Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.Financial support for this investigation by National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), Brazilian Biosciences National Laboratory (LNBioCNPEM/MCTI), Foundation for Support of Scientific and Technological Research in the State of Santa Catarina (FAPESC), and Portuguese Foundation for Science and Technology (FCT) is acknowledged. The research fellowship granted by CNPq to the first author is also acknowledged. The work was partially funded by a CNPq and FCT agreement through the PropMine grant

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant’s resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( λ= 280-400 ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination

Chemical characterization, antioxidant, cytotoxic and antibacterial activity of propolis extracts and isolated compounds from the Brazilian stingless bees <i>Melipona quadrifasciata</i> and <i>Tetragonisca angustula</i>

<p>Since chemical or pharmacological studies on the propolis of Brazilian native bees are scarce, the aim of this study was to evaluate the chemical profile, the antioxidant, cytotoxic and antibacterial activity of aqueous and hydro-alcoholic extracts and isolated compounds of propolis of the native bees, <i>Melipona quadrifasciata</i> and <i>Tetragonisca angustula</i>, against bacteria with (Gram-positive and Gram-negative) and without cell wall (mollicutes). The extracts presented a peculiar feature between green and yellow propolis. Despite the low content of flavonoid and phenolic compounds, a promising level of antioxidant activity without toxicity in the propolis extracts of <i>M. quadrifasciata</i> was observed. The best antimicrobial activity was that of the hydro-alcoholic extract against <i>Mycoplasma pneumoniae</i> and <i>Ureaplasma urealyticum</i> (MIC 125 μg/ml). We isolated two compounds and identified them from the aqueous and hydro-alcoholic extracts as the flavononol sakuranetin and gallic acid. Sakuranetin and gallic acid presented MICs of 50 and 25 μg/ml against <i>Mycoplasma hominis</i> and <i>Mycoplasma genitalium</i> respectively. Propolis from Brazilian native bees may constitute an alternative and undervalued source of compounds with biological activity. The mollicutes are the smallest self-replicating bacteria that constitute a model of cellular and molecular biology studies due to their small genome and restrict biochemical machinery. This is the first report investigating the potential of antibacterial molecules isolated from propolis of Brazilian native bees using this microorganism model. Our results contribute to a better understanding of the chemical and biological properties of these propolis types and provide evidence for its potential medicinal use.</p