In-depth quantitative analysis and comparison of the human hepatocyte and hepatoma cell line HepG2 proteomes

Label-Free and Standard-Free Absolute Quantitative Proteomics Using the "Total Protein" and "Proteomic Ruler" Approaches

Understanding biological systems and their variation upon stimuli requires knowledge on their composition, primarily including information on organization and dynamics of proteomes. The total protein approach (TPA) is a label- and standard-free method for absolute protein quantitation of proteins using large-scale proteomic data. The method relies on the assumption that the total MS signal from all identified proteins in the dataset reflects-in a biochemical sense-the total protein and the MS signal from a single protein corresponds its abundance in the studied sample. The method offers an easy way to quantify thousands of protein per sample. A related method, the "Proteomic Ruler," enables conversion of the protein abundance data calculated by TPA to compute numbers of protein copies per cell. TPA and the Proteomic Ruler are powerful tools for studying dynamics of cell architecture.

Evidences for salbutamol metabolism by respiratory and liver cell lines

This study aimed to investigate the enantiomeric biotransformation of salbutamol in the human respiratory and liver cells. The cells from the different cell growth cycles were treated with various concentrations of salbutamol sulfate. Salbutamol and its metabolites were analyzed using chiral liquid chromatography and mass spectrometry. There were no metabolites of salbutamol found in the extracellular medium, intracellular, and cell lysate of respiratory cell lines. The S/R ratios of salbutamol were found to be 0.99-1.10 in all cell lines, cell cycles, and salbutamol concentrations in this study. Salbutamol metabolites were found only in intracellular HepG2 cells. The S/R ratios of the salbutamol inside the liver cells were 10 times greater than the S/R ratios of the salbutamol in the liver extracellular medium (0.99-1.10). It is important to note that the S/R ratios of salbutamol in liver cell lysate enzyme were 0.99-1.10 whereas the S/R salbutamol metabolites inside the liver cell were around 1.91-2.14. Both salbutamol and sulfate conjugation metabolites were detected in MS chromatograms with an m/z of 239.2 and 317.6, respectively. Hence, the delivery of salbutamol directly to the respiratory system is a right target that can avoid first-pass metabolism.

Impact of Membrane Drug Transporters on Resistance to Small-Molecule Tyrosine Kinase Inhibitors

Small-molecule inhibitors of tyrosine kinases (TKIs) are the mainstay of treatment for many malignancies and represent novel treatment options for other diseases such as idiopathic pulmonary fibrosis. Twenty-five TKIs are currently FDA-approved and >130 are being evaluated in clinical trials. Increasing evidence suggests that drug exposure of TKIs may significantly contribute to drug resistance, independently from somatic variation of TKI target genes. Membrane transport proteins may limit the amount of TKI reaching the target cells. This review highlights current knowledge on the basic and clinical pharmacology of membrane transporters involved in TKI disposition and their contribution to drug efficacy and adverse drug effects. In addition to non-genetic and epigenetic factors, genetic variants, particularly rare ones, in transporter genes are promising novel factors to explain interindividual variability in the response to TKI therapy.

Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO). However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR) and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid) and H838 cells, we combined mathematical modeling with a method for feature selection, the L₁ regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR) in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR). The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in erythroid progenitor cells unaffected. Thus, the proposed modeling strategy can be employed as a general procedure to identify cell type-specific parameters and to recommend treatment strategies for the selective targeting of specific cell types.

A major challenge in the development of therapeutic interventions is the selective inhibition of a signal transduction pathway in one cell type such as a cancer cell leaving the other cell type such as a healthy cell as unaffected as possible. Here, we propose a new approach that combines mathematical modeling based on quantitative experimental data with statistical methods. We demonstrate based on simulated data that our approach can determine which parameters are the same and which parameters differ in two exemplary cell types. We compare a lung cancer cell line to the precursor cells of red blood cells. We show that the same signal transduction network induced by erythropoietin (EPO), a hormone that is frequently employed to treat anemia in cancer patients, regulates survival of both cell types. Based on our experimental data in combination with our computational approach, we identify seven cell type-specific differences in this signaling pathway. Our strategy allows predicting therapeutic targets that could be inhibited to interfere with survival of lung cancer cells while leaving production of red blood cells unaffected.

Impairment of oxidative phosphorylation increases the toxicity of SYD-1 on hepatocarcinoma cells (HepG2)

Toxicity of the SYD-1 mesoionic compound (3-[4-chloro-3-nitrophenyl]-1,2,3-oxadiazolium-5-olate) was evaluated on human liver cancer cells (HepG2) grown in either high glucose (HG) or galactose (GAL) medium, and also on suspended cells kept in HG medium. SYD-1 was able to decrease the viability of cultured HepG2 cells in a dose-dependent manner, as assessed by MTT, LDH release and dye with crystal violet assays, but no effect was observed on suspended cells after 1-40 min of treatment. Respiration analysis was performed after 2 min (suspended cells) or 24 h (cultured cells) of treatment: no change was observed in suspended cells, whereas SYD-1 inhibited as well basal, leak and uncoupled states of the respiration in cultured cells with HG medium. These inhibitions were consistent with the decrease in pyruvate level and increase in lactate level. Even more extended results were obtained with HepG2 cells grown in GAL medium where, additionally, the ATP amount was reduced. Furthermore, SYD-1 appears not to be transported by the main ABC multidrug transporters. These results show that SYD-1 is able to change the metabolism of HepG2 cells, and suggest that its cytotoxicity is related to impairment of mitochondrial metabolism. Therefore, we may propose that SYD-1 is a potential candidate for hepatocarcinoma treatment.

A Proteomics Approach to the Protein Normalization Problem: Selection of Unvarying Proteins for MS-Based Proteomics and Western Blotting

Proteomics and other protein-based analysis methods such as Western blotting all face the challenge of discriminating changes in the levels of proteins of interest from inadvertent changes in the amount loaded for analysis. Mass-spectrometry-based proteomics can now estimate the relative and absolute amounts of thousands of proteins across diverse biological systems. We reasoned that this new technology could prove useful for selection of very stably expressed proteins that could serve as better loading controls than those traditionally employed. Large-scale proteomic analyses of SDS lysates of cultured cells and tissues revealed deglycase DJ-1 as the protein with the lowest variability in abundance among different cell types in human, mouse, and amphibian cells. The protein constitutes 0.069 ± 0.017% of total cellular protein and occurs at a specific concentration of 34.6 ± 8.7 pmol/mg of total protein. Since DJ-1 is ubiquitous and therefore easily detectable with several peptides, it can be helpful in normalization of proteomic data sets. In addition, DJ-1 appears to be an advantageous loading control for Western blot that is superior to those used commonly used, allowing comparisons between tissues and cells originating from evolutionarily distant vertebrate species. Notably, this is not possible by the detection and quantitation of housekeeping proteins, which are often used in the Western blot technique. The approach introduced here can be applied to select the most appropriate loading controls for MS-based proteomics or Western blotting in any biological system.

Prediction of Altered Bile Acid Disposition Due to Inhibition of Multiple Transporters: An Integrated Approach Using Sandwich-Cultured Hepatocytes, Mechanistic Modeling, and Simulation

Transporter-mediated alterations in bile acid disposition may have significant toxicological implications. Current methods to predict interactions are limited by the interplay of multiple transporters, absence of protein in the experimental system, and inaccurate estimates of inhibitor concentrations. An integrated approach was developed to predict altered bile acid disposition due to inhibition of multiple transporters using the model bile acid taurocholate (TCA). TCA pharmacokinetic parameters were estimated by mechanistic modeling using sandwich-cultured human hepatocyte data with protein in the medium. Uptake, basolateral efflux, and biliary clearance estimates were 0.63, 0.034, and 0.074 mL/min/g liver, respectively. Cellular total TCA concentrations (Ct,Cells) were selected as the model output based on sensitivity analysis. Monte Carlo simulations of TCA Ct,Cells in the presence of model inhibitors (telmisartan and bosentan) were performed using inhibition constants for TCA transporters and inhibitor concentrations, including cellular total inhibitor concentrations ([I]t,cell) or unbound concentrations, and cytosolic total or unbound concentrations. For telmisartan, the model prediction was accurate with an average fold error (AFE) of 0.99-1.0 when unbound inhibitor concentration ([I]u) was used; accuracy dropped when total inhibitor concentration ([I]t) was used. For bosentan, AFE was 1.2-1.3 using either [I]u or [I]t This difference was evaluated by sensitivity analysis of the cellular unbound fraction of inhibitor (fu,cell,inhibitor), which revealed higher sensitivity of fu,cell,inhibitor for predicting TCA Ct,Cells when inhibitors exhibited larger ([I]t,cell/IC50) values. In conclusion, this study demonstrated the applicability of a framework to predict hepatocellular bile acid concentrations due to drug-mediated inhibition of transporters using mechanistic modeling and cytosolic or cellular unbound concentrations.