Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway; Centre of Excellence "Centre for Environmental Radiation" (CERAD), Norway.
Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway; Centre of Excellence "Centre for Environmental Radiation" (CERAD), Norway.
Sci Total Environ. 2020 May 15;717:137068. doi: 10.1016/j.scitotenv.2020.137068. Epub 2020 Feb 4.
Robust biomarkers of exposure to chronic low dose stressors such as ionizing radiation, particularly following chronic low doses and dose-rates, are urgently needed. MicroRNAs (miRNA) have emerged as promising markers of exposure to high dose and dose-rate. Here, we evaluated the feasibility of classifying γ-radiation exposure at different dose rates based on miRNA expression levels. Our objective was to identify miRNA-signatures discriminating between exposure to γ-radiation or not, including exposure to chronic low dose rates. We exposed male CBA/CaOlaHsd and C57BL/6NHsd wild-type mice to 0, 2.5, 10 and 100 mGy/h γ-irradiation (3 Gy total-dose). From an initial screening of 576 miRNAs, a set of 21 signature-miRNAs was identified based on differential expression (>± 2-fold or p < 0.05). This 21-signature miRNA panel was investigated in 39 samples from 4/5 livers/group/mouse strain. A set of significantly differentially expressed miRNAs was identified in all γ-irradiated samples. Most miRNAs were upregulated in all γ-irradiated groups compared to control, and functional analysis of these miRNAs revealed involvement in several cancer-related signaling pathways. To identify miRNAs that distinguished exposed mice from controls, nine prediction methods; i.e., six variants of generalized regression models, random-forest, boosted-tree and nearest-shrunken-centroid (PAM) were used. The generalized regression methods seem to outperform the other prediction methods for classification of irradiated and control samples. Using the 21-miRNA panel in the prediction models, we identified sets of candidate miRNA-markers that predict exposure to γ-radiation. Among the top10 miRNA predictors, contributing most in each of the three γ-irradiated groups, three miRNA predictors (miR-140-3p, miR-133a-5p and miR-145a-5p) were common. Three miRNAs, miR-188-3p/26a-5p/26b-5p, were specific for lower dose-rate γ-radiation. Similarly, exposure to the high dose-rates was also correctly predicted, including mice exposed to X-rays. Our approach identifying miRNA-based signature panels may be extended to classify exposure to environmental, nutritional and life-style-related stressors, including chronic low-stress scenarios.
目前迫切需要寻找能够稳健反映慢性低剂量应激源（如电离辐射）暴露的生物标志物，尤其是在慢性低剂量和低剂量率条件下。微小 RNA（miRNA）已成为高剂量和高剂量率暴露的有前途的标志物。在这里，我们评估了基于 miRNA 表达水平对不同剂量率 γ 辐射暴露进行分类的可行性。我们的目标是确定能够区分γ辐射暴露与非暴露的 miRNA 特征，包括区分慢性低剂量率的暴露。我们将雄性 CBA/CaOlaHsd 和 C57BL/6NHsd 野生型小鼠暴露于 0、2.5、10 和 100 mGy/h γ 辐射（总剂量 3 Gy）。从最初筛选的 576 个 miRNA 中，基于差异表达（>±2 倍或 p < 0.05）确定了一组 21 个特征 miRNA。在每组 4/5 只小鼠的 39 个肝脏样本中研究了这 21 个 miRNA 特征。在所有 γ 辐射样本中都确定了一组显著差异表达的 miRNA。与对照组相比，所有 γ 辐射组的大多数 miRNA 都上调，对这些 miRNA 的功能分析表明它们参与了几种与癌症相关的信号通路。为了确定能够将暴露小鼠与对照小鼠区分开来的 miRNA，我们使用了九种预测方法，即六种广义回归模型变体、随机森林、增强树和最近收缩中心（PAM）。广义回归方法似乎优于其他预测方法，更适合分类 γ 辐射照射和未照射的样本。我们使用 21 个 miRNA 面板在预测模型中，确定了一组候选 miRNA 标记物，这些标记物可以预测 γ 辐射暴露。在排名前 10 的 miRNA 预测因子中，在三个 γ 辐射组中每个组贡献最大的三个 miRNA 预测因子（miR-140-3p、miR-133a-5p 和 miR-145a-5p）是共有的。三个 miRNA，miR-188-3p/26a-5p/26b-5p，是特定于低剂量率 γ 辐射的。同样，也正确预测了高剂量率的暴露，包括暴露于 X 射线的小鼠。我们这种识别基于 miRNA 的特征面板的方法可以扩展到分类环境、营养和生活方式相关应激源的暴露，包括慢性低应激情况。
