Biology subjects

Simonneau, T.

Publications and source records attributed to Simonneau, T..

7 recordsLinked to original sources

bioRxiv2026

Genetic control of leaf carbon- and water-related traits in grapevine under greenhouse and field conditions

Understanding the genetic architecture of ecophysiological traits related to carbon gain and water loss is crucial to develop cultivars tolerant to abiotic stresses and that maintain productivity in the context of climate change. This can be achieved both through experiments in the greenhouse on potted plants under controlled conditions and in the field. However, the stability of genetic determinism of ecophysiological traits across environmental conditions has rarely been evaluated. We studied a grapevine diversity panel grown both in a greenhouse and in the field, to investigate the impact of growing conditions on leaf ecophysiological traits related to leaf morphology, fluorescence, gas exchange and water use efficiency. The correlation structure between traits was conserved within each environment even though, for most traits, cultivars ranking was not conserved between both environments. However, a set of cultivars, sharing the same functioning pattern in terms of gas exchange and fluorescence in both environments, was identified. Single- and multi-environment Genome-Wide Association Studies (GWAS), based on a dense genotyping matrix, were used to dissect the genetic architecture of leaf traits. The Quantitative Trait Loci (QTLs) identified through single-environment analyses differed substantially from those detected using multi-environment ones. Multi-environment approaches highlighted shared QTLs across environments for gas exchange and fluorescence traits. Candidate genes associated with carbon- and water-related functions were further identified in these genomic regions. Despite the weak correlation between greenhouse and field trait values, combining both environments enabled the identification of robust genetic regions associated with ecophysiological traits of interest.

plant biology↗

bioRxiv2025

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry

Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity (R{superscript 2} > 0.58), while the poro-fluorometer was efficient for predicting net CO2 assimilation (R{superscript 2} > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate (R{superscript 2} > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, non-destructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.

plant biology↗

bioRxiv2023

Impact of water deficit on single grapevine berry ripening

The effect of water deficit on grapevine fruit ripening has most often been addressed under the assumption that individual berries behave identically to their blend in the future harvest, both kinetically and metabolically. However, mixing unsynchronized berries, whose water and sucrose import pathways critically change according to their own developmental stages, intrinsically blurs the physiological and phenological effects of stress. We investigated the consequences of water deprivation on berry growth and primary metabolites content (glucose, fructose, tartaric and malic acids) on sixteen genetically distant genotypes of Vitis vinifera and fungus-tolerant hybrids submitted to 10 watering regimes, from well-watered to partial leaf shedding. Then, six genotypes were selected for comprehensive single berry analyses. Own-rooted potted plants bearing berries at the late herbaceous plateau stage were subjected to the different water treatments for four weeks in a greenhouse with automated regulation of soil water content. Berry and cluster growth were monitored by image analysis, before performing a final destructive sampling to determine berry weight and composition. Grape phenology was highly dependent on water availability. In some cultivars, ripening was considerably delayed or even prevented under well-watered conditions. These cultivars required an intermediate water deficit to trigger the second berry growth period along with sugar accumulation and malate breakdown, typical of the ripening process. Ripening still occurred in all genotypes upon severe water deprivation, although sugar accumulation and concentration were dramatically impaired, and the second growth period was annihilated or even replaced by shrivelling. Water deficit increased malate breakdown, uncoupling it from sugar accumulation. Single berry analyses suggest that although asynchronicity in berry ripening was reduced upon stress, individual fruits within the same cluster may undergo heterogeneous water budgets, expansion or shrivelling.

physiology↗

bioRxiv2023

Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images

BackgroundGrapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, as well as its impacts on both vine yield and wine quality, remain nearly unknown. To address these limitations, we propose to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. ResultFirst, a deep-learning approach is used to detect berries with at least 50{+/-}10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. ConclusionsWe successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one genotype, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.

plant biology↗

bioRxiv2022

Leaf starch metabolism sets the phase of stomatal rhythm

In leaves of C3 and C4 plants, stomata open during the day to favour CO2 entry for photosynthesis, and close at night to prevent inefficient transpiration of water vapour. The circadian clock paces rhythmic stomatal movements throughout the diel (24-h) cycle. Leaf transitory starch is also thought to regulate the diel stomatal movements, yet the underlying mechanisms across time (key moments) and space (relevant leaf tissues) remains elusive. Here, we developed PhenoLeaks, a pipeline to analyse the diel dynamics of transpiration, and used it to screen a series of Arabidopsis mutants impaired in starch metabolism. We detected a sinusoidal, endogenous rhythm of transpiration that overarches days and nights. We uncovered that a number of severe mutations in starch metabolism affect the endogenous rhythm through a phase shift, resulting in delayed stomatal movements throughout the daytime and reduced stomatal preopening during the night. Nevertheless, analysis of tissue-specific mutations revealed that neither guard-cell nor mesophyll-cell starch metabolism are strictly required for normal diel patterns of transpiration. We propose that leaf starch influences the timing of transpiration rhythm through an interplay between the clock and sugars across tissues, while the energetic effect of starch-derived sugars is usually non-limiting for endogenous stomatal movements. One-sentence summaryThe PhenoLeaks pipeline for monitoring diel transpiration dynamics reveals that leaf starch metabolism sets the timing of the endogenous stomatal rhythm.

plant biology↗

bioRxiv2020

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Viticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a potential key to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs, multivariate methods being potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. We applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental grapevine progeny, in order to compare their ability to predict genotypic values and detect QTLs. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. Using simulations, we recommend the penalized regression method Elastic Net (EN) as a default for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. These methods can be applied to other traits and species.

genetics↗

bioRxiv2019

HydroShoot: a functional-structural plant model for simulating hydraulic structure, gas and energy exchange dynamics of complex plant canopies under water deficit - application to grapevine (Vitis vinifera L.)

This paper aims at presenting HydroShoot, a functional-structural plant model (FSPM) that is developed to simulate gas-exchange rates of complex plant canopies under water deficit conditions, by scaling up gas-exchange rates from the leaf to the canopy levels. The main hypothesis is that simulating both the hydraulic structure of the shoot together with the energy budget of individual leaves is the asset for successfully achieving this up-scaling task. HydroShoot was hence built as the ensemble of three interacting modules: hydraulic which calculates the distribution of xylem water potential across shoot hydraulic segments, energy which calculates the complete energy budget of individual leaves, and exchange which calculates net assimilation and transpiration rates of individual leaves. HydroShoot was coupled with irradiance interception and soil water balance models, and was evaluated on virtual and real grapevines having strongly contrasted canopies, under well-watered and water-deficit conditions. HydroShoot captured accurately the impact of canopy architecture and the varying soil water deficit conditions on plant-scale gas-exchange rates and leaf-scale temperature and water potential distributions. Both shoot hydraulic structure and leaf energy budget simulations were, as postulated, required to adequately scaling-up leaf to canopy gas-exchange rates. Notwithstanding, simulating the hydraulic structure of the shoot was found far more necessary to adequately performing this scaling task than simulating leaf energy balance. That is, the intra-canopy variability of leaf water potential was a better predictor of the reduction of whole plant gas-exchange rates under water deficit than the intra-canopy variability of leaf temperature. We conclude therefore that simulating the shoot hydraulic structure is a prerequisite if FSPM's are to be used to assess gas-exchange rates of complex plant canopies as those of grapevines. Finally HydroShoot is available through the OpenAlea platform (https://github.com/openalea/hydroshoot) as a set of reusable modules.

plant biology↗