(374f) Transcriptomics Data Integrated Rice Kernel Metabolic Model Identified Histidine As a Marker of Chalkiness

This study investigates the effects of warmer night temperatures (WNT), a consequence of global warming, on the quality of rice kernel, focusing on the grain chalkiness. Through an integrated computational and experimental approach, we explored the rice kernel metabolic network that governs the emergence of chalkiness in rice kernels. To investigate rice kernel chalkiness, we reconstructed the largest rice kernel genome-scale metabolic model (GSM), iOSA3474-K. We also incorporated transcriptomics data with iOSA3474-K of three different times of the day (dawn, dawn 7h, and dusk) for both control and WNT conditions through EXTREAM. Distinct growth phases—namely, anoxia, normoxia, and hyperoxia—were identified in rice kernels from the GSMs, highlighting the grain-filling pattern during different oxygen levels. We identified histidine as a biomarker of normoxia, during which kernel chalkiness occurs. Moreover, we identified tyrosine as a biomarker for the hyperoxic growth phase. In addition, our investigation highlighted the crucial role of monodehydroascorbate reductase—an enzyme with evolutionary significance dating back to the Carboniferous era—in regulating the hyperoxic growth phase. Next, through metabolic bottleneck analysis, a tool that expands the flux range of a reaction and determines if that reaction improves the biomass growth rate, we identified nucleoside diphosphate kinase as a central regulator of metabolic flux under both control and wild-type conditions. These findings provide targeted insights into the complex metabolic network governing rice grain chalkiness under global warming. By integrating GSM and transcriptomics data, this approach not only enhances our understanding of the intricate relationship between environmental factors, metabolic processes, and crop quality but also offers practical avenues for improving crop resilience.