Dataset Information

Systems pharmacology dissection of cholesterol regulation reveals determinants of large pharmacodynamic variability between cell lines

ABSTRACT: In individuals, heterogeneous drug response phenotypes result from a complex interplay of dose, drug specificity, genetic background, and environmental factors, thus challenging our understanding of the underlying processes and optimal use of drugs in the clinic. Here, we use mass spectrometry-based quantification of molecular response phenotypes and logic modeling to explain drug response differences in a panel of cell lines. We apply this approach to cellular cholesterol regulation, a biological process with high clinical relevance. From the quantified molecular phenotypes elicited by various targeted pharmacologic or genetic treatments, we generated cell-line-specific models that quantified the processes beneath the idiotypic intracellular drug responses. The models revealed that in addition to drug uptake and metabolism further cellular processes displayed significant pharmacodynamic response variability between the cell lines, resulting in cell-line-specific drug response phenotypes. This study demonstrates the importance of integrating different types of quantitative systems-level molecular measurements with modeling to understand the effect of pharmacological perturbations on complex biological processes.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell, Cell Culture, Hela Cell

SUBMITTER: Peter Blattmann

LAB HEAD: Ruedi Aebersold

PROVIDER: PXD005955 | Pride | 2017-12-07

REPOSITORIES: Pride

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Publications

Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines.

Blattmann Peter P Henriques David D Zimmermann Michael M Frommelt Fabian F Sauer Uwe U Saez-Rodriguez Julio J Aebersold Ruedi R

Cell systems 20171206 6

In individuals, heterogeneous drug-response phenotypes result from a complex interplay of dose, drug specificity, genetic background, and environmental factors, thus challenging our understanding of the underlying processes and optimal use of drugs in the clinical setting. Here, we use mass-spectrometry-based quantification of molecular response phenotypes and logic modeling to explain drug-response differences in a panel of cell lines. We apply this approach to cellular cholesterol regulation, ...[more]

PMID: 29226804

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Project description:Bromodomains (BRDs) have emerged as compelling targets for cancer therapy. The development of selective and potent BET inhibitors and their significant activity in diverse tumor models has rapidly translated into clinical studies and has motivated drug development efforts targeting non-BET BRDs. However, the complex multidomain/subunit architecture of bromodomain protein complexes complicates predictions of consequences of their pharmacological targeting. To address this issue we developed a promiscuous bromodomain inhibitor (bromosporine, BSP) that broadly targets BRDs (including BETs) with nanomolar affinity, creating a tool for the identification of cellular processes and diseases where BRDs have a regulatory function. As a proof of principle we studied the effect of BSP in leukemic cell-lines known to be sensitive to BET inhibition and found as expected strong anti-proliferative activity. Comparison of the modulation of transcriptional profiles by BSP at short inhibitor exposure resulted in a BET inhibitor signature but no significant additional changes in transcription that could account for inhibition of other BRDs. Thus, non-selective targeting of BRDs identified BETs, but not other BRDs, as master regulators of a context dependent primary transcription response. Cells were seeded the day prior to treatment at 2x10^5 per ml. Treatments were performed so that a final concentration of 0.1 % DMSO (Cat.#D1435; Sigma) was achieved and cells were incubated with DMSO or 500 nM test compound (BSP, JQ1, LP99, GSK2801, iCBP112 or OF1) for 6 h prior to isolation of RNA. Total RNA was isolated using a standard TRIzol (Invitrogen) protocol and prepared using RNeasy columns (Cat.#74106 plus; Qiagen). RNA was quantified using a Nanodrop spectrophotometer (model ND1000; Thermo Fisher) and integrity assessed on a BioAnalyzer (2100; Agilent Laboratories, USA). All samples had a RNA Integrity Number (RIN) â¥ 9. Biological triplicates were performed for each condition tested.

Dataset Information

Systems pharmacology dissection of cholesterol regulation reveals determinants of large pharmacodynamic variability between cell lines

Publications

Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines.

Similar Datasets

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