In this study, we investigated the chemical composition, and antioxidant and antibacterial properties of ethanolic extracts of propolis (EEP) from Melipona quadrifasciata quadrifasciata and Tetragonisca angustula. Chemical composition of EEP was determined by colorimetry and chromatographic (HPLC-DAD and UPLC-Q/TOF-MS/MS) analysis. Antimicrobial activity of EEP was evaluated against gram-positive (S. aureus, methicillin-resistant S. aureus, E. faecalis) and gram-negative (E. coli and K. pneumoniae) bacteria by the minimal inhibitory concentration (MIC) test using the microdilution method. Furthermore, the growth curve and integrity of cell membrane of S. aureus and E. coli were investigated using standard microbiological methods. HPLC-DAD analysis showed that the EEP of M. quadrifasciata quadrifasciata has a more complex chemical composition than the EEP of T. angustula. Moreover, UPLC-MS analyses of M. quadrifasciata quadrifascita indicated flavonoids and terpenes as major constituents. The bactericidal activity of both EEPs was higher against gram-positive bacteria than for gram-negative bacteria. The EEP from M. quadrifasciata quadrifasciata presented MIC values lower than the EEP from T. angustula for all tested bacteria. The EEP from M. quadrifasciata quadrifasciata caused lysis of the bacterial wall and release of intracellular components from both E. coli and S. aureus. Our findings indicate that the chemical composition of propolis from stingless bees is complex and depends on the species. The extract from M. quadrifasciata quadrifascita was more effective against gram-positive than gram-negative strains, especially against S. aureus and methicillin-resistant S. aureus compared to T. angustula extract, by a mechanism that involves disturbance of the bacterial cell membrane integrity.

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.

No abstract

No abstract

Passiflora actinia Hook (common name: ‘maracujá do mato’) is an important medicinal species due to significant sedative and anxiolytic activities. In order to commercially exploit the plant, however, studies on propagation to improve rooting in stem cuttings are needed. The present study was designed to evaluate the effect of the brown seaweed (Ascophyllum nodosum (L.) Le Jol.) extract when applied on P. actinia stem cuttings bases. Five concentrations of the extract in distilled water were evaluated: 0% (pure distilled water), 10%, 20%, 30% and 40%. The experimental design was completely randomized with 4 repetitions and 12 cuttings per experimental unit. Cuttings were evaluated 45 days from planting. Data were analyzed through polynomial regression analysis and Pearson’s correlation coefficients were calculated for all the variables. On average, 51.27% rooting was achieved. Rooting percentage increased linearly according to the brown seaweed extract concentrations. When compared to the control treatment, about 10% increase in rooting was observed at the treatment with 40% seaweed extract. Leaf retention response to A. nodosum increasing concentrations was also adjusted in a positive linear model. A 15.6% increase in leaf retention was observed at the 40% seaweed concentration when compared to the control. Positive correlations were observed for leaf retention and rooting percentage and leaf retention and roots length. The immersion of P. actinia stem cuttings bases in A. nodosum extract at a concentration of 40% promote positive effects on rooting and facilitate the species propagation.

Baccharis dracunculifolia DC. is a Brazilian native plant, presenting wide chemical diversity and numerous pharmaceutical and industrial applications. This research assessed the yield, antioxidant activity and the chemical similarity of essential oils from 10 populations of B. dracunculifolia in the state of Paraná, southern Brazil. The extraction of the volatile compounds was carried out by hydrodistillation, the chemical composition was determined by GC/FID and GC/MS and the antioxidant activity by the DPPH method. The essential oil yield of wild B. dracunculifolia populations ranged from 0.14 to 0.87%. The oils were predominantly composed of oxygenated sesquiterpenes (34.16 -51.01%), monoterpene hydrocarbons (18.02 -46.17%) and sesquiterpenes hydrocarbons (9.60 -17.70%). The major compounds found in all populations were β-pinene (7.65 -29.8%) and (E)-nerolidol (9.11 -21.68%). Essential oil solutions (20%) from different populations presented antioxidant capacity ranging from 27.78 to 91.67%. A negative correlation was found between the antioxidant activity and spathulenol (r = -0.696). Multivariate analyses HIGHLIGHTS  Essential oils from populations of B. dracunculifolia were investigated.  β-pinene and (E)-nerolidol were the main compounds in B. dracunculifolia populations.  The difference in the chemical profile of the essential oils is quantitative only. There is a negative correlation between the antioxidant activity and spathulenol.Tomazzoli, M.M.; et al.

No abstract

Summary 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.