黄瓜花叶病毒浙江白术分离物全基因组序列测定与分析

正白术(Atractylodes macrocephala Koidz.)为菊科(Compositae)苍术属(Atractylodes)多年生草本植物,其根茎入药具补脾健胃、燥湿利水、止汗安胎等功效,在我国广泛栽培,并以于术(浙江临安于潜)品质最佳[1]。近年来白术生产由于品种单一,长期连作等因素导致病毒病害日趋严重。据报道,黄瓜花叶病毒(Cucumber mosaic virus,CMV)~([2])、蚕豆萎蔫病毒2号(Broad bean wilt virus 2,     

纳米磁珠联合PCR检测甘蔗杆状病毒方法的建立和初步应用

本研究通过对DNA提取裂解液的成分和用量以及裂解时间等因素进行系统优化,建立了一种高通量的磁珠法提取甘蔗总DNA,再进行PCR检测甘蔗杆状病毒(SCBV)的方法。采用核酸自动提取仪,应用纳米磁珠提取甘蔗总DNA,经PCR检测甘蔗杆状病毒,并与以试剂盒提取DNA为模板的PCR检测结果进行比较。结果显示,经优化的裂解液成分为EDTA·Na20.08 mol/L、NaCl 0.8 mol/L、SDS 2%和Tris-HCl0.10 mol/L,裂解时间为20 min,可用于纳米磁珠提取甘蔗总DNA进行SCBV检测。所建立的方法通量高、成本低、耗时短,可用于SCBV的批量检测。     

水稻育性调控相关基因RNAi载体的构建及其表型鉴定

利用RNAi干扰技术研究不同基因对花粉发育、卵细胞发育和合子胚发育的影响是一种重要的手段。本研究通过筛选水稻在生殖发育过程中的9个重要调控基因,构建基因的RNAi表达载体,分析转基因植株育性及相关性状表型,以期探明RNAi表达载体对靶标性状的干扰效应。其中,AT61~AT63、AT64~AT66、AT67~AT69表达载体分别靶标花粉育性、卵细胞发育以及合子胚发育的调控。结果表明,除RNAi表达载体AT64没有获得转基因植株外,其余8个RNAi表达载体均获得了转基因植株;对T0代转基因植株的花粉育性、结实率以及潮霉素筛选(40 mg/L)发芽率检测的统计结果显示,RNAi表达载体AT62(花粉发育调控)、AT65(卵细胞发育调控)和AT67(合子胚发育调控)的干扰效应较强。本研究结果将为创制新型水稻基因工程不育系提供策略和选择。     

鸡胚及禽类细胞系在生物医药领域的应用及研究进展

鸡胚因发育过程清楚，长久以来作为基础和应用科学研究领域重要的实验模型，尤其在鸡胚发育早期绒毛尿囊膜阶段，因其血管丰富，是天然的免疫缺陷宿主，是病理学、药理学和肿瘤学等研究领域的理想实验模型。作者简述了发育早期的鸡胚组织结构，并介绍了鸡胚绒毛尿囊膜在肿瘤研究、血管生成、器官移植、烧伤等疾病机理研究中的应用，以及在鸡胚病理模型基础上进行的抗肿瘤药物筛选的应用。重点介绍了鸡胚及禽类细胞系在病毒繁殖和疫苗生产、治疗性蛋白和单抗生产方面的应用研究进展。多种人源病毒、禽源病毒、支原体等可在鸡胚及禽类细胞上增殖，并用于疫苗生产。作者对常用的禽类纤维原细胞和多能干细胞的发展和特点进行了阐述，并总结了商业化的禽类细胞系来源以及部分易感病毒。鸡胚表达系统能够在目的蛋白特定位点产生人源化糖基，减少目的蛋白对人的过敏反应，且禽蛋廉价易得，可作为生产人用单克隆抗体和治疗性蛋白的合适供体。作者介绍了鸡胚及禽类细胞系在生物医药领域应用的最新进展，并对鸡胚作为动物模型在未来的应用进行了展望。     

Milk vetch dwarf virus infection in the Solanaceae and Caricaceae families in Southeast Asia

Milk vetch dwarf virus (MDV) is an important member of the genus Nanovirus and is transmitted by the aphid Aphis craccivora. MDV has multiple single-stranded DNA genome components, each approximately 1 kb, and two or three alpha-satellite molecules. It mainly infects plants of the legume family Fabaceae. Recently, papaya (Carica papaya) collected in Yesan, South Korea, displaying symptoms of leaf yellowing and dwarfism, was identified as a new host for MDV. To examine the geographical distribution of MDV, papaya samples with symptoms were harvested in South Korea, Vietnam, and Taiwan in August 2018, along with tomato and pepper samples grown in adjacent fields in Vietnam. The results revealed the presence of MDV not only in papaya but also in pepper and tomato. This MDV infection in members of the Solanaceae family was confirmed by Southern blot hybridization performed using a PCR product of segment S as a probe. Based on sequence analysis of three MDV components (M, S, and C3), we verified the presence of three different isolates of MDV in these three countries and homology between sequences of isolates from papaya and from members of the Solanaceae from Vietnam. Taken together, our results clearly demonstrate MDV infection in Vietnam and Taiwan for the first time and confirm that MDV can infect economically important pepper and tomato.     

利用高巧(60%吡虫啉悬浮种衣剂)防治小豆蚜虫效果研究

[目的]探讨小豆蚜虫防治新技术,比较研究种衣剂拌种的防治效果。[方法]选用高巧(60%吡虫啉悬浮种衣剂)进行播前拌种试验,采用50 mL水分别加入高巧2、4、6、8 mL拌种1.0kg的处理方法,播种后比较其出苗率及幼苗的发育情况、蚜虫防治效果、小豆百粒重、产量及增产率。[结果]高巧拌种不会影响小豆出苗,可有效防治小豆蚜虫,但防效随时间延长而降低。防效保持在85%以上,可显著提高小豆百粒重和产量。[结论]该技术可在小豆生产中进行推广应用。     

利用酵母双杂交膜系统筛选与小麦蓝矮植原体免疫膜蛋白互作的异沙叶蝉蛋白

正小麦蓝矮病(Wheat blue dwarf,WBD)是我国西北麦区一种重要植原体病害,在我国西部地区危害严重。该病害由异沙叶蝉(Psammotettix alienus L.)专化性传播,介体传毒成为病害流行的重要中心环节[1]。本实验室前期通过免疫荧光标记研究发现WBD植原体免疫膜蛋白(immunodominant membrane protein, IMP)与介体异沙叶蝉肌动蛋白互作,说明IMP在植原体传播和致病过程中起关键作用。     

刺葡萄棒状拟盘多毛孢叶斑病病原菌的分离鉴定及其防治药剂筛选

葡萄叶斑病是一种严重影响葡萄产量的病害。为了明确引起刺葡萄叶斑病的病原菌种类及筛选有效防治药剂,本研究从福建农林大学实践教学基地采集刺葡萄‘高山二号’10片典型叶斑病病叶片,对分离得到的菌株进行形态学和分子鉴定。结果表明,通过形态学特征结合rDNA-ITS(ITS)、β-tubulin和EF-1α序列分析确定分离到的菌株均为棒状拟盘多毛孢Pestalotiopsis clavispora。防治药剂筛选的结果显示,不同的防治药剂对炭疽病菌的抑制效果不同,60%唑醚·代森联水分散粒剂和25%嘧菌酯悬乳剂对该病原菌菌丝生长的抑制效果最好,其EC_(50)分别为0.119 mg/L和0.178 mg/L;其次为80%波尔多液可湿性粉剂,其EC_(50)为1.766 mg/L;抑制效果最差的药剂为50%多菌灵可湿性粉剂,仅表现出轻微的抑制作用。防治棒状拟盘多毛孢菌丝生长和孢子萌发的最佳药剂是60%唑醚·代森联水分散粒剂和25%嘧菌酯悬乳剂,可作为备选药剂进行田间刺葡萄叶斑病防控试验。     

The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.