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<p>Indigenous rice (<i>Oryza sativa</i> L.), a key dietary food in Asia, is known for its rich nutrient and phytochemical profiles. In this study, LC–Q–TOF–MS was used for comprehensive metabolite profiling. Rice samples were prepared through fine grinding, methanolic extraction, sonication, centrifugation, and Millipore filtration, followed by analysis using a mass spectrometer. A total of 257 metabolites in the positive and 269 in the negative ionization mode were identified, of which 32 metabolites were classified into lipids, amino acids, organic acids, choline, amines, sugars, and isoprenoid. Pathway enrichment analysis using the KEGG database revealed significant metabolic pathways influenced by altered metabolite profiles. Four key metabolites, i.e., lecithin, cephalin, phosphatidylserine, and 2-lysolecithin, were notably linked to glycerophospholipid metabolism, with lecithin also involved in the linoleic acid, arachidonic acid, and alpha-linolenic acid pathways. Cephalin participated in GPI-anchor biosynthesis, whereas triacylglycerol was associated with glycerolipid metabolism. This comprehensive analysis enhances our understanding of rice metabolism, providing valuable insights for rice breeding, nutritional improvement, and agricultural practices aimed at enhancing rice quality and nutritional profiles.</p>

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Metabolomic analyses for the characterization of traditional rice germplasm

  • 期刊论文

  • 发表日期：

    2025年10月14日

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  • Saurabh Kadam,
  • Vitthal T. Barvkar,
  • Pooja Dwivedi,
  • Santosh Sawardekar,
  • Sanjay Patil,
  • Sagar Barage

摘要

Indigenous rice (Oryza sativa L.), a key dietary food in Asia, is known for its rich nutrient and phytochemical profiles. In this study, LC–Q–TOF–MS was used for comprehensive metabolite profiling. Rice samples were prepared through fine grinding, methanolic extraction, sonication, centrifugation, and Millipore filtration, followed by analysis using a mass spectrometer. A total of 257 metabolites in the positive and 269 in the negative ionization mode were identified, of which 32 metabolites were classified into lipids, amino acids, organic acids, choline, amines, sugars, and isoprenoid. Pathway enrichment analysis using the KEGG database revealed significant metabolic pathways influenced by altered metabolite profiles. Four key metabolites, i.e., lecithin, cephalin, phosphatidylserine, and 2-lysolecithin, were notably linked to glycerophospholipid metabolism, with lecithin also involved in the linoleic acid, arachidonic acid, and alpha-linolenic acid pathways. Cephalin participated in GPI-anchor biosynthesis, whereas triacylglycerol was associated with glycerolipid metabolism. This comprehensive analysis enhances our understanding of rice metabolism, providing valuable insights for rice breeding, nutritional improvement, and agricultural practices aimed at enhancing rice quality and nutritional profiles.