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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.