Cell surface receptors linked to tyrosine kinases control a host of important cellular activities, including proliferation, differentiation, and motility. Disregulated tyrosine kinase signaling is a common feature of many human cancers, thus tyrosine kinases and their downstream effectors are targets for the development of new drugs for the treatment of cancer. In order to take full advantage of such promising new therapies, however, we need an understanding of how tyrosine kinase signaling networks process information on a systems level. While considerable progress has been made in developing quantitative models describing tyrosine kinase signaling networks, these efforts are severely hampered by a lack of quantitative information on how changes in tyrosine phosphorylation are coupled to their downstream effectors containing modular phosphotyrosine binding domains. The goals of this collaborative project are to take advantage of new experimental approaches to address this gap in knowledge directly. Specifically, we will use SH2 profiling, a phosphoproteomic approach that is highly complementary to mass spectrometry-based methods, to quantify dynamic changes in binding sites for specific effector proteins upon receptor tyrosine kinase activation. Responses to different receptors and in different cell types will be compared, allowing systems-level behavior to be correlated with biological outputs. We will also use single-molecule imaging methods to monitor the coupling of specific effectors to receptors in the intracellular environment. These studies will afford unprecedented insight into the interaction dynamics of receptor signaling complexes that will enable much more powerful and accurate models of tyrosine kinase signaling.
Signaling from receptors with tyrosine kinase activity plays an important role in a number of human diseases, in particular cancer. Quantitative computer-based models that accurately describe the signaling mechanism used by these receptors will be very useful in designing new therapies for cancer and in deciding which patients will benefit most from those therapies (individualized medicine). The proposed studies use innovative experimental approaches to reveal new mechanistic insights necessary for building more powerful and accurate models.
|Curran, Timothy G; Bryson, Bryan D; Reigelhaupt, Michael et al. (2013) Computer aided manual validation of mass spectrometry-based proteomic data. Methods 61:219-26|