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Theanine, a unique and the most abundant non-proteinogenic amino acid in tea plants, endows tea infusion with the umami taste and anti-stress effects. Its content in tea correlates highly with green tea quality. Theanine content in new shoots of tea plants is high in mid-spring and greatly decreases in late spring. However, how the decrease is regulated is largely unknown. In a genetic screening, we observed that a yeast mutant, glutamate dehydrolase 2 (gdh2), was hypersensitive to 40 mM theanine and accumulated more theanine. This result implied a role of CsGDH2s in theanine accumulation in tea plants. Therefore, we identified the two homologs of GDH2, CsGDH2.1 and CsGDH2.2, in tea plants. Yeast complementation assay showed that the expression of CsGDH2.1 in yeast gdh2 mutant rescued the theanine hypersensitivity and hyperaccumulation of this mutant. Subcellular localization and tissue-specific expression showed CsGDH2.1 localized in the mitochondria and highly expressed in young tissues. Importantly, CsGDH2.1 expression was low in early spring, and increased significantly in late spring, in the new shoots of tea plants. These results all support the idea that CsGDH2.1 regulates theanine accumulation in the new shoots. Moreover, the in vitro enzyme assay showed that CsGDH2.1 had glutamate catabolic activity, and knockdown of CsGDH2.1 expression increased glutamate and theanine accumulation in the new shoots of tea plants. These findings suggested that CsGDH2.1-mediated glutamate catabolism negatively regulates theanine accumulation in the new shoots in late spring, and provides a functional gene for improving late-spring green tea quality. [ABSTRACT FROM AUTHOR]

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the USs., Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients., Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), 3 adenosarcomas, 2 carcinosarcomas, and 1 uterine tumor resembling an ovarian sex-cord tumor (UTROSCT). ESS (including high-grade ESS and low-grade ESS) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A - PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uDEGs were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named MMN-MIL showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804., Conclusion: USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.