Protein kinase G and focal adhesion kinase converge on Src/Akt/ß-catenin signaling module in osteoblast mechanotransduction.

Mechanical loading of bone induces interstitial fluid flow, leading to fluid shear stress (FSS) of osteoblasts. FSS rapidly increases the intracellular calcium concentration ([Ca(2+)]) and nitric oxide (NO) synthesis in osteoblasts and activates the protein kinase Akt. Activated Akt stimulates osteoblast proliferation and survival, but the mechanism(s) leading to Akt activation is not well defined. Using pharmacological and genetic approaches in primary human and mouse osteoblasts and mouse MC3T3 osteoblast-like cells, we found that Akt activation by FSS occurred through two parallel pathways; one required calcium stimulation of NO synthase and NO/cGMP/protein kinase G II-dependent activation of Src, and the other required calcium activation of FAK and Src, independent of NO. Both pathways cooperated to increase PI3K-dependent Akt phosphorylation and were necessary for FSS to induce nuclear translocation of ß-catenin, c-fos, and cox-2 gene expression and osteoblast proliferation. These data explain how mechanical stimulation of osteoblasts leads to increased signaling through a growth regulatory pathway essential for maintaining skeletal integrity.

Prediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression.

BACKGROUND: Many kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems. Predictions of drug effectiveness based on a comprehensive network model of cellular signalling are difficult, due to our partial knowledge of the complex biological processes downstream of the targeted kinases. RESULTS: We have developed the Kinase Inhibitors Elastic Net (KIEN) method, which integrates information contained in drug-kinase networks with in vitro screening. The method uses the in vitro cell response of single drugs and drug pair combinations as a training set to build linear and nonlinear regression models. Besides predicting the effectiveness of untested drugs, the KIEN method identifies sets of kinases that are statistically associated to drug sensitivity in a given cell line. We compared different versions of the method, which is based on a regression technique known as elastic net. Data from two-drug combinations led to predictive models, and we found that predictivity can be improved by applying logarithmic transformation to the data. The method was applied to the A549 lung cancer cell line, and we identified specific kinases known to have an important role in this type of cancer (TGFBR2, EGFR, PHKG1 and CDK4). A pathway enrichment analysis of the set of kinases identified by the method showed that axon guidance, activation of Rac, and semaphorin interactions pathways are associated to a selective response to therapeutic intervention in this cell line. CONCLUSIONS: We have proposed an integrated experimental and computational methodology, called KIEN, that identifies the role of specific kinases in the drug response of a given cell line. The method will facilitate the design of new kinase inhibitors and the development of therapeutic interventions with combinations of many inhibitors.