Gene expression signatures of radiation exposure in peripheral white blood cells of smokers and non-smokers

Translating genetic findings to epigenetics: identifying the mechanisms associated with aging after high-radiation exposure on earth and in space

Purpose

Exposure to radiation is a health concern within and beyond the Earth's atmosphere for aircrew and astronauts in their respective austere environments. The biological effects of radiation exposure from a multiomics standpoint are relatively unexplored and stand to shed light on tailored monitoring and treatment for those in these career fields. To establish a reference variable for genetic damage, biological age seems to be closely associated with the effect of radiation. Following a genetic-based study, this study explores the epigenetic landscape of radiation exposure along with its associative effects on aging processes.

Methods

We imported the results of the genetics-based study that was a secondary analysis of five publicly available datasets (noted as Data1). The overlap of these genes with new data involving methylation data from two datasets (noted as Data2) following similar secondary analysis procedures is the basis of this study. We performed the standard statistical analysis on these datasets along with supervised and unsupervised learning to create preranked gene lists used for functional analysis in Ingenuity Pathway Analysis (IPA).

Results

There were 664 genes of interest from Data1 and 577 genes from Data2. There were 40 statistically significant methylation probes within 500 base pairs of the gene's transcription start site and 10 probes within 100 base pairs, which are discussed in depth. IPA yielded 21 significant pathways involving metabolism, cellular development, cell death, and diseases. Compared to gold standards for gestational age, we observed relatively low error and standard deviation using newly identified biomarkers.

Conclusion

We have identified 17 methylated genes that exhibited particular interest and potential in future studies. This study suggests that there are common trends in oxidative stress, cell development, and metabolism that indicate an association between aging processes and the effects of ionizing radiation exposure.

Sex differences in radiation research

PURPOSE

The Sex Differences in Radiation Research workshop addressed the role of sex as a confounder in radiation research and its implication in real-world radiological and nuclear applications.

METHODS

In April 2022, HHS-wide partners from the Radiation and Nuclear Countermeasures Program, the Office of Research on Women's Health National Institutes of Health Office of Women's Health, U.S. Food and Drug Administration, and the Radiological and Nuclear Countermeasures Branch at the Biomedical Advanced Research and Development Authority conducted a workshop to address the scientific implication and knowledge gaps in understanding sex in basic and translational research. The goals of this workshop were to examine sex differences in 1. Radiation animal models and understand how these may affect radiation medical countermeasure development; 2. Biodosimetry and/or biomarkers used to assess acute radiation syndrome, delayed effects of acute radiation exposure, and/or predict major organ morbidities; 3. medical research that lacks representation from both sexes. In addition, regulatory policies that influence inclusion of women in research, and the gaps that exist in drug development and device clearance were discussed. Finally, real-world sex differences in human health scenarios were also considered.

RESULTS

This report provides an overview of the two-day workshop, and open discussion among academic investigators, industry researchers, and U.S. government representatives.

CONCLUSIONS

This meeting highlighted that current study designs lack the power to determine statistical significance based on sex, and much is unknown about the underlying factors that contribute to these differences. Investigators should accommodate both sexes in all stages of research to ensure that the outcome is robust, reproducible, and accurate, and will benefit public health.

Impact of GADD45A on Radiation Biodosimetry Using Mouse Peripheral Blood

High-dose-radiation exposure in a short period of time leads to radiation syndromes characterized by severe acute and delayed organ-specific injury accompanied by elevated organismal morbidity and mortality. Radiation biodosimetry based on gene expression analysis of peripheral blood is a valuable tool to detect exposure to radiation after a radiological/nuclear incident and obtain useful biological information that could predict tissue and organismal injury. However, confounding factors, including chronic inflammation, can potentially obscure the predictive power of the method. GADD45A (Growth arrest and DNA damage-inducible gene a) plays important roles in cell growth control, differentiation, DNA repair, and apoptosis. GADD45A-deficient mice develop an autoimmune disease, similar to human systemic lupus erythematosus, characterized by severe hematological disorders, kidney disease, and premature death. The goal of this study was to elucidate how pre-existing inflammation in mice, induced by GADD45A ablation, can affect radiation biodosimetry. We exposed wild-type and GADD45A knockout male C57BL/6J mice to 7 Gy of X rays and 24 h later RNA was isolated from whole blood and subjected to whole genome microarray and gene ontology analyses. Dose reconstruction analysis using a gene signature trained on gene expression data from irradiated wild-type male mice showed accurate reconstruction of either a 0 Gy or 7 Gy dose with root mean square error of ± 1.05 Gy (R^2 = 1.00) in GADD45A knockout mice. Gene ontology analysis revealed that irradiation of both wild-type and GADD45A-null mice led to a significant overrepresentation of pathways associated with morbidity and mortality, as well as organismal cell death. However, based on their z-score, these pathways were predicted to be more significantly overrepresented in GADD45A-null mice, implying that GADD45A deletion may exacerbate the deleterious effects of radiation on blood cells. Numerous immune cell functions and quantities were predicted to be underrepresented in both genotypes; however, differentially expressed genes from irradiated GADD45A knockout mice predicted an increased deterioration in the numbers of T lymphocytes, as well as myeloid cells, compared with wild-type mice. Furthermore, an overrepresentation of genes associated with radiation-induced hematological malignancies was associated with GADD45A knockout mice, whereas hematopoietic and progenitor cell functions were predicted to be downregulated in irradiated GADD45A knockout mice. In conclusion, despite the significant differences in gene expression between wild-type and GADD45A knockout mice, it is still feasible to identify a panel of genes that could accurately distinguish between irradiated and control mice, irrespective of pre-existing inflammation status.

Candidate biomarkers and persistent transcriptional responses after low and high dose ionizing radiation at high dose rate

PURPOSE

Development of an integrated time and dose model to explore the dynamics of gene expression alterations and identify biomarkers for biodosimetry following low- and high-dose irradiations at high dose rate.

MATERIAL AND METHODS

We utilized multiple transcriptome datasets (GSE8917, GSE43151, and GSE23515) from Gene Expression Omnibus (GEO) for identifying candidate biological dosimeters. A linear mixed-effects model with random intercept was used to explore the dose-time dynamics of transcriptional responses and to functionally characterize the time- and dose-dependent changes in gene expression.

RESULTS

We identified genes that are correlated with dose and time and discovered two clusters of genes that are either positively or negatively correlated with both dose and time based on the parameters of the model. Genes in these two clusters may have persistent transcriptional alterations. Twelve potential transcriptional markers for dosimetry-ARHGEF3, BAX, BBC3, CCDC109B, DCP1B, DDB2, F11R, GADD45A, GSS, PLK3, TNFRSF10B, and XPC were identified. Of these genes, BAX, GSS, and TNFRSF10B are positively associated with both dose and time course, have a persistent transcriptional response, and might be better biological dosimeters.

CONCLUSIONS

With the proposed approach, we may identify candidate biomarkers that change monotonically in relation to dose, have a persistent transcriptional response, and are reliable over a wide dose range.

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.

Establishing a genomic radiation-age association for space exploration supplements lung disease differentiation

Purpose

One possible way to quantify each individual's response or damage from ionizing radiation is to estimate their accelerated biological age following exposure. Since there is currently no definitive way to know if biological age estimations are accurate, we aim to establish a rad-age association using genomics as its foundation.

Methods

Two datasets were combined and used to empirically find the age cutoff between young and old patients. With age as both a categorical and continuous variable, two other datasets that included radiation exposure are used to test the interaction between radiation and age. The gene lists are oriented in preranked lists for both pathway and diseases analysis. Finally, these genes are used to evaluate another dataset on the clinical relevance in differentiating lung disease given ethnicity and sex using both pairwise t-tests and linear models.

Results

Using 12 well-known genes associated with aging, a threshold of 29-years-old was found to be the difference between young and old patients. The two interaction tests yielded 234 unique genes such that pathway analysis flagged IL-1 signaling and PRPP biosynthesis as significant with high cell proliferation diseases and carcinomas being a common trend. LAPTM4B was the only gene with significant interaction among lung disease, ethnicity, and sex, with fold change greater than two.

Conclusion

The results corroborate an initial association between radiation and age, given inflammation and metabolic pathways and multiple genes emphasizing mitochondrial function, oxidation, and histone modification. Being able to tie rad-age genes to lung disease supplements future work for risk assessment following radiation exposure.

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.

Impact of Radiation Therapy on Biological Parameters in Cancer Patients: Sub-analysis from the RIT Prospective Epidemiological Study

Scarce data investigate the impact of radiotherapy (RT) on biology markers. An analysis of ancillary study of RIT (Radiation Impact on Thromboembolic events) prospective trial was carried out. All patients with non-metastatic solid tumors and treated with radiotherapy and/or brachytherapy in curative and consenting to have blood samples were included. A significant decrease in white blood count, (i.e. lymphocytes, monocytes, neutrophils and basophils) and platelet counts was observed after RT and maintained at 6 months. Whereas, eosinophils, D-dimers and hemoglobin levels were affected respectively 3 months and 6 months after RT initiation. Conversely, red cells count and CRP level were not affected by RT. This study is an advocacy to develop an understanding of basic immune system in relation with RT.

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.

The transcriptomic revolution and radiation biology

PURPOSE

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

CONCLUSIONS

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

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.

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.

Methylation of Promoters of Apoptosis-Related Genes in Blood Lymphocytes of Workers Exposed to Occupational External Irradiation

We studied the effect of technogenic radiation on the degree of promoter methylation in genes involved in apoptosis in blood lymphocytes of workers exposed to long-term γ-radiation during their professional activities. Blood samples for the analysis were obtained from 11 conventionally healthy men aged from 54 to 71 years (mean 66 years), workers of the Siberian Group of Chemical Enterprises working experience from 27 to 40 years (mean 30 years); the external exposure dose was 175.88 mSv (158.20-207.81 mSv). In all examined subjects, the degree of methylation of the promoters of apoptosis-related genes ranged from 0.22 to 50.00%. A correlation was found between the degree of methylation of BCLAF1 promoters (p=0.035) with the age of workers, BAX promoters (p=0.0289) with high content of aberrant cells, and APAF1 promoters (p=0.0152) with increased number of dicentric chromosomes. A relationship was found between the dose of external irradiation and the degree of methylation of gene promoters of BAD (p=0.0388), BID (р=0.0426), and HRK (р=0.0101) genes.

An ionising radiation-induced specific transcriptional signature of inflammation-associated genes in whole blood from radiotherapy patients: a pilot study

BACKGROUND

This communication reports the identification of a new panel of transcriptional changes in inflammation-associated genes observed in response to ionising radiation received by radiotherapy patients.

METHODS

Peripheral blood samples were taken with ethical approval and informed consent from a total of 20 patients undergoing external beam radiotherapy for breast, lung, gastrointestinal or genitourinary tumours. Nanostring nCounter analysis of transcriptional changes was carried out in samples prior and 24 h post-delivery of the 1st radiotherapy fraction, just prior to the 5th or 6th fraction, and just before the last fraction.

RESULTS

Statistical analysis with BRB-ArrayTools, GLM MANOVA and nSolver, revealed a radiation responsive panel of genes which varied by patient group (type of cancer) and with time since exposure (as an analogue for dose received), which may be useful as a biomarker of radiation response.

CONCLUSION

Further validation in a wider group of patients is ongoing, together with work towards a full understanding of patient specific responses in support of personalised approaches to radiation medicine.

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.

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.

Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3^rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates’ 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

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.

Impact of Inter-Individual Variance in the Expression of a Radiation-Responsive Gene Panel Used for Triage

In previous studies we determined a gene expression signature in baboons for predicting the severity of hematological acute radiation syndrome. We subsequently validated a set of eight of these genes in leukemia patients undergoing total-body irradiation. In the current study, we addressed the effect of intra-individual variability on the basal level of expression of those eight radiation-responsive genes identified previously, by examining baseline levels in 200 unexposed healthy human donors (122 males and 88 females with an average age of 46 years) using real-time PCR. In addition to the eight candidate genes ( DAGLA, WNT3, CD177, PLA2G16, WLS, POU2AF1, STAT4 and PRF1), we examined two more genes ( FDXR and DDB2) widely used in ex vivo whole blood experiments. Although significant sex- (seven genes) and age-dependent (two genes) differences in expression were found, the fold changes ranged only between 1.1-1.6. These were well within the twofold differences in gene expression generally considered to represent control values. Age and sex contributed less than 20-30% to the complete inter-individual variance, which is calculated as the fold change between the lowest (reference) and the highest Ct value minimum-maximum fold change (min-max FC). Min-max FCs ranging between 10-17 were observed for most genes; however, for three genes, min-max FCs of complete inter-individual variance were found to be 37.1 ( WNT3), 51.4 ( WLS) and 1,627.8 ( CD177). In addition, to determine whether discrimination between healthy and diseased baboons might be altered by replacing the published gene expression data of the 18 healthy baboons with that of the 200 healthy humans, we employed logistic regression analysis and calculated the area under the receiver operating characteristic (ROC) curve. The additional inter-individual variance of the human data set had either no impact or marginal impact on the ROC area, since up to 32-fold change gene expression differences between healthy and diseased baboons were observed.

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.

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.

Global transcriptome profile reveals abundance of DNA damage response and repair genes in individuals from high level natural radiation areas of Kerala coast

The high level natural radiation areas (HLNRA) of Kerala coast in south west India is unique for its wide variation in the background radiation dose (<1.0mGy to 45mGy/year) and vast population size. Several biological studies conducted in this area did not reveal any adverse effects of chronic low dose and low dose rate radiation on human population. In the present study, global transcriptome analysis was carried out in peripheral blood mono-nuclear cells of 36 individuals belonging to different background dose groups [NLNRA, (Group I, ≤1.50 mGy/year) and three groups of HLNRA; Group II, 1.51–5.0 mGy/year), Group III, 5.01-15mGy/year and Group IV, >15.0 mGy/year] to find out differentially expressed genes and their biological significance in response to chronic low dose radiation exposure. Our results revealed a dose dependent increase in the number of differentially expressed genes with respect to different background dose levels. Gene ontology analysis revealed majority of these differentially expressed genes are involved in DNA damage response (DDR) signaling, DNA repair, cell cycle arrest, apoptosis, histone/chromatin modification and immune response. In the present study, 64 background dose responsive genes have been identified as possible chronic low dose radiation signatures. Validation of 30 differentially expressed genes was carried out using fluorescent based universal probe library. Abundance of DDR and DNA repair genes along with pathways such as MAPK, p53 and JNK in higher background dose groups (> 5.0mGy/year) indicated a possible threshold dose for DDR signaling and are plausible reason of observing in vivo radio-adaptive response and non-carcinogenesis in HLNRA population. To our knowledge, this is the first study on molecular effect of chronic low dose radiation exposure on human population from high background radiation areas at transcriptome level using high throughput approach. These findings have tremendous implications in understanding low dose radiation biology especially, the effect of low dose radiation exposure in humans.

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.

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.

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.

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.

Transcriptome Alterations In X-Irradiated Human Gingiva Fibroblasts

Ionizing radiation is known to induce genomic lesions, such as DNA double strand breaks, whose repair can lead to mutations that can modulate cellular and organismal fate. Soon after radiation exposure, cells induce transcriptional changes and alterations of cell cycle programs to respond to the received DNA damage. Radiation-induced mutations occur through misrepair in a stochastic manner and increase the risk of developing cancers years after the incident, especially after high dose radiation exposures. Here, the authors analyzed the transcriptomic response of primary human gingival fibroblasts exposed to increasing doses of acute high dose-rate x rays. In the dataset obtained after 0.5 and 5 Gy x-ray exposures and two different repair intervals (0.5 h and 16 h), the authors discovered several radiation-induced fusion transcripts in conjunction with dose-dependent gene expression changes involving a total of 3,383 genes. Principal component analysis of repeated experiments revealed that the duration of the post-exposure repair intervals had a stronger impact than irradiation dose. Subsequent overrepresentation analyses showed a number of KEGG gene sets and WikiPathways, including pathways known to relate to radioresistance in fibroblasts (Wnt, integrin signaling). Moreover, a significant radiation-induced modulation of microRNA targets was detected. The data sets on IR-induced transcriptomic alterations in primary gingival fibroblasts will facilitate genomic comparisons in various genotoxic exposure scenarios.

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.

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.

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.

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

The online version of this article (doi:10.1186/s12920-015-0097-x) contains supplementary material, which is available to authorized users.

LIM-only protein FHL2 critically determines survival and radioresistance of pancreatic cancer cells

Numerous factors determine the current poor prognosis of pancreatic ductal adenocarcinoma (PDAC). One of the greatest challenges to overcome is treatment resistance. Among a large repertoire of intrinsic resistance mechanisms, integrin-mediated cell adhesion to extracellular matrix (ECM) has been identified to be fundamental. Coalesced in focal adhesion complexes, integrins, receptor tyrosine kinases, protein kinases and adapter proteins mediate prosurvival signaling. Four and a half LIM domains protein 2 (FHL2) is one of these adapter proteins, which operates through protein-protein interactions and shows tumor-specific expression. Based on this, we investigated FHL2 expression in PDAC specimens and three-dimensionally grown cell lines and how FHL2 mechanistically contributes to cell survival, cell cycling and radiation resistance. PDAC exhibited a significantly increased and heterogeneous FHL2 expression. Upon FHL2 depletion, pancreatic cancer cell lines showed significantly decreased cell survival, proliferation and radioresistance as well as enhanced apoptosis and MEK/ERK signaling and cyclin D1, E, A and B1 expression were strongly induced. Targeting of FHL2 and MEK1 was similarly effective than FHL2 depletion alone, suggesting MEK1 as a downstream signaling mediator of FHL2. Taken together, our results provide evidence for the importance of the focal adhesion protein FHL2 in pancreatic cancer cell survival, proliferation and radiosensitivity.

FHL2 regulates hematopoietic stem cell functions under stress conditions

FHL2, a member of the four and one half LIM domain protein family, is a critical transcriptional modulator. Here, we identify FHL2 as a critical regulator of hematopoietic stem cells (HSCs) that is essential for maintaining HSC self-renewal under regenerative stress. We find that Fhl2 loss has limited effects on hematopoiesis under homeostatic conditions. In contrast, Fhl2-null chimeric mice reconstituted with Fhl2-null bone marrow cells developed abnormal hematopoiesis with significantly reduced numbers of HSCs, hematopoietic progenitor cells (HPCs), red blood cells and platelets as well as hemoglobin levels. In addition, HSCs displayed a significantly reduced self-renewal capacity and were skewed toward myeloid lineage differentiation. We find that Fhl2 loss reduces both HSC quiescence and survival in response to regenerative stress, probably as a consequence of Fhl2-loss-mediated down-regulation of cyclin dependent kinase (CDK)-inhibitors, including p21(Cip) and p27(Kip1). Interestingly, FHL2 is regulated under control of a tissue specific promoter in hematopoietic cells and it is down-regulated by DNA hypermethylation in the leukemia cell line and primary leukemia cells. Furthermore, we find that down-regulation of FHL2 frequently occurs in myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) patients, raising a possibility that FHL2 down-regulation plays a role in the pathogenesis of myeloid malignancies.

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.

Widespread decreased expression of immune function genes in human peripheral blood following radiation exposure

We report a large-scale reduced expression of genes in pathways related to cell-type specific immunity functions that emerges from microarray analysis 48 h after ex vivo γ-ray irradiation (0, 0.5, 2, 5, 8 Gy) of human peripheral blood from five donors. This response is similar to that seen in patients at 24 h after the start of total-body irradiation and strengthens the rationale for the ex vivo model as an adjunct to human in vivo studies. The most marked response was in genes associated with natural killer (NK) cell immune functions, reflecting a relative loss of NK cells from the population. T- and B-cell mediated immunity genes were also significantly represented in the radiation response. Combined with our previous studies, a single gene expression signature was able to predict radiation dose range with 97% accuracy at times from 6-48 h after exposure. Gene expression signatures that may report on the loss or functional deactivation of blood cell subpopulations after radiation exposure may be particularly useful both for triage biodosimetry and for monitoring the effect of radiation mitigating treatments.

Gene expression as a biomarker for human radiation exposure

Accidental exposure to ionizing radiation can be unforeseen, rapid, and devastating. The detonation of a radiological device leading to such an exposure can be detrimental to the exposed population. The radiation-induced damage may manifest as acute effects that can be detected clinically or may be more subtle effects that can lead to long-term radiation-induced abnormalities. Accurate identification of the individuals exposed to radiation is challenging. The availability of a rapid and effective screening test that could be used as a biomarker of radiation exposure detection is mandatory. We tested the suitability of alterations in gene expression to serve as a biomarker of human radiation exposure. To develop a useful gene expression biomonitor, however, gene expression changes occurring in response to irradiation in vivo must be measured directly. Patients undergoing radiation therapy provide a suitable test population for this purpose. We examined the expression of CC3, MADH7, and SEC PRO in blood samples of these patients before and after radiotherapy to measure the in vivo response. The gene expression after ionizing radiation treatment varied among different patients, suggesting the complexity of the response. The expression of the SEC PRO gene was repressed in most of the patients. The MADH7 gene was found to be upregulated in most of the subjects and could serve as a molecular marker of radiation exposure.

DNA repair and cell cycle biomarkers of radiation exposure and inflammation stress in human blood

DNA damage and repair are hallmarks of cellular responses to ionizing radiation. We hypothesized that monitoring the expression of DNA repair-associated genes would enhance the detection of individuals exposed to radiation versus other forms of physiological stress. We employed the human blood ex vivo radiation model to investigate the expression responses of DNA repair genes in repeated blood samples from healthy, non-smoking men and women exposed to 2 Gy of X-rays in the context of inflammation stress mimicked by the bacterial endotoxin lipopolysaccharide (LPS). Radiation exposure significantly modulated the transcript expression of 12 genes of 40 tested (2.2E-06<p<0.03), of which 8 showed no overlap between unirradiated and irradiated samples (CDKN1A, FDXR, BBC3, PCNA, GADD45a, XPC, POLH and DDB2). This panel demonstrated excellent dose response discrimination (0.5 to 8 Gy) in an independent human blood ex vivo dataset, and 100% accuracy for discriminating patients who received total body radiation. Three genes of this panel (CDKN1A, FDXR and BBC3) were also highly sensitive to LPS treatment in the absence of radiation exposure, and LPS co-treatment significantly affected their radiation responses. At the protein level, BAX and pCHK2-thr68 were elevated after radiation exposure, but the pCHK2-thr68 response was significantly decreased in the presence of LPS. Our combined panel yields an estimated 4-group accuracy of ∼90% to discriminate between radiation alone, inflammation alone, or combined exposures. Our findings suggest that DNA repair gene expression may be helpful to identify biodosimeters of exposure to radiation, especially within high-complexity exposure scenarios.