196 research outputs found
Using numerical plant models and phenotypic correlation space to design achievable ideotypes
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 Environmen
Schémas de sélection : de la représentation généalogique au modèle statistique. Elaboration du modèle
Schémas de sélection: de la représentation généalogique au modèle statistique. Élaboration du modèle
International audienc
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
Genetic control of protein, oil and fatty acids content under partial drought stress and late sowing conditions in sunflower (Helianthus annuus)
The purpose of the present study was to map quantitative trait locus (QTLs) associated with percentage of seed protein, oil and fatty acids content under different conditions in a population of recombinant inbred lines (RILs) of sunflower. Three independent field experiments were conducted with well-, partial-irrigated and late-sowing conditions in randomized complete block design with three replications. High significant variation among genotypes is observed for the studied traits in all conditions. Several specific and non-specific QTLs for the aforementioned traits were detected. Under late-sowing condition, a specific QTL of palmitic acid content on linkage group 6 (PAC-LS.6) is located between ORS1233 and SSL66_1 markers. Common chromosomic regions are observed for percentage of seed oil and stearic acid content on linkage group 10 (PSO-PI.10 and SAC-WI.10) and 15 (PSO-PI.15 and SAC-LS.15). Overlapping occurs for QTLs of oleic and linoleic acids content on linkage groups 10, 11 and 16. Seven QTLs associated with palmitic, stearic, oleic and linoleic acids content are identified on linkage group 14. These common QTLs are linked to HPPD homologue, HuCL04260C001. Coincidence of the position for some detected QTLs and candidate genes involved in enzymatic and non-enzymatic antioxidants would be useful for the function of the respective genes in fatty acid stability.Key words: Sunflower, quantitative trait locus, simple sequence repeats, oil content, protein content, fatty acids
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
Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks
BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets
Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks
BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets
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