利用 FDXR 基因表达生物剂量学估算辐射剂量的校准曲线--前提与陷阱。

Kamil Brzóska, Michael Abend, Grainne O'Brien, Eric Gregoire, Matthias Port, Christophe Badie
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引用次数: 0

摘要

目的:辐射诱导的基因表达变化为剂量重建和急性健康影响的严重程度预测带来了巨大希望。在几种被探究为潜在生物标志物的基因中,FDXR 因其在辐照后白细胞中的高上调而被广泛使用。然而,缺乏基于基因表达的生物模拟的标准化方案是一个明显的差距,需要引起重视,以提高准确性、可重复性和可靠性。本研究的目的是评估转录生物定量法对不同实验室所用方案差异的敏感性,并制定使用 FDXR 表达数据计算校准曲线的准则:使用了两组在 RENEB 运动中产生的辐照血液样本。材料和方法:使用了两组在 RENEB 运动中产生的辐照血液样本:0、0.25、0.5、1、2、3 和 4 Gy。第二组包括三个 "盲 "样品,分别用 1.8 Gy、0.4 Gy 和一个假辐照样品进行辐照。辐照后,样本在 37 ° C 孵育 24 小时,然后送往参与实验室,使用每个实验室专用的引物/探针组和参考基因进行 RNA 分离和 qPCR FDXR 表达分析。使用非线性和线性回归法生成基于 FDXR 表达数据的校准曲线,并用于 "盲 "样本的剂量估算:结果:假辐照样本(0.020-0.024 Gy)和0.4 Gy辐照样本(0.369-0.381 Gy)的剂量估计值在所有实验室中显示出显著的一致性,接近真实剂量,而不考虑所用引物/探针和参考基因的差异。对于用 1.8 Gy 照射的样本,剂量估算的精确度较低(1.198-2.011 Gy),但仍在可接受的范围内,可在高剂量范围内进行分流:结论:用于 FDXR 表达测量的参考基因和引物/探针在方法上的差异不会对生成的剂量估算值产生重大影响,前提是所有参考基因的表现均符合预期,且引物/探针针对的是一组相似的转录本变体。根据 FDXR 表达数据构建校准曲线的首选方法是利用线性回归建立一个函数,描述吸收剂量的对数与 FDXR ΔCt 值之间的关系。不过,在使用非辐照样本数据时应小心谨慎,因为这些数据无法用对数刻度准确表示。使用这种方法生成的标准曲线至少可以在 50 mGy 至 4 Gy 的剂量范围内提供可靠的剂量估算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Calibration curve for radiation dose estimation using FDXR gene expression biodosimetry - premises and pitfalls.

Purpose: Radiation-induced alterations in gene expression show great promise for dose reconstruction and for severity prediction of acute health effects. Among several genes explored as potential biomarkers, FDXR is widely used due to high upregulation in white blood cells following radiation exposure. Nonetheless, the absence of a standardized protocols for gene expression-based biodosimetry is a notable gap that warrants attention to enhance the accuracy, reproducibility and reliability. The objective of this study was to evaluate the sensitivity of transcriptional biodosimetry to differences in protocols used by different laboratories and establish guidelines for the calculation of calibration curve using FDXR expression data.

Material and methods: Two sets of irradiated blood samples generated during RENEB exercise were used. The first included samples irradiated with known doses including: 0, 0.25, 0.5, 1, 2, 3 and 4 Gy. The second set consisted of three 'blind' samples irradiated with 1.8 Gy, 0.4 Gy and a sham-irradiated sample. After irradiation, samples were incubated at 37 °C over 24 h and sent to participating laboratories, where RNA isolation and FDXR expression analysis by qPCR were performed using sets of primers/probes and reference genes specific for each laboratory. Calibration curves based on FDXR expression data were generated using non-linear and linear regression and used for dose estimation of 'blind' samples.

Results: Dose estimates for sham-irradiated sample (0.020-0.024 Gy) and sample irradiated with 0.4 Gy (0.369-0.381 Gy) showed remarkable consistency across all laboratories, closely approximating the true doses regardless variation in primers/probes and reference genes used. For sample irradiated with 1.8 Gy the dose estimates were less precise (1.198-2.011 Gy) but remained within an acceptable margin for triage within the context of high dose range.

Conclusion: Methodological differences in reference genes and primers/probes used for FDXR expression measurement do not have a significant impact on the dose estimates generated, provided that all reference genes performed as expected and the primers/probes target a similar set of transcript variants. The preferred method for constructing a calibration curve based on FDXR expression data involves employing linear regression to establish a function that describes the relationship between the logarithm of absorbed dose and FDXR ΔCt values. However, one should be careful with using non-irradiated sample data as these cannot be accurately represented on a logarithmic scale. A standard curve generated using this approach can give reliable dose estimations in a dose range from 50 mGy to 4 Gy at least.

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