Application of Gamma Ray-Responsive Genes for Transcriptome-Based Phytodosimetry in Rice

• Main Authors: Jin-Hong Kim, Kwon Hwangbo, Eujin Lee, Shubham Kumar Dubey, Moon-Soo Chung, Byung-Yeoup Chung, Sungbeom Lee
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Plants
• Subjects: gamma radiation; phytodosimetry; rice; <i>Arabidopsis</i>; transcriptome; genotoxicity
• Online Access: https://www.mdpi.com/2223-7747/10/5/968

Transcriptome-based dose–response curves were recently applied to the phytodosimetry of gamma radiation in a dicot plant, <i>Arabidopsis thaliana</i>, as an alternative biological assessment of genotoxicity using DNA damage response (DDR) genes. In the present study, we characterized gamma ray-responsive marker genes for transcriptome-based phytodosimetry in a monocot plant, rice (<i>Oryza sativa</i> L.), and compared different phytodosimetry models between rice and <i>Arabidopsis</i> using gamma-H2AX, comet, and quantitative transcriptomic assays. The transcriptome-based dose–response curves of four marker genes (<i>OsGRG, OsMutS</i>, <i>OsRAD51</i>, and <i>OsRPA1</i>) were reliably fitted to quadratic or exponential decay equations (<i>r</i>² > 0.99). However, the single or integrated dose–response curves of these genes were distinctive from the conventional models obtained by the gamma-H2AX or comet assays. In comparison, rice displayed a higher dose-dependency in the comet signal and <i>OsRAD51</i> transcription, while the gamma-H2AX induction was more dose-dependent in <i>Arabidopsis</i>. The dose-dependent transcriptions of the selected gamma-ray-inducible marker genes, including <i>OsGRG</i>, <i>OsMutS</i>, <i>OsRAD51</i>, and <i>OsRPA1</i> in rice and <i>AtGRG</i>, <i>AtPARP1</i>, <i>AtRAD51</i>, and <i>AtRPA1E</i> in <i>Arabidopsis</i>, were maintained similarly at different vegetative stages. These results suggested that the transcriptome-based phytodosimetry model should be further corrected with conventional genotoxicity- or DDR-based models despite the high reliability or dose-dependency of the model. In addition, the relative weighting of each gene in the integrated transcriptome-based dose–response model using multiple genes needs to be considered based on the trend and amplitude of the transcriptional change.