In Silico Enzymatic Synthesis of a 400 000 Compound Biochemical Database for Nontargeted Metabolomics

Current methods of structure identification in mass-spectrometry-based nontargeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database, it cannot be identified, and if it cannot be identified, it cannot be included in a database. Thus, there is an urgent need to augment metabolomics databases with rationally designed biochemical structures using alternative means. Here we present the In Vivo/In Silico Metabolites Database (IIMDB), a database of in silico enzymatically synthesized metabolites, to partially address this problem. The database, which is available at http://metabolomics.pharm.uconn.edu/iimdb/, includes ∼23 000 known compounds (mammalian metabolites, drugs, secondary plant metabolites, and glycerophospholipids) collected from existing biochemical databases plus more than 400 000 computationally generated human phase-I and phase-II metabolites of these known compounds. IIMDB features a user-friendly web interface and a programmer-friendly RESTful web service. Ninety-five percent of the computationally generated metabolites in IIMDB were not found in any existing database. However, 21 640 were identical to compounds already listed in PubChem, HMDB, KEGG, or HumanCyc. Furthermore, the vast majority of these in silico metabolites were scored as biological using BioSM, a software program that identifies biochemical structures in chemical structure space. These results suggest that in silico biochemical synthesis represents a viable approach for significantly augmenting biochemical databases for nontargeted metabolomics applications

Unraveling the metabolome of grapevine through FT-ICR-MS : from nutritional value to pathogen resistance

Grapevine (Vitis vinifera L.) is one of most important fruit crops in the world due to its numerous food products, namely fresh and dried table grapes, wine and intermediate products, with a high economic importance worldwide. Concerning nutritional value, grapes are highly studied and a great diversity of secondary bioactive metabolites has already been identified. However, an important grapevine by-product, also containing a high nutritional value, but sometimes disregarded is grapevine leaves. They are an abundant source of compounds with interest in human health and are already included in human diet in several countries. The study of the nutritional values of this by-product is essential towards the improvement of food systems. Hence, in this PhD dissertation an untargeted metabolomic profiling of the leaves of Vitis vinifera cultivar ‘Pinot noir’ was performed by Fourier-transform ion cyclotron-resonance mass spectrometry (FT-ICR-MS), (CHAPTER II). Numerous compounds with diverse nutritional and pharmacological properties, particularly polyphenols and phenolic compounds, several phytosterols and fatty acids (the most represented lipids’ secondary class), were identified. Grapevine leaves were also evaluated for their antioxidant capacity. It was found that leaves present a high antioxidant capacity, similar to berries, putting grapevine leaves at the top of the list of foods with the highest antioxidant activity. Traditional premium cultivars of wine and table grapes are highly susceptible to various diseases. Grapevine downy mildew, powdery mildew and gray mold are caused, respectively, by the biotrophic oomycete Plasmopara viticola (Berk. & Curt.) Berl. & de Toni) Beri, et de Toni], by the biotrophic fungus Erysiphe necator (Schweinf.) Burrill) and by the necrotrophic fungus Botrytis cinerea Pers.). In Europe, disease management became one of the main tasks for viticulture, being the current strategy, for disease control, the massive use of fungicides and pesticides in each growing season. This practice has several associated problems, from the environmental impact to the economical level, and even in human health. The alternative approach to the application of pesticides is breeding for resistance, clearly the most effective and sustainable approach, particularly if coupled to the selection of desirable traits from local grapevine cultivars. However, a successful breeding program of grape plants with increased resistance traits against pathogens requires not only an understanding of the innate resistance mechanisms of cultivars against fungi/oomycetes, but also the identification of biomarkers of tolerance or susceptibility. Among these, metabolic biomarkers may prove particularly useful, not only because they can be determined in a high throughput way but, above all, because metabolites provide an accurate image of the metabolic state of the plant. To better understand the metabolic differences associated with intrinsic defence mechanisms of grapevine to pathogens, the metabolome of several genotypes with different tolerance degrees to fungal/oomycete pathogens was compared through an untargeted metabolomics approach by FT-ICR-MS (CHAPTERS III, IV and V). First, a comparison of two Vitis vinifera (V. vinifera cv. Trincadeira e V. vinifera cv. Regent, susceptible and tolerant, respectively, to pathogens) was performed and discriminatory compounds between these two cultivars, were identified (CHAPTER III). Also, through the comparison of the metabolome of one Vitis vinifera (V. vinifera cv. Cabernet Sauvignon, susceptible to pathogens) and one Vitis species (Vitis rotundifolia, tolerant), was possible to distinguish both genotypes and determine that Vitis rotundifolia metabolome appeared to be more complex according to the chemical formulas analysed (CHAPTER IV). Albeit grapevine metabolome is complex, it is possible to distinguish Vitis species and different genotypes within the same species. Ultimately, to identify compounds that contribute to the segregation between susceptible and tolerant grapevines, eleven Vitis genotypes, were compared at the metabolite level (CHAPTER V). From all the metabolites identified, seven compounds with a higher accumulation on susceptible genotypes were selected. Their metabolic pathways were analysed and the expression profile of biosynthesis and/or degradation enzymes coding genes was evaluated by Real-time Polymerase Chain Reaction (qPCR). qPCR studies require as internal controls one or more reference genes. Hence, in this study, ten possible reference genes were tested and the three most stable reference genes (ubiquitin-conjugating enzyme – UBQ, SAND family protein - SAND and elongation factor 1-alpha - EF1α) were established for our analysis and selected for qPCR data normalization. Our data revealed that the leucoanthocyanidin reductase 2 gene (LAR2) presented a significant increase of expression in susceptible genotypes, in accordance with catechin accumulation in this analysis group, being a possible metabolic constitutive biomarker, associated to susceptibility. The interaction of grapevine-P.viticola was also analysed by FT-ICR-MS (CHAPTERS VI and VII). The metabolome of Vitis vinifera cv. Trincadeira after 24 hours post-infection (hpi) was analysed and, based only on the chemical profile and representation plots, the discrimination between infected and non-infected grapevine leaves was possible (CHAPTER VI). A further analysis of Vitis vinifera cv. Trincadeira infected with P. viticola was performed through Matrix-assisted laser desorption/ionization (MALDI) FT-ICR-MS imaging, to identify leaf surface compounds related to the grapevine-pathogen interaction (CHAPTER VII). Putatively identified sucrose ions were more abundant on P. viticola infected leaves when compared to control ones. Also, sucrose was mainly located around the veins, which is an indicator of the correlation of putatively identified sucrose at P. viticola infection sites, leading to the hypothesis that the pathogen is extracting sucrose from grapevine to reproduce. Each chapter was written as a scientific article and has its own abstract, introduction, materials and methods, results and discussion, conclusion, acknowledgments and references. The results obtained in this PhD thesis are a starting point on the elucidation of the molecular mechanisms related to the intrinsic tolerance/susceptibility to different pathogens. Also, these results can be used for the development of new approaches and help to improve breeding and introgression line programs

Development of in silico models for the prediction of toxicity incorporating ADME information

Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges. A variety of computational approaches have been developed in the last two decades, these must be evaluated to ensure confidence in their use. The research presented in this thesis has assessed a range of existing tools for the prediction of toxicity and absorption, distribution, metabolism and elimination (ADME) parameters with an emphasis on absorption and xenobiotic metabolism. These two ADME properties largely determine bioavailability of a drug and, in turn, also influence toxicity. In vitro (Caco-2 cells and the parallel artificial membrane permeation assay) and in silico approaches, such as various druglikeness filters, can be used to estimate human intestinal absorption; a comparison between different methods was performed to identify relative strengths and weaknesses of the approaches. In terms of xenobiotic metabolism it is not only important to predict metabolites correctly, but it is also crucial to identify those compounds that can be biotransformed into species that can covalently bind to biomolecules. Structural alerts are routinely used to screen for such potential reactive metabolites. The balance between sensitivity and specificity of such reactive metabolite alerts has been discussed in the context of correctly predicting reactive metabolites of pharmaceuticals (using data available from DrugBank). Off-target toxicity, exemplified by human Ether-à-go-go-Related Gene (hERG) channel inhibition, was also explored. A number of novel structural alerts for hERG toxicity were developed based on groups of structurally similar compounds. Finally, the importance of predicting potential ecotoxicological effects of drugs was also considered. The utility of zebrafish embryos to distinguish between baseline and excess toxicity was investigated. In evaluating this selection of existing tools, improvements to the methods have been proposed where possible