrogen (N) availability. In this study, we performed the first metabolomic analysis of the response to high-N fertilizer during wheat grain development using non-targeted gas chromatography-mass spectrometry (GCeMS). Quality parameter analyses demonstrated that high-N fertilizer application led to a significant increase in grain protein content and improvement in starch and bread-making quality. Comparative metabolomic profiling of six grain developmental stages resulted in identification of 74 metabolites, including amino acids, carbohydrates, organic acids and lipids/alcohol, which are primarily involved in carbon and N metabolism. Under high-N fertilizer treatment, numerous metabolites accumulated significantly during grain development. Principal component analysis revealed two principal components as being responsible for the variances resulting from N-fertilizer treatments. Metaboliteemetabolite correlation analysis demonstrated that the high-N treatment group had a greater number of positive correlations among metabolites, suggesting that high-N fertilizer treatment induced a concerted metabolic change that resulted in improved grain development. Particularly, the high-N treatment-mediated significant accumulation of metabolites involved in the TCA cycle, starch and storage protein synthesis could be responsible for the improvement of grain yield and quality. Our results provide new insight into the molecular mechanisms of wheat grain development and yield and quality. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Wheat grain Nitrogen fertilizer Metabolome Flour quality Metaboliteemetabolite correlations

1. Introduction Wheat (Triticum aestivum L., 2n ¼ 6x ¼ 42, AABBDD), an allohexaploid species, is one of the most important and widely cultivated cereal crops, which serves as the main food source for >40%

Abbarevations: DPA, days post-anthesis; EI, electron-impact; FDR, false discovery rate; GCeMS, gas chromatography-mass spectrometry; GI, gluten index; GY, grain yield; HMW-GS, high molecular weight glutenin subunits; LMW-GS, low molecular weight glutenin subunits; N, nitrogen; NADPH, nicotinamide adenine nucleotide phosphate hydrogen; PC, principal component; PCA, principal component analysis; PLSDA, partial least-squares discriminant analysis; RVA, rapid viscosity analyzer; TCA, tricarboxylic acid; TKW, thousand kernel weight. * Corresponding author. ** Corresponding author. Laboratory of Molecular Genetics and Proteomics, College of Life Science, Capital Normal University, 100048 Beijing, China. E-mail addresses: [email protected] (S. Zhen), [email protected] (J. Zhou), [email protected] (X. Deng), [email protected] (G. Zhu), [email protected] (H. Cao), [email protected] (Z. Wang), [email protected] (Y. Yan). http://dx.doi.org/10.1016/j.jcs.2016.02.014 0733-5210/© 2016 Elsevier Ltd. All rights reserved.

of the global population (Shewry, 2009). The mature wheat grain comprises three major components, starch, proteins, and cell-wall polysaccharides, in addition to various minor components, including lipids, terpenoids, phenolics, minerals, and vitamins (Shewry et al., 2013). Thus, wheat contributes essential amino acids, minerals, vitamins, beneficial phytochemicals, and fiber to the human diet (Shewry, 2009). Seed starch, which comprises ~70% of the grain, is a major determinant of grain yield and has important effects on flour quality. Meanwhile, wheat seed storage proteins, mainly gliadins and glutenins, are important determinants of gluten quality, as they are responsible for the dough elasticity and extensibility that determine the quality of a range of end-products (Rasheed et al., 2014). In particular, bread-making quality is influenced mainly by the gluten proteins that form a viscoelastic dough. This dough has the gas retention ability necessary to produce loaves with large volumes and the desired texture (Flæte et al., 2005). Nitrogen (N) is the most abundant element in the atmosphere. In modern agriculture, N fertilizer is widely produced to nourish

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plants, increase yield, and improve harvest quality. Studies showed that N application exerted a significant increase in protein content and improved dough quality (Lerner et al., 2006). To date, several studies have investigated N fertilizer uptake, assimilation and effects on plant development (Arsova et al., 2012; Thomsen et al., 2014). N is taken up mainly as nitrate and ammonium salts, but organic N uptake in the form of urea or amino acids can also occur. The assimilation of inorganic N into organic N involves a series of tightly regulated, enzyme-catalyzed steps. Nitrate is reduced to nitrite by nitrate reductase. Nitrite reductase located in plastids produces ammonium, which is then incorporated into glutamine by glutamine synthase. Ammonium can also be fixed by cytosolic glutamine synthase isoforms, again producing glutamine as the first organic N-containing compound. Glutamine is then incorporated into proteins, thus influencing the quality of flour, or is transferred to other amino acids. N is an essential component of chlorophyll that will reduce chlorophyll formation if lacking from the mineral nutrient supply of a plant, with concomitant effects on photosynthesis (Scheible et al., 2004). On the contrary, the addition of 200 kg N/ha to a wheat crop increased both the chlorophyll content by ~15% per unit leaf area and the activity of ribulose 1,5diphosphate carboxylase (Thomas and Thorne, 1975). Several studies at the genome and proteome levels have investigated plant responses to the presence of nitrate at various concentrations (Wang et al., 2000; Bahrman et al., 2004). However, transcriptomic and proteomic methods are focused on macromolecules and cannot detect smaller metabolites that play important roles in plant growth and development. Thus, metabolite profiling is rapidly becoming a key tool for functional annotation of genes, and it facilitates comprehensive understanding of cellular responses to variations in biological conditions (Schauer and Fernie, 2006). A few metabolomics studies have been conducted in crop grains, such as rice (Hu et al., 2014), maize (Rao et al., 2014) and soybean (Lin et al., 2014). In contrast, the metabolomic profile of bread wheat grains has not been investigated extensively. The reported works focused only on the metabolite profiling of wheat € grains derived from organic and conventional agriculture (ZOrb et al., 2006), chemical analysis of semolina and the volatile composition of semolina and pasta samples from durum wheat cultivars (Beleggia et al., 2009) and metabolite profiling of a diverse collection of wheat lines to assess the effects of environmental factors on their characteristics (Matthews et al., 2012). However, the effects of high-N fertilizer application on the metabolome during wheat grain development remain unclear. In this study, we performed the first comparative metabolomic analysis of developing grains in the Chinese elite bread wheat cultivar Zhongmai 175 under high-N and normal-N fertilizer treatments. Our aim was to investigate the effects of N fertilizer levels on metabolite composition during various developmental stages of whole grains and particularly on yield and bread-making quality. 2. Experimental 2.1. Plant materials, field experiment design and N-fertilizer treatment The Chinese elite bread wheat cultivar Zhongmai 175 (T. aestivum L.), which was released in 2008 and widely cultivated in the main wheat production areas of China, was used for this study. The main advantages of this cultivar include high yield potential, water and N fertilizer use efficiency, superior dough quality and broad adaptation (Chen et al., 2009). Field experiments were performed in the experimental fields of the China Agricultural University Research Center, Wuqiao, Hebei Province, during the 2014e2015 wheat-growing season. The soil is glue test bed for low light loamy

salinization soil, the qualitative organic content of 0e20-cm-depth soil is 11.7 g/kg, total N is 1 g/kg, available N is 67.1 mg/kg, rapidly available phosphorus is 26.8 mg/kg, and rapidly available potassium is 92.5 mg/kg. Based on our experience and Xu et al. (2015), urea (NH2)2CO was used as N fertilizer. The field experiments included two groups: a control group that received normal N fertilization of 180 kg/hm2 and a treatment group that received high-N fertilization of 240 kg/ hm2. The time of N application was prior to sowing (180 kg/hm2) for both groups and jointing (60 kg/hm2) for the treatment groups. Each group comprised six biological replicates (each plot, 20 m2). In the 2014e2015 growing season, the average annual sunshine duration at the experiment location was 2690 h, and the average annual temperature was 12.6  C. Cultivation and management practices were identical to those for local field cultivation. After flowering, the plants were marked with colored lines, and the grains from middle ears at 10, 15, 20, 25, 30 and 35 days postanthesis (DPA) were collected from 9:00e11:00 am. The collected samples were rapidly transferred to liquid nitrogen and then stored at 80  C prior to analysis. Mature seeds were harvested for quality testing. 2.2. Agronomic traits, grain yield and quality testing The grains of each plot were harvested and thrashed using a mini-Vogel machine. The grain yield was measured using six replicate plots. Several main agronomic traits were tested, including plant height, effective spikelet number, grain number per spike, thousand grain weight (TKW) and grain yield (GY, kg/hm2). The quality parameters were evaluated according to Sun et al., 2010 with minor modifications. Flour moisture and ash contents (% dry basis) were determined according to the AACC, 2000 44-15A and 08-02, respectively. Protein content (%N 5.7, 14% moisture basis) was determined by N combustion analysis using the LECO (Model FP analyzer; St. Joseph, MI, USA) calibrated against EDTA. Falling number (Perten, Sweden) was determined by the method of AACC 56-81B. The Rapid Visco Analyzer (RVA) profile was obtained using the AACC, (2000) 76-21 with a RVA (Newport Scientific Series 3). The AACC, 2000 54-21 was followed to obtain Farinograph parameters (10 g Brabender Farinograh-E). Image analysis of crumb bread grains was performed using C-Cell image analysis software and equipment (Calibre Control International Ltd.; Warrington). 2.3. Sample preparation and untargeted metabolomic analysis Metabolite analysis by GCeMS was carried out following a method described previously (Beleggia et al., 2009) with minor modifications. Six biological replicates of each 500-mg grain sample were placed into six pre-cooling mortars and ground evenly for 30 min into fine powder using liquid nitrogen to avoid degradation. After lyophilization, 50 mg powders were homogenized in 1.4 mL 100% methanol, and 60 mL ribitol (0.2 mg/mL in water) were added to the extraction solution as a quantification standard. The mixtures were shaken vigorously for 1 min at room temperature and subjected to ultrasonic treatment for 15 min and then centrifuged for 10 min at 14,000 rpm. The supernatant was transferred to a fresh tube, and 750 mL CHCl3 and 1.5 mL water were added. The samples were mixed vigorously and centrifuged at 4000 rpm for 15 min, and the supernatant (containing extracted metabolites) was transferred to a fresh tube and dried under vacuum. The residues were resuspended and derivatized for 2 h at 37  C in 40 mL 20 mg/mL methoxyamine hydrochloride in pyridine, followed by 30 min treatment with 70 mL N-methyl-N-[trimethylsilyl] trifluoroacetamide (MSTFA) at 37  C. The samples were then subjected to GCeMS analysis.

48 ± 1 59 ± 2* 14.59 ± 0.27 1.5 ± 0.03 9.21 ± 0.175** 1.8 ± 0.01* 56.2 ± 0.85 0.7 ± 0.001 65.1 ± 0.44* 0.8 ± 0.002* 603.2 ± 40.5** 139.4 ± 0.4 245,817 ± 1406 1848 ± 17 500.8 ± 6.2 150.3 ± 0.5** 255,980.5 ± 1450** 1896 ± 2* CK NT

Circumference/ Attenuation Cell contrast px ratio

Volume of course cell

Cell extension

Cell Cell diameter/px quantity

Cell density

Wall thickness/ px 18.5 ± 0.21 2246 ± 24 0.009 ± 0.0005 3.6 ± 0.03 710 ± 3.5 14.1 ± 0.11** 3196 ± 43** 0.012 ± 0.0002** 3.2 ± 0.03** 720 ± 4.0

1.9 ± 0.10 2.7 ± 0.05 1.7 ± 0.01* 2.3 ± 0.15* Loaf parameters Loaf volume Score (ml3) (100) 51.8 ± 0.2 51.4 ± 0.2 321.5 ± 8.5 305 ± 5.0* 482 ± 0.1 500 ± 1.0* 2578.5 ± 12.5 3138.5 ± 24.5 1240.5 ± 53.5 111 ± 3.0 2637 ± 1.0** 3208.5 ± 9.5** 1300 ± 9.0* 136 ± 1.5* 3.0 ± 0.01 3.2 ± 0.01* 25.8 ± 1.02 49.3 ± 1.34**

CK 7.8 ± 0.08 14.5 ± 0.04 0.48 ± 0.01 NT 8.3 ± 0.06* 15.3 ± 0.06* 0.44 ± 0.02* Treatments C-cell parameters Hardness Slice Slice area/px brightness

Development Stability time (min) (min) Water absorption (500FU) Total gluten Peak viscosity Final viscosity Setback Protein content

Moisture

Ash

Gluten index

RVA parameters Treatments Basic quality parameters

Table 1 Comparison of main quality parameters between control (CK) and high N-treated group (NT).

Softening degree (BU)

Thickness (FU)

Falling number

Farinograph parameters

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Prepared samples were injected onto the GC column in splitless mode using the GCeMS instrument Agilent 7890A/5975C (Agilent Technologies; State of California, USA). The samples were randomized to reduce system error. The data were acquired in one batch and finished in 1 day. The GC column (HP-5MS capillary column 30 m  250 mm i.d., 0.25 mm film thickness; Agilent J & W Scientific; Folsom, CA, USA) contained 5% (w/v) methylphenylsiloxane, with an injection volume of 1 ml and a 20:1 split ratio. The injection temperature was 280  C, the transfer line was set to 150  C, and the ion source was adjusted to 250  C. Helium was used as the carrier gas at a constant flow rate of 1 mL/min. The initial temperature was 40  C, which was maintained for 5 min, increased by 10  C/min to 300  C, and maintained for 5 min. The spectrometer was operated in electron-impact (EI) mode, the ionization voltage was 70 eV, and mass spectra were recorded at 2 scans/s using an m/z 35e780 scanning range. Metabolites were identified by automated comparison of data to those in the NIST MS Search Program database (http://www.nist. gov/srd/nist1a.html) and the Wiley 9 metabolome database (http://www.sisweb.com/software/ms/wiley.html). The absolute concentration of most metabolites was determined by comparison with standard calibration curve response ratios of various concentrations of standard, including the internal standard ribitol, together with the samples. All chemicals were purchased from SigmaeAldrich Co. Ltd (Shanghai, China). The quality control, a mixture of all 72 samples, was also used to test the stability of the system. Quality control involved monitoring the deviations of the analytical results of the mixtures and comparison with the errors caused by the analytical instrument itself. One quality control sample was included for every 10 samples. 2.4. Statistical analysis Analysis of variance (ANOVA) was carried out for each compound detected in the control and treatment groups. Missing values (if any) were assumed to be below the limits of detection and were imputed using a function implemented in XCMS (http:// xcmsonline.scripps.edu/) prior to the normalization step. The Rlanguage was used to perform one-way ANOVA. In addition, to avoid false-positive results in the screening for significant metabolites, the false discovery rate (FDR) significance criterion was used and an FDR limit of 0.05 selected. To perform a general and comprehensive characterization of the control and treatment groups, the detected compounds were subjected to a principal components analysis (PCA), followed by SIMCA-P11.0 (http://www. umetrics.com/SIMCA), the acquiescent set was Pareto-scaled, and the key metabolites of the developing grains were identified by partial least-squares discriminant analysis (PLS-DA). Pearson's product-moment correlations (r values) were calculated using Perl (http://www.pm.org/). The corresponding p-values and FDRs were calculated using the aid of the cor. test function. Identified metabolites were mapped onto general biochemical pathways using the annotations from PMN (http://www.plantcyc.org/) and KEGG. A metabolic network map of wheat grain development incorporating the identified and annotated metabolites was constructed using the aid of PMN and KEGG. The Cytoscape 3.1.0 software (http://www. cytoscape.org/) was used to produce the correlation figures. 3. Results and discussion 3.1. Comparative analysis of agronomic traits and quality parameters between the control and high-N treatment groups The grain morphological changes during six developmental stages did not differ markedly between the control and high-N

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Table 2 Metabolite comparison with significant differences between CK and high N-treated group (NT)*.

Metabolites

NT/CK (10 DPA)

NT/CK (15 DPA)

NT/CK (20 DPA)

NT/CK (25 DPA)

NT/CK (30 DPA)

NT/CK (35 DPA)

1,3-Diaminopropane 1-Monohexadecanoylglycerol 2,4-Dimethyl-1-heptene 2-Amino-Adipinic acid 2-Octenoic acid 3-Methylgluconate 4,6-Dimethyldodecane 4-Aminobutyric acid Acetamide Adenosine Alanine Aminomalonic acid Asparagine Aspartic acid Carbodiimide Cellobiose Eicosanoic acid Ethanolamine Ethylamine Fructose Fumaric acid Galactono-1,4-lactone Glucaric acid Glucose Glutamic acid Glutamine Glyceric acid Glycerol Glycine Heptanoic acid Hexitol Hydrated Formaldehyde Inositol Isoleucine Lactic acid Leucine Linoleic acid Lysine Malic acid Mannose-6-Phosphate

1.61 1.53 1.05 1.16 0.98 0.37 1.10 354.77 4.76 12.87 53.32 1.39 1.06 16.52 1.07 0.77 0.77 4.31 3.94 0.72 0.73 1.40 1.44 0.74 3.24 0.48 0.90 0.91 6.27 1.07 0.96 1.09 1.13 1.49 0.60 15.94 1.39 0.97 0.82 1.55

1.06 1.02 0.76 0.85 0.69 0.01 0.84 1.94 1.19 1.29 1.74 0.96 0.76 1.45 0.81 0.48 0.49 1.11 1.10 0.42 0.45 0.97 0.98 0.47 1.08 0.03 0.57 0.60 1.28 0.81 0.66 0.82 0.84 0.99 0.31 1.35 0.97 0.67 0.49 1.03

0.88 0.88 1.20 1.01 1.43 5.32 1.13 0.47 0.67 0.66 0.49 0.97 1.19 0.52 1.16 1.94 1.84 0.68 0.68 2.07 2.07 0.95 0.93 2.06 0.77 4.50 1.71 1.71 0.67 1.16 1.49 1.15 1.10 0.90 2.64 0.63 0.96 1.46 1.76 0.88

1.04 1.34 1.15 1.13 0.72 1.82 1.19 0.97 1.11 1.92 1.31 0.84 1.00 1.41 1.00 0.82 1.26 1.29 1.02 0.85 1.18 0.98 1.16 1.26 1.00 1.22 0.87 1.06 1.01 1.12 2.50 1.02 0.77 0.80 0.72 0.88 3.58 0.96 1.14 1.04

1.17 2.01 1.08 0.80 1.13 1.44 0.98 0.93 1.11 1.50 0.77 0.64 1.52 1.09 1.58 1.02 1.11 0.93 0.92 0.90 0.83 0.97 1.00 1.84 1.67 1.31 0.84 1.13 0.94 0.95 0.68 1.29 0.84 0.69 1.73 0.70 0.85 0.98 0.92 1.12

12.87 0.79 0.87 0.90 0.69 0.85 0.78 1.72 1.00 1.04 0.88 1.12 1.61 1.53 1.02 1.72 0.76 1.16 0.92 1.82 0.78 0.81 0.82 1.11 0.92 1.85 0.82 0.81 1.07 0.74 0.23 0.88 0.90 1.61 0.49 1.51 1.43 1.77 0.89 0.56

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treatment groups (Figure S1A), and the grain fresh weight in both groups increased significantly with grain development, with the exception of a slight decrease from 30 to 35 DPA (Figure S1B). Main agronomic traits and yield analysis demonstrated that high-N fertilizer treatment significantly increased plant height and ear length, etc (Table S1). The majority of the quality parameters, including basic quality parameters, RVA parameters, and bread-making quality parameters, were highly increased significantly under high-N fertilizer treatment (Table 1). In particular, high-N fertilizer application significantly increased grain protein and gluten contents, which are major RVA parameters related to several properties of starch, such as viscosity, setback and softening degree. Although loaf volume did not change markedly, the major C-Cell parameters (related to bread slice structure and quality) were significantly improved under high-N fertilizer treatment (Figure S1CeE); these included decreased hardness, wall thickness and cell diameter and increased slice brightness and area, cell density, quantity, contrast and extension. The improvement of these parameters resulted in a significantly higher loaf score (Table 1). Farinograph parameters showed that high-N fertilizer treatment did not exert a marked effect on water absorption; however, falling number, development time and stability were decreased significantly. Our results

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demonstrate that application of high-N fertilizer can significantly promote protein and gluten content, dough viscosity and softening degree, leading ultimately to improved bread inner structures and loaf scores. 3.2. Metabolic profiling of the response of developing grains to high-N treatment GC-MSebased metabolomic analysis of the control and high-N treatment groups resulted in identification of 74 metabolites (Table S2). The total ion chromatogram is shown in Figure S2. We € detected the majority of the metabolites reported by ZOrb et al., 2006, i.e., 20 amino acids, 16 carbohydrates, 14 organic acids, 10 lipids/alcohol, 6 amines, 3 nucleotides and several others. A multiple hypothesis correction analysis was performed using a FDR at a p-value of 0.05 (Table S3). The results showed that 69 metabolites were significantly affected by the high-N treatment (Table 2). To determine the differences in metabolite profiles between the two groups, the multivariate statistical tool PCA was applied to the datasets. The results revealed the gradually clear clustering of metabolite samples from the control and high-N treatment groups, for example: 20DPA and 35DPA (Fig. 1). The first principal component (PC1), which had the greatest variance (29.81%) across

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Fig. 1. Principal component analysis (PCA) of the metabolite from the control and high-N-fertilizer treatment in Zhongmai 175. Different marker colours represent these developmental grain samples of CK and NT groups (2 groups  6 periods  6 biology replicates ¼ 72 samples).

the dataset, separated the samples according to sampling day. Three clusters were included in PC1: 10e15 DPA, 20e30 DPA and 35 DPA. This is in accordance with the three distinct phases of wheat grain development: cell division and differentiation, grain filling, and desiccation/maturation (Shewry, 2007). The 20 DPA and 35 DPA separated clearly between the two groups, indicating that N fertilizer plays a critical role during the both stages of grain development (20DPA and 35DPA) (Fig. 1). Pyroglutamic acid, galactose, fructose, glucose, serine, alanine, sucrose and acetamide were the main metabolites contributing to the dispersion of the samples on PC1 (Table S4). PC2 accounted for 22.85% of the variance and separated the samples between the control and high-N treatment groups. The changes in the abundance of glycerol, glucose, 4aminobutyric acid, fructose, and cellobiose (Table S4) were responsible for the sample dispersion on PC2. Our results demonstrated that the differences in the metabolomic profile among grain developmental stages were greater than those due to high-N treatment. The influence of developmental stages and high-N treatment on grain development could also be separated into distinct clusters according to the results of PLS-DA (PLS-DA: R2X ¼ 0.846, R2Y ¼ 0.889 and Q2 ¼ 0.774), which further revealed distinct metabolic alterations among developmental stages and between the N-treatment groups (Figure S3a). PLS-DA analysis also revealed replicates, substrate expression patterns, and the contributions of individual materials to seed development according to N level. According to a permutation test, the Q2Y > 0.5 and 0 < R2YQ2Y < 0.2; thus, the validation plot strongly indicated that the PLSDA model was valid, as the Q2 regression line (blue) had a negative

intercept, and all permuted R2-values (green) on the left were lower than the original point of the R2-value on the right (Figure S3b). The metabolite expression patterns during grain development shown in the heat map (Fig. 2) suggested three main tendencies in both groups. Pattern I included 26 metabolites and showed an upedown change in expression, while all 33 metabolites in pattern II were downregulated. The 15 metabolites in pattern III were generally upregulated throughout grain development. Although both groups showed similar expression patterns, high-N fertilizer treatment resulted in significant changes in the expression levels of individual metabolites (Table S5). 3.3. Comparison of the metaboliteemetabolite relationships between the control and high-N fertilizer treatments Correlation analysis can be used to reveal relationships among metabolites (Hu et al., 2014). The correlation analysis performed here indicated positive and negative correlations among metabolites in both groups (Table S6). A total of 2701 correlations were found in the control group, with values ranging from 0.99 for galactose and fructose to 0.91 for putrescine and fumaric acid (Figure S4a and Table S6). In the high-N treatment group, correlation values ranged from 0.99 for galactose and fructose to 0.85 for sucrose and sedoheptulose (Figure S4b and Table S6). Further screening resulted in identification of 655 significant correlations, with r2  0.49 (r  0.7 and r  0.7) and FDR  0.05 in both groups (Fig. 3). Among them, 66 negative correlations and 242 positive correlations were present in the control group. However, the high-

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Fig. 2. Clustering analysis of metabolome data from the various periods of grain development in CK and high-N-treatment group (NT). A heat map representation of the levels of 74 metabolites from developing grains. Three evident classes are shown. Each line in the heat map represents a metabolite. Red colors indicate metabolite levels greater than the median value, and green colors indicate metabolite levels lower than the median value. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

N treatment group had fewer negative correlations (13) and relatively more positive correlations (234). This indicates that the highN fertilizer treatment induced a concerted metabolic change that led to improved grain development. For example, the correlation of

Lys and Val was 0.82 (FDR: 1.65E-08) in the control group but increased to 0.87 (FDR: 3.41E-10) in the high-N treatment group. The r-value of b-Ala with Val was 0.77 (FDR: 6.04E-07) in the control group but 0.85 in the N treatment group (FDR: 4.13E-09).

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Fig. 3. Comparison of metaboliteemetabolite correlations (r2 > 0.49, FDR < 0.05) between CK and high-N-treatment (NT) in developing grains of Zhongmai 175. Different colours represent different function categories. The red lines represent the negative correlations of the metabolites and gray lines indicate positive correlations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

This suggested that high-N fertilizer treatment enhanced the relations of amino acids and accelerated the transformation of one amino acid to others. We propose that these amino acids accelerate the synthesis of storage proteins, thus benefitting flour quality. Generally, the high correlations among metabolites seen in the two groups (Table S6) were either among amino acids or organic acids or between amino acids and carbohydrates. This demonstrates the importance of carbon (C) and N metabolism for the development of wheat grains. Our results indicated that the correlations between metabolites of the same class were relatively conserved in both groups, similar to the results of a previous study in rice (Hu et al., 2014). The results also demonstrated that C and N metabolism were increased significantly after high-N treatment. Therefore, high-N fertilizer affects mainly C and N metabolism, which are closely related to grain yield and quality. 3.4. Influence of high-N treatment on metabolites related to yield and quality Several metabolites were significantly upregulated in developing grains under high-N treatment. These were involved mainly in C and N metabolism and were closely related to yield and quality (Table 2). Several important sugars involved in C metabolism were detected in this study, including glucose, galactose, fructose, sucrose, sedoheptulose, mannose-6-P, ribose and cellobiose. The levels of most were significantly increased by high-N fertilizer treatment (Table 2). These metabolites play vital roles in grain metabolism and starch biosynthesis, and thus ultimately influence yield performance and flour quality (Fig. 4). In most plants, carbohydrates represent the major energy store and are the building blocks for essential structural polymers (Fettke and Fernie, 2015). Glucose, fructose and sucrose are used to synthesize starch, and sucrose plays an important role in regulation of the C/N balance in plant cells (Zheng, 2009). In particular, these metabolites were significantly accumulated at 20 DPA (Table 2), indicating high

activity during the middle period of grain development. Thus, highN fertilizer can enhance sugar synthesis. The sugars are then used to synthesize starch, the most important determinant of grain weight and yield. Twenty amino acids involved in N metabolism were significantly upregulated by high-N fertilizer treatment during the grainfilling stage, 13 of which were markedly increased at 35 DPA (Table S5). Meanwhile, these metabolites accumulated more rapidly in the high-N fertilizer group than the control group during grain development. In particular, the levels of several essential amino acids important for humans, such as Phe, Ile, Leu, Lys, Thr, and Val, were significantly increased by the high-N fertilizer treatment, which could enhance the nutrition quality of flour. Alanine constitutes a significant proportion of the free amino acid pool under anaerobic and other stress conditions (Sousa and Sodek, 2003). Asparagine serves both to store N and transport it from the roots to leaves and from leaves to developing seeds (Sebastia et al., 2005). N-containing molecules, such as glutamate, glutamine, and asparagine, play key roles in N assimilation, recycling, translocation and storage (Sebastia et al., 2005). High-molecular weight glutenin subunits (HMW-GS) are the main determinants of glutenin elasticity, while low-molecular-weight glutenin subunits (LMW-GS) influence dough viscosity (Shewry, 2009), and both contain large proportions of Gln, Gly, Pro and Glu. High-N fertilizer treatment accelerated the accumulation of these N-containing amino acids, resulting in an increase in protein content (Table 1). The C/N balance is important for the functions of plants. CO2 is assimilated through photosynthesis, and the resulting sucrose and glucose are converted through glycolysis and the TCA cycle to 2oxoglutarate (2OG) or a-ketoglutarate to provide energy for the plant. According to Zheng, (2009), 2OG serves as a C skeleton for the synthesis of glutamate (Glu) by incorporating photorespiratory þ NHþ 4 . NH4 from primary N assimilation is then incorporated into Glu, resulting in the production of glutamine (Gln). Glu and Gln donate NHþ 4 , which is used for the synthesis of all other amino

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Fig. 4. Comprehensive metabolic pathways involved in wheat grain development of CK and NT. The red font means the up-regulated metabolites after high-N-fertilizer treatment. Three main metabolisms are shown: C metabolism, N metabolism and lipid metabolism. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

acids, including aspartate (Asp) and asparagine (Asn), which serve as an active NHþ 4 donor and a transport/storage compound, respectively. Thus, integrated C/N metabolism could be improved by high-N treatment, which would in turn promote grain development. N is also an essential component of proteins that build cell materials and plant tissue. In this study, urea (NH2)2CO, which is the most commonly used source of organic N, was used as N fertilizer. The role of N in agricultural production is intimately connected to photosynthesis. Through photosynthesis, the “physical energy” of photons is converted into the “chemical energy” of ATP and reduced metabolic intermediates, primarily nicotinamide adenine nucleotide phosphate hydrogen (NADPH), which are used in the synthesis of C and N assimilates of many types, particularly carbohydrates and amino acids (Foyer et al., 2001). Hence, the yield and quality of wheat grains could be highly improved by high-N fertilizer treatment. The TCA cycle provides C skeletons for biosynthetic processes and is also involved in the metabolism of organic acids generated by other pathways, such as the glyoxylate cycle during lipid mobilization (Sweetlove et al., 2010). Several metabolites involved

in the TCA cycle were found to be significantly upregulated by highN fertilizer, including pyruvic acid, malic acid and fumaric acid. Of these, pyruvic acid and fumaric acid levels were dramatically increased at 20 DPA by high-N fertilizer treatment (Table 2 and Fig. 4), which could enhance production of energy and C skeletons for amino acid synthesis and thereby increase grain protein and amino acid contents. 4. Conclusion High-N fertilizer treatment significantly increased wheat biomass, grain number and yield and improved flour quality in terms of protein and gluten content, dough rheological properties, and starch and bread-making quality. Comparative metabolomic analysis resulted in identification of 69 metabolites with significantly higher levels under high-N treatment. These metabolites comprised mainly amino acids, carbohydrates, organic acids and lipids/alcohol and were involved primarily in C and N metabolism. Two principal components (PC1 and PC2) were found to be responsible for the main variances caused by N-fertilizer treatment. Compared with normal-N fertilizer treatment, a greater number of

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positive correlations of metabolites were identified in the high-N treatment group, suggesting that high-N fertilizer treatment resulted in metabolic changes that benefit grain development. The increased levels of pyruvic acid, malic acid, and fumaric acid under high-N treatment could result in enhanced energy supply and C skeleton availability. Particularly, high-N treatment greatly increased the accumulation of metabolites involved in starch and storage protein synthesis, which could enhance grain yield and superior quality. These findings strengthen our understanding of the molecular mechanisms underlying the promotion of wheat grain yield and quality by N-containing fertilizer. Acknowledgments This research was financially supported by grants from the National Natural Science Foundation of China (31471485), Natural Science Foundation of Beijing City and the Key Developmental Project of Science and Technology from Beijing Municipal Commission of Education (KZ201410028031), and International Science & Technology Cooperation Program of China (2013DFG30530). Thanks for the help of data analysis from Bionovogene (Suzhou). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jcs.2016.02.014. References AACC, 2000. Approved Methods of the American Association of Cereal Chemists. Methods 44-15A, 08-02, 56e81B, 76-21 and 54-21, tenth ed. The Association, St. Paul, MN. Arsova, B., Kierszniowska, S., Schulze, W.X., 2012. The use of heavy nitrogen in quantitative proteomics experiments in plants. Trends. Plant. Sci. 17, 102e112. , A.L., Jaminon, O., Bahrman, N., Gouis, J.L., Negroni, L., Amilhat, L., Leroy, P., Laine 2004. Differential protein expression assessed by two-dimensional gel electrophoresis for two wheat varieties grown at four nitrogen levels. Proteomics 4, 709e719. Beleggia, R., Platani, C., Spano, G., Monteleone, M., Cattivelli, L., 2009. Metabolic profiling and analysis of volatile composition of durum wheat semolina and pasta. J. Cereal Sci. 49, 301e309. Chen, X.M., He, Z.H., Wang, D.S., 2009. The approval of new wheat variety Zhongmai 175. China Seed. Ind. 7, 69. Fettke, J., Fernie, A.R., 2015. Intracellular and cell-to-apoplast compartmentation of carbohydrate metabolism. Trends. Plant. Sci. 8, 490e497. Flæte, N.E.S., Hollung, K., Ruud, L., Sogn, T., Færgestad, E.M., Skarpeid, H.J., Magnus, E.M., Uhlen, A.K., 2005. Combined nitrogen and sulphur fertilisation and its effect on wheat quality and protein composition measured by SE-FPLC and proteomics. J. Cereal Sci. 41, 357e369. Foyer, C.H., Ferrario-Mery, S., Noctor, G., 2001. Interactions between carbon and nitrogen metabolism. Plant. Nitrogen 4, 237e254. Hu, C.Y., Shi, J.X., Quan, S., Cui, B., Kleessen, S., Nikoloski, Z., Tohge, T., Alexander, D., Guo, L.N., Lin, H., Wang, J., Cui, X., Rao, J., Luo, Q., Zhao, X.X., Fernie, A.R., Zhang, D.B., 2014. Metabolic variation between japonica and indica rice cultivars

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