Filamentous fungi produce a wide range of bioactive compounds with important pharmaceutical applications, such as antibiotic penicillins and cholesterol-lowering statins. However, less attention has been paid to fungal secondary metabolites compared to those from bacteria. In this study, we sequenced the genomes of 9 Penicillium species and, together with 15 published genomes, we investigated the secondary metabolism of Penicillium and identified an immense, unexploited potential for producing secondary metabolites by this genus. A total of 1,317 putative biosynthetic gene clusters (BGCs) were identified, and polyketide synthase and non-ribosomal peptide synthetase based BGCs were grouped into gene cluster families and mapped to known pathways. The grouping of BGCs allowed us to study the evolutionary trajectory of pathways based on 6-methylsalicylic acid (6-MSA) synthases. Finally, we cross-referenced the predicted pathways with published data on the production of secondary metabolites and experimentally validated the production of antibiotic yanuthones in Penicillia and identified a previously undescribed compound from the yanuthone pathway. This study is the first genus-wide analysis of the genomic diversity of Penicillia and highlights the potential of these species as a source of new antibiotics and other pharmaceuticals.

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.

SUMMARYBrown 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.

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.

Tomato is a major crop suffering substantial yield losses from diseases, as fruit decay at a postharvest level can claim up to 50% of the total production worldwide. Due to the environmental risks of fungicides, there is an increasing interest in exploiting plant immunity through priming, which is an adaptive strategy that improves plant defensive capacity by stimulating induced mechanisms. Broad-spectrum defence priming can be triggered by the compound ß-aminobutyric acid (BABA). In tomato plants, BABA induces resistance against various fungal and bacterial pathogens and different methods of application result in durable protection. Here, we demonstrate that the treatment of tomato plants with BABA resulted in a durable induced resistance in tomato fruit against Botrytis cinerea, Phytophthora infestans and Pseudomonas syringae. Targeted and untargeted metabolomics were used to investigate the metabolic regulations that underpin the priming of tomato fruit against pathogenic microbes that present different infection strategies. Metabolomic analyses revealed major changes after BABA treatment and after inoculation. Remarkably, primed responses seemed specific to the type of infection, rather than showing a common fingerprint of BABA-induced priming. Furthermore, top-down modelling from the detected metabolic markers allowed for the accurate prediction of the measured resistance to fruit pathogens and demonstrated that soluble sugars are essential to predict resistance to fruit pathogens. Altogether, our results demonstrate that metabolomics is particularly insightful for a better understanding of defence priming in fruit. Further experiments are underway in order to identify key metabolites that mediate broad-spectrum BABA-induced priming in tomato fruit.

Grapevine canes are an abundant byproduct of the wine industry. The stilbene contents of Vitis vinifera cultivars have been largely studied, but little is known about the stilbene contents of wild Vitis accessions. Moreover, there have only been few studies on the quantification of other phenolic compounds in just pruned grapevine canes. In our study, we investigated the polyphenol profile of 51 genotypes belonging to 15 Vitis spp. A total of 36 polyphenols (20 stilbenes, 6 flavanols, 7 flavonols, and 3 phenolic acids) were analyzed by high-performance liquid chromatography coupled with a triple quadrupole mass spectrometer. Our results suggest that some wild Vitis accessions could be of interest in terms of the concentration of bioactive polyphenols and that flavanols contribute significantly to the antioxidant activity of grapevine cane extracts. To the best of our knowledge, this is the most exhaustive study of the polyphenolic composition of grapevine canes of wild Vitis spp.

To understand the mechanisms that link metabolism to phenotypes, which would help to target breeding strategies, eight fleshy fruit species were compared during development and ripening. Three herbaceous (eggplant, pepper, cucumber), three tree (apple, peach, clementine) and two vine (kiwifruit, 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). Moreover, the strong link between biomass composition and RGR was further evidenced by generalised linear models that predicted RGR with R-values exceeding 0.9. Fruit comparison also showed that climacteric fruit (apple, peach, kiwifruit) contained more non-cellulosic cell-wall-glucose and -fucose and 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.

Ascorbate is a major antioxidant buffer in plants, so several approaches have been developed to increase the ascorbate contents of fruits and vegetables. In this study, we combined forward genetics with mapping-by-sequencing approaches using an EMSMicro-Tom population to identify putative regulators underlying a high ascorbate phenotype in fruits. Among the ascorbate-enriched mutants, the family with the highest fruit ascorbate level (P17C5 line, up to 5 times the WT) strongly impaired flower development and produced seedless fruit. Without progeny, genetic characterization was performed by outcrossing the P17C5 line with S. Lycopersicum cv. M82. We successfully identified the mutation responsible for the high ascorbate trait in a cis-acting upstream open reading frame (uORF) that is 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-enrichment trait in tomato plants by phenotyping the original P17C5 EMS mutant, the population of outcrossed P17C5xM82 plants, and the CRISPR-mutated line. These studies revealed that a high ascorbate content is linked to impaired floral organ architecture, particularly anthers and pollen development, thus leading to male sterility. RNAseq analysis suggests that uORF-GGP1 acts as a regulator of ascorbate synthesis that maintains redox homeostasis to allow appropriate plant development.