Publication: Deciphering rice metabolic flux reprograming under salinity stress via in silico metabolic modeling
Issued Date
2020-01-01
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ISSN
20010370
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2-s2.0-85096865058
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Mahidol University
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SCOPUS
Bibliographic Citation
Computational and Structural Biotechnology Journal. Vol.18, (2020), 3555-3566
Suggested Citation
Kwanjeera Wanichthanarak, Chuthamas Boonchai, Thammaporn Kojonna, Supachitra Chadchawan, Wichian Sangwongchai, Maysaya Thitisaksakul Deciphering rice metabolic flux reprograming under salinity stress via in silico metabolic modeling. Computational and Structural Biotechnology Journal. Vol.18, (2020), 3555-3566. doi:10.1016/j.csbj.2020.11.023 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/60414
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Title
Deciphering rice metabolic flux reprograming under salinity stress via in silico metabolic modeling
Abstract
© 2020 The Author(s) Rice is one of the most economically important commodities globally. However, rice plants are salt susceptible species in which high salinity can significantly constrain its productivity. Several physiological parameters in adaptation to salt stress have been observed, though changes in metabolic aspects remain to be elucidated. In this study, rice metabolic activities of salt-stressed flag leaf were systematically characterized. Transcriptomics and metabolomics data were combined to identify disturbed pathways, altered metabolites and metabolic hotspots within the rice metabolic network under salt stress condition. Besides, the feasible flux solutions in different context-specific metabolic networks were estimated and compared. Our findings highlighted metabolic reprogramming in primary metabolic pathways, cellular respiration, antioxidant biosynthetic pathways, and phytohormone biosynthetic pathways. Photosynthesis and hexose utilization were among the major disturbed pathways in the stressed flag leaf. Notably, the increased flux distribution of the photorespiratory pathway could contribute to cellular redox control. Predicted flux statuses in several pathways were consistent with the results from transcriptomics, end-point metabolomics, and physiological studies. Our study illustrated that the contextualized genome-scale model together with multi-omics analysis is a powerful approach to unravel the metabolic responses of rice to salinity stress.