Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field
Recent advances in “-omics” technologies have simplified capacity to concurrently assess expression profiles of thousands of targets in a cellular system. However, compilation and analysis of “omics” data in support of human health protection remains a challenge. Benchmark dose (BMD) modeling is currently being employed in chemical risk assessment to estimate acceptable levels of exposure. Although typically applied to conventional endpoints, newer software has enabled this application to be extended to transcriptomic datasets. BMD analytical tools now have the capacity to model transcriptional dose-response data to derive meaningful BMD values for genes, pathways and gene ontologies. In this report, radiation data obtained from the Gene Expression Omnibus (GEO) were analyzed to generate BMD values for transcriptional responses. The datasets comprised microarray analyses of human blood gamma-irradiated ex vivo (0–20 Gy) and human-derived cell lines exposed to alpha particle radiation (0.5–1.5 Gy). The distributions of BMDs for statistically significant genes and pathways in response to radiation exposure were examined and compared across studies. BMD modeling could identify pathway/gene sensitivities across wide radiation dose ranges, experimental conditions (time-points, cell types) and radiation qualities. BMD analysis offered a new approach to examine transcriptional data. The results were shown to provide information on transcriptional thresholds of effects to support refined risk assessments for low dose ionizing radiation exposures, derive gene-based values for relative biological effectiveness and identify pathways involved in radiation sensitivities across cell types which may extend to applications a clinical setting. Environ. Mol. Mutagen. 57:589–604, 2016.
|Keywords||point of departure, radiation, relative biological effectiveness, risk assessment|
|Journal||Environmental and Molecular Mutagenesis|
Chauhan, V. (Vinita), Kuo, B. (Byron), McNamee, J.P. (James P.), Wilkins, R.C, & Yauk, C.L. (Carole L.). (2016). Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field. Environmental and Molecular Mutagenesis, 57(8), 589–604. doi:10.1002/em.22043