Project description:Identifying the Mechanism of Action (MoA) of drugs is critical for the development of new drugs, understanding their side effects, and drug repositioning. However, identifying drug MoA has been challenging and has been traditionally attempted only though large experimental setups with little success. While advances in computational power offers the opportunity to achieve this in-silico, methods to exploit existing computational resources are still in their infancy. To overcome this, we developed a novel method to identify Drug Mechanism of Action using Network Dysregulation (DeMAND). The method is based on the realization that drugs affect the protein activity of their targets, but not necessarily their mRNA expression levels. In contrast, the change in protein activity directly affects the mRNA expression levels of downstream genes. Based on this hypothesis, DeMAND identifies drug MoA by comparing gene expression profiles following drug perturbation with control samples, and computing the change in the individual interactions within a pre-determined integrated transcriptional and post-translational regulatory model (interactome). This dataset includes GEPs in 3 different B-cell lymphoma cell lines (OCI-LY3, OCI-LY7 and U2932) at 6, 12, and 24hrs. 92 FDA approved compounds were used at a concentration of IC20 at 24h. DMSO was used as control at each time-point. A total of 828 samples and 29 control samples were available for analysis. Total RNA was isolated with the RNAqueous-96 Automated Kit (Ambion) on the Janus automated liquid handling system (Perkin Elmer Inc.), quantified by NanoDrop 6000 spectrophotometer and quality checked by Agilent Bioanalyzer. 300ng of each of the samples with RIN value >7 were converted to biotinylated cRNA with the Illumina TotalPrep-96 RNA Amplification Kit (Ambion) using a standard T7-based amplification protocol and hybridized on the Human Genome U219 96-Array Plate (Affymetrix). Hybridization, washing, staining and scanning of the array plates were performed on the GeneTitan Instrument (Affymetrix) according to manufacturer’s protocols.

Project description:Mass spectrometry-based omics technologies are of increasing relevance for drug discovery. In order to map drug treatments to biological pathways for mode of action (MoA) deconvolution, they rely on the determination of multiple testing corrected significant molecular events. The main contributors to statistical significance are the effect size and the variation of a given molecule during the drug treatment. While the former is largely determined by the biological system and the impact of the drug, the latter reflects the robustness of the experimental workflow. The impact of sample preparation and instrumentation on variation are well characterized, but the impact of cell culture conditions on variation of omics readouts is less explored, specifically related to subculture conditions during cell expansion. Here, we provide evidence that memory effects from subculture directly influence the variation of multilevel omics readouts, including oxylipins and fatty acids, proteins and phosphorylated sites, and phenotypic parameters, e.g. cell motility. When comparing drug treatments after homogeneous and heterogeneous subculture, the coefficients of variation (CVs) of controls increased from 1% to 3% for oxylipins and fatty acids, 11% to 23% for proteomics, 24% to 53% for phosphoproteomics and 6% to 14% for cell motility, respectively. The number of identifications remained comparable and the fold-change distributions were also largely similar after both subculture conditions. Therefore, the decrease in CVs seems to be the main contributor to the increased number of significant molecular events in drug treatments after homogeneous subculture. This in turn increased information content in the obtained perturbation network, based on proteomics and phosphoproteomics data, from 58 to 321 causal conjectures. Perturbation networks after homogeneous and heterogeneous subculture allowed for delineating the established MoA of arsenic trioxide in detail, including enhanced kinase signaling and reduced MYC-related gene expression, induction of heat shock proteins and cell cycle disruption. However, only the perturbation network after homogeneous subculture enabled the discovery of unprecedented drug effects, including down-regulation and quadruple dephosphorylation of the transcription factor RB1, among others. In summary, memory effects from subculture have broad relevance for MoA testing in drug discovery since this impact clearly manifests in supernatants, whole cell lysates and phenotypic properties and its control enables a considerably enhanced MoA deconvolution, facilitating discovery efforts.

			Action	DRS
	QE-A_2019-11-03_ruprechb_A01_A549_DMSO_1.raw	Raw
	QE-A_2019-11-03_ruprechb_A02_A549_DMSO_2.raw	Raw
	QE-A_2019-11-03_ruprechb_A03_A549_DMSO_3.raw	Raw
	QE-A_2019-11-03_ruprechb_A04_A549_DMSO_4.raw	Raw
	QE-A_2019-11-03_ruprechb_A05_2030_DMSO_1.raw	Raw

Dataset Information

A mass spectrometry-based proteome map of drug action in lung cancer cell lines Part 6

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Publications

A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

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