Global Phosphoproteomics Reveals Crosstalk Between Bcr-Abl and Negative Feedback Mechanisms Controlling Src Signaling

Liudmilla Rubbi, Björn Titz, Lauren Brown, Erica Galvan, Evangelia Komisopoulou, Sharon S. Chen, Tracey Low, Martik Tahmasian, Brian Skaggs, Markus Müschen, Matteo Pellegrini, Thomas G. Graeber*

*To whom correspondence should be addressed. E-mail: tgraeber{at}mednet.ucla.edu

This PDF file includes:

• Analysis: Unsupervised Hierarchical Clustering
• Fig. S1. Phosphoproteomics approach to delineate the bifurcation and coupling of the Bcr-Abl and Src family kinase (SFK) signaling network.
• Fig. S2. Global quantitative phosphoprofiling of dasatinib dose-escalation experiments reveals phosphorylation events with distinct inhibitor sensitivities and response patterns.
• Fig. S3. Comparison of dasatinib dose-response results using wild-type Bcr-Abl (p210) versus dasatinib-resistant T315A Bcr-Abl.
• Fig. S4. SFK-related properties are more enriched than Abl-related properties when phosphorylation events are ranked by their SFK perturbation response score.
• Fig. S5. The network of PPIs between the 40 proteins associated with the top-ranked SFK perturbation–correlated phosphosites and the 40 proteins with the smallest change in response to SFK perturbation.
• Fig. S6. The consensus sequence motifs of the top 50 correlated phosphosites based on SFK overexpression, drug inhibition (dasatinib), and combined rankings.
• Fig. S7. Unsupervised hierarchical clustering analysis of the Bcr-Abl and SFK network perturbation data.
• Fig. S8. The constitutive kinase activity of Bcr-Abl induces increased phosphorylation of the SFK activation domain tyrosine in Ba/F3 pro-B lymphoid cells.
• Fig. S9. Stable Csk knockdown in Ba/F3 Bcr-Abl (p210) cells.
• Fig. S10. Phosphorylation of the activation domain and C-terminal tyrosines of SFKs in response to a time course treatment with imatinib.
• Fig. S11. MS alignment and quantitation analysis pipeline and representative examples.
• Fig. S12. Global changes in phosphorylation amounts upon SFK perturbation compared to differences between biological replicates.
• Fig. S13. Global phosphorylation changes detected in independently derived biological replicates are sufficient to correctly cluster like samples in an unsupervised fashion.
• Fig. S14. Comparison of label-free and SILAC-based MS quantitation and representative examples.
• Table S1. Enrichment of SFK-related properties at the top of our SFK perturbation response score–ranked lists.
• Table S2. SFK-related properties of the two clusters generated by the unsupervised hierarchical clustering analysis.
• References
  

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Other Supplementary Material for this manuscript includes the following:

• Table S3. Quantitative fold change values for 493 phosphorylation sites upon SFK perturbation by genetics and inhibitors (Microsoft Excel format).
• Table S4. Kinetic fold change values for 74 phosphorylation sites upon imatinib inhibition of Bcr-Abl (Microsoft Excel format).

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© 2011 American Association for the Advancement of Science