Systems analysis of EGF receptor signaling dynamics with microwestern arrays

• Published in: Nature methods Vol. 7; no. 2; pp. 148 - 155
• Main Authors: Jones, Richard B, Ciaccio, Mark F, Wagner, Joel P, Chuu, Chih-Pin, Lauffenburger, Douglas A
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-02-2010; Nature Publishing Group
• Subjects: 631/1647/2230/1882; 631/553/2710; 631/80/86; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biophysics; Blotting, Western - instrumentation; Blotting, Western - methods; Cancer; Cellular signal transduction; Electrophoresis; Equipment Design; Equipment Failure Analysis; Experimental data; Kinases; Life Sciences; Protein Array Analysis - instrumentation; Protein Array Analysis - methods; Proteins; Proteome - metabolism; Proteomics; Receptor, Epidermal Growth Factor - metabolism; Reproducibility of Results; Sensitivity and Specificity; Signal Transduction - physiology; Systems analysis; Systems Biology; Topology; United States
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.1418

Microwestern arrays combine the advantages of scalability of reverse phase protein arrays and the information content of western blotting for analyzing protein abundance and modification state with high sensitivity and throughput. The method is demonstrated for analyzing phosphorylation state changes in the EGF receptor signaling network using Bayesian network modeling.
We describe microwestern arrays, which enable quantitative, sensitive and high-throughput assessment of protein abundance and modifications after electrophoretic separation of microarrayed cell lysates. This method allowed us to measure 91 phosphosites on 67 proteins at six time points after stimulation with five epidermal growth factor (EGF) concentrations in A431 human carcinoma cells. We inferred the connectivities among 15 phosphorylation sites in 10 receptor tyrosine kinases (RTKs) and two sites from Src kinase using Bayesian network modeling and two mutual information-based methods; the three inference methods yielded substantial agreement on the network topology. These results imply multiple distinct RTK coactivation mechanisms and support the notion that small amounts of experimental data collected from phenotypically diverse network states may enable network inference.