Metabolite Profiling and Bioactivities of Leaves, Stems, and Flowers of Rumex usambarensis (Dammer) Dammer, a Traditional African Medicinal Plant.

The comprehensive identification of secondary metabolites represents a fundamental step for the assessment of bioactivities and pharmacological properties of traditional herbal drugs. Rumex usambarensis (Dammer) Dammer has been described as a multipurpose remedy in different African traditional pharmacopoeias, but its phytochemical profile has not been properly investigated. Herein we report a high throughput metabolomic screening, based on ultra-high performance liquid chromatography-travelling wave ion mobility spectrometry quadrupole time-of-flight (UHPLC-TWINS-QTOF), which was performed for the first time on different R. usambarensis plant parts. By applying high-resolution mass spectrometry-based metabolomics and chemometric analysis, a complete discrimination of different aerial parts was obtained, with the annotation of 153 significant metabolites in leaves, stems, and flowers, suggesting an easy authentication and discrimination route. Phytochemical data were correlated to antimicrobial and antioxidant properties. Flavonoids, benzopyranes, chromones, and xanthones derivatives, along with a richer phytocomplex, might be responsible for the stronger bioactivities obtained from flowers.

The ECAT dataset: expert-validated distribution data of endemic and sub-endemic trees of Central Africa (Dem. Rep. Congo, Rwanda, Burundi).

In this data paper, we present a specimen-based occurrence dataset compiled in the framework of the Conservation of Endemic Central African Trees (ECAT) project with the aim of producing global conservation assessments for the IUCN Red List. The project targets all tree species endemic or sub-endemic to the Central African region comprising the Democratic Republic of the Congo (DR Congo), Rwanda, and Burundi. The dataset contains 6361 plant collection records with occurrences of 8910 specimens from 337 taxa belonging to 153 genera in 52 families. Many of these tree taxa have restricted geographic ranges and are only known from a small number of herbarium specimens. As assessments for such taxa can be compromised by inadequate data, we transcribed and geo-referenced specimen label information to obtain a more accurate and complete locality dataset. All specimen data were manually cleaned and verified by botanical experts, resulting in improved data quality and consistency.