Dataset Information

Proteome Dynamics Underlying Drought Resistance Heterosis in Rice Flag Leaves

ABSTRACT: This study investigates the molecular mechanisms underlying drought resistance heterosis (DRH) in rice, specifically using the water-saving and drought-resistant rice hybrid Hanyou73 (HY73) and its parental lines Hanhui3 (HH3) and Huhan7B (HH7B). Given the significant impact of drought stress on global food security, improving crop drought resistance is paramount. Our research focused on the proteome-level responses in flag leaves during the critical grain-filling stage under both well-watered (WW) and drought-stressed (DS) conditions. Our primary aim was to unravel the complex proteome dynamics and identify key regulatory pathways that contribute to DRH. Through comprehensive DIA-MS-based proteomic analysis, we sought to pinpoint proteins differentially regulated in response to drought and those exhibiting non-additive effects (NAE) in the hybrid. Specifically, we clarified how proteome remodeling supports enhanced carbon metabolism, identified critical protein post-translational modifications (PTMs) such as lysine acetylation (Kac), and uncovered key metabolic hubs like the "MOS-AcCoA-FA/PR" pathway in conferring HY73's drought resistance advantage. This rich dataset serves as a valuable resource for understanding the proteomic underpinnings of drought adaptation and heterosis in rice, offering promising targets for future crop improvement strategies.

INSTRUMENT(S):

ORGANISM(S): Oryza Sativa (rice)

TISSUE(S): Leaf

SUBMITTER: Wenbo Cheng

LAB HEAD: Wei Ding

PROVIDER: PXD064898 | Pride | 2026-06-28

REPOSITORIES: Pride

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Publications

Integrated multi-omics analysis reveals carbon metabolic reprogramming as a mechanism of drought resistance in hybrid rice.

Ding Wei W Bi Junguo J Chen Shoujun S Wu Zhen Z Liao Zhigang Z Huang Lin L Luo Lijun L Chen Liang L Zhang Xumin X

Plant communications 20260526

Drought severely limits global rice production, and the exploitation of heterosis offers a promising route for improving drought resistance. However, the molecular mechanisms that underlie drought resistance in hybrid rice remain largely unknown. Here, we performed integrated multi-omics analyses of flag leaves from the elite hybrid rice Hanyou73 (HY73) and its parental lines, Hanhui3 (HH3) and Huhan7B (HH7B), during grain filling under drought. We discovered that non-additive effect (NAE)-drive ...[more]

PMID: 42198838

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Project description:Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4–6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.

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Dataset Information

Proteome Dynamics Underlying Drought Resistance Heterosis in Rice Flag Leaves

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Publications

Integrated multi-omics analysis reveals carbon metabolic reprogramming as a mechanism of drought resistance in hybrid rice.

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