Mining of trace natural products from Pyrrosia calvata with feature-based molecular network strategy

Pyrrosia calvata is a traditional Chinese medicinal fern endemic to Guangxi, China. However, its phytochemical profile remains largely unexplored, and the comprehensive characterization of its chemical constituents is still lacking. In this study, the ethanolic crude extract of P. calvata was analyzed using ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in both negative and positive ionization modes, employing a data-dependent acquisition (DDA) strategy. The acquired data were processed using MS-DIAL and further analyzed through the Feature-Based Molecular Networking (FBMN) workflow on the Global Natural Products Social (GNPS) platform to facilitate the discovery and annotation of both known and putatively novel natural products. As a result, fourteen compounds were identified or tentatively characterized, with flavonoids representing the predominant chemical class. Notably, eleven of these compounds were reported for the first time from P. calvata. The results demonstrate the effectiveness of FBMN-assisted metabolomics in expanding the chemical coverage of trace constituents and provide a foundation for future targeted isolation and pharmacological evaluation of bioactive compounds from this medicinal fern.