Investigates biochemical responses in transgenic soybeans under copper nanoparticle exposure, indicating significant stress effects.
Soybean calluses constitute a robust in vitro model for investigating biochemical and elemental responses to external stressors. In this study, transgenic soybean calluses (Glycine max) were exposed to increasing concentrations of copper nanoparticles (CuNPs; 10, 50, and 100 µg L⁻1), and their chemical responses were investigated using an integrated online HILIC-ESI-HRMS-ICP-MS/MS platform. This hyphenated analytical approach enabled the simultaneous chromatographic separation, molecular characterization, and elemental detection of co-eluting chemical species, providing complementary metabolomic and speciation information within a single analytical workflow. The online coupling revealed marked concentration-dependent effects, with calluses exposed to 10 and 50 µg L−1 CuNPs exhibiting reduced chemical diversity compared with those exposed to 100 µg L−1. Multivariate and pathway-level analyses indicated that CuNP exposure progressively reshaped the metabolic landscape, with the most pronounced alterations observed at the highest concentration. These changes were primarily associated with cellular defense responses, redox imbalance, and membrane remodeling, as evidenced by the increased accumulation of oxylipins and lysophospholipids, including 12(13)-Ep-9-KODE, (9Z,12Z)-7,8,16-trihydroxyoctadeca-9,12-dienoic acid, and LysoPC(16:0/0:0). Importantly, the integrated speciomics strategy afforded by the online HILIC-ESI-HRMS-ICP-MS/MS platform enabled the putative annotation of metal- and heteroatom-containing species, suggesting homeostatic adjustments involving essential elements such as Mn, Mg, Zn, P, and S associated with organic ligands. These findings highlight the central role of elemental redistribution and coordination chemistry in shaping the biochemical response of soybean calluses to nanoparticle-induced stress and demonstrate the analytical potential of integrated molecular and elemental detection for investigating complex biological systems.
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Gamela et al. (2026) studied this question.
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