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Keywords:

  • ear;
  • genetics;
  • maize;
  • nitrogen (N);
  • quantitative trait loci (QTLs);
  • variability

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • Quantitative trait loci (QTLs) for the main steps of nitrogen (N) metabolism in the developing ear of maize (Zea mays L.) and their co-localization with QTLs for kernel yield and putative candidate genes were searched in order to identify chromosomal regions putatively involved in the determination of yield.
  • During the grain-filling period, the changes in physiological traits were monitored in the cob and in the developing kernels, representative of carbon and N metabolism in the developing ear. The correlations between these physiological traits and traits related to yield were examined and localized with the corresponding QTLs on a genetic map.
  • Glycine and serine metabolism in developing kernels and the cognate genes appeared to be of major importance for kernel production. The importance of kernel glutamine synthesis in the determination of yield was also confirmed.
  • The genetic and physiological bases of N metabolism in the developing ear can be studied in an integrated manner by means of a quantitative genetic approach using molecular markers and genomics, and combining agronomic, physiological and correlation studies. Such an approach leads to the identification of possible new regulatory metabolic and developmental networks specific to the ear that may be of major importance for maize productivity.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Nitrogen (N) fertilization and the development of new plant breeding strategies, such as the production of hybrids, have been the two most powerful tools for increasing kernel yield (KY), particularly in maize (Moose & Mumm, 2008). Nowadays, a combination of both agricultural and economic constraints means that farmers must optimize the application of N fertilizers to prevent pollution by nitrates and the release of nitric oxides to the atmosphere, whilst, at the same time, preserving their economic margin (Hirel et al., 2007a). Cereal kernels provide 60% of the world’s nutrition, either directly in the human diet or indirectly as animal feed. Worldwide, maize is the most important single crop, comprising 35% of overall cereal production. Recent improvements in maize yield of c. 1% each year from 1955 onwards have been estimated to be a result of improvements in agronomic practices (40%) and genetic gains (60%) (Hallauer & Carena, 2009). Maize is not only recognized as a major crop, but also as a model species that is well adapted for fundamental research into the understanding of the genetic basis of yield performance to improve kernel productivity and quality in terms of nutritional value to feed the world’s population (Hirel et al., 2007b).

Therefore, it has become of major importance to select for maize genotypes that take up and utilize N in the most efficient way for silage and kernel production. To reach such an objective, various complementary approaches, including conventional breeding, molecular genetics, whole-plant physiology and the use of improved or alternative farming techniques, have been developed (Hirel et al., 2007b, 2011; Moose & Below, 2009). Whatever the mode of N fertilization, an increased knowledge of the mechanisms controlling plant N economy is essential to improve nitrogen use efficiency (NUE) and to reduce excessive input of fertilizers, whilst maintaining an acceptable yield. Using plants grown under agronomic conditions at low and high N mineral fertilizer input, whole-plant and physiological studies have been combined with gene, protein and metabolite profiling. This has allowed the development of a comprehensive picture depicting the different steps of N uptake, assimilation and recycling in maize to produce either biomass in vegetative organs or proteins in storage organs (Hirel & Lea, 2011).

Moreover, the development of quantitative genetic studies associated with the use of molecular markers has become a powerful tool to identify putative candidate genes involved in the genetic variation of complex physiological traits, such as NUE (Hirel et al., 2007a). Furthermore, the availability of the maize genome sequence (Schnable et al., 2009) and of more detailed genetic maps allows the precise location of chromosomal regions and, ultimately, the key genes influencing the expression of desired traits. In turn, this strategy will be of great potential for plant breeders to carry out marker-assisted selection for improved NUE in relation to yield, particularly under low fertilization input (Ribaud & Hoisington, 1998; Moose & Below, 2009).

Recently, the main steps of N metabolism in the developing ear of the two maize lines F2 and Io have been characterized (Cañas et al., 2009). During the kernel-filling period, the changes in metabolite concentration, enzyme activities and transcript abundance for marker genes of amino acid synthesis and interconversion in both the cob and kernels are strongly dependent on the genetic background (Cañas et al., 2009). This has given rise to the conclusion that, in maize, there is genetic and environmental control of N metabolism not only in vegetative source organs, but also in reproductive sink organs, which could cooperatively contribute to plant productivity.

This preliminary study prompted us to develop a quantitative genetic approach, similar to that already performed on vegetative organs (Hirel et al., 2001; Gallais & Hirel, 2004) and on germinating kernels (Limami et al., 2002), to obtain more information on the genetic basis of N metabolism in the developing ear and its possible relationship to yield. The aim of such a study was to identify coincidences between QTLs for agronomic traits and QTLs for physiological traits related to N metabolism, in both the cob and developing kernel, during the kernel-filling period. In addition, co-mapping of agronomic and physiological QTLs with genes encoding enzymes involved in N and carbon (C) metabolism, and other metabolic and developmental processes, was also investigated in order to provide a genetic meaning for the QTLs.

To further explore the possible relationship between N metabolism in the developing ear, whole-plant and organ-specific NUE-related traits and KY traits, correlation studies were carried out using the entire available dataset for these traits, measured either in the present studies or gathered from previously published work by Coque et al. (2008). These correlation studies were performed on datasets obtained from different years of experimentation in order to overcome potential environmental effects and to strengthen the significance of the correlations between the different agronomic and physiological traits.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Plant material for agronomic and physiological studies

Data obtained by Hirel et al. (2001) and Coque et al. (2008) served as an agronomic reference for the studies performed on the developing ear of maize (Zea mays L.). A total of 100 recombinant inbred lines (RILs) and the two parental lines Io and F2 were grown in the field over two consecutive years, 2008 and 2009, as described by Bertin & Gallais (2000, 2001) at the Institut National de la Recherche Agronomique, Versailles, France (48°48.133′N, 2°04.942′E). The phenotypes of the two parental lines and a number of RILs exhibiting a large genetic diversity in terms of vegetative and ear biomass and structure are shown in Supporting Information Fig. S1(a,b). The soil was a deep silt loam without any stone. The level of N fertilization was 175 kg N ha−1 and N provided by the soil was estimated at c. 60 kg ha−1. Both phosphorus (P205) and potassium (K20) were also applied at 100 kg ha−1. The RILs were grown side by side in separate lines of 25 plants, in two separate blocks of 25 m × 25 m, with an outside border area of 3 m (line UH002) included in each block. Plants were sown on 6 May in both 2008 and 2009. Two ears from two individual plants for each RIL were harvested per block for the physiological studies, making four replicates per sample point and year. For all the RILs, the ears were harvested 14 d after silking (14 DAS), a time corresponding to the beginning of the kernel-filling period (Méchin et al., 2007), when genetic variability for the different measured traits in the two parental lines, Io and F2, was optimal (Cañas et al., 2009). Moreover, this date corresponds to major physiological changes in terms of amino acid biosynthesis and interconversion (Seebauer et al., 2004; Cañas et al., 2009). For the ears, the husk and shank were discarded and the remainder was separated into cobs and kernels (including pedicels) (Fig. S1c). Cobs and kernels were immediately frozen in liquid N2. All frozen tissues were reduced to a homogeneous powder and stored at − 80°C until required for the metabolite and enzyme activity measurements. All the harvesting of fresh material was carried out concomitantly between 10:00 h and 13:00 h. At maturity, the total ear number per plant was determined on 10 plants of each line and block. Ten ears of each line and block were harvested and their lengths measured.

Several traits were measured in the cob and in developing kernels at 14 DAS. Physiological traits were as follows: total free amino acid concentration (AAK for kernels and AAC for the cob), dry weight/fresh weight (DW : FW) ratio (DWFWK for kernels and DWFWC for the cob), glutamate dehydrogenase activity (GDHK for kernels and GDHC for the cob), glutamine synthetase activity (GSK for kernels and GSC for the cob), soluble protein concentration (PROTK for kernels and PROTC for the cob), starch concentration (STARK for kernels and STARC for the cob), sugar concentration (SUGK for kernels and SUGC for the cob) and the concentration of specific amino acids, including alanine, aspartate, asparagine, glutamate, glutamine, glycine, proline, serine and threonine (ALAK, ASPK, ASNK, GLUK, GLNK, GLYK, PROK, SERK and THRK, respectively, for kernels and ALAC, ASPC, ASNC, GLUC, GLNC, GLYC, PROC, SERC and THRC, respectively, for the cob). Two additional phenotypic traits were measured: the total number of ears per plant (EARN) and the ear length (EARL).

The QTLs for the agronomic trait KY and its components (kernel number per plant (KN) and thousand kernel weight (TKW)), used previously to identify co-localization with ear physiological traits (already listed), were those originally described by Hirel et al. (2001) using the same RIL population. The additional agronomic and NUE traits measured on line per se or in test cross Io × F2 derived RIL populations used for the correlation studies were obtained as described in Coque et al. (2008). Only the traits showing significant correlation with physiological traits measured in the present study are listed in Table 1.

Table 1.   Nitrogen use efficiency (NUE)-related and agronomic traits listed in alphabetical order and used to perform correlations with the developing ear physiological traits
TraitAbbreviation
  1. The agronomic and NUE traits correspond to those previously described by Coque et al. (2008).

% N from N uptake within kernel%NupG
Anthesis dateAD
Anthesis–silking interval (SD–AD)ASI
Whole-plant dry matter per plant at silkingDMsilk/pl
Kernel dry matter per plantGDM/pl
Kernel moistureGMoist
Kernel N concentrationGNC
Kernel N yieldGNY
Kernel yield m−2KY
Harvest indexHI
Kernel number per plantKN
Nitrogen concentration at silkingNcsilk
N harvest indexNHI
N nutrition indexNNI
N remobilized (15N method)Nrem
N remobilized (balance method)NremB
N from N uptake within kernelNupG
N utilization efficiencyNute
% of 15N remaining at silking/whole plant 15NRE15NF/M
Silking dateSD
Visual notation of leaf senescence at silking + 10 dSEN
Visual notation of leaf senescence at silking + 45 dSEN1
Visual notation of leaf senescence at maturitySEN2
Silking N uptake per plantSilkNup/pl
Stover dry matter yield per plant at maturityStDM/pl
% of sterile plantsSterile
Stover N per plant at maturityStN/pl
Stover N concentration at maturityStNC
Proportion of post-silking N uptake allocated to kernelstG
Thousand kernel weightTKW
Proportion of remobilized N (balance method)tremB
Proportion of N remobilized corrected by residual post-silking 15N uptaketremC
Whole-plant dry matter per plant at maturityWpDM/pl
Whole-plant N yield at maturityWpNY

Protein extraction, enzyme assays, metabolite extraction and analyses

Protein extraction was carried out on 100 mg of frozen cob and kernel material, as described previously (Cañas et al., 2009). Soluble protein concentration was determined using a commercially available kit (Coomassie Protein assay reagent; Biorad, München, Germany), with bovine serum albumin as a standard (Bradford, 1976). Enzymes were extracted from frozen material stored at − 80°C. All extractions were performed at 4°C. GS activity was measured according to the method of O’Neal & Joy (1973). GDH (NADH-GDH) was measured in the direction of glutamate synthesis as described by Turano et al. (1996), except that the extraction buffer was the same as for GS. Amino acids were extracted from frozen tissue with 2% 5-sulfosalicylic acid (100 mg FW ml−1) (Ferrario-Méry et al., 1998). Total free amino acids were determined by the Rosen colorimetric method using glutamine as a standard (Rosen, 1957). The composition of individual amino acids was performed by ion exchange chromatography, followed by detection with ninhydrin using an AminoTac JLC-500/V amino acid analyzer, according to the instructions of the manufacturer (JEOL, Tokyo, Japan). Sucrose, glucose, fructose and starch were extracted from aliquots (100 mg FW ml−1) of fresh plant material using a three-step ethanol–water procedure, as described by Lemaître et al. (2008). Soluble sugars (glucose, fructose and sucrose) were measured enzymatically using a commercially available kit assay (Roche, Boehringer Mannheim, Mannheim, Germany). Starch concentration was determined as described by Ferrario-Méry et al. (1998).

Statistical analysis

SAS software was used for the ANOVA, to calculate the heritability and Pearson correlation (SAS, 1990, SAS procedures guide, version 6, 3rd edn). The statistical model for the ANOVA was a mixed model in which we used fixed effects for the year and random effects for the lines or the line × year interaction. For each trait considered, the ANOVA allowed the estimation of the genetic variance among lines (VG), the line × year interaction variance (VGY) and residual variance (VE), from which the heritability at the level of means is derived: h2 = VG/(VG + VGY/2 + VE/4) (Table 2). In order to determine the importance of the genotypic and environmental interactions (G × E), a Fisher’s test was performed (Table S3). As the G × E effect was much lower than the genotypic effect over the 2 yr of experimentation, both the data presented and the detection of QTLs correspond to the mean of the 2-yr experimentation. The phenotypic correlations, and not the genotypic correlations, between the agronomic experiment and the physiological experiment were considered, because the accuracy is greater on the phenotypic than on the genotypic correlations. Moreover, as the two experiments are statistically independent, the phenotypic covariances between means are also the genotypic covariances. For the physiological traits, a heat map of the Pearson correlation matrix was obtained using Excel software. Network diagrams of the Pearson correlation matrix were obtained using Cytoscape 2.8 software (Smoot et al., 2011) with the network analyzer plug-in (Assenov et al., 2008).

Table 2.   Heritabilities for the ear physiological and phenotypic traits
Kernelsh2Cobh2
  1. AA, amino acid; DW, dry weight; FW, fresh weight; GDH, glutamate dehydrogenase; GS, glutamine synthetase.

  2. Results are the mean of a 2-yr field experiment.

Alanine0.85Alanine0.74
Asparagine0.90Asparagine0.67
Aspartate0.62Aspartate0.76
DW : FW0.80DW : FW0.81
GDH activity0.75GDH activity0.83
Glutamate0.67Glutamate0.65
Glutamine0.76Glutamine0.65
Glycine0.89Glycine0.75
GS activity0.84GS activity0.76
Proline0.83Proline0.47
Serine0.91Serine0.85
Sol. proteins0.43Sol. proteins0.77
Sol. sugars0.86Sol. sugars0.82
Starch0.79Starch0.83
Threonine0.89Threonine0.69
Total AA0.78Total AA0.71
Earh2Mean kernel0.74
Ear length0.88Mean cob0.79
Ear number0.77Mean ear0.82

Genetic map

In the present study, the genetic map originally constructed using the RILs derived from the crossing between Io × F2 (Causse et al., 1996), and further updated by Coque et al. (2008), was used. This reference map contains 410 loci covering 2147 cM. A subset of 203 markers, well distributed along the chromosomes, was used for QTL detection. The mean interval between two markers, depending on the chromosome, varies from 8 to 18 cM. As a result of the choice of the subset of markers, it was not possible to reduce the maximum values of such an interval by adding more markers.

QTL detection

QTLs were detected using the Plab-QTL software (Utz & Melchinger, 1995) following simple interval mapping. Only QTLs with a logarithm of the odds ratio (LOD) score > 2 were considered (Lander & Botstein, 1989). To represent a QTL on the map taking into account the error in the location, chromosomal regions corresponding to a LOD greater than the maximum LOD – 1 are shown, which is not a true confidence interval, and is called a LOD − 1 interval. Two QTLs for different traits are declared as coincident when their LOD − 1 intervals overlap. A coincidence is said to be positive when there is coincidence of favorable (or unfavorable) alleles for both traits. A coincidence is said to be negative when there is coincidence of a favorable allele for one trait with an unfavorable allele for the other trait. For each trait, the percentages of phenotypic (R2p) and genotypic (R2g) variation identified by the markers were calculated. R2g was equal to R2p divided by the heritabilities (h2). In addition, for each QTL detected, the estimated additive effect (half of the difference between the estimated values of the two homozygous genotypes at the QTL) is presented.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

QTL detection for ear physiological traits and coincidence with yield traits and candidate genes

QTLs (with LOD ≥ 2) for the main physiological traits representative of C and N metabolism, in both the cob and developing kernels, were detected using the dataset obtained from two consecutive year experiments. The QTLs detected over the 2-yr experiment are presented for the kernels in Table 3, and for the cob and the ear physiological and phenotypic traits in Table 4. The position of the different QTLs on the maize restriction fragment length polymorphism (RFLP) map is shown in Fig. 1 for the developing kernel traits, and in Fig. 2 for the cob and ear phenotypic traits.

Table 3.   Quantitative trait loci (QTLs) detected for the different physiological traits measured in developing kernels 14 d after silking
TraitR2paR2gbChrcLocationConfidence intervaldLODAdditive effecte
Marker+ cMDistance (cM)
  1. AA, amino acid; GDH, glutamate dehydrogenase; GS, glutamine synthetase; LOD, logarithm of the odds ratio.

  2. Results are the mean of a 2-yr field experiment.

  3. aPercentage of phenotypic variance explained by the markers.

  4. bPercentage of genotypic variance explained by the markers. R2g = R2p/h2.

  5. cChromosome number.

  6. dApproximate confidence interval (LOD − 1).

  7. eAdditive effect with positive value for the parental line Io.

Total AA0.150.191SC143B9173–352.6826.11
1SC282A22175167–1873.2333.421
GDH activity0.200.271SC143B8168–233.510.151
1UMC39C9219207–2303.030.145
GS activity0.160.195UMC39B3153138–1643.96− 0.211
Sol. sugars0.100.1210SC348A19181–1042.36126.237
Serine0.090.101SC61_B5113103–1214.224.509
1UMC83A1139134–1503.524.170
Glycine0.140.162SC108156350–743.01− 0.839
2UMC16A78174–972.63− 0.771
3SC224A86549–802.970.943
Proline0.240.295PGM2-I115038–642.723.573
10SC170B37353–833.13.533
Alanine0.310.363SC224A86552–774.9510.692
5PGM2_I155446–643.517.551
5SC154107869–933.187.586
Table 4.   Quantitative trait loci (QTLs) detected for the different physiological and phenotypic traits measured in the cob 14 d after silking
TraitR2paR2gbChrcLocationConfidence intervaldLODAdditive effecte
Marker+ cMDistance (cM)
  1. AA, amino acid; DW, dry weight; FW, fresh weight; GDH, glutamate dehydrogenase; GS, glutamine synthetase; LOD, logarithm of the odds ratio.

  2. Results are the mean of a 2-yr field experiment.

  3. aPercentage of phenotypic variance explained by the markers.

  4. bPercentage of genotypic variance explained by the markers. R2g = R2p/h2.

  5. cChromosome number.

  6. dApproximate confidence interval (LOD − 1).

  7. eAdditive effect with positive value for the parental line Io.

DW : FW0.090.126SC2241144–232.140.014
Total AA0.090.125SC15437157–822.1210.897
GDH activity0.090.119BNL51024739–612.810.091
GS activity0.130.174SC29238665–962.97− 0.179
Sol. proteins0.120.164SC431106956–842.98− 2.13
Sol. sugars0.080.101UMC6788872–1082.57− 166.452
1UMC83A4142128–1612.44− 159.774
Aspartate0.100.135SC258A26194183–2092.342.459
Serine0.280.331SC614112100–1222.281.354
2SC10845246–642.93− 1.679
5SC168126650–812.431.549
Asparagine0.100.159SC143A411384–1302.43− 3.304
Glutamine0.110.175SC168116557–763.254.005
Glycine0.170.222SC10835143–712.78− 0.269
10SC412A58173–882.470.253
Ear number0.150.193UMC6048557–953.420.308
Ear length0.100.119BNL142212698–1432.6− 7.305
image

Figure 1. Coincidences between quantitative trait loci (QTLs) for developing maize kernel physiological traits and traits related to kernel yield and its components. The locations of the QTLs for physiological traits on the maize genetic map are indicated by vertical bars with a dot at both ends: black, amino acid concentration; dark blue, glutamate dehydrogenase activity; green, glutamine synthetase activity; yellow, soluble sugar concentration. The locations of the QTLs for specific amino acids are indicated by vertical bars: black, alanine concentration; yellow, glycine concentration; red, serine concentration; brown, proline concentration. The locations of QTLs for yield and its components are indicated by dotted vertical bars: brown bars, kernel yield (KY); green bars, kernel number (KN per plant); blue bars, thousand kernel weight (TKW). A favorable allele from the parental line Io is indicated by (+) and an unfavorable allele from the parental line F2 is indicated by (−). Coincident QTLs between the cob and developing kernels are shown with an open oval symbol (see also Fig. 2). The positions of the loci for genes encoding enzymes involved in nitrogen (N) or carbon (C) assimilation are indicated in bold italics: AlaAT14 (alanine aminotransferase 1–4); AspAT1.1, AspAT1.2, AspAT1.3, AspAT2.1 and AspAT2.2 (aspartate aminotransferase 1.1–2.1); AS1–4 (asparagine synthetase 1–4); Fd-GOGAT (ferredoxin-dependent glutamate synthase); GDH1 and GDH2 (glutamate dehydrogenase 1 and 2); GS1.1–1.5 (cytosolic glutamine synthetase 1–5); Inv (invertase); NADH-GOGAT 13 (NADH glutamate synthase 1–3); NR (nitrate reductase); PEPC (phosphoenolpyruvate carboxylase); P5CS13 (pyrroline-5-carboxylate synthetase 1–3); SHMT15 (serine hydroxymethyltransferase 1–5).

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image

Figure 2. Coincidences between quantitative trait loci (QTLs) for the maize cob and phenotypic ear traits and traits related to kernel yield and its components. The locations of the QTLs for physiological traits on the maize restriction fragment length polymorphism (RFLP) genetic map are indicated by vertical bars with a dot at both ends: black, amino acid concentration; red, dry weight (DW) : fresh weight (FW) ratio; blue, ear number; brown, ear length; dark blue, glutamate dehydrogenase activity; green, glutamine synthetase activity; pink, protein concentration; yellow, soluble sugar concentration. The locations of the QTLs for specific amino acids are indicated by vertical bars: pink, aspartate concentration; green, asparagine concentration; blue, glutamine concentration; yellow, glycine concentration; red, serine concentration; brown, proline concentration. The locations of ear phenotypic QTLs are indicated by oval symbols: blue, ear number; brown, ear length. The locations of the QTLs for yield and its components are indicated by dotted vertical bars: brown bars, KY (kernel yield); green bars, KN (kernel number per plant); blue bars, TKW (thousand kernel weight). A favorable allele from the parental line Io is indicated by (+) and an unfavorable allele from the parental line F2 is indicated by (−). Coincident QTLs between cob and kernels are shown with an open oval symbol (see also Fig. 1). The positions of the loci for genes encoding enzymes involved in nitrogen (N) or carbon (C) assimilation are indicated in bold italics: AlaAT14 (alanine aminotransferase 1–4); AspAT1.1, AspAT1.2, AspAT1.3, AspAT2.1 and AspAT2.2 (aspartate aminotransferase 1.1–2.1); AS1–4 (asparagine synthetase 1–4); Fd-GOGAT (ferredoxin-dependent glutamate synthase); GDH1 and GDH2 (glutamate dehydrogenase 1 and 2); GS1.1–1.5 (cytosolic glutamine synthetase 1–5); Inv (invertase); NADH-GOGAT 13 (NADH glutamate synthase 1–3); NR (nitrate reductase); PEPC (phosphoenolpyruvate carboxylase); P5CS13 (pyrroline-5-carboxylate synthetase 1–3); SHMT15 (serine hydroxymethyltransferase 1–5).

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The heritabilities for the physiological traits measured in the developing kernels ranged from 0.91 for serine concentration to 0.43 for soluble protein concentration (Table 2). For the cob, the highest heritability was 0.85 for serine concentration and the lowest was 0.47 for proline concentration. The heritabilities for the number of ears and ear length were 0.77 and 0.88, respectively (Table 2).

For the developing kernels, the following QTLs were identified: two for total free amino acid concentration on chromosome 1, two for GDH activity on chromosome 1, one for GS activity on chromosome 5, one for soluble sugar concentration on chromosome 10, two for serine concentration on chromosome 1, three for glycine concentration (two on chromosome 2 and one on chromosome 3), two for proline concentration (on chromosome 5 and on chromosome 10), and three for alanine concentration (one on chromosome 3 and two on chromosome 5). The lowest percentages of phenotypic and genetic variation identified by the QTLs for physiological traits were 9% and 10%, respectively, for serine concentration. The highest were 31% and 36%, respectively, for alanine concentration (Table 3).

For the cob, the following QTLs were identified: one for the DW : FW ratio on chromosome 6, one for total free amino acid concentration on chromosome 5, one for GDH activity on chromosome 9, one for GS activity on chromosome 4, one for soluble protein concentration on chromosome 4, two for soluble sugar concentration on chromosome 1, one for aspartate concentration on chromosome 5, three for serine concentration on chromosomes 1 and 5, one for asparagine concentration on chromosome 9, one for glutamine concentration on chromosome 5, and two for glycine concentration on chromosome 2 and chromosome 10. For these QTLs, the lowest percentages of phenotypic and genetic variation were 8% and 10%, respectively, for soluble sugars. The highest were 28% and 33%, respectively, for serine concentration (Table 4). Interestingly, a QTL for serine concentration located on chromosome 1 and a QTL for glycine concentration located on chromosome 2 were detected in both the cob and developing kernels (Figs 1, 2). For the number of ears, a QTL was identified on chromosome 3, and, for ear length, a QTL was located on chromosome 9.

A number of co-localizations between QTLs for the physiological traits of developing kernels and QTLs for KY and its components (Hirel et al., 2001) and putative candidate genes were identified, as shown in Fig. 1.

On chromosome 1, two QTLs for serine, a QTL for total free amino acid concentration and a QTL for the GDH activity in the kernels co-localized with QTLs for yield (KN and KY). The parental line Io provided the favorable allele for the four physiological QTLs and for the two yield QTLs. On this chromosome, an interesting co-localization with a candidate gene encoding one of the two GDH isoenzymes (GDH1) was found between the corresponding enzyme activity and two QTLs for yield.

On chromosome 2, a QTL for glycine concentration partially overlapped with a QTL for yield (TKW). For both QTLs, the favorable allele originated from line F2. On chromosome 3, a co-localization between two physiological QTLs (one for glycine concentration and one for alanine concentration) and a QTL for KY was identified. For these three QTLs, the positive allele was from the parental line Io. A putative candidate gene was found in the chromosomal region containing these QTLs, namely that encoding serine hydroxymethyltransferase (SHMT3). On chromosome 5, a QTL for GS activity partially overlapped with QTLs for KY and TKW. On this occasion, the favorable allele for yield was provided by line Io, whereas, for GS activity, it was provided by line F2, thus having a negative effect. Although no QTLs were found to co-localize with yield on chromosome 5, there was a co-localization between the alanine concentration of the kernels and the gene encoding an alanine aminotransferase (AlaAT1), the enzyme catalyzing the interconversion of this amino acid.

Several QTLs detected for the physiological traits of the cob also co-localized with QTLs for yield (Fig. 2). On chromosome 1, two QTLs for soluble sugar concentration and a QTL for serine concentration were coincident with QTLs for KY, KN and TKW. On chromosome 2, two QTLs for glycine and serine concentrations co-localized with a QTL for TKW. On chromosome 5, the QTL for aspartate concentration partially overlapped with a QTL for TKW and KY. Finally, a QTL for soluble protein concentration was coincident with a QTL for GS activity on chromosome 4.

In the cob, the positive or negative additive effects of the various yield and physiological QTLs showed a more complex distribution pattern compared with that of the developing kernels. For example, on chromososme 2, the favorable allele was provided by line F2 for the coincident QTL, whereas, on chromosome 5, the favorable allele originated from Io. On chromosome 1, the QTL for serine concentration and the QTL for KN had positive additive effects, whereas the QTL for soluble sugar concentration and the QTLs for KN and KY had an opposite negative additive effect. The favorable allele was from line F2 for the two coincident QTLs represented by the soluble sugar concentration and TKW.

In the cob, co-localizations with candidate genes were less obvious when compared with those found between kernels and yield traits. On chromosome 5, a gene encoding an aspartate aminotranferase (AspAT1.2) was close to a QTL for the aspartate concentration in the cob. On chromosome 9, there was a co-localization of a QTL for the cob asparagine concentration, a QTL for the ear length and a gene encoding the enzyme asparagine synthetase (AS4) catalyzing asparagine synthesis. Interestingly, on chromosome 3, a co-localization was detected between a group of QTLs involved in yield determination and a QTL for ear number, all of which were positively controlled by alleles from Io. In this chromosomal region, several QTLs for the glycine and alanine concentrations of the kernel were also found.

Correlations between physiological traits in the developing ear, agronomic traits for yield and traits related to NUE

In order to identify possible functional relationships between the physiological traits measured in the cob or kernel, or between these two parts of the developing ear, their Pearson correlation coefficients were calculated. The coefficients are shown in a graphical manner in Fig. 3(a–c). Their values are presented in Table S1 and visualized in the heat map presented in Fig. 3(d). Together, a total of 561 correlations were detected, only 295 of which were significant ( 0.05). There were 78/120 significant correlations between traits measured in the developing kernel (Fig. 3a) and 98/120 significant correlations between traits measured in the cob (Fig. 3b). Of 256 correlations, only 111 were significant when only the interaction between the traits of the cob and the developing kernel were considered (Fig. 3c).

image

Figure 3. Correlation matrix and network diagrams for maize ear, developing kernel and cob physiological traits. (a–c) Network diagrams showing significant correlations (P < 0.05) between traits based on the calculation of Pearson coefficients. Traits with a larger number of correlations are represented by the largest and darkest red dots. Traits with a smaller number of correlations are represented by smaller and darker green dots. The lines (edges) represent a significant correlation between two traits. Thicker and darker red lines represent the highest positive correlations. Thinner and darker green lines represent the highest negative correlations. (a) Network diagram of the correlations between physiological kernel traits. (b) Network diagram of the correlations between cob physiological traits. (c) Network diagram of the correlations between developing kernels and cob physiological traits. (d) Heat map of the correlation matrix for kernel and cob traits based on the calculation of Pearson coefficients. Darkest red squares, coefficients closest to 1; darkest green squares, coefficients closest to − 1; yellow squares, coefficients closest to 0 (see scale). The group of negative correlations between cob and kernel DW : FW and other cob physiological traits is outlined with a black rectangle, whereas the group of positive correlations between physiological cob traits is outlined with a black triangle. See Materials and Methods section for definitions of abbreviations.

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For the length of the ear, no correlations were found with the physiological traits of either the cob or developing kernels. For this phenotypic trait, only a negative correlation (− 0.211) was found with the number of ears (Table S1, Fig. 3d). By contrast, for the number of ears, only 8/33 significant correlations were found with the physiological traits measured in the developing kernels. Of these, the highest significant correlation (0.422) was with the threonine concentration (Table S1 and Fig. 3a,d).

When the physiological traits of the developing kernels were considered separately, the highest positive significant correlation was found between the serine and glycine concentrations (0.762). For the cob, the highest correlation was found between the total free amino acid and threonine concentrations (0.765). When the interactions between the ear and kernel traits were considered, it was found that the most strongly correlated physiological trait was their serine concentration (0.762), as shown in Fig. 3(c).

The highest negative correlation was found between the soluble sugar concentration and the DW : FW ratio when the kernel and cob traits were analyzed separately (− 0.854 for the kernels and − 0.895 for the cob). The highest negative correlation was found between the soluble protein concentration and the DW : FW ratio when the interaction between the developing kernel and cob traits was analyzed (− 0.750) (Table S1, Fig. 3d).

In order to identify any relationship between the physiological traits measured in the two parts of the developing ear and the agronomic traits related to whole-plant NUE and yield, their Pearson correlation coefficients were calculated. This was carried out using the set of physiological data obtained in the present study and the set of NUE and agronomic traits gathered in the database used previously by Coque et al. (2008). As, in the present investigation, plants were grown under high N fertilization, only the previous agronomic data corresponding to plants grown under the same N regime were used. The 34 yield or NUE traits exhibiting significant correlations with the physiological traits of the ear are presented in Table 1. The correlations obtained for the entire set of traits are shown in Table S2.

Of 1156 correlations, only 263 were significant ( 0.05). For clarity, the investigation focused on the main correlations with a high level of significance (≥ 0.4 or ≤ − 0.4, P < 0.0002) (Table 5 and Fig. 4). Twenty-five correlations with a Pearson coefficient higher than 0.4 were obtained, which were mostly related to the physiological traits measured in the kernels, except for the cob glutamine concentration and the ear number.

Table 5.   Main correlations observed between physiological ear traits, nitrogen use efficiency (NUE)-related traits and agronomic traits corresponding to those previously described by Coque et al. (2008)
 ASPKSERKGLNKGLYKALAKEARNGLNC
  1. See Table 1 for definitions of abbreviations.

AD0.4220.474
DMsilk/pl0.417
GMoist0.4420.443
GNC0.414
GNY0.539
KY0.576
KN0.504
NHI0.5190.437
NNI0.5260.447
Nrem0.433
Nute0.424
RE15NF/M0.4040.401
SEN2− 0.437
SilkNup/pl0.4020.449
StDM/pl0.554
Sterile− 0.405
StN/pl0.556
StNC0.426
WpDM/pl0.585
image

Figure 4. Network diagram showing the main correlations found between physiological traits and agronomic traits related to nitrogen use efficiency (NUE) and yield in maize. Network diagrams show significant correlations ( 0.05) between traits based on positive and negative Pearson coefficients > 0.4 (P < 0.0002). Traits with a larger number of correlations are represented by larger and darker red dots. Traits with a smaller number of correlations are represented by smaller and darker green dots. Lines represent a significant correlation between two traits. Thicker and darker red lines represent the highest positive correlations. Thinner and darker green lines represent the highest negative correlations. See text and Table 1 for definitions of abbreviations.

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In the developing kernels, the alanine and glycine concentrations were the two traits that showed the highest number of significant correlations with the agronomic and NUE-related traits. Among these correlations, that between the alanine concentration and the whole-plant dry matter accumulated at maturity (WpDM/pl) was the highest (Pearson coefficient, 0.585). Several correlations with a Pearson coefficient ranging from 0.42 to 0.57 were also found for a number of yield and NUE traits and the alanine concentration of the developing kernels. The glycine concentration of the developing kernels also showed a number of significant correlations with traits more specifically related to NUE and a trait corresponding to dry matter accumulation at silking (DMsilk/pl). Interestingly, both the glycine and alanine concentrations of the kernels were strongly correlated with the nutrition harvest index (NHI) and the nitrogen nutrition index (NNI).

The highest negative correlation (− 0.437) was obtained between the serine concentration of the developing kernels and the visual annotation of leaf senescence at maturity (SEN2). All the traits exhibiting a positive or negative correlation were interconnected directly or indirectly, except for the percentage of sterile plants (Sterile) and the number of ears, which exhibited an independent negative correlation coefficient of − 0.405.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Previous studies have demonstrated that N metabolism in maize ears is an important component controlling N allocation during the grain-filling period (Seebauer et al., 2004; Cañas et al., 2009, 2010). Moreover, it is well established that N translocation and, presumably, N assimilation in the kernels facilitate the utilization of carbohydrates, thus being a major component in the determination of yield (Below et al., 2000). However, there is a paucity of data on both the physiological and molecular control of this process, although it has been suggested that a strong relationships exists between source and sink organs during kernel filling (Seebauer et al., 2004; Cañas et al., 2010).

Therefore, the present study focused on the identification of key metabolic reactions and candidate genes involved in the control of N assimilation in maize ears by exploiting the genetic variability in a population of maize RILs. In previous investigations, this RIL population allowed the identification of important components of NUE in both vegetative and reproductive organs in relation to yield (Hirel et al., 2001; Limami et al., 2002; Coque et al., 2008).

A number of physiological traits representative of C and N assimilation in both the cob and developing kernels (Cañas et al., 2009) were first measured in the RIL population grown over two consecutive years. As the heritability over the 2-yr experiment was high (0.7–0.8) for most of the physiological traits of both cob and developing kernels, it can be concluded that there is a highly significant genotypic effect for all measured physiological traits. This finding strengthens the power of the QTLs detected for these traits, and indicates that they may represent good putative biological markers to be used in breeding programs (Moose & Mumm, 2008).

Among the various QTLs or groups of QTLs detected for these sets of ear physiological traits, some showed interesting co-localization with putative candidate genes. One of the most interesting groups of QTLs identified concerned those controlling the concentrations of glycine and serine in the two parts of the developing ear. On chromosome 1, a QTL for serine concentration and, on chromosome 2, a QTL for glycine concentration were found in both the developing kernels and cob. These results are in agreement with correlation studies showing that there is a strong positive correlation between the serine and glycine concentrations of the cob and ear (Fig. 3, Table S1). It was also found that QTLs for both serine and glycine concentrations co-localized with QTLs for yield. This suggests that there is a genetic mechanism shared by the two parts of the developing ear that controls the synthesis and use of these two amino acids in an interactive manner, and that this control is important for the determination of yield. Moreover, it was shown that the glycine concentration in the developing kernels is highly correlated with several traits related to the plant N metabolic status, plant growth and development, such as NHI and NNI, and dry matter accumulation (Table 5, Fig. 4). In addition, it was observed that the glycine concentration of developing kernels is highly correlated with that of alanine, an amino acid shown to be of major importance in plant NUE (Good et al., 2007; Shrawat et al., 2008; Good & Beatty, 2011). In line with this finding, strong relationships were also found between the alanine concentration of the kernels, NHI, NNI, most of the yield traits (KY, KN) and plant dry matter accumulation (Fig. 4). It is therefore probable that alanine and glycine metabolism are strongly interrelated, as revealed by the high level of correlation obtained between these two traits in both the cob and developing kernels. This interaction between glycine and alanine metabolism may occur through the activity of the enzyme alanine:glyoxyate aminotransferase (AGT) which catalyzes the conversion of alanine to glycine. This enzyme has been shown to be mainly involved in the pathway of photorespiration in the leaves of C3 plants (Igarashi et al., 2006). The importance of glycine and alanine metabolism and accumulation during kernel filling is further strengthened by the presence, on chromosome 3, of a group of QTLs for the concentrations of glycine and alanine in the kernels, for ear number and for yield. In this chromosomal region, a gene encoding serine hydroxymethyltransferase (SHMT3), an enzyme that plays an important role in cellular one-carbon pathways by catalyzing the conversion of serine to glycine (which can be reversible depending on the metabolic pathway involved), was also detected. As the reaction catalyzed by the enzyme SHMT provides the largest part of the one-carbon units available to the cell (Douce et al., 2001; Maurino & Peterhansel, 2010), it is probable that this metabolic pathway is of major importance for the determination of yield during kernel development.

On chromosome 5, a QTL for kernel GS activity co-localized with QTLs for KY and TKW, previously found to be coincident with the Gln1.3 gene locus and a QTL for leaf GS activity (Hirel et al., 2001). Such findings reinforce the validity of previous quantitative genetic approaches, as the same QTL for GS activity was found in developing kernels (present study), in leaves (Hirel et al., 2001) and in germinating kernels (Limami et al., 2002). Surprisingly, the parental line F2 provided the favorable allele for kernel GS activity, whereas, for leaf GS activity, it originated from the parental line Io. In agreement with this finding, a low but significant negative correlation between TKW and GS activity in developing kernels was determined (Table S2), whereas it was found that there was a positive correlation with leaf GS activity (Hirel et al., 2001). It is probable therefore that GS activity at the Gln1.3 locus may be controlled by alleles of opposite effects according to the organ examined, which could represent an example of advantageous additive gene expression relative to one or both parents, which can increase the yield in hybrids (Springer & Stupar, 2007).

In developing kernels, there was a positive coincidence of a QTL for GDH activity and QTLs for yield and its components (TKW and KN) at the end of chromosome 1. Moreover, this group of QTLs also showed a coincidence with the Gdh1 locus, which therefore appears to be a good candidate gene, influencing yield. Previously, two QTLs for leaf GDH activity were found to be positively coincident with two QTLs for KY (Dubois et al., 2003; Gallais & Hirel, 2004), which confirms the hypothesis that GDH activity may be an important factor controlling plant productivity (Ameziane et al., 2000; Dubois et al., 2003; Loulakakis et al., 2009).

On chromosome 9, it was found that a QTL for asparagine concentration in the cob co-localized with a gene encoding asparagine synthetase (AS4). Interestingly, it has been shown previously that this gene is strongly induced during the process of N remobilization and recycling within the developing ear (Cañas et al., 2010). Moreover, the finding that the asparagine concentration of the cob is highly correlated with that of the kernels (Fig. 3c, Table S1) and, within the cob, with the other physiological traits related to amino acid interconversion (Fig. 3b, Table S1), further supports the hypothesis that asparagine is of major importance for C and N translocation between the cob and the developing kernels (Seebauer et al., 2004; Lea et al., 2007; Cañas et al., 2010). By contrast, asparagine may not be directly involved in grain yield if the negative correlations observed between yield components and the asparagine concentration of the developing kernels are considered. These negative correlations are in line with a previous investigation, in which a large accumulation of asparagine was observed during kernel abortion, thus being, in turn, detrimental to the final yield (Cañas et al., 2010). By contrast, the importance of asparagine during the process of ear elongation is an attractive hypothesis, as co-localization between the ear length and asparagine concentration was found on the same region of chromosome 9.

Although a QTL for the DW : FW ratio was only found for the cob on chromosome 6, with no co-localization with other QTLs or any particular candidate gene, this trait may be a good predictor of the water status of the whole plant. This hypothesis is supported by the fact that a significant negative correlation was found between kernel moisture (GMoist) and DW : FW of the kernels (Table S2). Furthermore, it was shown that a high DW : FW in the cob negatively affects KY (Table S2). A negative relationship was also found between DW : FW of both the cob and the kernels and most of the physiological traits measured in the cob (Fig. 3, Table S1). This finding indicates that, when there is a deficit of water in the developing ear, independent of dry matter production, most of the metabolites are not actively synthesized and transported, which may lead to kernel abortion (Zinsemeier et al., 1995; Ribaud et al., 2009). In line with these conclusions, DW : FW ratios of both the cob and developing kernels were highly correlated (Table S1, Fig. 3d), indicating that water deficit occurs in both parts of the developing ear.

In addition to studying QTL detection and the interpretation of their physiological meaning in terms of plant performance, interesting correlations between several physiological and agronomic traits were identified. If we consider that the genotypic effect predominates for most of the traits, in comparison with the genotype × year interaction, these correlation studies can be very informative for identifying important relationships between physiological and agronomic traits. Similarly, in the agronomic study of Coque et al. (2008), the genotype × year interaction was much smaller than the genotypic effect alone. Therefore, when measuring different sets of traits, the presence of genotype × year interaction experiments can only reduce the value of the correlation coefficients, which, in turn, does not bias the interpretation of the results based on the highest correlation coefficients. These correlations were calculated using a large dataset of agronomic and physiological traits obtained over several years of experimentation in order to circumvent potential environmental effects caused by climatic changes and variable N nutrition under field growth conditions. Moreover, stable relationships between traits will be essential if used by breeders to improve plant performance, both in terms of yield and N use. Together, they were consistent with well-known relationships existing between these traits, thus reinforcing the validity of this quantitative genetic approach and correlation studies performed with maize. For example, there is a strong negative correlation between the percentage of sterile plants and the total number of ears per plant (Tables 5, S2), simply because sterile plants have less ears or empty ears. By contrast, the number of ears is positively correlated with yield and its components (Table S2), as a plant with several ears produces generally more kernels than a plant with only one ear (Pan et al., 1986). The total or individual amino acid concentration of the developing ear and visual leaf senescence at 14 DAS were positively correlated, whereas this correlation was negative with visual leaf senescence at 45 DAS and at maturity. Thus, if the level of leaf senescence is high between 10 and 14 DAS, there will be an accumulation of amino acids in the ear because N remobilization to this organ is already occurring. By contrast, if there is a shortage of amino acids at the end of the grain-filling period, premature leaf senescence will occur to provide more amino acids to the developing ear through the N remobilization process.

It is also worth noting that the majority of the physiological traits of the cob are positively correlated with each other (Fig. 3d), suggesting that the C and N metabolic pathways in this organ are interconnected and that, when there is active metabolism, all metabolites are rapidly synthesized and transported.

Conclusions and perspectives

From both the study of correlations among traits and the detection of QTLs for various agronomic and physiological traits, one can conclude that genetic variability for N metabolism may be an important determinant for the yield and its components, not only in vegetative organs, but also in the developing ear of maize. This genetic variability mainly concerns amino acid metabolism and interconversion, mostly in developing kernels and, to a lesser extent, in the cob. One of the major breakthroughs from these studies concerns the metabolism of glycine and serine, and, presumably, the interconversion of glycine to alanine in the kernels, and the putative role of the cognate genes encoding the enzymes involved in these pathways. It is well established that glycine and serine play a major role during photorespiration (Maurino & Peterhansel, 2010), although, in C4 plants, this process is limited, but necessary, for proper functioning of photosynthesis (Lacuesta et al., 1997; Zelitch et al., 2009). Further work is thus needed to investigate the regulation of this metabolic pathway in the kernels, an organ in which photorespiration is normally absent.

Finally, this work has confirmed that both the GS enzyme (Martin et al., 2006) and, possibly, GDH are important in the determination of yield. Experiments are now in progress to overexpress these genes encoding the two enzymes, either constitutively or in an organ-specific manner in both source and sink organs, in order to verify whether grain filling and grain yield are improved.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We thank Professor P. J. Lea for helpful comments on the manuscript. The determination of the composition of individual amino acids and the N and C concentrations was carried out at the Plant Chemistry Platform of the Institute Jean-Pierre Bourgin, INRA, Versailles-Grignon, France.

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  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Fig. S1 Phenotype of the two parental lines and of representative members of their RIL progeny.

Table S1 Pearson correlation matrix between the different ear traits

Table S2 Pearson correlation matrix between the different ear and agronomic traits and NUE traits

Table S3 Fisher tests of the line effect and the line × year effect for different ear traits

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