Functional genomics in radiation biology: A gateway to cellular systems-level studies

Abstract

Cells respond to ionizing radiation through an intricate network of interacting signaling cascades that are engaged in the regulation of diverse cellular functions, such as cell cycle arrest, DNA repair, and apoptosis. While changes in protein modification, activity, and sub-cellular localization may directly mediate these responses, alterations in gene expression also represent a central component of the pathways involved. Studies of altered gene expression have historically played an important role in elucidating the molecular mechanisms underlying cellular radiation response. In recent years, functional genomics approaches, such as microarray profiling, have been developed that can simultaneously monitor changes in gene expression across essentially the entire genome. However, analogous methods for global measurements of protein expression or modification have lagged behind. As global transcription profiling has become increasingly accessible, the quantity of information on gene expression responses to irradiation has increased dramatically. While many such experiments have provided improved insight into various aspects of radiation response, the diversity of experimental models and details of radiation dose, timing, and data analysis that have been employed means that no single consistent picture has emerged yet. More sophisticated methods for data analysis, data mining, and reverse engineering to reconstruct the underlying response pathways are continually being developed, and can extract additional value from profiling studies. As methods for the global study of other biomolecules become more routine, it will be important to integrate the results of radiation response profiling across multiple biological levels, and to build from simpler experimental systems toward more complex multi-cellular and in vivo systems. The future development of "integromic" models of radiation response should add substantially to the understanding gained from gene expression studies alone. © 2007 Springer-Verlag.