Genotype determines Arbutus unedo L. physiological and metabolomic responses to drought and recovery

Strawberry tree (Arbutus unedo) is a small resilient species with a circum-Mediterranean distribution, high ecological relevance in southern European forests and with several economical applications. As most orchards are usually installed on marginal lands where plants usually face severe drought, selecting plants that can better cope with water restriction is critical, and a better understanding of the tolerance mechanisms is required. Strawberry tree plants under drought follow a typical isohydric strategy, by limiting transpiration through stomata closure. However, the contribution of genotype and its bio-geographic origin on plant performance needs clarification, as well as the involvement of a specific metabolic reactions associated with the mechanical response. To test this hypothesis, several eco-physiological and biochemical parameters were assessed on different genotypes, and the metabolic profiles studied, including important stress-related phytohormones, on plants under different water regimes (plants watered to 70% and 18% field capacity) and a recovery assay. A contrasting drought tolerance was found in plants from different genotypes, associated with physiological and metabolic responses. Metabolomics revealed more than 500 metabolic features were differentially accumulated, including abscisic and salicylic acids, for the genotype with better performance under drought (A4). This genotype also recovered faster when the imposed stress was interrupted, thus indicating the relevance of metabolic adaptation under water deficit conditions. By correlating carbon assimilation with identified metabolites, some proved to be satisfactory predictors of plant performance under drought and might be used for marker assisted breeding. Therefore, our study proves the importance of genotype as a major selection criterion of resistant plants to drought and provides empirical knowledge of the metabolic response involved. We also hypothesized the involvement of phenolics on response mechanisms under drought, which is worth to be explored to shed light on the metabolic pathways involved in plant response to water stress

Impaired cell growth under ammonium stress explained by modeling the energy cost of vacuole expansion in tomato leaves

Ammonium (NH4+)-based fertilization efficiently mitigates the adverse effects of nitrogen fertilization on the environment. However, high concentrations of soil NH4+ provoke growth inhibition, partly caused by the reduction of cell enlargement and associated with modifications of cell composition, such as an increase of sugars and a decrease in organic acids. Cell expansion depends largely on the osmotic-driven enlargement of the vacuole. However, the involvement of subcellular compartmentation in the adaptation of plants to ammonium nutrition has received little attention, until now. To investigate this, tomato (Solanum lycopersicum) plants were cultivated under nitrate and ammonium nutrition and the fourth leaf was harvested at seven developmental stages. The vacuolar expansion was monitored and metabolites and inorganic ion contents, together with intracellular pH, were determined. A data-constrained model was constructed to estimate subcellular concentrations of major metabolites and ions. It was first validated at the three latter developmental stages by comparison with subcellular concentrations obtained experimentally using non-aqueous fractionation. Then, the model was used to estimate the subcellular concentrations at the seven developmental stages and the net vacuolar uptake of solutes along the developmental series. Our results showed ammonium nutrition provokes an acidification of the vacuole and a reduction in the flux of solutes into the vacuoles. Overall, analysis of the subcellular compartmentation reveals a mechanism behind leaf growth inhibition under ammonium stress linked to the higher energy cost of vacuole expansion, as a result of alterations in pH, the inhibition of glycolysis routes and the depletion of organic acids.TP benefited from a cotutelle PhD (University of Bordeaux and University of the Basque Country) and thanks the University of the Basque Country (UPV/EHU, Spain) for his PhD grant during the execution of this work. This research was financially supported by the Basque Government (IT-932-16) and the Spanish Government (BIO2017-84035-R co-funded by Fondo Europeo para el Desarrollo Regional [FEDER]). Analytics were supported by MetaboHUB (ANR-11-INBS-0010) and PHENOME (ANR-11-INBS-0012) projects. Technical support was provided by Cedric Cassan, Ana Renovales and Mandy Bordas. The authors also thank SGIker (UPV/EHU, FEDER, EU) for the technical and human support provided

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management

International audienceIntroduction Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. Objectives To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. Methods We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. Results PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. Conclusion PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest