Transcriptomics for radiation biodosimetry: progress and challenges

Development of a Point-of-Care Microfluidic RNA Extraction Slide for Gene Expression Diagnosis after Irradiation

In times of war, radiological/nuclear emergency scenarios have become a reemphasized threat. However, there are challenges in transferring whole-blood samples to laboratories for specialized diagnostics using RNA. This project aims to miniaturize the process of unwieldy conventional RNA extraction with its stationed technical equipment using a microfluidic-based slide (MBS) for point-of-care diagnostics. The MBS is thought to be a preliminary step toward the development of a so-called lab-on-a-chip microfluidic device. A MBS would enable early and fast field care combined with gene expression (GE) analysis for the prediction of hematologic acute radiation syndrome (HARS) severity or identification of RNA microbes. Whole blood samples from ten healthy donors were irradiated with 0, 0.5 and 4 Gy, simulating different ARS severity degrees. RNA quality and quantity of a preliminary MBS was compared with a conventional column-based (CB) RNA extraction method. GE of four HARS severity-predicting radiation-induced genes (FDXR, DDB2, POU2AF1 and WNT3) was examined employing qRT-PCR. Compared to the CB method, twice as much total RNA from whole blood could be extracted using the MBS (6.6 ± 3.2 µg vs. 12.0 ± 5.8 µg) in half of the extraction time, and all MBS RNA extracts appeared DNA-free in contrast to the CB method (30% were contaminated with DNA). Using MBS, RNA quality [RNA integrity number equivalent (RINe)] values decreased about threefold (3.3 ± 0.8 vs. 9.0 ± 0.4), indicating severe RNA degradation, while expected high-quality RINe ≥ 8 were found using column-based method. However, normalized cycle threshold (Ct) values, as well as radiation-induced GE fold-changes appeared comparable for all genes utilizing both methods, indicating that no RNA degradation took place. In summary, the preliminary MBS showed promising features such as: 1. halving the RNA extraction time without the burden of heavy technical equipment (e.g., a centrifuge); 2. absence of DNA contamination in contrast to CB RNA extraction; 3. reduction in blood required, because of twice the biological output of RNA; and 4. equal GE performance compared to CB, thus, increasing its appeal for later semi-automatic parallel field applications.

Applicability of Gene Expression in Saliva as an Alternative to Blood for Biodosimetry and Prediction of Radiation-induced Health Effects

As the great majority of gene expression (GE) biodosimetry studies have been performed using blood as the preferred source of tissue, searching for simple and less-invasive sampling methods is important when considering biodosimetry approaches. Knowing that whole saliva contains an ultrafiltrate of blood and white blood cells, it is expected that the findings in blood can also be found in saliva. This human in vivo study aims to examine radiation-induced GE changes in saliva for biodosimetry purposes and to predict radiation-induced disease, which is yet poorly characterized. Furthermore, we examined whether transcriptional biomarkers in blood can also be found equivalently in saliva. Saliva and blood samples were collected in parallel from radiotherapy (RT) treated patients who suffered from head and neck cancer (n = 8) undergoing fractioned partial-body irradiations (1.8 Gy/fraction and 50-70 Gy total dose). Samples were taken 12-24 h before first irradiation and ideally 24 and 48 h, as well as 5 weeks after radiotherapy onset. Due to the low quality and quantity of isolated RNA samples from one patient, they had to be excluded from further analysis, leaving a total of 24 saliva and 24 blood samples from 7 patients eligible for analysis. Using qRT-PCR, 18S rRNA and 16S rRNA (the ratio being a surrogate for the relative human RNA/bacterial burden), four housekeeping genes and nine mRNAs previously identified as radiation responsive in blood-based studies were detected. Significant GE associations with absorbed dose were found for five genes and after the 2nd radiotherapy fraction, shown by, e.g., the increase of CDKN1A (2.0 fold, P = 0.017) and FDXR (1.9 fold increased, P = 0.002). After the 25th radiotherapy fraction, however, all four genes (FDXR, DDB2, POU2AF1, WNT3) predicting ARS (acute radiation syndrome) severity, as well as further genes (including CCNG1 [median-fold change (FC) = 0.3, P = 0.013], and GADD45A (median-FC = 0.3, P = 0.031)) appeared significantly downregulated (FC = 0.3, P = 0.01-0.03). A significant association of CCNG1, POU2AF1, HPRT1, and WNT3 (P = 0.006-0.04) with acute or late radiotoxicity could be shown before the onset of these clinical outcomes. In an established set of four genes predicting acute health effects in blood, the response in saliva samples was similar to the expected up- (FDXR, DDB2) or downregulation (POU2AF1, WNT3) in blood for up to 71% of the measurements. Comparing GE responses (PHPT1, CCNG1, CDKN1A, GADD45A, SESN1) in saliva and blood samples, there was a significant linear association between saliva and blood response of CDKN1A (R2 = 0.60, P = 0.0004). However, the GE pattern of other genes differed between saliva and blood. In summary, the current human in vivo study, (I) reveals significant radiation-induced GE associations of five transcriptional biomarkers in salivary samples, (II) suggests genes predicting diverse clinical outcomes such as acute and late radiotoxicity as well as ARS severity, and (III) supports the view that blood-based GE response can be reflected in saliva samples, indicating that saliva is a "mirror of the body" for certain but not all genes and, thus, studies for each gene of interest in blood are required for saliva.

Effects of cyclophosphamide and mitomycin C on radiation-induced transcriptional biomarkers in human lymphoblastoid cells

PURPOSE

Ionizing radiation (IR)-induced transcriptional changes are considered a potential biodosimetry for dose evaluation and health risk monitoring of acute or chronic radiation exposure. It is crucial to understand the impact of confounding factors on the radiation-responsive gene expressions for accurate and reproducible dose assessment. This study aims to explore the potential influence of exposures to chemotherapeutic agents such as cyclophosphamide (CP) and mitomycin C (MMC) on IR-induced transcriptional biomarkers.

METHODS

The human B lymphoblastoid cells (AHH-1) were exposed to 0, 20, 50, 100, 200 and 500 μg/ml CP or 0, 0.025, 0.05, 0.1 and 1 μg/ml MMC, respectively. The appropriate concentrations of CP and MMC were added for 1 h before irradiation with 0, 2, 4 and 6 Gy of 60Co γ-rays at a dose rate of 1 Gy/min. Cell viability was evaluated by CCK-8 assay. The gene expression responses of 18 radiation-induced transcriptional biomarkers were examined at 24 h after exposures to CP and MMC, respectively. The expression levels of five crucial DNA interstrand crosslinks (ICLs) repair genes were also evaluated. The biodosimetry models were established based on the specific radiation-responsive gene combinations.

RESULTS

The baseline transcriptional levels of the 18 selected genes were slightly affected by CP treatment in the absence of IR, while the transcript responses to IR could be inhibited as the concentration of CP up to 50 μg/ml. MMC treatment up-regulated the background levels in most radiation-responsive gene expressions. Of 18 genes, only the relative mRNA expression levels of CDKN1A and BBC3 were repressed after treatment with IR and MMC in combination. The relative mRNA level of RAD51 was significantly up-regulated after exposure to CP, while the expression of FANCD2, RAD51 and BLM showed an overall increase in response to MMC treatment. After irradiation, the relative mRNA expression levels of FANCD2, BRCA2 and RAD51 exhibited dose-dependent increases in IR alone and MMC treatment groups. In addition, the biodosimetry models were established using 2-4 radiation-responsive genes based on different radiation exposure scenarios.

CONCLUSION

Our findings suggested that IR-induced gene expression changes were slightly affected after exposure to a relatively low concentration of CP and MMC. Gene expression combinations might improve the broad applicability of transcriptional biodosimetry across diverse radiation exposures.

Paper-Based Vertical Flow Immunoassay for the Point-of-Care Multiplex Detection of Radiation Dosimetry Genes

In a nuclear or radiological incident, first responders must quickly and accurately measure radiation exposure among civilians as medical countermeasures are radiation dose-dependent and time-sensitive. Although several approaches have been explored to measure absorbed radiation dose, there is an important need to develop point-of-care (POC) bioassay devices that can be used immediately to triage thousands of individuals potentially exposed to radiation. Here we present a proof-of-concept study showing the use of a paper-based vertical flow immunoassay (VFI) to detect radiation dosimetry genes. Using labeled primers during amplification and a multiplex membrane, our results showed that the nucleic acid VFI can simultaneously detect two biodosimetry genes, CDKN1A and DDB2, as well as one housekeeping gene MRPS5. The assay demonstrated good linearity and precision with an inter- and intra-assay coefficient of variance <20% and <10%, respectively. Moreover, the assay showed its ability to discriminate non-irradiated controls (0 Gy) from irradiated samples (1 + 2 Gy) with an overall sensitivity of 62.5% and specificity of 100% (AUC = 0.8672, 95% CI: 0.723-1.000; p = 0.004). Interestingly, the gene combination also showed a dose-dependent response for 0, 1, and 2 Gy, similar to data obtained by real-time PCR benchmark. These preliminary results suggest that a VFI platform can be used to detect simultaneously multiple genes that can be then quantified, thus offering a new approach for a POC biodosimetry assay that could be rapidly deployed on-site to test a large population and help triage and medical management after radiological event.

Biomarkers for Biodosimetry and Their Role in Predicting Radiation Injury

Radiation-related normal tissue injury sustained during cancer radiotherapy or in a radiological or mass casualty nuclear incident is a major health concern. Reducing the risk and mitigating consequences of radiation injury could have a broad impact on cancer patients and citizens. Efforts to discover biomarkers that can determine radiation dose, predict tissue damage, and aid medical triage are underway. Exposure to ionizing radiation causes changes in gene, protein, and metabolite expression that needs to be understood to provide a holistic picture for treating acute and chronic radiation-induced toxicities. We present evidence that both RNA (mRNA, microRNA, long noncoding RNA) and metabolomic assays may provide useful biomarkers of radiation injury. RNA markers may provide information on early pathway alterations after radiation injury that can predict damage and implicate downstream targets for mitigation. In contrast, metabolomics is impacted by changes in epigenetics, genetics, and proteomics and can be considered a downstream marker that incorporates all these changes to provide an assessment of what is currently happening within an organ. We highlight research from the past 10 years to understand how biomarkers may be used to improve personalized medicine in cancer therapy and medical decision-making in mass casualty scenarios.

RENEB Inter-Laboratory Comparison 2021: Inter-Assay Comparison of Eight Dosimetry Assays

Tools for radiation exposure reconstruction are required to support the medical management of radiation victims in radiological or nuclear incidents. Different biological and physical dosimetry assays can be used for various exposure scenarios to estimate the dose of ionizing radiation a person has absorbed. Regular validation of the techniques through inter-laboratory comparisons (ILC) is essential to guarantee high quality results. In the current RENEB inter-laboratory comparison, the performance quality of established cytogenetic assays [dicentric chromosome assay (DCA), cytokinesis-block micronucleus assay (CBMN), stable chromosomal translocation assay (FISH) and premature chromosome condensation assay (PCC)] was tested in comparison to molecular biological assays [gamma-H2AX foci (gH2AX), gene expression (GE)] and physical dosimetry-based assays [electron paramagnetic resonance (EPR), optically or thermally stimulated luminescence (LUM)]. Three blinded coded samples (e.g., blood, enamel or mobiles) were exposed to 0, 1.2 or 3.5 Gy X-ray reference doses (240 kVp, 1 Gy/min). These doses roughly correspond to clinically relevant groups of unexposed to low exposed (0-1 Gy), moderately exposed (1-2 Gy, no severe acute health effects expected) and highly exposed individuals (>2 Gy, requiring early intensive medical care). In the frame of the current RENEB inter-laboratory comparison, samples were sent to 86 specialized teams in 46 organizations from 27 nations for dose estimation and identification of three clinically relevant groups. The time for sending early crude reports and more precise reports was documented for each laboratory and assay where possible. The quality of dose estimates was analyzed with three different levels of granularity, 1. by calculating the frequency of correctly reported clinically relevant dose categories, 2. by determining the number of dose estimates within the uncertainty intervals recommended for triage dosimetry (±0.5 Gy or ±1.0 Gy for doses <2.5 Gy or >2.5 Gy), and 3. by calculating the absolute difference (AD) of estimated doses relative to the reference doses. In total, 554 dose estimates were submitted within the 6-week period given before the exercise was closed. For samples processed with the highest priority, earliest dose estimates/categories were reported within 5-10 h of receipt for GE, gH2AX, LUM, EPR, 2-3 days for DCA, CBMN and within 6-7 days for the FISH assay. For the unirradiated control sample, the categorization in the correct clinically relevant group (0-1 Gy) as well as the allocation to the triage uncertainty interval was, with the exception of a few outliers, successfully performed for all assays. For the 3.5 Gy sample the percentage of correct classifications to the clinically relevant group (≥2 Gy) was between 89-100% for all assays, with the exception of gH2AX. For the 1.2 Gy sample, an exact allocation to the clinically relevant group was more difficult and 0-50% or 0-48% of the estimates were wrongly classified into the lowest or highest dose categories, respectively. For the irradiated samples, the correct allocation to the triage uncertainty intervals varied considerably between assays for the 1.2 Gy (29-76%) and 3.5 Gy (17-100%) samples. While a systematic shift towards higher doses was observed for the cytogenetic-based assays, extreme outliers exceeding the reference doses 2-6 fold were observed for EPR, FISH and GE assays. These outliers were related to a particular material examined (tooth enamel for EPR assay, reported as kerma in enamel, but when converted into the proper quantity, i.e. to kerma in air, expected dose estimates could be recalculated in most cases), the level of experience of the teams (FISH) and methodological uncertainties (GE). This was the first RENEB ILC where everything, from blood sampling to irradiation and shipment of the samples, was organized and realized at the same institution, for several biological and physical retrospective dosimetry assays. Almost all assays appeared comparably applicable for the identification of unexposed and highly exposed individuals and the allocation of medical relevant groups, with the latter requiring medical support for the acute radiation scenario simulated in this exercise. However, extreme outliers or a systematic shift of dose estimates have been observed for some assays. Possible reasons will be discussed in the assay specific papers of this special issue. In summary, this ILC clearly demonstrates the need to conduct regular exercises to identify research needs, but also to identify technical problems and to optimize the design of future ILCs.

Candidate Gene Expression in Regional Population and Its Relevance for Radiation Triage

Quantification of gene expression signatures has been substantiated as a potential and rapid marker for radiation triage and biodosimetry during nuclear emergencies. Similar to the established biodosimetry assays, the gene expression assay has drawbacks such as being highly dynamic and transient, not specific to ionizing radiation, and also influenced by confounding factors such as gender, health status, lifestyle, and inflammation. In view of that, prior knowledge of baseline expression of certain candidate genes in a population could complement the discrimination of the unexposed from the exposed individuals without the need for individual pre-exposure controls. We intended to establish a baseline expression of reported radiation-responsive genes such as CDKN1A, DDB2, FDXR, and PCNA in the blood samples of healthy human participants and then compare it with diabetic/hypertension participants (as a chronic inflammatory condition) drawn from south Indian population. Further, we have examined the appropriateness of the assay for radiation triage-like situations; i.e., the expression profiles of those genes were examined in the participants who underwent X-ray-based medical imaging. Acute inflammation induced by lipopolysaccharide exposure in the blood significantly increased the fold expression of those genes (p < 0.0001) compared to the control. Whereas the basal expression level of those genes among the participants with the inflammatory condition is marginally higher than those observed in the healthy participants; despite the excess, the fold increase in those genes between the groups did not differ significantly. Consistent with the inflammatory participants, the basal expression level of those genes in the blood sample of participants who received X-radiation during neuro-interventional and computed tomography imaging is marginally higher than those observed in the pre-exposure of respective groups. Nevertheless, the fold increase in those genes did not differ significantly as the fold change fell within the two folds. Thus, overall results suggest that the utility of CDKN1A, DDB2, FDXR, and PCNA gene expression for radiation triage specific after very low-dose radiation exposure needs to be interpreted with caution for a much more reliable triage.

Transcriptome profile changes in the jejunum of nonhuman primates exposed to supralethal dose of total- or partial-body radiation

The risk of exposure of the general public or military personnel to high levels of ionizing radiation from nuclear weapons or radiological accidents is a dire national security matter. The development of advanced molecular biodosimetry methods, those that measure biological response, such as transcriptomics, to screen large populations of radiation-exposed victims is key to improving survival outcomes during radiological mass casualty scenarios. In this study, nonhuman primates were exposed to either 12.0 Gy cobalt-60 gamma (total-body irradiation, TBI) or X-ray (partial-body irradiation, PBI) 24 h after administration of a potential radiation medical countermeasure, gamma-tocotrienol (GT3). Changes in the jejunal transcriptomic profiles in GT3-treated and irradiated animals were compared to healthy controls to assess the extent of radiation damage. No major effect of GT3 on radiation-induced transcriptome at this radiation dose was identified. About 80% of the pathways with a known activation or repression state were commonly observed between both exposures. Several common pathways activated due to irradiation include FAK signaling, CREB signaling in the neurons, phagosome formation, and G-protein coupled signaling pathway. Sex-specific differences associated with excessive mortality among irradiated females were identified in this study, including Estrogen receptor signaling. Differential pathway activation was also identified across PBI and TBI, pointing towards altered molecular response for different degrees of bone marrow sparing and radiation doses. This study provides insight into radiation-induced changes in jejunal transcriptional profiles, supporting the investigation for the identification of biomarkers for radiation injury and countermeasure efficacy.

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction

Background

Molecular radiation biomarkers are an emerging tool in radiation research with applications for cancer radiotherapy, radiation risk assessment, and even human space travel. However, biomarker screening in genome-wide expression datasets using conventional tools is time-consuming and underlies analyst (human) bias. Machine Learning (ML) methods can improve the sensitivity and specificity of biomarker identification, increase analytical speed, and avoid multicollinearity and human bias.

Aim

To develop a resource-efficient ML framework for radiation biomarker discovery using gene expression data from irradiated normal tissues. Further, to identify biomarker panels predicting radiation dose with tissue specificity.

Methods

A strategic search in the Gene Expression Omnibus database identified a transcriptomic dataset (GSE44762) for normal tissues radiation responses (murine kidney cortex and medulla) suited for biomarker discovery using an ML approach. The dataset was pre-processed in R and separated into train and test data subsets. High computational cost of Genetic Algorithm/k-Nearest Neighbor (GA/KNN) mandated optimization and 13 ML models were tested using the caret package in R. Biomarker performance was evaluated and visualized via Principal Component Analysis (PCA) and dose regression. The novelty of ML-identified biomarker panels was evaluated by literature search.

Results

Caret-based feature selection and ML methods vastly improved processing time over the GA approach. The KNN method yielded overall best performance values on train and test data and was implemented into the framework. The top-ranking genes were Cdkn1a, Gria3, Mdm2 and Plk2 in cortex, and Brf2, Ccng1, Cdkn1a, Ddit4l, and Gria3 in medulla. These candidates successfully categorized dose groups and tissues in PCA. Regression analysis showed that correlation between predicted and true dose was high with R² of 0.97 and 0.99 for cortex and medulla, respectively.

Conclusion

The caret framework is a powerful tool for radiation biomarker discovery optimizing performance with resource-efficiency for broad implementation in the field. The KNN-based approach identified Brf2, Ddit4l, and Gria3 mRNA as novel candidates that have been uncharacterized as radiation biomarkers to date. The biomarker panel showed good performance in dose and tissue separation and dose regression. Further training with larger cohorts is warranted to improve accuracy, especially for lower doses.

Gene Expression Changes in a Prefinal Health Stage of Lethally Irradiated Male and Female Rhesus Macaques

Radiation-induced gene expression (GE) changes can be used for early and high-throughput biodosimetry within the first three days postirradiation. However, is the method applicable in situations such as the Alexander Litvinenko case or the Goiania accident, where diagnosis occurred in a prefinal health stage? We aimed to characterize gene expression changes in a prefinal health stage of lethally irradiated male and female rhesus macaques. Peripheral blood was drawn pre-exposure and at the prefinal stage of male and female animals, which did not survive whole-body exposure with 700 cGy (LD66/60). RNA samples originated from a blinded randomized Good Laboratory Practice study comprising altogether 142 irradiated rhesus macaques of whom 60 animals and blood samples (15 samples for both time points and sexes) were used for this analysis. We evaluated GE on 34 genes widely used in biodosimetry and prediction of the hematological acute radiation syndrome severity (H-ARS) employing quantitative real-time polymerase chain reaction (qRT-PCR). These genes were run in duplicate and triplicate and altogether 96 measurements per time point and sex could be performed. In addition, 18S ribosomal RNA (rRNA) was measured to depict the ribosome/transcriptome status as well as for normalization purposes and 16S rRNA was evaluated as a surrogate for bacteremia. Mean differential gene expression (DGE) was calculated for each gene and sex including all replicate measurements and using pre-exposure samples as the reference. From 34 genes, altogether 27 genes appeared expressed. Pre-exposure samples revealed no signs of bacteremia and 18S rRNA GE was in the normal range in all 30 samples. Regarding prefinal samples, 46.7% and 40% of animals appeared infected in females and males, respectively, and for almost all males this was associated with out of normal range 18S rRNA values. The total number of detectable GE measurements was sixfold (females) and 15-fold (males) reduced in prefinal relative to pre-exposure samples and about tenfold lower in 80% of prefinal compared to pre-exposure samples (P < 0.0001). An overall 11-fold (median) downregulation in prefinal compared to pre-exposure samples was identified for most of the 27 genes and even FDXR appeared 4-14-fold downregulated in contrast to a pronounced up-regulation according to cited work. This pattern of overall downregulation of almost all genes and the rapid reduction of detectable genes at a prefinal stage was found in uninfected animals with normal range 18S rRNA as well. In conclusion, in a prefinal stage after lethal radiation exposure, the ribosome/transcriptome status remains present (based on normal range 18S rRNA values) in 60-67% of animals, but the whole transcriptome activity in general appears silenced and cannot be used for biodosimetry purposes, but probably as an indicator for an emerging prefinal health stage.

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

The focus of radiation biodosimetry has changed recently, and a paradigm shift for using molecular technologies of omic platforms in addition to cytogenetic techniques has been observed. In our study, we have used a nonhuman primate model to investigate the impact of a supralethal dose of 12 Gy radiation on alterations in the lung transcriptome. We used 6 healthy and 32 irradiated animal samples to delineate radiation-induced changes. We also used a medical countermeasure, γ-tocotrienol (GT3), to observe any changes. We demonstrate significant radiation-induced changes in the lung transcriptome for total-body irradiation (TBI) and partial-body irradiation (PBI). However, no major influence of GT3 on radiation was noted in either comparison. Several common signaling pathways, including PI3K/AKT, GADD45, and p53, were upregulated in both exposures. TBI activated DNA-damage-related pathways in the lungs, whereas PTEN signaling was activated after PBI. Our study highlights the various transcriptional profiles associated with γ- and X-ray exposures, and the associated pathways include LXR/RXR activation in TBI, whereas pulmonary wound-healing and pulmonary fibrosis signaling was repressed in PBI. Our study provides important insights into the molecular pathways associated with irradiation that can be further investigated for biomarker discovery.

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

In the search for biological markers after a large-scale exposure of the human population to radiation, gene expression is a sensitive endpoint easily translatable to in-field high throughput applications. Primarily, the ex-vivo irradiated healthy human blood model has been used to generate available gene expression datasets. This model has limitations i.e., lack of signaling from other irradiated tissues and deterioration of blood cells cultures over time. In vivo models are needed; therefore, we present our novel approach to define a gene signature in mouse blood cells that quantitatively correlates with radiation dose (at 1 Gy/min). Starting with available microarray datasets, we selected 30 radiation-responsive genes and performed cross-validation/training–testing data splits to downselect 16 radiation-responsive genes. We then tested these genes in an independent cohort of irradiated adult C57BL/6 mice (50:50 both sexes) and measured mRNA by quantitative RT-PCR in whole blood at 24 h. Dose reconstruction using net signal (difference between geometric means of top 3 positively correlated and top 4 negatively correlated genes with dose), was highly improved over the microarrays, with a root mean square error of ± 1.1 Gy in male and female mice combined. There were no significant sex-specific differences in mRNA or cell counts after irradiation.

Application of radiation omics in the development of adverse outcome pathway networks: an example of radiation-induced cardiovascular disease

BACKGROUND

Epidemiological studies have indicated that exposure of the heart to doses of ionizing radiation as low as 0.5 Gy increases the risk of cardiac morbidity and mortality with a latency period of decades. The damaging effects of radiation to myocardial and endothelial structures and functions have been confirmed radiobiologically at high dose, but much less is known at low dose. Integration of radiation biology and epidemiology data is a recommended approach to improve the radiation risk assessment process. The adverse outcome pathway (AOP) framework offers a comprehensive tool to compile and translate mechanistic information into pathological endpoints which may be relevant for risk assessment at the different levels of a biological system. Omics technologies enable the generation of large volumes of biological data at various levels of complexity, from molecular pathways to functional organisms. Given the quality and quantity of available data across levels of biology, omics data can be attractive sources of information for use within the AOP framework. It is anticipated that radiation omics studies could improve our understanding of the molecular mechanisms behind the adverse effects of radiation on the cardiovascular system. In this review, we explored the available omics studies on radiation-induced cardiovascular disease (CVD) and their applicability to the proposed AOP for CVD.

RESULTS

The results of 80 omics studies published on radiation-induced CVD over the past 20 years have been discussed in the context of the AOP of CVD proposed by Chauhan et al. Most of the available omics data on radiation-induced CVD are from proteomics, transcriptomics, and metabolomics, whereas few datasets were available from epigenomics and multi-omics. The omics data presented here show great promise in providing information for several key events of the proposed AOP of CVD, particularly oxidative stress, alterations of energy metabolism, extracellular matrix and vascular remodeling.

CONCLUSIONS

The omics data presented here shows promise to inform the various levels of the proposed AOP of CVD. However, the data highlight the urgent need of designing omics studies to address the knowledge gap concerning different radiation scenarios, time after exposure and experimental models. This review presents the evidence to build a qualitative omics-informed AOP and provides views on the potential benefits and challenges in using omics data to assess risk-related outcomes.

Serum RNA biomarkers for predicting survival in non-human primates following thoracic radiation

In a mass radiation exposure, the healthcare system may rely on differential expression of miRNA to determine exposure and effectively allocate resources. To this end, miRNome analysis was performed on non-human primate serum after whole thorax photon beam irradiation of 9.8 or 10.7 Gy with dose rate 600 cGy/min. Serum was collected up to 270 days after irradiation and sequenced to determine immediate and delayed effects on miRNA expression. Elastic net based GLM methods were used to develop models that predicted the dose vs. controls at 81% accuracy at Day 15. A three-group model at Day 9 achieved 71% accuracy in determining if an animal would die in less than 90 days, between 90 and 269 days, or survive the length of the study. At Day 21, we achieved 100% accuracy in determining whether an animal would later develop pleural effusion. These results demonstrate the potential ability of miRNAs to determine thorax partial-body irradiation dose and forecast survival or complications early following whole thorax irradiation in large animal models. Future experiments incorporating additional doses and independent animal cohorts are warranted to validate these results. Development of a serum miRNA assay will facilitate the administration of medical countermeasures to increase survival and limit normal tissue damage following a mass exposure.

Effect of the p38 Mitogen-Activated Protein Kinase Signaling Cascade on Radiation Biodosimetry

Radiation biodosimetry based on transcriptomic analysis of peripheral blood is a valuable tool to detect radiation exposure after a radiological/nuclear event and obtain useful biological information that could predict tissue and organismal injury. However, confounding factors, including chronic inflammation or immune suppression, can potentially obscure the predictive power of the method. Members of the p38 mitogen-activated protein kinase (MAPK) family respond to pro-inflammatory signals and environmental stresses, whereas genetic ablation of the p38 signaling pathway in mice leads to reduced susceptibility to collagen-induced arthritis and experimental autoimmune encephalomyelitis that model human rheumatoid arthritis and multiple sclerosis, respectively. p38 is normally regulated by the MAP3K-MAP2K pathway in mammalian cells. However, in T cells there is an alternative pathway for p38 activation that plays an important role in antigen-receptor-activated T cells and participates in immune and inflammatory responses. To examine the role of p38 in response to radiation, we used two mouse models expressing either a p38α dominant negative (DN) mutation that globally suppresses p38 signaling or a p38αβ double-knock-in (DKI) mutant, which inhibits specifically T-cell receptor activation. We exposed p38 wild-type (p38WT) and mutant male mice to 7 Gy X rays and 24 h later whole blood was isolated subjected to whole-genome microarray and gene ontology analysis. Irradiation of p38WT mice led to a significant overrepresentation of pathways associated with morbidity and mortality, as well as organismal cell death. In contrast, these pathways were significantly underrepresented in p38DN and p38DKI mutant mice, suggesting that p38 attenuation may protect blood cells from the deleterious effects of radiation. Furthermore, radiation exposure in p38 mutant mice resulted in an enrichment of phagocytosis-related pathways, suggesting a role for p38 signaling in restricting phagocytosis of apoptotic cells after irradiation. Finally, despite the significant changes in gene expression, it was still feasible to identify a panel of genes that could accurately distinguish between irradiated and control mice, irrespective of p38 status.

Early molecular markers for retrospective biodosimetry and prediction of acute health effects

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.

The Potential of Omics in Biological Dosimetry

Biological dosimetry is an internationally recognized method for quantifying and estimating radiation dose following suspected or verified excessive exposure to ionising radiation. In severe radiation accidents where a large number of people are potentially affected, it is possible to distinguish irradiated from non-irradiated people in order to initiate appropriate medical care if necessary. In addition to severe incidents caused by technical failure, environmental disasters, military actions, or criminal abuse, there are also radiation accidents in which only one or a few individuals are affected in the frame of occupational or medical exposure. The requirements for biological dosimetry are fundamentally different for these two scenarios. In particular, for large-scale radiation accidents, pre-screening methods are necessary to increase the throughput of samples for a rough first-dose categorization. The rapid development and increasing use of omics methods in research as well as in individual applications provides new opportunities for biological dosimetry. In addition to the discovery and search for new biomarkers, dosimetry assays based on omics technologies are becoming increasingly interesting and hold great potential, especially for large-scale dosimetry. In the following review, the different areas of biological dosimetry, the problems in finding suitable biomarkers, the current status of biomarker research based on omics, the potential applications of assays using omics technologies, and also the limitations for the different areas of biological dosimetry are discussed.

The transcriptomic revolution and radiation biology

PURPOSE

This article will briefly review the origins and evolution of functional genomics, first describing the experimental technology, and then some of the approaches applied to data analysis and visualization. It will emphasize application of functional genomics to radiation biology, using examples from the author's work to illustrate several key types of analysis. It concludes with a look at non-coding RNA, alternative reading of the genome, and single-cell transcriptomics, some of the innovative areas that may help to shape future research in radiation biology and oncology.

CONCLUSIONS

Transcriptomic approaches have provided insight into many areas of radiation biology and medicine, and innovations in technology and data analysis approaches promise continued contributions to radiation science in the future.

Development of Biomarkers for Radiation Biodosimetry and Medical Countermeasures Research: Current Status, Utility, and Regulatory Pathways

Biomarkers are important indicators of biological processes in health or disease. For this reason, they play a critical role in advanced development of radiation biodosimetry tools and medical countermeasures (MCMs). They can aid in the assessment of radiation exposure level, extent of radiation-induced injury, and/or efficacy of an MCM. This meeting report summarizes the presentations and discussions from the 2020 workshop titled, "Biomarkers in Radiation Biodosimetry and Medical Countermeasures," sponsored by the Radiation and Nuclear Countermeasures Program (RNCP) at the National Institute of Allergy and Infectious Diseases (NIAID). The main goals of this meeting were to: 1. Provide an overview on biomarkers and to focus on the state of science with regards to biomarkers specific to radiation biodosimetry and MCMs; 2. Understand developmental challenges unique to the role of biomarkers in the fields of radiation biodosimetry and MCM development; and 3. Identify existing gaps and needs for translational application.

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

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.