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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. [ABSTRACT FROM AUTHOR]

SUMMARY: 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. Significance Statement: This work shows how a panel of eight fruit species unravels specific metabolic flux patterns throughout fruit development. For this, a medium‐size metabolic model of primary metabolism is constrained by compositional time‐series data. The predicted metabolic fluxes highlight a growth‐defence trade‐off with high nitrogen uptake and carbon used in cell wall synthesis for fast‐growing fruits from herbaceous plants. Conversely, slow‐growing fruits, from trees, require the tricarboxylic acid cycle for early phenolic compounds synthesis. [ABSTRACT FROM AUTHOR]

Summary: 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 H2O2 availability, while providing a new tool to the scientific community to investigate redox metabolism in fruits. [ABSTRACT FROM AUTHOR]

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. Metabolomic and proteomic responses of diverse maize hybrids to progressive decreasing temperatures were larger from 20 to 16°C, partly mimicked those in the field, and involved primary and specialized metabolisms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 , a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. 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 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 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 was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that 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. [ABSTRACT FROM AUTHOR]

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, Ecto GEM (). 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. Ecto GEM 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. Ecto GEM 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. [ABSTRACT FROM AUTHOR]