Bénédicte Quilot-Turion

Comparison of the genetic determinism of two key phenological traits, flowering and maturity dates, in three Prunus species: peach, apricot and sweet cherry.

The present study investigates the genetic determinism of flowering and maturity dates, two traits highly affected by global climate change. Flowering and maturity dates were evaluated on five progenies from three Prunus species, peach, apricot and sweet cherry, during 3–8 years. Quantitative trait locus (QTL) detection was performed separately for each year and also by integrating data from all years together. High heritability estimates were obtained for flowering and maturity dates. Several QTLs for flowering and maturity dates were highly stable, detected each year of evaluation, suggesting that they were not affected by climatic variations. For flowering date, major QTLs were detected on linkage groups (LG) 4 for apricot and sweet cherry and on LG6 for peach. QTLs were identified on LG2, LG3, LG4 and LG7 for the three species. For maturity date, a major QTL was detected on LG4 in the three species. Using the peach genome sequence data, candidate genes underlying the major QTLs on LG4 and LG6 were investigated and key genes were identified. Our results provide a basis for the identification of genes involved in flowering and maturity dates that could be used to develop cultivar ideotypes adapted to future climatic conditions.

Whole-Genome Analysis of Diversity and SNP-Major Gene Association in Peach Germplasm.

Peach was domesticated in China more than four millennia ago and from there it spread world-wide. Since the middle of the last century, peach breeding programs have been very dynamic generating hundreds of new commercial varieties, however, in most cases such varieties derive from a limited collection of parental lines (founders). This is one reason for the observed low levels of variability of the commercial gene pool, implying that knowledge of the extent and distribution of genetic variability in peach is critical to allow the choice of adequate parents to confer enhanced productivity, adaptation and quality to improved varieties. With this aim we genotyped 1,580 peach accessions (including a few closely related Prunus species) maintained and phenotyped in five germplasm collections (four European and one Chinese) with the International Peach SNP Consortium 9K SNP peach array. The study of population structure revealed the subdivision of the panel in three main populations, one mainly made up of Occidental varieties from breeding programs (POP1OCB), one of Occidental landraces (POP2OCT) and the third of Oriental accessions (POP3OR). Analysis of linkage disequilibrium (LD) identified differential patterns of genome-wide LD blocks in each of the populations. Phenotypic data for seven monogenic traits were integrated in a genome-wide association study (GWAS). The significantly associated SNPs were always in the regions predicted by linkage analysis, forming haplotypes of markers. These diagnostic haplotypes could be used for marker-assisted selection (MAS) in modern breeding programs.

Profiling sugar metabolism during fruit development in a peach progeny with different fructose-to-glucose ratios

BackgroundFruit taste is largely affected by the concentration of soluble sugars and organic acids and non-negligibly by fructose concentration, which is the sweetest-tasting sugar. To date, many studies investigating the sugars in fruit have focused on a specific sugar or enzyme and often on a single variety, but only a few detailed studies addressing sugar metabolism both as a whole and dynamic system are available. In commercial peach fruit, sucrose is the main sugar, followed by fructose and glucose, which have similar levels. Interestingly, low fructose-to-glucose ratios have been observed in wild peach accessions. A cross between wild peach and commercial varieties offers an outstanding possibility to study fruit sugar metabolism.ResultsThis work provides a large dataset of sugar composition and the capacities of enzymes that are involved in sugar metabolism during peach fruit development and its genetic diversity. A large fraction of the metabolites and enzymes involved in peach sugar metabolism were assayed within a peach progeny of 106 genotypes, of which one quarter displayed a low fructose-to-glucose ratio. This profiling was performed at six stages of growth using high throughput methods. Our results permit drawing a quasi-exhaustive scheme of sugar metabolism in peach. The use of a large number of genotypes revealed a remarkable robustness of enzymatic capacities across genotypes and years, despite strong variations in sugar composition, in particular the fructose-to-glucose ratio, within the progeny. A poor correlation was also found between the enzymatic capacities and the accumulation rates of metabolites.ConclusionsThese results invalidate the hypothesis of the straightforward enzymatic control of sugar concentration in peach fruit. Alternative hypotheses concerning the regulation of fructose concentration are discussed based on experimental data. This work lays the foundation for a comprehensive study of the mechanisms involved in sugar metabolism in developing fruit.

Model-assisted phenotyping and ideotype design

By formalizing traits as the result of genotypic and environmental effects and the relations among traits, ecophysiological models (or process-based models) provide a platform for integrative analyses of trait impacts on whole-plant and crop phenotypes. Over the past two decades, model development has been increasingly driven by the need to account for genotypic differences across environments, and improvements in this area have developed around well-defined traits such as leaf elongation, early vigor and flowering time. Process-based models are now increasingly used to define and characterize crop environments at various scales and help breeding programs take advantage of G × E interactions. Ecophysiological models are also used to assist plant phenotyping and could provide necessary links between controlled-conditions phenotyping and plant performance in the field. The integration of genetic controls in ecophysiological models has allowed analysis of the genetic control of phenotypic plasticity across wide ranges of environments, and the G × E × M space is now explored using efficient algorithms to find ideotypes optimizing many antagonist criteria. This later approach lies in finding combinations of values of the genetic and agronomic parameters that best satisfy the pre-defined objectives, but it is currently limited by the lack of quantitative relationships between genes and model parameters. Considerable efforts are still needed to develop robust links between genetic controls, physiological determinants and traits relevant to breeders.

Determination of the composition in sugars and organic acids in peach using mid infrared spectroscopy: comparison of prediction results according to data sets and different reference methods.

The prediction of internal quality properties, such as sweetness and acidity, in peach fruit by mid infrared spectroscopy is of interest for rapid determination. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) was tested here on two populations of peach fruits issued from contrasting genitors providing a large phenotypic variability. Over two successive years, 284 samples in 2006 and 483 samples in 2007 were characterized for soluble solids content (SSC), titratable acidity (TA), glucose, fructose, sucrose, malic acid, and citric acid contents. Sugar and organic acid composition were determined by three methods: colorimetric enzymatic measurements (ENZ), high-performance liquid chromatography (HPLC), or proton NMR spectroscopy ((1)H NMR), depending on the samples. For all samples, fruit homogenates were analyzed in ATR-FTIR using the same methodology and the same spectrometer. The objective here was to evaluate the effect of reference methods on the prediction performance. The best results were generally observed for SSC and TA, the percentage of the root-mean-square error of cross validation (RMSECV%) ranging respectively between 5.8% and 8.7% and between 5.9% and 8.0%, depending on the samples. For individual sugars and organic acids, the best correlations were obtained between ATR-FTIR data and ENZ reference data followed by HPLC and (1)H NMR ones.

Dynamic QTLs for sugars and enzyme activities provide an overview of genetic control of sugar metabolism during peach fruit development

Highlight Forty-five QTLs controlling sugars and enzyme activities related to sugar metabolism in peach fruit were identified. Dynamic QTLs revealed changing effects of alleles during fruit development.

Brown Rot Strikes Prunus Fruit: An Ancient Fight Almost Always Lost

Brown rot (BR) caused by Monilinia spp., has been an economic problem for the stone fruit market due to dramatic losses, mainly during the postharvest period. There is much literature about basic aspects of Monilinia spp. infection, which indicates that environment significantly influences its occurrence in the orchard. However, progress is needed to sustainably limit this disease: the pathogen is able to develop resistance to pesticides, and most of BR resistance research programs in plant models perish. Solving this problem becomes important due to the need to decrease chemical treatments and reduce residues on fruit. Thus, research has recently increased, exploring a wide range of disease control strategies (e.g., genetic, chemical, physical). Summarizing this information is difficult, as studies evaluate different Monilinia and Prunus model species, with diverse strategies and protocols. Thus, the purpose of this review is to present the diversity and distribution of agents causing BR, focusing on the biochemical mechanisms of Monilinia spp. infection both of the fungi and of the fruit, and report on the resistance sources in Prunus germplasm. This review comprehensively compiles the information currently available to better understand mechanisms related to BR resistance.

Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies

BackgroundHighly polygenic traits such as fruit weight, sugar content and acidity strongly influence the agroeconomic value of peach varieties. Genomic Selection (GS) can accelerate peach yield and quality gain if predictions show higher levels of accuracy compared to phenotypic selection. The available IPSC 9K SNP array V1 allows standardized and highly reliable genotyping, preparing the ground for GS in peach.ResultsA repeatability model (multiple records per individual plant) for genome-enabled predictions in eleven European peach populations is presented. The analysis included 1147 individuals derived from both commercial and non-commercial peach or peach-related accessions. Considered traits were average fruit weight (FW), sugar content (SC) and titratable acidity (TA). Plants were genotyped with the 9K IPSC array, grown in three countries (France, Italy, Spain) and phenotyped for 3–5 years. An analysis of imputation accuracy of missing genotypic data was conducted using the software Beagle, showing that two of the eleven populations were highly sensitive to increasing levels of missing data. The regression model produced, for each trait and each population, estimates of heritability (FW:0.35, SC:0.48, TA:0.53, on average) and repeatability (FW:0.56, SC:0.63, TA:0.62, on average). Predictive ability was estimated in a five-fold cross validation scheme within population as the correlation of true and predicted phenotypes. Results differed by populations and traits, but predictive abilities were in general high (FW:0.60, SC:0.72, TA:0.65, on average).ConclusionsThis study assessed the feasibility of Genomic Selection in peach for highly polygenic traits linked to yield and fruit quality. The accuracy of imputing missing genotypes was as high as 96%, and the genomic predictive ability was on average 0.65, but could be as high as 0.84 for fruit weight or 0.83 for titratable acidity. The estimated repeatability may prove very useful in the management of the typical long cycles involved in peach productions. All together, these results are very promising for the application of genomic selection to peach breeding programmes.

Optimization of Allelic Combinations Controlling Parameters of a Peach Quality Model

Process-based models are effective tools to predict the phenotype of an individual in different growing conditions. Combined with a quantitative trait locus (QTL) mapping approach, it is then possible to predict the behavior of individuals with any combinations of alleles. However the number of simulations to explore the realm of possibilities may become infinite. Therefore, the use of an efficient optimization algorithm to intelligently explore the search space becomes imperative. The optimization algorithm has to solve a multi-objective problem, since the phenotypes of interest are usually a complex of traits, to identify the individuals with best tradeoffs between those traits. In this study we proposed to unroll such a combined approach in the case of peach fruit quality described through three targeted traits, using a process-based model with seven parameters controlled by QTL. We compared a current approach based on the optimization of the values of the parameters with a more evolved way to proceed which consists in the direct optimization of the alleles controlling the parameters. The optimization algorithm has been adapted to deal with both continuous and combinatorial problems. We compared the spaces of parameters obtained with different tactics and the phenotype of the individuals resulting from random simulations and optimization in these spaces. The use of a genetic model enabled the restriction of the dimension of the parameter space toward more feasible combinations of parameter values, reproducing relationships between parameters as observed in a real progeny. The results of this study demonstrated the potential of such an approach to refine the solutions toward more realistic ideotypes. Perspectives of improvement are discussed.

A model for temporal dynamics of brown rot spreading in fruit orchards

Brown rot, caused by Monilinia spp., is a major disease of stone fruit and, in favorable environmental conditions and in the absence of fungicide treatments, it causes important economic losses. In the present work, we propose a modification of classical susceptible, exposed, infectious and removed compartmental models to grasp the peculiarities of the progression of brown rot epidemics in stone fruit orchards in the last stage of the fruit growth (i.e., from the end of the pit hardening to harvest time). Namely, we took into account (i) the lifespan of airborne spores; (ii) the dependence of the latent period on the cuticle crack surface area, which itself varies in time with fruit growth; (iii) the impossibility of recovery in infectious fruit; and (iv) the abrupt interruption of disease development by the elimination of the host fruit at harvest time. We parametrized the model by using field data from a peach Prunus persica orchard infected by Monilinia laxa and M. fructicola in Avignon (southern France). The basic reproduction number indicates that the environmental conditions met in the field were extremely favorable to disease development and the model closely fitted the temporal evolution of the fruit abundance in the different epidemiological compartments. The model permits us to highlight crucial mechanisms undergoing brown rot build up and to evaluate the consequences of different agricultural practices on the quantity and quality of the yield. We found that winter sanitation practices (which decrease the initial infection incidence) and the control of the fruit load (which affects the host fruit density and the single fruit growth trajectory) can be effective in controlling brown rot in conjunction with or in place of fungicide treatments.