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

Modeling Radiation-Induced Epithelial Cell Injury in Murine Three-Dimensional Esophageal Organoids

Esophageal squamous cell carcinoma (ESCC) is a deadly consequence of radiation exposure to the esophagus. ESCC arises from esophageal epithelial cells that undergo malignant transformation and features a perturbed squamous cell differentiation program. Understanding the dose- and radiation quality-dependence of the esophageal epithelium response to radiation may provide insights into the ability of radiation to promote ESCC. We have explored factors that may play a role in esophageal epithelial radiosensitivity and their potential relationship to ESCC risk. We have utilized a murine three-dimensional (3D) organoid model that recapitulates the morphology and functions of the stratified squamous epithelium of the esophagus to study persistent dose- and radiation quality-dependent changes. Interestingly, although high-linear energy transfer (LET) Fe ion exposure induced a more intense and persistent alteration of squamous differentiation and 53BP1 DNA damage foci levels as compared to Cs, the MAPK/SAPK stress pathway signaling showed similar altered levels for most phospho-proteins with both radiation qualities. In addition, the lower dose of high-LET exposure also revealed nearly the same degree of morphological changes, even though only ~36% of the cells were predicted to be hit at the lower 0.1 Gy dose, suggesting that a bystander effect may be induced. Although p38 and ERK/MAPK revealed the highest levels following high-LET exposure, the findings reveal that even a low dose (0.1 Gy) of both radiation qualities can elicit a persistent stress signaling response that may critically impact the differentiation gradient of the esophageal epithelium, providing novel insights into the pathogenesis of radiation-induced esophageal injury and early stage esophageal carcinogenesis.

Advanced Technologies in Radiation Research

The U.S. Government is committed to maintaining a robust research program that supports a portfolio of scientific experts who are investigating the biological effects of radiation exposure. On August 17 and 18, 2023, the Radiation and Nuclear Countermeasures Program, within the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), partnered with the National Cancer Institute, NIH, the National Aeronautics and Space Administration, and the Radiation Injury Treatment Network to convene a workshop titled, Advanced Technologies in Radiation Research (ATRR), which focused on the use of advanced technologies under development or in current use to accelerate radiation research. This meeting report provides a comprehensive overview of the research presented at the workshop, which included an assembly of subject matter experts from government, industry, and academia. Topics discussed during the workshop included assessments of acute and delayed effects of radiation exposure using modalities such as clustered regularly interspaced short palindromic repeats (CRISPR) - based gene editing, tissue chips, advanced computing, artificial intelligence, and immersive imaging techniques. These approaches are being applied to develop products to diagnose and treat radiation injury to the bone marrow, skin, lung, and gastrointestinal tract, among other tissues. The overarching goal of the workshop was to provide an opportunity for the radiation research community to come together to assess the technological landscape through sharing of data, methodologies, and challenges, followed by a guided discussion with all participants. Ultimately, the organizers hope that the radiation research community will benefit from the workshop and seek solutions to scientific questions that remain unaddressed. Understanding existing research gaps and harnessing new or re-imagined tools and methods will allow for the design of studies to advance medical products along the critical path to U.S. Food and Drug Administration approval.

Screening radiation-differentially expressed circular RNAs and establishing dose classification models in the human lymphoblastoid cell line AHH-1

PURPOSE

In the event of a large-scale radiological accident, rapid and high-throughput biodosimetry is the most vital basis in medical resource allocation for the prompt treatment of victims. However, the current biodosimeter is yet to be rapid and high-throughput. Studies have shown that ionizing radiation modulates expressions of circular RNAs (circRNAs) in healthy human cell lines and tumor tissue. circRNA expressions can be quantified rapidly and high-throughput. However, whether circRNAs are suitable for early radiation dose classification remains unclear.

METHODS

We employed transcriptome sequencing and bioinformatics analysis to screen for radiation-differentially expressed circRNAs in the human lymphoblastoid cell line AHH-1 at 4 h following exposure to 0, 2, and 5 Gy 60Co γ-rays. The dose-response relationships between differentially expressed circRNA expressions and absorbed doses were investigated using real-time polymerase chain reaction and linear regression analysis at 4 h, 24 h, and 48 h post-exposure to 0, 2, 4, 6, and 8 Gy. Six distinct dose classification models of circRNA panels were established and validated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 11 radiation-differentially expressed circRNAs were identified and validated. Based on dose-response effects, those circRNAs changed in a dose-responsive or dose-dependent manner were combined into panels A through F at 4 h, 24 h, and 48 h post-irradiation. ROC curve analysis showed that panels A through C had the potential to effectively classify exposed and non-exposed conditions, which area under the curve (AUC) of these three panels were all 1.000, and the associate p values were .009. Panels D through F excellently distinguished between different dose groups (AUC = 0.963-1.000, p < .05). The validation assay showed that panels A through F demonstrated consistent excellence in sensitivity and specificity in dose classification.

CONCLUSIONS

Ionizing radiation can indeed modulate the circRNA expression profile in the human lymphoblastoid cell line AHH-1. The differentially expressed circRNAs exhibit the potential for rapid and high-throughput dose classification.

Humanized Mice as a Model to Assess the Response of Human Hematopoietic Stem Cells to Irradiation

NOD SCID mice were humanized by transplanting human hematopoietic cells isolated from umbilical cord blood. A dose-dependent death of hematopoietic cells and their subsequent recovery were shown after acute external γ-irradiation in the model of humanized mice. The proposed approach can be used for preclinical studies of radioprotective agents and for assessment of the impact of adverse factors on the survival rate and functional properties of human hematopoietic stem cells in vivo.

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.

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.

Biomarkers for Biodosimetry and Their Role in Predicting Radiation Injury

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

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

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

Biomarkers to Predict Lethal Radiation Injury to the Rat Lung

Currently, there are no biomarkers to predict lethal lung injury by radiation. Since it is not ethical to irradiate humans, animal models must be used to identify biomarkers. Injury to the female WAG/RijCmcr rat has been well-characterized after exposure to eight doses of whole thorax irradiation: 0-, 5-, 10-, 11-, 12-, 13-, 14- and 15-Gy. End points such as SPECT imaging of the lung using molecular probes, measurement of circulating blood cells and specific miRNA have been shown to change after radiation. Our goal was to use these changes to predict lethal lung injury in the rat model, 2 weeks post-irradiation, before any symptoms manifest and after which a countermeasure can be given to enhance survival. SPECT imaging with 99mTc-MAA identified a decrease in perfusion in the lung after irradiation. A decrease in circulating white blood cells and an increase in five specific miRNAs in whole blood were also tested. Univariate analyses were then conducted on the combined dataset. The results indicated that a combination of percent change in lymphocytes and monocytes, as well as pulmonary perfusion volume could predict survival from radiation to the lungs with 88.5% accuracy (95% confidence intervals of 77.8, 95.3) with a p-value of < 0.0001 versus no information rate. This study is one of the first to report a set of minimally invasive endpoints to predict lethal radiation injury in female rats. Lung-specific injury can be visualized by 99mTc-MAA as early as 2 weeks after radiation.

Non-Clinical Cell Therapy Development Using the NCG Mouse Model as a Test System

The NCG triple immunodeficient mice on a NOD/Nju background lack functional/mature T, B, and NK cells, and have reduced macrophage and dendritic cell function. This study characterized the NCG mouse model for toxicity, engraftment and tumorigenicity assessments of cell therapies, using CD34⁺ hHSPC adult mobilized cells with two myeloablation regimens.Mice received sub-lethal irradiation or busulfan and were then injected intravenously with CD34⁺ hHSPCs (1.0 x 106 cells/mouse) or PBS (control), while positive control animals received 2 x 106 HL-60 cells/mouse. hCD34+ cell donors were treated with the mobilizing agent G-CSF prior to leukapheresis. Following injections, mouse blood samples were collected to assess engraftment rates by flow cytometry with body weights recorded periodically up to 20 weeks post-cell injection. No significant clinical signs or body weight changes were observed. At week 10 post-cell injection, the peripheral blood chimerism of hCD45+ cells was above 20%. While mCD45+ concentration was constant between week 10 and 17 in whole blood samples, hCD45+ concentration and chimerism slightly decreased at week 17. However, chimerism remained above 10%, with busulfan-treated mice presenting higher values. Chimerism was further assessed by quantifying human Alu sequences in blood and multiple organs using qPCR. Alu sequences were most abundant in the spleen and bone marrow, while lowest in the testes. In the positive control group, expected mortalities due to tumorigenesis were observed between days 27 and 40 post-cell injection. Overall, study results may be used to inform study design and potential toxicological endpoints relevant to non-clinical cell therapy development.

Microarray analysis identifies coding and non-coding RNA markers of liver injury in whole body irradiated mice

Radiation injury from medical, accidental, or intentional sources can induce acute and long-term hepatic dysregulation, fibrosis, and cancer. This long-term hepatic dysregulation decreases quality of life and may lead to death. Our goal in this study is to determine acute changes in biological pathways and discover potential RNA biomarkers predictive of radiation injury. We performed whole transcriptome microarray analysis of mouse liver tissue (C57BL/6 J) 48 h after whole-body irradiation with 1, 2, 4, 8, and 12 Gray to identify significant expression changes in mRNAs, lncRNAs, and miRNAs, We also validated changes in specific RNAs through qRT-PCR. We used Ingenuity Pathway Analysis (IPA) to identify pathways associated with gene expression changes. We observed significant dysregulation of multiple mRNAs across all doses. In contrast, miRNA dysregulation was observed upwards of 2 Gray. The most significantly upregulated mRNAs function as tumor suppressors: Cdkn1a, Phlda3, and Eda2r. The most significantly downregulated mRNAs were involved in hemoglobin synthesis, inflammation, and mitochondrial function including multiple members of Hbb and Hba. The most significantly upregulated miRNA included: miR-34a-5p, miR-3102-5p, and miR-3960, while miR-342-3p, miR-142a-3p, and miR-223-3p were most significantly downregulated. IPA predicted activation of cell cycle checkpoint control pathways and inhibition of pathways relevant to inflammation and erythropoietin. Clarifying expression of mRNA, miRNA and lncRNA at a short time point (48 h) offers insight into potential biomarkers, including radiation markers shared across organs and animal models. This information, once validated in human models, can aid in development of bio-dosimetry biomarkers, and furthers our understanding of acute pathway dysregulation.

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.

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

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

Adverse Outcome Pathways and Linkages to Transcriptomic Effects Relevant to Ionizing Radiation Injury

BACKGROUND

In the past three decades, a large body of data on the effects of exposure to ionizing radiation and the ensuing changes in gene expression has been generated. These data have allowed for an understanding of molecular-level events and shown a level of consistency in response despite the vast formats and experimental procedures being used across institutions. However, clarity on how this information may inform strategies for health risk assessment needs to be explored. An approach to bridge this gap is the adverse outcome pathway (AOP) framework. AOPs represent an illustrative framework characterizing a stressor associated with a sequential set of causally linked key events (KEs) at different levels of biological organization, beginning with a molecular initiating event (MIE) and culminating in an adverse outcome (AO). Here, we demonstrate the interpretation of transcriptomic datasets in the context of the AOP framework within the field of ionizing radiation by using a lung cancer AOP (AOP 272: https://www.aopwiki.org/aops/272) as a case example.

METHODS

Through the mining of the literature, radiation exposure-related transcriptomic studies in line with AOP 272 related to lung cancer, DNA damage response, and repair were identified. The differentially expressed genes within relevant studies were collated and subjected to the pathway and network analysis using Reactome and GeneMANIA platforms. Identified pathways were filtered (p < 0.001, ≥ 3 genes) and categorized based on relevance to KEs in the AOP. Gene connectivities were identified and further grouped by gene expression-informed associated events (AEs). Relevant quantitative dose-response data were used to inform the directionality in the expression of the genes in the network across AEs.

RESULTS

Reactome analyses identified 7 high-level biological processes with multiple pathways and associated genes that mapped to potential KEs in AOP 272. The gene connectivities were further represented as a network of AEs with associated expression profiles that highlighted patterns of gene expression levels.

CONCLUSIONS

This study demonstrates the application of transcriptomics data in AOP development and provides information on potential data gaps. Although the approach is new and anticipated to evolve, it shows promise for improving the understanding of underlying mechanisms of disease progression with a long-term vision to be predictive of adverse outcomes.

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

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

The Potential of Omics in Biological Dosimetry

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

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

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

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

PURPOSE

In a nuclear or radiological event, an early diagnostic or prognostic tool is needed to distinguish unexposed from low- and highly exposed individuals with the latter requiring early and intensive medical care. Radiation-induced gene expression (GE) changes observed within hours and days after irradiation have shown potential to serve as biomarkers for either dose reconstruction (retrospective dosimetry) or the prediction of consecutively occurring acute or chronic health effects. The advantage of GE markers lies in their capability for early (1-3 days after irradiation), high-throughput, and point-of-care (POC) diagnosis required for the prediction of the acute radiation syndrome (ARS).

CONCLUSIONS

As a key session of the ConRad conference in 2021, experts from different institutions were invited to provide state-of-the-art information on a range of topics including: (1) Biodosimetry: What are the current efforts to enhance the applicability of this method to perform retrospective biodosimetry? (2) Effect prediction: Can we apply radiation-induced GE changes for prediction of acute health effects as an approach, complementary to and integrating retrospective dose estimation? (3) High-throughput and point-of-care diagnostics: What are the current developments to make the GE approach applicable as a high-throughput as well as a POC diagnostic platform? (4) Low level radiation: What is the lowest dose range where GE can be used for biodosimetry purposes? (5) Methodological considerations: Different aspects of radiation-induced GE related to more detailed analysis of exons, transcripts and next-generation sequencing (NGS) were reported.

Transcriptomics for radiation biodosimetry: progress and challenges

PURPOSE

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

CONCLUSIONS

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

Reduction of pTau and APP levels in mammalian brain after low-dose radiation

Brain radiation can occur from treatment of brain tumors or accidental exposures. Brain radiation has been rarely considered, though, as a possible tool to alter protein levels involved in neurodegenerative disorders. We analyzed possible molecular and neuropathology changes of phosphorylated-Tau (pTau), all-Tau forms, β-tubulin, amyloid precursor protein (APP), glial fibrillary acidic protein (GFAP), ionized calcium binding adaptor molecule 1 (IBA-1), myelin basic protein (MBP), and GAP43 in Frontal Cortex (FC), Hippocampus (H) and Cerebellum (CRB) of swine brains following total-body low-dose radiation (1.79 Gy). Our data show that radiated-animals had lower levels of pTau in FC and H, APP in H and CRB, GAP43 in CRB, and higher level of GFAP in H versus sham-animals. These molecular changes were not accompanied by obvious neurohistological changes, except for astrogliosis in the H. These findings are novel, and might open new perspectives on brain radiation as a potential tool to interfere with the accumulation of specific proteins linked to the pathogenesis of various neurodegenerative disorders.

Mice Are Not Humans: The Case of p53

Mouse models represent powerful tools in cancer research that have influenced much of our understanding of tumor development and our current anticancer strategies. Yet, central signaling pathways differ considerably between mouse and human, including gene regulatory networks (GRNs) of the most important tumor suppressor p53 and its oncogenic sibling p63.

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

BACKGROUND AND PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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