Novel surface expression of reticulocalbin 1 on bone endothelial cells and human prostate cancer cells is regulated by TNF-alpha.

An unbiased cDNA expression phage library derived from bone-marrow endothelial cells was used to identify novel surface adhesion molecules that might participate in metastasis. Herein we report that reticulocalbin 1 (RCN1) is a cell surface-associated protein on both endothelial (EC) and prostate cancer (PCa) cell lines. RCN1 is an H/KDEL protein with six EF-hand, calcium-binding motifs, found in the endoplasmic reticulum. Our data indicate that RCN1 also is expressed on the cell surface of several endothelial cell lines, including human dermal microvascular endothelial cells (HDMVECs), bone marrow endothelial cells (BMEC), and transformed human bone marrow endothelial cells (TrHBMEC). While RCN1 protein levels were highest in lysates from HDMVEC, this difference was not statistically significant compared BMEC and TrHBMEC. Given preferential adhesion of PCa to bone-marrow EC, these data suggest that RCN1 is unlikely to account for the preferential metastasis of PCa to bone. In addition, there was not a statistically significant difference in total RCN1 protein expression among the PCa cell lines. RCN1 also was expressed on the surface of several PCa cell lines, including those of the LNCaP human PCa progression model and the highly metastatic PC-3 cell line. Interestingly, RCN1 expression on the cell surface was upregulated by tumor necrosis factor alpha treatment of bone-marrow endothelial cells. Taken together, we show cell surface localization of RCN1 that has not been described previously for either PCa or BMEC and that the surface expression on BMEC is regulated by pro-inflammatory TNF-alpha.

Nonlinear model-based estimates of IC(50) for studies involving continuous therapeutic dose-response data.

We present statistical details for estimating an in vitro 50% inhibitory concentration (IC(50)), based on several models for continuous response data fit to bone-marrow endothelial cell lines replicated in vehicle and at several dose increments. Nonlinear models are fit via maximum likelihood assuming normal errors, and primary attention is given to exponential, Gompertz, and scaled logistic dose-response curves that admit increasing or decreasing monotonic and sigmoidal patterns. Careful consideration is given to dose axis scaling, comparative model fit via mean squared error and graphical assessment, analogues to weighted least squares analysis to address heterogeneity of variance across doses, and potential hormetic effects. Standard error estimation is discussed in detail, highlighting the advantage of reparameterizing dose-response models directly in terms of IC(50). Specific results for two cell lines are provided, along with a sample commercial software-based program for implementing a selection of the methods discussed.