Calibration curve for radiation dose estimation using FDXR gene expression biodosimetry – premises and pitfalls

Abstract

Purpose

Radiation-induced alterations in gene expression show great promise for dose reconstruction and for severity prediction of acute health effects. Among several genes explored as potential biomarkers, FDXR is widely used due to high upregulation in white blood cells following radiation exposure. Nonetheless, the absence of a standardized protocols for gene expression-based biodosimetry is a notable gap that warrants attention to enhance the accuracy, reproducibility and reliability. The objective of this study was to evaluate the sensitivity of transcriptional biodosimetry to differences in protocols used by different laboratories and establish guidelines for the calculation of calibration curve using FDXR expression data.

Material and Methods

Two sets of irradiated blood samples generated during RENEB exercise were used. The first included samples irradiated with known doses including: 0, 0.25, 0.5, 1, 2, 3 and 4 Gy. The second set consisted of three ‘blind’ samples irradiated with 1.8 Gy, 0.4 Gy and a sham-irradiated sample. After irradiation, samples were incubated at 37 °C over 24 h and sent to participating laboratories, where RNA isolation and FDXR expression analysis by qPCR were performed using sets of primers/probes and reference genes specific for each laboratory. Calibration curves based on FDXR expression data were generated using non-linear and linear regression and used for dose estimation of ‘blind’ samples.

Results

Dose estimates for sham-irradiated sample (0.020–0.024 Gy) and sample irradiated with 0.4 Gy (0.369–0.381 Gy) showed remarkable consistency across all laboratories, closely approximating the true doses regardless variation in primers/probes and reference genes used. For sample irradiated with 1.8 Gy the dose estimates were less precise (1.198–2.011 Gy) but remained within an acceptable margin for triage within the context of high dose range.

Conclusion

Methodological differences in reference genes and primers/probes used for FDXR expression measurement do not have a significant impact on the dose estimates generated, provided that all reference genes performed as expected and the primers/probes target a similar set of transcript variants. The preferred method for constructing a calibration curve based on FDXR expression data involves employing linear regression to establish a function that describes the relationship between the logarithm of absorbed dose and FDXR ΔCt values. However, one should be careful with using non-irradiated sample data as these cannot be accurately represented on a logarithmic scale. A standard curve generated using this approach can give reliable dose estimations in a dose range from 50 mGy to 4 Gy at least.

Keywords:

• FDXR
• gene expression
• biological dosimetry
• qPCR

Acknowledgements

We are very thankful for the technical support by Eva Grumpelt.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work was partly funded by the NIHR HPRU in Chemical and Radiation Threats and Hazards, a partnership between UKHSA and Imperial College London. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA, or the Department of Health and Social Care. This work was supported by the Institute of Nuclear Chemistry and Technology statutory grant, Poland.

Notes on contributors

Kamil Brzóska

Kamil Brzóska, PhD, is an Associate Professor of Radiobiology at the Center for Radiobiology and Biological Dosimetry, Institute of Nuclear Chemistry and Technology, Warsaw, Poland.

Michael Abend

Michael Abend, MD, is a Professor of Radiobiology and Deputy Head of the Bundeswehr Institute of Radiobiology, Munich, Germany.

Grainne O’Brien

Grainne O'Brien, PhD, is a Scientist in the Cancer Mechanisms and Biomarkers group of the Department of Radiation Effects, UK Health Security Agency.

Eric Gregoire

Eric Gregoire, is a Scientist in the Laboratory of Radiobiology of Accidental Exposures (LRAcc), Institute of Radioprotection and Nuclear Safety (IRSN), Fontenay-aux-roses, France.

Matthias Port

Matthias Port, MD, is a Professor of Radiobiology and Internal Medicine and Head of the Bundeswehr Institute of Radiobiology, Munich, Germany.

Christophe Badie

Christophe Badie, PhD, is Leader of and head of the Department of Radiation Effects, UK Health Security Agency.