Purpose: In a nuclear or radiological event, an early diagnostic or prognostic tool is needed to distinguish unexposed from low- and highly exposed individuals with the latter requiring early and intensive medical care. Radiation-induced gene expression (GE) changes observed within hours and days after irradiation have shown potential to serve as biomarkers for either dose reconstruction (retrospective dosimetry) or the prediction of consecutively occurring acute or chronic health effects. The advantage of GE markers lies in their capability for early (1–3 days after irradiation), high-throughput, and point-of-care (POC) diagnosis required for the prediction of the acute radiation syndrome (ARS).
Conclusions: As a key session of the ConRad conference in 2021, experts from different institutions were invited to provide state-of-the-art information on a range of topics including: (1) Biodosimetry: What are the current efforts to enhance the applicability of this method to perform retrospective biodosimetry? (2) Effect prediction: Can we apply radiation-induced GE changes for prediction of acute health effects as an approach, complementary to and integrating retrospective dose estimation? (3) High-throughput and point-of-care diagnostics: What are the current developments to make the GE approach applicable as a high-throughput as well as a POC diagnostic platform? (4) Low level radiation: What is the lowest dose range where GE can be used for biodosimetry purposes? (5) Methodological considerations: Different aspects of radiation-induced GE related to more detailed analysis of exons, transcripts and next-generation sequencing (NGS) were reported.
Bibliographical noteFunding Information: This work was supported by the German Ministry of Defense. SAA and SAG were supported through the Center for High-Throughput Minimally-Invasive Radiation Biodosimetry, National Institute of Allergy and Infectious Diseases Grant Number U19 AI067773. AE was supported by the National Cancer Institute Grant Number R01 CA172067. Work was also performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344.
Open Access: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way
Publisher Copyright: © Copyright © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
Citation: Patrick Ostheim, Sally A. Amundson, Christophe Badie, Dimitry Bazyka, Angela C. Evans, Shanaz A. Ghandhi, Maria Gomolka, Milagrosa López Riego, Peter K. Rogan, Robert Terbrueggen, Gayle E. Woloschak, Frederic Zenhausern, Hanns L. Kaatsch, Simone Schüle, Reinhard Ullmann, Matthias Port & Michael Abend (2021) Gene expression for biodosimetry and effect prediction purposes: promises, pitfalls and future directions – key session ConRad 2021, International Journal of Radiation Biology,
- effect prediction
- Gene expression
- high dose
- low dose
- radiation exposure
- PARTIAL-BODY IRRADIATION
- DOSE ESTIMATION