酿酒酵母合成木栓酮及其衍生物的研究现状及展望

木栓酮及其衍生物在植物中普遍存在且种类繁多,具有丰富的生理药理学活性。木栓酮衍生物是以木栓酮为骨架经细胞色素氧化酶P450(cytochromeP450,CYP450)及UDP葡萄糖醛酸转移酶(UDP-glucuronosyltransferase, UGT)修饰而来。植物中天然木栓酮及其衍生物的含量极低,传统的萃取分离和化学合成效率低、能耗高且污染环境,因此,利用酿酒酵母作为宿主菌生产木栓酮及其衍生物是一种高效且环保的策略。本文从增加前体含量、提高酶活性和产物合成的亚细胞定位等方面介绍并展望了木栓酮在酿酒酵母中高效生产的策略,并介绍了目前几种常见的木栓酮衍生物研究现状,从根据碳骨架相似性挖掘CYP450、蛋白质工程改造CYP450和合成代谢基因簇的挖掘等方面展望了木栓酮衍生物的合成途径解析的新思路。     

Comprehensive Evaluation and Screening for 1,2,4-Trichlorobenzene Tolerance in Rice Cultivars at Different Growth Stages

Journal of Plant Growth Regulation - 1,2,4-trichlorobenzene (1,2,4-TCB) pollution in fields has become a potential threat to rice growth, yet little is known about the different response of many...     

海水酸化及升温对刺参生长及能量收支的影响

以东亚浅海生态系统中的关键种——刺参(Apostichopus japonicus)为实验对象,研究了CO₂驱动的海水酸化及升温对其生长及能量收支的影响。实验设置对照组(大连近海水温, pCO₂ 400μatm)、升温组(大连近海水温+3℃, pCO₂ 400μatm)、酸化组(大连近海水温, pCO₂ 1100μatm)和酸化升温组(大连近海水温+3℃,pCO₂ 1100μatm)。结果表明:与对照组相比,温度升高3℃对刺参的生长无显著影响;酸化组刺参的特定生长率最低,较对照组降低0.19%/d,个体体重的变异系数最大;酸化升温组刺参的终末体重和特定生长率与对照组相较无显著差异,但其摄食率和排粪率均显著高于对照组。升温组和酸化组的刺参能量的分配模式与对照组相比未发生明显改变,但酸化升温组刺参的能量分配模式发生显著变化,其粪便能所占摄食能的比例显著升高。研究表明,海水酸化抑制了刺参的生长但未改变其能量的分配,生长的降低主要取决于摄食减少;而海水酸化与温度升高的共同作用可能会通过...     

苄基异喹啉类化合物 D20抑制钙调素激活磷酸二酯酶的研究

苄基异喹啉化合物是一类钙调素拮抗剂.对新合成的双苄基异喹啉化合物 D₂₀对钙调素依赖的磷酸二酯酶的抑制作用进行了研究,IC₅₀=5μmol/L,表明其拮抗作用大于三氟啦嗪,是强的拮抗剂.荧光分析表明,钙调素与化合物 D₂₀的结合常数为2.64(μmol/L)^-1,一个化合物 D₂₀分子与两个钙调素分子结合,并显示了结合方向性及空间位阻影响.     

LDL的氧化修饰和氧化修饰LDL的组成和结构变化

与低密度脂蛋白(LDL)相比,氧化修饰LDL(O-LDL)的组成、结构和生物学特性发生了深刻的变化,而组成和结构的改变是生物学特性改变的基础.本文根据最近文献资料.结合我们实验室的工作.对LDL的氧化修饰、O-LDL的组成、结构改变,以及它们的机理作一简要综述.     

杨树拟茎点菌溃疡病的研究

本病系我国杨树新发生的一种病害。病原菌的分生孢子有2种形状,无色透明、单胞,呈纺缍形孢子大小为1.64—2.74×9.59—13.7μm。线形孢子顶端弯钩状大小为1.37—1.78×15—27.4μm。病原菌适宜生长的培养基为PDA、麦芽粉、豆饼粉培养基;温度为25—32℃和pH6—10。本病于4月下旬发病,5月中下旬分生孢子成熟,6月中下旬为分生孢子飞散高峰期。经室内采用多菌灵、甲基托布津、福美砷的200倍液浸泡插穗30、60、90分钟处理均有药害,采用100—200倍液浸泡10分钟无药害,对病害有防效。造林地幼树可采用上述药剂的100倍液进行刮破病斑涂抹,防效可达87.5％。     

一种重组人α1型干扰素突变体IFN-α1/86D的纯化与鉴定

本文研究了重组人α1型干扰素突变体IFN-α1/86D在摇瓶培养和分批发酵培养的重组大肠杆菌DH5α株中的表达动态,并采用阳离子交换层析和抗α1型干扰素单克隆抗体亲和层析的两步流程对其进行了纯化,得到SDS-PAGE纯及高效液相层析(HPLC)纯的IFN-α1/86D产品,共比活性为2.3×10~7单位/毫克蛋白。N端氨基酸序列测定的结果表明,纯化产品的纯度在95％以上,但其N端不均一,产品中含有两种N端序列正确的IFN-α1/86D活性分子,即约75％为去Met分子和约25％为带Met分子。     

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302–0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299–0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.