Aloe plant species have been used for centuries in traditional medicine and are reported to be an important source of natural products. However, despite the large number of species within the Aloe genus, only a few have been investigated chemotaxonomically. A Molecular Network approach was used to highlight the different chemical classes characterizing the leaves of five Aloe species: Aloe macra, Aloe vera, Aloe tormentorii, Aloe ferox, and Aloe purpurea. Aloe macra, A. tormentorii, and A. purpurea are endemic from the Mascarene Islands comprising Reunion, Mauritius, and Rodrigues. UHPLC-MS/MS analysis followed by a dereplication process allowed the characterization of 93 metabolites. The newly developed MolNotator algorithm was usedfor molecular networking and allowed a better exploration of the Aloe metabolome chemodiversity. The five species appeared rich in polyphenols (anthracene derivatives, flavonoids, phenolic acids). Therefore, the total phenolic content and antioxidant activity of the five species were evaluated, and a DPPH-On-Line-HPLC assay was used to determine the metabolites responsible for the radical scavenging activity. The use of computational tools allowed a better description of the comparative phytochemical profiling of five Aloe species, which showed differences in their metabolite composition, both qualitative and quantitative. Moreover, the molecular network approach combined with the On-Line-HPLC assay allowed the identification of 9 metabolites responsible for the antioxidant activity. Two of them, aloeresin A and coumaroylaloesin, could be the principal metabolites responsible for the activity. From 374 metabolites calculated by MolNator, 93 could be characterized. Therefore, the Aloe species can be a rich source of new chemical structures that need to be discovered.

This study compares the antioxidant properties and phytochemical profiles of three pairs of widely used medicinal plant species to their counterparts from Provence, France: Arnica montana with Pentanema montanum (formerly known as Inula montana), Helichrysum italicum with Helichrysum stoechas, and Satureja hortensis with Satureja montana. Phytochemical composition has been investigated using UHPLC-HRMS/MS and molecular networking, revealing chemical profiles dominated by phenylpropanoids and flavonoids, with lignans, sesquiterpene lactones, or polyketides aside. Well-plate DPPH/ABTS assays were used to evaluate the antioxidant activity of extracts, and post-column assays were used to identify antioxidant compounds. The three Provence species demonstrated comparable or superior antioxidant activities to their counterparts, primarily attributed to phenolic compounds such as mono- and di-caffeoylquinic acids, quercetagetin-7-O-glucoside, and myricetin acetylhexoside. These findings show the potential of Provence species to be substituted for some overharvested medicinal plants. This research supports biodiversity conservation while promoting the integration of these local species into pharmaceutical, nutraceutical, cosmetic, and food industries.

Tuberculosis (TB) is still a significant public health threat, with rising drug resistance and high incidence in multiple areas worldwide. In the search for novel antitubercular agents, this study explores the application of a bioactivity-guided molecular networking approach to identify bioactive compounds from seven plant species (Curatella americana, Davilla nitida, Dipteryx punctata, Indigofera suffruticosa, Quassia amara, Tetradenia riparia, and Zingiber zerumbet) collected in French Guiana. Using ultrasound-assisted extraction followed by liquid–liquid partitioning and UHPLC-HRMS/MS analysis, a library of 72 samples was tested against Mycobacterium tuberculosis. The non-polar fractions from Indigofera suffruticosa, Tetradenia riparia, and Zingiber zerumbet showed the highest activity. The integration of metabolomic and bioassay data on molecular networks allowed the prioritization and annotation of active compounds, revealing flavonoids as contributors to the antitubercular activity of the active samples. In addition, the use of computational tools such as GNPS, SIRIUS, and TIMA-R enabled dereplication and increased the confidence in the structural prediction of active metabolites. This approach demonstrated its potential in accelerating the identification of both known and novel bioactive compounds without requiring exhaustive isolation, offering a robust strategy for natural product-based drug development against TB.