Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits

IntroductionPredicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects.MethodsIn this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits.ResultsThe results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used.DiscussionThese results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances

A role for Dynlt3 in melanosome movement, distribution, acidity and transfer

Skin pigmentation is dependent on cellular processes including melanosome biogenesis, transport, maturation and transfer to keratinocytes. However, how the cells finely control these processes in space and time to ensure proper pigmentation remains unclear. Here, we show that a component of the cytoplasmic dynein complex, Dynlt3, is required for efficient melanosome transport, acidity and transfer. In Mus musculus melanocytes with decreased levels of Dynlt3, pigmented melanosomes undergo a more directional motion, leading to their peripheral location in the cell. Stage IV melanosomes are more acidic, but still heavily pigmented, resulting in a less efficient melanosome transfer. Finally, the level of Dynlt3 is dependent on beta -catenin activity, revealing a function of the Wnt/beta -catenin signalling pathway during melanocyte and skin pigmentation, by coupling the transport, positioning and acidity of melanosomes required for their transfer. Aktary et al. identify novel roles for the dynein light chain Dynlt3 in melanosome transport, maturation, and transfer to keratinocytes. They also find that the Wnt/beta catenin signalling pathway controls Dynlt3 levels and thus also contributes to the regulation of melanocyte transport and skin pigmentation

Suppression of Autophagy Dysregulates the Antioxidant Response and Causes Premature Senescence of Melanocytes

YesAutophagy is the central cellular mechanism for delivering organelles and cytoplasm to lysosomes for degradation and recycling of their molecular components. To determine the contribution of autophagy to melanocyte (MC) biology, we inactivated the essential autophagy gene Atg7 specifically in MCs using the Cre-loxP system. This gene deletion efficiently suppressed a key step in autophagy, lipidation of microtubule-associated protein 1 light chain 3 beta (LC3), in MCs and induced slight hypopigmentation of the epidermis in mice. The melanin content of hair was decreased by 10–15% in mice with autophagy-deficient MC as compared with control animals. When cultured in vitro, MCs from mutant and control mice produced equal amounts of melanin per cell. However, Atg7-deficient MCs entered into premature growth arrest and accumulated reactive oxygen species (ROS) damage, ubiquitinated proteins, and the multi-functional adapter protein SQSTM1/p62. Moreover, nuclear factor erythroid 2–related factor 2 (Nrf2)–dependent expression of NAD(P)H dehydrogenase, quinone 1, and glutathione S-transferase Mu 1 was increased, indicating a contribution of autophagy to redox homeostasis in MCs. In summary, the results of our study suggest that Atg7-dependent autophagy is dispensable for melanogenesis but necessary for achieving the full proliferative capacity of MCs

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Intégration de la modélisation écophysiologique et génétique pour la définition et l'estimation des valeurs génétiques d'idéotypes variétaux - application au cas de la croissance en biomasse chez le sorgho

La modélisation écophysiologique est de plus en plus attendue pour optimiser la mise en relation de l'information Génétique et Phénotypique (G2P), prédire des idéotypes variétaux et leur valeur génétique. Cela devient d'autant plus essentiel lorsqu'il s'agit de prendre en compte les effets, sur la performance des plantes, d'environnements cibles de sélection de plus en plus diverses et changeants. Cela est en particulier le cas des céréales, comme le sorgho, qui sont cultivées en Europe comme en Afrique de l'ouest. Cet enjeu implique que les modèles utilisés intègrent davantage les processus biologiques aux échelles où se jouent la réponse de la plante à l'environnement. Par-là, les modèles se complexifient en nombre et interdépendances des paramètres, rendant leurs effets sur les sorties du modèle non linéaires. Alors que ce type de modélisation pourrait être un outil puissant pour la mise en relation G2P, elle n'a que peu été éprouvée dans ce contexte. L'objectif de cette thèse est d'élaborer et évaluer un framework intégratif de conception d'idéotypes variétaux de sorgho et d'estimation de leur valeur génétique, basé sur un outil de modélisation d'un système complexe qu'est la croissance de la plante de sorgho, en se focalisant ici sur l'accumulation de biomasse végétative. Pour cela le modèle écophysiologique (CGM) Ecomeristem, qui décrit de façon détaillée la croissance et le développement de la plante à l'échelle de l'organe, a été utilisé et appliqué grâce à un jeu de données adapté pour l'évaluer et le calibré, acquis sur la plateforme de phénotypage Phénoarch, sur un panel de diversité de sorghos africains. Plusieurs améliorations ont été apportées au modèle Ecomeristem afin de mieux capturer les processus contrôlant la variabilité de l'accumulation de biomasse végétative du sorgho en fonction du génotype et de l'environnement abiotique. Ces processus ont notamment concerné les relations source-puits en carbone (C), afin de mieux simuler la croissance, l'avortement et la mortalité des talles en fonction de la compétition entre elles pour la ressource carbonée. Le modèle amélioré a montré sa capacité à prédire les traits constituant la croissance en biomasse structurale et non structurale (stockage en C) de 8 génotypes de sorgho biomasse au champ. Une étude comparative de plusieurs approches de calibration du modèle écophysiologique a ensuite été réalisée afin de sélectionner l'approche qui permet au mieux de relier la valeur de ses paramètres à des facteurs génétiques. L'algorithme métaheuristique « évolution différentielle », en utilisant des données de calibration basées sur la prédiction linéaire sans biais (BLUP) des traits phénotypiques, a donné les meilleurs résultats en termes de simulation du phénotype et de prédiction génomique (WGP) de ces paramètres. Ces résultats de prédiction génomique en passant par un modèle écophysiologique sont au moins aussi précis, voire meilleure, que la prédiction génomique traditionnel. Enfin, des méthodes d'apprentissage automatique ont été développées afin d'améliorer la précision de prédiction de la prédiction génomique sous l'effet des paramètres considérés et les trade-offs qu'ils impliquent.Ces résultats soulignent la valeur ajoutée par l'approche intégrative CGM-WGP basé sur un CGM considérant un nombre important de paramètres génotypiques, pour prédire des phénotypes dynamiques et leur valeurs génétiques en considérant un grand nombre de marqueurs moléculaires. Ils mettent également en évidence les risques potentiels de l'utilisation des paramètres pour capturer l'information génotypique en raison des sources de variabilités des valeurs de ces paramètres. Les résultats sont discutés au regard de l'apport de la modélisation écophysiologique dans la prédiction d'idéotypes et plus largement dans l'amélioration variétale pour des environnements variés, ainsi qu'au regard des perspectives d'application de ce framework sur des jeux de données multi-locaux.Ecophysiological modeling is increasingly expected to optimize the relationship of Genetic and Phenotypic information (G2P), predict varietal ideotypes and their genetic value. This becomes even more essential when it comes to accounting for the effects on plant performance of increasingly diverse and changing breeding target environments. This is particularly the case for cereals, such as sorghum, which are grown in Europe as well as in West Africa. This implies that the models used must further integrate biological processes at the scales where the plant's response to the environment takes place. As a result, the models become more complex in terms of number and interdependencies of the parameters, making their effects on the model outputs non-linear. While this type of modeling could be a powerful tool for G2P linking, it has only been little tested in this context. The objective of this thesis is to develop and evaluate an integrative framework for the design of sorghum varietal ideotypes and the estimation of their genetic value, based on a tool for modeling a complex system that is the growth of the sorghum plant, focusing on the accumulation of vegetative biomass. For this, the Ecomeristem crop growth model (CGM), which describes in detail the growth and development of the plant at the organ level, was used and applied using a dataset adapted to evaluate and calibrate the model, acquired on the Phénoarch phenotyping platform, on a diversity panel of African sorghums. Several improvements have been made to the Ecomeristem model in order to better capture the processes controlling the variability of the accumulation of vegetative biomass in sorghum depending on the genotype and the abiotic environment. These processes concerned in particular the carbon (C) source-sink relationships, in order to better simulate the growth, abortion and mortality of tillers according to the competition between them for the carbon resource. The improved model showed its ability to predict the traits constituting the growth in structural and non-structural biomass (storage in C) of eight genotypes of biomass sorghum in the field. A comparative study of several approaches to calibrate the ecophysiological model was then carried out in order to select the approach that best allows the value of its parameters to be linked to genetic factors. The “differential evolution” metaheuristic algorithm, using calibration data based on best unbiased linear predictions (BLUP) of phenotypic traits, gave the best results in terms of phenotype simulation and genomic prediction (WGP) of these parameters. These results of genomic prediction using an ecophysiological model are at least as precise, and even better in some cases, than traditional genomic prediction. Finally, machine learning methods have been developed to improve the predictive accuracy of genomic prediction under the effect of the parameters considered and the trade-offs they involve. These results underline the added value by the CGM-WGP integrative approach based on a CGM considering a large number of genotypic parameters, to predict dynamic phenotypes and their genetic values by considering a large number of molecular markers. They also highlight the potential risks of using parameters to capture genotypic information due to the sources of variability in the values of these parameters. The results are discussed with regard to the contribution of ecophysiological modeling in the prediction of ideotypes and more generally in varietal improvement for various environments, as well as with regard to the perspectives of application of this framework on multi-local datasets

Transcriptomic Analysis of Mouse Embryonic Skin Cells Reveals Previously Unreported Genes Expressed in Melanoblasts

Studying the development of melanoblasts, precursors of melanocytes, is challenging owing to their scarcity and dispersion in the skin embryo. However, this is an important subject because diverse diseases are associated with defective melanoblast development. Consequently, characterizing patterns of expression in melanoblasts during normal development is an important issue. This requires isolating enough melanoblasts during embryonic development to obtain sufficient RNA to study their transcriptome. ZEG reporter mouse line crossed with Tyr::Cre mouse line was used to label melanoblasts by enhanced green fluorescent protein (EGFP) autofluorescence. We isolated melanoblasts by FACS from the skin of E14.5–E16.5 embryos, and obtained sufficient cells for large-scale transcriptomic analysis after RNA isolation and amplification. We confirmed our array-based data for various genes of interest by standard quantitative real-time RT-PCR. We demonstrated that phosphatase and tensin homolog was expressed in melanoblasts but BRN2 was not, although both are involved in melanomagenesis. We also revealed the potential contribution of genes not previously implicated in any function in melanocytes or even in neural crest derivatives. Finally, the Schwann cell markers, PLP1 and FABP7, were significantly expressed in melanoblasts, melanocytes, and melanoma. This study demonstrates the feasibility of the transcriptomic analysis of purified melanoblasts at different embryonic stages and reveals the involvement of previously unreported genes in melanoblast development