Functional interaction between Brn-3a and Src-1 co-activates Brn-3a-mediated transactivation

A simple technique for the prediction of interacting proteins reveals a direct Brn-3a-androgen receptor interaction

The formation of multiprotein complexes constitutes a key step in determining the function of any translated gene product. Thus, the elucidation of interacting partners for a protein of interest is of fundamental importance to cell biology. Here we describe a simple methodology for the prediction of novel interactors. We have applied this to the developmental transcription factor Brn-3a to predict and verify a novel interaction between Brn-3a and the androgen receptor (AR). We demonstrate that these transcription factors form complexes within the nucleus of ND7 neuroblastoma cells, while in vitro pull-down assays show direct association. As a functional consequence of the Brn-3a-AR interaction, the factors bind cooperatively to multiple elements within the promoter of the voltage-gated sodium channel, Nav1.7, leading to a synergistic increase in its expression. Thus, these data define AR as a direct Brn-3a interactor and verify a simple interacting protein prediction methodology that is likely to be useful for many other proteins.

Normal and cancer-related functions of the p160 steroid receptor co-activator (SRC) family

The three homologous members of the p160 SRC family (SRC1, SRC2 and SRC3) mediate the transcriptional functions of nuclear receptors and other transcription factors, and are the most studied of all the transcriptional co-activators. Recent work has indicated that the SRCgenes are subject to amplification and overexpression in various human cancers. Some of the molecular mechanisms responsible for SRC overexpression, along with the mechanisms by which SRCs promote breast and prostate cancer cell proliferation and survival, have been identified, as have the specific contributions of individual SRC family members to spontaneous breast and prostate carcinogenesis in genetically manipulated mouse models. These studies have identified new challenges for cancer research and therapy.