Functional proteomic pattern identification under low dose ionizing radiation

High dose radiation has been well known for increasing the risk of carcinogenesis. However, the understanding of biological effects of low dose radiation is limited. Low dose radiation is reported to affect several signaling pathways including deoxyribonucleic acid repair, survival, cell cycle, cell growth, and cell death. The goal of this study is to reveal the proteomic patterns influencing these pathways.

To detect the possibly regulatory proteins/kinases, an emerging reverse-phase protein microarray (RPPM) in conjunction with quantum dots nano-crystal technology is used as a quantitative detection system. The dynamic responses are observed under different time points and radiation doses. To quantitatively determine the responsive protein/kinases and to discover the network motifs, we present a discriminative feature pattern identification system (DFPIS). Instead of simply identifying proteins contributing to the pathways, our methodology takes into consideration of protein dependencies which are represented as strong jumping emerging patterns (SJEPs). Furthermore, infrequent patterns, though occurred, will be considered irrelevant.

Computational results using DFPIS to analyze ataxia-telangiectasia mutated (ATM) cells treated under six different ionizing radiation doses (0cGy, 4cGy, 10cGy, 50cGy, 1Gy, and 5Gy) are presented. For each dose, the dynamic response was observed at different time points (1, 6, 24, 48, and 72h). The sets of different responsive proteins/kinases at different dose are reported. For each dose, the SJEPs for ATM-proficient and ATM-deficient cells are shown and compared.

By using the new RPPM technology and the DFPIS algorithm, we can observe the change of signaling patterns even at a very low radiation dosage where conventional technologies tend to fail.