E-MTAB-1272 - Exposure of NHBE cells to cell cycle inhibitor specific for CDK4 / CDK6 (PD-0332991)
Submitted on 1 September 2011, released on 1 April 2013, last updated on 3 May 2014
Modern ‘omics’ technologies, where changes in the expression of thousands of molecular species can be investigated in even a simple experiment, provide contemporary biomedical researchers with an unprecedented level of molecular granularity. An outstanding challenge is transformation of this experimental detail and complexity into meaningful biological insight. This is especially true for drug development and toxicological risk assessment, where molecular profiling data is increasingly being used to investigate the biological effects of a variety of exposures, from drugs to environmental factors, on human systems. Recently, we proposed a systems-based strategy for objectively predicting the mechanistic impact of biologically active substances from transcriptomic data, and report here on an initial implementation of this strategy. We applied a set of biological network models and novel scoring algorithms to investigate transcriptomic data from a range of experimental systems. Importantly, this approach enabled continuity between investigation at the molecular, pathway, and systems levels. When applied to targeted perturbations on in vitro systems, the method was able to identify and provide relative quantitation for the precise mechanisms that were impacted. In normal human bronchial epithelial (NHBE) cells, the method correctly indicated the temporal activation of the cell cycle following removal of a small molecule cyclin-dependent kinase inhibitor. In rat nasal epithelium exposed to formaldehyde, the method identified known mechanistic effects caused by exposure, including increased cell proliferation and necrosis. Importantly, many of the effects indicated by the methodology were supported by experimental endpoint data, providing further objective validation of the general approach. We propose that various fields of human disease research, from drug development to consumer product testing, could benefit from using this or similar approaches to evaluate the biological impact of exposures.
transcription profiling by array, compound treatment, in vitro, time series
Mechanistic Assessment of Biological Impact Using Transcriptomic Data and Hierarchically Structured Network Models. Ty Thomson, Alain Sewer, Florian Martin, Walter K. Schlage, Stephan Gebel, Jurjen W. Westra, Dmitry Vasilyev, Jennifer Park, Marja Talikka, Vincenzo Belcastro, Brian Frushour, Renee Deehan, Julia Hoeng, Manuel C. Peitsch.
Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset. Belcastro V, Poussin C, Gebel S, Mathis C, Schlage WK, Lichtner RB, Quadt-Humme S, Wagner S, Hoeng J, Peitsch MC. :217-230 (2013), Europe PMC 23926424