Partial-body Models of Radiation Exposure

The events of 9/11 sparked a revitalization of civil defense in the U.S. for emergency planning and preparedness for future radiological or nuclear event scenarios and specifically for mass casualty medical management of radiation exposure and injury. Research in medical countermeasure development in the form of novel pharmaceuticals to treat radiation injury and new radiation biodosimetry diagnostics, primarily focused on development of research models of uniform total-body irradiation (TBI). With the success of those models, it was recognized that most radiation exposures in the field will involve non-uniform heterogeneous irradiations and many partial-body or organ-specific irradiation models have been utilized. This review examines partial-body models of irradiations developed in the last decade for heterogeneous radiation exposures and organ-specific radiation exposure patterns. These research models have been used to further our understanding of radiation injury, novel medical countermeasures and biodosimetry diagnostics in development for future radiological and nuclear event scenarios.

Organ-specific Biodosimetry Modeling Using Proteomic Biomarkers of Radiation Exposure

In future mass casualty medical management scenarios involving radiation injury, medical diagnostics to both identify those who have been exposed and the level of exposure will be needed. As almost all exposures in the field are heterogeneous, determination of degree of exposure and which vital organs have been exposed will be essential for effective medical management. In the current study we sought to characterize novel proteomic biomarkers of radiation exposure and develop exposure and dose prediction algorithms for a variety of exposure paradigms to include uniform total-body exposures, and organ-specific partial-body exposures to only the brain, only the gut and only the lung. C57BL6 female mice received a single total-body irradiation (TBI) of 2, 4 or 8 Gy, 2 and 8 Gy for lung or gut exposures, and 2, 8 or 16 Gy for exposure to only the brain. Plasma was then screened using the SomaScan v4.1 assay for ∼7,000 protein analytes. A subset panel of protein biomarkers demonstrating significant (FDR<0.05 and |logFC|>0.2) changes in expression after radiation exposure was characterized. All proteins were used for feature selection to build 7 different predictive models of radiation exposure using different sample cohort combinations. These models were structured according to practical field considerations to differentiate level of exposure, in addition to identification of organ-specific exposures. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. The overall predictive accuracy for all models was 100% at the model training level. When tested with reserved samples Model 1 which compared an "exposure" group inclusive of all TBI and organ-specific partial-body exposures in the study vs. control, and Model 2 which differentiated between control, TBI and partials (all organ-specific partial-body exposures) the resulting prediction accuracy was 92.3% and 95.4%, respectively. For identification of organ-specific exposures vs. control, Model 3 (only brain), Model 4 (only gut) and Model 5 (only lung) were developed with predictive accuracies of 78.3%, 88.9% and 94.4%, respectively. Finally, for Models 6 and 7, which differentiated between TBI and separate organ-specific partial-body cohorts, the testing predictive accuracy was 83.1% and 92.3%, respectively. These models represent novel predictive panels of radiation responsive proteomic biomarkers and illustrate the feasibility of development of biodosimetry algorithms with utility for simultaneous classification of total-body, partial-body and organ-specific radiation exposures.

The delayed effects of acute radiation exposure (DEARE): characteristics, mechanisms, animal models, and promising medical countermeasures

PURPOSE

Terrorist use of nuclear weapons and radiation accidents put the human population at risk for exposure to life-threatening levels of radiation. Victims of lethal radiation exposure face potentially lethal acute injury, while survivors of the acute phase are plagued with chronic debilitating multi-organ injuries for years after exposure. Developing effective medical countermeasures (MCM) for the treatment of radiation exposure is an urgent need that relies heavily on studies conducted in reliable and well-characterized animal models according to the FDA Animal Rule. Although relevant animal models have been developed in several species and four MCM for treatment of the acute radiation syndrome are now FDA-approved, animal models for the delayed effects of acute radiation exposure (DEARE) have only recently been developed, and there are no licensed MCM for DEARE. Herein, we provide a review of the DEARE including key characteristics of the DEARE gleaned from human data as well as animal, mechanisms common to multi-organ DEARE, small and large animal models used to study the DEARE, and promising new or repurposed MCM under development for alleviation of the DEARE.

CONCLUSIONS

Intensification of research efforts and support focused on better understanding of mechanisms and natural history of DEARE are urgently needed. Such knowledge provides the necessary first steps toward the design and development of MCM that effectively alleviate the life-debilitating consequences of the DEARE for the benefit of humankind worldwide.

Prediction of Total-Body and Partial-Body Exposures to Radiation Using Plasma Proteomic Expression Profiles

There is a need to identify new biomarkers of radiation exposure for not only systemic total-body irradiation (TBI) but also to characterize partial-body irradiation and organ specific radiation injury. In the current study, we sought to develop novel biodosimetry models of radiation exposure using TBI and organ specific partial-body irradiation to only the brain, lung or gut using a multivariate proteomics approach. Subset panels of significantly altered proteins were selected to build predictive models of radiation exposure in a variety of sample cohort configurations relevant to practical field application of biodosimetry diagnostics during future radiological or nuclear event scenarios. Female C57BL/6 mice, 8-15 weeks old, received a single total-body or partial-body dose of 2 or 8 Gy TBI or 2 or 8 Gy to only the lung or gut, or 2, 8 or 16 Gy to only the brain using a Pantak X-ray source. Plasma was collected by cardiac puncture at days 1, 3 and 7 postirradiation for total-body exposures and only the lung and brain exposures, and at days 3, 7 and 14 postirradiation for gut exposures. Plasma was then screened using the aptamer-based SOMAscan proteomic assay technology, for changes in expression of 1,310 protein analytes. A subset panel of protein biomarkers which demonstrated significant changes (P < 0.01) in expression after irradiation were used to build predictive models of radiation exposure using different sample cohorts. Model 1 compared controls vs. all pooled irradiated samples, which included TBI and all organ specific partial irradiation. Model 2 compared controls vs. TBI vs. partial irradiation (with all organ specific partial exposure pooled within the partial-irradiated group), and model 3 compared controls vs. each individual organ specific partial-body exposure separately (brain, gut and lung). Detectable values were obtained for all 1,310 proteins included in the SOMAscan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies of 89%, 78% and 55% resulted for models 1-3, respectively, representing novel predictive panels of radiation responsive proteomic biomarkers. Though relatively high overall predictive accuracies were achieved for models 1 and 2, all three models showed limited accuracy at differentiating between the controls and partial-irradiated body samples. In our study we were able to identify novel panels of radiation responsive proteins useful for predicting radiation exposure and to create predictive models of partial-body exposure including organ specific radiation exposures. This proof-of-concept study also illustrates the inherent physiological limitations of distinguishing between small-body exposures and the unirradiated using proteomic biomarkers of radiation exposure. As use of biodosimetry diagnostics in future mass casualty settings will be complicated by the heterogeneity of partial-body exposure received in the field, further work remains in adapting these diagnostic tools for practical use.

Lung transcriptome of nonhuman primates exposed to total- and partial-body irradiation

The focus of radiation biodosimetry has changed recently, and a paradigm shift for using molecular technologies of omic platforms in addition to cytogenetic techniques has been observed. In our study, we have used a nonhuman primate model to investigate the impact of a supralethal dose of 12 Gy radiation on alterations in the lung transcriptome. We used 6 healthy and 32 irradiated animal samples to delineate radiation-induced changes. We also used a medical countermeasure, γ-tocotrienol (GT3), to observe any changes. We demonstrate significant radiation-induced changes in the lung transcriptome for total-body irradiation (TBI) and partial-body irradiation (PBI). However, no major influence of GT3 on radiation was noted in either comparison. Several common signaling pathways, including PI3K/AKT, GADD45, and p53, were upregulated in both exposures. TBI activated DNA-damage-related pathways in the lungs, whereas PTEN signaling was activated after PBI. Our study highlights the various transcriptional profiles associated with γ- and X-ray exposures, and the associated pathways include LXR/RXR activation in TBI, whereas pulmonary wound-healing and pulmonary fibrosis signaling was repressed in PBI. Our study provides important insights into the molecular pathways associated with irradiation that can be further investigated for biomarker discovery.

mRNA and small RNA gene expression changes in peripheral blood to detect internal Ra-223 exposure

PURPOSE

Excretion analysis is the established method for detection of incorporated alpha-emitting radionuclides, but it is laborious and time consuming. We sought a simplified method in which changes in gene expression might be measured in human peripheral blood to detect incorporated radionuclides. Such an approach could be used to quickly determine internal exposure in instances of a radiological dispersal device or a radiation accident.

MATERIALS AND METHODS

We evaluated whole blood samples from five patients with castration-resistant prostate cancer and multiple bone metastases (without visceral or nodal involvement), who underwent treatment with the alpha emitting isotope Radium-223 dichloride (Ra-223, Xofigo^®). Patients received about 4 MBq per cycle and, depending on survival and treatment tolerance, were followed for six months. We collected 24 blood samples approximately monthly corresponding to treatment cycle.

RESULTS

Firstly, we conducted whole genome screening of mRNAs (mRNA seq) and small RNAs (small RNA seq) using next generation sequencing in one patient at eight different time points during all six cycles of Ra-223-therapy. We identified 1900 mRNAs and 972 small RNAs (222 miRNAs) that were differentially up- or down-regulated during follow-up after the first treatment with Ra-223. Overall candidate RNA species inclusion criteria were a general (≥|2|-fold) change or with peaking profiles (≥|5|-fold) at specific points in time. Next we chose 72 candidate mRNAs and 101 small RNAs (comprising 29 miRNAs) for methodologic (n = 8 samples, one patient) and independent (n = 16 samples, four patients) validation by qRT-PCR. In total, 15 mRNAs (but no small RNAs) were validated by methodologic and independent testing. However, the deregulation occurred at different time points, showing a large inter-individual variability in response among patients.

CONCLUSIONS

This proof of concept provides support for the applicability of gene expression measurements to detect internalized alpha-emitting radionuclides, but further work is needed with a larger sample size. While our approach has merit for internal deposition monitoring, it was complicated by the severe clinical condition of the patients we studied.

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.