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=Worldwide the fame on traditional rice is increasing as they possess high nutritional and pharmaceutical value besides having high resistance to abiotic and biotic stresses. This has attracted significant attention of breeders, nutritionist, plant protection scientists in recent years. Hence it is the need of hour to investigate the grain metabolome to reveals germination and nutritional importance. This research aimed to explore non targeted metabolites of five traditional rice varieties viz., Chinnar, 2 Chitiraikar, Karunguruvai, Kichili samba and Thooyamalli for its nutritional and therapeutic properties. About 149 metabolites were identified using National Institute of Standards and Technology (NIST) library and Human Metabolome Database (HMDB) and were grouped into 34 chemical classes. Major classes includes fatty acids (31.1 -56.3%), steroids and its derivatives (1.80 -22.4%), dihydrofurans (8.98 -11.6%), prenol lipids (0.66 -4.44%), organo-oxygen 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 metabolites 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 (PC) 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 seventeen variable important in projection (VIP scores) metabolites. 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 the traditional rice grain metabolism to increase grain quality and production with various nutritional and therapeutical benefits.