Abstract
Introduction
Lipids are a diverse group of macromolecules that occur in rice grains and are known to impact rice grain properties. Identifying the relationships between specific lipids and traits of quality is important to improve varietal selection for high quality rice.
Objectives
Using untargeted lipidomics, this study aims to understand the role of lipids on different traits of quality by identifying the genotypic effect of lipids and their impact on traits of cooking and eating quality of a rice mapping population.
Methods
Lipids from milled rice grains of three sets of rice samples were screened by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) in the positive ionisation mode. Lipid features were putatively identified using analytical standards and online databases. Multivariate statistics were carried out to identify the lipid profile of varieties across three experiments. Correlation analysis was carried out between lipid features and 12 quality traits across a rice mapping population that segregates for grain physical and texture-associated traits.
Results
Thousands of features in rice grain lipids were detected, and were grouped into six categories—fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids and prenol lipids. A strong genotypic basis for the lipid profile was observed among the four varieties grown under five nitrogen treatments. Clear differentiation in lipid profiles between waxy and non-waxy rice was observed. Strong correlations were observed for putative lipids that form the amylose–lipid complex and with amylose content and viscosity parameters.
Conclusions
This study demonstrates the strength of untargeted lipidomics in putatively determining features that differentiate varieties from each other, and reveals the role of specific lipids on the physical and textural quality of rice.
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Acknowledgements
The authors would like to thank Dr Paul Goulding of Waters Corporation for his technical assistance with the lipidomics data of the mapping population. Christopher Proud for his assistance in creating the Manhattan plots in R and for reading the manuscript. Dr Jennifer Waanders of the School of Agriculture and Food Sciences at the University of Queensland for her assistance in using the UPLC-MS. Margrit Martin of the Yanco Agricultural Research Centre for providing samples used in the nitrogen experiment (Experiment 1). The International Rice Research Institute for providing the rice grain samples of the diverse rice varieties. Bingyue Wang for the assistance in sample preparation. We also like to thank the Australian Research Council—Industry Transformation Training Centre (Agents of Change) IC130100011 and the Australian Centre for International Agricultural Research (ACIAR) for funding this research and for the John Allwright Fellowship of JCTC.
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JCTC performed the analytical experiments, data processing, metabolite annotation, PCA, presentation and interpretation of results, MC and MAF optimised the method for the UPLC-MS, JCTC performed the majority of the writing, MAF, MC and MJG provided guidance to the lead author. All authors have read and approved the manuscript.
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Concepcion, J.C.T., Calingacion, M., Garson, M.J. et al. Lipidomics reveals associations between rice quality traits. Metabolomics 16, 54 (2020). https://doi.org/10.1007/s11306-020-01670-6
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DOI: https://doi.org/10.1007/s11306-020-01670-6