Coexpression network and phenotypic analysis identify metabolic pathways associated with the effect of warning on grain yield components in wheat
Christine Girousse, Jane Roche, Claire Guerin, Jacques Le Gouis, Sandrine Balzegue, Said Mouzeyar, Mouhamed Fouad Bouzidi - PLOS one, 2018
Abstract: 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. DOI 13(6): e0199434
High throughput SNP discovery and genotyping in hexaploid wheat
Hélène Rimbert, Benoit Darrier, Julien Navarro, Jonathan Kitt, Frederic Choulet, Magalie Leveugle, Jorge Duarte, Nathalie Rivière, Kellye Eversole on behalf of The International Wheat Genome Sequencing Consortium, Jacques Le Gouis on behalf The BreedWheat Consortium, Alessandro Davassi, Francois Balfourier, Marie-Christine Le Paslier, Aurelie Berard, Dominique Brunel, Catherine Feuillet, Charles Poncet, Pierre Sourdille, Etienne Paux - Field Crops Research, 2018
Abstract: Because of their abundance and their amenability to high-throughput genotyping techniques, Single Nucleotide Polymorphisms (SNPs) are powerful tools for efficient genetics and genomics studies, including characterization of genetic resources, genome-wide association studies and genomic selection. In wheat, most of the previous SNP discovery initiatives targeted the coding fraction, leaving almost 98% of the wheat genome largely unexploited. Here we report on the use of whole-genome resequencing data from eight wheat lines to mine for SNPs in the genic, the repetitive and non-repetitive intergenic fractions of the wheat genome. Eventually, we identified 3.3 million SNPs, 49% being located on the B-genome, 41% on the A-genome and 10% on the D-genome. We also describe the development of the TaBW280K high-throughput genotyping array containing 280,226 SNPs. Performance of this chip was examined by genotyping a set of 96 wheat accessions representing the worldwide diversity. Sixty-nine percent of the SNPs can be efficiently scored, half of them showing a diploid-like clustering. The TaBW280K was proven to be a very efficient tool for diversity analyses, as well as for breeding as it can discriminate between closely related elite varieties. Finally, the TaBW280K array was used to genotype a population derived from a cross between Chinese Spring and Renan, leading to the construction a dense genetic map comprising 83,721 markers. The results described here will provide the wheat community with powerful tools for both basic and applied research. DOI 10.1371/journal.pone.0186329
Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression
Delphine Ly, Sylvie Huet, Arnaud Gauffreteau, Renaud Rincent, Gaëtan Touzy, Agathe Mini, Jean-Luc Janninke, Fabien Cormier, Etienne Paux, Stéphane Lafarge, Jacques Le Gouis, Gilles Charmet - Field Crops Research, 2018
Abstract: Plant breeding has always sought to develop crops able to withstand environmental stresses, but this is all the more urgent now as climate change is affecting the agricultural regions of the world. It is currently difficult to screen genetic material to determine how well a crop will tolerate various stresses. Multi-environment trials (MET) which include a particular stress condition could be used to train a genomic selection model thanks to molecular marker information that is now readily available. Our study focuses on understanding how and predicting whether a plant is adapted to a particular environmental stress. We propose a way to use genomic random regression, an extension of factorial regression, to model the reaction norms of a genotype to an environmental stress: the factorial regression genomic best linear unbiased predictor (FR-gBLUP). Twenty-eight wheat trials in France (3 years, 12 locations, nitrogen or water stress treatments) were split into two METs where different stresses limited grain number and yield. In MET1, drought at flowering was responsible for 46.7% of the genotype-by-environment (G ×E) interactions for yield while in MET2, heat stress during booting was identified as the main factor responsible for G× E interactions, but that explained less of the interaction variance (33.6%). Since drought at flowering explained a fairly large variance in G ×E in MET1, the FR-gBLUP model was more accurate than the additive gBLUP across all types of cross validation. Accuracy gains varied from 2.4% to 12.9% for the genomic regression to drought. In MET2 accuracy gains were modest, varying from −5.7% to 2.4%. When a major stress influencing G ×E is identified, the FR-gBLUP strategy makes it possible to predict the level of adaptation of genotyped individuals to varying stress intensities, and thus to select them in silico. Our study demonstrates how genome-wide selection can facilitate breeding for adaptation. Keywords: Genotype-by-environment interaction, Factorial regression, Genomic prediction, Reaction norm, Drought adaptation. DOI 10.1016/j.fcr.2017.08.020
Optimization of multi‑environment trials for genomic selection based on crop models
Renaud Rincent, E. Kuhn, H. Monod, F.‑X. Oury, M. Rousset, V. Allard, J. Le Gouis - Theoretical and Applied Genetics, 2017
Summary: Key message We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Abstract Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed. DOI 10.1007/s00122-017-2922-4
Grain subproteome responses to nitrogen and sulfur supply in diploid wheat Triticum monococcum ssp monococcum
Titouan Bonnot, Emmanuelle Bancel, David Alvarez, Marlène Davanture, Julie Boudet, Marie Pailloux, Michel Zivy, Catherine Ravel, Pierre Martre - The Plant Journal, 2017
Summary: Wheat grain storage proteins (GSPs) make up most of the protein content of grain and determine flour enduse value. The synthesis and accumulation of GSPs depend highly on nitrogen (N) and sulfur (S) availability and it is important to understand the underlying control mechanisms. Here we studied how the einkorn (Triticum monococcum ssp. monococcum) grain proteome responds to different amounts of N and S supply during grain development. GSP composition at grain maturity was clearly impacted by nutrition treatments, due to early changes in the rate of GSP accumulation during grain filling. Large-scale analysis of the nuclear and albumin-globulin subproteomes during this key developmental phase revealed that the abundance of 203 proteins was significantly modified by the nutrition treatments. Our results showed that the grain proteome was highly affected by perturbation in the N:S balance. S supply strongly increased the rate of accumulation of S-rich a/b-gliadin and c-gliadin, and the abundance of several other proteins involved in glutathione metabolism. Post-anthesis N supply resulted in the activation of amino acid metabolism at the expense of carbohydrate metabolism and the activation of transport processes including nucleocytoplasmic transit. Protein accumulation networks were analyzed. Several central actors in the response were identified whose variation in abundance was related to variation in the amounts of many other proteins and are thus potentially important for GSP accumulation. This detailed analysis of grain subproteomes provides information on how wheat GSP composition can possibly be controlled in low-level fertilization condition. Keywords: Triticum monococcum, grain, nitrogen, sulfur, storage proteins, nuclear proteins, albumin-globulin, network. DOI: 10.1111/tpj.13615
Modeling the spatial distribution of plants on the row for wheat crops: Consequences on the green fraction at the canopy level
Shouyang Liu, Frédéric Baret, Bruno Andrieu, Mariem Abichou, Denis Allard, Benoit de Solan, Philippe Burger - Computers and Electronics in Agriculture, 2017
Abstract: This work investigates the spatial distribution of wheat plants and its consequences on the canopy structure. A set of RGB images were taken from nadir on a total 14 plots showing a range of sowing densities, cultivars and environmental conditions. The coordinates of the plants were extracted from RGB images. Results show that the distance between-plants along the row follows a gamma distribution law, with no dependency between the distances. Conversely, the positions of the plants across rows follow a Gaussian distribution, with strongly interdependent. A statistical model was thus proposed to simulate the possible plant distribution pattern. Through coupling the statistical model with 3D Adel-Wheat model, the impact of the plant distribution pattern on canopy structure was evaluated using emerging properties such as the green fraction (GF) that drives the light interception efficiency. Simulations showed that the effects varied over different development stages but were generally small. For the intermediate development stages, large zenithal angles and directions parallel to the row, the deviations across the row of plant position increased the GF by more than 0.1. These results were obtained with a wheat functionalstructural model that does not account for the capacity of plants to adapt to their local environment. Nevertheless, our work will extend the potential of functional-structural plant models to estimate the optimal distribution pattern for given conditions and subsequently guide the field management practices. Keywords: Plant distribution pattern, Green fraction, FSPMs, Wheat. DOI: 10.1016/j.compag.2017.02.022
Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery
Shouyang Liu, Fred Baret, Bruno Andrieu, Philippe Burger and Matthieu Hemmerlé - Frontiers in Plant Science, 2017
Abstract: Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds.m-2. Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages. Keywords: plant density, RGB imagery, neural network, wheat, recursive feature elimination, Hough transform. DOI: 10.3389/fpls.2017.00739
A method to estimate plant density and plant spacing heterogeneity: application to wheat crops
Shouyang Liu, Fred Baret, Denis Allard, Xiuliang Jin, Bruno Andrieu, Philippe Burger, Matthieu Hemmerlé and Alexis Comar - Plant Methods, 2017
Abstract: Background: Plant density and its non-uniformity drive the competition among plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the plant density in wheat crops from plant counting and reach a given precision. Results: Three experiments were conducted in 2014 resulting in 14 plots across varied sowing density, cultivars and environmental conditions. The coordinates of the plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate plant density for a given precision. Results suggest that measuring the length of segments containing 90 plants will achieve a precision better than 10%, independently from the plant density. This approach appears more efficient than the usual method based on fixed length segments where the number of plants are counted: the optimal length for a given precision on the density estimation will depend on the actual plant density. The gamma count model parameters may also be used to quantify the heterogeneity of plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive plants should be measured. Conclusions: This method provides an optimal sampling strategy to estimate the plant density and quantify the plant spacing heterogeneity along the row. Keywords: Wheat, Gamma-count model, Density, RGB imagery, Sampling strategy, Plant spacing heterogeneity. DOI 10.1186/s13007-017-0187-1
Bridging the gap between ideotype and genotype: Challenges and prospects for modelling as exemplified by the case of adapting wheat (Triticum aestivum L.) phenology to climate change in France
David Gouache, Matthieu Bogard, Marie Pegard, Stéphanie Thepot,Cécile Garcia, Delphine Hourcade, Etienne Paux, Francois-Xavier Oury, Michel Rousset, Jean-Charles Deswarte, Xavier Le Bris - Field Crops Research, 2017
Abstract: Simulations using crop models can assist designing ideotypes for current and future agricultural conditions. This approach consists in running simulations for different “in silico genotypes” obtained by varying the most sensitive genotypic parameters of these models, and analyzing results obtained for different environments, so as to identify the best genotypes for a target population of environments. However, this approach has rarely been used to guide commercial breeding programs so far. In this paper, we attempt to address some of the gaps yet to be filled before this kind of approach can be implemented,and identify some remaining issues that should be addressed in future research. Our focus is on optimizing wheat phenology, integrating simulations from a modified version of the ARCWHEAT model of wheat growth stages with available knowledge on the genetic control of wheat phenology obtained via molecular markers. Based on simulations, stem extension could be advanced by 10 days in 2025–2049 without increasing frost risks, thus opening up opportunities for lengthening the rapid growth period.Analysis of the current genetic variability for major phenology genes in French elite varieties, showed that the insensitive PpdD1—spring Vrn3 allele combination appears undesirable and current genotypes with early stem extensions are unstable (i.e. show a strong response to temperature and can start the stem extension very early in case of mild winter temperatures). We finally use a case study on gene-based modelling of wheat phenology in France to illustrate how it can be used to dissect the genetic basis of the quantitative nature of the three components of earliness, beyond the effects of major genes. We identify the need to link the variability for optimized model parameters and the allelic variations at the gene level as a critical step of this type of approach. Keywords: Wheat, Earliness, Stem extension, Marker-based model, Plasticity, Photoperiod sensitivity. DOI: 10.1016/j.fcr.2015.12.012
Fortune telling: metabolic markers of plant performance
Olivier Fernandez, Maria Urrutia, Stéphane Bernillon, Catherine Giauffret, François Tardieu, Jacques Le Gouis, Nicolas Langlade, Alain Charcosset, Annick Moing, Yves Gibon - Metabolomics, 2016
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<br /> 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. Keywords Breeding: Metabolic marker, Metabolomics, Plant performance, Prediction. DOI: 10.1007/s11306-016-1099-1
CN-Wheat, a functional-structural model of carbon and nitrogen metabolism in wheat culms after anthesis. I. Model description
Romain Barillot, Camille Chambon and Bruno Andrieu - Annals of Botany, 2016
Abstract: Background and Aims Improving crops requires better linking of traits and metabolic processes to whole plant performance. In this paper, we present CN-Wheat, a comprehensive and mechanistic model of carbon (C) and nitrogen (N) metabolism within wheat culms after anthesis. Methods The culm is described by modules that represent the roots, photosynthetic organs and grains. Each of them includes structural, storage and mobile materials. Fluxes of C and N among modules occur through a common pool and through transpiration flow. Metabolite variations are represented by differential equations that depend on the physiological processes occurring in each module. A challenging aspect of CN-Wheat lies in the regulation of these processes by metabolite concentrations and the environment perceived by organs. Key Results CN-Wheat simulates the distribution of C and N into wheat culms in relation to photosynthesis, N-uptake, metabolite turnover, root exudation and tissue death. Regulation of physiological activities by local concentrations of metabolites appears to be a valuable feature for understanding how the behaviour of the whole plant can emerge from local rules. Conclusions The originality of CN-Wheat is that it proposes an integrated view of plant functioning based on a mechanistic approach. The formalization of each process can be further refined in the future as knowledge progresses. This approach is expected to strengthen our capacity to understand plant responses to their environment and investigate plant traits adapted to changes in agronomical practices or environmental conditions. A companion paper will evaluate the model. Key words: Amino acids, carbon, cytokinins, fructans, process-based functional–structural plant model, nitrogen, proteins, plant metabolism and physiology, sink–source relations, sucrose, Triticum aestivum, wheat. DOI:
CN-Wheat, a functional–structural model of carbon and nitrogen metabolism in wheat culms after anthesis. II. Model evaluation
Romain Barillot, Camille Chambon and Bruno Andrieu - Annals of Botany, 2016
Abstract: Background and Aims Simulating resource allocation in crops requires an integrated view of plant functioning and the formalization of interactions between carbon (C) and nitrogen (N) metabolisms. This study evaluates the functional–structural model CN-Wheat developed for winter wheat after anthesis. Methods In CN-Wheat the acquisition and allocation of resources between photosynthetic organs, roots and grains are emergent properties of sink and source activities and transfers of mobile metabolites. CN-Wheat was calibrated for field plants under three N fertilizations at anthesis. Model parameters were taken from the literature or calibrated on the experimental data. Key Results The model was able to predict the temporal variations and the distribution of resources in the culm. Thus, CN-Wheat accurately predicted the post-anthesis kinetics of dry masses and N content of photosynthetic organs and grains in response to N fertilization. In our simulations, when soil nitrates were non-limiting, N in grains was ultimately determined by availability of C for root activity. Dry matter accumulation in grains was mostly affected by photosynthetic organ lifespan, which was regulated by protein turnover and C-regulated root activity.<br /> Conclusions The present study illustrates that the hypotheses implemented in the model were able to predict realistic dynamics and spatial patterns of C and N. CN-Wheat provided insights into the interplay of C and N metabolism and how the depletion of mobile metabolites due to grain filling ultimately results in the cessation of resource capture. This enabled us to identify processes that limit grain mass and protein content and are potential targets for plant breeding. Key words: Amino acids, carbon, cytokinins, fructans, process-based functional–structural plant model, nitrogen, proteins, plant metabolism and physiology, sink-source relations, sucrose, Triticum aestivum, wheat. DOI:
Proteomic Approach to Identify Nuclear Proteins in Wheat Grain
Emmanuelle Bancel, Titouan Bonnot, Marlène Davanture, Gérard Branlard, Michel Zivy, and Pierre Martre - Journal of Proteome Research, 2015
Abstract: The nuclear proteome of the grain of the two cultivated wheat species Triticum aestivum (hexaploid wheat; genomes A, B, and D) and T. monococcum (diploid wheat; genome A) was analyzed in two early stages of development using shotgun-based proteomics. A procedure was optimized to purify nuclei, and an improved protein sample preparation was developed to efficiently remove nonprotein substances (starch and nucleic acids). A total of 797 proteins corresponding to 528 unique proteins were identified, 36% of which were classified in functional groups related to DNA and RNA metabolism. A large number (107 proteins) of unknown functions and hypothetical proteins were also found. Some identified proteins may be multifunctional and may present multiple localizations. On the basis of the MS/MS analysis, 368 proteins were present in the two species, and in two stages of development, some qualitative differences between species and stages of development were also found. All of these data illustrate the dynamic function of the grain nucleus in the early stages of development. Keywords: cereal, grain development, bread wheat (Triticum aestivum), einkorn wheat (Triticum monococcum), LC−MS/MS, nuclear proteome. DOI: 10.1021/acs.jproteome.5b00446
Changes in the nuclear proteome of developing wheat (Triticum aestivumL.) grain
Titouan Bonnot, Emmanuelle Bancel, Christophe Chambon, Julie Boudet, Gérard Branlard, and Pierre Martre - Frontiers in Plant Science, 2015
Abstract: Wheat grain end-use value is determined by complex molecular interactions that occur during grain development, including those in the cell nucleus. However, our knowledge of how the nuclear proteome changes during grain development is limited. Here, we analyzed nuclear proteins of developing wheat grains collected during the cellularization, effective grain-filling, and maturation phases of development, respectively. Nuclear proteins were extracted and separated by two-dimensional gel electrophoresis. Image analysis revealed 371 and 299 reproducible spots in gels with first dimension separation along pH 4–7 and pH6–11 isoelectric gradients, respectively. The relative abundance of 464 (67%) protein spots changed during grain development. Abundance profiles of these proteins clustered in six groups associated with the major phases and phase transitions of grain development. Using nano liquid chromatography-tandem mass spectrometry to analyse 387 variant and non-variant protein spots, 114 different proteins were identified that were classified into 16 functional classes. We noted that some proteins involved in the regulation of transcription, like HMG1/2-like protein and histone deacetylase HDAC2, were most abundant before the phase transition from cellularization to grain-filling, suggesting that major transcriptional changes occur during this key developmental phase. The maturation period was characterized by high relative abundance of proteins involved in ribosome biogenesis. Data are available via ProteomeXchange with identifier PXD002999. Keywords: wheat, developing grain, nuclear proteins, 2D gel electrophoresis, LC-MS/MS. DOI: 10.3389/fpls.2015.00905
RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat –Omics Data
Jonathan Vincent, Pierre Martre, Benjamin Gouriou, Catherine Ravel, Zhanwu Dai, Jean-Marc Petit, Marie Pailloux - PLOS one, 2015
With the increasing amount of –omics data available, a particular effort has to be made to provide suitable analysis tools. A major challenge is that of unraveling the molecular regulatory networks from massive and heterogeneous datasets. Here we describe RulNet, a weboriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data by means of rule discovery. Queries for rule discovery can be written in an extended form of the RQL query language, which has a syntax similar to SQL. RulNet also offers users interactive features that progressively adjust and refine the inferred networks. In this paper, we present a functional characterization of RulNet and compare inferred networks with correlation-based approaches. The performance of RulNet has been evaluated using the three benchmark datasets used for the transcriptional network inference challenge DREAM5. Overall, RulNet performed as well as the best methods that participated in this challenge and it was shown to behave more consistently when compared across the three datasets. Finally, we assessed the suitability of RulNet to analyze experimental –omics data and to infer regulatory networks involved in the response to nitrogen and sulfur supply in wheat (Triticum aestivum L.) grains. The results highlight putative actors governing the response to nitrogen and sulfur supply in wheat grains. We evaluate the main characteristics and features of RulNet as an all-in-one solution for RN inference, visualization and editing. Using simple yet powerful RulNet queries allowed RNs involved in the adaptation of wheat grain to N and S supply to be discovered.We demonstrate the effectiveness and suitability of RulNet as a platform for the analysis of RNs involving different types of –omics data. The results are promising since they are consistent with what was previously established by the scientific community. DOI: 10.1371/journal.pone.0127127
Evolution de l'organisation de la recherche et du secteur des semences
Aline Fugeray-Scarbel & Stéphane Lemarie - Le selectionneur français, 2013
Depuis son émergence à la fin du XIXème siècle, le secteur des semences a connu des évolutions importantes conduisant à une réorganisation générale de la recherche en amélioration des plantes. Le premier fait marquant de cette évolution concerne le positionnement relatif de la recherche publique et de la recherche privée. L'effort privé en recherche a augmenté suite aux évolutions réglementaires (DHS, VAT), à la mise en place de droits de propriété (COV) et, dans certains cas, au développement des semences hybrides. La recherche publique s'est alors repositionnée sur les domaines pour lesquels il existait des défaillances du marché (recherche amont, recherche méthodologique, segments orphelins). Le deuxième fait marquant de cette évolution concerne la structure interne du secteur des semences. Bien qu'il soit encore globalement peu concentré, ce secteur a vu progressivement émerger des acteurs majeurs ayant des positions fortes à la fois sur les semences et dans le domaine des biotechnologies, ces positions étant renforcées par le développement de brevets sur le vivant. Cette concentration croissante s'explique également par les coûts (fixes) croissants liés à la recherche, la réglementation, et la gestion de la propriété intellectuelle. Key words: amélioration des plantes, biotechnologie, structure industrielle, recherche publique