Gene expression-based biodosimetry for radiological incidents: assessment of dose and time after radiation exposure

Research output: Contribution to journalArticle

Institutes & Expert groups

Documents & links

DOI

Abstract

Purpose: In order to ensure efficient use of medical resources following a radiological incident, there is an urgent need for high-throughput time-efficient biodosimetry tools. In the present study, we tested the applicability of a gene expression signature for the prediction of exposure dose as well as the time elapsed since irradiation. Materials and methods: We used whole blood samples from seven healthy volunteers as reference samples (X-ray doses: 0, 25, 50, 100, 500, 1000, and 2000 mGy; time points: 8, 12, 24, 36 and 48 h) and samples from seven other individuals as ‘blind samples’ (20 samples in total). Results: Gene expression values normalized to the reference gene without normalization to the unexposed controls were sufficient to predict doses with a correlation coefficient between the true and the predicted doses of 0.86. Importantly, we could also classify the samples according to the time since exposure with a correlation coefficient between the true and the predicted time point of 0.96. Because of the dynamic nature of radiation-induced gene expression, this feature will be of critical importance for adequate gene expression-based dose prediction in a real emergency situation. In addition, in this study we also compared different methodologies for RNA extraction available on the market and suggested the one most suitable for emergency situation which does not require on-spot availability of any specific reagents or equipment. Conclusions: Our results represent an important advancement in the application of gene expression for biodosimetry purposes.

Details

Original languageEnglish
Pages (from-to)64-75
Number of pages12
JournalInternational Journal of Radiation Biology
Volume95
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • Biodosimetry, gene expression, radiation, time point prediction, dose prediction

ID: 4753211