A Combined Theoretical and Experimental Approach to unravel zearalenone degradation pathway by Laccase Mediator System

Enzymatic degradation is a promising strategy to reduce mycotoxin contamination due to its mild conditions and high specificity. Laccases (E.C. 1.10.3.2), copper-containing oxidases, are widely applied to degrade zearalenone (ZEN), especially in combination with the redox mediator acetosyringone (AS) within Laccase Mediator Systems (LMS). Despite demonstrated efficacy, the detailed degradation mechanisms and the identities of LMS-derived ZEN products remain largely unknown. High-resolution mass spectrometry (HRMS) offers powerful capabilities for identifying degradation products, but challenges such as lack of standards, low abundance, and compound instability complicate confident identification. In this study, a novel combined theoretical and experimental tool was developed to investigate ZEN degradation products and pathways. Density functional theory (DFT) was applied to ZEN, AS, and potential degradation products to predict molecular reactivity and generate a customized database to support HRMS data interpretation. DFT also provided mechanistic insights into enzymatic oxidation processes. Experimentally, HRMS coupled with statistical analysis—specifically volcano plots—was used to detect and characterize degradation products. DFT calculations revealed the most reactive atoms on ZEN as C11, C13, C15, and O22, all located within the phenolic moiety. These theoretical predictions were supported by HRMS detection of an oxidation product consistent with 14-hydroxyZEN quinone (C₁₈H₂₀O₆, 332.1259 Da). Additionally, novel coupling products between AS and ZEN were identified, including oxidized forms at C13 or C15 (AS-ZEN, C₂₈H₃₄O₉, m/z 514.2202 Da; AS-hydroxyZEN, C₂₈H₃₂O₈, 527.1923 Da). Despite these identifications, volcano plot analyses of HRMS data showed that these compounds accounted for only a minor fraction of the total signal variation, indicating the presence of other, unidentified degradation products and pathways. This work represents the first integrated use of DFT, HRMS, and advanced statistical tools to elucidate the complex degradation pathways of ZEN by LMS. By combining computational predictions with experimental validation, the study provides a deeper understanding of LMS-mediated detoxification mechanisms.