The genus Camellia comprises more than 200 evergreen species of major economic and ornamental importance, characterised by high morphological and chemical diversity. While several species have been extensively studied for their bioactive compounds, the metabolic basis of floral trait variation across the genus remains poorly understood. In this study, a predictive metabolomics framework was applied to investigate the relationship between leaf metabolic profiles and floral traits, focusing on flower colour and floral form. Leaves from 315 individual trees, including 15 Camellia species and representing 1,160 samples, were analysed by untargeted metabolomics, generating a large-scale metabolic profiling dataset. A dedicated quality control strategy was implemented to ensure analytical stability across multiple injection series and flowering seasons. Penalised generalised linear models were used to uncover robust metabolic predictors associated with floral traits and to evaluate model performance through internal and external validation. Distinct sets of metabolites were associated with flower colour and floral form, with limited overlap between traits. Predictive performance was consistently higher for colour than for floral form, indicating more structured metabolic signatures for chromatic traits. The selected predictors spanned multiple major chemical classes, supporting a systemic organisation of the metabolome rather than reliance on single biosynthetic pathways. Consistently high predictive accuracies were obtained, reaching approximately 87% for both flower colour and floral form, and remaining clearly above the corresponding no-information rates (≈ 43%). Together, these results demonstrate that leaf metabolomics can be used to robustly predict floral traits in Camellia and highlight the potential of predictive metabolomics as a tool for early phenotype inference, quality control and selection in long-lived ornamental species. ### Competing Interest Statement The authors have declared no competing interest.

Bud outgrowth is a major component of plant architectural plasticity and is influenced by light conditions. While the inhibitory effect of low light intensity on branching is well documented, the underlying regulators remain debated and, especially, the role of sugar availability has never been thoroughly evaluated. Here, we combined experiments with a computational approach quantifying carbon source-sink balance in single-axis rose plants to investigate how continuous and transient light limitation regulate bud outgrowth. Continuous low light reduced photosynthesis, leading to decreased sugar availability and inhibited bud outgrowth. In contrast, a transient period of low light followed by high light unexpectedly stimulated bud outgrowth, shortened the delay between outgrowth of successive buds, and produced an over-branched phenotype. This response resulted from a non-reversible reduction in the growth of apical organs appearing under low light, which lowered carbon demand and caused sugar over-accumulation after the return to high light. Manipulating carbon supply and demand through leaf masking, photosynthetic inhibition, and targeted sucrose feeding supported a causal contribution of sugar availability in these contrasting responses. Beyond these findings, key requirements for models simulating branching plasticity were identified and this work provides a basis for predicting branching responses under fluctuating and complex light environments.

The genus Camellia comprises more than 200 evergreen species of major economic and ornamental importance, characterised by high morphological and chemical diversity. While several species have been extensively studied for their bioactive compounds, the metabolic basis of floral trait variation across the genus remains poorly understood. In this study, a predictive metabolomics framework was applied to investigate the relationship between leaf metabolic profiles and floral traits, focusing on flower colour and floral form. Leaves from 315 individual trees, including 15 Camellia species and representing 1,224 samples, were analysed by untargeted metabolomics, generating a large-scale metabolic profiling dataset. A dedicated quality control strategy was implemented to ensure analytical stability across multiple injection series and flowering seasons. Penalised generalised linear models were used to uncover robust metabolic predictors associated with floral traits and to evaluate model performance through internal and external validation. Distinct sets of metabolites were associated with flower colour and floral form, with limited overlap between traits. Predictive performance was consistently higher for colour than for floral form, indicating more structured metabolic signatures for chromatic traits. The selected predictors spanned multiple major chemical classes, supporting a systemic organisation of the metabolome rather than reliance on single biosynthetic pathways. Consistently high predictive accuracies were obtained, reaching approximately 87% for both flower colour and floral form, and remaining clearly above the corresponding no-information rates (≈ 43%). Together, these results demonstrate that leaf metabolomics can be used to robustly predict floral traits in Camellia and highlight the potential of predictive metabolomics as a tool for early phenotype inference, quality control and selection in long-lived ornamental species.

Leaf senescence is regulated by several genetic mechanisms and environmental factors. During senescence, cellular organelles are degraded and chlorophylls are lost. Concomitantly, macromolecules are broken down into smaller molecules that are transported into tissues like seeds, a process called nutrient remobilization. By reducing photosynthesis and increasing nutrient remobilization, senescence affects seed yield and seed protein. Moreover, its understanding is relevant for agro-ecology, because it could increase nitrogen use efficiency. Sunflower, the fourth oilseed worldwide, is characterized by low water and nitrogen requirements. It shows rapid onset of senescence after anthesis, which hinders its productivity. In this crop, the interaction among senescence, water stress and cover crops for green manure has already been explored from an agronomic perspective. Here, we studied this interaction at the molecular level by comparing morpho-physiological traits and biochemical responses, transcriptome and metabolome of two sunflower near-isogenic lines with contrasting haplotypes for the senescence related LES10.179 QTL. We characterized our plants under water stress and in presence of residues of two cover crops, namely vetch and rye. The overall response to drought in our findings revealed several novel insights, such as the global decrease of secondary metabolites and the involvement of potentially antioxidant minor compounds. The characterization of the LES10.179 QTL, instead, suggested that senescence was likely controlled by a homolog to NYC1, a gene involved in chlorophyll degradation. The LES10.179 QTL also affected seed protein content: the haplotype associated with early senescence lowered it, the other one acting the opposite way. Cover crops had a minor impact on molecular profiles, and no interactive effects were observed between drought and the LES10.179 QTL. This is the first characterization of a QTL associated with leaf senescence in sunflower. This QTL has also an impact on seed protein content and it is not affected by drought. Therefore, it could prove especially useful for breeding applications. Anyway, further evidence is needed to precisely state whether the observed stay-green phenotype is functional or cosmetic.

The genus Camellia comprises more than 200 evergreen species of major economic and ornamental importance, characterised by high morphological and chemical diversity. While several species have been extensively studied for their bioactive compounds, the metabolic basis of floral trait variation across the genus remains poorly understood. In this study, a predictive metabolomics framework was applied to investigate the relationship between leaf metabolic profiles and floral traits, focusing on flower colour and floral form. Leaves from 315 individual trees, including 15 Camellia species and representing 1,160 samples, were analysed by untargeted metabolomics, generating a large-scale metabolic profiling dataset. A dedicated quality control strategy was implemented to ensure analytical stability across multiple injection series and flowering seasons. Penalised generalised linear models were used to uncover robust metabolic predictors associated with floral traits and to evaluate model performance through internal and external validation. Distinct sets of metabolites were associated with flower colour and floral form, with limited overlap between traits. Predictive performance was consistently higher for colour than for floral form, indicating more structured metabolic signatures for chromatic traits. The selected predictors spanned multiple major chemical classes, supporting a systemic organisation of the metabolome rather than reliance on single biosynthetic pathways. Consistently high predictive accuracies were obtained, reaching approximately 87% for both flower colour and floral form, and remaining clearly above the corresponding no-information rates (≈ 43%). Together, these results demonstrate that leaf metabolomics can be used to robustly predict floral traits in Camellia and highlight the potential of predictive metabolomics as a tool for early phenotype inference, quality control and selection in long-lived ornamental species.

Summary Plant ecological and evolutionary strategies are shaped by interactions between phylogenetic history and environmental constraints, resulting in leaf and stomatal traits. However, traditional trait-based and phylogenetic approaches often fail to fully explain biochemical mechanisms underlying ecological strategies, particularly for leaf and stomatal traits. Plant metabolomes integrate genetic, physiological, and environmental information and therefore represent a promising intermediate phenotype for investigating links between biochemical diversity, functional traits, and evolutionary patterns. We analysed metabolomic profiles from 74 plant species with various growth forms and ecological types. Using machine learning approaches, we explored whether metabolic variation could predict plant functional divisions, growth forms and phenological types, but also physiological traits related to drought resistance. Metabolomic data contained structured information associated with variation in plant functional traits, ecological strategies, and phylogenetic relationships. Machine learning models identified with high accuracy distinct metabolic signatures linked to differences among plant functional divisions, growth forms, phenology, and trait values. Our study demonstrates that predictive metabolomics provides a powerful and integrative framework to investigate plant ecological and evolutionary strategies. By linking biochemical diversity with plant phylogeny, and ecophysiological traits across multiple species, this approach offers new opportunities to explore the mechanistic basis of plant evolution.

Connecting the characterization of juvenile (pre-anthesis) plant stress responses in controlled environments to field agronomic performance is a challenge. The oilseed crop Camelina sativa (camelina), with its innate resilience and plasticity, presents an opportunity to understand the underlying mechanisms of juvenile resilience and identify the implications for yield in diverse pedoclimates. A better understanding of camelina's abiotic stress resilience is important in the context of climate change and the development of breeding programs for climate-tolerant crops. In this study, 54 accessions representing the genetic diversity observed in the wider publicly available population were used to investigate the plasticity of camelina's early stage response to drought and heat stress, combined with an evaluation of field performance in multilocation field trials. A combinatorial phenotyping approach of early stage drought and heat stress identified stress-responsive signatures within the diversity panel. The substantial variation in the morphophysiological line-specific responses to stress indicated that juvenile and mature camelina plants have significant plasticity and access different stress response strategies. In response to stress, we observed significant molecular metabolic adjustment alongside significant lipid remodeling and physiological compensation. Camelina was resilient to drought stress, and certain metabolites were identified as indicators of abiotic stress response. Applying an integrated approach, early stage phenotyping and multilocation field trials provided a complete assessment of the camelina stress response and facilitated a connection to crop productivity. This approach facilitates improved breeding programs, addresses the restrictions of limited genetic diversity in camelina, and supports the development of local varieties optimized for climate resilience.

Tomato is both one of the most consumed horticultural crops and the model species for fleshy fruits. While impressive yields are now obtained, the tomato sector must face the numerous threats to these advances in productivity due to the consequences of climate change. Since the first draft genome sequence in 2012, our understanding of tomato genetics has advanced dramatically, greatly facilitating the identification of mechanisms involved in growth, development, resistance to stress, and organoleptic quality. This intensive investigation has broadened our understanding of the integration and coordination of these mechanisms by phytohormones and transcription factors, as well as the roles played by metabolism, particularly in establishing fruit quality, with the aim to better target breeding programmes and to develop innovative cultivation practices.

Linking genotype and phenotype is a fundamental challenge in biology. In this respect, machine learning is playing a pivotal role in systems biology. As central phenotypic traits, fruit development and relative growth rate (RGR) result from interactions between gene regulation, metabolism, and environment. In the present study, we carried out a multispecies transcriptomic analysis of nine different fruits. To illustrate fruit transcriptomes, transcripts were first compared using multivariate methods, revealing similar main profiles. They were then used as variables to predict four growth traits, that is RGR, developmental progress, fruit weight, and protein content, using generalized linear models to decipher the mechanisms involving gene expression in development. The predictions were highly satisfactory despite disparities when the model did not include the entire panel of fruit species. Based on orthogroups derived from BLAST and annotated consensus sequences from gene ontology terminology, variables annotated for metabolic processes, especially those involving cell wall carbohydrates and proteins, were found to be the most effective in predicting growth. In addition, predictions were improved for RGR when introducing a 7 d lag between transcript contents and growth traits, suggesting the necessity of considering the proteins produced to enhance phenotypic trait predictions. These original results showed that growth traits can be predicted very well with generalized linear models based on orthogroups from multi-species transcriptomes.

Mycorrhizal symbiosis is a ubiquitous plant-fungal association widespread across the plant kingdom. Considering the different characteristics and distributions of arbuscular (AM) and ectomycorrhizal (EM) fungal types across biomes, mycorrhizal types can be associated with different climatic and edaphic conditions, reinforced by feedback between soil conditions and plant traits. Although it is becoming clear that AM and EM trees differ in leaf litter quality and nutrient acquisition strategies, studies investigating how leaf traits differ across mycorrhizal associations have led to contrasting results. Here for the first time, we used a combination of quantitative targeted measurements and an untargeted metabolomic approach on 32 European tree species to demonstrate that AM and EM-associated tree species show distinct leaf metabolic fingerprints. Finally, we discuss the link between AM and EM function with key leaf metabolites that emerged from our integrated metabolomic approach.

Nitrogen (N) use efficiency (NUE) in crops is a critical challenge, as only 40 % of applied nitrogen is typically recovered at harvest. Ammonium-based nutrition is proposed as a strategy to increase NUE in agrosystems. However, crops display better performance when growing with a nitrate-based nutrition. To advance in the current understanding of the interspecific variability to ammonium nutrition, we investigated the performance of the three annual species of Brachypodium genus: B. distachyon, B. stacei, and B. hybridum, focusing on their differential responses to ammonium and nitrate nutrition. B. stacei appeared as a tolerant species, with equal growth regardless of the N source, and B. distachyon as the most sensitive, while B. hybridum showed an intermediate phenotype. Metabolomic analysis highlighted critical differences in N metabolism, where B. stacei and B. hybridum exhibited more robust N assimilation in terms of protein content. In addition, several metabolic pathways were found associated with ammonium nutrition. Notably, flavonoid biosynthesis and tricarboxylic acid cycle pathways together with ethylene precursors and iron homeostasis-related compounds contributed to explain species-specific responses to ammonium nutrition. Altogether, these findings pinpoint potential strategies for improving N utilization and ammonium stress tolerance in cereals.

High temperatures increase the sugar concentration of grape (Vitis vinifera L.) berries, which can negatively affect the composition and quality of wine, and global climate change is expected to exacerbate this problem. Modifying the source-to-sink ratio of grapevines by selective pruning is a potential strategy to mitigate this. To investigate the effects of low source-to-sink ratio (retaining three leaves per cluster) on carbon metabolism of grape (cv. Cabernet Sauvignon) berries, we conducted an analysis of 42 metabolites and 21 enzyme activities at nine berry developmental stages，as well as transcriptomes from berries grown under two leaves per cluster. The results revealed that the metabolic pathways were coordinately regulated to maintain homeostasis under low source-to-sink ratio conditions. Because of a delay between metabolites and enzyme activities, the metabolites were loosely correlated with enzyme activities, and a lower density of connectivity between them appeared in low source-to-sink conditions. Otherwise, transcripts of the carbohydrate and amino acid metabolism pathways were enriched by carbon limitation. In summary, this integrated analysis reveals a coordinated regulation of various metabolic pathways that maintains the balance of carbon metabolism and ensures survival in challenging environments, highlighting the high metabolic plasticity of grape berries.

Nitrogen (N) fertilization is essential to maximize crop production. However, around half of the applied N is lost to the environment, causing water and air pollution and contributing to climate change. Understanding the natural genetic and metabolic basis underlying plants N use efficiency is of great interest to attain an agriculture with less N demand and thus more sustainable. The study of ammonium (NH4+) nutrition is of particular interest, because it mitigates N losses due to nitrate (NO3-) leaching or denitrification. In this work, we studied Brachypodium distachyon, the model plant for C-3 grasses, grown with NH4+ or NO3- supply. We performed gene expression analysis in the root of the B. distachyon reference accession Bd21 and examined the phenotypic variation across 52 natural accessions through analyzing plant growth and a panel of 22 metabolic traits in leaf and root. We found that the adjustment of primary metabolism to NH4+ nutrition is essential for the natural variation of NH4+ tolerance, notably involving NH4+ assimilation and phosphoenolpyruvate carboxylase (PEPC) activity. Additionally, genome-wide association studies (GWAS) indicated several loci associated with B. distachyon growth and metabolic adaptation to NH4+ nutrition. We found that the GDH2 gene was associated with the induction of root glutamate dehydrogenase activity under NH4+ nutrition and that two genes encoding malic enzyme were associated with leaf PEPC activity. Altogether, our work underlines the value of natural variation and the key role of primary metabolism to improve NH4+ tolerance.

Plant defence peptides are paramount endogenous danger signals secreted after a challenge, intensifying the plant immune response. The peptidic hormone Systemin (Sys) was shown to participate in resistance in several plant pathosystems, although the mechanisms behind Sys-induced resistance when exogenously applied remain elusive. We performed proteomic, metabolomic, and enzymatic studies to decipher the Sys-induced changes in tomato plants in either the absence or the presence of Botrytis cinerea infection. Sys treatments triggered direct proteomic rearrangement mostly involved in carbon metabolism and photosynthesis. However, the final induction of defence proteins required concurrent challenge, triggering priming of pathogen-targeted proteins. Conversely, at the metabolomic level, Sys-treated plants showed an alternative behaviour following a general priming profile. Of the primed metabolites, the flavonoids rutin and isorhamnetin and two alkaloids correlated with the proteins 4-coumarate-CoA-ligase and chalcone-flavanone-isomerase triggered by Sys treatment. In addition, proteomic and enzymatic analyses revealed that Sys conditioned the primary metabolism towards the production of available sugars that could be fuelling the priming of callose deposition in Sys-treated plants; furthermore, PR1 appeared as a key element in Sys-induced resistance. Collectively, the direct induction of proteins and priming of specific secondary metabolites in Sys-treated plants indicated that post-translational protein regulation is an additional component of priming against necrotrophic fungi.

The primary metabolite contents of ripe fruits result from complex regulations during their development. For Solanaceae, these regulations have been widely studied in tomato. The fruit metabolite contents of other fruit species, such as pepper (Capsicum annuum L.) and eggplant (Solanum melongena L.), constitute a valuable resource for the community to study the regulation of fruit metabolism and identify common or species-dependent regulations. This dataset about major polar metabolites is part of a larger project that integrates other omics data for pepper and eggplant, and other fruit species for metabolomics and other omics. We provide quantitative metabolite data of pepper and eggplant fruit along development. We sampled pepper and eggplant fruit cultivated in a tunnel or a greenhouse at 10 or 11 stages from anthesis to ripe fruit. We used proton nuclear magnetic resonance (1H-NMR) metabolomic profiling of polar extracts to quantify the major metabolites and expressed the data in µmol per g fresh weight. Twenty-four metabolites were determined in pepper and 27 in eggplant. Nineteen common metabolites were quantified in both fruit species including three soluble sugars and one sugar-alcohol, five organic acids and nine free amino acids. These data can be combined with similar quantitative data on other species or complemented with other omics data to perform cross-species or cross-omics comparisons.

IntroductionA better understanding of the physiological response of silage maize to a mild reduction in nitrogen (N) fertilization and the identification of predictive biochemical markers of N utilization efficiency could contribute to limit the detrimental effect of the overuse of N inputs.ObjectivesWe integrated phenotypic and biochemical data to interpret the physiology of maize in response to a mild reduction in N fertilization under agronomic conditions and identify predictive leaf metabolic and proteic markers that could be used to pilot and rationalize N fertilization.MethodsEco-physiological, developmental and yield-related traits were measured and complemented with metabolomic and proteomic approaches performed on young leaves of a core panel of 29 European genetically diverse dent hybrids cultivated in the field under non-limiting and reduced N fertilization conditions.ResultsMetabolome and proteome data were analyzed either individually or in an integrated manner together with eco-physiological, developmental, phenotypic and yield-related traits. They allowed to identify (i) common N-responsive metabolites and proteins that could be used as predictive markers to monitor N fertilization, (ii) silage maize hybrids that exhibit improved agronomic performance when N fertilization is reduced.ConclusionsAmong the N-responsive metabolites and proteins identified, a cytosolic NADP-dependent malic enzyme and four metabolite signatures stand out as promising markers that could be used for both breeding and agronomic purposes.

Different wheat QTLs were associated to the free asparagine content of grain grown in four different conditions. Environmental effects are a key factor when selecting for low acrylamide-forming potential. The amount of free asparagine in grain of a wheat genotype determines its potential to form harmful acrylamide in derivative food products. Here, we explored the variation in the free asparagine, aspartate, glutamine and glutamate contents of 485 accessions reflecting wheat worldwide diversity to define the genetic architecture governing the accumulation of these amino acids in grain. Accessions were grown under high and low nitrogen availability and in water-deficient and well-watered conditions, and plant and grain phenotypes were measured. Free amino acid contents of grain varied from 0.01 to 1.02 mg g−1 among genotypes in a highly heritable way that did not correlate strongly with grain yield, protein content, specific weight, thousand-kernel weight or heading date. Mean free asparagine content was 4

The best ideotypes are under mounting pressure due to increased aridity. Understanding the conserved molecular mechanisms that evolve in wild plants adapted to harsh environments is crucial in developing new strategies for agriculture. Yet our knowledge of such mechanisms in wild species is scant. We performed metabolic pathway reconstruction using transcriptome information from 32 Atacama and phylogenetically related species that do not live in Atacama (sister species). We analyzed reaction enrichment to understand the commonalities and differences of Atacama plants. To gain insights into the mechanisms that ensure survival, we compared expressed gene isoform numbers and gene expression patterns between the annotated biochemical reactions from 32 Atacama and sister species. We found biochemical convergences characterized by reactions enriched in at least 50% of the Atacama species, pointing to potential advantages against drought and nitrogen starvation, for instance. These findings suggest that the adaptation in the Atacama Desert may result in part from shared genetic legacies governing the expression of key metabolic pathways to face harsh conditions. Enriched reactions corresponded to ubiquitous compounds common to extreme and agronomic species and were congruent with our previous metabolomic analyses. Convergent adaptive traits offer promising candidates for improving abiotic stress resilience in crop species.

Background Abiotic stresses in plants include all the environmental conditions that significantly reduce yields, like drought and heat. One of the most significant effects they exert at the cellular level is the accumulation of reactive oxygen species, which cause extensive damage. Plants possess two mechanisms to counter these molecules, i.e. detoxifying enzymes and non-enzymatic antioxidants, which include many classes of specialized metabolites. Sunflower, the fourth global oilseed, is considered moderately drought resistant. Abiotic stress tolerance in this crop has been studied using many approaches, but the control of specialized metabolites in this context remains poorly understood. Here, we performed the first genome-wide association study using abiotic stress-related specialized metabolites as molecular phenotypes in sunflower. After analyzing leaf specialized metabolites of 450 hybrids using liquid chromatography-mass spectrometry, we selected a subset of these compounds based on their association with previously known abiotic stress-related quantitative trait loci. Eventually, we characterized these molecules and their associated genes. Results We putatively annotated 30 compounds which co-localized with abiotic stress-related quantitative trait loci and which were associated to seven most likely candidate genes. A large proportion of these compounds were potential antioxidants, which was in agreement with the role of specialized metabolites in abiotic stresses. The seven associated most likely candidate genes, instead, mainly belonged to cytochromes P450 and glycosyltransferases, two large superfamilies which catalyze greatly diverse reactions and create a wide variety of chemical modifications. This was consistent with the high plasticity of specialized metabolism in plants. Conclusions This is the first characterization of the genetic control of abiotic stress-related specialized metabolites in sunflower. By providing hints concerning the importance of antioxidant molecules in this biological context, and by highlighting some of the potential molecular mechanisms underlying their biosynthesis, it could pave the way for novel applications in breeding. Although further analyses will be required to better understand this topic, studying how antioxidants contribute to the tolerance to abiotic stresses in sunflower appears as a promising area of research.