The cuticle, covering the surface of all primary plant organs, plays important roles in plant development and protection against the biotic and abiotic environment. In contrast to vegetative organs, very little molecular information has been obtained regarding the surfaces of reproductive organs such as fleshy fruit. To broaden our knowledge related to fruit surface, comparative transcriptome and metabolome analyses were carried out on peel and flesh tissues during tomato (Solanum lycopersicum) fruit development. Out of 574 peel-associated transcripts, 17% were classified as putatively belonging to metabolic pathways generating cuticular components, such as wax, cutin, and phenylpropanoids. Orthologs of the Arabidopsis (Arabidopsis thaliana) SHINE2 and MIXTA-LIKE regulatory factors, activating cutin and wax biosynthesis and fruit epidermal cell differentiation, respectively, were also predominantly expressed in the peel. Ultra-performance liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer and gas chromatography-mass spectrometry using a flame ionization detector identified 100 metabolites that are enriched in the peel tissue during development. These included flavonoids, glycoalkaloids, and amyrin-type pentacyclic triterpenoids as well as polar metabolites associated with cuticle and cell wall metabolism and protection against photooxidative stress. Combined results at both transcript and metabolite levels revealed that the formation of cuticular lipids precedes phenylpropanoid and flavonoid biosynthesis. Expression patterns of reporter genes driven by the upstream region of the wax-associated SlCER6 gene indicated progressive activity of this wax biosynthetic gene in both fruit exocarp and endocarp. Peel-associated genes identified in our study, together with comparative analysis of genes enriched in surface tissues of various other plant species, establish a springboard for future investigations of plant surface biology.

The cuticle covering plants' aerial surfaces is a unique structure that plays a key role in organ development and protection against diverse stress conditions. A detailed analysis of the tomato colorless-peel y mutant was carried out in the framework of studying the outer surface of reproductive organs. The y mutant peel lacks the yellow flavonoid pigment naringenin chalcone, which has been suggested to influence the characteristics and function of the cuticular layer. Large-scale metabolic and transcript profiling revealed broad effects on both primary and secondary metabolism, related mostly to the biosynthesis of phenylpropanoids, particularly flavonoids. These were not restricted to the fruit or to a specific stage of its development and indicated that the y mutant phenotype is due to a mutation in a regulatory gene. Indeed, expression analyses specified three R2R3-MYB–type transcription factors that were significantly down-regulated in the y mutant fruit peel. One of these, SlMYB12, was mapped to the genomic region on tomato chromosome 1 previously shown to harbor the y mutation. Identification of an additional mutant allele that co-segregates with the colorless-peel trait, specific down-regulation of SlMYB12 and rescue of the y phenotype by overexpression of SlMYB12 on the mutant background, confirmed that a lesion in this regulator underlies the y phenotype. Hence, this work provides novel insight to the study of fleshy fruit cuticular structure and paves the way for the elucidation of the regulatory network that controls flavonoid accumulation in tomato fruit cuticle.

Plant metabolic engineering is commonly used in the production of functional foods and quality trait improvement. However, to date, computational model-based approaches have only been scarcely used in this important endeavor, in marked contrast to their prominent success in microbial metabolic engineering. In this study we present a computational pipeline for the reconstruction of fully compartmentalized tissue-specific models of Arabidopsis thaliana on a genome scale. This reconstruction involves automatic extraction of known biochemical reactions in Arabidopsis for both primary and secondary metabolism, automatic gap-filling, and the implementation of methods for determining subcellular localization and tissue assignment of enzymes. The reconstructed tissue models are amenable for constraint-based modeling analysis, and significantly extend upon previous model reconstructions. A set of computational validations (i.e., cross-validation tests, simulations of known metabolic functionalities) and experimental validations (comparison with experimental metabolomics datasets under various compartments and tissues) strongly testify to the predictive ability of the models. The utility of the derived models was demonstrated in the prediction of measured fluxes in metabolically engineered seed strains and the design of genetic manipulations that are expected to increase vitamin E content, a significant nutrient for human health. Overall, the reconstructed tissue models are expected to lay down the foundations for computational-based rational design of plant metabolic engineering. The reconstructed compartmentalized Arabidopsis tissue models are MIRIAM-compliant and are available upon request.C urrent challenges in using plants as factories for bio-energy and nutraceuticals require predesigned and efficient strategies for metabolic engineering (1). Currently, plant metabolic engineering mostly involves trial-and-error approaches, without the utilization of computational modeling procedures to rationally design genetic modifications. The marginal contribution played by metabolic modeling in plants until now stands in marked contrast to its prominent success in microbial metabolic engineering (2, 3). Metabolic network reconstructions were manually reconstructed for dozens of bacterial species (3), and automated approaches recently generated draft models for a total of 130 bacteria (4). A modeling approach, called constraintbased modeling (CBM), serves to analyze the function of such large-scale metabolic networks by solely relying on simple physical-chemical constraints, overcoming the problem of missing enzyme kinetic data (3). Applications of CBM for large-scale microbial networks has proven to be highly successful in predicting metabolic phenotypes in metabolic engineering and many other applications (3).The reconstruction of metabolic network models for multicellular eukaryotes is significantly more challenging than that for bacteria, because of the larger size of the networks, the subcellular compartmentalization of me...

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Motivation: Revealing the subcellular localization of proteins within membrane-bound compartments is of a major importance for inferring protein function. Though current high-throughput localization experiments provide valuable data, they are costly and time-consuming, and due to technical difficulties not readily applicable for many Eukaryotes. Physical characteristics of proteins, such as sequence targeting signals and amino acid composition are commonly used to predict subcellular localizations using computational approaches. Recently it was shown that protein–protein interaction (PPI) networks can be used to significantly improve the prediction accuracy of protein subcellular localization. However, as high-throughput PPI data depend on costly high-throughput experiments and are currently available for only a few organisms, the scope of such methods is yet limited.Results: This study presents a novel constraint-based method for predicting subcellular localization of enzymes based on their embedding metabolic network, relying on a parsimony principle of a minimal number of cross-membrane metabolite transporters. In a cross-validation test of predicting known subcellular localization of yeast enzymes, the method is shown to be markedly robust, providing accurate localization predictions even when only 20% of the known enzyme localizations are given as input. It is shown to outperform pathway enrichment-based methods both in terms of prediction accuracy and in its ability to predict the subcellular localization of entire metabolic pathways when no a-priori pathway-specific localization data is available (and hence enrichment methods are bound to fail). With the number of available metabolic networks already reaching more than 600 and growing fast, the new method may significantly contribute to the identification of enzyme localizations in many different organisms.Contact: shira.mintz@weizmann.ac.il; tomersh@cs.technion.ac.il