Transcriptomic profiling reveals gene expression in human peripheral blood after exposure to low-dose ionizing radiation

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.

PUM1 and PGK1 are Favorable Housekeeping Genes over Established Biodosimetry-related Housekeeping Genes such as HPRT1, ITFG1, DPM1, MRPS5, 18S rRNA and Others after Radiation Exposure

In gene expression (GE) studies, housekeeping genes (HKGs) are required for normalization purposes. In large-scale inter-laboratory comparison studies, significant differences in dose estimates are reported and divergent HKGs are employed by the teams. Among them, the 18S rRNA HKG is known for its robustness. However, the high abundance of 18S rRNA copy numbers requires dilution, which is time-consuming and a possible source of errors. This study was conducted to identify the most promising HKGs showing the least radiation-induced GE variance after radiation exposure. In the screening stage of this study, 35 HKGs were analyzed. This included selected HKGs (ITFG1, MRPS5, and DPM1) used in large-scale biodosimetry studies which were not covered on an additionally employed pre-designed 96-well platform comprising another 32 HKGs used for different exposures. Altogether 41 samples were examined, including 27 ex vivo X-ray irradiated blood samples (0, 0.5, 4 Gy), six X-irradiated samples (0, 0.5, 5 Gy) from two cell lines (U118, A549), as well as eight non-irradiated tissue samples to encompass multiple biological entities. In the independent validation stage, the most suitable candidate genes were examined from another 257 blood samples, taking advantage of already stored material originating from three studies. These comprise 100 blood samples from ex vivo X-ray irradiated (0-4 Gy) healthy donors, 68 blood samples from 5.8 Gy irradiated (cobalt-60) Rhesus macaques (RM) (LD29/60) collected 0-60 days postirradiation, and 89 blood samples from chemotherapy-(CTx) treated breast tumor patients. CTx and radiation-induced GE changes in previous studies appeared comparable. RNA was isolated, converted into cDNA, and GE was quantified employing TaqMan assays and quantitative RT-PCR. We calculated the standard deviation (SD) and the interquartile range (IQR) as measures of GE variance using raw cycle threshold (Ct) values and ranked the HKGs accordingly. Dose, time, age, and sex-dependent GE changes were examined employing the parametrical t-test and non-parametrical Kruskal Wallis test, as well as linear regression analysis. Generally, similar ranking results evolved using either SD or IQR GE measures of variance, indicating a tight distribution of GE values. PUM1 and PGK1 showed the lowest variance among the first ten most suitable genes in the screening phase. MRPL19 revealed low variance among the first ten most suitable genes in the screening phase only for blood and cells, but certain comparisons indicated a weak association of MRPL19 with dose (P = 0.02-0.09). In the validation phase, these results could be confirmed. Here, IQR Ct values from, e.g., X-irradiated blood samples were 0.6 raw Ct values for PUM1 and PGK1, which is considered to represent GE differences as expected due to methodological variance. Overall, when compared, the GE variance of both genes was either comparable or lower compared to 18S rRNA. Compared with the IQR GE values of PUM1 and PGKI, twofold-fivefold increased values were calculated for the biodosimetry HKG HPRT1, and comparable values were calculated for biodosimetry HKGs ITFG1, MRPS5, and DPM1. Significant dose-dependent associations were found for ITFG1 and MRPS5 (P = 0.001-0.07) and widely absent or weak (P = 0.02-0.07) for HPRT1 and DPM1. In summary, PUM1 and PGK1 appeared most promising for radiation exposure studies among the 35 HKGs examined, considering GE variance and adverse associations of GE with dose.

Distinguish response of low-dose radiation with different dose-rate on gene expression of human coronary artery endothelial cells: a bioinformatic study based on transcriptomic sequencing

PURPOSE

People are exposed to low-dose radiation in medical diagnosis, occupational, or life circumstances, but the effect of low-dose radiation on human health is still controversial. The biological effects of radiation below 100 mGy are still unproven. In this study, we observed the effects of low-dose radiation (100 mGy) on gene expression in human coronary artery endothelial cells (HCAECs) and its effect on molecular signaling.

MATERIALS AND METHODS

HCAECs were exposed to 100 mGy ionizing radiation at 6 mGy/h (low-dose-rate) or 288 mGy/h (high-dose-rate). After 72 h, total RNA was extracted from sham or irradiated cells for Quant-Seq 3'mRNA-Seq, and bioinformatic analyses were performed using Metascape. Gene profiling was validated using qPCR.

RESULTS

Compared to the non-irradiated control group, 100 mGy of ionizing radiation at 6 mGy/h altered the expression of 194 genes involved in signaling pathways related to heart contraction, blood circulation, and cardiac myofibril assembly differentially. However, 100 mGy at 288 mGy/h altered expression of 450 genes involved in cell cycle-related signaling pathways, including cell division, nuclear division, and mitosis differentially. Additionally, gene signatures responding to low-dose radiation, including radiation dose-specific gene profiles (HIST1H2AI, RAVER1, and POTEI) and dose-rate-specific gene profiles (MYL2 for the low-dose-rate and DHRS9 and CA14 for the high-dose-rate) were also identified.

CONCLUSIONS

We demonstrated that 100 mGy low-dose radiation could alter gene expression and molecular signaling pathways at the low-dose-rate and the high-dose-rate differently. Our findings provide evidence for further research on the potential impact of low-dose radiation on cardiovascular function.

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.

Tritium: Its relevance, sources and impacts on non-human biota

Tritium (³H) is a radioactive isotope of hydrogen that is abundantly released from nuclear industries. It is extremely mobile in the environment and in all biological systems, representing an increasing concern for the health of both humans and non-human biota (NHB). The present review examines the sources and characteristics of tritium in the environment, and evaluates available information pertaining to its biological effects at different levels of biological organisation in NHB. Despite an increasing number of publications in the tritium radiobiology field, there exists a significant disparity between data available for the different taxonomic groups and species, and observations are heavily biased towards marine bivalves, fish and mammals (rodents). Further limitations relate to the scarcity of information in the field relative to the laboratory, and lack of studies that employ forms of tritium other than tritiated water (HTO). Within these constraints, different responses to HTO exposure, from molecular to behavioural, have been reported during early life stages, but the potential transgenerational effects are unclear. The application of rapidly developing "omics" techniques could help to fill these knowledge gaps and further elucidate the relationships between molecular and organismal level responses through the development of radiation specific adverse outcome pathways (AOPs). The use of a greater diversity of keystone species and exposures to multiple stressors, elucidating other novel effects (e.g., by-stander, germ-line, transgenerational and epigenetic effects) offers opportunities to improve environmental risk assessments for the radionuclide. These could be combined with artificial intelligence (AI) including machine learning (ML) and ecosystem-based approaches.