3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking

The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5–117.1% accuracy surpassed 1.2–14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics

UHPLC-Q-Orbitrap-Based Integrated Lipidomics and Proteomics Reveal Propane-1,2-diol Exposure Accelerating Degradation of Lipids <i>via</i> the Allosteric Effect and Reducing the Nutritional Value of Milk

The scandal of detecting the flavoring solvent propane-1,2-diol (PD) in milk has brought a crisis to the trust of consumers in the dairy industry, while its deposition and transformation are still indistinct. Pseudo-targeted lipidomics revealed that PD accelerated the degradation of glycerolipid (33,638.3 ± 28.9 to 104,54.2 ± 28.4 mg kg–1), phosphoglyceride (467.4 ± 8.2 to 56.6 ± 4.2 mg kg–1), and sphingolipids (11.4 ± 0.3 to 0.7 ± 0.2 mg kg–1), which extremely decreased the milk quality. Recoveries and relative standard deviations (RSDs) of the established method were 85.0–109.9 and 0.1–14.9%, respectively, indicating that the approach was credible. Protein–lipid interactions demonstrated that 10 proteins originating from fat globules were upregulated significantly and the activities of 7 enzymes related to lipid degradation were improved. Diacylglycerol cholinephosphotransferase was the only enzyme with decreased activity, and the molecular docking results indicated that PD adjusted its activity through regulating the conformation of the active center and weakening the hydrogen bond force between the enzyme and substrate. This study firstly revealed the mechanism of deposition and transformation of PD in milk, which contributed to the knowledge on the milk quality control and provided key indicators to evaluate the adverse risks of PD in dairy products