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INTRODUCTION
With the increasing human population globally, the demand of crop yield accelerated dramatically in last few decades. One of the common practices for higher crop yield is the application of over-dose fertilizer. Of the three basic nutrients (N, P, and K) for plant growth and development, N is believed to be the most essential nutrient for plant growth and a major restriction factor in plant productivity (Tabuchi et al., 2007). Cereal yield and N fertilizer have been highly correlated in a linear way during the past 40 yr (Smil, 2001; Cassman et al., 2002; Ladha et al., 2016). Statistics showed that the N fertilizer consumption has augmented for approximately 17-fold in the last four decades (Ali et al., 1999; Dobermann and Cassman, 2004; Rahn et al., 2009). However, N nutrient in soil has not been fully applied for crop growth and development, and finally converted into crop yield. It is estimated that only 50% or less N fertilizer applied has been used for aboveground biomass production in cereals, and the rest has been wasted by dissipating into environment via surface runoff, leaching, volatilization, and denitrification, which have resulted in severe environmental disasters (Giles, 2005; Nestler et al., 2011; Simplicio et al., 2016). Therefore, how to achieve efficient use of N via cultivation techniques and fertilizer management is of great concern for the agronomist focusing on crop cultivation.
N fertilization significantly increases tissue N concentration (Ye et al., 2014). It was reported that approximate 70%-90% of the plant total N come from the remobilized N from vegetative organs (Mae, 1997). Nitrogen partitioning within plant contributes to the growth dynamics, portioning of biomass, and the grain quality. Due to the complexity caused by inherent factors, including 1000-grain weight, grain number per panicle and productive culms in addition to plant N management, studies focused on the N use efficiency among different rice genotypes are limited (Samonte et al., 2006). It was reported that the increase of N in leaves could increase the hull size and grain filling, whilst the number of degenerate spikelet decreased (Singh and Verma, 2013).
The critical step restricting the N use efficiency is the capability of plant in acquiring N from applied fertilizer (Shrawat et al., 2008). Urea, as a form of N fertilizer with lower application cost than other nitrogenous sources, is the major N carrier worldwide in crop production (Fageria et al., 2011). The assimilation of urea N (NH4) was achieved by a coupled reaction mediated by glutamine synthetase (GS) and glutamate synthase (GOGAT). In rice, there are two types of GS, cytosolic and chloroplastic. Cytosolic GS is encoded by three genes (OsGS1;1, OsGS1;2 and OsGS1;3), and chloroplastic GS is encoded by GS2 (OsGS2) (Tabuchi et al., 2007). OsGS1;1, OsGS1;2, OsGS1;3 and OsGS2 showed tissue specific expression pattern, which are abundant in shoot, root, spikelets, and leaf respectively (Tabuchi et al., 2005). For the GOGAT, there were three genes encoding either ferredoxin (Fd)-GOGAT, NADH-GOGAT1, or NADH-GOGAT2, of which OsNADH-GOGAT2 is a newly identified gene expressed preferentially in fully expanded leaf blades and leaf sheaths (Tamura et al., 2011). The expression pattern could to some extent reflect the ammonium assimilation status of plant.
Unveiling of the response pattern of crops to large regime of N supply would be especially essential for better N management in crops. The objective of this study was to gain a better understanding of the effects on N partitioning, plant growth, grain quality, and expression pattern of key genes involved in glutamate synthesis among three rice genotypes under elevated N input, and provide essential information for better N management in cultivation.
MATERIALS AND METHODS
Plant materials and treatments
Two rice cultivars (Oryza sativa L. subsp. indica Kato), Quanliangyou 1 and Quanliangyou 681, were provided by Hubei All-win High-tech Seed Co., Ltd., and 'Huanghuazhan' was provided by Huazhong Agricultural University. Experiments were performed by orthogonal design, in which three different cultivars and four distinct N input (0, 120, 180, and 250 kg ha-1), namely control, low N, moderate N and high N, and each treatment were supplied with basal fertilization of 122.5 kg P ha-1 and 150 kg K ha-1. For each treatment, and increasing N doses were 0, 120, 180, and 250 kg ha-1. Different cultivars and different N input were used as two factors. Rice seedlings were transplanted to soil filled pot after germination, and three plants in each pot were retained. Three pots were planted as technical replicates and each treatment was repeated for three times as biological replicates within the same growing season. All experiments in this study were carried out in the open-air conditions similar to field test, during the time period from 2 May to 30 August 2015 at Yangtze University, China.
Phenotypic analysis
During the plant growth, we characterized the growth period, including tillering date, heading date, booting date, flowering date, maturation date, plant height, effective tiller number and length, leaf area, panicle number and length, and leaf chlorophyll content, were characterized at heading stage, leaf weight, culm weight were determined after maturation. Flag leaf area (FLA) was calculated by L (leaf length) x W (width) in cm2 as described by Wang et al. (2012). Leaf chlorophyll content was determined by SPAD-502 meter (Ling et al., 2011).
Glutamine synthetase activity
Raw protein containing glutamine synthetase (GS) was extracted from leaf tissue by the extraction buffer (2 mmol Mg2+ L-1, 2 mmol dithiothreitol L-1, 0.4 mol sucrose L-1), then protein content was determined by Bradford protein assay (Bradford, 1976).
The GS activity was determined by measuring the absorbance of y-glutamyl hydroxamate (GH) and ferric chloride formed complex at 540 nm with modifications (Hakeem et al., 2011). Firstly, the raw enzyme extract was incubated with reaction A (80 mmol Mg2+ L-1, 20 mmol sodium glutamate L-1, 20 mmol cysteine L-1, 2 mmol EGTA L-1, 80 mmol hydroxylamine hydrochloride L-1, pH 7.4) and 0.7 mL adenosine triphosphate (ATP, 40 mmol L-1) for 30 min, then 0.37 mol L-1 ferric chloride containing 0.2 mol L-1 trichloroacetic acid and 0.6 mol L-1 hydrochloric acid was added to mixture to display color for measurement. The absorbance was determined by automatic microplate reader (BioTek, Synergy 2). Enzyme activity was calculated by GS activity = A/(P V T), where A, P, V, and T represents micromoles of y-GH, protein content, volume of raw enzyme extract used, and reaction time.
Nitrogen content and grain quality traits measurement
Different tissues of plant under different N supply conditions, namely root, stem, leaf, and seeds, were harvested at maturation stage for N content determination. Total N content of each tissue was determined by standard macro-Kjeldahl procedure (Horneck and Miller, 1998) using Kjeldahl apparatus (Ketuo KDY-9820, China). Brown rice of each treatment was made by brown rice machine (THU35-C, Satake, Penrith, New South Wales, Australia), and then grain quality related traits, i.e. protein content, fatty acid content, amylose and amylopectin content, and quality index were determined by standard program of near infrared spectrometer (DA7200, Perten Instruments, Hágersten, Sweden).
RNA extraction and real time PCR
Flag leaves of each plant in each pot at heading stage were harvested for RNA extraction. Total RNA was extracted from 100 mg leaf powder by RNAiso Plus (TaKaRa Code: D9108A). RNA was then treated by Recombinant DNase I (Ambion, AM2235, Life Technologies, Carlsbad, California, USA) to eliminate genomic DNA contamination. Approximately 2 µg DNase-treated RNA was then reverse transcribed into first strand (Takara, Code: D6110A) according to the user's manual.
Amplification of real-time PCR products was detected with a QuantStudio qPCR Detection System in a reaction mixture of 10 µL: 1 µL cDNA first strand product, 1 µL gene specific primer, 3 µL MiliQ water, and 5 µL SYBR Premix ExTaq II (Takara, RR82LR). The real time PCR was performed by the following programs: template denatured at 95 °C for 30 s, then followed by 40 cycles of 95 °C for 10 s, 60 °C for 10 s, and 72 °C for 5 s, after that 30 cycles at 60 °C were performed to obtain a melting curve. Geometric mean of expression level of Actin (AK100267), Ubiquitin (AK101547), and Gapdh (AK064960) were used as internal standard (Moraes et al., 2015). The relative expression level of OsGS1;1 and OsNADH-GOGAT2 were calculated against internal standard by 2 (-Delta Delta C(T)) method as described previously (Livak and Schmittgen, 2001). Gene specific primer targeted to OsGS1;1 (LOC_Os02g50240.1) and OsNADH-GOGAT2(LOC_Os05g48200.1) could be found in Table 1.
Table 1 Primer sequence designed for qRT-PCR.
| Primer name | Sequence(5'-3') |
| OsGOGAT2F | CCTGTCGAAGGATGATGAAGGTGAAACC |
| OsGOGAT2R | TGCATGGCCCTACTATCTTCGCATCA |
| OsGS1;1F | CAAGTCTTTTGGGCGTGATATTGTTGAC |
| OsGS1;1R | CACCTGATCACCGGCAGAAATGCCGACA |
| GAPDH1F | AAGCCAGCATCCTATGATCAGATT |
| GAPDH1R | CGTAACCCAGAATACCCTTGAGTTT |
| UBQ10-1F | TGGTCAGTAATCAGCCAGTTTGG |
| UBQ10-1R | GCACCACAAATACTTGACGAACAG |
| ACT11-1F | CAGCCACACTGTCCCCATCTA |
| ACT11-1R | AGCAAGGTCGAGACGAAGGA |
Data analysis
All experiments of this study were based on the orthogonal design described in plant materials and treatment. Mean value of three individual plants of each pot were deemed as one individual replicate of each treatment. Differences among treatments and different cultivars were analyzed by One-way or Two-way ANOVA with significant level of p < 0.05 or p < 0.01 according to fixed model of Duncan's shortest significant range-test.
RESULTS
N partitioning in response to elevated N input
Clarification of the processes governing N fluxes, especially N assimilation and allocation in plant is of great significance to both environmental concerns and the quality of crop products (Ata-Ul-Karim et al., 2017). We studied the N partitioning among root, culm, leaf and seed at maturation stage as this stage could reflect the final N accumulation status. Under moderate N input (180 kg ha-1), N contents were most abundant in seed (0.91 ± 0.02%), then followed by leaf (0.73 ± 0.03%), root (0.44 ± 0.03%) and culm (0.34 ± 0.01%) (Figure 1A). Compared with control (0 kg N ha-1), low and moderate N input did not change N contents in root, culm, seed, and leaf. However high N input (250 kg ha-1) significantly increased the N content in all these tissues (Figure 1B), indicating that higher N input exceeding 180 kg ha-1 could enhance overall N content of rice plant. Compared with N content under moderate N input (180 kg ha-1), higher N dose (250 kg ha-1) increased N content by 29.55%, 58.82% 31.87%, and 67.12% in root, culm, seed and leaf respectively (Figure 1B), revealing that high N supply had stronger impact on above-ground vegetative organs than on reproductive organs and under-ground organs. Correlation analysis showed that seed N content was highly correlated to N content in culm and leaf (Pearson correlation coefficient r > 0.84, p < 0.01, Table 2), but less correlated to N content in root (Pearson correlation coefficient r = 0.754, p < 0.01, Table 2).
Impact of elevated N input on plant growth
Since N is an essential element for plant growth, the vegetative and productive growths among three distinct rice genotypes (Q1, Q681, and HHZ) under different N input (0, 120, 180, and 250 kg ha-1) were investigated in our study. The plant height without extra N input (control) was 65.22 ± 0.58 cm on average, and increased by 32.6% and 45.7% respectively when the N input was 120 and 180 kg ha-1, but under 250 kg ha-1 the plant height was nonsignificantly different from that under 180 kg ha-1 (Figure 2A), indicated 180 kg ha-1 N was possibly the optimal N input for plant height. However, the average effective tiller number kept climbing from 7.78 ± 0.11 per plant to 27.78 ± 2.30 per plant as the N input increased from 0 to 250 kg ha-1 (Figure 2B). Similar trend was observed in panicle number, leaf weight, and culm weight as well (Figures 2C, 2D, 2E). Correlation analysis showed that tiller number, leaf number, stem number, leaf weight, and culm weight were highly correlated to each other (Pearson correlation coefficient r > 0.8, p < 0.01, Table 2). These results indicated that tillering was more sensitive than plant height in response to high N input.
Elevated N input enhanced leaf area, leaf chlorophyll content, panicle length, and 1000-grain weight
Leaf is an important trait in cereal crops, which serves as the main source of photosynthetic products for primary sink (Wang et al., 2012). Flag leaves were reported to be associated with improved 1000-grain weight and panicle length in rice (Lee et al., 2017). In this study, leaf area, leaf chlorophyll content of flag leaves, 1000-grain weight and panicle length from each rice genotype under different N input were characterized. Leaf area of flag leaves was measured at heading stage. Under 0 kg ha-1 N input the leaf area was 21.25 ± 1.06 cm2 and significantly increased to 57.68 ± 7.24 cm2 (p < 0.01) as the N supply increased to 250 kg ha-1 (Figure 3A). Leaf chlorophyll content (SPAD) of flag leaves without N input (Control: 0 kg ha-1) was 30.91 ± 0.63 on average, which significantly increased by 19.9% to 37.05 ± 1.17 under 180 kg ha-1 (p < 0.01, Figure 3B). However, the leaf chlorophyll content under N input of 250 kg ha-1 only increased by 4.99% compared with that under 180 kg ha-1 (p < 0.05), suggested the maximum leaf chlorophyll content could be expected within small N input regime around 250 kg ha-1.
To better understand the impact of high N input on yield, panicle length and 1000-grain weight was studied among these three rice genotypes. Under 0 and 120 kg ha-1 N input, the panicle length was almost the same (22.95 ± 0.65 and 23.39 ± 0.45 cm, respectively), and significantly increased to 24.13 ± 0.51 and 25.20 ± 0.51 cm under 180 and 250 kg ha-1, respectively (p < 0.01, Figure 3C). The average 1000-grain weight under N input of 0 kg ha-1 was 20.93 ± 2.30 g, which was slightly improved by 8.27% to 22.66 ± 2.48 g under 180 kg ha-1 (p < 0.01), but under 250 kg ha-1 N input 1000-grain weight decreased to 21.26 ± 2.76 g, however nonsignificant with that of moderate N input (180 kg ha-1), indicating that high N input had limited impact on 1000-grain weight.
(A) Nitrogen content in different tissues (stem, seed, leaf and root) under moderate N dose (180 kg ha-1), One-way ANOVA; (B) Nitrogen content in stem, seed, leaf and root under different N doses, Two-way ANOVA; (C) Nitrogen content in different tissues among Q1, Q681, and HHZ, One-way ANOVA. Control: 0 kg N ha-1; Low: 120 kg N ha-1; Moderate: 180 kg N ha-1; High: 250 kg N ha-1. Q1: 'Quangliangyou 1', Q681: 'Quangliangyou 681', HHZ: 'Huanghuazhan'. Significance of 0.05 and 0.01 probability level is indicated by lower-case and uppercase letters on top of error bar (standard error). These experiments were performed by three independent biological replicates.
Figure 1 Nitrogen distribution in different tissues.
Table 2 Correlation between different phenotypic indexes.
*Pearson correlation efficient larger than 0.80 were highlighted by yellow; ** p value < 0.01 were indicated by red.
(A) - (E) Plant height (cm), tiller number, leaf weight (g), panicle number and culm weight (g). Different N inputs are indicated by control (0 kg ha-1), low (120 kg ha-1), moderate (180 kg ha-1), and high (250 kg ha-1). Lower-case and uppercase letters on the top of error bar (standard error) represent significant level of 0.05 and 0.01 respectively by One-way ANOVA test. These experiments were performed by three independent biological replicates.
Figure 2 Alteration of plant growth related indexes in response to different N input.
Impact of elevated N input on grain quality
Grain quality is one of the major objectives apart from yield in plant breeding, and in our study N input showed significant influence on rice plant growth and N content partitioning in different organs, grain quality under different N doses was studied as well. The overall quality index, ranged from 61.53% to 72.5%, did not change under low and moderate N doses, but significantly elevated at 250 kg ha-1 (p < 0.01, Figure 4A). Notably, the quality index of grain seed without N input (Control) was 72.06 ± 1.11%, which was significantly higher than low and moderate N input (120 and 180 kg ha-1 respectively), indicating that the N input reduced grain quality. As for starch, amylose was 14.19% ~ 17.88%, which is slightly higher than amylopectin under different N input, but both of them did not show significant difference among different N input (p > 0.05, Figures 4B, C). Fatty acid content of grain seed was 11.59% when no N was applied, and fatty acid content was significantly changed under 120 and 180 kg ha-1, but elevated significantly by 14.8% under 250 kg ha-1 (Figure 4D), indicating the higher N input exceed 180 kg ha-1 is beneficial for fatty acid accumulation. Interestingly positive correlation was observed between fatty acid content and leaf chlorophyll content (Pearson correlation coefficient r = 0.724, p < 0.01, Table 2). In terms of seed protein content, it was found to be highly correlated to seed N content (Pearson correlation coefficient r = 0.812, p < 0.01, Table 2), and higher seed protein content in Q681 was observed when high N dose was applied, but not in Q1 and HHZ (Table 3).
A-D: Leaf area (cm2), leaf chlorophyll content (SPAD), panicle length (cm) and 1000-grain weight (g). Different N inputs are indicated by control (0 kg ha-1), low (120 kg ha-1), moderate (180 kg ha-1), and high (250 kg ha-1). Lower-case and uppercase letters on the top of error bar (standard error) represent significant level of 0.05 and 0.01 respectively by One-way ANOVA test. These experiments were performed by three independent biological replicates.
Figure 3 Leaf and panicle characteristics in response to different N input.
GS activity variance to different N input
Urea is an organic fertilizer made of two amine group and a carbonyl group, which could be broken up and converted into ammonium in face of water (Kleiner, 1981). In higher plants, the ammonium ions (NH4 +) are taken up by the roots and then assimilated into amide residue of glutamine (Gln) catalyzed by glutamine synthetase (GS). The Gln is then converted into glutamate (Glu) by glutamate synthase (GOGAT) (Lea and Miflin, 1974; Tabuchi et al., 2007; Gaur et al., 2012). As OsGS1;1 and OsNADH-GOGAT2 were mainly expressed in vascular tissues of mature leaf blades (Tabuchi et al., 2007; Tamura et al., 2011), the expression pattern of these two genes was investigated in our study. OsGS1;1 in flag leaves expressed at very low level (0.33 ± 0.03, normalized by geometric average expression level of actin, ubiquitin, and GAPDH) in control flag leaves. OsGS1;1's expression level was significantly increased by ~ 11.2 fold to 3.70 ± 0.47 under low N input at 120 kg ha-1 compared with control, and maintained unchanged when the N input at 180 kg ha-1 (p > 0.05, Figure 5A), but OsGS1;1 decreased slightly at 250 kg ha-1 in all three genotypes tested (p < 0.05, Figure 5A, Table 3). This trend could be observed in the enzymatic activity of GS in Q681 and HHZ, while in Q1 the GS activity was higher in control but decreased when extra N was applied (Figure 5C), showed a genotype dependent manner. In flag leaves, the OsNADH-GOGAT2 was expressed highly in control (0 kg ha1) but decreased dramatically under low and moderate N input, however high N input could induce the OsNADH-GOGAT2 slightly (Figure 5B). With regard to each rice genotype, OsGS1;1 and OsNADH-GOGAT2 showed similar expression pattern (Figures 5D, E).
Grain quality index comparison under different nitrogen doses: protein content (A), amylopectin content (B), amylose content (C), fatty acid content (D) (Two-way ANOVA, n = 9). Control: 0 kg N ha-1; Low: 120 kg N ha-1; Moderate: 180 kg N ha-1; High: 250 kg N ha-1. Significance of 0.05 and 0.01 probability level is indicated by lower-case and uppercase letters on top of error bar (standard error). These experiments were performed by three independent biological replicates.
Figure 4 Grain quality indexes under different nitrogen input.
Response pattern to elevated N input among rice genotypes
Generally, HHZ has less 1000-grain weight than that of Q1 and Q618 under any N input conditions. When no extra N was applied (control), most of the phenotypic indexes related to plant growth, N partitioning and grain quality were similar among Q1, Q681, and HHZ, except that HHZ had less amylose content than Q681 (Table 3). And with the increase of N input, phenotypic indexes difference among rice genotypes expanded (5, 7, and 10 phenotypic indexes under 120, 180, and 250 kg ha-1, respectively). Overall HHZ showed more tiller and panicle number, larger plant height, higher N content in root, leaf and culm, Q681 had better performance in tiller number and leaf N content, while Q1 were outstanding in panicle number, leaf area, and plant height (Table 3). These results indicated that although different rice genotype showed similar response pattern to N input, each genotype varied a lot in certain phenotypic indexes.
DISCUSSION
Nitrogen content in leaf, culm, seed and root were all significantly elevated under high N input (250 kg ha-1), however the elevated N preferentially deposited in seed and leaf rather than culm and root (Figure 1), which is consistent with the report by Ye et al. (2014). The high N content in leaf could be explained by the increased OsGS1;1, OsNADH-GOGAT2, and GS activity in flag leaves, which probably contributed to the N accumulation in leaf (Li et al., 2016). Leaf is the organ where photosynthesis takes place, and the N content in leaf is closely related to photosynthetic process, and hence correlates to crop yield (Ata-Ul-Karim et al., 2017). The increase of N content in leaves and accompanied by the expansion of leaf area under high N input probably promoted the CO2 assimilation and finally for plant growth and reproductive growth. This was further evidenced by the high positive correlation among N content in leaf, culm and seed, and also the increase of tiller number, plant height, panicle number, leaf weight, and culm weight (Table 3). Although the elevation of plant height and tiller number by high N input in rice has been documented in different studies (Gaur et al., 2012; Singh et al., 2014), in our study the plant height maximized when the N input was 180 kg ha-1, and further increase of N input inhibited the plant to grow vertically, whilst the tiller number and panicle number continued increasing under N input of 250 kg ha-1. Probably inner regulation process might exist in rice to prevent the plant height from growing taller and consequent lodging.
With respect to grain quality, high N input caused decrease in amylopectin content, but increase in the seed protein content, while amylose and fatty acid content remained unaffected (Figure 4). Previously it has been documented that amylose content of super rice cultivar could be decreased by applying N input (Wei et al., 2014). This is probably because that the N input adopted in Wei' s research was 300 kg ha-1, which was higher than 250 kg N ha-1 in our study. These results implied that grain quality is a kind of seed trait that could be improved by breeding with high N input.
The assimilation of ammonium ions in plant mainly depends on the cooperation between GS and GOGAT, which served as the key catalytic step during glutamate synthesis (Lea and Miflin, 1974). The GS expression under high N input has been somewhat inhibited but GOGAT was slightly induced, this was possibly because that high N could induce the conversion of glutamine to glutamate. This was consistent with the observation of the elevated raw seed protein content under high N input (Figure 4A).
Table 3 Multiple comparison among different rice genotypes in terms of different phenotypic and molecular indexes.
*SE: Standard error; **Comparison with different among rice genotypes were highlighted in red. Significance of 0.05 and 0.01 probability level is indicated by lower-case and uppercase, respectively
Relative expression of OsGS1;1 (A) and OsNADH-GOGAT2 (B) were normalized against actin, ubiquitin, and GAPDH. (C) Average GS activity [A/(mg × h)] among three rice genotypes under different N input. Best fit nonlinear regression curve was calculated by third order polynomial (cubic), R2 was shown by the side of each curve. Relative expression of OsGS1;1 (D) and OsNADH-GOGAT2 (E) in each rice genotype under different N input. Control: 0 kg ha-1; Low: 120 kg ha-1; Moderate: 180 kg ha-1; High: 250 kg ha-1. Q1: 'Quanliangyou 1', Q681: 'Quanliangyou 681', HHZ: 'Huanghuazhan'. Significance of 0.05 and 0.01 probability level is indicated by lower-case and uppercase letters on top of error bar (standard error). These experiments were performed by three independent biological replicates.
Figure 5 OsGS1;1 and OsNADH-GOGAT2 expression level and glutamine synthetase (GS) activity in flag leaves under different N inputs.
We utilized three distinct rice genotypes, 'Quanliangyou 1', 'Quanliangyou 681' and 'Huanghuazhan', for response pattern analysis under high N input. Although different rice genotypes response similarly with the increase of N input, there was a big difference among rice genotypes (Table 3), suggesting that different N management practice was necessarily needed for these cultivars for better production. Unfortunately, no interaction was observed between rice genotype and different N input treatments (data not shown), which contradicted with the results by Singh et al. (2014). This is probably the result of the higher N input adopted (250 kg ha-1) in our study. Taken together, these results implied that the response pattern of N partitioning, plant growth, OsGS1;1 and OsNADH-GOGAT2 expression to high N input was independent of the genotype.
CONCLUSIONS
Overall, the N content in leaf, culm, seed and root had all been increased but preferentially deposited in leaf and culm, which led to the increase in leaf area, leaf chlorophyll content, leaf weight and culm weight under high N input. The plant growth has also been significantly promoted in terms of tiller number, panicle number and length, but not the plant height and 1000-grain weight under high N input. In contrast, high N input did have negative impact on amylopectin content, while fatty acid and amylose content remained stable, but seed protein increased. Although three rice genotypes were investigated, the response pattern of all these phenotypic characteristics to high N input was independent of rice genotype. Taken together, the overview of N partitioning and the impact on plant growth and key genes expression involved in glutamate biosynthesis provided physiological and molecular foundation for rice breeding and cultivation.
