{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chowdhury NB"],"funding":["National Institute of Food and Agriculture","National Science Foundation"],"pagination":["16432"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12069599"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Rising temperatures due to global warming can negatively impact rice grain quality and yield. This study investigates the effects of increased warmer night temperatures (WNT), a consequence of global warming, on the quality of rice kernel, particularly grain chalkiness. By integrating computational and experimental approaches, we used a rice grain metabolic network to discover the metabolic factors of chalkiness. For this, we reconstructed the rice grain genome-scale metabolic model (GSM), iOSA3474-G and incorporated transcriptomics data from three different times of the day (dawn, dawn 7 h, and dusk) for both control and WNT conditions with iOSA3474-G. Three distinct growth phases: anoxia, normoxia, and hyperoxia, were identified in rice kernels from the GSMs, highlighting the grain-filli"],"journal":["Scientific reports"],"pubmed_title":["Transcriptome enhanced rice grain metabolic model identifies histidine level as a marker for grain chalkiness."],"pmcid":["PMC12069599"],"funding_grant_id":["2024-67013-42385","1943310"],"pubmed_authors":["Saha R","Chandran AKN","Walia H","Chowdhury NB"],"additional_accession":[]},"is_claimable":false,"name":"Transcriptome enhanced rice grain metabolic model identifies histidine level as a marker for grain chalkiness.","description":"Rising temperatures due to global warming can negatively impact rice grain quality and yield. This study investigates the effects of increased warmer night temperatures (WNT), a consequence of global warming, on the quality of rice kernel, particularly grain chalkiness. By integrating computational and experimental approaches, we used a rice grain metabolic network to discover the metabolic factors of chalkiness. For this, we reconstructed the rice grain genome-scale metabolic model (GSM), iOSA3474-G and incorporated transcriptomics data from three different times of the day (dawn, dawn 7 h, and dusk) for both control and WNT conditions with iOSA3474-G. Three distinct growth phases: anoxia, normoxia, and hyperoxia, were identified in rice kernels from the GSMs, highlighting the grain-filli","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 May","modification":"2026-06-02T21:04:35.809Z","creation":"2026-04-20T03:14:32.466Z"},"accession":"S-EPMC12069599","cross_references":{"pubmed":["40355482"],"doi":["10.1038/s41598-025-00504-6"]}}