Bystander responses are, there fore, especially relevant to cancer risk assessment in low Ganetespib buy dose low dose rate radiation exposure situations such as domestic radon exposure or extended space tra vel, and also in partial body exposures such as from medical radiation. It is important to understand not only the physiologi cal and DNA damage effects of radiation on cells but also the global inflammatory and stress responses of cells and tissues. For instance, irradiated fibroblasts are known to promote tumor formation in neighboring epithelial cells by altering the tumor microenvironment. With this in mind, we studied gene expression over time in normal human lung fibroblasts, at the mRNA level, to provide insight into the mechanisms and timing of signaling in irradiated and bystander cells.

We have previously studied the gene expression response of bystander fibroblasts to 0. 5 Gy a particle irradiation, 4 hours after exposure. To better under stand both early and sustained signaling associated with responding genes, we have now extended the study, measuring global gene expression at 0. 5 hour, 1 hour, 2 hours, 4 hours, 6 hours, and 24 hours after irradiation. We studied the direct radiation and bystander gene expression responses separately to compare trends because, although much is known about the effects of radiation on gene expression in cells, the full effect of radiation encompasses cells that are hit and those that are not. Also, over time the response in tissues comes from the convergence of signaling and respond ing genes from both types of cells.

In the previous study of the 4 hour response, we identified 238 genes that were significantly changed 4 hours after exposure in irradiated and or bystander cells. In the current study, we focused our analysis on the response of these genes over time, and applied a novel time course clustering technique to identify genes with potential regulatory similarities. The choice of methodology is a crucial issue in the use of clustering methods to examine structure in a given data set. It is important to choose and or devise a methodology appropriate for the given data. Time series data are often analyzed using standard clustering algo rithms such as hierarchical clustering, k means and self organizing maps.

Although these algorithms have yielded biological insights, the fundamental pro blem Carfilzomib is that these methods typically treat measurements taken at different time points as independent, ignoring the sequential nature of time series data. Further more, most methods that have been developed specifi cally for time course data are designed for longer time series. In contrast, most microarray based studies encompass relatively few time points. In this study, six time points and four biological replicates were measured, yielding sparsity in both the number of time points and the number of replicates.