A translatable predictor of human radiation exposure

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.

The DNA damage response to radiological imaging: from ROS and γH2AX foci induction to gene expression responses in vivo

Candidate ionising radiation exposure biomarkers must be validated in humans exposed in vivo. Blood from patients undergoing positron emission tomography-computed tomography scan (PET-CT) and skeletal scintigraphy (scintigraphy) was drawn before (0 h) and after (2 h) the procedure for correlation analyses of the response of selected biomarkers with radiation dose and other available patient information. FDXR, CDKN1A, BBC3, GADD45A, XPC, and MDM2 expression was determined by qRT-PCR, DNA damage (γH2AX) by flow cytometry, and reactive oxygen species (ROS) levels by flow cytometry using the 2', 7'-dichlorofluorescein diacetate test in peripheral blood mononuclear cells (PBMC). For ROS experiments, 0- and 2-h samples were additionally exposed to UVA to determine whether diagnostic irradiation conditioned the response to further oxidative insult. With some exceptions, radiological imaging induced weak γH2AX foci, ROS and gene expression fold changes, the latter with good coherence across genes within a patient. Diagnostic imaging did not influence oxidative stress in PBMC successively exposed to UVA. Correlation analyses with patient characteristics led to low correlation coefficient values. γH2AX fold change, which correlated positively with gene expression, presented a weak positive correlation with injected activity, indicating a radiation-induced subtle increase in DNA damage and subsequent activation of the DNA damage response pathway. The exposure discrimination potential of these biomarkers in the absence of control samples as frequently demanded in radiological emergencies, was assessed using raw data. These results suggest that the variability of the response in heterogeneous populations might complicate identifying individuals exposed to low radiation doses.

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.

Radiation-Induced Gene Expression Changes Used for Biodosimetry and Clinical Outcome Prediction: Challenges and Promises

As the war in Ukraine progresses, the radiological and nuclear threat has never been as real as now. The formation of life-threatening acute radiation syndrome (ARS), in particular after the deployment of a nuclear weapon or an attack on a nuclear power station, must be considered realistic. ARS is caused by massive cell death, leading to functional organ deficits and, via systemic inflammatory responses, finally aggravates into multiple organ failure. As a deterministic effect, the severity of the disease dictates the clinical outcome. Hence, predicting ARS severity via biodosimetry or alternative approaches appears straightforward. Because the disease occurs delayed, therapy starting as early as possible has the most significant benefit. A clinically relevant diagnosis should be carried out within the diagnostic time window of about 3 days after exposure. Biodosimetry assays providing retrospective dose estimations within this time frame will support medical management decision-making. However, how closely can dose estimates be associated with the later developing ARS severity degrees when considering dose as one among other determinants of radiation exposure and cell death? From a clinical/triage point of view, ARS severity degrees can be further aggregated into unexposed, weakly diseased (no acute health effects expected), and strongly diseased patient groups, with the latter requiring hospitalization as well as an early and intensive treatment. Radiation-induced gene expression (GE) changes occur early after exposure and can be quickly quantified. GE can be used for biodosimetry purposes. Can GE be used to predict later developing ARS severity degrees and allocate individuals to the three clinically relevant groups as well?

Considerations for application of benchmark dose modeling in radiation research: workshop highlights

BACKGROUND

Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples.

CONCLUSIONS

Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

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.

Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research

PURPOSE

Benchmark dose (BMD) modeling is a method commonly used in chemical toxicology to identify the point of departure (POD) from a dose-response curve linked to a health-related outcome. Recently, it is being explored on transcriptional data and in adverse outcome pathways (AOPs). As AOPs are informed by diverse data types, it is important to understand the impact of study parameters such as dose selection, number of replicates and dose range on BMD outputs for radiation induced genes and pathways.

MATERIALS AND METHODS

Data were selected from the Gene Expression Omnibus (GSE52403) that featured gene expression profiles of peripheral blood samples from C57BL/6 mice 6 hours post-exposure to ¹³⁷Cs gamma-radiation at 0, 1, 2, 3, 4.5, 6, 8 and 10.5 Gy. The dataset comprised a broad dose-range over multiple dose-points with consistent dose spacing and multiple biological replicates. This dataset was ideal for systematically transforming across three categories: (1) dose-range, (2) dose-spacing and (3) number of controls/replicates. Across these categories, 29 transformed datasets were compared to the original dataset to determine the impact of each transformation on the BMD outputs.

RESULTS

Most of the experimental changes did not impact the BMD outputs. The transformed datasets were largely consistent with the original dataset in terms of number of reproduced genes modeled and absolute BMD values for genes and pathways. Variations in dose selection identified the importance of the absolute value of the lowest and second dose. It was determined that dose selection should include at least two doses <1 Gy and two >5 Gy to achieve meaningful BMD outputs. Changes to the number of biological replicates in the control and non-zero dose groups impacted the overall accuracy and precision of the BMD outputs as well as the ability to fit dose-response models consistent with the original dataset.

CONCLUSION

Successful application of transcriptomic BMD modeling for radiation datasets requires considerations of the exposure dose and the number of biological replicates. Most important is the selection of the lowest doses and dose spacing. Reflections on these parameters in experimental design will provide meaningful BMD outputs that could correlate well to apical endpoints of relevance to radiation exposure assessment.

Transcriptional Dynamics of DNA Damage Responsive Genes in Circulating Leukocytes during Radiotherapy

Simple Summary

In this study, the transcriptional response of a panel of radiation responsive genes was monitored over time in blood samples after radiation exposure in vivo. For this aim, cancer patients treated by radiotherapy were recruited after consent forms were obtained. Following the first fraction of radiotherapy, 2 mL blood samples were collected at different time points during the first 24h hours (before the second fraction was delivered) and at mid and end of treatment. Amongst the 9 genes studied, the gene FDXR stood out as the most sensitive and responsive to the low dose of radiation received from the localised radiation treatment by the circulating white blood cells. The activation of FDXR was found to depend on the volume of the body exposed with a peak of expression around 8–9 hours after irradiation was delivered. Finally results obtained ex vivo confirmed the results obtained in vivo.

Abstract

External beam radiation therapy leads to cellular activation of the DNA damage response (DDR). DNA double-strand breaks (DSBs) activate the ATM/CHEK2/p53 pathway, inducing the transcription of stress genes. The dynamic nature of this transcriptional response has not been directly observed in vivo in humans. In this study we monitored the messenger RNA transcript abundances of nine DNA damage-responsive genes (CDKN1A, GADD45, CCNG1, FDXR, DDB2, MDM2, PHPT1, SESN1, and PUMA), eight of them regulated by p53 in circulating blood leukocytes at different time points (2, 6–8, 16–18, and 24 h) in cancer patients (lung, neck, brain, and pelvis) undergoing radiotherapy. We discovered that, although the calculated mean physical dose to the blood was very low (0.038–0.169 Gy), an upregulation of Ferredoxin reductase (FDXR) gene transcription was detectable 2 h after exposure and was dose dependent from the lowest irradiated percentage of the body (3.5% whole brain) to the highest, (up to 19.4%, pelvic zone) reaching a peak at 6–8 h. The radiation response of the other genes was not strong enough after such low doses to provide meaningful information. Following multiple fractions, the expression level increased further and was still significantly up-regulated by the end of the treatment. Moreover, we compared FDXR transcriptional responses to ionizing radiation (IR) in vivo with healthy donors’ blood cells exposed ex vivo and found a good correlation in the kinetics of expression from the 8-hours time-point onward, suggesting that a molecular transcriptional regulation mechanism yet to be identified is involved. To conclude, we provided the first in vivo human report of IR-induced gene transcription temporal response of a panel of p53-dependant genes. FDXR was demonstrated to be the most responsive gene, able to reliably inform on the low doses following partial body irradiation of the patients, and providing an expression pattern corresponding to the % of body exposed. An extended study would provide individual biological dosimetry information and may reveal inter-individual variability to predict radiotherapy-associated adverse health outcomes.

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.

Predicting Space Radiation Single Ion Exposure in Rodents: A Machine Learning Approach

This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Radiation (GCR) ion exposure for ⁴He, ¹⁶O, ²⁸Si, ⁴⁸Ti, or ⁵⁶Fe up to 150 mGy, based on Attentional Set-shifting (ATSET) experimental tests. The ATSET assay consists of a series of cognitive performance tasks on irradiated male Wistar rats. The GCR ion doses represent the expected cumulative radiation astronauts may receive during a Mars mission on an individual ion basis. The primary objective is to synthesize and assess predictive models on a per-subject level through Machine Learning (ML) classifiers. The raw cognitive performance data from individual rodent subjects are used as features to train the models and to explore the capabilities of three different ML techniques for elucidating a range of correlations between received radiation on rodents and their performance outcomes. The analysis employs scores of selected input features and different normalization approaches which yield varying degrees of model performance. The current study shows that support vector machine, Gaussian naive Bayes, and random forest models are capable of predicting individual ion exposure using ATSET scores where corresponding Matthews correlation coefficients and F₁ scores reflect model performance exceeding random chance. The study suggests a decremental effect on cognitive performance in rodents due to ≤150 mGy of single ion exposure, inasmuch as the models can discriminate between 0 mGy and any exposure level in the performance score feature space. A number of observations about the utility and limitations in specific normalization routines and evaluation scores are examined as well as best practices for ML with imbalanced datasets observed.

Whole blood gene expression within days after total-body irradiation predicts long term survival in Gottingen minipigs

Gottingen minipigs mirror the physiological radiation response observed in humans and hence make an ideal candidate model for studying radiation biodosimetry for both limited-sized and mass casualty incidents. We examined the whole blood gene expression profiles starting one day after total-body irradiation with increasing doses of gamma-rays. The minipigs were monitored for up to 45 days or time to euthanasia necessitated by radiation effects. We successfully identified dose- and time-agnostic (over a 1-7 day period after radiation), survival-predictive gene expression signatures derived using machine-learning algorithms with high sensitivity and specificity. These survival-predictive signatures fare better than an optimally performing dose-differentiating signature or blood cellular profiles. These findings suggest that prediction of survival is a much more useful parameter for making triage, resource-utilization and treatment decisions in a resource-constrained environment compared to predictions of total dose received. It should hopefully be possible to build such classifiers for humans in the future.

Two-miRNA-based finger-stick assay for estimation of absorbed ionizing radiation dose

Nuclear radiation and radioactive fallouts resulting from a nuclear weapon detonation or reactor accidents could result in injuries affecting multiple sensitive organs, defined as acute radiation syndrome (ARS). Rapid and early estimation of injuries to sensitive organs using markers of radiation response is critical for identifying individuals who could potentially exhibit ARS; however, there are currently no biodosimetry assays approved for human use. We developed a sensitive microRNA (miRNA)-based blood test for radiation dose reconstruction with ±0.5 Gy resolution at critical dose range. Radiation dose-dependent changes in miR-150-5p in blood were internally normalized by a miRNA, miR-23a-3p, that was nonresponsive to radiation. miR-23a-3p was not highly expressed in blood cells but was abundant in circulation and was released primarily from the lung. Our assay showed the capability for dose estimation within hours to 1 week after exposure using a drop of blood from mice. We tested this biodosimetry assay for estimation of absorbed ionizing radiation dose in mice of varying ages and after exposure to both improvised nuclear device (IND)-spectrum neutrons and gamma rays. Leukemia specimens from patients exposed to fractionated radiation showed depletion of miR-150-5p in blood. We bridged the exposure of these patients to fractionated radiation by comparing responses after fractionated versus single acute exposure in mice. Although validation in nonhuman primates is needed, this proof-of-concept study suggests the potential utility of this assay in radiation disaster management and clinical applications.

Transcriptomics for radiation biodosimetry: progress and challenges

PURPOSE

Transcriptomic-based approaches are being developed to meet the needs for large-scale radiation dose and injury assessment and provide population triage following a radiological or nuclear event. This review provides background and definition of the need for new biodosimetry approaches, and summarizes the major advances in this field. It discusses some of the major model systems used in gene signature development, and highlights some of the remaining challenges, including individual variation in gene expression, potential confounding factors, and accounting for the complexity of realistic exposure scenarios.

CONCLUSIONS

Transcriptomic approaches show great promise for both dose reconstruction and for prediction of individual radiological injury. However, further work will be needed to ensure that gene expression signatures will be robust and appropriate for their intended use in radiological or nuclear emergencies.

Expression of genes and pathways associated with the B7-CD28 superfamily in response to irradiation of blood cells using 137Cs

PURPOSE

DNA damage is one of the main consequences of exposure to ionizing irradiation (IR). Recent studies indicate that IR can modulate the expression of immune system-related genes. However, the effects of IR on the expression of genes and pathways of the B7-CD28 superfamily remain poorly defined. The aim of this study was to evaluate the modulation of genes and pathways related to the B7-CD28 superfamily in response to IR.

MATERIALS AND METHODS

In this study, we used transcriptome data available from the Gene Expression Omnibus (GEO) database to investigate the modulation of the response of genes and pathways of samples of human peripheral blood irradiated with doses of 150, 300, and 600 cGy. The data were obtained at 6 and 24 h after irradiation. The relationship between genes and pathways was established through the Reactome database. The behavior of these pathways was analyzed using mathematical methods based on relative activity and diversity. Analysis of variance (ANOVA) followed by multiple comparisons tests (Bonferroni and Tamhanes) was used to identify differentially expressed genes. Data on transcriptomes were analyzed through ViaComplex V.1.0 and IBM SPSS Statistics 22.

RESULTS

For the pathways investigated in this study, we observed that the effects produced by these doses significantly modified the behavior of five pathways associated with the immune system. Also, the dose of 300 cGy might trigger signaling for the activation of T cells through the negative regulation (p < .05) of the co-inhibitory PDCD1LG2 gene. Positive regulation caused by 300 cGy (p < .05) of the CD80 receptor was observed by us, which might be related to a stimulatory signal. According to our findings, this dose induced the production of cytokines and genes that are associated with the activation and differentiation of T cells.

CONCLUSIONS

Our findings indicate that the irradiation modulated the organization of the biological system, suggesting that 300 cGy is more efficient in activating the immune system.

Gene Expression Profiles from Heart, Lung and Liver Samples of Total-Body-Irradiated Minipigs: Implications for Predicting Radiation-Induced Tissue Toxicity

In the event of a major accidental or intentional radiation exposure incident, the affected population could suffer from total- or partial-body exposures to ionizing radiation with acute exposure to organs that would produce life-threatening injury. Therefore, it is necessary to identify markers capable of predicting organ-specific damage so that appropriate directed or encompassing therapies can be applied. In the current work, gene expression changes in response to total-body irradiation (TBI) were identified in heart, lungs and liver tissue of Göttingen minipigs. Animals received 1.7, 1.9, 2.1 or 2.3 Gy TBI and were followed for 45 days. Organ samples were collected at the end of day 45 or sooner if the animal displayed morbidity necessitating euthanasia. Our findings indicate that different organs respond to TBI in a very specific and distinct manner. We also found that the liver was the most affected organ in terms of gene expression changes, and that lipid metabolic pathways were the most deregulated in the liver samples of non-survivors (survival time <45 days). We identified organ-specific gene expression signatures that accurately differentiated non-survivors from survivors and control animals, irrespective of dose and time postirradiation. At what point did these radiation-induced injury markers manifest and how this information could be used for applying intervention therapies are under investigation.

High-throughput meta-analysis and validation of differentially expressed genes as potential biomarkers of ionizing radiation-response

BACKGROUND AND PURPOSE

The high-throughput analysis of gene expression in ionizing radiation (IR)-exposed human peripheral white blood cells (WBC) has emerged as a novel method for biodosimetry markers detection. We aimed to detect IR-exposure differential expressed genes (DEGs) as potential predictive biomarkers for biodosimetry and radioinduced-response.

MATERIALS AND METHODS

We performed a meta-analysis of raw data from public microarrays of ex vivo low linear energy transfer-irradiated human peripheral WBC. Functional enrichment and transcription factors (TF) detection from resulting DEGs were assessed. Six selected DEGs among studies were validated by qRT-PCR on mRNA from human peripheral blood samples from nine healthy human donors 24 h after ex vivo X-rays-irradiation.

RESULTS

We identified 275 DEGs after IR-exposure (parameters: |lfc| ≥ 0.7, q value <0.05), enriched in processes such as regulation after IR-exposure, DNA damage checkpoint, signal transduction by p53 and mitotic cell cycle checkpoint. Among these DEGs, DRAM1, NUDT15, PCNA, PLK2 and TIGAR were selected for qRT-PCR validation. Their expression levels significantly increased at 1-4 Gy respect to non-irradiated controls. Particularly, PCNA increased dose dependently. Curiously, TCF4 (Entrez Gene: 6925), detected as overrepresented TF in the radioinduced DEGs set, significantly decreased post-irradiation.

CONCLUSION

These six DEGs show potential to be proposed as candidates for IR-exposure biomarkers, considering their observed molecular radioinduced-response. Among them, TCF4, bioinformatically detected, was validated herein as an IR-responsive gene.

CT Irradiation-induced Changes of Gene Expression within Peripheral Blood Cells

Computed tomography (CT) is a crucial element of medical imaging diagnostics. The widespread application of this technology has made CT one of the major contributors to medical radiation burden, despite the fact that doses per individual CT scan steadily decrease due to the advancement of technology. Epidemiological risk assessment of CT exposure is hampered by the fact that moderate adverse effects triggered by low doses of CT exposure are likely masked by statistical fluctuations. In light of these limitations, there is need of further insights into the biological processes induced by CT scans to complement the existing knowledge base of risk assessment. This prompted us to investigate the early transcriptomic response of ex vivo irradiated peripheral blood of three healthy individuals. Samples were irradiated employing a modern dual-source-CT-scanner with a tube voltage of 150 kV, resulting in an estimated effective dose of 9.6 mSv. RNA was isolated 1 h and 6 h after exposure, respectively, and subsequently analyzed by RNA deep sequencing. Differential gene expression analysis revealed shared upregulation of AEN, FDXR, and DDB2 6 h after exposure in all three probands. All three genes have previously been discussed as radiation responsive genes and have already been implicated in DNA damage response and cell cycle control after DNA damage. In summary, we substantiated the usefulness of AEN, FDXR, and DDB2 as RNA markers of low dose irradiation. Moreover, the upregulation of genes associated with DNA damage reminds one of the genotoxic nature of CT diagnostics even with the low doses currently applied.

Discordant gene responses to radiation in humans and mice and the role of hematopoietically humanized mice in the search for radiation biomarkers

The mouse (Mus musculus) is an extensively used model of human disease and responses to stresses such as ionizing radiation. As part of our work developing gene expression biomarkers of radiation exposure, dose, and injury, we have found many genes are either up-regulated (e.g. CDKN1A, MDM2, BBC3, and CCNG1) or down-regulated (e.g. TCF4 and MYC) in both species after irradiation at ~4 and 8 Gy. However, we have also found genes that are consistently up-regulated in humans and down-regulated in mice (e.g. DDB2, PCNA, GADD45A, SESN1, RRM2B, KCNN4, IFI30, and PTPRO). Here we test a hematopoietically humanized mouse as a potential in vivo model for biodosimetry studies, measuring the response of these 14 genes one day after irradiation at 2 and 4 Gy, and comparing it with that of human blood irradiated ex vivo, and blood from whole body irradiated mice. We found that human blood cells in the hematopoietically humanized mouse in vivo environment recapitulated the gene expression pattern expected from human cells, not the pattern seen from in vivo irradiated normal mice. The results of this study support the use of hematopoietically humanized mice as an in vivo model for screening of radiation response genes relevant to humans.

New Approaches for Quantitative Reconstruction of Radiation Dose in Human Blood Cells

In the event of a nuclear attack or large-scale radiation event, there would be an urgent need for assessing the dose to which hundreds or thousands of individuals were exposed. Biodosimetry approaches are being developed to address this need, including transcriptomics. Studies have identified many genes with potential for biodosimetry, but, to date most have focused on classification of samples by exposure levels, rather than dose reconstruction. We report here a proof-of-principle study applying new methods to select radiation-responsive genes to generate quantitative, rather than categorical, radiation dose reconstructions based on a blood sample. We used a new normalization method to reduce effects of variability of signal intensity in unirradiated samples across studies; developed a quantitative dose-reconstruction method that is generally under-utilized compared to categorical methods; and combined these to determine a gene set as a reconstructor. Our dose-reconstruction biomarker was trained using two data sets and tested on two independent ones. It was able to reconstruct dose up to 4.5 Gy with root mean squared error (RMSE) of ± 0.35 Gy on a test dataset using the same platform, and up to 6.0 Gy with RMSE of ± 1.74 Gy on a test set using a different platform.

Transcriptomic responses in mouse blood during the first week after in vivo gamma irradiation

Due to limitations of available human models for development of gene expression based radiation biodosimetry, many such studies have made use of mouse models. To provide a broad view of the gene expression response to irradiation in the mouse, we have exposed male C57BL/6 mice to 0, 1.5, 3, 6 or 10 Gy of gamma rays, sacrificing groups of the mice at 1, 2, 3, 5, or 7 days after exposure. We then profiled global gene expression in blood from individual mice using Agilent microarrays. In general, we found increasing numbers of genes differentially expressed with increasing dose, with more prolonged responses after the higher doses. Gene ontology analysis showed a similar pattern, with more biological processes enriched among the genes responding to higher doses, and at later times after exposure. Clustering the timecourse expression data using maSigPro identified four broad patterns of response, representing different gene ontology functions. The largest of these clusters included genes with initially decreased expression followed by increased expression at later times, a pattern of expression previously reported for several genes following neutron exposure. Another gene cluster showing consistent down regulation suggests genes useful for biodosimetry throughout the first week after exposure can be identified.

Is Emergency Medical Services (EMS) in Islamic Republic of Iran Practical and Efficient in Facing Ebola?

Objective

To evaluate the efficacy and preparedness of Emergency Medical Services (EMS) in Islamic Republic of Iran to face Ebola.

Methods

The present study is cross-sectional and somehow descriptive. We used a standard questionnaire that was designed by "center for disease control and prevention". This questionnaire was captioned "Emergency Medical Services (EMS) checklist for Ebola preparedness". We collected a lot of data by studying that questionnaire and every manager in every province of Iran were informed about that info in 2016. This data was analyzed by using SSPP software version 16.

Results

Findings have showed the average score related to preparation level of EMS in facing Ebola in Iran was 63.73±12.77 percent. There was no significant difference between the country regions regarding the preparedness of to detect (p=0.975), protect (p=0.275) and respond (p=0.344) to ebola outbreaks.The highest score had been achieved by region number 5 and the lowest score belonged to region number 7.

Conclusion

Although the acquired average score in this study is higher than standards, considering the increased threat of breaking out biologic threats especially Ebola infection, using and practicing some measures in order to enhance preparation level of Emergency Medical Services counter this infection and similar infectious diseases is inevitable.

Impact of inflammatory signaling on radiation biodosimetry: mouse model of inflammatory bowel disease

Background

Ionizing Radiation (IR) is a known pro-inflammatory agent and in the process of development of biomarkers for radiation biodosimetry, a chronic inflammatory disease condition could act as a confounding factor. Hence, it is important to develop radiation signatures that can distinguish between IR-induced inflammatory responses and pre-existing disease. In this study, we compared the gene expression response of a genetically modified mouse model of inflammatory bowel disease (Il10^−/−) with that of a normal wild-type mouse to potentially develop transcriptomics-based biodosimetry markers that can predict radiation exposure in individuals regardless of pre-existing inflammatory condition.

Results

Wild-type (WT) and Il10^−/− mice were exposed to whole body irradiation of 7 Gy X-rays. Gene expression responses were studied using high throughput whole genome microarrays in peripheral blood 24 h post-irradiation. Analysis resulted in identification of 1962 and 1844 genes differentially expressed (p < 0.001, FDR < 10%) after radiation exposure in Il10^−/− and WT mice respectively. A set of 155 genes was also identified as differentially expressed between WT and Il10^−/− mice at the baseline pre-irradiation level. Gene ontology analysis revealed that the 155 baseline differentially expressed genes were mainly involved in inflammatory response, glutathione metabolism and collagen deposition. Analysis of radiation responsive genes revealed that innate immune response and p53 signaling processes were strongly associated with up-regulated genes, whereas B-cell development process was found to be significant amongst downregulated genes in the two genotypes. However, specific immune response pathways like MHC based antigen presentation, interferon signaling and hepatic fibrosis were associated with radiation responsive genes in Il10^−/− mice but not WT mice. Further analysis using the IPA prediction tool revealed significant differences in the predicted activation status of T-cell mediated signaling as well as regulators of inflammation between WT and Il10^−/− after irradiation.

Conclusions

Using a mouse model we established that an inflammatory disease condition could affect the expression of many radiation responsive genes. Nevertheless, we identified a panel of genes that, regardless of disease condition, could predict radiation exposure. Our results highlight the need for consideration of pre-existing conditions in the population in the process of development of reliable biodosimetry markers.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5689-y) contains supplementary material, which is available to authorized users.

A Meta-Analysis of the Performance of a Blood-Based Exposure Response Gene Signature Across Clinical Studies on the Tobacco Heating System 2.2 (THS 2.2)

As part of emerging tobacco harm reduction strategies, modified risk tobacco products (MRTP) are being developed to offer alternatives that have the potential to reduce the individual risk and population harm compared with smoking cigarettes for adult smokers who want to continue using tobacco and nicotine products. MRTPs are defined as any tobacco products that are distributed for use to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. One such candidate MRTP is the Tobacco Heating System (THS) 2.2, which does not burn tobacco but instead heats it, thus producing significantly reduced levels of harmful and potentially harmful constituents compared with cigarettes. The clinical assessment of candidate MRTPs requires the development of exposure-response markers to distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Toward this end, a whole blood-derived gene signature was previously developed and reported. Four randomized, controlled, open-label, three-arm parallel group reduced exposure clinical studies have been conducted with subjects randomized to three arms: switching from cigarettes to THS 2.2, continuous use of cigarettes, or smoking abstinence. These clinical studies had an investigational period of 5 days in confinement, which was followed by an 85-day ambulatory period in two studies. Here we tested the previously developed blood-derived signature on the samples derived from those clinical studies. We showed that in all four studies, the signature scores were reduced consistently in subjects who either stopped smoking or switched to THS 2.2 compared with subjects who continued smoking cigarettes.

Bridging the gaps: using an NHP model to predict single dose radiation absorption in humans

Purpose: Design and characterization of a radiation biodosimetry device are complicated by the fact that the requisite data are not available in the intended use population, namely humans exposed to a single, whole-body radiation dose. Instead, one must turn to model systems. We discuss our studies utilizing healthy, unexposed humans, human bone marrow transplant patients undergoing total body irradiation (TBI), non-human primates subjected to the same irradiation regimen received by the human TBI patients and NHPs given a single, whole-body dose of ionizing radiation.Materials and Methods: We use Bayesian linear mixed models to characterize the association between NHP and human expression patterns in radiation response genes when exposed to a common exposure regimen and across exposure regimens within the same species.Results: We show that population average differences in expression of our radiation response genes from one to another model system are comparable to typical differences between two randomly sampled members of a given model system and that these differences are smaller, on average, for linear combinations of the probe data and for the model-based combinations employed for dose prediction as part of a radiation biodosimetry device.Conclusions: Our analysis suggests that dose estimates based on our gene list will be accurate when applied to humans who have received a single, whole-body exposure to ionizing radiation.

Biodosimetric transcriptional and proteomic changes are conserved in irradiated human tissue

Transcriptional dosimetry is an emergent field of radiobiology aimed at developing robust methods for detecting and quantifying absorbed doses using radiation-induced fluctuations in gene expression. A combination of RNA sequencing, array-based and quantitative PCR transcriptomics in cellular, murine and various ex vivo human models has led to a comprehensive description of a fundamental set of genes with demonstrable dosimetric qualities. However, these are yet to be validated in human tissue due to the scarcity of in situ-irradiated source material. This represents a major hurdle to the continued development of transcriptional dosimetry. In this study, we present a novel evaluation of a previously reported set of dosimetric genes in human tissue exposed to a large therapeutic dose of radiation. To do this, we evaluated the quantitative changes of a set of dosimetric transcripts consisting of FDXR, BAX, BCL2, CDKN1A, DDB2, BBC3, GADD45A, GDF15, MDM2, SERPINE1, TNFRSF10B, PLK3, SESN2 and VWCE in guided pre- and post-radiation (2 weeks) prostate cancer biopsies from seven patients. We confirmed the prolonged dose-responsivity of most of these transcripts in in situ-irradiated tissue. BCL2, GDF15, and to some extent TNFRSF10B, were markedly unreliable single markers of radiation exposure. Nevertheless, as a full set, these genes reliably segregated non-irradiated and irradiated tissues and predicted radiation absorption on a patient-specific basis. We also confirmed changes in the translated protein product for a small subset of these dosimeters. This study provides the first confirmatory evidence of an existing dosimetric gene set in less-accessible tissues-ensuring peripheral responses reflect tissue-specific effects. Further work will be required to determine if these changes are conserved in different tissue types, post-radiation times and doses.

Gene Expression in Parp1 Deficient Mice Exposed to a Median Lethal Dose of Gamma Rays

There is a current interest in the development of biodosimetric methods for rapidly assessing radiation exposure in the wake of a large-scale radiological event. This work was initially focused on determining the exposure dose to an individual using biological indicators. Gene expression signatures show promise for biodosimetric application, but little is known about how these signatures might translate for the assessment of radiological injury in radiosensitive individuals, who comprise a significant proportion of the general population, and who would likely require treatment after exposure to lower doses. Using Parp1^-/- mice as a model radiation-sensitive genotype, we have investigated the effect of this DNA repair deficiency on the gene expression response to radiation. Although Parp1 is known to play general roles in regulating transcription, the pattern of gene expression changes observed in Parp1^-/- mice 24 h postirradiation to a LD_50/30 was remarkably similar to that in wild-type mice after exposure to LD_50/30. Similar levels of activation of both the p53 and NFκB radiation response pathways were indicated in both strains. In contrast, exposure of wild-type mice to a sublethal dose that was equal to the Parp1^-/- LD_50/30 resulted in a lower magnitude gene expression response. Thus, Parp1^-/- mice displayed a heightened gene expression response to radiation, which was more similar to the wild-type response to an equitoxic dose than to an equal absorbed dose. Gene expression classifiers trained on the wild-type data correctly identified all wild-type samples as unexposed, exposed to a sublethal dose or exposed to an LD_50/30. All unexposed samples from Parp1^-/- mice were also correctly classified with the same gene set, and 80% of irradiated Parp1^-/- samples were identified as exposed to an LD_50/30. The results of this study suggest that, at least for some pathways that may influence radiosensitivity in humans, specific gene expression signatures have the potential to accurately detect the extent of radiological injury, rather than serving only as a surrogate of physical radiation dose.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Global Gene Expression Response in Mouse Models of DNA Repair Deficiency after Gamma Irradiation

In the event of an improvised nuclear device or "dirty bomb" in a highly populated area, potentially hundreds of thousands of people will require screening to ensure that exposed individuals receive appropriate treatment. For this reason, there is a need to develop tools for high-throughput radiation biodosimetry. Gene expression represents an emerging approach to biodosimetry and could potentially provide an estimate of both absorbed dose and individual radiation-induced injury. Since approximately 2-4% of humans are thought to be radiosensitive, and would suffer greater radiological injury at a given dose than members of the general population, it is of interest to explore the potential impact of such sensitivity on the biodosimetric gene expression signatures being developed. In this study, we used wild-type mice and genetically engineered mouse models deficient in two DNA repair pathways that can contribute to radiation sensitivity to estimate the maximum effect of differences in radiosensitivity. We compared gene expression in response to a roughly equitoxic (LD_50/30) dose of gamma rays in wild-type C57BL/6 (8 Gy) and DNA double-strand break repair-deficient Atm^-/- (4 Gy) and Prkdc^scid (3 Gy) mutants of C57BL/6. Overall, 780 genes were significantly differentially expressed in wild-type mice one day postirradiation, 232 in Atm^-/- and 269 in Prkdc^scid. Upstream regulators including TP53 and NFκB were predicted to be activated by radiation exposure in the wild-type mice, but not in either of the DNA repair-deficient mutant strains. There was also a significant muting of the apparent inflammatory response triggered by radiation in both mutant strains. These differences impacted the ability of gene expression signatures developed in wild-type mice to detect potentially fatal radiation exposure in the DNA repair-deficient mice, with the greatest impact on Atm^-/- mice. However, the inclusion of mutant mice in gene selection vastly improved performance of the classifiers.

FDXR is a biomarker of radiation exposure in vivo

Previous investigations in gene expression changes in blood after radiation exposure have highlighted its potential to provide biomarkers of exposure. Here, FDXR transcriptional changes in blood were investigated in humans undergoing a range of external radiation exposure procedures covering several orders of magnitude (cardiac fluoroscopy, diagnostic computed tomography (CT)) and treatments (total body and local radiotherapy). Moreover, a method was developed to assess the dose to the blood using physical exposure parameters. FDXR expression was significantly up-regulated 24 hr after radiotherapy in most patients and continuously during the fractionated treatment. Significance was reached even after diagnostic CT 2 hours post-exposure. We further showed that no significant differences in expression were found between ex vivo and in vivo samples from the same patients. Moreover, potential confounding factors such as gender, infection status and anti-oxidants only affect moderately FDXR transcription. Finally, we provided a first in vivo dose-response showing dose-dependency even for very low doses or partial body exposure showing good correlation between physically and biologically assessed doses. In conclusion, we report the remarkable responsiveness of FDXR to ionising radiation at the transcriptional level which, when measured in the right time window, provides accurate in vivo dose estimates.

Developing Human Radiation Biodosimetry Models: Testing Cross-Species Conversion Approaches Using an Ex Vivo Model System

In the event of a large-scale radiation exposure, accurate and quick assessment of radiation dose received would be critical for triage and medical treatment of large numbers of potentially exposed individuals. Current methods of biodosimetry, such as the dicentric chromosome assay, are time consuming and require sophisticated equipment and highly trained personnel. Therefore, scalable biodosimetry approaches, including gene expression profiles in peripheral blood cells, are being investigated. Due to the limited availability of appropriate human samples, biodosimetry development has relied heavily on mouse models, which are not directly applicable to human response. Therefore, to explore the feasibility of using non-human primate (NHP) models to build and test a biodosimetry algorithm for use in humans, we irradiated ex vivo peripheral blood samples from both humans and rhesus macaques with doses of 0, 2, 5, 6 and 7 Gy, and compared the gene expression profiles 24 h later using Agilent human microarrays. Among the dose-responsive genes in human and using non-human primate, 52 genes showed highly correlated expression patterns between the species, and were enriched in p53/DNA damage response, apoptosis and cell cycle-related genes. When these interspecies-correlated genes were used to build biodosimetry models with using NHP data, the mean prediction accuracy on non-human primate samples was about 90% within 1 Gy of delivered dose in leave-one-out cross-validation. However, tests on human samples suggested that human gene expression values may need to be adjusted prior to application of the NHP model. A "multi-gene" approach utilizing all gene values for cross-species conversion and applying the converted values on the NHP biodosimetry models, gave a leave-one-out cross-validation prediction accuracy for human samples highly comparable (up to 94%) to that for non-human primates. Overall, this study demonstrates that a robust NHP biodosimetry model can be built using interspecies-correlated genes, and that, by using multiple regression-based cross-species conversion of expression values, absorbed dose in human samples can be accurately predicted by the NHP model.

Impact of Neutron Exposure on Global Gene Expression in a Human Peripheral Blood Model

The detonation of an improvised nuclear device would produce prompt radiation consisting of both photons (gamma rays) and neutrons. While much effort in recent years has gone into the development of radiation biodosimetry methods suitable for mass triage, the possible effect of neutrons on the endpoints studied has remained largely uninvestigated. We have used a novel neutron irradiator with an energy spectrum based on that 1-1.5 km from the epicenter of the Hiroshima blast to begin examining the effect of neutrons on global gene expression, and the impact this may have on the development of gene expression signatures for radiation biodosimetry. We have exposed peripheral blood from healthy human donors to 0.1, 0.3, 0.5 or 1 Gy of neutrons ex vivo using our neutron irradiator, and compared the transcriptomic response 24 h later to that resulting from sham exposure or exposure to 0.1, 0.3, 0.5, 1, 2 or 4 Gy of photons (X rays). We identified 125 genes that responded significantly to both radiation qualities as a function of dose, with the magnitude of response to neutrons generally being greater than that seen after X-ray exposure. Gene ontology analysis suggested broad involvement of the p53 signaling pathway and general DNA damage response functions across all doses of both radiation qualities. Regulation of immune response and chromatin-related functions were implicated only following the highest doses of neutrons, suggesting a physiological impact of greater DNA damage. We also identified several genes that seem to respond primarily as a function of dose, with less effect of radiation quality. We confirmed this pattern of response by quantitative real-time RT-PCR for BAX, TNFRSF10B, ITLN2 and AEN and suggest that gene expression may provide a means to differentiate between total dose and a neutron component.

Ionizing radiation biomarkers in epidemiological studies - An update

Recent epidemiology studies highlighted the detrimental health effects of exposure to low dose and low dose rate ionizing radiation (IR): nuclear industry workers studies have shown increased leukaemia and solid tumour risks following cumulative doses of <100mSv and dose rates of <10mGy per year; paediatric patients studies have reported increased leukaemia and brain tumours risks after doses of 30-60mGy from computed tomography scans. Questions arise, however, about the impact of even lower doses and dose rates where classical epidemiological studies have limited power but where subsets within the large cohorts are expected to have an increased risk. Further progress requires integration of biomarkers or bioassays of individual exposure, effects and susceptibility to IR. The European DoReMi (Low Dose Research towards Multidisciplinary Integration) consortium previously reviewed biomarkers for potential use in IR epidemiological studies. Given the increased mechanistic understanding of responses to low dose radiation the current review provides an update covering technical advances and recent studies. A key issue identified is deciding which biomarkers to progress. A roadmap is provided for biomarker development from discovery to implementation and used to summarise the current status of proposed biomarkers for epidemiological studies. Most potential biomarkers remain at the discovery stage and for some there is sufficient evidence that further development is not warranted. One biomarker identified in the final stages of development and as a priority for further research is radiation specific mRNA transcript profiles.

State-of-the-Art Advances in Radiation Biodosimetry for Mass Casualty Events Involving Radiation Exposure

With the possibility of large-scale terrorist attacks around the world, the need for modeling and development of new medical countermeasures for potential future chemical, biological, radiological and nuclear (CBRN) has been well established. Project Bioshield, initiated in 2004, provided a framework to develop and expedite research in the field of CBRN exposures. To respond to large-scale population exposures from a nuclear event or radiation dispersal device (RDD), new methods for determining received dose using biological modeling became necessary. The field of biodosimetry has advanced significantly beyond this original initiative, with expansion into the fields of genomics, proteomics, metabolomics and transcriptomics. Studies are ongoing to evaluate the use of lymphocyte kinetics for dose assessment, as well as the development of field-deployable EPR technology. In addition, expansion of traditional cytogenetic assessment methods through the use of automated platforms and the development of laboratory surge capacity networks have helped to advance our biodefense preparedness. In this review of the latest advances in the field of biodosimetry we evaluate our progress and identify areas that still need to be addressed to achieve true field-deployment readiness.

MDP: A Deinococcus Mn2+-Decapeptide Complex Protects Mice from Ionizing Radiation

The radioprotective capacity of a rationally-designed Mn²⁺-decapeptide complex (MDP), based on Mn antioxidants in the bacterium Deinococcus radiodurans, was investigated in a mouse model of radiation injury. MDP was previously reported to be extraordinarily radioprotective of proteins in the setting of vaccine development. The peptide-component (DEHGTAVMLK) of MDP applied here was selected from a group of synthetic peptides screened in vitro for their ability to protect cultured human cells and purified enzymes from extreme damage caused by ionizing radiation (IR). We show that the peptides accumulated in Jurkat T-cells and protected them from 100 Gy. MDP preserved the activity of T4 DNA ligase exposed to 60,000 Gy. In vivo, MDP was nontoxic and protected B6D2F1/J (female) mice from acute radiation syndrome. All irradiated mice treated with MDP survived exposure to 9.5 Gy (LD_70/30) in comparison to the untreated mice, which displayed 63% lethality after 30 days. Our results show that MDP provides early protection of white blood cells, and attenuates IR-induced damage to bone marrow and hematopoietic stem cells via G-CSF and GM-CSF modulation. Moreover, MDP mediated the immunomodulation of several cytokine concentrations in serum including G-CSF, GM-CSF, IL-3 and IL-10 during early recovery. Our results present the necessary prelude for future efforts towards clinical application of MDP as a promising IR countermeasure. Further investigation of MDP as a pre-exposure prophylactic and post-exposure therapeutic in radiotherapy and radiation emergencies is warranted.

In vivo Signatures of Genotoxic and Non-genotoxic Chemicals

This chapter reviews the findings from a broad array of in vivo genomic studies with the goal of identifying a general signature of genotoxicity (GSG) that is indicative of exposure to genotoxic agents (i.e. agents that are active in either the bacterial mutagenesis and/or the in vivo micronucleus test). While the GSG has largely emerged from systematic studies of rat and mouse liver, its response is evident across a broad collection of genotoxic treatments that cover a variety of tissues and species. Pathway-based characterization of the GSG indicates that it is enriched with genes that are regulated by p53. In addition to the GSG, another pan-tissue signature related to bone marrow suppression (a common effect of genotoxic agent exposure) is reviewed. Overall, these signatures are quite effective in identifying genotoxic agents; however, there are situations where false positive findings can occur, for example when necrotizing doses of non-genotoxic soft electrophiles (e.g. thioacetamide) are used. For this reason specific suggestions for best practices for generating for use in the creation and application of in vivo genomic signatures are reviewed.

Radiation-induced alternative transcription and splicing events and their applicability to practical biodosimetry

Accurate assessment of the individual exposure dose based on easily accessible samples (e.g. blood) immediately following a radiological accident is crucial. We aimed at developing a robust transcription-based signature for biodosimetry from human peripheral blood mononuclear cells irradiated with different doses of X-rays (0.1 and 1.0 Gy) at a dose rate of 0.26 Gy/min. Genome-wide radiation-induced changes in mRNA expression were evaluated at both gene and exon level. Using exon-specific qRT-PCR, we confirmed that several biomarker genes are alternatively spliced or transcribed after irradiation and that different exons of these genes exhibit significantly different levels of induction. Moreover, a significant number of radiation-responsive genes were found to be genomic neighbors. Using three different classification models we found that gene and exon signatures performed equally well on dose prediction, as long as more than 10 features are included. Together, our results highlight the necessity of evaluating gene expression at the level of single exons for radiation biodosimetry in particular and transcriptional biomarker research in general. This approach is especially advisable for practical gene expression-based biodosimetry, for which primer- or probe-based techniques would be the method of choice.

Quantification of damage due to low-dose radiation exposure in mice: construction and application of a biodosimetric model using mRNA indicators in circulating white blood cells

Biodosimetry, the measurement of radiation damage in a biologic sample, is a reliable tool for increasing the accuracy of dose estimation. Although established chromosome analyses are suitable for estimating the absorbed dose after high-dose irradiation, biodosimetric methodology to measure damage following low-dose exposure is underdeveloped. RNA analysis of circulating blood containing radiation-sensitive cells is a candidate biodosimetry method. Here we quantified RNA from a small amount of blood isolated from mice following low-dose body irradiation (<0.5 Gy) aimed at developing biodosimetric tools for situations that are difficult to study in humans. By focusing on radiation-sensitive undifferentiated cells in the blood based on Myc RNA expression, we quantified the relative levels of RNA for DNA damage-induced (DDI) genes, such as Bax, Bbc3 and Cdkn1a. The RNA ratios of DDI genes/Myc in the blood increased in a dose-dependent manner 4 h after whole-body irradiation at doses ranging from 0.1 to 0.5 Gy (air-kerma) of X-rays, regardless of whether the mice were in an active or resting state. The RNA ratios were significantly increased after 0.014 Gy (air-kerma) of single X-ray irradiation. The RNA ratios were directly proportional to the absorbed doses in water ranging from 0.1 to 0.5 Gy, based on gamma-irradiation from (137)Cs. Four hours after continuous irradiation with gamma-rays or by internal contamination with a beta-emitter, the increased RNA ratios resembled those following single irradiation. These findings indicate that the RNA status can be utilized as a biodosimetric tool to estimate low-dose radiation when focusing on undifferentiated cells in blood.

Effect of 90Sr internal emitter on gene expression in mouse blood

Background

The radioactive isotope Strontium-90 (⁹⁰Sr) may be released as a component of fallout from nuclear accidents, or in the event of a radiological incident such as detonation of an improvised nuclear device, and if ingested poses a significant health risk to exposed individuals. In order to better understand the response to ⁹⁰Sr, using an easily attainable and standard biodosimetry sample fluid, we analyzed the global transcriptomic response of blood cells in an in vivo model system.

Results

We injected C57BL/6 mice with a solution of 90SrCl2 and followed them over a 30-day period. At days 4, 7, 9, 25 and 30, we collected blood and isolated RNA for microarray analyses. These days corresponded to target doses in a range from 1–5 Gy. We investigated changes in mRNA levels using microarrays, and changes in specific microRNA (miRNA) predicted to be involved in the response using qRT-PCR. We identified 8082 differentially expressed genes in the blood of mice exposed to ⁹⁰Sr compared with controls. Common biological functions were affected throughout the study, including apoptosis of B and T lymphocytes, and atrophy of lymphoid organs. Cellular functions such as RNA degradation and lipid metabolism were also affected during the study. The broad down regulation of genes observed in our study suggested a potential role for miRNA in gene regulation. We tested candidate miRNAs, mmu-miR-16, mmu-miR-124, mmu-miR-125 and mmu-mir-21; and found that all were induced at the earliest time point, day 4.

Conclusions

Our study is the first to report the transcriptomic response of blood cells to the internal emitter ⁹⁰Sr in mouse and a possible role for microRNA in gene regulation after ⁹⁰Sr exposure. The most dramatic effect was observed on gene expression related to B-cell development and RNA maintenance. These functions were affected by genes that were down regulated throughout the study, suggesting severely compromised antigen response, which may be a result of the deposition of the radioisotope proximal to the hematopoietic compartment in bone.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1774-z) contains supplementary material, which is available to authorized users.

Radiation Dose-Rate Effects on Gene Expression in a Mouse Biodosimetry Model

In the event of a nuclear accident or radiological terrorist attack, there will be a pressing need for biodosimetry to triage a large, potentially exposed population and to assign individuals to appropriate treatment. Exposures from fallout are likely, resulting in protracted dose delivery that would, in turn, impact the extent of injury. Biodosimetry approaches that can distinguish such low-dose-rate (LDR) exposures from acute exposures have not yet been developed. In this study, we used the C57BL/6 mouse model in an initial investigation of the impact of low-dose-rate delivery on the transcriptomic response in blood. While a large number of the same genes responded to LDR and acute radiation exposures, for many genes the magnitude of response was lower after LDR exposures. Some genes, however, were differentially expressed (P < 0.001, false discovery rate <5%) in mice exposed to LDR compared with mice exposed to acute radiation. We identified a set of 164 genes that correctly classified 97% of the samples in this experiment as exposed to acute or LDR radiation using a support vector machine algorithm. Gene expression is a promising approach to radiation biodosimetry, enhanced greatly by this first demonstration of its potential for distinguishing between acute and LDR exposures. Further development of this aspect of radiation biodosimetry, either as part of a complete gene expression biodosimetry test or as an adjunct to other methods, could provide vital triage information in a mass radiological casualty event.