High-confidence structural annotation of substances via multi-layer molecular network reveals the system-wide constituent alternations in milk interfered with diphenylolpropane

The spectral database-based mass spectrometry (MS) matching strategy is versatile for structural annotating in ingredient fluctuation profiling mediated by external interferences. However, the systematic variability of MS pool attributable to aliasing peaks and inadequacy of present spectral database resulted in a substantial metabolic feature depletion. An amended procedure termed multiple-charges overlap peaks extraction algorithm (MCOP) was proposed involving identifying collision-trigged dissociation precursor ions through iteratively matching mass features of fragmentations to expand the spectral reference library. We showcased the versatility and utility of established strategy in an investigation centered on the stimulation of milk mediated by diphenylolpropane (BPA). MCOP enabled efficient unknown annotations at metabolite-lipid-protein level, which elevated the accuracy of substance annotation to 85.3% after manual validation. Arginase and α-amylase (|r| > 0.75, p < 0.05) were first identified as the crucial issues via graph neural network-based virtual screening in the abnormal metabolism of urea triggered by BPA, resulting in the accumulation of arginine (original: 1.7 μg kg-1 1.7 times) and maltodextrin (original: 6.9 μg kg-1 2.9 times) and thus, exciting the potential dietary risks. Conclusively, MCOP demonstrated generalisation and scalability and substantially advanced the discovery of unknown metabolites for complex matrix samples, thus deciphering dark matter in multi-omics. As a widespread polycarbonate-plastics-related hazardous contaminant, diphenylolpropane has been ubiquitously found in water, dust and sediment with endocrine disruption and reproductive toxicity. Since the diet is the primary fountainhead of exposure in all stage of the population, it is necessary to investigate the potential risks of animal-derived substrates contaminated with diphenylolpropane to submit a reliable support for the reparative cure and questioned real innocuity of specific migration limit threshold for diphenylolpropane. The present MCOP protocol enabled large-scale annotation for multi-dimensional-omics without comprehensive standard spectral library, advancing the mechanism mining of abnormal variations for complex biological events subjected to external interference.