Identification of Geographical Chemical Markers in Neolitsea pallens Using UHPLC-Q-TOF-MS/MS and Multivariate Analysis.

Abstract

RATIONALE: Comprehensive characterization of plant metabolomes is essential for understanding phytochemical diversity and identifying location-specific metabolic signatures. However, the high complexity of untargeted metabolomics datasets requires robust analytical workflows that integrate high-resolution mass spectrometry with chemometric and pathway-based analyses. This study aimed to characterize the metabolomic landscape of Neolitsea pallens leaves collected from diverse Himalayan locations using an untargeted UHPLC-QTOF-MS/MS metabolomics approach. METHODS: Leaf samples collected from 13 geographically distinct Himalayan populations were analyzed using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS) in negative electrospray ionization mode. Metabolites were annotated using accurate mass measurements and MS/MS fragmentation patterns. Frequency-based feature filtering was applied to refine the metabolite dataset, followed by principal component analysis (PCA) and hierarchical clustering analysis (HCA) to identify discriminant metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-based pathway mapping was performed to investigate the biological significance of the selected biomarkers. RESULTS: UHPLC-QTOF-MS/MS analysis enabled the annotation of 56 metabolites encompassing diverse primary and secondary metabolites. Frequency-based filtering combined with PCA and HCA identified 10 discriminant biomarker metabolites that effectively differentiated N. pallens populations. KEGG pathway analysis revealed that these biomarkers were predominantly associated with central carbon metabolism, with propanoate and butanoate metabolism serving as key metabolic hubs linking glycolysis, the tricarboxylic acid cycle, amino acid degradation, and fatty acid metabolism. Conserved metabolites, including citric acid, succinic acid, and β-alanine, reflected metabolic stability across populations, whereas low-frequency metabolites represented adaptive metabolic variation associated with geographical diversity. CONCLUSION: The integration of untargeted UHPLC-QTOF-MS/MS metabolomics, multivariate chemometric analysis, and pathway-level interpretation provides a robust strategy for resolving complex plant metabolomes and identifying biologically relevant metabolic biomarkers. This workflow offers a comprehensive framework for investigating phytochemical diversity, metabolic adaptation, and biomarker discovery in medicinal plant species.

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