A Novel Signaling Pathway Mediated by the Nuclear Targeting of C-Terminal Fragments of Mammalian Patched 1

Background: Patched 1 (Ptc1) is a polytopic receptor protein that is essential for growth and differentiation. Its extracellular domains accept its ligand, Sonic Hedgehog, while the function of its C-terminal intracellular domain is largely obscure. Principal Findings: In this study, we stably expressed human Ptc1 protein in HeLa cells and found that it is subjected to proteolytic cleavage at the C-terminus, resulting in the generation of soluble C-terminal fragments. These fragments accumulated in the nucleus, while the N-terminal region of Ptc1 remained in the cytoplasmic membrane fractions. Using an anti-Ptc1 C-terminal domain antibody, we provide conclusive evidence that C-terminal fragments of endogenous Ptc1 accumulate in the nucleus of C3H10T1/2 cells. Similar nuclear accumulation of endogenous C-terminal fragments was observed not only in C3H10T1/2 cells but also in mouse embryonic primary cells. Importantly, the C-terminal fragments of Ptc1 modulate transcriptional activity of Gli1. Conclusions: Although Ptc1 protein was originally thought to be restricted to cell membrane fractions, our findings sugges

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response