Comparative Untargeted LC-MS/MS Metabolic Profiling of Medicinal Phyllanthus and Euphorbia Species from an Arid Ecosystem

Arid-zone medicinal plants experience strong environmental constraints, yet their chemical diversity is often described through identified compounds rather than the broader metabolic feature space. We used untargeted LC–MS/MS profiling to compare root, stem, and leaf extracts of four Phyllanthus and three Euphorbia species collected from western Rajasthan, India. The analysis focused on chemical features shared across species and those associated with individual taxa and tissues. The main Phyllanthus dataset contained 39,592 recurrent grouped features, including 7,678 shared across all four species and 11,841 shared among P. airyshawii, P. amarus, and P. urinaria. Despite this large common feature pool, 1,034 features showed tissue-associated patterns, indicating differentiation among roots, stems, and leaves. A feature consistent with a hypophyllanthin-like lignan candidate (m/z 453.40607, RT 18.52 min) occurred across all four Phyllanthus species and all three tissues, although its identity remains putative. The Euphorbia-containing dataset showed substantial chemical differentiation within the 2–25 min chromatographic region, with 2,756 positive- and 1,806 negative-mode tissue-associated features. Species-associated feature pools were also evident among E. hirta, E. jodhpurensis, and E. thymifolia. Chemical overlap extended across genera, including 21 features shared between P. maderaspatensis and E. hirta. A substantial fraction of recurrent features remained unannotated. Together, these results reveal a combination of shared chemical background, tissue partitioning, and species-associated chemical diversity across arid-zone Phyllanthus and Euphorbia, providing candidate features for further MS/MS-based structural characterization. Although many LC-MS features remained unannotated, these unresolved signals represent an important part of the plant dark metabolome and may contain ecologically relevant metabolites not yet well represented in public spectral resources. The study highlights the value of LC-MS feature profiling for exploring tissue-level metabolic differentiation in arid-zone medicinal plants and provides candidate feature groups for future targeted LC-MS/MS validation and compound-level identification.