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Systems analysis of EGF receptor signaling dynamics with microwestern arrays

Nature Methods volume 7, pages 148–155 (2010)Cite this article

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Abstract

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.

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Figure 1: Microwestern array (MWA) method.
Figure 2: MWA validation of linear response.
Figure 3: Comparison of MWA to traditional western blot.
Figure 4: An MWA containing 6 cell lysates probed with 192 antibodies.
Figure 5: A clustered heatmap profile of fold changes for antibody bands representing specific phosphorylation sites of proteins in A431 cells over the indicated six time points for four EGF stimulation concentrations and the no-EGF control.
Figure 6: Consensus model of EGF receptor level influences modeled by Bayesian network inference with comparison to ARACNe and CLR.

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References

  1. Burnette, W. “Western blotting”: electrophoretic transfer of proteins from sodium dodecyl sulfate polyacrylamide gels to unmodified nitrocellulose and radiographic detection with antibody and radioiodinated protein A. Anal. Biochem. 112, 195–203 (1981).

    Article  CAS  Google Scholar 

  2. Paweletz, C.P., Liotta, L.A. & Petricoin, E.F. New technologies for biomarker analysis of prostate cancer progression: Laser capture microdissection and tissue proteomics. Urology 57, 160–163 (2001).

    Article  CAS  Google Scholar 

  3. Paweletz, C.P. et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20, 1981–1989 (2001).

    Article  CAS  Google Scholar 

  4. Sevecka, M. & MacBeath, G. State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling. Nat. Methods 3, 825–831 (2006).

    Article  CAS  Google Scholar 

  5. Sachs, K., Perez, O., Pe′er, D., Lauffenburger, D.A. & Nolan, G.P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005).

    Article  CAS  Google Scholar 

  6. Rikova, K. et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131, 1190–1203 (2007).

    Article  CAS  Google Scholar 

  7. Olsen, J.V. et al. Global, in vivo and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648 (2006).

    Article  CAS  Google Scholar 

  8. Wolf-Yadlin, A. et al. Effects of HER2 overexpression on cell signaling networks governing proliferation and migration. Mol. Syst. Biol. 2, 54 (2006).

    Article  Google Scholar 

  9. Tibes, R. et al. Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells. Mol. Cancer Ther. 5, 2512–2521 (2006).

    Article  CAS  Google Scholar 

  10. Jones, R.B., Gordus, A., Krall, J.A. & MacBeath, G. A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439, 168–174 (2006).

    Article  CAS  Google Scholar 

  11. Sunada, H., Magun, B.E., Mendelsohn, J. & MacLeod, C.L. Monoclonal antibody against epidermal growth factor receptor is internalized without stimulating receptor phosphorylation. Proc. Natl. Acad. Sci. USA 83, 3825–3829 (1986).

    Article  CAS  Google Scholar 

  12. Gill, G.N. & Lazar, C.S. Increased phosphotyrosine content and inhibition of proliferation in EGF-treated A431 cells. Nature 293, 305–307 (1981).

    Article  CAS  Google Scholar 

  13. Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D.A. & White, F.M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl. Acad. Sci. USA 104, 5860–5865 (2007).

    Article  CAS  Google Scholar 

  14. Chang, F. et al. Involvement of PI3K//Akt pathway in cell cycle progression, apoptosis and neoplastic transformation: a target for cancer chemotherapy. Leukemia 17, 590–603 (2003).

    Article  CAS  Google Scholar 

  15. Pe'er, D. Bayesian network analysis of signaling networks: a primer. Sci. STKE 2005, l4 (2005).

    Google Scholar 

  16. Koivisto, M. & Sood, K. Exact Bayesian structure discovery in Bayesian networks. J. Mach. Learn. Res. 5, 549–573 (2004).

    Google Scholar 

  17. Eaton, D. & Murphy, K. Exact Bayesian structure learning from uncertain interventions. in Proc. 12th Conf. on AI and Stats, 107–114 (2007).

  18. Chickering, D.M. Learning equivalence classes of bayesian-network structures. J. Mach. Learn. Res. 2, 445–498 (2002).

    Google Scholar 

  19. Downward, J., Parker, P. & Waterfield, M.D. Autophosphorylation sites on the epidermal growth factor receptor. Nature 311, 483–485 (1984).

    Article  CAS  Google Scholar 

  20. Saito, Y., Haendeler, J., Hojo, Y., Yamamoto, K. & Berk, B.C. Receptor heterodimerization: essential mechanism for platelet-derived growth factor-induced epidermal growth factor receptor transactivation. Mol. Cell. Biol. 21, 6387–6394 (2001).

    Article  CAS  Google Scholar 

  21. Duchesne, L., Tissot, B., Rudd, T.R., Dell, A. & Fernig, D.G. N-glycosylation of fibroblast growth factor receptor 1 regulates ligand and heparan sulfate co-receptor binding. J. Biol. Chem. 281, 27178–27189 (2006).

    Article  CAS  Google Scholar 

  22. Ekman, S., Kallin, A., Engström, U., Heldin, C. & Rönnstrand, L. SHP-2 is involved in heterodimer specific loss of phosphorylation of Tyr771 in the PDGF beta-receptor. Oncogene 21, 1870–1875 (2002).

    Article  CAS  Google Scholar 

  23. Gould, K.L. & Hunter, T. Platelet-derived growth factor induces multisite phosphorylation of pp60c-src and increases its protein-tyrosine kinase activity. Mol. Cell. Biol. 8, 3345–3356 (1988).

    Article  CAS  Google Scholar 

  24. Margolin, A.A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 (Suppl. 1), S7 (2006).

    Article  Google Scholar 

  25. Faith, J.J. et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5, e8 (2007).

    Article  Google Scholar 

  26. Taylor, R.C., Acquaah-Mensah, G., Singhal, M., Malhotra, D. & Biswal, S. Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress. PLOS Comput. Biol. 4, e1000166 (2008).

    Article  Google Scholar 

  27. Cantone, I. et al. A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137, 172–181 (2009).

    Article  CAS  Google Scholar 

  28. Husmeier, D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19, 2271–2282 (2003).

    Article  CAS  Google Scholar 

  29. Lou, Y.W. et al. Redox regulation of the protein tyrosine phosphatase PTP1B in cancer cells. FEBS J. 275, 69–88 (2008).

    Article  CAS  Google Scholar 

  30. Lu, W., Shen, K. & Cole, P.A. Chemical dissection of the effects of tyrosine phosphorylation of SHP-2. Biochemistry 42, 5461–5468 (2003).

    Article  CAS  Google Scholar 

  31. Friedman, N. & Koller, D. Being Bayesian about Bayesian network structure: a Bayesian approach to structure discovery in Bayesian networks. Mach. Learn. 50, 95–125 (2003).

    Article  Google Scholar 

  32. Heckerman, D., Geiger, D. & Chickering, D.M. Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn. 20, 197–243 (1995).

    Google Scholar 

  33. Madigan, D. & York, J. Bayesian graphical models for discrete data. Int. Stat. Rev. 63, 215–232 (1995).

    Article  Google Scholar 

  34. Meyer, P., Lafitte, F. & Bontempi, G. minet: a R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics 9, 461 (2008).

    Article  Google Scholar 

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Acknowledgements

A431 cells were kindly provided by S. Liao (The University of Chicago). We thank C.Y. Chuang for technical assistance with MWAs, M.R. Rosner, K.P. White and W.L. McKeehan for helpful discussions, C. May for the graphic design of Figure 1, and J. Barkinge for operating support with microarraying. This work was supported, in part, by awards from The University of Chicago Cancer Research Center, The American Cancer Society, The University of Chicago Breast Cancer Specialized Program of Research Excellence, The Cancer Research Foundation and The Illinois Department of Health (86280156 to R.B.J.), the US National Institutes of General Medical Sciences P50-GM0686762 Cell Decision Processes Center grant and US National Cancer Institute CA96504 to D.A.L.; M.F.C. was supported by a National Institutes of Health Systems Biology of Oxygen Predoctoral Training grant; J.P.W. was supported by a National Science Foundation Graduate fellowship; and C.-P.C. was supported by a National Institutes of Health Cancer Biology Postdoctoral Training grant.

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Author notes

  1. Chih-Pin Chuu

    Present address: Present address: Institute of Cellular and Systems Medicine, National Health Research Institutes, Miaoli County, Taiwan.,

Authors and Affiliations

  1. The Ben May Department for Cancer Research and the Institute for Genomics and Systems Biology,

    Mark F Ciaccio, Chih-Pin Chuu & Richard B Jones

  2. The University of Chicago, Chicago, Illinois, USA

    Mark F Ciaccio, Chih-Pin Chuu & Richard B Jones

  3. Center for Cell Decision Processes and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Joel P Wagner & Douglas A Lauffenburger

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  1. Mark F Ciaccio
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Contributions

C.-P.C., M.F.C. and R.B.J. designed the experiments. C.-P.C. and M.F.C. performed the cell culture and growth factor stimulations. M.F.C. and R.B.J. designed the MWA method, M.F.C. carried out microwestern experiments and organized the data into heat maps. J.P.W. and D.A.L. performed Bayesian network, CLR and ARACNe analysis of the data. M.F.C., J.P.W., D.A.L. and R.B.J. wrote the manuscript. All authors read and revised the manuscript.

Corresponding author

Correspondence to Richard B Jones.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Notes 1–3, Supplementary Tables 3,5 (PDF 4837 kb)

Supplementary Table 1

Fold changes, standard deviations, signal/background ratios, antibody catalog numbers, protein sizes and local epitope sequence for protein abundances and modifications quantified by microwestern arrays. (XLS 285 kb)

Supplementary Table 2

Antibody layout for Figure 4. (XLS 32 kb)

Supplementary Table 4

The epitope recognition sequence surrounding the 20 phosphosites for which signals were quantified in this study compared to the sequences of all 57 human-encoded receptor tyrosine kinases. (XLS 37 kb)

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Ciaccio, M., Wagner, J., Chuu, CP. et al. Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat Methods 7, 148–155 (2010). https://doi.org/10.1038/nmeth.1418

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