A Label-Free, Multi-Omic Study to Qualitatively and Quantitatively Characterize the Effects of A Glucosylceramide Inhibitor on Obesity

Library Number:
Gertjan Kramer, Nicholas Dekker, Lee A Gethings, John P Shockcor, Victoria Lee, Robert J. Beynon, Robert Tonge, James I Langridge, Johannes PC Vissers, Johannes M.F.G. Aerts
Waters, University of Amsterdam, University of Liverpool
Content Type:
Content Subtype:
Other Symposium
Related Products:
Xevo G2-S Tof


Obesity is one of the risk-factors associated with metabolic syndrome, causing excess body fat to be accumulated, adversely affecting health and life expectancy. Manipulating the function of glucosylceramides with small molecule drug compounds has shown that symptoms can be negated. Here we present a multi-omic analysis of protein and lipid liver extracts from control and obese mouse models undergoing treatment to prevent or revert obesity. 


Proteomic and lipidomic data were acquired using LC/MS with the utilization of ion mobility in the acquisition scheme. Data were processed and searched using Progenesis QI and dedicated protein sequence and lipid compound databases, providing normalized label-free quantitation results prior to pathway analysis.


Proteomic analysis resulted in over 2000 identified proteins, across technical and biological replicates. 50 proteins exhibit a fold change ≥ 2 with significant analysis of variance. Unique peptides were used for relative label-free quantitation with median abundance normalization across all samples. Lipid extracts were analyzed in triplicate with QC samples injected routinely. Lipid classes identified included phosphatidylcholines, sphingomyelins, triglycerides and lysophosphatidylcholines. Mass accuracy, isotoptic fit and fragmentation were used for scoring. Unsupervised multivariate analyses showed distinction between obese and control groups in both datasets. Pathway analysis tools were used to review the complimentary datasets.  


A multi-omic, biochemical and network investigation for the study of obesity using a mouse model has revealed a number of targets within carbohydrate and lipid metabolism pathways being influenced by treatment with a glucosylcermaide synthase inhibitor.

Title Format File Size
Download PDF 2107.73kB