Editor's Choice for October 2016

The October 2016 Editor's choice article is "Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field" by Vinita Chauhan, Byron Kuo, James P. McNamee, Ruth C. Wilkins and Carole L. Yauk.

Dr. Chauhan and colleagues applied benchmark dose modeling to high density transcriptomic datasets that describe gene expression changes in human cells damaged by ionizing radiations. The technology of gene expression profiling using DNA microarrays has advanced to the point now that virtually every mRNA that is expressed in human cells can be quantified and the effects of toxic environmental chemicals and radiation determined. Sophisticated and powerful data analysis algorithms have been developed to identify transcripts that respond to radiation. Benchmark dose modeling evaluates dose-response data and computes a benchmark dose as the dose that is estimated to induce a pre-defined (e.g., 10%) change in the measured parameter. The benchmark dose, so computed, is a dose that produces a small but significant biological effect. This approach may be applied to evaluating the adverse risks associated with exposures to radiation, as well as toxic environmental chemicals.

The application of benchmark dose modeling to regulatory risk assessment holds promise for managing the thousands of new chemicals entering the environment each year. Science-based determination of doses that produce adverse effects is essential to sound policy. The Chauhan et al. paper shows a variety of potential uses for benchmark dose modeling in the radiation field. The authors’ analysis reveals that radiation induces a modal distribution of genes associated with specific biological functions. The authors further show how benchmark dose modeling can identify pathways that change at doses lower than sub-threshold. These changes appear to be consistent across cell types, however interestingly certain cell types were shown to be more sensitive to radiation exposures. For example, pathways that respond to a low dose of radiation in leukocytes may respond to a higher dose in fibroblasts and vice versa. In leukocytes treated with gamma rays, apoptotic cell death and DNA damage checkpoint signaling displayed greatest sensitivity with very low benchmark doses. Overall, the authors demonstrate that benchmark dose modeling provides quantitative measures of radiation responses that facilitate comparison of radiation qualities, cell-line sensitivities and the identification of pathways associated with sub-pathological changes.

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