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Published on: October 6, 2020
Revealing the Formation Mechanism of Key Metabolites During Japonica Rice Storage Driven by Microbial Functional
Xinwei Li1, Wei Deng1, Zongrui Zhang1
1Liaoning Academy of Agricultural Sciences, Liaoning Grain Science Research Institute, Shenyang 110161, China.
Background:
To elucidate the evolution of metabolites and fungal communities during storage of fragrant japonica rice (Liaoxiangjing 1396), and to investigate the biosynthetic mechanisms of key compounds and their association with quality deterioration, this study examined rice samples stored under simulated conditions for 16 months.
Method:
Samples were collected at 4-month intervals (designated R20, R14, R13, R12, and R11). Metabolites were identified using GC-MS non-targeted metabolomics, while fungal community structure was analyzed through metagenomics. Core mechanisms were further elucidated via PLS-DA, KEGG pathway enrichment, and multiomics association analysis.
Result:
Results demonstrated that the fatty acid content of rice increased initially and then stabilized (from 12.24 mg/g in R20 to 17.63 mg/g in R12). A total of 263 metabolites were identified, with oxygenated organic compounds (38 species) and lipids/lepidid molecules (24 species) as the predominant categories. Twelve key differential metabolites were screened from the R20 and R12 groups, involving five major metabolic pathways, including amino acid metabolism and lipid metabolism. In the fungal community, Pseudomonas (60.2%) and Pantoea (38.19%) were dominant taxa, with a specific Pantoea species (Pantoea sp.) identified as a core potential biomarker. Multiomics association analysis revealed that Klebsiella dominated the ndhB energy metabolism pathway, while multiple bacteria cooperatively regulated the mcp chemotaxis pathway, interacting with monosaccharide and amino acid accumulation.
Conclusions:
This study reveals that the storage quality deterioration of fragrant japonica rice is driven by the "metabolite-microbe-pathway" chain regulation, and the dynamic changes in key metabolites and fungal communities can serve as quality early warning targets.
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