A Label-free, Multi-omic Study to Qualitatively and Quantitatively Characterize the Effects of a Glucosylceramide Inhibitor on Obesity

Significance of the Topic

The global rise in obesity affects over 500 million people and is linked to type 2 diabetes, heart and liver disease, and various cancers. Gaining mechanistic insights into how glucosylceramide inhibitors modulate metabolic pathways is critical for developing effective treatments. A label-free multi-omic workflow enables simultaneous proteomic and lipidomic profiling from a single experiment, offering comprehensive pathway-level understanding.

Objectives and Study Overview

This study aimed to qualitatively and quantitatively characterize the effects of the glucosylceramide inhibitor MZ-21 on liver tissue from obese mice. By applying a single label-free LC-DIA-IM-MS analysis, the work sought to identify regulated proteins and lipids, then integrate these findings to elucidate affected biological processes and pathways.

Methodology

Three control and three MZ-21–treated obese mice provided liver samples. Proteins were extracted with RapiGest SF, reduced, alkylated, and digested with trypsin. Lipids were isolated using a chloroform–methanol Bligh and Dyer extraction. Proteomic separations employed a nanoACQUITY UPLC system with a Symmetry C18 trap and an HSS T3 analytical column at 35 °C; data were acquired by HDMSE on a SYNAPT G2-Si mass spectrometer over 50–2000 m/z at 0.5 s per scan. Lipidomics used an ACQUITY UPLC BEH C8 column at 65 °C with MSE acquisition on a Xevo G2 S instrument at 0.1 s per scan. Data processing and label-free quantification were performed in Progenesis QI and Progenesis QI for Proteomics, followed by multivariate analysis in EZInfo and pathway interrogation in Ingenuity Pathway Analysis.

Used Instrumentation

• SYNAPT G2-Si Mass Spectrometer
• Xevo G2 S Mass Spectrometer
• nanoACQUITY UPLC System
• ACQUITY UPLC System
• RapiGest SF Surfactant
• NanoEase Columns
• Progenesis QI and Progenesis QI for Proteomics Software

Main Results and Discussion

Unsupervised PCA clearly separated control and treated groups in both proteomic and lipidomic datasets. Over 1,200 proteins were identified, with roughly 30 percent showing greater than two-fold changes and ANOVA p ≤ 0.05. Volcano plots and hierarchical clustering confirmed significant protein regulation. Lipidomic S-plots highlighted key features including phosphatidylcholines, sphingomyelins, triglycerides, and lysophosphatidylcholines with stringent mass accuracy (< 3 ppm), p ≤ 5 × 10⁻⁵, and fold change > 2. Integrated pathway analysis revealed modulation of carbohydrate metabolism, inflammatory response, hepatic system development, lipid metabolism, and molecular transport networks, with links to diabetes and inflammation significantly down-regulated after MZ-21 treatment.

Benefits and Practical Applications

The label-free multi-omic approach consolidates proteomic and lipidomic profiling into one workflow without isotopic labeling, accelerating data acquisition and reducing sample requirements. Integrated Progenesis workflows enable rapid data processing and pathway mapping. Findings on MZ-21’s molecular impact support biomarker discovery and rational design of anti-obesity therapies.

Future Trends and Applications

• Applying label-free multi-omics to clinical cohorts and other metabolic diseases
• Combining with genomics, metabolomics, and single-cell analyses
• Adopting higher-throughput instrumentation and automated sample preparation
• Leveraging machine learning for advanced pathway interpretation
• Enabling personalized interventions via comprehensive molecular phenotyping

Conclusion

This label-free LC-DIA-IM-MS multi-omic strategy effectively profiled proteomic and lipidomic alterations induced by the glucosylceramide inhibitor MZ-21 in obese mouse liver. The observed changes and implicated pathways provide a mechanistic basis for the inhibitor’s therapeutic action and guide future obesity research.

References

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