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Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition

Nature Methods volume 10, pages 1239–1245 (2013)Cite this article

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Abstract

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

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Figure 1: AP-SWATH pipeline.
Figure 2: AP-SWATH for scoring protein interactions.
Figure 3: Selected biological samples.
Figure 4: Identification of differential interactomes for CDK4 cancer-associated mutants.
Figure 5: Use of AP-SWATH to probe drug-modulated interactions.

References

  1. Barrios-Rodiles, M. et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307, 1621–1625 (2005).

    Article  CAS  Google Scholar 

  2. Delmore, J.E. et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 146, 904–917 (2011).

    Article  CAS  Google Scholar 

  3. Vacic, V. & Iakoucheva, L.M. Disease mutations in disordered regions—exception to the rule? Mol. Biosyst. 8, 27–32 (2012).

    Article  CAS  Google Scholar 

  4. Steward, R.E., MacArthur, M.W., Laskowski, R.A. & Thornton, J.M. Molecular basis of inherited diseases: a structural perspective. Trends Genet. 19, 505–513 (2003).

    Article  CAS  Google Scholar 

  5. Lahiry, P., Torkamani, A., Schork, N.J. & Hegele, R.A. Kinase mutations in human disease: interpreting genotype-phenotype relationships. Nat. Rev. Genet. 11, 60–74 (2010).

    Article  CAS  Google Scholar 

  6. Schuster-Böckler, B. & Bateman, A. Protein interactions in human genetic diseases. Genome Biol. 9, R9 (2008).

    Article  Google Scholar 

  7. Zhong, Q. et al. Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5, 321 (2009).

    Article  Google Scholar 

  8. Gingras, A.C., Gstaiger, M., Raught, B. & Aebersold, R. Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 8, 645–654 (2007).

    Article  CAS  Google Scholar 

  9. Ideker, T. & Krogan, N.J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012).

    Article  Google Scholar 

  10. Gingras, A.C. & Raught, B. Beyond hairballs: the use of quantitative mass spectrometry data to understand protein-protein interactions. FEBS Lett. 586, 2723–2731 (2012).

    Article  CAS  Google Scholar 

  11. Tabb, D.L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 9, 761–776 (2010).

    Article  CAS  Google Scholar 

  12. Tate, S., Larsen, B., Bonner, R. & Gingras, A.C. Label-free quantitative proteomics trends for protein-protein interactions. J. Proteomics 81, 91–101 (2013).

    Article  CAS  Google Scholar 

  13. Bisson, N. et al. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 29, 653–658 (2011).

    Article  CAS  Google Scholar 

  14. Zheng, Y. et al. Temporal regulation of EGF signalling networks by the scaffold protein Shc1. Nature 499, 166–171 (2013).

    Article  CAS  Google Scholar 

  15. Picotti, P. & Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods 9, 555–566 (2012).

    Article  CAS  Google Scholar 

  16. Addona, T.A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).

    Article  CAS  Google Scholar 

  17. Venable, J.D., Dong, M.Q., Wohlschlegel, J., Dillin, A. & Yates, J.R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

    Article  CAS  Google Scholar 

  18. Gillet, L.C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

    Article  Google Scholar 

  19. Liu, Y. et al. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS. Proteomics 13, 1247–1256 (2013).

    Article  CAS  Google Scholar 

  20. Collins, B.C. et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat. Methods 10.1038/nmeth.2703 (27 October 2013).

  21. Andrews, G.L., Simons, B.L., Young, J.B., Hawkridge, A.M. & Muddiman, D.C. Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). Anal. Chem. 83, 5442–5446 (2011).

    Article  CAS  Google Scholar 

  22. Kean, M.J., Couzens, A.L. & Gingras, A.C. Mass spectrometry approaches to study mammalian kinase and phosphatase associated proteins. Methods 57, 400–408 (2012).

    Article  CAS  Google Scholar 

  23. Gingras, A.C., Raught, B. & Sonenberg, N. eIF4 initiation factors: effectors of mRNA recruitment to ribosomes and regulators of translation. Annu. Rev. Biochem. 68, 913–963 (1999).

    Article  CAS  Google Scholar 

  24. Yang, H.S. et al. The transformation suppressor Pdcd4 is a novel eukaryotic translation initiation factor 4A binding protein that inhibits translation. Mol. Cell. Biol. 23, 26–37 (2003).

    Article  Google Scholar 

  25. Jeronimo, C. et al. Systematic analysis of the protein interaction network for the human transcription machinery reveals the identity of the 7SK capping enzyme. Mol. Cell 27, 262–274 (2007).

    Article  CAS  Google Scholar 

  26. Wölfel, T. et al. A p16INK4a-insensitive CDK4 mutant targeted by cytolytic T lymphocytes in a human melanoma. Science 269, 1281–1284 (1995).

    Article  Google Scholar 

  27. Zuo, L. et al. Germline mutations in the p16INK4a binding domain of CDK4 in familial melanoma. Nat. Genet. 12, 97–99 (1996).

    Article  CAS  Google Scholar 

  28. Serrano, M., Hannon, G.J. & Beach, D. A new regulatory motif in cell-cycle control causing specific inhibition of cyclin D/CDK4. Nature 366, 704–707 (1993).

    Article  CAS  Google Scholar 

  29. Coleman, K.G. et al. Identification of CDK4 sequences involved in cyclin D1 and p16 binding. J. Biol. Chem. 272, 18869–18874 (1997).

    Article  CAS  Google Scholar 

  30. Shimamura, T., Lowell, A.M., Engelman, J.A. & Shapiro, G.I. Epidermal growth factor receptors harboring kinase domain mutations associate with the heat shock protein 90 chaperone and are destabilized following exposure to geldanamycins. Cancer Res. 65, 6401–6408 (2005).

    Article  CAS  Google Scholar 

  31. Grbovic, O.M. et al. V600E B-Raf requires the Hsp90 chaperone for stability and is degraded in response to Hsp90 inhibitors. Proc. Natl. Acad. Sci. USA 103, 57–62 (2006).

    Article  CAS  Google Scholar 

  32. Taipale, M. et al. Quantitative analysis of Hsp90-client interactions reveals principles of substrate recognition. Cell 150, 987–1001 (2012).

    Article  CAS  Google Scholar 

  33. Prodromou, C. Strategies for stalling malignancy: targeting cancer's addiction to Hsp90. Curr. Top. Med. Chem. 9, 1352–1368 (2009).

    Article  CAS  Google Scholar 

  34. Brough, P.A. et al. 4,5-Diarylisoxazole Hsp90 chaperone inhibitors: potential therapeutic agents for the treatment of cancer. J. Med. Chem. 51, 196–218 (2008).

    Article  CAS  Google Scholar 

  35. Taipale, M., Jarosz, D.F. & Lindquist, S. HSP90 at the hub of protein homeostasis: emerging mechanistic insights. Nat. Rev. Mol. Cell Biol. 11, 515–528 (2010).

    Article  CAS  Google Scholar 

  36. da Rocha Dias, S. et al. Activated B-RAF is an Hsp90 client protein that is targeted by the anticancer drug 17-allylamino-17-demethoxygeldanamycin. Cancer Res. 65, 10686–10691 (2005).

    Article  CAS  Google Scholar 

  37. Liu, G. et al. ProHits: integrated software for mass spectrometry–based interaction proteomics. Nat. Biotechnol. 28, 1015–1017 (2010).

    Article  CAS  Google Scholar 

  38. Mellacheruvu, D. et al. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data. Nat. Methods 10, 730–736 (2013).

    Article  CAS  Google Scholar 

  39. Dunham, W.H. et al. A cost-benefit analysis of multidimensional fractionation of affinity purification-mass spectrometry samples. Proteomics 11, 2603–2612 (2011).

    Article  CAS  Google Scholar 

  40. Luke-Glaser, S. et al. CIF-1, a shared subunit of the COP9/signalosome and eukaryotic initiation factor 3 complexes, regulates MEL-26 levels in the Caenorhabditis elegans embryo. Mol. Cell. Biol. 27, 4526–4540 (2007).

    Article  CAS  Google Scholar 

  41. Shilov, I.V. et al. The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol. Cell. Proteomics 6, 1638–1655 (2007).

    Article  CAS  Google Scholar 

  42. Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 8, 430–435 (2011).

    Article  CAS  Google Scholar 

  43. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank L. Taylor for input and initial SWATH data analysis, E. Polvi for help with subcloning, K. Colwill (Lunenfeld-Tanenbaum Research Institute) for the construction of pDEST-5′Triple-Flag-pcDNA5-FRT-TO and B. Raught for critical reading of the manuscript. The website for the supplementary material was designed by G. Liu and J.P. Zhang. This work was supported by a Venture Sinai award to A.-C.G.; the Canadian Institute of Health Research (to A.-C.G.; MOP-84314); the US National Institutes of Health (to A.-C.G.; 5R01GM94231); the Ontario Research Fund via a Global Leadership Award Round 2 (to T.P. and A.-C.G.).; the European Research Council (to R.A.; #ERC-2008-AdG 233226); SystemsX.ch, the Swiss Initiative for Systems Biology (to R.A.); the US National Human Genome Research Institute (to M.V.; R01HG001715 and P50HG004233) and the US National Cancer Institute (to M.V.; R33CA132073); a Canada Research Chair in Functional Proteomics and the Lea Reichmann Chair in Cancer Proteomics (to A.-C.G.).; and postdoctoral awards from the Canadian Institutes of Health Research and the Canadian National Sciences and Engineering Research Council postdoctoral award (to J.-P.L.).

Author information

Author notes

  1. Tony Pawson: Deceased.

  2. Jean-Philippe Lambert and Gordana Ivosev: These authors contributed equally to this work.

Authors and Affiliations

  1. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada

    Jean-Philippe Lambert, Amber L Couzens, Brett Larsen, Zhen-Yuan Lin, Tony Pawson & Anne-Claude Gingras

  2. AB Sciex, Concord, Ontario, Canada

    Gordana Ivosev, Ron Bonner & Stephen Tate

  3. Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA

    Mikko Taipale & Susan Lindquist

  4. Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Quan Zhong & Marc Vidal

  5. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Quan Zhong & Marc Vidal

  6. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Quan Zhong & Marc Vidal

  7. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Susan Lindquist

  8. Howard Hughes Medical Institute, Cambridge, Massachusetts, USA

    Susan Lindquist

  9. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

    Ruedi Aebersold

  10. Faculty of Science, University of Zurich, Zurich, Switzerland

    Ruedi Aebersold

  11. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Tony Pawson & Anne-Claude Gingras

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  1. Jean-Philippe Lambert
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Contributions

J.-P.L. generated all CDK4 samples and performed validation experiments; G.I. developed the pipeline for the normalization and fold-change calculation and performed statistical analysis; A.L.C. generated all GRK6 samples and performed validation experiments; M.T. performed LUMIER analysis and provided constructs; Z.-Y.L. prepared samples for mass spectrometry; B.L. and S.T. performed mass spectrometric measurements and iTRAQ data analysis; Q.Z. and M.V. provided initial constructs and input on the project; S.L. supervised M.T., and T.P. cosupervised J.-P.L.; R.A., R.B. and S.T. co-developed the SWATH approach; J.-P.L., A.L.C., B.L., A.-C.G., G.I. and S.T. analyzed the SWATH data; A.-C.G. wrote the manuscript with input from all authors; A.-C.G. conceived of the study and directed the project.

Corresponding authors

Correspondence to Stephen Tate or Anne-Claude Gingras.

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Competing interests

G.I., R.B. and S.T. are employees of AB Sciex. AB Sciex provided support for the Ontario Research Fund grant to T.P. and A.-C.G.

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Lambert, JP., Ivosev, G., Couzens, A. et al. Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nat Methods 10, 1239–1245 (2013). https://doi.org/10.1038/nmeth.2702

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