Ecophysiological analysis of genotypic variation in peach fruit growth

Crop management impacts the efficiency of quantitative trait loci (QTL) detection and use: case study of fruit load×QTL interactions

Mapping studies using populations with introgressed marker-defined genomic regions are continuously increasing knowledge about quantitative trait loci (QTL) that correlate with variation in important crop traits. This knowledge is useful for plant breeding, although combining desired traits in one genotype might be complicated by the mode of inheritance and co-localization of QTL with antagonistic effects, and by physiological trade-offs, and feed-back or feed-forward mechanisms. Therefore, integrating advances at the genetic level with insight into influences of environment and crop management on crop performance remains difficult. Whereas mapping studies can pinpoint correlations between QTL and phenotypic traits for specific conditions, ignoring or overlooking the importance of environment or crop management can jeopardize the relevance of such assessments. Here, we focus on fruit load (a measure determining competition among fruits on one plant) and its strong modulation of QTL effects on fruit size and composition. Following an integral approach, we show which fruit traits are affected by fruit load, to which underlying processes these traits can be linked, and which processes at lower and higher integration levels are affected by fruit load (and subsequently influence fruit traits). This opinion paper (i) argues that a mechanistic framework to interpret interactions between fruit load and QTL effects is needed, (ii) pleads for consideration of the context of agronomic management when detecting QTL, (iii) makes a case for incorporating interacting factors in the experimental set-up of QTL mapping studies, and (iv) provides recommendations to improve efficiency in QTL detection and use, with particular focus on model-based marker-assisted breeding.

Virtual profiling: a new way to analyse phenotypes

Simulation models can be used to perform virtual profiling in order to analyse eco-physiological processes controlling plant phenotype. To illustrate this, an eco-physiological model has been used to compare and contrast the status of a virtual fruit system under two situations of carbon supply. The model simulates fruit growth, accumulation of sugar, citric acid and water, transpiration, respiration and ethylene emission, and was successfully tested on peach (Prunus persica L. Batsch) for two leaf-to-fruit ratios (6 and 18 leaves per fruit). The development stage and the variation in leaf number had large effects of the fruit model variables dealing with growth, metabolism and fruit quality. A sensitivity analysis showed that changing a single parameter value, which could correspond to a genotypic change induced by a mutation, either strongly affects most of the processes, or affects a specific process or none. Correlation analysis showed that, in a complex system such as fruit, the intensity of many physiological processes and quality traits co-varies. It also showed unexpected co-variations resulting from emergent properties of the system. This virtual profiling approach opens a new route to explore the impact of mutations, or naturally occurring genetic variations, under differing environmental conditions.

Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case-study of fruit and grain quality traits

Detailed information has arisen from research at gene and cell levels, but it is still incomplete in the context of a quantitative understanding of whole plant physiology. Because of their integrative nature, process-based simulation models can help to bridge the gap between genotype and phenotype and assist in deconvoluting genotype-by-environment (GxE) interactions for complex traits. Indeed, GxE interactions are emergent properties of simulation models, i.e. unexpected properties generated by complex interconnections between subsystem components and biological processes. They co-occur in the system with synergistic or antagonistic effects. In this work, different kinds of GxE interactions are illustrated. Approaches to link model parameters to genes or quantitative trait loci (QTL) are briefly reviewed. Then the analysis of GxE interactions through simulation models is illustrated with an integrated model simulation of peach (Prunus persica (L.) Batsch) fruit mass and sweetness, and with a model of wheat (Triticum aestivum L.) grain yield and protein concentration. This paper suggests that the management of complex traits such as fruit and grain quality may become possible, thanks to the increasing knowledge concerning the genetic and environmental regulation of organ size and composition and to the development of models simulating the complex aspects of metabolism and biophysical behaviours at the plant and organ levels.

A simplified conceptual model of carbon/nitrogen functioning for QTL analysis of winter wheat adaptation to nitrogen deficiency

Breeding new varieties adapted to low-input agricultural practices is of particular interest in light of current economical and environmental concerns. Improving nitrogen (N) uptake and N utilization efficiency (NUE) are two ways of producing varieties tolerant to low N input. To offer new possibilities to breeders, it is necessary to acquire more knowledge about these two processes. Knowing C and N metabolisms are linked and knowing N uptake is partly explained by root characteristics, we carried out a QTL analysis for traits associated with N uptake and NUE by using both a conceptual model of C/N plant functioning and a root architecture description. A total of 120 lines were selected according to their genotype among 241 doubled haploids derived from two varieties, one N stress tolerant and the other N stress sensitive. They were grown in hydroponic rhizotrons under N-limited nutritional conditions. Initial conditions varied among genotypes; therefore, total root length on day 1 was used to correct traits. Heritabilities ranged from 13 to 84%. Thirty-two QTL were located: 6 associated with root architecture (on chromosomes 4B, 5A, 5D and 7B), 6 associated with model efficiencies (1B, 2B, 6A, 6B, 7A, 7B and 7D) and 20 associated with state variables (1A, 1B, 2B, 4B, 5A, 5B and 6B). The effects of the dwarfing gene Rht-B1 on root traits are discussed, as well as the features of a conceptual plant functioning model, as a useful tool to assess pertinent traits for QTL detection. It is suggested that further studies that couple QTL with a functioning model and a root architecture description could serve in the search for ideotypes.

Simulating genotypic variation of fruit quality in an advanced peach x Prunus davidiana cross

Ecophysiological models are increasingly expected to describe genotypic variation within breeding populations. Accordingly, the ability of an ecophysiological model of peach to explain variation in fruit quality among 100 genotypes of a second backcross progeny derived from a clone of wild peach (Prunus davidiana) crossed with two commercial nectarine (Prunus persica) varieties was explored. Experimental measurements were carried out to calibrate the model for each genotype. The predictive quality of the model was tested on several independent datasets. The genotypic variation in dry and fresh growth of the fruit and the stone were effectively described by the model. Prediction of the amount of total sugar in flesh at maturity was accurate, whereas prediction of flesh dry matter content and total sugar concentration was suitable but less accurate. This approach and the results have allowed physiological processes to be ranked according to their contribution to the variation in fruit quality between genotypes. Fruit growth demand and the hydraulic conductance in the fruit were the main processes that explained the fruit quality variation. Shortcomings and further potential uses of the model are discussed.

Complex quality traits: now time to model

A recently published dynamic peach model by Françoise Lescourret and Michel Génard simulates changes in quality during final stages of fruit growth. It predicts quality, emerging from complex processes and influenced by environment and management, remarkably well and describes complex emerging behaviour. The model could prove an excellent tool to integrate novel insight from metabolic profiling and pathways of taste-related compounds, ultimately enabling the analysis of gene networks responsible for fruit maturation processes.

Environmental and genetic control of morphogenesis in crops: towards models simulating phenotypic plasticity

As molecular biologists are realising the importance of physiology in understanding functional genomics of quantitative traits, and as physiologists are realising the formidable prospects for improving their phenotypic models with information on the underlying gene networks, researchers worldwide are working on linked physiological–genetic models. These efforts are in their early methodological stage despite, or because of, the availability of many different types of models, the problem being to bring together the different ways that scientists see the plant. This paper describes some current efforts to adapt phenotype models to the objective of simulating gene-phene processes at the plant or crop scale. Particular emphasis is given to the models’ capacity to simulate genotype × environment interaction and the resulting phenotypic plasticity, assuming that this permits the defining of model parameters that are closer to specific gene action. Three different types of approaches are presented: (1) a generic, mathematical-architectural model called GREENLAB that simulates resource-modulated morphogenesis; (2) an ecophysiological model of peach tree fruit development and filling, parameterised for a mapping population to evaluate the potential of plugging quantitative trait locus (QTL) effects into the model; and (3) the new model Ecomeristem that constructs plant architecture and its phenotypic plasticity from meristem behaviour, the principal hypothesis being that resource limitations and stresses feed back on the meristems. This latter choice is based on the fact that gene expression happens to a large extent in the meristems. The model is evaluated on the basis of preliminary studies on vegetative-stage rice. The different modelling concepts are critically discussed with respect to their ability to simulate phenotypic plasticity and to operate with parameters that approximate specific gene action, particularly in the area of morphogenesis.