Dominant Role of PI3K p110α over p110ß in Insulin and ß-Adrenergic Receptor Signalling.

Attribution of specific roles to the two ubiquitously expressed PI 3-kinase (PI3K) isoforms p110α and p110ß in biological functions they have been implicated, such as in insulin signalling, has been challenging. While p110α has been demonstrated to be the principal isoform activated downstream of the insulin receptor, several studies have provided evidence for a role of p110ß. Here we have used isoform-selective inhibitors to estimate the relative contribution of each of these isoforms in insulin signalling in adipocytes, which are a cell type with essential roles in regulation of metabolism at the systemic level. Consistent with previous genetic and pharmacological studies, we found that p110α is the principal isoform activated downstream of the insulin receptor under physiological conditions. p110α interaction with Ras enhanced the strength of p110α activation by insulin. However, this interaction did not account for the selectivity for p110α over p110ß in insulin signalling. We also demonstrate that p110α is the principal isoform activated downstream of the ß-adrenergic receptor (ß-AR), another important signalling pathway in metabolic regulation, through a mechanism involving activation of the cAMP effector molecule EPAC1. This study offers further insights in the role of PI3K isoforms in the regulation of energy metabolism with implications for the therapeutic application of selective inhibitors of these isoforms.

RANK-C Expression Sensitizes ER-Negative, EGFR-Positive Breast Cancer Cells to EGFR-Tyrosine Kinase Inhibitors (TKIs).

BACKGROUND: We have previously shown that overexpression of RANK-c in ER-negative breast cancer cell lines attenuates aggressive properties of cancer cells, partially through a RANK-c/EGFR interaction. EGFR inhibition through TKIs in breast cancer has been tested in triple-negative disease settings with limited clinical benefit for patients. Here we test if expression of RANK-c in ER-negative breast cancer cells in conjunction with treatment with TK inhibitors (erlotinib or gefitinib) can affect survival and colony-forming capacity of cancer cells. METHODS: Stably expressing MDA-MB-231-RANK-c and SKBR3-RANK-c cells were employed to test proliferation and colony formation in the presence of TKIs. In addition, Western blot analysis was performed to dissect EGFR related signaling cascades upon TK inhibition in the presence of RANK-c. RESULTS: Interestingly the two RANK-c expressing, ER-negative cells lines presented with a distinct phenotype concerning TKI sensitivity upon treatment. MDA-MB-231-RANK-c cells had a higher sensitivity upon gefitinib treatment, while erlotinib decreased the proliferation rate of SKBR3-RANK-c cells. Further, colony formation assays for MDA-MB-231-RANK-c cells showed a decrease in the number and size of colonies developed in the presence of erlotinib. In addition, RANK-c seems to alter signaling through EGFR after TKI treatment in a cell type-specific manner. CONCLUSIONS: Our results indicate that ER-negative breast cancer cells that express RANK-c alter their sensitivity profile against tyrosine kinase inhibitors (erlotinib and gefitinib) in a cell type-specific and culture substrate-dependent manner.

Novel Cost Function Definition for Minimum-Cost Tractography in MR Diffusion Tensor Imaging.

Magnetic resonance imaging (MRI) is an established clinical technique that measures diffusion-weighted signals, applied primarily in brain studies. Diffusion tensor imaging (DTI) is a technique that uses the diffusion-weighted signals to obtain information about tissue connectivity, which recently started to become established in clinical use. The extraction of tracts (tractography) is an issue under active research. In this work we present an algorithm for recovering tracts, based on Dijkstra's minimum-cost path. A novel cost definition algorithm is presented that allows tract reconstruction, considering the tract's curvature, as well as its alignment with the diffusion vector field. The proposed cost function is able to adapt to linear, planar, and spherical diffusion. Thus, it can handle issues of fiber crossing, which pose considerable problems to tractography algorithms. A simple method for generating synthetic diffusion - weighted MR signals from known fibers - is also presented and utilized in this work. Results are shown for two (2D)- and three-dimensional (3D) synthetic data, as well as for a clinical MRI-DTI brain study.