Saliva, as a non-invasive and easily accessible biofluid, has been shown to contain RNA biomarkers for prediction and diagnosis of several diseases. However, systematic analysis done by our group identified two problematic issues not coherently described before: (1) most of the isolated RNA originates from the oral microbiome and (2) the amount of isolated human RNA is comparatively low. The degree of bacterial contamination showed ratios up to 1:900,000, so that only about one out of 900,000 RNA copies was of human origin, but the RNA quality (average RIN 6.7 + /− 0.8) allowed for qRT-PCR. Using 12 saliva samples from healthy donors, we modified the methodology to (1) select only human RNA during cDNA synthesis by aiming at the poly(A)+-tail and (2) introduced a pre-amplification of human RNA before qRT-PCR. Further, the manufacturer's criteria for successful pre-amplification (Ct values ≤ 35 for unamplified cDNA) had to be replaced by (3) proofing linear preamplification for each gene, thus, increasing the number of evaluable samples up to 70.6%. When considering theses three modifications unbiased gene expression analysis on human salivary RNA can be performed.

Large-scale radiation emergency scenarios involving protracted low dose rate radiation exposure (e.g. a hidden radioactive source in a train) necessitate the development of high throughput methods for providing rapid individual dose estimates. During the RENEB (Running the European Network of Biodosimetry) 2019 exercise, four EDTA-blood samples were exposed to an Iridium-192 source (1.36 TBq, Tech-Ops 880 Sentinal) at varying distances and geometries. This resulted in protracted doses ranging between 0.2 and 2.4 Gy using dose rates of 1.5–40 mGy/min and exposure times of 1 or 2.5 h. Blood samples were exposed in thermo bottles that maintained temperatures between 39 and 27.7 °C. After exposure, EDTA-blood samples were transferred into PAXGene tubes to preserve RNA. RNA was isolated in one laboratory and aliquots of four blinded RNA were sent to another five teams for dose estimation based on gene expression changes. Using an X-ray machine, samples for two calibration curves (first: constant dose rate of 8.3 mGy/min and 0.5–8 h varying exposure times; second: varying dose rates of 0.5–8.3 mGy/min and 4 h exposure time) were generated for distribution. Assays were run in each laboratory according to locally established protocols using either a microarray platform (one team) or quantitative real-time PCR (qRT-PCR, five teams). The qRT-PCR measurements were highly reproducible with coefficient of variation below 15% in ≥ 75% of measurements resulting in reported dose estimates ranging between 0 and 0.5 Gy in all samples and in all laboratories. Up to twofold reductions in RNA copy numbers per degree Celsius relative to 37 °C were observed. However, when irradiating independent samples equivalent to the blinded samples but increasing the combined exposure and incubation time to 4 h at 37 °C, expected gene expression changes corresponding to the absorbed doses were observed. Clearly, time and an optimal temperature of 37 °C must be allowed for the biological response to manifest as gene expression changes prior to running the gene expression assay. In conclusion, dose reconstructions based on gene expression measurements are highly reproducible across different techniques, protocols and laboratories. Even a radiation dose of 0.25 Gy protracted over 4 h (1 mGy/min) can be identified. These results demonstrate the importance of the incubation conditions and time span between radiation exposure and measurements of gene expression changes when using this method in a field exercise or real emergency situation.

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A suite of software tools has been developed for dose estimation (BAT, WinFRAT) and prediction of acute health effects (WinFRAT, H-Module) using clinical symptoms and/or changes in blood cell counts. We constructed a database of 191 ARS cases using the METREPOL (n = 167) and the SEARCH-database (n = 24). The cases ranged from unexposed (RC0), to mild (RC1), moderate (RC2), severe (RC3), and lethal ARS (RC4). From 2015–2019, radiobiology students and participants of two NATO meetings predicted clinical outcomes (RC, H-ARS, and hospitalization) based on clinical symptoms. We evaluated the prediction outcomes using the same input datasets with a total of 32 teams and 94 participants. We found that: (1) unexposed (RC0) and mildly exposed individuals (RC1) could not be discriminated; (2) the severity of RC2 and RC3 were systematically overestimated, but almost all lethal cases (RC4) were correctly predicted; (3) introducing a prior education component for non-physicians significantly increased the correct predictions of RC, ARS, and hospitalization by around 10% (p<0.005) with a threefold reduction in variance and a halving of the evaluation time per case; (4) correct outcome prediction was independent of the software tools used; and (5) comparing the dose estimates generated by the teams with H-ARS severity reflected known limitations of dose alone as a surrogate for H-ARS severity. We found inexperienced personnel can use software tools to make accurate diagnostic and treatment recommendations with up to 98% accuracy. Educational training improved the quality of decision making and enabled participants lacking a medical background to perform comparably to experts.

We examined the transcriptome/post-transcriptome for persistent gene expression changes after radiation exposure in a baboon model. Eighteen baboons were irradiated with a whole body equivalent dose of 2.5 or 5 Gy. Blood samples were taken before, 7, 28 and 75–106 days after radiation exposure. Stage I was a whole genome screening for mRNA combined with a qRT-PCR platform for detection of 667 miRNAs. Candidate mRNAs and miRNAs differentially up- or down-regulated in stage I were chosen for validation in stage II using the remaining samples. Only 12 of 32 candidate genes provided analyzable results with two mRNAs showing significant 3–5-fold differences in gene expression over the reference (p < 0.0001). From 667 candidate miRNAs, 290 miRNA were eligible for analysis with 21 miRNAs independently validated using qRT-PCR. These miRNAs showed persistent expression changes on each day and over days 7–106 days after exposure (n = 7). In particular miR-212 involved in radiosensitivity and immune modulation appeared persistently and 48–77-fold up-regulated over the entire time period. We are finally trying to put our results into a context of clinical implications and provide possible hints on underlying molecular mechanisms to be examined in future studies.

No abstract

Radiation-induced biological changes occurring within hours and days after irradiation can be potentially used for either exposure reconstruction (retrospective dosimetry) or the prediction of consecutively occurring acute or chronic health effects. The advantage of molecular protein or gene expression (GE) (mRNA) marker lies in their capability for early (1–3 days after irradiation), high-throughput and point-of-care diagnosis, required for the prediction of the acute radiation syndrome (ARS) in radiological or nuclear scenarios. These molecular marker in most cases respond differently regarding exposure characteristics such as e.g. radiation quality, dose, dose rate and most importantly over time. Changes over time are in particular challenging and demand certain strategies to deal with. With this review, we provide an overview and will focus on already identified and used mRNA GE and protein markers of the peripheral blood related to the ARS. These molecules are examined in light of ‘ideal’ characteristics of a biomarkers (e.g. easy accessible, early response, signal persistency) and the validation degree. Finally, we present strategies on the use of these markers considering challenges as their variation over time and future developments regarding e.g. origin of samples, point of care and high-throughput diagnosis.