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Sunflower (Helianthus annuus L.) is used as “seed snacks”, so called “pipas”, in the Mediterranean countries which may offer an interesting opportunity for agricultural diversification of this crop. The morpho-phenological variability already demonstrated in the Tunisian local populations can be a basis for the creation of new varieties well adapted to climate and soil conditions in that region. The molecular characterization of 59 accessions and reference lines generated 194 alleles from 30 SSR loci (3–10 alleles per locus) and 54 haplotypes, built from 117 SNP detected by NGS sequencing of 7 genes (4–16 haplotypes per gene). These data highlight some uniqueness of the Tunisian material compared to 7 control lines but a low genetic dispersion between accessions. However, a core collection of 8 populations, capturing 88 % of the Tunisian genetic diversity, could be proposed for a future sunflower breeding program.

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity., Comment: 25 pages, 5 figures, 2017, 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.

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.

Genetic architecture of the relationship between morphological and developmental traits, and sunflower resistance traits to Phoma macdonaldii. (DOCX 28 kb)

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.

Characterization of plant morphological and physiological responses is a limiting step to breed crops adapted to drought-limiting conditions. Automation of plant management on a phenotyping platform overcomes it by allowing large scale experimentation with yet accurate and individual plant monitoring. In response to both genetic and eco-physiological experimentation requirements, we developed the HELIAPHEN platform. This unique outdoor platform can host 1300 plants, such as sunflower, in 15L pots. It allows plant growth in climatic conditions similar to field, as well as a precise and automated monitoring of plant water consumption thanks to a prototype robot. Its primary functions are to move autonomously on the 600m2 platform, and to treat each pot at its location (including weighing and watering up to a targeted weight). Beyond these functions, the robot takes at each handling, plant images from multiple angles with four cameras, to follow the evolution of morphological traits along with the description of the water status. In addition, a ultrasound radar measures automatically plant height and a laser measures stem diameter at the plant basis. These secondary functions are currently improved with new captors such as a light curtain and a 3D laser in order to reconstitute a 3D representation of the plant. To validate the meaning of the HELIAPHEN outputs, we confirmed the impact of drought stress managed with the robot on seed weight, number and thousand kemel weight (TKW). Furthermore, we observed a correlation between field and HELIAPHEN data for TKW and seed number observed on 45 sunflower hybrids.

La résistance quantitative du tournesol (Helianthus annuus) au mildiou (Plasmopara halstedii), mise en évidence récemment, est difficile à évaluer. Un réseau de onze expérimentations associant la GEVESSNES, deux laboratoires de l’INRA et Terres Inovia a été construit afin de proposer une méthodologie permettant de mieux caractériser et quantifier cette résistance. En plus de lignées témoins, 17 lignées recombinantes obtenues par l’INRA et portant entre 0 et 3 QTL (quantitative trait loci) impliqués dans la résistance quantitative du tournesol au mildiou ont été testées face à 5 pathotypes de mildiou : 710, 704, 714, 304 et 334. Bien que des différences aient été observées entre laboratoires, les résultats ont permis de poser les bases d’un futur protocole utilisable en routine pour valoriser ce type de résistance dès la création variétale jusqu’à l’inscription puis la post-inscription des variétés., A quantitative resistance of sunflower (Helianthus annuus) to downy mildew (Plasmopara halstedii) has been identified a few years ago. An experimental network involving several labs (GEVES-SNES, INRA and Terres Inovia) over several years aimed at describing and quantifying such a quantitative response. It was hoped to propose a methodology useable to improve sustainable resistance. In addition to check varieties and breeding lines, 17 recombinant inbred lines carrying 0 to 3 quantitative trait loci alleles for quantitative resistance were tested with five downy mildew pathotypes (710, 704, 714, 304, 334). While the experimental results revealed some discrepancies between experiments, this study provided a first experimental protocol to evaluate quantitative resistance both for breeding and for variety registration and development

Trabajo presentado en el 13th World Congress on Parasitic Plants (Parasitic plants: the good, the bad, and the mysterious), celebrado en Kunming (China) del 5 al 10 de julio de 2015., Sunflower is an important crop and is mainly used for oil production. Orobanche cumana is a major disease in cultivated areas around the black sea and in Spain. The pathogen spread more recently to several other countries (France, China ...). During the last ten years, several new O. cumana races have emerged but very few efficient methods were available to control their development. Genetic resistance was the more efficient and introgression of major resistance loci was successfully used to produce new resistant sunflower varieties (from races A to E). With the recent emergence of new virulent races (called F and F+), novel resistance loci need to be mapped and characterized. A recombinant inbred line population, derived from the cross between the lines HA89 and LR1, has been developed by INRA. It has been previously characterized for the resistance to the race E of O. cumana. We used this population to map QTLs controlling quantitative resistance to race F. The phenotyping has been conducted on the 107 lines of the population at different stages of the interaction. We evaluated each line for (i) the capacity of their root exudate to induce germination of O. cumana seeds, (ii) their ability to induce incompatible attachment, (iii) the number of broomrape tubercles in growth chamber, and (iv) the number of broomrape emergences in the field. Different response profiles were observed at these 4 stages of development, indicating several resistance mechanisms in sunflower. Interestingly, even if the two parental lines showed a close resistant phenotype, we observed a large diversity of the resistance level in the population. Combined with this detailed phenotyping analysis, we performed the genotyping of the sunflower recombinant inbred lines using an AXIOM array of 586 985 SNPs. QTLs will be mapped for the different traits.

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

Trabajo presentado en el Third Internacional Symposium on broomrape (Orobanche spp.) in Sunflower, celebrado en Córdoba (España) del 3 al 6 de junio de 2014., The plant parasite Orobanche cumana is a major threat for the sunflower crop. The emergence of new, virulent “races” during the ten past years reinforced the need to develop new approaches, knowledge and tools in order to control this pest efficiently. Breeding is the most sustainable approach to control broomrape in the field. A RIL population derived from a cross between HA89 and LR1, an inbred line bred from an interspecific cross with Helianthus debilis had been previously characterized for resistance to O. cumana race E, but no data is available for new races. The aim of this study was to characterize the HA89xLR1 RIL population for resistance to race F and to identify QTLs associated with this resistance. The population was phenotyped by counting the number of healthy broomrape tubercles and the rate of tubercle necrosis on young sunflower plants raised in a growth chamber with four biological replications. Differences in l resistance were observed among the RIL population, with some resistant genotypes and some highly susceptible genotypes. The polymorphism of 111 SNP markers previously mapped on a consensus genetic map was used for the QTL detection. Four QTLs were detected on four linkage groups (LG01, LG07, LG15 and LG17), with two QTLs controlling the number of tubercles per plant and two others controlling necrosis. This study suggests that the resistance to O. cumana race F is controlled by several QTLs affecting differently the number of tubercle and the induction of tubercle necrosis., This research was supported by the French Association for the Promotion of Oilseed Crops Breeding (PROMOSOL).

Gene regulatory networks (GRN) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We examined the responses of 32,423 expressed sequences to drought and to abscisic acid and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST) at nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity. This systems biology approach combined molecular data at different time scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments., New Phytologist, 2014, in press

La culture du tournesol est confrontée aux attaques de différents bioagresseurs. En France, il s’agit essentiellement du mildiou Plasmopara halstedii, du sclérotinia Sclerotinia sclerotiorum, du phomopsis Diaporthe helianthi, et du phoma Phoma macdonaldii dont les attaques au collet entrainent le dessèchement précoce. Les efforts de recherche et de sélection ont permis de conserver à cette culture son statut respectueux de l’environnement. Cependant, les dialogues moléculaires entre la plante et ces différents pathogènes et qui aboutissent à l’immunité ou à la sensibilité, ainsi que sa variabilité génétique, sont encore largement inconnus. Après une revue des principaux résultats récents concernant l’interaction entre le tournesol et ces pathogènes, nous soulevons deux questions d’intérêtpour la poursuite de ces recherches : celle de la répartition des rôles et de la hiérarchisation des questions de recherche d’une part, et d’autre part celle du regard à porter sur les réseaux de gènes que révèle l’analyse du transcriptome grâce aux nouvelles technologies de séquençage., The sunflower crop is subjected to several diseases, among of which Downy Mildew, Sclerotinia, Phomopsis and premature death caused by Phoma macdonaldii are the most important in France today. The research and breeding efforts during the past fifty years have been efficient enough to ensure an “environmentally safe” status to this cultivation. However, little is known about the moleculartalk which leads to immunity or susceptibility between the plant and the pathogens as well as about its genetic variability. After a review of the main recent results on the these sunflower*microorganisms interactions, we are discussing on two questions of interest for the research perspectives: How to establish the research priorities? How to decipher the genes networks revealed by transcriptome analysis with a greater involvement of the genetic variability thanks to the development of the new sequencing technologies?

In sunflower, the active leaf area during the grain filling period has been shown to be an important character, which can be used to predict seed yield. Total and residual leaf areas of inbred lines and their hybrids are significantly correlated, but there may be some interaction between parents. Strict sense heritability is moderate. The relation between residual leaf area and seed yield has been confirmed using simplified leaf measurements on recombinant inbred lines and estimation of leaf area index with a ceptometer on hybrids. Studies of use of residual leaf area as a character in breeding for seed yield showed that F2 or F3 plants with the smallest leaf areas can be discarded, but, according to genotype, those with medium or largest leaf area give the best seed yields.

International audience