As the major contributors to the floral odors of tea products, terpenoid volatiles play critical roles in the defense response of plants to multiple stresses. Until now, only a few TPS genes in tea plants (Camellia sinensis) have been functionally validated. In this study, by comparative studies conducted at gene, protein, and metabolite levels during oolong tea processing, we isolated an ocimene synthase gene, CsOCS, which displays a low similarity to previously characterized tea ocimene synthases. Further prokaryotic expression and subcellular localization analysis showed that it is plastid-located and could produce (E)-β-ocimene and (Z)-β-ocimene using GPP as the substrate. The optimum temperature and pH of the enzyme were 30 °C and 7.5, respectively. Treatment with exogenous methyl jasmonate elevated the transcript level of CsOCS and enhanced the emission of ocimene from tea leaves. Collectively, CsOCS is implicated as a key enzyme for β-ocimene synthesis during oolong tea processing.
BackgroundPrimary central nervous system lymphoma (PCNSL) is a type of extranodal non-Hodgkin lymphoma. Although there are widely used prognostic scores, their accuracy and practicality are insufficient. Thus, a novel prognostic prediction model was developed for risk stratification of PCNSL patients in our research.MethodsWe retrospectively collected 122 patients with PCNSL from two medical centers in China from January 2010 to June 2022. Among them, 72 patients were used as the development cohort to construct a new model, and 50 patients were used for the validation. Then, by using univariate and multivariate Cox regression analsis and Lasso analysis, the Xijing model was developed and composed of four variables, including lesion number, β2-microglobulin (β2-MG), systemic inflammation response index (SIRI) and Karnofsky performance status (KPS). Finally, we evaluated the Xijing model through internal and external validation.ResultsCompared with the original prognostic scores, the Xijing model has an overall improvement in predicting the prognosis of PCNSL according to the time-dependent area under the curve (AUC), Harrell’s concordance index (C-index), decision curve analysis (DCA), integrated discrimination improvement (IDI) and continuous net reclassification index (NRI). For overall survival (OS) and progression-free survival (PFS), the Xijing model can divide PCNSL patients into three groups, and shows more accurate stratification ability. In addition, the Xijing model can still stratify and predict prognosis similarly better in the elderly with PCNSL and subgroups received high-dose methotrexate (HD-MTX) or Bruton’s tyrosine kinase inhibitors (BTKi). Finally, external validation confirmed the above results.ConclusionsIntegrating four prognostic factors, including imaging findings, tumor burden, systemic inflammation response index, and comprehensive physical condition, we provided a novel prognostic model for PCNSL based on real-world data and evaluated its predictive capacity.
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