Abstract
Accurate assessment of the individual exposure dose based on easily accessible samples (e.g. blood) immediately following a radiological accident is crucial. We aimed at developing a robust transcription-based signature for biodosimetry from human peripheral blood mononuclear cells irradiated with different doses of X-rays (0.1 and 1.0 Gy) at a dose rate of 0.26 Gy/min. Genome-wide radiation-induced changes in mRNA expression were evaluated at both gene and exon level. Using exon-specific qRT-PCR, we confirmed that several biomarker genes are alternatively spliced or transcribed after irradiation and that different exons of these genes exhibit significantly different levels of induction. Moreover, a significant number of radiation-responsive genes were found to be genomic neighbors. Using three different classification models we found that gene and exon signatures performed equally well on dose prediction, as long as more than 10 features are included. Together, our results highlight the necessity of evaluating gene expression at the level of single exons for radiation biodosimetry in particular and transcriptional biomarker research in general. This approach is especially advisable for practical gene expression-based biodosimetry, for which primer- or probe-based techniques would be the method of choice.
Highlights
Several recent studies have shown that transcriptome analysis at the individual exon level may significantly add to our understanding of the transcriptional response to radiation exposure[11,12,13]
We evaluated the predictive performance of gene and exon signatures using three different statistical models, which were further used to assess the robustness of our gene signature on an independent, publicly available dataset (Fig. 1)
Prompted by the rapid development of high-throughput genomic profiling technologies, several groups have explored the potential of gene expression signatures as biomarkers of exposure to ionising radiation[6,8,18,19,20,21,22]
Summary
Several recent studies have shown that transcriptome analysis at the individual exon level may significantly add to our understanding of the transcriptional response to radiation exposure[11,12,13]. Alternative transcription and alternative pre-mRNA splicing dramatically expand the translational repertoire. We hypothesise that alternative transcription and splicing analyses applied in the context of radiation exposure may generate additional radiation biomarkers with potentially increased sensitivity. We established gene and exon signatures that may serve as radiation biomarkers and subsequently compared their reliability and effectiveness. Our results yield new insights into transcriptional biomarker identification studies using genome-wide strategies and underline the importance of investigating gene expression at the single exon level
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