Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice
Enhancing the dietary properties of rice is crucial to contribute to alleviating hidden hunger and non-communicable diseases in rice-consuming countries. Germination is a bioprocessing approach to increase the bioavailability of nutrients in rice. However, there is a scarce information on how germin...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Nature Publishing Group
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/137963 |
| _version_ | 1855522245769691136 |
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| author | Tiozon, Rhowell N., Jr. Sreenivasulu, Nese Alseekh, Saleh Sartagoda, Kristel June D. Usadel, Björn Fernie, Alisdair R. |
| author_browse | Alseekh, Saleh Fernie, Alisdair R. Sartagoda, Kristel June D. Sreenivasulu, Nese Tiozon, Rhowell N., Jr. Usadel, Björn |
| author_facet | Tiozon, Rhowell N., Jr. Sreenivasulu, Nese Alseekh, Saleh Sartagoda, Kristel June D. Usadel, Björn Fernie, Alisdair R. |
| author_sort | Tiozon, Rhowell N., Jr. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Enhancing the dietary properties of rice is crucial to contribute to alleviating hidden hunger and non-communicable diseases in rice-consuming countries. Germination is a bioprocessing approach to increase the bioavailability of nutrients in rice. However, there is a scarce information on how germination impacts the overall nutritional profile of pigmented rice sprouts (PRS). Herein, we demonstrated that germination resulted to increase levels of certain dietary compounds, such as free phenolics and micronutrients (Ca, Na, Fe, Zn, riboflavin, and biotin). Metabolomic analysis revealed the preferential accumulation of dipeptides, GABA, and flavonoids in the germination process. Genome-wide association studies of the PRS suggested the activation of specific genes such as CHS1 and UGT genes responsible for increasing certain flavonoid compounds. Haplotype analyses showed a significant difference (P < 0.05) between alleles associated with these genes. Genetic markers associated with these flavonoids were incorporated into the random forest model, improving the accuracy of prediction of multi-nutritional properties from 89.7% to 97.7%. Deploying this knowledge to breed rice with multi-nutritional properties will be timely to address double burden nutritional challenges. |
| format | Journal Article |
| id | CGSpace137963 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Nature Publishing Group |
| publisherStr | Nature Publishing Group |
| record_format | dspace |
| spelling | CGSpace1379632025-12-08T10:29:22Z Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice Tiozon, Rhowell N., Jr. Sreenivasulu, Nese Alseekh, Saleh Sartagoda, Kristel June D. Usadel, Björn Fernie, Alisdair R. metabolomics machine learning germination rice nutrition Enhancing the dietary properties of rice is crucial to contribute to alleviating hidden hunger and non-communicable diseases in rice-consuming countries. Germination is a bioprocessing approach to increase the bioavailability of nutrients in rice. However, there is a scarce information on how germination impacts the overall nutritional profile of pigmented rice sprouts (PRS). Herein, we demonstrated that germination resulted to increase levels of certain dietary compounds, such as free phenolics and micronutrients (Ca, Na, Fe, Zn, riboflavin, and biotin). Metabolomic analysis revealed the preferential accumulation of dipeptides, GABA, and flavonoids in the germination process. Genome-wide association studies of the PRS suggested the activation of specific genes such as CHS1 and UGT genes responsible for increasing certain flavonoid compounds. Haplotype analyses showed a significant difference (P < 0.05) between alleles associated with these genes. Genetic markers associated with these flavonoids were incorporated into the random forest model, improving the accuracy of prediction of multi-nutritional properties from 89.7% to 97.7%. Deploying this knowledge to breed rice with multi-nutritional properties will be timely to address double burden nutritional challenges. 2023-10-02 2024-01-18T07:26:52Z 2024-01-18T07:26:52Z Journal Article https://hdl.handle.net/10568/137963 en Open Access application/pdf Nature Publishing Group Tiozon, R.J.N., Sreenivasulu, N., Alseekh, S. et al. Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice. Commun Biol 6, 1000 (2023). https://doi.org/10.1038/s42003-023-05379-9 |
| spellingShingle | metabolomics machine learning germination rice nutrition Tiozon, Rhowell N., Jr. Sreenivasulu, Nese Alseekh, Saleh Sartagoda, Kristel June D. Usadel, Björn Fernie, Alisdair R. Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice |
| title | Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice |
| title_full | Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice |
| title_fullStr | Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice |
| title_full_unstemmed | Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice |
| title_short | Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice |
| title_sort | metabolomics and machine learning technique revealed that germination enhances the multi nutritional properties of pigmented rice |
| topic | metabolomics machine learning germination rice nutrition |
| url | https://hdl.handle.net/10568/137963 |
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