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

Importance of the Topic

Rice plants exposed to ionizing radiation undergo complex defense responses at molecular level. Understanding how low doses of gamma radiation affect seed biology is critical for assessing environmental safety in contaminated regions and for improving crop resilience after nuclear incidents. Multi-omic strategies that integrate transcriptome and metabolome profiling enable a holistic view of stress adaptation mechanisms and identification of reliable molecular markers.

Objectives and Study Overview

This study aimed to discover transcriptomic and metabolomic biomarkers linked to gamma radiation stress in rice seeds. Researchers compared seeds harvested from plants grown in Fukushima’s radionuclide-contaminated soil to those from an uncontaminated control site. By leveraging Agilent’s multi-omics workflow, the investigation sought to map altered pathways and validate key stress-related genes and metabolites.

Methods

• Sample collection: Rice cultivar Koshihikari seeds were obtained from a paddy field in Iitate village (contaminated) and Minamisoma (control). Seeds were dehusked and stored at –80 °C.
• Transcriptomics: Total RNA was isolated with CTAB, phenol–chloroform extraction and column purification. Microarray analysis employed Agilent rice 4×44 K arrays, with data processed in GeneSpring 13.1 and validated by qRT-PCR using Brilliant III SYBR Green chemistry on the AriaMx system.
• Metabolomics: Polar and nonpolar metabolites were extracted using a biphasic chloroform–methanol–water protocol. Samples were analyzed by GC/Q-TOF after derivatization and by LC/MS with an Infinity 1290 LC coupled to a 6550 Accurate-Mass Q-TOF. MassHunter and ProFinder software were used for peak deconvolution and compound identification against the Fiehn and METLIN libraries.
• Data integration: Differential genes and metabolites (fold change ≥2, p ≤ 0.05) were integrated in the Multi-Omic Analysis module of GeneSpring/MPP Pathway Architect 13.1 to reveal overlapping pathways and correlation networks.

Instrumentation Used

• Agilent 2100 Bioanalyzer (RNA quality)
• Agilent SureScan Microarray Scanner
• Agilent AriaMx Real-time PCR System
• Agilent 7200 GC/Q-TOF with Fiehn library
• Agilent 1290 Infinity LC and 6550 Accurate-Mass Q-TOF
• Agilent MassHunter Workstation, ProFinder, and GeneSpring/MPP software suites

Key Results and Discussion

• Transcriptome: 2 331 genes were differentially expressed in seeds from contaminated soil (1 891 up-regulated, 440 down-regulated). Stress-responsive pathways, including phenylpropanoid biosynthesis, antioxidant defense and fatty acid metabolism, were significantly activated.
• Validation: PR10, a pathogenesis-related gene, showed 8.2-fold induction, consistent across microarray and qRT-PCR platforms.
• Metabolome: 383 compounds were detected, 50 showing significant changes. Stress markers such as proline (+17.5 FC), trehalose (+26 FC) and raffinose (+24.5 FC) accumulated in radiated seeds.
• Multi-omic integration: Combined analysis highlighted overlaps in purine, linolenic acid and amino acid metabolism. Up-regulation of linolenic acid and 12-OPDA pointed to jasmonate signaling activation.
• Correlation networks: Positive associations were observed between key genes and metabolites within carbohydrate and fatty acid pathways, suggesting coordinated regulation of cell wall reinforcement (cutin/suberin) and energy metabolism under radiation stress.

Benefits and Practical Applications

Identified molecular signatures provide potential biomarkers for monitoring low-level radiation exposure in crops. Understanding defense-related metabolic adjustments can inform breeding programs aimed at improving stress tolerance. The Agilent multi-omic workflow demonstrates a robust platform for plant stress research and environmental safety assessments.

Future Trends and Potential Applications

Advances in high-resolution mass spectrometry, single-cell omics and data integration algorithms will enable deeper characterization of radiation effects at cellular and subcellular levels. Incorporation of proteomics and epigenomics promises to reveal additional regulatory layers. Development of rapid field-deployable assays based on identified biomarkers could enhance real-time monitoring of contaminated environments.

Conclusion

The integrated transcriptomic and metabolomic analysis uncovered a coordinated defense response in rice seeds exposed to low-level gamma radiation. Activation of phenylpropanoid, fatty acid and carbohydrate pathways, along with accumulation of stress-protective metabolites, underscores the resilience mechanisms deployed by plants in radionuclide-laden soils. This multi-omic approach paves the way for targeted biomarker development and contributes to risk assessment and crop improvement strategies.

Reference

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