Comparative constraint-based modelling of fruit development across species highlights nitrogen metabolism in the growth-defence trade-off.

Although primary metabolism is well conserved across species, it is useful to explore the specificity of its network to assess the extent to which some pathways may contribute to particular outcomes. Constraint-based metabolic modelling is an established framework for predicting metabolic fluxes and phenotypes and helps to explore how the plant metabolic network delivers specific outcomes from temporal series. After describing the main physiological traits during fruit development, we confirmed the correlations between fruit relative growth rate (RGR), protein content and time to maturity. Then a constraint-based method is applied to a panel of eight fruit species with a knowledge-based metabolic model of heterotrophic cells describing a generic metabolic network of primary metabolism. The metabolic fluxes are estimated by constraining the model using a large set of metabolites and compounds quantified throughout fruit development. Multivariate analyses showed a clear common pattern of flux distribution during fruit development with differences between fast- and slow-growing fruits. Only the latter fruits mobilise the tricarboxylic acid cycle in addition to glycolysis, leading to a higher rate of respiration. More surprisingly, to balance nitrogen, the model suggests, on the one hand, nitrogen uptake by nitrate reductase to support a high RGR at early stages of cucumber and, on the other hand, the accumulation of alkaloids during ripening of pepper and eggplant. Finally, building virtual fruits by combining 12 biomass compounds shows that the growth-defence trade-off is supported mainly by cell wall synthesis for fast-growing fruits and by total polyphenols accumulation for slow-growing fruits.

Ecological and metabolic implications of the nurse effect of Maihueniopsis camachoi in the Atacama Desert.

Plant-plant positive interactions are key drivers of community structure. Yet, the underlying molecular mechanisms of facilitation processes remain unexplored. We investigated the 'nursing' effect of Maihueniopsis camachoi, a cactus that thrives in the Atacama Desert between c. 2800 and 3800 m above sea level. We hypothesised that an important protective factor is thermal amelioration of less cold-tolerant species with a corresponding impact on molecular phenotypes. To test this hypothesis, we compared plant cover and temperatures within the cactus foliage with open areas and modelled the effect of temperatures on plant distribution. We combined eco-metabolomics and machine learning to test the molecular consequences of this association. Multiple species benefited from the interaction with M. camachoi. A conspicuous example was the extended distribution of Atriplex imbricata to colder elevations in association with M. camachoi (400 m higher as compared to plants in open areas). Metabolomics identified 93 biochemical markers predicting the interaction status of A. imbricata with 79% accuracy, independently of year. These findings place M. camachoi as a key species in Atacama plant communities, driving local biodiversity with an impact on molecular phenotypes of nursed species. Our results support the stress-gradient hypothesis and provide pioneer insights into the metabolic consequences of facilitation.

Multi-regulated GDP-l-galactose phosphorylase calls the tune in ascorbate biosynthesis.

Ascorbate is involved in numerous vital processes, in particular in response to abiotic but also biotic stresses whose frequency and amplitude increase with climate change. Ascorbate levels vary greatly depending on species, tissues, or stages of development, but also in response to stress. Since its discovery, the ascorbate biosynthetic pathway has been intensely studied and it appears that GDP-l-galactose phosphorylase (GGP) is the enzyme with the greatest role in the control of ascorbate biosynthesis. Like other enzymes of this pathway, its expression is induced by various environmental and also developmental factors. Although mRNAs encoding it are among the most abundant in the transcriptome, the protein is only present in very small quantities. In fact, GGP translation is repressed by a negative feedback mechanism involving a small open reading frame located upstream of the coding sequence (uORF). Moreover, its activity is inhibited by a PAS/LOV type photoreceptor, the action of which is counteracted by blue light. Consequently, this multi-level regulation of GGP would allow fine control of ascorbate synthesis. Indeed, experiments varying the expression of GGP have shown that it plays a central role in response to stress. This new understanding will be useful for developing varieties adapted to future environmental conditions.

Phylogenetically diverse wild plant species use common biochemical strategies to thrive in the Atacama Desert.

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.

Multi-omics quantitative data of tomato fruit unveils regulation modes of least variable metabolites.

BACKGROUND: The composition of ripe fruits depends on various metabolites which content evolves greatly throughout fruit development and may be influenced by the environment. The corresponding metabolism regulations have been widely described in tomato during fruit growth and ripening. However, the regulation of other metabolites that do not show large changes in content have scarcely been studied. RESULTS: We analysed the metabolites of tomato fruits collected on different trusses during fruit development, using complementary analytical strategies. We identified the 22 least variable metabolites, based on their coefficients of variation. We first verified that they had a limited functional link with the least variable proteins and transcripts. We then posited that metabolite contents could be stabilized through complex regulations and combined their data with the quantitative proteome or transcriptome data, using sparse partial-least-square analyses. This showed shared regulations between several metabolites, which interestingly remained linked to early fruit development. We also examined regulations in specific metabolites using correlations with individual proteins and transcripts, which revealed that a stable metabolite does not always correlate with proteins and transcripts of its known related pathways. CONCLUSIONS: The regulation of the least variable metabolites was then interpreted regarding their roles as hubs in metabolic pathways or as signalling molecules.

STracking: a free and open-source Python library for particle tracking and analysis.

SUMMARY: Analysis of intra- and extracellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking, etc.) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a Python library that allows combining algorithms into standardized particle tracking pipelines. AVAILABILITY AND IMPLEMENTATION: STracking is available as a Python library using 'pip install' and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using 'pip install'. The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Enzyme-based kinetic modelling of ASC-GSH cycle during tomato fruit development reveals the importance of reducing power and ROS availability.

The ascorbate-glutathione (ASC-GSH) cycle is at the heart of redox metabolism, linking the major redox buffers with central metabolism through the processing of reactive oxygen species (ROS) and pyridine nucleotide metabolism. Tomato fruit development is underpinned by changes in redox buffer contents and their associated enzyme capacities, but interactions between them remain unclear. Based on quantitative data obtained for the core redox metabolism, we built an enzyme-based kinetic model to calculate redox metabolite concentrations with their corresponding fluxes and control coefficients. Dynamic and associated regulations of the ASC-GSH cycle throughout the whole fruit development were analysed and pointed to a sequential metabolic control of redox fluxes by ASC synthesis, NAD(P)H and ROS availability depending on the developmental phase. Furthermore, we highlighted that monodehydroascorbate reductase and the availability of reducing power were found to be the main regulators of the redox state of ASC and GSH during fruit growth under optimal conditions. Our kinetic modelling approach indicated that tomato fruit development displayed growth phase-dependent redox metabolism linked with central metabolism via pyridine nucleotides and H2 O2 availability, while providing a new tool to the scientific community to investigate redox metabolism in fruits.

Sparse denoising and adaptive estimation enhances the resolution and contrast of fluorescence emission difference microscopy based on an array detector.

An array detector allows a resolution gain for confocal microscopy by combining images sensed by a set of photomultipliers tubes (or sub-detectors). Several methods have been proposed to reconstruct a high-resolution image by linearly combining sub-detector images, especially the fluorescence emission difference (FED) technique. To improve the resolution and contrast of FED microscopy based on an array detector, we propose to associate sparse denoising with spatial adaptive estimation. We show on both calibration slides and real data that our approach applied to the full stack of spatially reassigned detector signals, enables us to achieve a higher reconstruction performance in terms of resolution, image contrast, and noise reduction.

Predictive metabolomics of multiple Atacama plant species unveils a core set of generic metabolites for extreme climate resilience.

Current crop yield of the best ideotypes is stagnating and threatened by climate change. In this scenario, understanding wild plant adaptations in extreme ecosystems offers an opportunity to learn about new mechanisms for resilience. Previous studies have shown species specificity for metabolites involved in plant adaptation to harsh environments. Here, we combined multispecies ecological metabolomics and machine learning-based generalized linear model predictions to link the metabolome to the plant environment in a set of 24 species belonging to 14 families growing along an altitudinal gradient in the Atacama Desert. Thirty-nine common compounds predicted the plant environment with 79% accuracy, thus establishing the plant metabolome as an excellent integrative predictor of environmental fluctuations. These metabolites were independent of the species and validated both statistically and biologically using an independent dataset from a different sampling year. Thereafter, using multiblock predictive regressions, metabolites were linked to climatic and edaphic stressors such as freezing temperature, water deficit and high solar irradiance. These findings indicate that plants from different evolutionary trajectories use a generic metabolic toolkit to face extreme environments. These core metabolites, also present in agronomic species, provide a unique metabolic goldmine for improving crop performances under abiotic pressure.

A targeted 3D EM and correlative microscopy method using SEM array tomography.

Using electron microscopy to localize rare cellular events or structures in complex tissue is challenging. Correlative light and electron microscopy procedures have been developed to link fluorescent protein expression with ultrastructural resolution. Here, we present an optimized scanning electron microscopy (SEM) workflow for volumetric array tomography for asymmetric samples and model organisms (Caenorhabditis elegans, Drosophila melanogaster, Danio rerio). We modified a diamond knife to simplify serial section array acquisition with minimal artifacts. After array acquisition, the arrays were transferred to a glass coverslip or silicon wafer support. Using light microscopy, the arrays were screened rapidly for initial recognition of global anatomical features (organs or body traits). Then, using SEM, an in-depth study of the cells and/or organs of interest was performed. Our manual and automatic data acquisition strategies make 3D data acquisition and correlation simpler and more precise than alternative methods. This method can be used to address questions in cell and developmental biology that require the efficient identification of a labeled cell or organelle.

Maize metabolome and proteome responses to controlled cold stress partly mimic early-sowing effects in the field and differ from those of Arabidopsis.

In Northern Europe, sowing maize one-month earlier than current agricultural practices may lead to moderate chilling damage. However, studies of the metabolic responses to low, non-freezing, temperatures remain scarce. Here, genetically-diverse maize hybrids (Zea mays, dent inbred lines crossed with a flint inbred line) were cultivated in a growth chamber at optimal temperature and then three decreasing temperatures for 2 days each, as well as in the field. Leaf metabolomic and proteomic profiles were determined. In the growth chamber, 50% of metabolites and 18% of proteins changed between 20 and 16°C. These maize responses, partly differing from those of Arabidopsis to short-term chilling, were mapped on genome-wide metabolic maps. Several metabolites and proteins showed similar variation for all temperature decreases: seven MS-based metabolite signatures and two proteins involved in photosynthesis decreased continuously. Several increasing metabolites or proteins in the growth-chamber chilling conditions showed similar trends in the early-sowing field experiment, including trans-aconitate, three hydroxycinnamate derivatives, a benzoxazinoid, a sucrose synthase, lethal leaf-spot 1 protein, an allene oxide synthase, several glutathione transferases and peroxidases. Hybrid groups based on field biomass were used to search for the metabolite or protein responses differentiating them in growth-chamber conditions, which could be of interest for breeding.

Overproduction of ascorbic acid impairs pollen fertility in tomato.

Ascorbate is a major antioxidant buffer in plants. Several approaches have been used to increase the ascorbate content of fruits and vegetables. Here, we combined forward genetics with mapping-by-sequencing approaches using an ethyl methanesulfonate (EMS)-mutagenized Micro-Tom population to identify putative regulators underlying a high-ascorbate phenotype in tomato fruits. Among the ascorbate-enriched mutants, the family with the highest fruit ascorbate level (P17C5, up to 5-fold wild-type level) had strongly impaired flower development and produced seedless fruit. Genetic characterization was performed by outcrossing P17C5 with cv. M82. We identified the mutation responsible for the ascorbate-enriched trait in a cis-acting upstream open reading frame (uORF) involved in the downstream regulation of GDP-l-galactose phosphorylase (GGP). Using a specific CRISPR strategy, we generated uORF-GGP1 mutants and confirmed the ascorbate-enriched phenotype. We further investigated the impact of the ascorbate-enriched trait in tomato plants by phenotyping the original P17C5 EMS mutant, the population of outcrossed P17C5 × M82 plants, and the CRISPR-mutated line. These studies revealed that high ascorbate content is linked to impaired floral organ architecture, particularly anther and pollen development, leading to male sterility. RNA-seq analysis suggested that uORF-GGP1 acts as a regulator of ascorbate synthesis that maintains redox homeostasis to allow appropriate plant development.

Modeling Protein Destiny in Developing Fruit.

Protein synthesis and degradation are essential processes that regulate cell status. Because labeling in bulky organs, such as fruits, is difficult, we developed a modeling approach to study protein turnover at the global scale in developing tomato (Solanum lycopersicum) fruit. Quantitative data were collected for transcripts and proteins during fruit development. Clustering analysis showed smaller changes in protein abundance compared to mRNA abundance. Furthermore, protein and transcript abundance were poorly correlated, and the coefficient of correlation decreased during fruit development and ripening, with transcript levels decreasing more than protein levels. A mathematical model with one ordinary differential equation was used to estimate translation (kt ) and degradation (kd ) rate constants for almost 2,400 detected transcript-protein pairs and was satisfactorily fitted for >1,000 pairs. The model predicted median values of ∼2 min for the translation of a protein, and a protein lifetime of ∼11 d. The constants were validated and inspected for biological relevance. Proteins involved in protein synthesis had higher kt and kd values, indicating that the protein machinery is particularly flexible. Our model also predicts that protein concentration is more strongly affected by the rate of translation than that of degradation.

Biomass composition explains fruit relative growth rate and discriminates climacteric from non-climacteric species.

Fleshy fruits are very varied, whether in terms of their composition, physiology, or rate and duration of growth. To understand the mechanisms that link metabolism to phenotypes, which would help the targeting of breeding strategies, we compared eight fleshy fruit species during development and ripening. Three herbaceous (eggplant, pepper, and cucumber), three tree (apple, peach, and clementine) and two vine (kiwifruit and grape) species were selected for their diversity. Fruit fresh weight and biomass composition, including the major soluble and insoluble components, were determined throughout fruit development and ripening. Best-fitting models of fruit weight were used to estimate relative growth rate (RGR), which was significantly correlated with several biomass components, especially protein content (R=84), stearate (R=0.72), palmitate (R=0.72), and lignocerate (R=0.68). The strong link between biomass composition and RGR was further evidenced by generalized linear models that predicted RGR with R-values exceeding 0.9. Comparison of the fruit also showed that climacteric fruit (apple, peach, kiwifruit) contained more non-cellulosic cell-wall glucose and fucose, and more starch, than non-climacteric fruit. The rate of starch net accumulation was also higher in climacteric fruit. These results suggest that the way biomass is constructed has a major influence on performance, especially growth rate.

Proton-NMR Metabolomics of Rainbow Trout Fed a Plant-Based Diet Supplemented with Graded Levels of a Protein-Rich Yeast Fraction Reveal Several Metabolic Processes Involved in Growth.

BACKGROUND: Plant raw materials are commonly used in aquafeeds, as marine resources are unsustainable. However, full plant-based diets lead to poorer fish growth performance. OBJECTIVE: We aimed to understand the metabolic effects of a yeast fraction as a protein supplement in a plant-based diet and to integrate such effects with phenotypic traits as a new approach to assess the interest of this raw material. METHODS: Juvenile (49 g) rainbow trout (Oncorhynchus mykiss) were fed graded levels of a yeast protein-rich fraction (5% YST05, 10% YST10, 15% YST15) in a plant-based diet (PB) for 84 d. Final body weight, feed conversion ratio, and hepatosomatic and viscerosomatic indexes were measured. Plasma, liver, and muscle 1H-NMR fingerprints were analyzed with principal component analyses, and their metabolite patterns were clustered according to the yeast level to identify concomitant metabolic effects. A regression modeling approach was used to predict tissue metabolite changes from plasma fingerprints. RESULTS: In tissues, the patterns of metabolite changes followed either linear trends with the gradual inclusion of a yeast fraction (2 patterns out of 6 in muscle, 1 in liver) or quadratic trends (4 patterns in muscle, 5 in liver). Muscle aspartate and glucose (395 and 138% maximum increase in relative content compared with PB, respectively) revealing modification in energy metabolism, as well as modification of liver betaine (163% maximum increase) and muscle histidine (57% maximum decrease) related functions, indicates that the yeast fraction could improve growth in several ways. The highest correlation between measured and predicted metabolite intensities in a tissue based on plasma fingerprints was observed for betaine in liver (r = 0.80). CONCLUSIONS: These findings herald a new approach to assess the plurality of metabolic effects induced by diets and establish the optimal level of raw materials. They open the way for using plasma as a noninvasive matrix in trout nutrition studies.

Genome-scale model of Rhodotorula toruloides metabolism.

The basidiomycete red yeast Rhodotorula toruloides is a promising platform organism for production of biooils. We present rhto-GEM, the first genome-scale model (GEM) of R. toruloides metabolism, that was largely reconstructed using RAVEN toolbox. The model includes 852 genes, 2,731 reactions, and 2,277 metabolites, while lipid metabolism is described using the SLIMEr formalism allowing direct integration of lipid class and acyl chain experimental distribution data. The simulation results confirmed that the R. toruloides model provides valid growth predictions on glucose, xylose, and glycerol, while prediction of genetic engineering targets to increase production of linolenic acid, triacylglycerols, and carotenoids identified genes-some of which have previously been engineered to successfully increase production. This renders rtho-GEM valuable for future studies to improve the production of other oleochemicals of industrial relevance including value-added fatty acids and carotenoids, in addition to facilitate system-wide omics-data analysis in R. toruloides. Expanding the portfolio of GEMs for lipid-accumulating fungi contributes to both understanding of metabolic mechanisms of the oleaginous phenotype but also uncover particularities of the lipid production machinery in R. toruloides.

Reconstruction of 24 Penicillium genome-scale metabolic models shows diversity based on their secondary metabolism.

Modeling of metabolism at the genome-scale has proved to be an efficient method for explaining the phenotypic traits observed in living organisms. Further, it can be used as a means of predicting the effect of genetic modifications for example, development of microbial cell factories. With the increasing amount of genome sequencing data available, there exists a need to accurately and efficiently generate such genome-scale metabolic models (GEMs) of nonmodel organisms, for which data is sparse. In this study, we present an automatic reconstruction approach applied to 24 Penicillium species, which have potential for production of pharmaceutical secondary metabolites or use in the manufacturing of food products, such as cheeses. The models were based on the MetaCyc database and a previously published Penicillium GEM and gave rise to comprehensive genome-scale metabolic descriptions. The models proved that while central carbon metabolism is highly conserved, secondary metabolic pathways represent the main diversity among species. The automatic reconstruction approach presented in this study can be applied to generate GEMs of other understudied organisms, and the developed GEMs are a useful resource for the study of Penicillium metabolism, for example, for the scope of developing novel cell factories.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond.

Brown algae (stramenopiles) are key players in intertidal ecosystems, and represent a source of biomass with several industrial applications. Ectocarpus siliculosus is a model to study the biology of these organisms. Its genome has been sequenced and a number of post-genomic tools have been implemented. Based on this knowledge, we report the reconstruction and analysis of a genome-scale metabolic network for E. siliculosus, EctoGEM (http://ectogem.irisa.fr). This atlas of metabolic pathways consists of 1866 reactions and 2020 metabolites, and its construction was performed by means of an integrative computational approach for identifying metabolic pathways, gap filling and manual refinement. The capability of the network to produce biomass was validated by flux balance analysis. EctoGEM enabled the reannotation of 56 genes within the E. siliculosus genome, and shed light on the evolution of metabolic processes. For example, E. siliculosus has the potential to produce phenylalanine and tyrosine from prephenate and arogenate, but does not possess a phenylalanine hydroxylase, as is found in other stramenopiles. It also possesses the complete eukaryote molybdenum co-factor biosynthesis pathway, as well as a second molybdopterin synthase that was most likely acquired via horizontal gene transfer from cyanobacteria by a common ancestor of stramenopiles. EctoGEM represents an evolving community resource to gain deeper understanding of the biology of brown algae and the diversification of physiological processes. The integrative computational method applied for its reconstruction will be valuable to set up similar approaches for other organisms distant from biological benchmark models.