Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India

Venkatesan Subramanian, Udhaya Nandhini Dhandayuthapani,Senthilraja Kandasamy, Jidhu Vaishnavi Sivaprakasam,Prabha Balasubramaniam, Mohan Kumar Shanmugam,Sriram Nagappan, Subramanian Elangovan, Umesh Kanna Subramani, Kumaresan Palaniyappan,Geethalakshmi Vellingiri,Raveendran Muthurajan

BMC Plant Biology(2024)

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摘要
Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.
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关键词
Traditional rice variety,Metabolite biomarkers,Principal compound analysis,Gas chromatography-mass spectrometry,SDG 2,KEGG pathway,Univariate and multivariate analysis
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