The intrinsic value of biodiversity extends beyond species diversity, genetic heritage, ecosystem variability and ecological services, such as climate regulation, water quality, nutrient cycling and the provision of reproductive habitats it is also an inexhaustible source of molecules and products beneficial to human well-being. To uncover the chemistry of Brazilian natural products, the Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products Database (NuBBEDB) was created as the first natural product library from Brazilian biodiversity. Since its launch in 2013, the NuBBEDB has proven to be an important resource for new drug design and dereplication studies. Consequently, continuous efforts have been made to expand its contents and include a greater diversity of natural sources to establish it as a comprehensive compendium of available biogeochemical information about Brazilian biodiversity. The content in the NuBBEDB is freely accessible online (https://nubbe.iq.unesp.br/portal/nubbedb.html) and provides validated multidisciplinary information, chemical descriptors, species sources, geographic locations, spectroscopic data (NMR) and pharmacological properties. Herein, we report the latest advancements concerning the interface, content and functionality of the NuBBEDB. We also present a preliminary study on the current profile of the compounds present in Brazilian territory.

Abstract Structure elucidation is an important and sometimes time-consuming step for natural products research. This step has evolved in the past few years to a faster and more automated process due to the development of several computational programs and analytical techniques. In this paper, the topics of NMR prediction and CASE programs are addressed. Furthermore, the elucidation of natural peptides is discussed.

Taurine is one of the most abundant amino acids in mammalian tissues. It is obtained from the diet and by de novo synthesis, from cysteic acid or hypotaurine. Despite the discovery in 1954 that the oxygenation of hypotaurine produces taurine, the identification of an enzyme catalyzing this reaction has remained elusive. In large part this is due to the incorrect assignment, in 1962, of the enzyme as an NAD-dependent hypotaurine dehydrogenase. For more than 55 years the literature has continued to refer to this enzyme as such. Here we show, both in vivo and in vitro, that the enzyme that oxygenates hypotaurine to produce taurine is flavin-containing monooxygenase 1 (FMO1). Metabolite analysis of the urine of Fmo1-null mice by 1 H NMR spectroscopy revealed a build-up of hypotaurine and a deficit of taurine in comparison with the concentrations of these compounds in the urine of wild-type mice. In vitro assays confirmed that human FMO1 catalyzes the conversion of hypotaurine to taurine utilizing either NADPH or NADH as co-factor. FMO1 has a wide substrate range and is best known as a xenobiotic-or drug-metabolizing enzyme. The identification that the endogenous molecule hypotaurine is a substrate for the FMO1catalyzed production of taurine resolves a long-standing mystery. This finding should help establish the role FMO1 plays in a range of biological processes in which taurine or its deficiency is implicated, including conjugation of bile acids, neurotransmitter, anti-oxidant and anti-inflammatory functions, and the pathogenesis of obesity and skeletal muscle disorders. Significance statement:The identity of the enzyme that catalyzes the biosynthesis of taurine from hypotaurine has remained elusive. Here we show, both in vivo and in This article has not been copyedited and formatted. The final version may differ from this version. DMD Fast Forward. Downloaded fromvitro, that flavin-containing monooxygenase 1 (FMO1) catalyzes the oxygenation of hypotaurine to produce taurine.

contribuiram igualmente para o trabalho e são co-autores.

Abstract Technological advances have contributed to the evolution of the natural product chemistry and drug discovery programs. Recently, computational methods for nuclear magnetic resonance (NMR) and mass spectrometry (MS) have speeded up and facilitated the process of structural elucidation even in high complex biological samples. In this chapter, the current computational tools related to NMR and MS databases and spectral similarity networks, as well as their applications on dereplication and determination of biological biomarkers, are addressed.

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A major challenge in metabolomic studies is how to extract and analyze an entire metabolome. So far, no single method was able to clearly complete this task in an efficient and reproducible way. In this work we proposed a sequential strategy for the extraction and chromatographic separation of metabolites from leaves Jatropha gossypifolia using a design of experiments and partial least square model. The effect of 14 different solvents on extraction process was evaluated and an optimized separation condition on liquid chromatography was estimated considering mobile phase composition and analysis time. The initial conditions of extraction using methanol and separation in 30 min between 5 and 100% water/methanol (1:1 v/v) with 0.1% of acetic acid, 20 μL sample volume, 3.0 mL min(-1) flow rate and 25°C column temperature led to 107 chromatographic peaks. After the optimization strategy using i-propanol/chloroform (1:1 v/v) for extraction, linear gradient elution of 60 min between 5 and 100% water/(acetonitrile/methanol 68:32 v/v with 0.1% of acetic acid), 30 μL sample volume, 2.0 mL min(-1) flow rate, and 30°C column temperature, we detected 140 chromatographic peaks, 30.84% more peaks compared to initial method. This is a reliable strategy using a limited number of experiments for metabolomics protocols.

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.