Gene expression for biodosimetry and effect prediction purposes: promises, pitfalls and future directions – key session ConRad 2021

Abstract

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 day after irradiation), high-throughput, and point-of-care diagnosis required for the prediction of the acute radiation syndrome (ARS). ConclusionsAs 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 point-of-care 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.

Lung transcriptome of nonhuman primates exposed to total- and partial-body irradiation

“…Animal studies have indicated that the effects of irradiation are manifested in males and females differently, with females being more sensitive to radiation injury. 18 , 61 , 62 In humans, long-term radiosensitivity is higher in females even when both sexes receive a comparable radiation dose. 63 , 64 In addition, females have a significantly higher risk of dying from radiation-associated cancers.…”

“… 11 , 12 , 13 Several studies from various investigators have profiled transcriptomic changes in various tissues of nonhuman primates (NHPs). 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 However, the majority of these publications are with baboons, which are no longer used for such studies. Therefore, there is a need for studies performed with rhesus and cynomolgus, which are commonly used NHPs.…”

“…Though significant progress has been made with transcriptomic studies using radiation injury models of NHPs, additional investigation is required to ensure that signatures are robust. 16 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 …”

Cross-platform validation of a mouse blood gene signature for quantitative reconstruction of radiation dose

“…With the caveat that there are species specific 36 and even strain specific differences 37 , 38 in radiation response, we trained an algorithm using the transcriptomics datasets available in the NCBI Gene Expression Omnibus database for quantitative radiation responses at 24 h. From this analysis we selected the top dose-correlated genes (positive and negative) and trained and tested our method using the net signal (N) as a correlate of dose. Typically, gene signatures that have potential for field applications are viewed to consist of a few genes to tens of genes 39 – 41 . Therefore, we started with 30 top ranked genes for quantitative RT-PCR testing, then further reduced the number of genes to a lower threshold, beyond which addition of more genes would not dramatically change the average error measured by RMSE.…”

Cross-platform validation of a mouse blood gene signature for quantitative reconstruction of radiation dose

“…With the caveat that there are species speci c 34 and even strain speci c differences 35,36 in radiation response, we trained an algorithm using the transcriptomics datasets available in the NCBI Gene Expression Omnibus database for quantitative radiation responses at 24 h. From this analysis we selected the top dose-correlated genes (positive and negative) and trained and tested our method using the net signal (N) as a correlate of dose. Typically, gene signatures that have potential for eld applications are viewed to consist of a few genes to tens of genes [37][38][39] . Therefore, we started with 30 top ranked genes for quantitative RT-PCR testing, then further reduced the number of genes to a lower threshold, beyond which addition of more genes would not dramatically change the average error measured by RMSE.…”