Rice Bran Metabolome Contains Amino Acids, Vitamins & Cofactors, and Phytochemicals with Medicinal and Nutritional Properties

Abstract

Background: Rice bran is a functional food that has shown protection against major chronic diseases (e.g. obesity, diabetes, cardiovascular disease and cancer) in animals and humans, and these health effects have been associated with the presence of bioactive phytochemicals. Food metabolomics uses multiple chromatography and mass spectrometry platforms to detect and identify a diverse range of small molecules with high sensitivity and precision, and has not been completed for rice bran.

Results: This study utilized global, non-targeted metabolomics to identify small molecules in rice bran, and conducted a comprehensive search of peer-reviewed literature to determine bioactive compounds. Three U.S. rice varieties (Calrose, Dixiebelle, and Neptune), that have been used for human dietary intervention trials, were assessed herein for bioactive compounds that have disease control and prevention properties. The profiling of rice bran by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) identified 453 distinct phytochemicals, 209 of which were classified as amino acids, cofactors & vitamins, and secondary metabolites, and were further assessed for bioactivity. A scientific literature search revealed 65 compounds with health properties, 16 of which had not been previously identified in rice bran. This suite of amino acids, cofactors & vitamins, and secondary metabolites comprised 46% of the identified rice bran metabolome, which substantially enhanced our knowledge of health-promoting rice bran compounds provided during dietary supplementation.

Conclusion: Rice bran metabolite profiling revealed a suite of biochemical molecules that can be further investigated and exploited for multiple nutritional therapies and medical food applications. These bioactive compounds may also be biomarkers of dietary rice bran intake. The medicinal compounds associated with rice bran can function as a network across metabolic pathways and this metabolite network may occur via additive and synergistic effects between compounds in the food matrix.

Keywords: Chronic diseases; Functional food; Infectious diseases; Medicinal properties; Metabolomics; Phytochemicals; Rice bran.

Fig. 1

Cytoscape network analysis of rice bran amino acid metabolic pathways. Pathway specific network visualization is shown for Calrose rice bran. Each metabolite is represented as a node (circle), extending from a central sub-metabolic pathway node. The central hexagon represents the super metabolic pathway. Node size corresponds to the Z-score using the relative abundance mean value for all three varieties

Fig. 2

Cytoscape network analysis of rice bran cofactor & vitamin metabolic pathways. Pathway specific network visualization is shown for Calrose rice bran. Each metabolite is represented as a node (circle), extending from a central sub-metabolic pathway node., The central hexagon node represents the super metabolic pathway. Node size corresponds to the Z-score using the relative abundance mean value for all three varieties

Fig. 3

Secondary metabolites detected in rice bran across 3 U.S. varieties. Rice bran metabolites were normalized by median of relative abundance for the entire dataset. a shows half of metabolites identified within secondary metabolites metabolic pathways and their difference in median-scaled relative abundance across three varieties. b shows another half of identified secondary metabolites and their difference in medin-scaled relative abundance across three varieties

Fig. 4

Cytoscape visualisation of 16 newly identified metabolites from the rice bran metabolome. Medicinal and nutritional value for these bioactive compounds are described as deonted by ¹ in Tables 1, 2 and 3. Pathway specific network visualization is shown for Calrose rice bran. Each metabolite is represented as a node (circle), extending from a central sub-metabolic pathway node. The central hexagon node represents the super metabolic pathway. Node size corresponds to the Z-score using the relative abundance mean value for all three varieties

Fig. 5

Principal Component Analysis of three U.S. varieties. Principal component analysis (PCA) revealed that the first component described 54% variation herein and between separation of Calrose, Dixiebelle and Neptune rice bran cultivars. The second component describing 46% of the variance did not separate the varieties. No statistical significance was established between the metabolites detected from each of the three U.S rice varieties analyzed