Metabolomics identifies plasma biomarkers of localized radiation injury

A radiological accident may result in the development of a local skin radiation injury (LRI) which may evolve, depending on the dose, from dry desquamation to deep ulceration and necrosis through unpredictable inflammatory waves. Therefore, early diagnosis of victims of LRI is crucial for improving medical care efficiency. This preclinical study aims to identify circulating metabolites as biomarkers associated with LRI using a C57BL/6J mouse model of hind limb irradiation. More precisely, two independent mice cohorts were used to conduct a broad-spectrum profiling study followed by a suspect screening analysis performed on plasma metabolites by mass spectrometry. An integrative analysis was conducted through a multi-block sparse partial least square discriminant analysis (sPLS-DA) to establish multi-scale correlations between specific metabolites levels and biological, physiological (injury severity), and functional parameters (skin perfusion). The identified biomarker signature consists in a 6-metabolite panel including putrescine, uracil, 2,3-dihydroxybenzoate, 3-hydroxybenzoate, L-alanine and pyroglutamate, that can discriminate mice according to radiation dose and injury severity. Our results demonstrate relevant molecular signature associated with LRI in mice and support the use of plasma metabolites as suitable molecular biomarkers for LRI prognosis and diagnosis.

Cohort Studies and Multi-omics Approaches to Low-Dose Ionizing Radiation-Induced Cardiovascular Disease: A Comprehensive Review

The effect of low-dose ionizing radiation exposure on the risk of cardiovascular disease (CVD) represents a significant concern in the field of radiation protection. The prevailing approach to mitigating the adverse effects of low-dose or low-dose-rate radiation does not currently incorporate the potential risk of CVD, despite the possibility that such risk may be a substantial contributor to overall health hazards. Current evidence suggests a potential association between radiation exposure and CVD; however, the overall findings remain inconclusive. This is particularly due to the uncertainty surrounding the influence of significant non-radiation risk factors on the associations reported in epidemiological studies. It is difficult to discern the underlying connection in observational epidemiology when there is substantial variation in baseline risk factors. The paucity of epidemiological research in this domain is being partially offset by the advancement of multi-omics approaches. These methods assist in identifying radiosensitive targets, comprehending underlying biological processes, and pinpointing biomarkers. This, in turn, fortifies the evidence gleaned from epidemiological studies. In this review, we delve into the body of epidemiological research pertaining to CVD induced by low-dose ionizing radiation and the application of multi-omics techniques. The integration of these two methodologies holds the promise of identifying specific molecules or biological pathways that can be employed to validate endpoints related to radiation risk assessment.

Metabolomics and transcriptomics based multi-omics integration reveals radiation-induced altered pathway networking and underlying mechanism

Recent advancement in integrated multi-omics has significantly contributed to many areas of the biomedical field. Radiation research has also grasped uprising omics technologies in biomarker identification to aid in triage management. Herein, we have used a combinatorial multi-omics approach based on transcriptomics together with metabolomics and lipidomics of blood from murine exposed to 1 Gy (LD) and 7.5 Gy (HD) of total-body irradiation (TBI) for a comprehensive understanding of biological processes through integrated pathways and networking. Both omics displayed demarcation of HD group from controls using multivariate analysis. Dysregulated amino acids, various PC, PE and carnitine were observed along with many dysregulated genes (Nos2, Hmgcs2, Oxct2a, etc.). Joint-Pathway Analysis and STITCH interaction showed radiation exposure resulted in changes in amino acid, carbohydrate, lipid, nucleotide, and fatty acid metabolism. Elicited immune response was also observed by Gene Ontology. BioPAN has predicted Elovl5, Elovl6 and Fads2 for fatty acid pathways, only in HD group. Collectively, the combined omics approach facilitated a better understanding of processes uncovering metabolic pathways. Presumably, this is the first in radiation metabolomics that utilized an integrated omics approach following TBI in mice. Our work showed that omics integration could be a valuable tool for better comprehending the mechanism as well as molecular interactions.

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

PURPOSE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Biodosimetry, can it find its way to the nuclear medicine clinic?

Personalised dosimetry based on molecular imaging is a field that has grown exponentially in the last decade due to the increasing success of Radioligand Therapy (RLT). Despite advances in imaging-based 3D dose estimation, the administered dose of a therapeutic radiopharmaceutical for RLT is often non-personalised, with standardised dose regimens administered every 4-6 weeks. Biodosimetry markers, such as chromosomal aberrations, could be used alongside image-based dosimetry as a tool for individualised dose estimation to further understand normal tissue toxicity and refine the administered dose. In this review we give an overview of biodosimetry markers that are used for blood dose estimation, followed by an overview of their current results when applied in RLT patients. Finally, an in-depth discussion will provide a perspective on the potential for the use of biodosimetry in the nuclear medicine clinic.

Application of radiation omics in the development of adverse outcome pathway networks: an example of radiation-induced cardiovascular disease

BACKGROUND

Epidemiological studies have indicated that exposure of the heart to doses of ionizing radiation as low as 0.5 Gy increases the risk of cardiac morbidity and mortality with a latency period of decades. The damaging effects of radiation to myocardial and endothelial structures and functions have been confirmed radiobiologically at high dose, but much less is known at low dose. Integration of radiation biology and epidemiology data is a recommended approach to improve the radiation risk assessment process. The adverse outcome pathway (AOP) framework offers a comprehensive tool to compile and translate mechanistic information into pathological endpoints which may be relevant for risk assessment at the different levels of a biological system. Omics technologies enable the generation of large volumes of biological data at various levels of complexity, from molecular pathways to functional organisms. Given the quality and quantity of available data across levels of biology, omics data can be attractive sources of information for use within the AOP framework. It is anticipated that radiation omics studies could improve our understanding of the molecular mechanisms behind the adverse effects of radiation on the cardiovascular system. In this review, we explored the available omics studies on radiation-induced cardiovascular disease (CVD) and their applicability to the proposed AOP for CVD.

RESULTS

The results of 80 omics studies published on radiation-induced CVD over the past 20 years have been discussed in the context of the AOP of CVD proposed by Chauhan et al. Most of the available omics data on radiation-induced CVD are from proteomics, transcriptomics, and metabolomics, whereas few datasets were available from epigenomics and multi-omics. The omics data presented here show great promise in providing information for several key events of the proposed AOP of CVD, particularly oxidative stress, alterations of energy metabolism, extracellular matrix and vascular remodeling.

CONCLUSIONS

The omics data presented here shows promise to inform the various levels of the proposed AOP of CVD. However, the data highlight the urgent need of designing omics studies to address the knowledge gap concerning different radiation scenarios, time after exposure and experimental models. This review presents the evidence to build a qualitative omics-informed AOP and provides views on the potential benefits and challenges in using omics data to assess risk-related outcomes.

-Omics potential of in vitro skin models for radiation exposure

There is a growing need to uncover biomarkers of ionizing radiation exposure that leads to a better understanding of how exposures take place, including dose type, rate, and time since exposure. As one of the first organs to be exposed to external sources of ionizing radiation, skin is uniquely positioned in terms of model systems for radiation exposure study. The simultaneous evolution of both MS-based -omics studies, as well as in vitro 3D skin models, has created the ability to develop a far more holistic understanding of how ionizing radiation affects the many interconnected biomolecular processes that occur in human skin. However, there are a limited number of studies describing the biomolecular consequences of low-dose ionizing radiation to the skin. This review will seek to explore the current state-of-the-art technology in terms of in vitro 3D skin models, as well as track the trajectory of MS-based -omics techniques and their application to ionizing radiation research, specifically, the search for biomarkers within the low-dose range.