Article | Published:

Systems analysis of EGF receptor signaling dynamics with microwestern arrays

Nature Methods volume 7, pages 148155 (2010) | Download Citation

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    “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).

  2. 2.

    , & New technologies for biomarker analysis of prostate cancer progression: Laser capture microdissection and tissue proteomics. Urology 57, 160–163 (2001).

  3. 3.

    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).

  4. 4.

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

  5. 5.

    , , , & Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005).

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

    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).

  10. 10.

    , , & A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439, 168–174 (2006).

  11. 11.

    , , & Monoclonal antibody against epidermal growth factor receptor is internalized without stimulating receptor phosphorylation. Proc. Natl. Acad. Sci. USA 83, 3825–3829 (1986).

  12. 12.

    & Increased phosphotyrosine content and inhibition of proliferation in EGF-treated A431 cells. Nature 293, 305–307 (1981).

  13. 13.

    , , & Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl. Acad. Sci. USA 104, 5860–5865 (2007).

  14. 14.

    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).

  15. 15.

    Bayesian network analysis of signaling networks: a primer. Sci. STKE 2005, l4 (2005).

  16. 16.

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

  17. 17.

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

  18. 18.

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

  19. 19.

    , & Autophosphorylation sites on the epidermal growth factor receptor. Nature 311, 483–485 (1984).

  20. 20.

    , , , & Receptor heterodimerization: essential mechanism for platelet-derived growth factor-induced epidermal growth factor receptor transactivation. Mol. Cell. Biol. 21, 6387–6394 (2001).

  21. 21.

    , , , & N-glycosylation of fibroblast growth factor receptor 1 regulates ligand and heparan sulfate co-receptor binding. J. Biol. Chem. 281, 27178–27189 (2006).

  22. 22.

    , , , & SHP-2 is involved in heterodimer specific loss of phosphorylation of Tyr771 in the PDGF beta-receptor. Oncogene 21, 1870–1875 (2002).

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

    , , , & Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress. PLOS Comput. Biol. 4, e1000166 (2008).

  27. 27.

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

  28. 28.

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

  29. 29.

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

  30. 30.

    , & Chemical dissection of the effects of tyrosine phosphorylation of SHP-2. Biochemistry 42, 5461–5468 (2003).

  31. 31.

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

  32. 32.

    , & Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn. 20, 197–243 (1995).

  33. 33.

    & Bayesian graphical models for discrete data. Int. Stat. Rev. 63, 215–232 (1995).

  34. 34.

    , & minet: a R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics 9, 461 (2008).

Download references

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.

Author information

Author notes

    • Chih-Pin Chuu

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

Affiliations

  1. The Ben May Department for Cancer Research and the Institute for Genomics and Systems BiologyThe University of Chicago, Chicago, Illinois, USA.

    • Mark F Ciaccio
    • , Chih-Pin Chuu
    •  & Richard B Jones
  2. Center for Cell Decision Processes and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Joel P Wagner
    •  & Douglas A Lauffenburger

Authors

  1. Search for Mark F Ciaccio in:

  2. Search for Joel P Wagner in:

  3. Search for Chih-Pin Chuu in:

  4. Search for Douglas A Lauffenburger in:

  5. Search for Richard B Jones in:

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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Richard B Jones.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–12, Supplementary Notes 1–3, Supplementary Tables 3,5

Excel files

  1. 1.

    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.

  2. 2.

    Supplementary Table 2

    Antibody layout for Figure 4.

  3. 3.

    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.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.1418

Further reading

  • Validating Antibodies for Quantitative Western Blot Measurements with Microwestern Array

    • Rick J. Koch
    • , Anne Marie Barrette
    • , Alan D. Stern
    • , Bin Hu
    • , Mehdi Bouhaddou
    • , Evren U. Azeloglu
    • , Ravi Iyengar
    •  & Marc R. Birtwistle

    Scientific Reports (2018)

  • Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease

    • Barbara E Stranger
    • , Lori E Brigham
    • , Richard Hasz
    • , Marcus Hunter
    • , Christopher Johns
    • , Mark Johnson
    • , Gene Kopen
    • , William F Leinweber
    • , John T Lonsdale
    • , Alisa McDonald
    • , Bernadette Mestichelli
    • , Kevin Myer
    • , Brian Roe
    • , Michael Salvatore
    • , Saboor Shad
    • , Jeffrey A Thomas
    • , Gary Walters
    • , Michael Washington
    • , Joseph Wheeler
    • , Jason Bridge
    • , Barbara A Foster
    • , Bryan M Gillard
    • , Ellen Karasik
    • , Rachna Kumar
    • , Mark Miklos
    • , Michael T Moser
    • , Scott D Jewell
    • , Robert G Montroy
    • , Daniel C Rohrer
    • , Dana R Valley
    • , David A Davis
    • , Deborah C Mash
    • , Sarah E Gould
    • , Ping Guan
    • , Susan Koester
    • , A Roger Little
    • , Casey Martin
    • , Helen M Moore
    • , Abhi Rao
    • , Jeffery P Struewing
    • , Simona Volpi
    • , Kasper D Hansen
    • , Peter F Hickey
    • , Lindsay F Rizzardi
    • , Lei Hou
    • , Yaping Liu
    • , Benoit Molinie
    • , Yongjin Park
    • , Nicola Rinaldi
    • , Li Wang
    • , Nicholas Van Wittenberghe
    • , Melina Claussnitzer
    • , Ellen T Gelfand
    • , Qin Li
    • , Sandra Linder
    • , Rui Zhang
    • , Kevin S Smith
    • , Emily K Tsang
    • , Lin S Chen
    • , Kathryn Demanelis
    • , Jennifer A Doherty
    • , Farzana Jasmine
    • , Muhammad G Kibriya
    • , Lihua Jiang
    • , Shin Lin
    • , Meng Wang
    • , Ruiqi Jian
    • , Xiao Li
    • , Joanne Chan
    • , Daniel Bates
    • , Morgan Diegel
    • , Jessica Halow
    • , Eric Haugen
    • , Audra Johnson
    • , Rajinder Kaul
    • , Kristen Lee
    • , Matthew T Maurano
    • , Jemma Nelson
    • , Fidencio J Neri
    • , Richard Sandstrom
    • , Marian S Fernando
    • , Caroline Linke
    • , Meritxell Oliva
    • , Andrew Skol
    • , Fan Wu
    • , Joshua M Akey
    • , Andrew P Feinberg
    • , Jin Billy Li
    • , Brandon L Pierce
    • , John A Stamatoyannopoulos
    • , Hua Tang
    • , Kristin G Ardlie
    • , Manolis Kellis
    • , Michael P Snyder
    •  & Stephen B Montgomery

    Nature Genetics (2017)

  • A bead-based western for high-throughput cellular signal transduction analyses

    • Fridolin Treindl
    • , Benjamin Ruprecht
    • , Yvonne Beiter
    • , Silke Schultz
    • , Anette Döttinger
    • , Annette Staebler
    • , Thomas O. Joos
    • , Simon Kling
    • , Oliver Poetz
    • , Tanja Fehm
    • , Hans Neubauer
    • , Bernhard Kuster
    •  & Markus F. Templin

    Nature Communications (2016)

  • Identification of thioridazine, an antipsychotic drug, as an antiglioblastoma and anticancer stem cell agent using public gene expression data

    • H-W Cheng
    • , Y-H Liang
    • , Y-L Kuo
    • , C-P Chuu
    • , C-Y Lin
    • , M-H Lee
    • , A T H Wu
    • , C-T Yeh
    • , E I-T Chen
    • , J Whang-Peng
    • , C-L Su
    •  & C-YF Huang

    Cell Death & Disease (2015)

  • A high-density immunoblotting methodology for quantification of total protein levels and phosphorylation modifications

    • F. Mazet
    • , J. L. Dunster
    • , C. I. Jones
    • , S. Vaiyapuri
    • , M. J. Tindall
    • , M. J. Fry
    •  & J. M. Gibbins

    Scientific Reports (2015)