Increased genetic diversity improves crop yield stability under climate variability: a computational study on sunflower

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

Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modeling and genome-wide association

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.Comment: 12 pages, 5 figures, Plant, Cell and Environmen

Genome-wide and comparative phylogenetic analysis of senescence-associated NAC transcription factors in sunflower (Helianthus annuus)

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. 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. This finding suggests HaNAC001 as an interesting candidate to explore the molecular regulation of senescence in sunflower

A Gene-Phenotype Network Based on Genetic Variability for Drought Responses Reveals Key Physiological Processes in Controlled and Natural Environments

Identifying the connections between molecular and physiological processes underlying the diversity of drought stress responses in plants is key for basic and applied science. Drought stress response involves a large number of molecular pathways and subsequent physiological processes. Therefore, it constitutes an archetypical systems biology model. We first inferred a gene-phenotype network exploiting differences in drought responses of eight sunflower (Helianthus annuus) genotypes to two drought stress scenarios. Large transcriptomic data were obtained with the sunflower Affymetrix microarray, comprising 32423 probesets, and were associated to nine morpho-physiological traits (integrated transpired water, leaf transpiration rate, osmotic potential, relative water content, leaf mass per area, carbon isotope discrimination, plant height, number of leaves and collar diameter) using sPLS regression. Overall, we could associate the expression patterns of 1263 probesets to six phenotypic traits and identify if correlations were due to treatment, genotype and/or their interaction. We also identified genes whose expression is affected at moderate and/or intense drought stress together with genes whose expression variation could explain phenotypic and drought tolerance variability among our genetic material. We then used the network model to study phenotypic changes in less tractable agronomical conditions, i.e. sunflower hybrids subjected to different watering regimes in field trials. Mapping this new dataset in the gene-phenotype network allowed us to identify genes whose expression was robustly affected by water deprivation in both controlled and field conditions. The enrichment in genes correlated to relative water content and osmotic potential provides evidence of the importance of these traits in agronomical conditions

Tinkering with the C-Function: A Molecular Frame for the Selection of Double Flowers in Cultivated Roses

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Proteomic data from leaves of twenty-four sunflower genotypes under water deficit

This article describes a proteomic data set produced from sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower. They included both inbred lines and their hybrids. Water deficit was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. We present here the identification of 3062 proteins and the quantification of 1211 of them in the leaves of the 24 genotypes grown under two watering conditions. These data allow the study of both the effects of genetic variations and watering conditions. They constitute a valuable resource for the community to study adaptation of crops to drought and the molecular basis of heterosis