Bioactivity-guided metabolomics reveals discriminant cytotoxic signatures in Siparuna guianensis
摘要
Natural products remain a privileged source of structurally diverse bioactive compounds with potential for the development of safer and more selective anticancer agents.
ObjectivesIn this study, a bioactivity-guided untargeted metabolomics approach was applied to investigate the cytotoxic chemical space of Siparuna guianensis.
MethodsThe hydroethanolic leaf extract and solvent-partitioned fractions (hexane, ethyl acetate, butanol, and aqueous) were evaluated for cytotoxic activity against MCF-7, 4T1, and MDA-MB-231 breast cancer cell lines, followed by metabolomic profiling using HPLC–HRMS.
ResultsCytotoxicity was predominantly associated with low- and intermediate-polarity fractions, which were classified as active and subsequently compared with inactive samples using chemometric methods. Structural annotation supported by spectral libraries enabled MSI level 2–3 annotation of 60 metabolites. Alkaloids and flavonoids were proportionally enriched in cytotoxic fractions despite the overall dominance of terpenoids. Multivariate and univariate statistical analyses demonstrated clear metabolic discrimination between active and inactive groups. Integration of VIP scores, Volcano analysis, and ROC-based prioritization identified isoquinoline and aporphine alkaloids, together with glycosylated flavonoids, as the principal contributors to cytotoxicity. The alkaloid norglaucine emerged as a key discriminant feature in the VIP–Volcano intersection, consistent with its previously reported cytotoxic activity against multiple tumor cell lines. Consensus discriminant ions included m/z 330.170 and 296.12, both showing high discriminative potential.
ConclusionsThis study suggests a strong association between metabolomic composition and cytotoxic activity in S. guianensis, highlighting isoquinoline-derived scaffolds as promising candidates for future isolation and mechanistic investigation while demonstrating the power of metabolomics-guided strategies for natural product discovery.