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.

Extensive admixture and selective pressure across the Sahel Belt

Genome-wide studies of African populations have the potential to reveal powerful insights into the evolution of our species, as these diverse populations have been exposed to intense selective pressures imposed by infectious diseases, diet, and environmental factors. Within Africa, the Sahel Belt extensively overlaps the geographical center of several endemic infections such as malaria, trypanosomiasis, meningitis, and hemorrhagic fevers. We screened 2.5 million single nucleotide polymorphisms in 161 individuals from 13 Sahelian populations, which together with published data cover Western, Central, and Eastern Sahel, and include both nomadic and sedentary groups. We confirmed the role of this Belt as a main corridor for human migrations across the continent. Strong admixture was observed in both Central and Eastern Sahelian populations, with North Africans and Near Eastern/Arabians, respectively, but it was inexistent in Western Sahelian populations. Genome-wide local ancestry inference in admixed Sahelian populations revealed several candidate regions that were significantly enriched for non-autochthonous haplotypes, and many showed to be under positive selection. The DARC gene region in Arabs and Nubians was enriched for African ancestry, whereas the RAB3GAP1/LCT/MCM6 region in Oromo, the TAS2R gene family in Fulani, and the ALMS1/NAT8 in Turkana and Samburu were enriched for non-African ancestry. Signals of positive selection varied in terms of geographic amplitude. Some genomic regions were selected across the Belt, the most striking example being the malaria-related DARC gene. Others were Western-specific (oxytocin, calcium, and heart pathways), Eastern-specific (lipid pathways), or even population-restricted (TAS2R genes in Fulani, which may reflect sexual selection).

Nanotechnological approaches using curcumin and Withania somnifera: neuroprotection and antimicrobial activities

Tese de Doutoramento em Biologia de Plantas

Stress-triggered synaptic malfunction: a gate along the path from depressionto dementia

Tese de Doutoramento em Ciências da Saúde.