Semi-targeted metabolomic analysis provides the basis for enhanced antioxidant capacities in pigmented rice grains

Kotamreddy, Jhansi Narmada Reddy; Hansda, Chitra; Mitra, Adinpunya

doi:10.1007/s11694-019-00367-2

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…Furthermore, most organic acids and amino acids significantly contributed to the separation of RR from BR in the PLS-DA loading plot and OPLS-DA VIP plot (Figures 1 and 2). Among "other" metabolites, γ-aminobutyric acid and asparagine are reportedly highly abundant in BR [37,38], which is consistent with the data presented herein. In addition, the t-test p-value for these metabolites comparing BR and RR was below 0.05, and these metabolites contributed to the separation of BR from RR in the PLS-DA loading plot and OPLS-DA VIP plot.…”

Section: Pathvisio Pathway Analysissupporting

confidence: 91%

Metabolite Profiling Reveals Distinct Modulation of Complex Metabolic Networks in Non-Pigmented, Black, and Red Rice (Oryza sativa L.) Cultivars

Kim

Park

et al. 2021

Metabolites

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Comprehensive profiling of primary and secondary metabolites was performed to understand metabolic differences associated with color formation in pigmented rice (Oryza sativa L.). Overall, 110 metabolites from non-pigmented, black, and red rice cultivars were identified. Black and red rice contained high levels of flavonoids associated with plant color. Black rice also contained high levels of terpenoids (carotenoids, tocopherols, phytosterols, and monoterpenes). The non-pigmented rice contained relatively low levels of secondary metabolites. Multivariate and pathway analyses were performed to data-mine the metabolite profiles. Hierarchical clustering analysis of correlation coefficients revealed metabolite clusters based on nitrogen and carbon sources. These clusters suggested a negative correlation between nitrogen and carbon. Pathway analysis revealed that black rice was rich in carbon-based secondary metabolites, with relatively low levels of primary metabolites compared with other rice cultivars. These data highlight the complex interactions between nitrogen and carbon metabolism of primary and secondary metabolites in rice. For the first time, the relationships and metabolic differences in terpenoid content (monoterpenes, triterpenes, and tetraterpenes) of non-pigmented and pigmented rice cultivars were analyzed. These findings should greatly contribute to the understanding of pigmented rice metabolome and inform breeding programs for new rice cultivars.

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Section: Pathvisio Pathway Analysissupporting

confidence: 91%

Metabolite Profiling Reveals Distinct Modulation of Complex Metabolic Networks in Non-Pigmented, Black, and Red Rice (Oryza sativa L.) Cultivars

Kim

Park

et al. 2021

Metabolites

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“…Hierarchical cluster analysis can visually present the differences in the content of metabolites in different types of rice in the form of a heatmap [ 16 ], thereby allowing origin traceability by comparing the difference in the metabolite content in different rice samples. In the heatmap of cluster analysis, the same row represents the same differential metabolites, and the same column represents the same rice samples.…”

Section: Resultsmentioning

confidence: 99%

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Tan

Deng

et al. 2022

Foods

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Many factors, such as soil, climate, and water source in the planting area, can affect rice taste and quality. Adulterated rice is common in the market, which seriously damages the production and sales of high-quality rice. Traceability analysis of rice has become one of the important research fields of food safety management. In this study, LC–MS-based non-targeted metabolomics technology was used to trace four rice samples from Heilongjiang and Jiangsu Provinces, namely, Daohuaxiang (DH), Huaidao No. 5 (HD), Songjing (SJ), and Changlixiang (CL). Results showed that the discrimination accuracy of the partial least squares discriminant analysis (PLS-DA) model was as high as 100% with satisfactory prediction ability. A total of 328 differential metabolites were screened, indicating significant differences in rice metabolites from different origins. Pathway enrichment analysis was carried out on the four rice samples based on the KEGG database to determine the three metabolic pathways with the highest enrichment degree. The main biochemical metabolic pathways and signal transduction pathways involved in differential metabolites in rice were obtained. This study provides theoretical support for the geographical origins of rice and elucidates the change mechanism of rice metabolic pathways, which can shed light on improving rice quality control.

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“…Similarly, an integrated semi-targeted metabolomics platform was incorporated for high throughput metabolite identification and quantitation to reveal distinct metabolic dysregulation in pleural effusion caused by tuberculosis and malignancy utilizing an orbitrap-based MS with DDA MS 2 acquisition [51]. GC coupled to orbitrap MS also showed an ability to perform quantitative as well as qualitative analyses (called semitargeted) to measure the differences in metabolite profiles in pigmented rice grains that might provide insight into the enhanced antioxidant capacities of those grains [89]. These studies demonstrated that the high selectivity of HRAM technology is crucial for the analysis of isobars but also low abundant metabolites in complex backgrounds.…”

Section: Semi-targeted Metabolomicsmentioning

confidence: 99%

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Amer¹,

Deshpande²,

Bird³

2023

Preprint

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Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two’s limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

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Semi-targeted metabolomic analysis provides the basis for enhanced antioxidant capacities in pigmented rice grains

Cited by 11 publications

References 35 publications

Metabolite Profiling Reveals Distinct Modulation of Complex Metabolic Networks in Non-Pigmented, Black, and Red Rice (Oryza sativa L.) Cultivars

Metabolite Profiling Reveals Distinct Modulation of Complex Metabolic Networks in Non-Pigmented, Black, and Red Rice (Oryza sativa L.) Cultivars

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

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