海洋微藻抑菌化合物的快速分离方法及抑菌活性研究

Searching for new antibiotics from secondary metabolites of marine microalgae is one of the hot areas in emerging marine drug discovery. Earlier study has indicated that the liposoluble components of microalgae have good antimicrobial activity. However, because of the lack of effective separation approaches and screening methods, most of the earlier studies were limited to screening crude extracts from microalgae for antibiotic activity. There are only limited efforts in attempting to understand the chemical structures of specific compounds and the mechanism of their antibacterial activities. Therefore, it is crucial to establish an effective method for rapidly isolating compounds from microalgae and to establish a new model for screening novel compounds for their antibacterial activities. In this study, an algorithm model will be built based on an artificial neural network, and we will use this model to screen appropriate three-phase solvent system for high-speed counter-current chromatography. Then, the research will be directed to understanding the mechanistic aspects of how to use the liquid/liquid/liquid high-speed counter-current chromatography to effectively and rapidly isolate various antibacterial compounds from microalgae and develop effective separation processes accordingly. Our research will be further directed to establishing a new screening model: using Caenorhabditis elegans (C.elegans) for screening compounds for their antibacterial activities. With this screening model, novel antibiotics with good in vitro and in vivo activity correlations can be identified quickly from a mixture. And with the model, the research will further determine the antimicrobial activity of compound, and elucidate its antimicrobial action way. In summary, our studies solve the drawbacks of the existing weaknesses in screening and separation of marine antibiotics in China. By establishing novel technology and mechanism for separation of compounds from microalgae and new model for screening these compounds for their antibacterial activities, our studies will also help to speed the process of identifying new lead compound for antibacterial drug discovery.

从海洋微藻次生代谢产物中寻找新的抗菌药物是新兴海洋药物研究的热点之一。研究发现，微藻中一些脂溶性成分具有良好的抗菌活性，但由于分离方法及活性筛选方法的限制，使得目前对微藻中抑菌成分的研究多处于粗提物活性初筛层面，而对具体化合物的结构及抑菌机制研究甚少。为深入发掘微藻中的抑菌成分，本项目结合自组织神经网络技术建立算法模型，筛选三相溶剂体系，在此基础上建立一种能从复杂体系中快速分离获得单体化合物的液/液/液三相高速逆流色谱分离方法,用于从海洋微藻中分离抗菌化合物；其后以秀丽隐杆线虫为模式生物，通过参数优化和筛选方法研究，建立一种化合物体内抗菌活性筛选模型，从微藻中快速筛选体内外相关性较好的新型抑菌物质；并以模型为基础，测定化合物的抗菌活性，探讨其抗菌作用途径。项目建立的关键技术原理，可填补微藻来源的抑菌化合物筛选、分离方法缺乏的薄弱环节，为加快新型抗菌类药物或其先导物的发现提供新的途径。

从海洋微藻次生代谢产物中寻找新的抗菌药物是新兴海洋药物研究的热点之一，但由于受到分离方法及活性筛选方法的限制，使得目前对微藻中抑菌成分的研究多处于粗提物活性初筛层面，且对具体化合物的结构及抑菌机制研究甚少。本研究结合自组织神经网络技术建立算法模型，筛选三相溶剂体系，在此基础上建立了液/液/液三相高速逆流色谱（HSCCC）分离技术，并结合传统的层析分离、高压制备型液相色谱分离，优化了高速逆流色谱分离的溶剂体系及分离条件，从卢氏藻（Ruttnera spectabilis）、旋链角毛藻（Chaetoceros curvisetus）、紫球藻（Porphyridium cruentum）、金藻（Sarcinochrysis marina Geitler）、聚球藻（Synechocoocus sp）、微拟球藻（Nannochloropsis salina）及小球藻（Chlorella Vulgaris）7种微藻中分离纯化获得植醇、4-羟基苯甲醛、β-谷甾醇、邻苯二甲酸二正丁酯等14种有机小分子单体化合物，并验证了其抗菌和抗氧化活性。以秀丽隐杆线虫为模式生物，建立了以铜绿假单孢菌和金黄色葡萄球菌为致病菌感染的抗细菌模型和以白色念珠菌和霉菌为感染菌的抗真菌模型，确定了感染菌浓度和培养时间、感染时间等模型建立参数，通过采用已知抗菌药物治疗感染线虫，观察线虫存活数量、行为变化及体内细菌数量变化，建立药物与感染菌的数量关系曲线，探讨以此构建新型抗菌物质筛选模型的可行性，并以模型为基础对微藻提取的粗提物及分步精制的产物进行了体外和体内抗菌活性的追踪与筛。采用实时定量PCR技术，通过检测感染线虫用药前后的卵黄原蛋白基因（VTG）vit-2、控制秀丽隐杆线虫凋亡相关蛋白基因ape-1和可以表征外源化合物引起的氧化胁迫等细胞毒性相关基因细胞色素P450家族蛋白(CYP) cyp-35a2的表达量，从分子水平考察化合物的抑菌机制，发现小球藻乙酸乙酯提取物治疗感染线虫时，三种特征蛋白的表达量有所降低，不同于环丙沙星的作用效果。研究建立的关键技术及方法，可填补微藻来源的抑菌化合物筛选、分离方法缺乏的薄弱环节，为加快新型抗菌类药物或其先导物的发现提供新的途径。