A LABEL-FREE, MULTI-OMIC STUDY TO QUALITATIVELY AND QUANTITATIVELY CHARACTERIZE THE EFFECTS OF A GLUCOSYLCERAMIDE INHIBITOR ON OBESITY

Significance of the Topic

Obesity is a global health crisis with over 500 million individuals affected, contributing to type 2 diabetes, cardiovascular and liver diseases, and certain cancers. Glucosylceramide synthase inhibitors (e.g., MZ-21) have shown potential to modulate lipid metabolism and improve insulin sensitivity in preclinical models.

Objectives and Study Overview

This study employed a label-free, multi-omic strategy to qualitatively and quantitatively assess how MZ-21 treatment affects the liver proteome and lipidome in obese mice. By combining data-independent acquisition with ion mobility, the workflow captures complementary proteomic and lipidomic information from a single experiment.

Methodology and Used Instrumentation

Sample preparation included liver tissue homogenization in chloroform-methanol (Bligh & Dyer extraction) to isolate lipids and proteins. Protein fractions were processed with RapiGest, reduced, alkylated, and digested with trypsin. Analytical platforms:

• Proteomics: nanoACQUITY UPLC with 1.8 µm HSS C18 column, 90 min gradient (5–40% ACN), data-independent acquisition (HDMSE) on Synapt G2-Si IMS-oaToF.
• Lipidomics: ACQUITY UPLC with 1.7 µm BEH C8 column, 20 min gradient (3–40% IPA:MeOH), DIA on Xevo G2-S QToF with ion mobility.
• Data Processing: Progenesis QI for Proteomics and Lipidomics; EZInfo for statistics; Ingenuity Pathway Analysis for network mapping.

Main Results and Discussion

PCA of both proteomic and lipidomic datasets showed clear separation between control and MZ-21 treated groups, indicating systematic biochemical shifts. Over 1,200 proteins were identified, with ~30% significantly differentially expressed (ANOVA p≤0.05, fold change >2). Key lipid classes—lysophosphatidylcholine (LPC), phosphatidylcholine (PC), sphingomyelin (SM), and triglycerides (TG)—drove the observed variance. Volcano plots and S-plots highlighted top contributors. Pathway analysis linked treatment effects to hepatic development, inflammatory response, carbohydrate and lipid metabolism.

Benefits and Practical Applications of the Method

The integrated label-free DIA workflow provides:

• Comprehensive coverage of proteins and lipids from a single sample.
• High-throughput, quantitative profiling without labeling barriers.
• Robust statistical metrics and network insights to support biomarker discovery.

This approach is well suited for metabolic research, pharmacology, and translational studies in obesity and related disorders.

Future Trends and Potential Applications

Future directions include:

• Integrating metabolomics and transcriptomics for deeper multi-omic insights.
• Advancements in ion mobility for enhanced molecular separation.
• Scaling to larger animal or human cohorts for biomarker validation.
• Application of machine learning to refine pathway predictions and treatment responses.

Conclusion

This study demonstrates that a label-free, ion mobility-enabled, multi-omic LC-DIA approach effectively captures the biochemical impact of glucosylceramide inhibition in obese mice. The method offers a powerful tool for dissecting complex metabolic changes and identifying potential therapeutic targets.

References

1. Aerts et al. Pharmacological inhibition of glucosylceramide synthase enhances insulin sensitivity. Diabetes. 2007;56:1341–1349.
2. Bligh & Dyer. A rapid method of total lipid extraction and purification. Can J Biochem. 1959;37:911–917.
3. Li et al. Database searching and accounting of multiplexed precursor and product ion spectra for DIA proteomics. Proteomics. 2009;9:1696–1719.
4. Richardson et al. A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS. OMICS. 2012;16:468–482.