Qualitative and Quantitative Characterization of the Metabolome, Lipidome and Proteome of Human Hepatocytes Stably Transfected with Cytochrome P450 2E1 Using Data Independent LC-MS.

Drug toxicity is a major reason for the failure of candidate pharmaceuticals during their development. It is therefore important to realize the potential for toxicity in a timely fashion. Many xenobiotics are bioactivated into toxic metabolites by cytochromes P450 (CYP). However, the activity of these enzymes typically falls in in-vitro systems. Recently, a transformed human hepatocyte cell line (THLE) became available in which the metabolic activity of specific CYP isoforms is maintained. THLE cells could be an ideal system in which to examine the potential toxicity of candidate pharmaceuticals. The baseline effect of the addition of CYP2E1 into THLE hepatocytes has been characterized to better understand the biochemistry of this model system. Dedicated and independent sample preparation protocols were applied in order to isolate metabolites lipids and proteins. Three independent replicates of THLE null or THLE +2E1 cells were investigated for all analyte classes. Proteins were recovered and digested with trypsin overnight. The same LC-MS Omics Research Platform was used for all experiments and generic, application dependent LC conditions applied throughout. In all instances, MS data were acquired using a data independent analysis (DIA) approach, whereby the energy applied to the collision cell was switched between a low and elevated energy state during alternate scans. For the proteomics experiments, ion mobility separation (IM) was incorporated into the analytical schema (IM-DIA). Multi-omic data were processed and searched using TransOmics software, allowing for normalized label-free quantitation. Pathway analysis and systems biology experiments were conducted to interrogate the datasets further using various bioinformatics tools. Comparison of the correlation variance and fold change between the two groups illustrates significant analyte expression. Data interpretation by means of clustering, statistical, and data analysis approaches have shown protein, lipid, and metabolite data to be complimentary and confirmative, which is further supported from the resulting pathway analysis output.