AUTHOR=Udhaya Nandhini Dhandayuthapani , Venkatesan Subramanian , Senthilraja Kandasamy , Janaki Ponnusamy , Prabha Balasubramaniam , Sangamithra Sadasivam , Vaishnavi Sivaprakasam Jidhu , Meena Sadasivam , Balakrishnan Natarajan , Raveendran Muthurajan , Geethalakshmi Vellingiri , Somasundaram Eagan TITLE=Metabolomic analysis for disclosing nutritional and therapeutic prospective of traditional rice cultivars of Cauvery deltaic region, India JOURNAL=Frontiers in Nutrition VOLUME=Volume 10 - 2023 YEAR=2023 URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2023.1254624 DOI=10.3389/fnut.2023.1254624 ISSN=2296-861X ABSTRACT=

Traditional rice is gaining popularity worldwide due to its high nutritional and pharmaceutical value, as well as its high resistance to abiotic and biotic stresses. This has attracted significant attention from breeders, nutritionists, and plant protection scientists in recent years. Hence, it is critical to investigate the grain metabolome to reveal germination and nutritional importance. This research aimed to explore non-targeted metabolites of five traditional rice varieties, viz., Chinnar, Chithiraikar, Karunguruvai, Kichili samba, and Thooyamalli, for their nutritional and therapeutic properties. Approximately 149 metabolites were identified using the National Institute of Standards and Technology (NIST) library and Human Metabolome Database (HMDB) and were grouped into 34 chemical classes. Major classes include fatty acids (31.1–56.3%), steroids and their derivatives (1.80–22.4%), dihydrofurans (8.98–11.6%), prenol lipids (0.66–4.44%), organooxygen compounds (0.12–6.45%), benzene and substituted derivatives (0.53–3.73%), glycerolipids (0.36–2.28%), and hydroxy acids and derivatives (0.03–2.70%). Significant variations in metabolite composition among the rice varieties were also observed through the combination of univariate and multivariate statistical analyses. Principal component analysis (PCA) reduced the dimensionality of 149 metabolites into five principle components (PCs), which explained 96% of the total variance. Two clusters were revealed by hierarchical cluster analysis, indicating the distinctiveness of the traditional varieties. Additionally, a partial least squares-discriminant analysis (PLS-DA) found 17 variables important in the projection (VIP) scores of metabolites. The findings of this study reveal the biochemical intricate and distinctive metabolomes of the traditional therapeutic rice varieties. This will serve as the foundation for future research on developing new rice varieties with traditional rice grain metabolisms to increase grain quality and production with various nutritional and therapeutic benefits.