A Multi-omic Approach to Reveal the Effect of Low-level Gamma Radiation on Rice Seeds

Importance of the Topic

Rice is a critical global staple crop and understanding the impact of environmental stressors on seed quality is essential for food security and crop resilience. Low-level gamma radiation, resulting from nuclear events, may trigger subtle molecular changes that influence plant defense mechanisms, metabolism, and yield. Multi-omics approaches that combine transcriptomics and metabolomics provide comprehensive insight into stress responses, enabling the identification of biomarkers and pathways that confer enhanced tolerance.

Study Objectives and Overview

This study aimed to investigate the molecular effects of chronic low-level gamma radiation on rice seeds cultivated on radionuclide-contaminated soil in Fukushima Prefecture. Using Agilent’s integrated workflow, researchers performed a combined transcriptomic and metabolomic analysis to identify stress-related biomarkers and elucidate pathways modulated by irradiation. Control seeds were obtained from plants grown in non-contaminated soil, allowing direct comparison.

Methodology and Instrumentation

Plant Material Collection and RNA Analysis:

• Harvested and flash-frozen rice seeds from contaminated and control fields.
• RNA extracted via CTAB and kit-based protocols; quality assessed by NanoDrop and Agilent 2100 Bioanalyzer.
• One-color microarray hybridization on Agilent rice 4×44k arrays; feature extraction with Agilent software.

Metabolite Extraction and Analysis:

• Biphasic extraction of metabolites (CHCl₃/MeOH/H₂O).
• GC/Q-TOF profiling with Agilent 7200 and Fiehn library for polar compounds.
• LC/Q-TOF analysis with Agilent 6550 Q-TOF and 1290 Infinity LC for organic and aqueous fractions.

Data Integration:

• GeneSpring 13.1 and Mass Profiler Professional for differential analysis.
• Moderated T-test with multiple testing correction for DE genes (≥2-fold, p≤0.05).
• Multi-Omic Analysis (MOA) and Pathway Architect for pathway mapping and correlation between genes and metabolites.

Main Results and Discussion

• Transcriptomics: 2,331 genes differentially expressed (1,891 up, 440 down) affecting defense, phenylpropanoid, fatty acid, carbohydrate, and glutathione metabolism.
• Metabolomics: Of 383 identified metabolites, 50 showed significant changes, including increased proline, trehalose, and linolenic acid, and decreased arginine and serine.
• Pathway overlap: Key shared pathways included alpha-linolenic acid metabolism, pentose phosphate pathway, and phenylpropanoid biosynthesis.
• Integrated correlations revealed coordinated up-regulation of genes and metabolites in the unsaturated fatty acid pathway and downstream suberin/cutin synthesis, suggesting reinforced cell walls and oxidative protection.

Benefits and Practical Applications

• Identified radio-markers (e.g., PR10, proline) for monitoring seed stress response.
• Workflow demonstrates a rapid, integrated platform for plant stress biology studies.
• Potential to guide breeding or agronomic strategies for radiation-resilient rice varieties.

Future Trends and Potential Applications

Advancements in multi-omics integration, machine learning for biomarker discovery, and high-throughput phenotyping will accelerate understanding of low-dose radiation effects. Extending similar workflows to other crops and environmental stressors may inform global food safety and sustainable agriculture under changing climates.

Conclusion

This multi-omic study demonstrates that chronic low-level gamma radiation induces a coordinated defense response in rice seeds, involving both gene expression and metabolite adjustments. Agilent’s integrated solutions facilitated the identification of stress biomarkers and pathways, highlighting their value for plant stress research and crop improvement.

Reference

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