Different wheat loci are associated to heritable free asparagine content in grain grown under different water and nitrogen availability

KEY MESSAGE

Different wheat QTLs were associated to the free asparagine content of grain grown in four different conditions. Environmental effects are a key factor when selecting for low acrylamide-forming potential. The amount of free asparagine in grain of a wheat genotype determines its potential to form harmful acrylamide in derivative food products. Here, we explored the variation in the free asparagine, aspartate, glutamine and glutamate contents of 485 accessions reflecting wheat worldwide diversity to define the genetic architecture governing the accumulation of these amino acids in grain. Accessions were grown under high and low nitrogen availability and in water-deficient and well-watered conditions, and plant and grain phenotypes were measured. Free amino acid contents of grain varied from 0.01 to 1.02 mg g-1 among genotypes in a highly heritable way that did not correlate strongly with grain yield, protein content, specific weight, thousand-kernel weight or heading date. Mean free asparagine content was 4% higher under high nitrogen and 3% higher in water-deficient conditions. After genotyping the accessions, single-locus and multi-locus genome-wide association study models were used to identify several QTLs for free asparagine content located on nine chromosomes. Each QTL was associated with a single amino acid and growing environment, and none of the QTLs colocalised with genes known to be involved in the corresponding amino acid metabolism. This suggests that free asparagine content is controlled by several loci with minor effects interacting with the environment. We conclude that breeding for reduced asparagine content is feasible, but should be firmly based on multi-environment field trials.

KEY MESSAGE

Different wheat QTLs were associated to the free asparagine content of grain grown in four different conditions. Environmental effects are a key factor when selecting for low acrylamide-forming potential.

Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios

KEY MESSAGE

Clustering 24 environments in four contrasting nitrogen stress scenarios enabled the detection of genetic regions determining tolerance to nitrogen deficiency in European elite bread wheats. Increasing the nitrogen use efficiency of wheat varieties is an important goal for breeding. However, most genetic studies of wheat grown at different nitrogen levels in the field report significant interactions with the genotype. The chromosomal regions possibly involved in these interactions are largely unknown. The objective of this study was to quantify the response of elite bread wheat cultivars to different nitrogen field stress scenarios and identify genomic regions involved in this response. For this purpose, 212 elite bread wheat varieties were grown in a multi-environment trial at different nitrogen levels. Genomic regions associated with grain yield, protein concentration and grain protein deviation responses to nitrogen deficiency were identified. Environments were clustered according to adjusted means for grain yield, yield components and grain protein concentration. Four nitrogen availability scenarios were identified: optimal condition, moderate early deficiency, severe late deficiency, and severe continuous deficiency. A large range of tolerance to nitrogen deficiency was observed among varieties, which were ranked differently in different nitrogen deficiency scenarios. The well-known negative correlation between grain yield and grain protein concentration also existed between their respective tolerance indices. Interestingly, the tolerance indices for grain yield and grain protein deviation were either null or weakly positive meaning that breeding for the two traits should be less difficult than expected. Twenty-two QTL regions were identified for the tolerance indices. By selecting associated markers, these regions may be selected separately or combined to improve the tolerance to N deficiency within a breeding programme.

Symbiotic Variations among Wheat Genotypes and Detection of Quantitative Trait Loci for Molecular Interaction with Auxin-Producing Azospirillum PGPR

Crop varieties differ in their ability to interact with Plant Growth-Promoting Rhizobacteria (PGPR), but the genetic basis for these differences is unknown. This issue was addressed with the PGPR Azospirillum baldaniorum Sp245, using 187 wheat accessions. We screened the accessions based on the seedling colonization by the PGPR and the expression of the phenylpyruvate decarboxylase gene ppdC (for synthesis of the auxin indole-3-acetic acid), using gusA fusions. Then, the effects of the PGPR on the selected accessions stimulating Sp245 (or not) were compared in soil under stress. Finally, a genome-wide association approach was implemented to identify the quantitative trait loci (QTL) associated with PGPR interaction. Overall, the ancient genotypes were more effective than the modern genotypes for Azospirillum root colonization and ppdC expression. In non-sterile soil, A. baldaniorum Sp245 improved wheat performance for three of the four PGPR-stimulating genotypes and none of the four non-PGPR-stimulating genotypes. The genome-wide association did not identify any region for root colonization but revealed 22 regions spread on 11 wheat chromosomes for ppdC expression and/or ppdC induction rate. This is the first QTL study focusing on molecular interaction with PGPR bacteria. The molecular markers identified provide the possibility to improve the capacity of modern wheat genotypes to interact with Sp245, as well as, potentially, other Azospirillum strains.

Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials

Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.

Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits

Roots are essential for water and nutrient uptake but are rarely the direct target of breeding efforts. To characterize the genetic variability of wheat root architecture, the root and shoot traits of 200 durum and 715 bread wheat varieties were measured at a young stage on a high-throughput phenotyping platform. Heritability of platform traits ranged from 0.40 for root biomass in durum wheat to 0.82 for the number of tillers. Field phenotyping data for yield components and SNP genotyping were already available for all the genotypes. Taking differences in earliness into account, several significant correlations between root traits and field agronomic performances were found, suggesting that plants investing more resources in roots in some stressed environments favored water and nutrient uptake, with improved wheat yield. We identified 100 quantitative trait locus (QTLs) of root traits in the bread wheat panels and 34 in the durum wheat panel. Most colocalized with QTLs of traits measured in field conditions, including yield components and earliness for bread wheat, but only in a few environments. Stress and climatic indicators explained the differential effect of some platform QTLs on yield, which was positive, null, or negative depending on the environmental conditions. Modern breeding has led to deeper rooting but fewer seminal roots in bread wheat. The number of tillers has been increased in bread wheat, but decreased in durum wheat, and while the root-shoot ratio for bread wheat has remained stable, for durum wheat it has been increased. Breeding for root traits or designing ideotypes might help to maintain current yield while adapting to specific drought scenarios.

Identification of QTLs affecting post-anthesis heat stress responses in European bread wheat

The response of a large panel of European elite wheat varieties to post-anthesis heat stress is influenced by 17 QTL linked to grain weight or the stay-green phenotype. Heat stress is a critical abiotic stress for winter bread wheat (Triticum aestivum L.) especially at the flowering and grain filling stages, limiting its growth and productivity in Europe and elsewhere. The breeding of new high-yield and stress-tolerant wheat varieties requires improved understanding of the physiological and genetic bases of heat tolerance. To identify genomic areas associated with plant and grain characteristics under heat stress, a panel of elite European wheat varieties (N = 199) was evaluated under controlled conditions in 2016 and 2017. A split-plot design was used to test the effects of high temperature for ten days after flowering. Flowering time, leaf chlorophyll content, the number of productive spikes, grain number, grain weight and grain size were measured, and the senescence process was modeled. Using genotyping data from a 280 K SNP chip, a genome-wide association study was carried out to test the main effect of each SNP and the effect of SNP × treatment interaction. Genotype × treatment interactions were mainly observed for grain traits measured on the main shoots and tillers. We identified 10 QTLs associated with the main effect of at least one trait and seven QTLs associated with the response to post-anthesis heat stress. Of these, two main QTLs associated with the heat tolerance of thousand-kernel weight were identified on chromosomes 4B and 6B. These QTLs will be useful for breeders to improve grain yield in environments where terminal heat stress is likely to occur.

Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection

Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.

Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection

There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker-trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.

Dissecting Bread Wheat Heterosis through the Integration of Agronomic and Physiological Traits

Simple Summary

To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Similar to what has been observed in maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis, i.e., the fact that a progeny surpasses the performances of its parents, is still limited and poorly understood. To better characterize this phenomenon, we developed and phenotyped for physiological and agronomic traits 91 hybrids and their nineteen female and sixteen male parents. We showed that hybrids had a longer grain filling phase that led to bigger grains and an increased thousand kernel weight. This resulted in a better yield for 86% of hybrids compared to the average yield of their parents. In addition, hybrids appeared to be less affected by the negative correlation between protein content and yield compared to pure lines. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids that can help to meet agriculture’s challenges.

Abstract

To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Following the example of maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis is still limited and poorly understood. Gaining a better understanding of hybrid vigor holds the key to breed for better varieties. To this aim, we have developed and phenotyped for physiological and agronomic traits an incomplete factorial design consisting of 91 hybrids and their nineteen female and sixteen male parents. Monitoring the plant development with normalized difference vegetation index revealed that 89% of the hybrids including the five higher yielding hybrids had a longer grain filling phase with a delayed senescence that results in larger grain size. This average increase of 7.7% in thousand kernel weight translated to a positive mid-parent heterosis for grain yield for 86% of hybrids. In addition, hybrids displayed a positive grain protein deviation leading to a +4.7% heterosis in protein yield. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids.

Wheat individual grain-size variance originates from crop development and from specific genetic determinism

Wheat grain yield is usually decomposed in the yield components: number of spikes / m2, number of grains / spike, number of grains / m2 and thousand kernel weight (TKW). These are correlated one with another due to yield component compensation. Under optimal conditions, the number of grains per m2 has been identified as the main determinant of yield. However, with increasing occurrences of post-flowering abiotic stress associated with climate change, TKW may become severely limiting and hence a target for breeding. TKW is usually studied at the plot scale as it represents the average mass of a grain. However, this view disregards the large intra-genotypic variance of individual grain mass and its effect on TKW. The aim of this study is to investigate the determinism of the variance of individual grain size. We measured yield components and individual grain size variances of two large genetic wheat panels grown in two environments. We also carried out a genome-wide association study using a dense SNPs array. We show that the variance of individual grain size partly originates from the pre-flowering components of grain yield; in particular it is driven by canopy structure via its negative correlation with the number of spikes per m2. But the variance of final grain size also has a specific genetic basis. The genome-wide analysis revealed the existence of QTL with strong effects on the variance of individual grain size, independently from the other yield components. Finally, our results reveal some interesting drivers for manipulating individual grain size variance either through canopy structure or through specific chromosomal regions.

Ancient wheat varieties have a higher ability to interact with plant growth-promoting rhizobacteria

Plant interactions with plant growth-promoting rhizobacteria (PGPR) are highly dependent on plant genotype. Modern plant breeding has largely sought to improve crop performance but with little focus on the optimization of plant × PGPR interactions. The interactions of the model PGPR strain Pseudomonas kilonensis F113 were therefore compared in 199 ancient and modern wheat genotypes. A reporter system, in which F113 colonization and expression of 2,4-diacetylphloroglucinol biosynthetic genes (phl) were measured on roots was used to quantify F113 × wheat interactions under gnotobiotic conditions. Thereafter, eight wheat accessions that differed in their ability to interact with F113 were inoculated with F113 and grown in greenhouse in the absence or presence of stress. F113 colonization was linked to improved stress tolerance. Moreover, F113 colonization and phl expression were higher overall on ancient genotypes than modern genotypes. F113 colonization improved wheat performance in the four genotypes that showed the highest level of phl expression compared with the four genotypes in which phl expression was lowest. Taken together, these data suggest that recent wheat breeding strategies have had a negative impact on the ability of the plants to interact with PGPR.

Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions

Plant breeders evaluate their selection candidates in multi-environment trials to estimate their performance in contrasted environments. The number of genotype/environment combinations that can be evaluated is strongly constrained by phenotyping costs and by the necessity to limit the evaluation to a few years. Genomic prediction models taking the genotype by environment interactions (GEI) into account can help breeders identify combination of (possibly unphenotyped) genotypes and target environments optimizing the traits under selection. We propose a new prediction approach in which a secondary trait available on both the calibration and the test sets is introduced as an environment specific covariate in the prediction model (trait-assisted prediction, TAP). The originality of this approach is that the phenotyping of the test set for the secondary trait is replaced by crop-growth model (CGM) predictions. So there is no need to sow and phenotype the test set in each environment which is a clear advantage over the classical trait-assisted prediction models. The interest of this approach, called CGM-TAP, is highest if the secondary trait is easy to predict with CGM and strongly related to the target trait in each environment (and thus capturing GEI). We tested CGM-TAP on bread wheat with heading date as secondary trait and grain yield as target trait. Simple CGM-TAP model with a linear effect of heading date resulted in high predictive abilities in three prediction scenarios (sparse testing, or prediction of new genotypes or of new environments). It increased predictive abilities of all reference GEI models, even those involving sophisticated environmental covariates.

Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.)

Environmental clustering helps to identify QTLs associated with grain yield in different water stress scenarios. These QTLs could be useful for breeders to improve grain yields and increase genetic resilience in marginal environments. Drought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world. The acquisition of new high-yielding and stress-tolerant varieties is therefore necessary and requires improved understanding of the physiological and genetic bases of drought resistance. A panel of 210 elite European varieties was evaluated in 35 field trials. Grain yield and its components were scored in each trial. A crop model was then run with detailed climatic data and soil water status to assess the dynamics of water stress in each environment. Varieties were registered from 1992 to 2011, allowing us to test timewise genetic progress. Finally, a genome-wide association study (GWAS) was carried out using genotyping data from a 280 K SNP chip. The crop model simulation allowed us to group the environments into four water stress scenarios: an optimal condition with no water stress, a post-anthesis water stress, a moderate-anthesis water stress and a high pre-anthesis water stress. Compared to the optimal water condition, grain yield losses in the stressed conditions were 3.3%, 12.4% and 31.2%, respectively. This environmental clustering improved understanding of the effect of drought on grain yields and explained 20% of the G × E interaction. The greatest genetic progress was obtained in the optimal condition, mostly represented in France. The GWAS identified several QTLs, some of which were specific of the different water stress patterns. Our results make breeding for improved drought resistance to specific environmental scenarios easier and will facilitate genetic progress in future environments, i.e., water stress environments.

Different grain-filling rates explain grain-weight differences along the wheat ear

Thousand grain weight is one of the components determining wheat grain yield. It represents the average value of individual grain weights which depends on position within the ear and on positon within the spikelet. Our objective was to quantify the influences of individual floret anthesis date, of carpel weight at anthesis and of rate and duration of grain filling, on variation in individual final grain weight. Two bread wheat cultivars were grown in a greenhouse and their ears were sampled from anthesis through to harvest. Each ear was divided into three parts-basal, central and apical-where the two proximal grains were dissected from each of two spikelets. We analysed (i) the flowering time shift within the ear and within the spikelet; and (ii) the growth kinetics during grain filling in relation to position along the ear. For both cultivars, florets located in the central part of the ear were the first to reach anthesis followed by those in the apical part and then the basal part. Within a spikelet, the floret located nearest the rachis flowered first followed by the more distal ones. We found no significant systematic effect of flowering time-shift on final grain weight. Nevertheless, grains in the central part were heavier than the basal ones (9.75% smaller) and than the apical ones (18.25% smaller). These differences were explained mainly by differences in mean grain filling rates. Analysis of growth kinetics enabled an improved explanation of the variability of individual grain weight along the ear.

Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar

Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.

Coexpression network and phenotypic analysis identify metabolic pathways associated with the effect of warming on grain yield components in wheat

Wheat grains are an important source of human food but current production amounts cannot meet world needs. Environmental conditions such as high temperature (above 30°C) could affect wheat production negatively. Plants from two wheat genotypes have been subjected to two growth temperature regimes. One set has been grown at an optimum daily mean temperature of 19°C while the second set of plants has been subjected to warming at 27°C from two to 13 days after anthesis (daa). While warming did not affect mean grain number per spike, it significantly reduced other yield-related indicators such as grain width, length, volume and maximal cell numbers in the endosperm. Whole genome expression analysis identified 6,258 and 5,220 genes, respectively, whose expression was affected by temperature in the two genotypes. Co-expression analysis using WGCNA (Weighted Gene Coexpression Network Analysis) uncovered modules (groups of co-expressed genes) associated with agronomic traits. In particular, modules enriched in genes related to nutrient reservoir and endopeptidase inhibitor activities were found to be positively associated with cell numbers in the endosperm. A hypothetical model pertaining to the effects of warming on gene expression and growth in wheat grain is proposed. Under moderately high temperature conditions, network analyses suggest a negative effect of the expression of genes related to seed storage proteins and starch biosynthesis on the grain size in wheat.

Fortune telling: metabolic markers of plant performance

BACKGROUND

In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC-MS, LC-MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained.

AIM OF REVIEW

(i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding.

KEY MESSAGE

Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.

Bread Wheat (Triticum aestivum L.) Grain Protein Concentration Is Related to Early Post-Flowering Nitrate Uptake under Putative Control of Plant Satiety Level

The strong negative correlation between grain protein concentration (GPC) and grain yield (GY) in bread wheat complicates the simultaneous improvement of these traits. However, earlier studies have concluded that the deviation from this relationship (grain protein deviation or GPD) has strong genetic basis. Genotypes with positive GPD have an increased ability to uptake nitrogen (N) during the post-flowering period independently of the amount of N taken up before flowering, suggesting that genetic variability for N satiety could enable the breakage of the negative relationship. This study is based on two genotypes markedly contrasted for GPD grown under semi-hydroponic conditions differentiated for nitrate availability both before and after flowering. This allows exploration of the genetic determinants of post-flowering N uptake (PANU) by combining whole plant sampling and targeted gene expression approaches. The results highlights the correlation (r² = 0.81) with GPC of PANU occurring early during grain development (flowering-flowering + 250 degree-days) independently of GY. Early PANU was in turn correlated (r² = 0.80) to the stem-biomass increment after flowering through its effect on N sink activity. Differences in early PANU between genotypes, despite comparable N statuses at flowering, suggest that genetic differences in N satiety could be involved in the establishment of the GPC. Through its strong negative correlation with genes implied in N assimilation, root nitrate concentration appears to be a good marker for evaluating instantaneous plant N demand, and may provide valuable information on the genotypic N satiety level. This trait may help breeders to identify genotypes having high GPC independently of their GY.

In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management

Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work.

Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat

Seedling root traits of wheat (Triticum aestivum L.) have been shown to be important for efficient establishment and linked to mature plant traits such as height and yield. A root phenotyping pipeline, consisting of a germination paper-based screen combined with image segmentation and analysis software, was developed and used to characterize seedling traits in 94 doubled haploid progeny derived from a cross between the winter wheat cultivars Rialto and Savannah. Field experiments were conducted to measure mature plant height, grain yield, and nitrogen (N) uptake in three sites over 2 years. In total, 29 quantitative trait loci (QTLs) for seedling root traits were identified. Two QTLs for grain yield and N uptake co-localize with root QTLs on chromosomes 2B and 7D, respectively. Of the 29 root QTLs identified, 11 were found to co-localize on 6D, with four of these achieving highly significant logarithm of odds scores (>20). These results suggest the presence of a major-effect gene regulating seedling root vigour/growth on chromosome 6D.

Post-flowering nitrate uptake in wheat is controlled by N status at flowering, with a putative major role of root nitrate transporter NRT2.1

In bread wheat (Triticum aestivum L.), the simultaneous improvement of both yield and grain protein is difficult because of the strong negative relationship between these two traits. However, some genotypes deviate positively from this relationship and this has been linked to their ability to take up nitrogen (N) during the post-flowering period, regardless of their N status at flowering. The physiological and genetic determinants of post-flowering N uptake relating to N satiety are poorly understood. This study uses semi-hydroponic culture of cv. Récital under controlled conditions to explore these controls. The first objective was to record the effects of contrasting N status at flowering on post-flowering nitrate (NO₃⁻) uptake under non-limiting NO₃⁻ conditions, while following the expression of key genes involved in NO₃⁻ uptake and assimilation. We found that post-flowering NO₃⁻ uptake was strongly influenced by plant N status at flowering during the first 300-400 degree-days after flowering, overlapping with a probable regulation of nitrate uptake exerted by N demand for growth. The uptake of NO₃⁻ correlated well with the expression of the gene TaNRT2.1, coding for a root NO₃⁻ transporter, which seems to play a major role in post-flowering NO₃⁻ uptake. These results provide a useful knowledge base for future investigation of genetic variability in post-flowering N uptake and may lead to concomitant gains in both grain yield and grain protein in wheat.

A genome-wide identification of chromosomal regions determining nitrogen use efficiency components in wheat (Triticum aestivum L.)

This study identified 333 genomic regions associated to 28 traits related to nitrogen use efficiency in European winter wheat using genome-wide association in a 214-varieties panel experimented in eight environments. Improving nitrogen use efficiency is a key factor to sustainably ensure global production increase. However, while high-throughput screening methods remain at a developmental stage, genetic progress may be mainly driven by marker-assisted selection. The objective of this study was to identify chromosomal regions associated with nitrogen use efficiency-related traits in bread wheat (Triticum aestivum L.) using a genome-wide association approach. Two hundred and fourteen European elite varieties were characterised for 28 traits related to nitrogen use efficiency in eight environments in which two different nitrogen fertilisation levels were tested. The genome-wide association study was carried out using 23,603 SNP with a mixed model for taking into account parentage relationships among varieties. We identified 1,010 significantly associated SNP which defined 333 chromosomal regions associated with at least one trait and found colocalisations for 39 % of these chromosomal regions. A method based on linkage disequilibrium to define the associated region was suggested and discussed with reference to false positive rate. Through a network approach, colocalisations were analysed and highlighted the impact of genomic regions controlling nitrogen status at flowering, precocity, and nitrogen utilisation on global agronomic performance. We were able to explain 40 ± 10 % of the total genetic variation. Numerous colocalisations with previously published genomic regions were observed with such candidate genes as Ppd-D1, Rht-D1, NADH-Gogat, and GSe. We highlighted selection pressure on yield and nitrogen utilisation discussing allele frequencies in associated regions.

Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model

Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base , representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.

Structural and functional partitioning of bread wheat chromosome 3B

We produced a reference sequence of the 1-gigabase chromosome 3B of hexaploid bread wheat. By sequencing 8452 bacterial artificial chromosomes in pools, we assembled a sequence of 774 megabases carrying 5326 protein-coding genes, 1938 pseudogenes, and 85% of transposable elements. The distribution of structural and functional features along the chromosome revealed partitioning correlated with meiotic recombination. Comparative analyses indicated high wheat-specific inter- and intrachromosomal gene duplication activities that are potential sources of variability for adaption. In addition to providing a better understanding of the organization, function, and evolution of a large and polyploid genome, the availability of a high-quality sequence anchored to genetic maps will accelerate the identification of genes underlying important agronomic traits.

A multi-environmental study of recent breeding progress on nitrogen use efficiency in wheat (Triticum aestivum L.)

By comparing 195 varieties in eight trials, this study assesses nitrogen use efficiency improvement in high and low nitrogen conditions in European winter wheat over the last 25 years. In a context where European agriculture practices have to deal with environmental concerns and nitrogen (N) fertiliser cost, nitrogen use efficiency (NUE) has to be improved. This study assessed genetic progress in winter wheat (Triticum aestivum L.) NUE. Two hundred and twenty-five European elite varieties were tested in four environments under two levels of N. Global genetic progress was assessed on additive genetic values and on genotype × N interaction, covering 25 years of European breeding. To avoid sampling bias, quality, precocity and plant height were added as covariates in the analyses when needed. Genotype × environment interactions were highly significant for all the traits studied to such an extent that no additive genetic effect was detected on N uptake. Genotype × N interactions were significant for yield, grain protein content (GPC), N concentration in straw, N utilisation, and NUE. Grain yield improvement (+0.45 % year(-1)) was independent of the N treatment. GPC was stable, thus grain nitrogen yield was improved (+0.39 % year(-1)). Genetic progress on N harvest index (+0.12 % year(-1)) and on N concentration in straw (-0.52 % year(-1)) possibly revealed improvement in N remobilisation. There has been an improvement of NUE additive genetic value (+0.33 % year(-1)) linked to better N utilisation (+0.20 % year(-1)). Improved yield stability was detected as a significant improvement of NUE in low compared to high N conditions. The application of these results to breeding programs is discussed.

Acclimation of leaf nitrogen to vertical light gradient at anthesis in wheat is a whole-plant process that scales with the size of the canopy

Vertical leaf nitrogen (N) gradient within a canopy is classically considered as a key adaptation to the local light environment that would tend to maximize canopy photosynthesis. We studied the vertical leaf N gradient with respect to the light gradient for wheat (Triticum aestivum) canopies with the aims of quantifying its modulation by crop N status and genetic variability and analyzing its ecophysiological determinants. The vertical distribution of leaf N and light was analyzed at anthesis for 16 cultivars grown in the field in two consecutive seasons under two levels of N. The N extinction coefficient with respect to light (b) varied with N supply and cultivar. Interestingly, a scaling relationship was observed between b and the size of the canopy for all the cultivars in the different environmental conditions. The scaling coefficient of the b-green area index relationship differed among cultivars, suggesting that cultivars could be more or less adapted to low-productivity environments. We conclude that the acclimation of the leaf N gradient to the light gradient is a whole-plant process that depends on canopy size. This study demonstrates that modeling leaf N distribution and canopy expansion based on the assumption that leaf N distribution parallels that of the light is inappropriate. We provide a robust relationship accounting for vertical leaf N gradient with respect to vertical light gradient as a function of canopy size.

The quantitative response of wheat vernalization to environmental variables indicates that vernalization is not a response to cold temperature

The initiation of flowering is a crucial trait that allows temperate plants to flower in the favourable conditions of spring. The timing of flowering initiation is governed by two main mechanisms: vernalization that defines a plant's requirement for a prolonged exposure to cold temperatures; and photoperiod sensitivity defining the need for long days to initiate floral transition. Genetic variability in both vernalization and photoperiod sensitivity largely explains the adaptability of cultivated crop plants such as bread wheat (Triticum aestivum L.) to a wide range of climatic conditions. The major genes controlling wheat vernalization (VRN1, VRN2, and VRN3) and photoperiod sensitivity (PPD1) have been identified, and knowledge of their interactions at the molecular level is growing. However, the quantitative effects of temperature and photoperiod on these genes remain poorly understood. Here it is shown that the distinction between the temperature effects on organ appearance rate and on vernalization sensu stricto is crucial for understanding the quantitative effects of the environmental signal on wheat flowering. By submitting near isogenic lines of wheat differing in their allelic composition at the VRN1 locus to various temperature and photoperiod treatments, it is shown that, at the whole-plant level, the vernalization process has a positive response to temperature with complex interactions with photoperiod. In addition, the phenotypic variation associated with the presence of different spring homoeoalleles of VRN1 is not induced by a residual vernalization requirement. The results demonstrate that a precise definition of vernalization is necessary to understand and model temperature and photoperiod effects on wheat flowering. It is suggested that this definition should be used as the basis for gene expression studies and assessment of functioning of the wheat flowering gene network, including an explicit account of the quantitative effect of environmental variables.

Deciphering the genetics of flowering time by an association study on candidate genes in bread wheat (Triticum aestivum L.)

Earliness is very important for the adaptation of wheat to environmental conditions and the achievement of high grain yield. A detailed knowledge of key genetic components of the life cycle would enable an easier control by the breeders. The objective of the study was to investigate the effect of candidate genes on flowering time. Using a collection of hexaploid wheat composed of 235 lines from diverse geographical origins, we conducted an association study for six candidate genes for flowering time and its components (vernalization sensitivity and earliness per se). The effect on the variation of earliness components of polymorphisms within the copies of each gene was tested in ANOVA models accounting for the underlying genetic structure. The collection was structured in five groups that minimized the residual covariance. Vernalization requirement and lateness tend to increase according to the mean latitude of each group. Heading date for an autumnal sowing was mainly determined by the earliness per se. Except for the Constans (CO) gene orthologous of the barley HvCO3, all gene polymorphisms had a significant impact on earliness components. The three traits used to quantify vernalization requirement were primarily associated with polymorphisms at Vrn-1 and then at Vrn-3 and Luminidependens (LD) genes. We found a good correspondence between spring/winter types and genotypes at the three homeologous copies of Vrn-1. Earliness per se was mainly explained by polymorphisms at Vrn-3 and to a lesser extent at Vrn-1, Hd-1 and Gigantea (GI) genes. Vernalization requirement and earliness as a function of geographical origin, as well as the possible role of the breeding practices in the geographical distribution of the alleles and the hypothetical adaptive value of the candidate genes, are discussed.

Anthesis date mainly explained correlations between post-anthesis leaf senescence, grain yield, and grain protein concentration in a winter wheat population segregating for flowering time QTLs

The genetic variability of the duration of leaf senescence during grain filling has been shown to affect both carbon and nitrogen acquisition. In particular, maintaining green leaves during grain filling possibly leads to increased grain yield, but its associated effect on grain protein concentration has not been studied. The aim of this study was to dissect the genetic factors contributing to correlations observed at the phenotypic level between leaf senescence during grain filling, grain protein concentration, and grain yield in winter wheat. With this aim in view, an analysis of quantitative trait locus (QTL) co-locations for these traits was carried out on a doubled haploid mapping population grown in a large multienvironment trial network. Pleiotropic QTLs affecting leaf senescence and grain yield and/or grain protein concentration were identified on chromosomes 2D, 2A, and 7D. These were associated with QTLs for anthesis date, showing that the phenotypic correlations with leaf senescence were mainly explained by flowering time in this wheat population. Study of the allelic effects of these pleiotropic QTLs showed that delaying leaf senescence was associated with increased grain yield or grain protein concentration depending on the environments considered. It is proposed that this differential effect of delaying leaf senescence on grain yield and grain protein concentration might be related to the nitrogen availability during the post-anthesis period. It is concluded that the benefit of using leaf senescence as a selection criterion to improve grain protein concentration in wheat cultivars may be limited and would largely depend on the targeted environments, particularly on their nitrogen availability during the post-anthesis period.

Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution

Monitoring nitrogen use efficiency (NUE) in plants is becoming essential to maintain yield while reducing fertilizer usage. Optimized NUE application in major crops is essential for long-term sustainability of agriculture production. Here, we report the precise identification of 11 major chromosomal regions controlling NUE in wheat that co-localise with key developmental genes such as Ppd (photoperiod sensitivity), Vrn (vernalization requirement), Rht (reduced height) and can be considered as robust markers from a molecular breeding perspective. Physical mapping, sequencing, annotation and candidate gene validation of an NUE metaQTL on wheat chromosome 3B allowed us to propose that a glutamate synthase (GoGAT) gene that is conserved structurally and functionally at orthologous positions in rice, sorghum and maize genomes may contribute to NUE in wheat and other cereals. We propose an evolutionary model for the NUE locus in cereals from a common ancestral region, involving species specific shuffling events such as gene deletion, inversion, transposition and the invasion of repetitive elements.

Using probe genotypes to dissect QTL × environment interactions for grain yield components in winter wheat

Yield is known to be a complex trait, the expression of which interacts strongly with environmental conditions. Understanding the genetic basis of these genotype × environment interactions, particularly under limited input levels, is a key objective when selecting wheat genotypes adapted to specific environments. Our principal objectives were thus: (1) to identify genomic regions [quantitative trait loci (QTL)] involving QTL × environment interactions (QEI) and (2) to develop a strategy to understand the specificity of these regions to certain environments. The two main components of yield were studied: kernel number (KN) and thousand-kernel weight (TKW). The Arche × Récital doubled-haploid population of 222 lines was grown in replicated field trials during 2000 and 2001 at three locations in France, under two nitrogen levels. The 12 environments were characterized in terms of water deficit, radiation, temperature and nitrogen stress based on measurements conducted on the four-probe genotypes: Arche, Récital, Ritmo and Soissons. A four-step strategy was developed to explain QTL specificity to some environments: (1) the detection of QTL for KN and TKW in each environment; (2) the estimation of genotypic sensitivities as the factorial regression slope of KN and TKW to environmental covariates and the detection of QTL for these genotypic sensitivities; (3) study of the co-locations of QTL for KN and TKW and of the QTL for sensitivities; in the event of a co-location partitioning the QEI, appropriate covariates were employed; (4) a description of the environments where QTL were detected for KN and TKW using the environmental covariates. A total of 131 QTL were found to be associated with KN, TKW and their sensitivity to environmental covariates across the 12 environments. Four of these QTL, for both KN and TKW, were located on linkage groups 1B, 2D1, 4B and 5A1, and displayed pleiotropic effects. Factorial regression explained from 15.1 to 83.2% of the QEI for KN and involved three major environmental covariates: cumulative radiation-days ±3 days at meiosis, cumulative degree-days >25°C ±3 days at meiosis and nitrogen stress at flowering. For TKW, 13.5-81.8% of the effect of the QEI was partitioned and involved three major environmental covariates: water deficit from flowering to the milk stage, cumulative degree-days >0°C from the milk stage to maturity and soil water deficit at maturity. A comparative analysis was then performed on the QTL detected during this and previous studies published on QEI and some interacting QTL may be common to different genetic backgrounds. Focusing on these QTL common to different genetic backgrounds would give some guidance to understand genotype × environment interaction.

Deviation from the grain protein concentration-grain yield negative relationship is highly correlated to post-anthesis N uptake in winter wheat

In plants, carbon and nitrogen (N) economies are intimately linked at the physiological and biochemical level. The strong genetic negative correlation between grain yield and grain protein concentration observed in various cereals is an illustration of this inter-relationship. Studies have shown that deviation from this negative relationship (grain protein deviation or GPD) has a genetic basis, but its physiological basis is still poorly understood. This study analysed data on 27 genotypes grown in multienvironment field trials, representing a wide range of agricultural practices and climatic conditions. The objective was to identify physiological processes related to the genetic variability in GPD. Under most environments, GPD was significantly related to post-anthesis N uptake independently of anthesis date and total N at anthesis. The underlying physiological trait might be related to genotypic differences in either access to soil N, regulation of N uptake by plant N status, or ability to maintain root activity during the grain-filling period. GPD is an interesting potential target in breeding as it appears to be relatively robust across different environments and would be valuable in increasing total N uptake by maturity.

A quantitative genetic study for elucidating the contribution of glutamine synthetase, glutamate dehydrogenase and other nitrogen-related physiological traits to the agronomic performance of common wheat

To better understand the genetic variability for nitrogen use efficiency in winter wheat is a necessity in the frame of the present economic and ecological context. The objective of this work was to investigate the role of the enzymes glutamine synthetase (GS) and glutamate dehydrogenase (GDH), and other nitrogen (N)-related physiological traits in the control of agronomic performance in wheat. A quantitative genetics approach was developed using the Arche x Récital population of doubled haploid lines grown for 3 years in the field. GS and GDH activities, ammonium, amino acid and protein contents were measured at different stages of plant development in different organs after flowering. Significant genotypic effects were observed for all measured physiological and agronomical traits. Heading date was negatively correlated with ammonium, amino acid, protein contents and GS activity in the flag leaf lamina. Grain protein content was positively correlated with both ammonium and amino acid content, and to a lesser extent with soluble protein content and GS activity. A total of 148 quantitative trait loci (QTLs) were detected, 104 QTLs for physiological traits and 44 QTLs for agronomic traits. Twenty-six QTLs were detected for GDH activity spread over 13 chromosomes and 25 QTLs for GS activity spread over 12 chromosomes. We found only a co-localization between a QTL for GS activity and GSe, a structural gene encoding cytosolic GS on chromosome 4B. A coincidence between a QTL for GDH activity and a gene encoding GDH was also found on chromosome 2B. QTL regions combining both physiological and agronomical QTLs were mainly identified on linkage groups 2A, 2B, 2D, 5A, 5B and 5D. This approach allowed us to propose possible functions of physiological traits to explain the variation observed for agronomic traits including yield and its components.

Using genotype x nitrogen interaction variables to evaluate the QTL involved in wheat tolerance to nitrogen constraints

Lower market prices and environmental concerns now orientate wheat (Triticum aestivum L.) breeding programs towards low input agricultural practices, and more particularly low nitrogen (N) input management. Such programs require knowledge of the genetic determination of plant reaction to N deficiency. Our aim was to characterize the genetic basis of N use efficiency and genotype x N interactions. The detection of QTL for grain yield, grain protein yield and their components was performed on a mapping population of 222 doubled haploid lines (DH), obtained from the cross between an N stress tolerant variety and an N stress sensitive variety. Experiments on the population were carried out in seven different environments, and in each case under high (N(+)) and low (N(-)) N supplies. In total, 233 QTL were detected for traits measured in each combination of environment and N supply, for "global" interaction variables (N(+)-N(-) and N(-)/N(+)), for sensitivity to N stress and for performance under N-limited conditions which were assessed using factorial regression parameters. The 233 QTL were detected on the whole genome and clustered into 82 genome regions. The dwarfing gene (Rht-B1), the photoperiod sensitivity gene (Ppd-D1) and the awns inhibitor gene (B1) coincided with regions that contained the highest numbers of QTL. Non-interactive QTL were detected on linkage groups 3D, 4B, 5A1 and 7B2. Interactive QTL were revealed by interaction or factorial regression variables (2D2, 3D, 5A1, 5D, 6A, 6B, 7B2) or by both variables (1B, 2A1, 2A2, 2D1, 4B, 5A2, 5B). The usefulness of QTL meta-analysis and factorial regression to study QTL x N interactions and the impact of Rht-B1, Ppd-D1 and B1, are discussed.

The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches

In this review, recent developments and future prospects of obtaining a better understanding of the regulation of nitrogen use efficiency in the main crop species cultivated in the world are presented. In these crops, an increased knowledge of the regulatory mechanisms controlling plant nitrogen economy is vital for improving nitrogen use efficiency and for reducing excessive input of fertilizers, while maintaining an acceptable yield. Using plants grown under agronomic conditions at low and high nitrogen fertilization regimes, it is now possible to develop whole-plant physiological studies combined with gene, protein, and metabolite profiling to build up a comprehensive picture depicting the different steps of nitrogen uptake, assimilation, and recycling to the final deposition in the seed. A critical overview is provided on how understanding of the physiological and molecular controls of N assimilation under varying environmental conditions in crops has been improved through the use of combined approaches, mainly based on whole-plant physiology, quantitative genetics, and forward and reverse genetics approaches. Current knowledge and prospects for future agronomic development and application for breeding crops adapted to lower fertilizer input are explored, taking into account the world economic and environmental constraints in the next century.

Estimation of genetic parameters of a DH wheat population grown at different N stress levels characterized by probe genotypes

Low market prices and environmental concerns in Europe favor lower input wheat production systems. To efficiently breed for new varieties adapted to low input management while maintaining high yield levels, our objective was to characterize the heritability and its components for yield and nitrogen traits under different nitrogen levels. Two hundred and twenty-two doubled-haploid (DH) lines from the cross between Arche (tolerant) and Récital (sensitive) were tested in France at four locations in 2000, and three in 2001, under high (N+) and low (N-) nitrogen supplies. The response of yield to the environment of four probe genotypes, the parents and two controls, were tested and used as descriptors of these environments. Grain yield (GY), its components, and grain and straw nitrogen, called nitrogen traits, were studied. A factorial regression was performed to assess the sensitivity (slope) of the DH lines to nitrogen stress and their performance to low nitrogen supply. An index based on the nitrogen nutrition index at flowering of the probe genotype Récital was the best descriptor of the environment stress. Heritabilities of yield and nitrogen traits for both nitrogen supplies were always above 0.6. When nitrogen stress increased, heritabilities decreased and genotype x nitrogen interaction variances increased. The decrease in heritability was mainly explained by a decrease in genetic variance. Genetic variation for sensitivity to nitrogen stress and performance under low nitrogen supply were shown in the population. GY decreased from 278 to 760 g/m2 per unit of nitrogen stress index increase and GY under moderate nitrogen stress varied from 340 to 613 g/m2. Those contrasted reactions revealed specific lines to include in breeding programs for improving GY under low nitrogen supply.

Combined agronomic and physiological aspects of nitrogen management in wheat highlight a central role for glutamine synthetase

In wheat the period of grain filling is characterized by a transition for all vegetative organs from sink to source status. To study this transition, the progression of physiological markers and enzyme activities representative of nitrogen metabolism was monitored from the vegetative stage to maturity in different leaf stages and stem sections of two wheat (Triticum aestivum) cultivars grown at high and low levels of N fertilization. In the two cultivars examined, we found a general decrease of the metabolic and enzyme markers occurred during leaf ageing, and that this decrease was enhanced when plants were N-limited. Both correlation studies and principal components analysis (PCA) showed that there was a strong relationship among total N, chlorophyll, soluble protein, ammonium, amino acids and glutamine synthetase (GS) activity. The use of a marker such as GS activity to predict the N status of wheat, as a function of both plant development and N availability, is discussed with the aim of selecting wheat genotypes with better N-use efficiency.

Changes in the Cellular and Subcellular Localization of Glutamine Synthetase and Glutamate Dehydrogenase During Flag Leaf Senescence in Wheat (Triticum aestivum L.)

In order to improve our understanding of the regulation of nitrogen assimilation and recycling in wheat (Triticum aestivum L.), we studied the localization of plastidic (GS2) and cytosolic (GS1) glutamine synthetase isoenzymes and of glutamate dehydrogenase (GDH) during natural senescence of the flag leaf and in the stem. In mature flag leaves, large amounts of GS1 were detected in the connections between the mestome sheath cells and the vascular cells, suggesting an active transfer of nitrogen organic molecules within the vascular system in the mature flag leaf. Parallel to leaf senescence, an increase of a GS1 polypeptide (GS1b) was detected in the mesophyll cytosol of senescing leaves, while the GS protein content represented by another polypetide (GS1a) in the phloem companion cells remained practically constant in both leaves and stems. Both GDH aminating activity and protein content were strongly induced in senescing flag leaves. The induction occurred both in the mitochondria and in the cytosol of phloem companion cells, suggesting that the shift in GDH cellular compartmentation is important during leaf nitrogen remobilization although the metabolic or sensing role of the enzyme remains to be elucidated. Taken together, our results suggest that in wheat, nitrogen assimilation and recycling are compartmentalized between the mesophyll and the vasculature, and are shifted in different cellular compartments within these two tissues during the transition of sink leaves to source leaves.

Wheat leaf proteome analysis using sequence data of proteins separated by two-dimensional electrophoresis

Identifying wheat leaf protein expression is a major challenge of functional genomics. Using two-dimensional gel electrophoresis 541 wheat leaf proteins were separated and 55 of them were sequenced by nano liquid chromatography-tandem mass spectrometry. Peptide sequence data were screened against protein banks and expressed sequence tag public banks. Among these 55 spots, 20 proteins were found in wheat and 21 in other grass families (http://www.ncbi.nlm.nih.gov/). Twelve proteins showed similarities with other eukaryotic plant species. One protein showed homology to a bacterial sequence and another protein remained unknown. In 18 cases a significant score was found for the wheat TUC (Tentative Unique Contigs) of the PlantGDB (http://www.plantgdb.org/) data. In several cases, different spots were identified as corresponding to the same protein that can probably be attributed to the hexaploid structure of wheat. The identified proteins were classified in six groups and their role is discussed. Most of them (31/55) are involved in carbohydrate metabolism.

Differential protein expression assessed by two-dimensional gel electrophoresis for two wheat varieties grown at four nitrogen levels

To limit N-fertilizer applied on wheat, cultivars that use N more efficiently are needed. Our objective was to investigate differences of nitrogen utilization in varieties by studying qualitative and quantitative proteins expression. Two wheat varieties, 'Arche' and 'Récital', were grown under controlled conditions at four N levels (0, 2, 8, and 20 mg N/plant/day) with two replicates. The number of tillers/plant, aerial dry weight/plant and total N content were measured after two months. Two-dimensional gel electrophoresis was also performed on leaf protein extracts. Analyses of variance showed that the N level effect was highly significant for the number of tillers/plant, aerial dry weight and N content. The variety x N level interaction was significant for N content. Analyses of variance on % volume carried out for 524 spots showed a significant variety effect for 55 spots and a significant N treatment effect for 76 spots. Twenty spots showed a significant variety x N treatment interaction. Fourteen proteins were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The possible role of these proteins, eight of which belong to the carbon metabolism, is discussed.