Biology subjects

Cabrera-Bosquet, L.

Publications and source records attributed to Cabrera-Bosquet, L..

9 recordsLinked to original sources

bioRxiv2026

How to be dispensable: genomic and transcriptomic determinants in maize genes

BackgroundPlant genomes harbour a substantial proportion of dispensable genes - present only in a subset of individuals - that differ from ubiquitously shared core genes in multiple genomic and expression features. While these differences have been repeatedly documented, the factors shaping gene dispensability remain poorly understood. ResultsWe assembled a pan-gene set from eight maize inbred lines from American and European germplasms, together with their transcriptomic profile across 22 tissues/conditions, revealing the genomic and transcriptomic determinants of maize gene dispensability. Multivariate analysis demonstrates that gene expression level and purifying selection - rather than gene size - are the primary factors distinguishing core from dispensable genes. Dispensable genes overlap Helitrons at 4.6 times the rate of core genes, implicating Helitron-mediated gene capture as a major mechanism of dispensable gene formation. Classifying genes into stably expressed, variably expressed, and on-off categories shows that all three classes contain dispensable genes, though in different proportions than for core genes. Contrary to previous assumptions, we show that dispensable genes can participate in basal biological functions just as core genes, and that gene duplication likely provides only a partial mechanism for functional complementation of accessory genes absence. ConclusionsOur results provide novel insights into the molecular and evolutionary factors distinguishing core from dispensable genes and into the biological mechanisms shaping gene dispensability in maize, and demonstrate that classifying genes by transcriptional patterns provides a powerful framework for understanding the biological functions and evolutionary dynamics of both core and dispensable genes.

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

Tillering structures the genetic variability of wheat vegetative growth and its plasticity under water deficit

Leaf expansion under drought drives the trade-off between water saving for later grain production and canopy photosynthesis. Fine-tuning leaf expansion could therefore become a target of genetic progress for drought-prone environments. However, its components (branching, leaf production and elongation) may have their own genetic variability and plasticity under drought, making hard to calibrate crop simulation models and specify breeding targets. In this study, we focused on the genetic diversity of bread wheat and durum wheat to determine the links and trade-offs between the underlying processes of leaf growth under drought and how it translates to leaf expansion at the whole plant and canopy level. For that, we used non-destructive imaging both in the field and controlled condition platforms to determine the dynamics of the components of shoot expansion and analyze their relative contribution to the genetic variability of whole-plant shoot expansion under drought. Results show that leaf expansion measured at plant level in controlled environment was associated with that measured at canopy level in the field, indicating that controlled phenotyping platforms can capture the genetic variability of growth in the field. Both whole-plant and canopy expansion were associated with tillering rate. In addition, the sensitivity of shoot growth and tillering to soil water deficit were correlated, indicating that both tillering ability and sensitivity to water deficit drive the genetic variability of shoot expansion. Overall, dissecting leaf expansion dynamics allowed determining the links between shoot expansion traits under drought, and provides key targets in phenotyping, modelling and breeding for drought environments.

plant biology↗

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

PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time

BackgroundHigh-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. ResultsWe propose PhenoTrack3D, a new pipeline to extract a 3D+t reconstruction of maize at organ level from plant images. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. PhenoTrack3D improves a former method limited to 3D reconstruction at a single time point [Artzet et al., 2019] by (i) a novel stem detection method based on deep-learning and (ii) a new and original multiple sequence alignment method to perform the temporal tracking of ligulated leaves. Our method exploits both the consistent geometry of ligulated leaves over time and the unambiguous topology of the stem axis. Growing leaves are tracked afterwards with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants x 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10 to 355 plants. ConclusionsWe developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise automatically and at a high-throughput the development of maize architecture at organ level. It has been validated for hundreds of plants during the entire development cycle, showing its applicability to the GxE analyses of large maize datasets.

plant biology↗

bioRxiv2021

High-throughput phenotyping reveals a link between transpiration efficiency and transpiration restriction under high evaporative demand and new loci controlling water use-related traits in African rice, Oryza glaberrima Steud.

Because water availability is the most important environmental factor limiting crop production, improving water use efficiency, the amount of carbon fixed per water used, is a major target for crop improvement. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand, remain unknown. These traits were measured in a panel of 147 African rice Oryza glaberrima genotypes, known as potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly differentiated genotypes confirmed these associations and suggested that the root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency and its residuals, that links to genes involved in water transport and cell wall patterning. Our data suggest that root shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

physiology↗

bioRxiv2021

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zurich.

genetics↗

bioRxiv2019

Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

In the era of high-throughput visual plant phenotyping, it is crucial to design fully automated and flexible workflows able to derive quantitative traits from plant images. Over the last years, several software supports the extraction of architectural features of shoot systems. Yet currently no end-to-end systems are able to extract both 3D shoot topology and geometry of plants automatically from images on large datasets and a large range of species. In particular, these software essentially deal with dicotyledons, whose architecture is comparatively easier to analyze than monocotyledons. To tackle these challenges, we designed the Phenomenal software featured with: (i) a completely automatic workflow system including data import, reconstruction of 3D plant architecture for a range of species and quantitative measurements on the reconstructed plants; (ii) an open source library for the development and comparison of new algorithms to perform 3D shoot reconstruction and (iii) an integration framework to couple workflow outputs with existing models towards model-assisted phenotyping. Phenomenal analyzes a large variety of data sets and species from images of high-throughput phenotyping platform experiments to published data obtained in different conditions and provided in a different format. Phenomenal has been validated both on manual measurements and synthetic data simulated by 3D models. It has been also tested on other published datasets to reproduce a published semi-automatic reconstruction workflow in an automatic way. Phenomenal is available as an open-source software on a public repository.

plant biology↗

bioRxiv2019

Integrating proteomics and genomics into systems genetics provides novel insights into the mechanisms of drought tolerance in maize

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms make the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype-phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein co-expression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified fifteen loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein co-expression network, and a reprogramming of the genetic control of the abundance of many proteins, notably those involved in stress response. Co-localizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the co-expression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

systems biology↗