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Background: Leaf senescence delay impacts positively in grain yield by maintaining the photosynthetic area during the reproductive stage and during grain filling. Therefore a comprehensive understanding of the gene families associated with leaf senescence is essential. NAC transcription factors (TF) form a large plant-specific gene family involved in regulating development, senescence, and responses to biotic and abiotic stresses. The main goal of this work was to identify sunflower NAC TF (HaNAC) and their association with senescence, studying their orthologous to understand possible functional relationships between genes of different species. Results: To clarify the orthologous relationships, we used an in-depth comparative study of four divergent taxa, in dicots and monocots, with completely sequenced genomes (Arabidopsis thaliana, Vitis vinifera, Musa acuminata and Oryza sativa). These orthologous groups provide a curated resource for large scale protein sequence annotation of NAC TF. From the 151 HaNAC genes detected in the latest version of the sunflower genome, 50 genes were associated with senescence traits. These genes showed significant differential expression in two contrasting lines according to an RNAseq assay. An assessment of overexpressing the Arabidopsis line for HaNAC001 (a gene of the same orthologous group of Arabidopsis thaliana ORE1) revealed that this line displayed a significantly higher number of senescent leaves and a pronounced change in development rate. Conclusions: This finding suggests HaNAC001 as an interesting candidate to explore the molecular regulation of senescence in sunflower. Instituto de Biotecnología Fil: Bengoa Luoni, Sofia Ailin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina. Fil: Cenci, Alberto. Bioversity International; Francia Fil: Moschen, Sebastian Nicolás. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina. Fil: Nicosia, Salvador. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina. Fil: Radonic, Laura Mabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina. Fil: Sabio Y Garcia, Julia Veronica. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina Fil: Langlade, Nicolas B. Université de Toulouse. LIPM-INRA-CNRS; Francia Fil: Langlade, Nicolas B. Université de Montpellier. LEPSE; Francia Fil: Vazquez Rovere, Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina Fil: Fernandez, Paula Del Carmen. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina.

International audience; This article describes how metabolomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower ( Helianthus annuus L.) genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied at the vegetative stage to plants cultivated in pots using the high-throughput phenotyping facility Heliaphen. Here, we provide untargeted and targeted metabolomic data of sunflower leaves. These compositional data differentiate both plant water status and different genotype groups. They constitute a valuable resource for the community to study the adaptation of crops to drought and the metabolic bases of heterosis.; Cet article décrit comment les données métabolomiques ont été produites sur des plants de tournesol soumis à un déficit hydrique. Vingt-quatre génotypes de tournesol ( Helianthus annuus L.) ont été sélectionnés pour représenter la diversité génétique du tournesol cultivé et comprennent à la fois des lignées consanguines et leurs hybrides. Une limitation hydrique a été appliquée au stade végétatif aux plantes cultivées en pots à l’aide de la plateforme de phénotypage à haut débit Heliaphen. Ici, nous mettons à disposition des données métabolomiques non ciblées et ciblées de feuilles de tournesol. Ces données de composition permettent de différencier l’état hydrique des plantes et différents groupes de génotypes. Elles constituent une ressource précieuse pour la communauté afin d’étudier l’adaptation des cultures à la sécheresse et les bases métaboliques de l’hétérosis.

International audience; As a rainfed spring-sown crop, sunflower ( Helianthus annuus L.) is increasingly exposed to negative impacts of climate change, especially to high temperatures and drought stress. Incremental, systemic and transformative adaptations have been suggested for reducing the crop vulnerability to these stressful conditions. In addition, innovative cropping systems based on low-input management, organic farming, soil and water conservation practices, intercropping, double-cropping, and/or agroforestry are undergoing marked in agriculture. Because of its plasticity and low-input requirements (nitrogen, water, pesticides), sunflower crop is likely to take part to these new agroecological systems. Aside from current production outputs (yield, oil and cake), ecosystem services ( e.g. bee feeding, soil phytoremediation…), and non-food industrial uses are now expected externalities for the crop. The combination of climatic and societal contexts could deeply modify the characteristics of genotypes to be cultivated in the main production areas (either traditional or adoptive). After reviewing these changes, we identify how innovative cropping systems and new environments could modify the traits classically considered up to now, especially in relation to expected ecosystem services. Finally, we consider how research could provide methods to help identifying traits of interest and design ideotypes.; En tant que culture d’été non irriguée, le tournesol ( Helianthus annuus L.) est davantage exposé aux impacts négatifs du changement climatique, et en particulier aux températures élevées et au stress hydrique. Des adaptations incrémentales, systémiques ou de rupture ont été suggérées pour réduire la vulnérabilité de la culture à ces conditions stressantes. Par ailleurs, des systèmes de culture innovants sont en fort développement qu’il s’agisse de systèmes à bas niveaux d’intrants, d’agriculture biologique, d’agriculture de conservation des sols, d’agroforesterie, ou de l’introduction d’associations de cultures et de doubles cultures. En raison de sa plasticité et de ses faibles besoins en intrants (eau, azote, pesticides), la culture de tournesol est amenée à s’insérer de manière privilégiée dans ces systèmes agro-écologiques. À côté de la production (grains, huile, tourteaux), de nouvelles externalités sont attendues comme la contribution aux autres services écosystémiques (pollinisateurs, phytoremédiation…) et de nouveaux usages non alimentaires. La conjugaison des contextes climatiques et sociétaux pourrait profondément changer les caractéristiques attendues des variétés cultivées dans les principales régions de production qu’elles soient traditionnelles ou en reconquête. Après une revue de ces changements, nous identifions comment systèmes innovants et changement climatique pourraient modifier les caractères phénotypiques considérés classiquement, en particulier dans la perspective de nouveaux services. Enfin, nous verrons comment la recherche pourrait fournir des méthodes pour faciliter l’identification de ces caractères d’intérêt et aider à la conception d’idéotypes.

This article describes how the transcriptomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower (Helianthus annuus) genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. Here, we provide transcriptomic data from sunflower leaves. These data differentiate both plant water status and the different genotypes. They constitute a valuable resource to the community to study adaptation of crops to drought and the transcriptomic basis of heterosis.; Données transcriptomiques de feuilles de huit lignées de tournesol et de leurs 16 hybrides soumis à un stress hydrique. Cet article décrit la production de données transcriptomiques sur des plantes de tournesol soumises à un stress hydrique. Vingt-quatre génotypes de tournesol (Helianthus annuus) ont été sélectionnés pour représenter la diversité génétique parmi le tournesol cultivé et comprennent à la fois des lignées et leurs hybrides. Le stress hydrique a été appliqué sur des plantes en pots au stade végétatif grâce à la plateforme de phénotypage haut-débit Heliaphen. Ici, nous mettons à disposition les données transcritomiques de feuilles de ces plantes. Les données permettent de différencier à la fois le statut hydrique et les différents génotypes. Elles constituent une ressource importante pour la communauté pour étudier l'adaptation des plantes à la sécheresse et les bases de l'hétérosis au niveau transcriptomique.

This article describes how RNA expression data of 173 genes were produced on 384 sunflowerhybrids grown infield conditions. Sunflower hybrids were selected to represent genetic diversity withincultivated sunflower. The RNA was extracted from mature leaves at one time seven days after anthesis.These data allow to differentiate the different genotype behaviours and constitute a valuable resource to thecommunity to study the adaptation of crops tofield conditions and the molecular basis of heterosis. It isavailable on data.inra.fr repository.; Cet article décrit la production des niveaux d’expression de 173 gènes dans 384 hybrides de tournesol cultivés en conditions de champ. Les hybrides sont issus de parents choisis pour représenter la diversité génétique dans le tournesol cultivé. Les ARN ont été extraits à partir de feuilles matures environ sept jours après la floraison. Ces données permettent de différencier les comportements des différents génotypes et constituent une ressource importante pour les chercheurs intéressés dans l’adaptation des espèces cultivées aux conditions agronomiques et aux bases moléculaires de l’hétérosis. Elles sont disponibles sur le portail Data INRAE : data.inra.fr.

This article describes how the transcriptomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower (Helianthus annuus) genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. Here, we provide transcriptomic data from sunflower leaves. These data differentiate both plant water status and the different genotypes. They constitute a valuable resource to the community to study adaptation of crops to drought and the transcriptomic basis of heterosis.; Données transcriptomiques de feuilles de huit lignées de tournesol et de leurs 16 hybrides soumis à un stress hydrique. Cet article décrit la production de données transcriptomiques sur des plantes de tournesol soumises à un stress hydrique. Vingt-quatre génotypes de tournesol (Helianthus annuus) ont été sélectionnés pour représenter la diversité génétique parmi le tournesol cultivé et comprennent à la fois des lignées et leurs hybrides. Le stress hydrique a été appliqué sur des plantes en pots au stade végétatif grâce à la plateforme de phénotypage haut-débit Heliaphen. Ici, nous mettons à disposition les données transcritomiques de feuilles de ces plantes. Les données permettent de différencier à la fois le statut hydrique et les différents génotypes. Elles constituent une ressource importante pour la communauté pour étudier l'adaptation des plantes à la sécheresse et les bases de l'hétérosis au niveau transcriptomique.

Key message This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Abstract Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids. Electronic supplementary material The online version of this article (doi:10.1007/s00122-017-3003-4) contains supplementary material, which is available to authorized users.

International audience; Seeds are a major means for transforming farming systems and thus occupy a prominent place in the transition to more sustainable agriculture. This article analyses the conditions involved in creating and diffusing new plant breeds that have agroecological benefits, called eco-innovation varieties. By combining theories on eco-innovation processes with various scales of analysis, this study makes an original contribution to understanding innovation and transformation dynamics in the seed industry. Drawing on a case study of the sunflower industry in France and in Europe, the findings reveal which factors promote and hinder eco-innovation varieties. The results confirm the positive interactions between technology, regulation, and the market in eco-innovation processes. They also show that economic incentives for companies are insufficient to push them to develop more systemic eco-innovation varieties. Moreover, changes in the mainstream seed company regime, with the increasing internationalization of their strategies, call into question their ability to foster farming practices that promote the environmental benefits of these new varieties.

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modeling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65,534 tested SNP, we identified nine QTL controlling oil yield plasticity to cold stress. Associated SNP are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, LTP, cystatin, alternative oxidase, or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions., 12 pages, 5 figures, Plant, Cell and Environment

International audience; Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore. GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.

The domesticated sunflower, Helianthus annuus L., is a global oil crop that has promise for climate change adaptation, because it can maintain stable yields across a wide variety of environmental conditions, including drought. Even greater resilience is achievable through the mining of resistance alleles from compatible wild sunflower relatives, including numerous extremophile species. Here we report a high-quality reference for the sunflower genome (3.6 gigabases), together with extensive transcriptomic data from vegetative and floral organs. The genome mostly consists of highly similar, related sequences and required single-molecule real-time sequencing technologies for successful assembly. Genome analyses enabled the reconstruction of the evolutionary history of the Asterids, further establishing the existence of a whole-genome triplication at the base of the Asterids II clade and a sunflower-specific whole-genome duplication around 29 million years ago. An integrative approach combining quantitative genetics, expression and diversity data permitted development of comprehensive gene networks for two major breeding traits, flowering time and oil metabolism, and revealed new candidate genes in these networks. We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs.

International audience; 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.

Given the diversification of oilseed-based products, sunflower is a competitive crop for obtaining high oil and protein concentrations; however, both are subject to genotypic and environmental variability. We analyzed individual and interaction effects of nitrogen (N), plant density (D) and genotype (G) in a 2-year field experiment. A set of “sink” (oil, protein, and hull concentrations and quantities) and “source” (leaf area duration, nitrogen uptake, nitrogen and biomass remobilizations) indicators were measured at harvest in a split-split-plot design with contrasting nitrogen (N+: 150 kg ha−1 ; N−: no fertilization), plant density (D1: 3 and D2: 4.5 plants m−2 ) and genotype (cv. LG5451HO in 2011, cv. Olledy in 2012 and cv. Kerbel in both years) treatments. We found that nitrogen had a significant positive effect on protein con- centration and plant density had a positive effect on nitrogen uptake after flowering. Oil concentration was not related to oil weight but was related to plant dry matter at flowering and biomass remobilization. Protein concentration was related to protein weight and nitrogen nutrition index at flowering and to nitrogen uptake and leaf area duration after flowering. Significant interaction effects were identified on sink (N × D, D × G) and source (N × G) indicators in the 2012 experiment, which was only partly explained by differences in initial states at flowering. In this study, the genotype that maximized oil concentration depended on nitrogen and plant density conditions, while the genotype that maximized protein concen- tration was the same regardless of cropping conditions. We highlight the importance of analyzing effects of determining factors on oil accumulation during grain filling.

A crop can be represented as a biotechnical system in which components are either chosen (cultivar, management) or given (soil, climate) and whose combination generates highly variable stress patterns and yield responses. Here, we used modeling and simulation to predict the crop phenotypic plasticity resulting from the interaction of plant traits (G), climatic variability (E) and management actions (M). We designed two in silico experiments that compared existing and virtual sunflower cultivars ( Helianthus annuus L. ) in a target population of cropping environments by simulating a range of indicators of crop performance. Optimization methods were then used to search for GEM combinations that matched desired crop specifications. Computational experiments showed that the fit of particular cultivars in specific environments is gradually increasing with the knowledge of pedo-climatic conditions. At the regional scale, tuning the choice of cultivar impacted crop performance the same magnitude as the effect of yearly genetic progress made by breeding. When considering virtual genetic material, designed by recombining plant traits, cultivar choice had a greater positive impact on crop performance and stability. Results suggested that breeding for key traits conferring plant plasticity improved cultivar global adaptation capacity whereas increasing genetic diversity allowed to choose cultivars with distinctive traits that were more adapted to specific conditions. Consequently, breeding genetic material that is both plastic and diverse may improve yield stability of agricultural systems exposed to climatic variability. We argue that process-based modeling could help enhancing spatial management of cultivated genetic diversity and could be integrated in functional breeding approaches