Anti-DOK5 monoclonal antibody Made to order

Tropomyosin-related kinase (Trk) family receptors are a group of high affinity receptors for neurotrophin growth factors, which have pivotal functions in many physiological processes of nervous system. Trk receptors can dimerize and autophosphory late upon neurotrophin stimulation, then recruit multiple adaptor proteins to transduct signal. In this report, we identified Dok5, a member of Dok family, as a new substrate of TrkB/C receptors. In yeast two-hybrid assay, Dok5 can interact with intracellular domain of TrkB and TrkC receptor through its PTB domain, but not with that of TrkA receptor. The interaction was then confirmed by GST pull-down assay and Co-IP experiment. Dok5 co-localized with TrkB and TrkC in differentiated PC12 cells, providing another evidence for their interaction. By using mutational analysis, we characterized that Dok5 PTB domain bound to Trk receptor NPQY motif in a kinase-activity-dependent manner. Furthermore, competition experiment indicated that Dok5 competed with N-shc for binding to the receptors at the same site. Finally, we showed that Dok5 was involved in the activation of MAPK pathway induced by neurotrophin stimulation. Taken together, these results suggest that Dok5 acts as substrate of TrkB/C receptors and is involved in neurotrophin induced MAPK signal pathway activation. (c) 2006 Elsevier Inc. All rights reserved.

Breast cancer metastasis is a demanding problem in clinical treatment of patients with breast cancer. It is necessary to examine the mechanisms of metastasis for developing therapies. Classification of the aggressiveness of breast cancer is an important issue in biological study and for clinical decisions. Although aggressive and non-aggressive breast cancer cells can be easily distinguished among different cell lines, it is very difficult to distinguish in clinical practice. The aim of the current study was to use the gene expression analysis from breast cancer cell lines to predict clinical outcomes of patients with breast cancer. Weighted gene co-expression network analysis (WGCNA) is a powerful method to account for correlations between genes and extract co-expressed modules of genes from large expression datasets. Therefore, WGCNA was applied to explore the differences in sub-networks between aggressive and non-aggressive breast cancer cell lines. The greatest difference topological overlap networks in both groups include potential information to understand the mechanisms of aggressiveness. The results show that the blue and red modules were significantly associated with the biological processes of aggressiveness. The sub-network, which consisted of TMEM47, GJC1, ANXA3, TWIST1 and C19orf33 in the blue module, was associated with an aggressive phenotype. The sub-network of LOC100653217, CXCL12, SULF1, DOK5 and DKK3 in the red module was associated with a non-aggressive phenotype. In order to validate the hazard ratio of these genes, the prognostic index was constructed to integrate them and examined using data from the Cancer Genomic Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with breast cancer from TCGA in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group (hazard ratio=1.231, 95% confidence interval=1.058-1.433, P=0.0071, by the Wald test). A similar result was produced from the GEO database. The findings may provide a novel strategy for measuring cancer aggressiveness in patients with breast cancer.