Identification of gene expression biomarkers for predicting radiation exposure

AT, Raavi V, Easwaramoorthy V, Murugaiyan P, Perumal V. Enhanced γ-H2AX Foci Frequency and Altered Gene Expression in Participants Exposed to Ionizing Radiation During I-131 Nuclear Medicine Procedures

Purpose

Ionizing radiation-based technologies are extensively used in the diagnosis and treatment of diseases. While utilizing the technologies, exposure to a certain amount of radiation is unavoidable. Data can be obtained from participants who received radiation during medical imaging and therapeutic purposes to predict the effects of low-dose radiation.

Methods

To understand the effects of low-dose radiation, participants (n = 22) who received radioactive I-131 for scan/therapy were used as a model in this study. Blood samples were drawn pre- and post-administration of I-131. Biological effects were measured using markers of DNA damage (γ-H2AX, micronucleus (MN), and chromosomal aberrations (CA)) and response to damage through gene expression changes (ATM, CDKN1A, DDB2, FDXR, and PCNA) in blood samples.

Results

Mean frequency of γ-H2AX foci in pre-samples was 0.28 ± 0.16, and post-samples were 1.03 ± 0.60. γ-H2AX foci frequency obtained from post-samples showed significant (p < 0.0001) and a heterogeneous increase in all the participants (received I-131 for scan/therapy) when compared to pre-samples. A significant increase (p < 0.0001) in MN and CA frequency was also observed in participants who received the I-131 therapy. Gene expression analysis indicates that all genes (ATM, CDKN1A, DDB2, FDXR, and PCNA) were altered in post-samples, although with varying degrees, suggesting that the cellular responses to DNA damage, such as damage repair, cell cycle regulation to aid in repair and apoptosis are increased, which priority is given to repair, followed by apoptosis.

Conclusion

The results of this study indicate that the participants who received I-131 (low doses of β- and γ-radiation) can produce substantial biological effects.

Comparative Analysis of Driver Mutations and Transcriptomes in Papillary Thyroid Cancer by Region of Residence in South Korea

BACKGRUOUND

Radiation exposure is a well-known risk factor for papillary thyroid cancer (PTC). South Korea has 24 nuclear reactors in operation; however, no molecular biological analysis has been performed on patients with PTC living near nuclear power plants.

METHODS

We retrospectively included patients with PTC (n=512) divided into three groups according to their place of residence at the time of operation: inland areas (n=300), coastal areas far from nuclear power plants (n=134), and nuclear power plant areas (n=78). After propensity score matching (1:1:1) by age, sex, and surgical procedure, the frequency of representative driver mutations and gene expression profiles were compared (n=50 per group). Epithelial-mesenchymal transition (EMT), BRAF, thyroid differentiation, and radiation scores were calculated and compared.

RESULTS

No significant difference was observed in clinicopathological characteristics, including radiation exposure history and the frequency of incidentally discovered thyroid cancer, among the three groups. BRAFV600E mutation was most frequently detected in the groups, with no difference among the three groups. Furthermore, gene expression profiles showed no statistically significant difference. EMT and BRAF scores were higher in our cohort than in cohorts from Chernobyl tissue bank and The Cancer Genome Atlas Thyroid Cancer; however, there was no difference according to the place of residence. Radiation scores were highest in the Chernobyl tissue bank but exhibited no difference according to the place of residence.

CONCLUSION

Differences in clinicopathological characteristics, frequency of representative driver mutations, and gene expression profiles were not observed according to patients' region of residence in South Korea.

DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns

In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker-related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry.

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.

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.

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.

FDXR Gene Expression after in Vivo Radiation Exposure of Pediatric Patients Undergoing Interventional Cardiology Procedures

BACKGROUND

Ferredoxin reductase (FDXR) has already been reported as a promising biomarker for estimating radiation doses in radiotherapy. This study aimed to investigate the responsiveness of FDXR on pediatric population exposed to ionizing radiation (X-rays) during pediatric interventional cardiology (IC) procedures.

PATIENTS AND METHODS

Peripheral blood was collected by venipuncture from 24 pediatric donors before and 24 hours after the IC procedure. To estimate the effective dose, demographic data and Air Kerma-Area Product (PKA) were recorded for each patient. The relative quantification (RQ) of the FDXR gene in irradiated patient blood samples compared to the non-irradiated blood samples was determined using qPCR analysis. The relative values of FDXR were log- transformed.

RESULTS

The effective dose ranged from 0.002 mSv to 8.004 mSv. Over this radiation exposure range, the FDXR gene expression varied randomly with the effective dose. Up-regulation in FDXR expression was observed in 17 patients and down-regulation in 7 patients.

CONCLUSIONS

Further studies in a larger cohort of pediatric patients along with the record of clinical data are needed to determine whether FDXR gene expression is an effective biomarker for radiation exposure estimation in pediatric imaging.

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

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

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.

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

Radioprotection and Radiomitigation: From the Bench to Clinical Practice

The development of protective agents against harmful radiations has been a subject of investigation for decades. However, effective (ideal) radioprotectors and radiomitigators remain an unsolved problem. Because ionizing radiation-induced cellular damage is primarily attributed to free radicals, radical scavengers are promising as potential radioprotectors. Early development of such agents focused on thiol synthetic compounds, e.g., amifostine (2-(3-aminopropylamino) ethylsulfanylphosphonic acid), approved as a radioprotector by the Food and Drug Administration (FDA, USA) but for limited clinical indications and not for nonclinical uses. To date, no new chemical entity has been approved by the FDA as a radiation countermeasure for acute radiation syndrome (ARS). All FDA-approved radiation countermeasures (filgrastim, a recombinant DNA form of the naturally occurring granulocyte colony-stimulating factor, G-CSF; pegfilgrastim, a PEGylated form of the recombinant human G-CSF; sargramostim, a recombinant granulocyte macrophage colony-stimulating factor, GM-CSF) are classified as radiomitigators. No radioprotector that can be administered prior to exposure has been approved for ARS. This differentiates radioprotectors (reduce direct damage caused by radiation) and radiomitigators (minimize toxicity even after radiation has been delivered). Molecules under development with the aim of reaching clinical practice and other nonclinical applications are discussed. Assays to evaluate the biological effects of ionizing radiations are also analyzed.

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

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

Identification of potential radiation-responsive biomarkers based on human orthologous genes with possible roles in DNA repair pathways by comparison between Arabidopsis thaliana and homo sapiens

Rapid and reliable ionization radiation (IR) exposure estimation has become increasingly important in environment due to the urgent requirement of medical evaluation and treatment in the event of nuclear accident emergency. Human DNA repair genes can be identified as important candidate biomarkers to assess IR exposure, while how to find the enough sensitive and specific biomarkers in the DNA repair networks is still challenged and not fully determined. The conserved features of DNA repair pathways may facilitate interdisciplinary studies that cross the traditional boundaries between animal and plant biology, with the aim of identifying undiscovered human DNA repair genes for potential radiation-responsive biomarkers. In this work, an in silico method of homologous comparison was performed to identify the human orthologues of A. thaliana DNA repair genes, and thereby to explore the sensitive and specific human radiation-responsive genes to evaluate the IR exposure levels. The results showed that a total of 16 putative candidate genes were involved in the human DNA repair pathways of homologous recombination (HR) and non-homologous end joining (NHEJ), and most of them were confirmed by previous experiments. Additionally, we analyzed the gene expression patterns of these 16 candidate genes in several human transcript microarray datasets with different IR treatments. The results indicated that most of the gene expression levels for these candidate genes were significantly changed under different radiation treatments. Based on these results, we integrated these putative human DNA repair genes into the DNA repair pathways to propose new insights of the HR and NHEJ pathways, which can also provide the potential targets for the development of radiation biomarkers. Notably, two putative DNA repair genes, named ERCC1 and ESCO2, were identified and were considered to be the sensitive and specific biomarkers in response to γ-ray exposures.

The hunt for radiation biomarkers: current situation

Purpose: The possibility of a large-scale acute radiation exposure necessitates the development of new methods that could provide a rapid assessment of the doses received by individuals using high-throughput technologies. There is also a great interest in developing new biomarkers of dose exposure, which could be used in large molecular epidemiological studies in order to correlate estimated doses received and health effects. The goal of this review was to summarize current literature focused on biological dosimetry, namely radiation-responsive biomarkers.Methods: The studies involved in this review were thoroughly selected according to the determined criteria and PRISMA guidelines.Results: We described briefly recent advances in radiation genomics and metabolomics, giving particular emphasis to proteomic analysis. The majority of studies were performed on animal models (rats, mice, and non-human primates). They have provided much beneficial information, but the most relevant tests have been done on human (oncological) patients. By inspecting the radiaiton biodosimetry literate of the last 10 years, we identified a panel of candidate markers for each -omic approach involved.Conslusions: We reviewed different methodological approaches and various biological materials, which can be exploited for dose-effect prediction. The protein biomarkers from human plasma are ideal for this specific purpose. From a plethora of candidate markers, FDXR is a very promising transcriptomic candidate, and importantly this biomarker was also confirmed by some studies at protein level in humans.

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

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

Dataset for dose and time-dependent transcriptional response to ionizing radiation exposure

Exposure to ionizing radiation associated with highly energetic and charged heavy particles is an inherent risk astronauts face in long duration space missions. We have previously considered the transcriptional effects that three levels of radiation (0.3 Gy, 1.5 Gy, and 3.0 Gy) have at an immediate time point (1 hr) post-exposure [1]. Our analysis of these results suggest effects on transcript levels that could be modulated at lower radiation doses [2]. In addition, a time dependent effect is likely to be present. Therefore, in order to develop a lab-on-a-chip approach for detection of radiation exposure in terms of both radiation level and time since exposure, we developed a time- and dose-course study to determine appropriate sensitive and specific transcript biomarkers that are detectable in blood samples. The data described herein was developed from a study measuring exposure to 0.15 Gy, 0.30 Gy, and 1.5 Gy of radiation at 1 hr, 2 hr, and 6 hr post-exposure using Affymetrix® GeneChip® PrimeView™ microarrays. This report includes raw gene expression data files from the resulting microarray experiments representing typical radiation exposure levels an astronaut may experience as part of a long duration space mission. The data described here is available in NCBI's Gene Expression Omnibus (GEO), accession GSE63952.

Transcriptome profiles of isolated murine Achilles tendon proper- and peritenon-derived progenitor cells

Progenitor cells of the tendon proper and peritenon have unique properties that could impact their utilization in tendon repair strategies. While a few markers have been found to aid in distinguishing progenitors cells from each region, there is great value in identifying more markers. In this study, we hypothesized that RNAseq could be used to improve our understanding of those markers that define these cell types. Transcriptome profiles were generated for pools of mouse Achilles tendon progenitor cells from both regions and catalogues of potential markers were generated. Moreover, common (e.g., glycoprotein, signaling, and proteinaceous extracellular matrix) and unique (e.g., cartilage development versus angiogenesis and muscle contraction) biological processes and molecular functions were described for progenitors from each region. Real-time quantitative PCR of a subset of genes was used to gain insight into the heterogeneity amongst individual progenitor colonies from each region. Markers like Scx, Mkx, Thbs4, and Wnt10a were consistently able to distinguish tendon proper progenitors from peritenon progenitors; expression variability for other genes suggested greater cell type complexity for potential peritenon progenitor markers. This is the first effort to define Achilles tendon progenitor markers by region. Further efforts to investigate the value of these cataloged markers are required by screening more individual colonies of progenitors for more markers. Clinical Significance: Findings from this study advance efforts in the discernment of cell type specific markers for tendon proper and peritenon progenitor cells; insight into marker sets could improve tracking and sorting strategies for these cells for future therapeutic strategies. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1409-1418, 2019.

THE ROLE OF RADIOACTIVE METHODS IN THE DIAGNOSTIC TYPE OF HYDRONEPHROSIS IN CLEAN-UP WORKERS OF CHORNOBYL ACCIDENT

RATIONALE

Since the introduction of radionuclide research methods into clinical practice, they occupy an importantplace in the diagnosis of hydronephrosis and, at the same time, sufficiently objective, sensitive and atraumaticmethods of investigation.

OBJECTIVE

On the basis of retrospective data, to investigate the diagnostic role of radionuclide renography (RRG)and the method of indirect radionuclide renangiography (IRAG) in clean-up workers of Chornobyl accident withhydronephrosis.

MATERIALS AND METHODS

A total of 257 patients with hydronephrosis (140 women and 117 men) aged 15 to 77 yearswere examined by the RRG and the IRAG. The RRG technique consists of intravenous administration of a solution of131I-hypurane (2.5 kBq/kg) and continuous registration for 20 minutes of the level of radioactivity above the kid-neys with the help of sensors of the renograph UR 1-1. The IRAG was conducted for 30-45 seconds with exposure1 frame per second after intravenous administration of a solution of 99mTc-pentatech (2 MBq/kg).

RESULTS

The results of the radionuclide study of the hemodynamics of patients with different stages ofhydronephrosis made it possible to draw a conclusion regarding the expediency of taking into account the condi-tion of the cup-pelvic system in the preoperative period, as well as the parameters of the arterial and venous circu-lation.

CONCLUSIONS

Combined use of X-ray and radionuclide methods allows establishing the cause and consequences ofhydronephrosis, to develop a rational treatment plan. RRG and IRAG are reliable methods of dynamic control in post-operative observation of clean-up workers of Chornobyl accident with hydronephrosis.

BIOLOGICAL MARKERS OF EXTERNAL AND INTERNAL EXPOSURE IN SHELTER CONSTRUCTION WORKERS: A 13-YEAR EXPERIENCE

OBJECTIVE

The aim of this study is to investigate the cellular, molecular, genetic and non-invasive functional biomarkers of occupational irradiation in workers exposed to a combination of external gamma-radiation and incorporation of transuranium elements.

RESULTS

A study was performed in 688 radiation workers of Shelter object conversion into a safe system with mean shift dose of external exposure of 26.06 mSv (range: 0.1-113.35) and risk of internal exposure with transuranium elements. Several biological parameters could serve as biomarkers of exposure at radiation doses below 100 mSv and even in 20-50 mSv interval. The parallel changes were shown in decline of brain electric activity, telomere length, differences in CCND1, CDKN1A, CDKN2A, VEGFA, TP53, DDB2 genes expression. An increase in counts of dicentrics, pair fragments and TCR-variant lymphocytes at doses over professional limits shows the need of biological dosimetry. The most sensitive markers include TCR-CD4+, γ-H2AX+ and CyclinD1+ cell counts. Implementation of flow cytometry approach for these markers enables quick obtaining of quantitative data. Confounding factors included respiratory function and smoking. The study of the radiation workers with the history of chronic exposure in radiation area during 3-5 years demonstrates changes of compensatory origin, i.e. absence of telomere shortening, increased number of NK-cells in combination with lower expression of intracellular γ-H2AX.

CONCLUSION

This study confirms the presence of radiation-induced changes in gene regulation of cell proliferation, telomere function and apoptosis in radiation workers exposed to external and internal exposure at doses above professional limits and increase of compensatory changes.

EXPRESSION OF BIOLOGICAL MARKERS INDUCED BY IONIZING RADIATION AT THE LATE PERIOD AFTER EXPOSURE IN A WIDE RANGE OF DOSES

OBJECTIVE

To study radiation induced biological markers of the late period after exposure.

SUBJECTS AND METHODS

A study was performed in 235 Chornobyl accident male clean-up workers exposed in 1986-1987 (doses of external exposure: (M ± SD: 419.48 ± 654.60; range 0.10-3,500 mSv); age 58,34 ± 6,57 years. Controlgroup included 45 non-exposed subjects (mean age: 50.60 ± 5.37 (M ± SD). Gene expression was performed by RT-PCR on 7900HT Analyzer using TLDA for BCL2, CDKN2A, CLSTN2, GSTM1, IFNG, IL1B, MCF2L, SERPINB9, STAT3, TERF1, TERF2,TERT, TNF, TP53, CCND1 genes. Relative telomere length (RTL) was analysed by flow-FISH; immune cell subsets,γ-H2AХ and CyclinD1 expression by flow cytometry.

RESULTS

A statistically significant and dose-dependent decrease in expression of the BCL2, SERPINB9, CDKN2A, andSTAT3 genes was demonstrated in parallel to a dose-dependent overexpression of MCF2L and upregulation of TP53 (upto 100 mSv). IL1B expression was the highest in exposed to doses from 0.1 to 100 mSv with a negative correlationbetween at IL1B expression and CD19+3-, CD3-HLA-DR+, CD4+8- cell counts and CD4+/CD8+ ratio. Hyperexpression ofTNF gene in doses above 100 mSv to 1,000 mSv was shown, and in higher doses a combination of TNF downregula-tion with increase in IFNG gene expression were demonstrated with correlations with numbers of CD3+16+56+ andCD25+ lymphocytes and inhibition of expression CLSTN2. An increased expression of γ-H2AХ and Cyclin D1 corre-lated to radiation dose, telomere shortening to age and concommittant pathology.

CONCLUSIONS

Cellular immunity, gene expression, telomere length, intracellular protein parameters are shown to beamong perspective biological markers at a late period after radiation exposure.

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.

Ionizing radiation exposure: hazards, prevention, and biomarker screening

Radiation is a form of energy derived from a source that is propagated through material in space. It consists of ionizing radiation or nonionizing radiation. Ionizing radiation is a feature of the environment and an important tool in medical treatment, but it can cause serious damage to organisms. A number of protective measures and standards of protection have been proposed to protect against radiation. There is also a need for biomarkers to rapidly assess individual doses of radiation, which can not only estimate the dose of radiation but also determine its effects on health. Proteomics, genomics, metabolomics, and lipidomics have been widely used in the search for such biomarkers. These topics are discussed in depth in this review.

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.

FDXR is a biomarker of radiation exposure in vivo

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

Development of a prediction model for radiosensitivity using the expression values of genes and long non-coding RNAs

Radiotherapy has become a popular and standard approach for treating cancer patients because it greatly improves patient survival. However, some of the patients receiving radiotherapy suffer from adverse effects and do not obtain survival benefits. This may be attributed to the fact that most radiation treatment plans are designed based on cancer type, without consideration of each individual's radiosensitivity. A model for predicting radiosensitivity would help to address this issue. In this study, the expression levels of both genes and long non-coding RNAs (lncRNAs) were used to build such a prediction model. Analysis of variance and Tukey's honest significant difference tests (P < 0.001) were utilized in immortalized B cells (GSE26835) to identify differentially expressed genes and lncRNAs after irradiation. A total of 41 genes and lncRNAs associated with radiation exposure were revealed by a network analysis algorithm. To develop a predictive model for radiosensitivity, the expression profiles of NCI-60 cell lines along, with their radiation parameters, were analyzed. A genetic algorithm was proposed to identify 20 predictors, and the support vector machine algorithm was used to evaluate their prediction performance. The model was applied to 2 datasets of glioblastoma, The Cancer Genome Atlas and GSE16011, and significantly better survival was observed in patients with greater predicted radiosensitivity.

Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia

BACKGROUND

Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data.

RESULTS

Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes.

CONCLUSION

Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data.

Biomarkers of Ionizing Radiation Exposure: A Multiparametric Approach

Humans are exposed to ionizing radiation not only through background radiation but also through the ubiquitous presence of devices and sources that generate radiation. With the expanded use of radiation in day-to-day life, the chances of accidents or misuse only increase. Therefore, a thorough understanding of the dynamic effects of radiation exposure on biological entities is necessary. The biological effects of radiation exposure on human cells depend on much variability such as level of exposure, dose rate, and the physiological state of the cells. During potential scenarios of a large-scale radiological event which results in mass casualties, dose estimates are essential to assign medical attention according to individual needs. Many attempts have been made to identify biomarkers which can be used for high throughput biodosimetry screening. In this study, we compare the results of different biodosimetry methods on the same irradiated cells to assess the suitability of current biomarkers and push forward the idea of employing a multiparametric approach to achieve an accurate dose and risk estimation.

Comparison of gene expression response to neutron and x-ray irradiation using mouse blood

Background

In the event of an improvised nuclear device detonation, the prompt radiation exposure would consist of photons plus a neutron component that would contribute to the total dose. As neutrons cause more complex and difficult to repair damage to cells that would result in a more severe health burden to affected individuals, it is paramount to be able to estimate the contribution of neutrons to an estimated dose, to provide information for those making treatment decisions.

Results

Mice exposed to either 0.25 or 1 Gy of neutron or 1 or 4 Gy x-ray radiation were sacrificed at 1 or 7 days after exposure. Whole genome microarray analysis identified 7285 and 5045 differentially expressed genes in the blood of mice exposed to neutron or x-ray radiation, respectively. Neutron exposure resulted in mostly downregulated genes, whereas x-rays showed both down- and up-regulated genes. A total of 34 differentially expressed genes were regulated in response to all ≥1 Gy exposures at both times. Of these, 25 genes were consistently downregulated at days 1 and 7, whereas 9 genes, including the transcription factor E2f2, showed bi-directional regulation; being downregulated at day 1, while upregulated at day 7. Gene ontology analysis revealed that genes involved in nucleic acid metabolism processes were persistently downregulated in neutron irradiated mice, whereas genes involved in lipid metabolism were upregulated in x-ray irradiated animals. Most biological processes significantly enriched at both timepoints were consistently represented by either under- or over-expressed genes. In contrast, cell cycle processes were significant among down-regulated genes at day 1, but among up-regulated genes at day 7 after exposure to either neutron or x-rays. Cell cycle genes downregulated at day 1 were mostly distinct from the cell cycle genes upregulated at day 7. However, five cell cycle genes, Fzr1, Ube2c, Ccna2, Nusap1, and Cdc25b, were both downregulated at day 1 and upregulated at day 7.

Conclusions

We describe, for the first time, the gene expression profile of mouse blood cells following exposure to neutrons. We have found that neutron radiation results in both distinct and common gene expression patterns compared with x-ray radiation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3436-1) contains supplementary material, which is available to authorized users.

Insights into a Critical Role of the FOXO3a-FOXM1 Axis in DNA Damage Response and Genotoxic Drug Resistance

FOXO3a and FOXM1 are two forkhead transcription factors with antagonistic roles in cancer and DNA damage response. FOXO3a functions like a typical tumour suppressor, whereas FOXM1 is a potent oncogene aberrantly overexpressed in genotoxic resistant cancers. FOXO3a not only represses FOXM1 expression but also its transcriptional output. Recent research has provided novel insights into a central role for FOXO3a and FOXM1 in DNA damage response. The FOXO3a-FOXM1 axis plays a pivotal role in DNA damage repair and the accompanied cellular response through regulating the expression of genes essential for DNA damage sensing, mediating, signalling and repair as well as for senescence, cell cycle and cell death control. In this manner, the FOXO3a-FOXM1 axis also holds the key to cell fate decision in response to genotoxic therapeutic agents and controls the equilibrium between DNA repair and cell termination by cell death or senescence. As a consequence, inhibition of FOXM1 or reactivation of FOXO3a in cancer cells could enhance the efficacy of DNA damaging cancer therapies by decreasing the rate of DNA repair and cell survival while increasing senescence and cell death. Conceptually, targeting FOXO3a and FOXM1 may represent a promising molecular therapeutic option for improving the efficacy and selectivity of DNA damage agents, particularly in genotoxic agent resistant cancer. In addition, FOXO3a, FOXM1 and their downstream transcriptional targets may also be reliable diagnostic biomarkers for predicting outcome, for selecting therapeutic options, and for monitoring treatments in DNA-damaging agent therapy.

Gene expression profiling in undifferentiated thyroid carcinoma induced by high-dose radiation

Published gene expression studies for radiation-induced thyroid carcinogenesis have used various methodologies. In this study, we identified differential gene expression in a human thyroid epithelial cell line after exposure to high-dose γ-radiation. HTori-3 cells were exposed to 5 or 10 Gy of ionizing radiation using two dose rates (high-dose rate: 4.68 Gy/min, and low-dose rate: 40 mGy/h) and then implanted into the backs of BALB/c nude mice after 4 (10 Gy) or 5 weeks (5 Gy). Decreases in cell viability, increases in giant cell frequency, anchorage-independent growth in vitro, and tumorigenicity in vivo were observed. Particularly, the cells irradiated with 5 Gy at the high-dose rate or 10 Gy at the low-dose rate demonstrated more prominent tumorigenicity. Gene expression profiling was analyzed via microarray. Numerous genes that were significantly altered by a fold-change of >50% following irradiation were identified in each group. Gene expression analysis identified six commonly misregulated genes, including CRYAB, IL-18, ZNF845, CYP24A1, OR4N4 and VN1R4, at all doses. These genes involve apoptosis, the immune response, regulation of transcription, and receptor signaling pathways. Overall, the altered genes in high-dose rate (HDR) 5 Gy and low-dose rate (LDR) 10 Gy were more than those of LDR 5 Gy and HDR 10 Gy. Thus, we investigated genes associated with aggressive tumor development using the two dosage treatments. In this study, the identified gene expression profiles reflect the molecular response following high doses of external radiation exposure and may provide helpful information about radiation-induced thyroid tumors in the high-dose range.

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.

Transcriptional profile of immediate response to ionizing radiation exposure

Astronauts participating in long duration space missions are likely to be exposed to ionizing radiation associated with highly energetic and charged heavy particles. Previously proposed gene biomarkers for radiation exposure include phosphorylated H2A Histone Family, Member X (γH2AX), Tumor Protein 53 (TP53), and Cyclin-Dependent Kinase Inhibitor 1A (CDKN1A). However, transcripts of these genes may not be the most suitable biomarkers for radiation exposure due to a lack of sensitivity or specificity. As part of a larger effort to develop lab-on-a-chip methods for detecting radiation exposure events using blood samples, we designed a dose–course microarray study in order to determine coding and non-coding RNA transcripts undergoing differential expression immediately following radiation exposure. The main goal was to elicit a small set of sensitive and specific radiation exposure biomarkers at low, medium, and high levels of ionizing radiation exposure. Four separate levels of radiation were considered: 0 Gray (Gy) control; 0.3 Gy; 1.5 Gy; and 3.0 Gy with four replicates at each radiation level. This report includes raw gene expression data files from the resulting microarray experiments from all three radiation levels ranging from a lower, typical exposure than an astronaut might see (0.3 Gy) to high, potentially lethal, levels of radiation (3.0 Gy). The data described here is available in NCBI's Gene Expression Omnibus (GEO), accession GSE64375.