Gene expression signatures of radiation response are specific, durable and accurate in mice and humans

Validation of genes for H-ARS severity prediction in leukemia patients - interspecies comparison, challenges, and promises

PURPOSE

In a previous baboon-study, a total of 29 genes were identified for clinical outcome prediction of the hematologic, acute, radiation, syndrome (H-ARS) severity. Among them, four genes (FDXR, DDB2, POU2AF1, WNT3) appeared promising and were validated in five leukemia patients. Within this study, we sought further in-vivo validation in a larger number of whole-body irradiated patients.

MATERIAL AND METHODS

Peripheral blood was drawn from 10 leukemia patients before and up to 3 days during a fractionated (2 Gy/day) total-body irradiation (TBI) with 2-12Gy. After RNA-isolation, gene expression (GE) was evaluated on 31 genes widely used in biodosimetry and H-ARS prediction employing qRT-PCR. A customized low-density-array (LDA) allowed simultanously analyzing all genes, the 96-well format further examined the four most promising genes. Fold-changes (FC) in GE relative to pre-irradiation were calculated.

RESULTS

Five patients suffering from acute-lymphoblastic-leukemia (ALL) respectively non-Hodgkin-lymphoma (NHL) revealed sufficient RNA-amounts and corresponding lymphocyte and neutrophile counts for running qRT-PCR, while acute-myeloid-leukemia (AML) and one myelofibrosis patient could not supply enough RNA. Generally, 1-2µg total RNA was isolated, whereas up to 10-fold differences in RNA-quantities (associated suppressed GE-changes) were identified among pre-exposure and exposure samples. From 31 genes, 23 were expressed in at least one of the pre-exposure samples. Relative to pre-exposure, the number of expressed genes could halve at 48 and 72h after irradiation. Using the LDA, 13 genes were validated in human samples. The four most promising genes (vid. sup.) were either undetermined or too close to pre-exposure. However, they were measured using the more sensitive 96-well format, except WNT3, which wasn´t detectable. As in previous studies, an opposite regulation in GE for FDXR in leukemia patients (up-regulated) relative to baboons (down-regulated) was reconfirmed. Radiation-induced GE-changes of DDB2 (up-regulated) and POU2AF1 (down-regulated) behaved similarly in both species. Hence, 16 out of 23 genes of two species showed GE-changes in the same direction, and up-regulated FDXR as in human studies were revalidated.

CONCLUSION

Identified genes for H-ARS severity prediction, previously detected in baboons, were validated in ALL but not in AML patients. Limitations related to leukemia type, associated reduced RNA amounts, suppressed GE changes, and methodological challenges must be considered as factors negatively affecting the total number of validated genes. Based on that, we propose additional controls including blood cell counts and preferably fluorescence-based RNA quantity measurements for selecting promising samples and using a more sensitive 96-well format for candidate genes with low baseline copy numbers.

RNA N6-methyladenosine modification-based biomarkers for absorbed ionizing radiation dose estimation

Radiation triage and biological dosimetry are critical for the medical management of massive potentially exposed individuals following radiological accidents. Here, we performed a genome-wide screening of radiation-responding mRNAs, whose N6-methyladenosine (m6A) levels showed significant alteration after acute irradiation. The m6A levels of three genes, Ncoa4, Ate1 and Fgf22, in peripheral blood mononuclear cells (PBMCs) of mice showed excellent dose-response relationships and could serve as biomarkers of radiation exposure. Especially, the RNA m6A of Ncoa4 maintained a high level as long as 28 days after irradiation. We demonstrated its responsive specificity to radiation, conservation across the mice, monkeys and humans, and the dose-response relationship in PBMCs from cancer patients receiving radiation therapy. Finally, NOCA4 m6A-based biodosimetric models were constructed for estimating absorbed radiation doses in mice or humans. Collectively, this study demonstrated the potential feasibility of RNA m6A in radiation accidents management and clinical applications.

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.

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.

Effect of Age and Sex on Gene Expression-Based Radiation Biodosimetry Using Mouse Peripheral Blood

Blood-based gene expression profiles that can reconstruct radiation exposure are being developed as a practical approach to radiation biodosimetry. However, age and sex could potentially limit the accuracy of the approach. In this study, we determined the impact of age on the peripheral blood cell gene expression profile of female mice exposed to radiation and identified differences and similarities with a previously obtained transcriptomic signature of male mice. Young (2 months) and old (24 months) female mice were irradiated with 4 Gy X-rays, total RNA was isolated from blood 24 hours later and subjected to whole-genome microarray analysis. Dose reconstruction analyses using a gene signature trained on gene expression data from irradiated young male mice showed accurate reconstruction of 0 or 4 Gy doses with root mean square error of ±0.75 Gy (R2 = 0.90) in young female mice. Although dose reconstruction for irradiated old female mice was less accurate than young female mice, the deviation from the actual radiation dose was not statistically significant. Pathway analysis of differentially expressed genes revealed that after irradiation, apoptosis-related functions were overrepresented, whereas functions related to quantities of various immune cell subtypes were underrepresented, among differentially expressed genes from young female mice, but not older animals. Furthermore, young mice significantly upregulated genes involved in phagocytosis, a process that eliminates apoptotic cells and preserves tissue homeostasis. Both functions were also overrepresented in young, but not old, male mice following 4 Gy X-irradiation. Lastly, functions associated with neutrophil activation that is essential for killing invading pathogens and regulating the inflammatory response were predicted to be uniquely enriched in young but not old female mice. This work supports the concept that peripheral blood gene expression profiles can be identified in mice that accurately predict physical radiation dose exposure irrespective of age and sex.

A Review of Radiation-Induced Alterations of Multi-Omic Profiles, Radiation Injury Biomarkers, and Countermeasures

Increasing utilization of nuclear power enhances the risks associated with industrial accidents, occupational hazards, and the threat of nuclear terrorism. Exposure to ionizing radiation interferes with genomic stability and gene expression resulting in the disruption of normal metabolic processes in cells and organs by inducing complex biological responses. Exposure to high-dose radiation causes acute radiation syndrome, which leads to hematopoietic, gastrointestinal, cerebrovascular, and many other organ-specific injuries. Altered genomic variations, gene expression, metabolite concentrations, and microbiota profiles in blood plasma or tissue samples reflect the whole-body radiation injuries. Hence, multi-omic profiles obtained from high-resolution omics platforms offer a holistic approach for identifying reliable biomarkers to predict the radiation injury of organs and tissues resulting from radiation exposures. In this review, we performed a literature search to systematically catalog the radiation-induced alterations from multi-omic studies and radiation countermeasures. We covered radiation-induced changes in the genomic, transcriptomic, proteomic, metabolomic, lipidomic, and microbiome profiles. Furthermore, we have covered promising multi-omic biomarkers, FDA-approved countermeasure drugs, and other radiation countermeasures that include radioprotectors and radiomitigators. This review presents an overview of radiation-induced alterations of multi-omics profiles and biomarkers, and associated radiation countermeasures.

Evaluating the Influences of Confounding Variables on Benchmark Dose using a Case Study in the Field of Ionizing Radiation

Purpose A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. Methods: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female non-smokers (F-NS), six male smokers (M-S), and six male non-smokers (M-NS). Results: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of responses in the robust common genes and pathways. Conclusion: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.

Clinical Applications of Biological Dosimetry in Patients Exposed to Low Dose Radiation Due to Radiological, Imaging or Nuclear Medicine Procedures

Radiation dosimetric biomarkers have found applications beyond radiation protection area and now are actively introduced into clinical practice. Cytogenetic assays appeared to be a valuable tool for individualized quantifying radiation effects in patients, with high capability for assessing genotoxicity of various medical exposure modalities and providing meaningful radiation dose estimates for prognoses of radiation-related cancer risk. This review summarized current data on the use of biological dosimetry methods in patients undergoing various medical irradiations to low doses. The highlighted topics include basic aspects of biological dosimetry and its limitations in the range of low radiation doses, and main patterns of in vivo induction of radiation biomarkers in clinical exposure scenarios, occurring in X-ray diagnostics, computed tomography, interventional radiology, low dose radiotherapy, and nuclear medicine (internally administered ¹³¹I and other radiopharmaceuticals). Additionally, several specific issues, examined by biodosimetry techniques, are analysed, such as contrast media effect, radiation response in pediatric patients, impact of magnetic resonance imaging, evaluation of radioprotectors, detection of patients' abnormal intrinsic radiosensitivity and dose estimation in persons involved in medical radiation incidents. A prognosis of possible directions for further improvements in this area includes the automation of cytogenetic analysis, introduction of molecular biodosimeters and development of multiparametric biodosimetry platforms. A potential approach to the advanced biodosimetry of internal exposure and/or low dose external irradiation is suggested; this can be a multiparametric platform based on the combination of the γ-H2AX foci, dicentric, and translocation assays, each applied in the optimum postexposure time range, with the amalgamation of the dose estimates. The study revealed the necessity of further research, which might clarify medical radiation safety concerns for patients via using stringent biodosimetry methodology.

Differential Analysis and Putative Roles of Genes, Cytokines and Apoptotic Proteins in Blood Samples of Patients with Respiratory Viral Infections: A Single Center Study

Insights into the molecular pathogenesis of respiratory viral infections were investigated using serum and peripheral blood from patients with clinical syndromes. Signatures of expression of cytokines, genes and apoptotic proteins that discriminate symptomatic individuals from healthy individuals were determined among 21 patients. In symptomatic patients, significant upregulation of IL-1β, IL-2, IL-4, IL-6, IL-8, IL-12, IL-15, TNF-a and IFN-g (P<0.05) was noted, while IL-10 was significantly downregulated (P<0.05). This is accompanied by either up or down-regulation of various pro-apoptotic and anti-apoptotic markers, suggesting a protective role of immune responses against viral infection and the capacity of viruses to subvert host cell apoptosis. Gene expression analysis for both T and B cells were categorized according to their functional status of activation, proliferation, and differentiation. Of note, genes SH2D1A and TCL1A were upregulated only in rhinovirus samples, while PSMB7, CD4, CD8A, HLA-DMA, HLA-DRA and CD69 were upregulated in samples of Flu A and RSV but were not significant in samples of rhinovirus as compared to healthy individuals. These results demonstrated Flu A and RSV elicit different alterations in human peripheral blood gene expression as compared to rhinovirus. Overall, despite the small number of study subjects, the current study for the first time has recognized signature genes, cytokines and proteins that are used by some respiratory viruses that may serve as candidates for rapid diagnosis as well as targets for therapeutic interventions.

Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures

PURPOSE

Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning (ML) based signatures (with 8-20% misclassification rates). These signatures can quantify therapeutically relevant as well as accidental radiation exposures. The prodromal symptoms of acute radiation syndrome (ARS) overlap those present in influenza and dengue fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy.

METHODS

This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-neoplastic blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to ML-based classifiers to predict radiation exposure in other hematological conditions.

RESULTS

Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8-54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thrombosis, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death or extrusion of DNA fragments. Riboviral infections (e.g. influenza or dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions.

CONCLUSIONS

This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.

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.

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.

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.

Impact of aging on gene expression response to x-ray irradiation using mouse blood

As a radiation biodosimetry tool, gene expression profiling is being developed using mouse and human peripheral blood models. The impact of dose, dose-rate, and radiation quality has been studied with the goal of predicting radiological tissue injury. In this study, we determined the impact of aging on the gene expression profile of blood from mice exposed to radiation. Young (2 mo) and old (21 mo) male mice were irradiated with 4 Gy x-rays, total RNA was isolated from whole blood 24 h later, and subjected to whole genome microarray analysis. Pathway analysis of differentially expressed genes revealed young mice responded to x-ray exposure by significantly upregulating pathways involved in apoptosis and phagocytosis, a process that eliminates apoptotic cells and preserves tissue homeostasis. In contrast, the functional annotation of senescence was overrepresented among differentially expressed genes from irradiated old mice without enrichment of phagocytosis pathways. Pathways associated with hematologic malignancies were enriched in irradiated old mice compared with irradiated young mice. The fibroblast growth factor signaling pathway was underrepresented in older mice under basal conditions. Similarly, brain-related functions were underrepresented in unirradiated old mice. Thus, age-dependent gene expression differences should be considered when developing gene signatures for use in radiation biodosimetry.

Integrative network analyses of transcriptomics data reveal potential drug targets for acute radiation syndrome

Recent political unrest has highlighted the importance of understanding the short- and long-term effects of gamma-radiation exposure on human health and survivability. In this regard, effective treatment for acute radiation syndrome (ARS) is a necessity in cases of nuclear disasters. Here, we propose 20 therapeutic targets for ARS identified using a systematic approach that integrates gene coexpression networks obtained under radiation treatment in humans and mice, drug databases, disease-gene association, radiation-induced differential gene expression, and literature mining. By selecting gene targets with existing drugs, we identified potential candidates for drug repurposing. Eight of these genes (BRD4, NFKBIA, CDKN1A, TFPI, MMP9, CBR1, ZAP70, IDH3B) were confirmed through literature to have shown radioprotective effect upon perturbation. This study provided a new perspective for the treatment of ARS using systems-level gene associations integrated with multiple biological information. The identified genes might provide high confidence drug target candidates for potential drug repurposing for ARS.

Dose and Dose-Rate Effects in a Mouse Model of Internal Exposure to 137Cs. Part 1: Global Transcriptomic Responses in Blood

Internal contamination by radionuclides may constitute a major source of exposure and biological damage after radiation accidents and potentially in a dirty bomb or improvised nuclear device scenario. We injected male C57BL/6 mice with radiolabeled cesium chloride solution (137CsCl) to evaluate the biological effects of varying cumulative doses and dose rates in a two-week study. Injection activities of 137CsCl were 5.71, 6.78, 7.67 and 9.29 MBq, calculated to achieve a target dose of 4 Gy at days 14, 7, 5 and 3, respectively. We collected whole blood samples at days 2, 3, 5, 7 and 14 so that we can publish the issue in Decemberfrom all injection groups and measured gene expression using Agilent Mouse Whole Genome microarrays. We identified both dose-rate-independent and dose-rate-dependent gene expression responses in the time series. Gene Ontology analysis indicated a rapid and persistent immune response to the chronic low-dose-rate irradiation, consistent with depletion of radiosensitive B cells. Pathways impacting platelet aggregation and TP53 signaling appeared activated, but not consistently at all times in the study. Clustering of genes by pattern and identification of dose-rate-independent and -dependent genes provided insight into possible drivers of the dynamic transcriptome response in vivo, and also indicated that TP53 signaling may be upstream of very different transcript response patterns. This characterization of the biological response of blood cells to internal radiation at varying doses and dose rates is an important step in understanding the effects of internal contamination after a nuclear event.

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.

Generation of a Transcriptional Radiation Exposure Signature in Human Blood Using Long-Read Nanopore Sequencing

In the event of a large-scale event leading to acute ionizing radiation exposure, high-throughput methods would be required to assess individual dose estimates for triage purposes. Blood-based gene expression is a broad source of biomarkers of radiation exposure which have great potential for providing rapid dose estimates for a large population. Time is a crucial component in radiological emergencies and the shipment of blood samples to relevant laboratories presents a concern. In this study, we performed nanopore sequencing analysis to determine if the technology can be used to detect radiation-inducible genes in human peripheral blood mononuclear cells (PBMCs). The technology offers not only long-read sequencing but also a portable device which can overcome issues involving sample shipment, and provide faster results. For this goal, blood from nine healthy volunteers was 2 Gy ex vivo X irradiated. After PBMC isolation, irradiated samples were incubated along with the controls for 24 h at 37°C. RNA was extracted, poly(A)+ enriched and reverse-transcribed before sequencing. The data generated was analyzed using a Snakemake pipeline modified to handle paired samples. The sequencing analysis identified a radiation signature consisting of 46 differentially expressed genes (DEGs) which included 41 protein-coding genes, a long non-coding RNA and four pseudogenes, five of which have been identified as radiation-responsive transcripts for the first time. The genes in which transcriptional expression is most significantly modified after radiation exposure were APOBEC3H and FDXR, presenting a 25- and 28-fold change on average, respectively. These levels of transcriptional response were comparable to results we obtained by quantitative polymerase chain reaction (qPCR) analysis. In vivo exposure analyses showed a transcriptional radioresponse at 24 h postirradiation for both genes together with a strong dose-dependent response in blood irradiated ex vivo. Finally, extrapolating from the data we obtained, the minimum sequencing time required to detect an irradiated sample using APOBEC3H transcripts would be less than 3 min for a total of 50,000 reads. Future improvements, in sample processing and bioinformatic pipeline for specific radiation-responsive transcript identification, will allow the provision of a portable, rapid, real-time biodosimetry platform based on this new sequencing technology. In summary, our data show that nanopore sequencing can identify radiation-responsive genes and can also be used for identification of new transcripts.

Rapid High-Throughput Diagnostic Triage after a Mass Radiation Exposure Event Using Early Gene Expression Changes

Radiological exposure scenarios involving large numbers of people require a rapid and high-throughput method to identify the unexposed, and those exposed to low- and high-dose radiation. Those with high-dose exposure, e.g., >2 Gy and depending on host characteristics, may develop severe hematological acute radiation syndrome (HARS), requiring hospitalization and treatment. Previously, we identified a set of genes that discriminated these clinically relevant groups. In the current work, we examined the utility of gene expression changes to classify 1,000 split blood samples into HARS severity scores of H0, H1 and H2-4, with the latter indicating likely hospitalization. In several previous radiation dose experiments, we determined that these HARS categories corresponded, respectively, to doses of 0 Gy (unexposed), 0.5 Gy and 5 Gy. The main purpose of this work was to assess the rapidity of blood sample processing using targeted next-generation sequencing (NGS). Peripheral blood samples from two healthy donors were X-ray irradiated in vitro and incubated at 37°C for 24 h. A total of 1,000 samples were evaluated by laboratory personnel blinded to the radiation dose. Changes in gene expression of FDXR, DDB2, POU2AF1 and WNT3 were examined with qRT-PCR as positive controls. Targeted NGS (TREX) was used on all samples for the same four genes. Agreement using both methods was almost 78%. Using NGS, all 1,000 samples were processed within 30 h. Classification of the HARS severity categories corresponding to radiation dose had an overall agreement ranging between 90-97%. Depending on the end point, either a combination of all genes or FDXR alone (H0 HARS or unexposed) provided the best classification. Using this optimized automated methodology, we assessed 100× more samples approximately three times faster compared to standard cytogenetic studies. We showed that a small set of genes, rather than a complex constellation of genes, provided robust positive (97%) and negative (97%) predictive values for HARS categories and radiation doses of 0, 0.5 and 5 Gy. The findings of this study support the potential utility of early radiation-induced gene expression changes for high-throughput biodosimetry and rapid identification of irradiated persons in need of hospitalization.

Development of a point-of-care radiation biodosimeter: studies using novel protein biomarker panels in non-human primates

Purpose: There is a need to rapidly triage individuals for absorbed radiation dose following a significant nuclear event. Since most exposed individuals will not have physical dosimeters, we are developing a method to assess exposure dose based on the analysis of a specific panel of blood proteins that can be easily obtained from a fingerstick blood sample.Materials and methods: In three large non-human primate (NHP) studies, animals were exposed to single acute total body doses of x-ray or gamma radiation. A total of 895 blood samples were obtained at baseline and for 7 days after exposure, to evaluate the temporal progression of markers in each of 10 animals (5M/5F) in six dose groups receiving 0-10 Gy. We used tandem mass spectrometry and immunoassay techniques to identify radiation-responsive proteins in blood plasma samples.Results: A blood protein biomarker panel was developed based on analysis of blood plasma samples obtained from several irradiation studies in NHPs that aimed to simulate acute radiation injury in humans from a nuclear exposure event. Panels of several subsets of proteins were shown to accurately classify plasma samples into two exposure groups either above or below a critical dose threshold with sensitivities and specificities exceeding 90%.Conclusion: This study lays the groundwork for developing a radiation biodosimetry triage tool. Our results in NHPs must be compared with those in human patients undergoing radiotherapy to determine if the biomarker panel proteins exhibit a similar radiation response and allow adequate classification power in humans.

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.

Gene expression-based biodosimetry for radiological incidents: assessment of dose and time after radiation exposure

PURPOSE

In order to ensure efficient use of medical resources following a radiological incident, there is an urgent need for high-throughput time-efficient biodosimetry tools. In the present study, we tested the applicability of a gene expression signature for the prediction of exposure dose as well as the time elapsed since irradiation.

MATERIALS AND METHODS

We used whole blood samples from seven healthy volunteers as reference samples (X-ray doses: 0, 25, 50, 100, 500, 1000, and 2000 mGy; time points: 8, 12, 24, 36 and 48 h) and samples from seven other individuals as 'blind samples' (20 samples in total).

RESULTS

Gene expression values normalized to the reference gene without normalization to the unexposed controls were sufficient to predict doses with a correlation coefficient between the true and the predicted doses of 0.86. Importantly, we could also classify the samples according to the time since exposure with a correlation coefficient between the true and the predicted time point of 0.96. Because of the dynamic nature of radiation-induced gene expression, this feature will be of critical importance for adequate gene expression-based dose prediction in a real emergency situation. In addition, in this study we also compared different methodologies for RNA extraction available on the market and suggested the one most suitable for emergency situation which does not require on-spot availability of any specific reagents or equipment.

CONCLUSIONS

Our results represent an important advancement in the application of gene expression for biodosimetry purposes.

Candidate gene biodosimetry markers of exposure to external ionizing radiation in human blood: A systematic review

Purpose

To compile a list of genes that have been reported to be affected by external ionizing radiation (IR) and to assess their performance as candidate biomarkers for individual human radiation dosimetry.

Methods

Eligible studies were identified through extensive searches of the online databases from 1978 to 2017. Original English-language publications of microarray studies assessing radiation-induced changes in gene expression levels in human blood after external IR were included. Genes identified in at least half of the selected studies were retained for bio-statistical analysis in order to evaluate their diagnostic ability.

Results

24 studies met the criteria and were included in this study. Radiation-induced expression of 10,170 unique genes was identified and the 31 genes that have been identified in at least 50% of studies (12/24 studies) were selected for diagnostic power analysis. Twenty-seven genes showed a significant Spearman’s correlation with radiation dose. Individually, TNFSF4, FDXR, MYC, ZMAT3 and GADD45A provided the best discrimination of radiation dose < 2 Gy and dose ≥ 2 Gy according to according to their maximized Youden’s index (0.67, 0.55, 0.55, 0.55 and 0.53 respectively). Moreover, 12 combinations of three genes display an area under the Receiver Operating Curve (ROC) curve (AUC) = 1 reinforcing the concept of biomarker combinations instead of looking for an ideal and unique biomarker.

Conclusion

Gene expression is a promising approach for radiation dosimetry assessment. A list of robust candidate biomarkers has been identified from analysis of the studies published to date, confirming for example the potential of well-known genes such as FDXR and TNFSF4 or highlighting other promising gene such as ZMAT3. However, heterogeneity in protocols and analysis methods will require additional studies to confirm these results.

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.

Whole thorax irradiation of non-human primates induces persistent nuclear damage and gene expression changes in peripheral blood cells

We investigated the cytogenetic and gene expression responses of peripheral blood cells of non-human primates (NHP, Macaca mulatta) that were whole-thorax irradiated with a single dose of 10 Gy. In this model, partial irradiation of NHPs in the thoracic region (Whole Thorax Lung Irradiation, WTLI) allows the study of late radiation-induced lung injury, while avoiding acute radiation syndromes related to hematopoietic and gastrointestinal injury. A transient drop in circulating lymphocytes and platelets was seen by 9 days, followed by elevations in respiratory rate, circulating neutrophils, lymphocytes, and monocytes at 60-100 days, corresponding to computed tomography (CT) and histologic evidence of pneumonitis, and elective euthanasia of four animals. To evaluate long-term DNA damage in NHP peripheral blood lymphocytes after 10 Gy WTLI, we used the cytokinesis-block micronucleus (CBMN) assay to measure chromosomal aberrations as post-mitotic micronuclei in blood samples collected up to 8 months after irradiation. Regression analysis showed significant induction of micronuclei in NHP blood cells that persisted with a gradual decline over the 8-month study period, suggesting long-term DNA damage in blood lymphocytes after WTLI. We also report transcriptomic changes in blood up to 30 days after WTLI. We isolated total RNA from peripheral blood at 3 days before and then at 2, 5 and 30 days after irradiation. We identified 1187 transcripts that were significantly changed across the 30-day time course. From changes in gene expression, we identified biological processes related to immune responses, which persisted across the 30-day study. Response to oxygen-containing compounds and bacteria were implicated by gene-expression changes at the earliest day 2 and latest, day 30 time-points. Gene expression changes suggest a persistent altered state of the immune system, specifically response to infection, for at least a month after WTLI.

GDF-15 gene expression alterations in human lymphoblastoid cells and peripheral blood lymphocytes following exposure to ionizing radiation

The identification of rapid, sensitive and high‑throughput biomarkers is imperative in order to identify individuals harmed by radiation accidents, and accurately evaluate the absorbed doses of radiation. DNA microarrays have previously been used to evaluate the alterations in growth/differentiation factor 15 (GDF15) gene expression in AHH‑1 human lymphoblastoid cells, following exposure to γ‑rays. The present study aimed to characterize the relationship between the dose of ionizing radiation and the produced effects in GDF‑15 gene expression in AHH‑1 cells and human peripheral blood lymphocytes (HPBLs). GDF‑15 mRNA and protein expression levels following exposure to γ‑rays and neutron radiation were assessed by reverse transcription‑quantitative polymerase chain reaction and western blot analysis in AHH‑1 cells. In addition, alterations in GDF‑15 gene expression in HPBLs following ex vivo irradiation were evaluated. The present results demonstrated that GDF‑15 mRNA and protein expression levels in AHH‑1 cells were significantly upregulated following exposure to γ‑ray doses ranging between 1 and 10 Gy, regardless of the dose rate. A total of 48 h following exposure to neutron radiation, a dose‑response relationship was identified in AHH‑1 cells at γ‑ray doses between 0.4 and 1.6 Gy. GDF‑15 mRNA levels in HPBLs were significantly upregulated following exposure to γ‑ray doses between 1 and 8 Gy, within 4‑48 h following irradiation. These results suggested that significant time‑ and dose‑dependent alterations in GDF‑15 mRNA and protein expression occur in AHH‑1 cells and HPBLs in the early phases following exposure to ionizing radiation. In conclusion, alterations in GDF‑15 gene expression may have potential as a biomarker to evaluate radiation exposure.

Gene and protein analysis reveals that p53 pathway is functionally inactivated in cytogenetically normal Acute Myeloid Leukemia and Acute Promyelocytic Leukemia

Background

Mechanisms that inactivate the p53 pathway in Acute Myeloid Leukemia (AML), other than rare mutations, are still not well understood.

Methods

We performed a bioinformatics study of the p53 pathway function at the gene expression level on our collection of 1153 p53-pathway related genes. Publically available Affymetrix data of 607 de-novo AML patients at diagnosis were analyzed according to the patients cytogenetic, FAB and molecular mutations subtypes. We further investigated the functional status of the p53 pathway in cytogenetically normal AML (CN-AML) and Acute Promyelocytic Leukemia (APL) patients using bioinformatics, Real-Time PCR and immunohistochemistry.

Results

We revealed significant and differential alterations of p53 pathway-related gene expression in most of the AML subtypes. We found that p53 pathway-related gene expression was not correlated with the accepted grouping of AML subtypes such as by cytogenetically-based prognosis, morphological stage or by the type of molecular mutation. Our bioinformatic analysis revealed that p53 is not functional in CN-AML and APL blasts at inducing its most important functional outcomes: cell cycle arrest, apoptosis, DNA repair and oxidative stress defense. We revealed transcriptional downregulation of important p53 acetyltransferases in both CN-AML and APL, accompanied by increased Mdmx protein expression and inadequate Chk2 protein activation.

Conclusions

Our bioinformatic analysis demonstrated that p53 pathway is differentially inactivated in different AML subtypes. Focused gene and protein analysis of p53 pathway in CN-AML and APL patients imply that functional inactivation of p53 protein can be attributed to its impaired acetylation. Our analysis indicates the need in further accurate evaluation of p53 pathway functioning and regulation in distinct subtypes of AML.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0249-2) contains supplementary material, which is available to authorized users.

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.

MicroRNA Expression for Early Prediction of Late Occurring Hematologic Acute Radiation Syndrome in Baboons

For effective medical management of radiation-exposed persons after a radiological/nuclear event, blood-based screening measures in the first few days that could predict hematologic acute radiation syndrome (HARS) are needed. For HARS severity prediction, we used microRNA (miRNA) expression changes measured on days one and two after irradiation in a baboon model. Eighteen baboons underwent different patterns of partial or total body irradiation, corresponding to an equivalent dose of 2.5 or 5 Gy. According to changes in blood cell counts (BCC) the surviving baboons (n = 17) exhibited mild (H1-2, n = 4) or more severe (H2-3, n = 13) HARS. In a two Stage study design we screened 667 miRNAs using a quantitative real-time polymerase chain reaction (qRT-PCR) platform. In Stage II we validated candidates where miRNAs had to show a similar regulation (up- or down-regulated) and a significant 2-fold miRNA expression difference over H0. Seventy-two candidate miRNAs (42 for H1-2 and 30 for H2-3) were forwarded for validation. Forty-two of the H1-2 miRNA candidates from the screening phase entered the validation step and 20 of them showed a statistically significant 2–4 fold up-regulation relative to the unexposed reference (H0). Fifteen of the 30 H2-3 miRNAs were validated in Stage II. All miRNAs appeared 2–3 fold down-regulated over H0 and allowed an almost complete separation of HARS categories; the strongest candidate, miR-342-3p, showed a sustained and 10-fold down-regulation on both days 1 and 2. In summary, our data support the medical decision making of the HARS even within the first two days after exposure where diagnostic tools for early medical decision are required but so far missing. The miRNA species identified and in particular miR-342-3p add to the previously identified mRNAs and complete the portfolio of identified mRNA and miRNA transcripts for HARS prediction and medical management.

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.

A Molecular Profile of the Endothelial Cell Response to Ionizing Radiation

Ionizing radiation exposure can cause acute radiation sickness (ARS) by damaging the hematopoietic compartment. Radiation damages quiescent hematopoietic stem cells (HSCs) and proliferating hematopoietic cells, resulting in neutropenia, thrombocytopenia and increased risk for long-term hematopoietic dysfunction and myelodysplasia. While some aspects of the hematopoietic response to radiation injury are intrinsic to hematopoietic cells, the recovery of the HSC pool and overall hematopoiesis is also dependent on signals from bone marrow endothelial cells (BM ECs) within the HSC vascular niche. The precise mechanisms through which BM ECs regulate HSC regeneration remain unclear. Characterization of the altered EC gene expression that occurs in response to radiation could provide a roadmap to the discovery of EC-derived mechanisms that regulate hematopoietic regeneration. Here, we show that 5 Gy total-body irradiation substantially alters the expression of numerous genes in BM ECs within 24 h and this molecular response largely resolves by day 14 postirradiation. Several unique and nonannotated genes, which encode secreted proteins were upregulated and downregulated in ECs in response to radiation. These results highlight the complexity of the molecular response of BM ECs to ionizing radiation and identify several candidate mechanisms that should be prioritized for functional analysis in models of hematopoietic injury and regeneration.

First Generation Gene Expression Signature for Early Prediction of Late Occurring Hematological Acute Radiation Syndrome in Baboons

We implemented a two-stage study to predict late occurring hematologic acute radiation syndrome (HARS) in a baboon model based on gene expression changes measured in peripheral blood within the first two days after irradiation. Eighteen baboons were irradiated to simulate different patterns of partial-body and total-body exposure, which corresponded to an equivalent dose of 2.5 or 5 Gy. According to changes in blood cell counts the surviving baboons (n = 17) exhibited mild (H1-2, n = 4) or more severe (H2-3, n = 13) HARS. Blood samples taken before irradiation served as unexposed control (H0, n = 17). For stage I of this study, a whole genome screen (mRNA microarrays) was performed using a portion of the samples (H0, n = 5; H1-2, n = 4; H2-3, n = 5). For stage II, using the remaining samples and the more sensitive methodology, qRT-PCR, validation was performed on candidate genes that were differentially up- or down-regulated during the first two days after irradiation. Differential gene expression was defined as significant (P < 0.05) and greater than or equal to a twofold difference above a H0 classification. From approximately 20,000 genes, on average 46% appeared to be expressed. On day 1 postirradiation for H2-3, approximately 2-3 times more genes appeared up-regulated (1,418 vs. 550) or down-regulated (1,603 vs. 735) compared to H1-2. This pattern became more pronounced at day 2 while the number of differentially expressed genes decreased. The specific genes showed an enrichment of biological processes coding for immune system processes, natural killer cell activation and immune response (P = 1 × E-06 up to 9 × E-14). Based on the P values, magnitude and sustained differential gene expression over time, we selected 89 candidate genes for validation using qRT-PCR. Ultimately, 22 genes were confirmed for identification of H1-3 classifications and seven genes for identification of H2-3 classifications using qRT-PCR. For H1-3 classifications, most genes were constantly three to fivefold down-regulated relative to H0 over both days, but some genes appeared 10.3-fold (VSIG4) or even 30.7-fold up-regulated (CD177) over H0. For H2-3, some genes appeared four to sevenfold up-regulated relative to H0 (RNASE3, DAGLA, ARG2), but other genes showed a strong 14- to 33-fold down-regulation relative to H0 (WNT3, POU2AF1, CCR7). All of these genes allowed an almost completely identifiable separation among each of the HARS categories. In summary, clinically relevant HARS can be independently predicted with all 29 irradiated genes examined in the peripheral blood of baboons within the first two days postirradiation. While further studies are needed to confirm these findings, this model shows potential relevance in the prediction of clinical outcomes in exposed humans and as an aid in the prioritizing of medical treatment.

A novel technology for multiplex gene expression analysis directly from whole blood samples stabilized at ambient temperature using an RNA-stabilizing buffer

We describe a novel method, based on target-dependent chemical ligation of probes, which simplifies the multiplexed quantitation of gene expression from blood samples by eliminating the RNA purification step. Gene expression from seven genes was evaluated over a range of sample inputs (16.7 to 0.25 μL of whole blood in serial dilutions) from three healthy donors. Mean CVs were ≤11% for five technical replicates for whole blood inputs ≥2.1 μL. The method showed a limit of detection of 300 copies of RNA by using titration of in vitro transcripts for four genes. Gene expression measured on stabilized blood samples was highly correlated (Spearman rank correlation method, ρ = 0.80) to gene expression results obtained with RNA isolated from matched samples (three donors, five technical replicates). Gene expression changes determined with seven radiation-responsive genes on six healthy donor blood samples before and after ex vivo irradiation were highly correlated (ρ = 0.93) to those measured with a TaqMan quantitative real-time RT-PCR assay on RNA purified from matched samples. Thus, this method is reproducible, sensitive, and correlated to quantitative real-time RT-PCR and may be used to streamline the multiplex gene expression analysis of large numbers of stabilized blood samples without RNA purification.

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.

Microfluidic module for blood cell separation for gene expression radiobiological assays

Advances in molecular techniques have improved discovery of biomarkers associated with radiation exposure. Gene expression techniques have been demonstrated as effective tools for biodosimetry, and different assay platforms with different chemistries are now available. One of the main challenges is to integrate the sample preparation processing of these assays into microfluidic platforms to be fully automated for point-of-care medical countermeasures in the case of a radiological event. Most of these assays follow the same workflow processing that comprises first the collection of blood samples followed by cellular and molecular sample preparation. The sample preparation is based on the specific reagents of the assay system and depends also on the different subsets of cells population and the type of biomarkers of interest. In this article, the authors present a module for isolation of white blood cells from peripheral blood as a prerequisite for automation of gene expression assays on a microfluidic cartridge. For each sample condition, the gene expression platform can be adapted to suit the requirements of the selected assay chemistry.

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.

Toward the development of transcriptional biodosimetry for the identification of irradiated individuals and assessment of absorbed radiation dose

The most frequently used and the best established method of biological dosimetry at present is the dicentric chromosome assay, which is poorly suitable for a mass casualties scenario. This gives rise to the need for the development of new, high-throughput assays for rapid identification of the subjects exposed to ionizing radiation. In the present study, we tested the usefulness of gene expression analysis in blood cells for biological dosimetry. Human peripheral blood from three healthy donors was X-irradiated with doses of 0 (control), 0.6, and 2 Gy. The mRNA level of 16 genes (ATF3, BAX, BBC3, BCL2, CDKN1A, DDB2, FDXR, GADD45A, GDF15, MDM2, PLK3, SERPINE1, SESN2, TNFRSF10B, TNFSF4, and VWCE) was assessed by reverse transcription quantitative PCR 6, 12, 24, and 48 h after exposure with ITFG1 and DPM1 used as a reference genes. The panel of radiation-responsive genes was selected comprising GADD45A, CDKN1A, BAX, BBC3, DDB2, TNFSF4, GDF15, and FDXR. Cluster analysis showed that ΔC t values of the selected genes contained sufficient information to allow discrimination between irradiated and non-irradiated blood samples. The samples were clearly grouped according to the absorbed doses of radiation and not to the time interval after irradiation or to the blood donor.

HEMODOSE: A Biodosimetry Tool Based on Multi-type Blood Cell Counts

Peripheral blood cell counts are important biomarkers of radiation exposure. In this work, a simplified compartmental modeling approach is applied to simulate the perturbation of the hematopoiesis system in humans after radiation exposure, and HemoDose software is reported to estimate individuals' absorbed doses based on multi-type blood cell counts. Testing with patient data in some historical accidents indicates that either single or serial granulocyte, lymphocyte, leukocyte, and platelet counts after exposure can be robust indicators of the absorbed doses. In addition, such correlation exists not only in the early time window (1 or 2 d) but also in the late phase (up to 4 wk) after exposure, when the four types of cell counts are combined for analysis. These demonstrate the capability of HemoDose as a rapid point-of-care diagnostic or centralized high-throughput assay system for personnel exposed to unintended high doses of radiation, especially in large-scale nuclear/radiological disaster scenarios involving mass casualties.

Radiation dose-rate effects on gene expression for human biodosimetry

Background

The effects of dose-rate and its implications on radiation biodosimetry methods are not well studied in the context of large-scale radiological scenarios. There are significant health risks to individuals exposed to an acute dose, but a realistic scenario would include exposure to both high and low dose-rates, from both external and internal radioactivity. It is important therefore, to understand the biological response to prolonged exposure; and further, discover biomarkers that can be used to estimate damage from low-dose rate exposures and propose appropriate clinical treatment.

Methods

We irradiated human whole blood ex vivo to three doses, 0.56 Gy, 2.23 Gy and 4.45 Gy, using two dose rates: acute, 1.03 Gy/min and a low dose-rate, 3.1 mGy/min. After 24 h, we isolated RNA from blood cells and these were hybridized to Agilent Whole Human genome microarrays. We validated the microarray results using qRT-PCR.

Results

Microarray results showed that there were 454 significantly differentially expressed genes after prolonged exposure to all doses. After acute exposure, 598 genes were differentially expressed in response to all doses. Gene ontology terms enriched in both sets of genes were related to immune processes and B-cell mediated immunity. Genes responding to acute exposure were also enriched in functions related to natural killer cell activation and cell-to-cell signaling. As expected, the p53 pathway was found to be significantly enriched at all doses and by both dose-rates of radiation. A support vectors machine classifier was able to distinguish between dose-rates with 100 % accuracy using leave-one-out cross-validation.

Conclusions

In this study we found that low dose-rate exposure can result in distinctive gene expression patterns compared with acute exposures. We were able to successfully distinguish low dose-rate exposed samples from acute dose exposed samples at 24 h, using a gene expression-based classifier. These genes are candidates for further testing as markers to classify exposure based on dose-rate.

Electronic supplementary material

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A translatable predictor of human radiation exposure

Terrorism using radiological dirty bombs or improvised nuclear devices is recognized as a major threat to both public health and national security. In the event of a radiological or nuclear disaster, rapid and accurate biodosimetry of thousands of potentially affected individuals will be essential for effective medical management to occur. Currently, health care providers lack an accurate, high-throughput biodosimetric assay which is suitable for the triage of large numbers of radiation injury victims. Here, we describe the development of a biodosimetric assay based on the analysis of irradiated mice, ex vivo-irradiated human peripheral blood (PB) and humans treated with total body irradiation (TBI). Interestingly, a gene expression profile developed via analysis of murine PB radiation response alone was inaccurate in predicting human radiation injury. In contrast, generation of a gene expression profile which incorporated data from ex vivo irradiated human PB and human TBI patients yielded an 18-gene radiation classifier which was highly accurate at predicting human radiation status and discriminating medically relevant radiation dose levels in human samples. Although the patient population was relatively small, the accuracy of this classifier in discriminating radiation dose levels in human TBI patients was not substantially confounded by gender, diagnosis or prior exposure to chemotherapy. We have further incorporated genes from this human radiation signature into a rapid and high-throughput chemical ligation-dependent probe amplification assay (CLPA) which was able to discriminate radiation dose levels in a pilot study of ex vivo irradiated human blood and samples from human TBI patients. Our results illustrate the potential for translation of a human genetic signature for the diagnosis of human radiation exposure and suggest the basis for further testing of CLPA as a candidate biodosimetric assay.

The current epidemiology and clinical decisions surrounding acute respiratory infections

Acute respiratory infection (ARI) is a common diagnosis in outpatient and emergent care settings. Currently available diagnostics are limited, creating uncertainty in the use of antibacterial, antiviral, or supportive care. Up to 72% of ambulatory care patients with ARI are treated with an antibacterial, despite only a small fraction actually needing one. Antibiotic overuse is not restricted to ambulatory care: ARI accounts for approximately 5 million emergency department (ED) visits annually in the USA, where 52-61% of such patients receive antibiotics. Thus, an accurate test for the presence or absence of viral or bacterial infection is needed. In this review, we focus on recent research showing that the host-response (genomic, proteomic, or miRNA) can accomplish this task.

Gene expression response of mice after a single dose of 137CS as an internal emitter

Cesium-137 is a radionuclide of concern in fallout from reactor accidents or nuclear detonations. When ingested or inhaled, it can expose the entire body for an extended period of time, potentially contributing to serious health consequences ranging from acute radiation syndrome to increased cancer risks. To identify changes in gene expression that may be informative for detecting such exposure, and to begin examining the molecular responses involved, we have profiled global gene expression in blood of male C57BL/6 mice injected with 137CsCl. We extracted RNA from the blood of control or 137CsCl-injected mice at 2, 3, 5, 20 or 30 days after exposure. Gene expression was measured using Agilent Whole Mouse Genome Microarrays, and the data was analyzed using BRB-ArrayTools. Between 466-6,213 genes were differentially expressed, depending on the time after 137Cs administration. At early times (2-3 days), the majority of responsive genes were expressed above control levels, while at later times (20-30 days) most responding genes were expressed below control levels. Numerous genes were overexpressed by day 2 or 3, and then underexpressed by day 20 or 30, including many Tp53-regulated genes. The same pattern was seen among significantly enriched gene ontology categories, including those related to nucleotide binding, protein localization and modification, actin and the cytoskeleton, and in the integrin signaling canonical pathway. We compared the expression of several genes three days after 137CsCl injection and three days after an acute external gamma-ray exposure, and found that the internal exposure appeared to produce a more sustained response. Many common radiation-responsive genes are altered by internally administered 137Cs, but the gene expression pattern resulting from continued irradiation at a decreasing dose rate is extremely complex, and appears to involve a late reversal of much of the initial response.