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    深圳大学高等研究院是深圳大学新成立的一个包含本科与研究生培养、侧重跨学科教学与研究的校内综合办学单位。作为深圳大学内部探索全面改革创新的学术特区,高等研究院与香港和海外著名高校合作,借鉴国外研究型大学通行的管理模式,引进具有一流视野的资深教授和发展潜力的青年教师,营造与国际接轨的学术氛围和培养环境,开展卓越的教学、研究和管理工作。 ······

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高等研究院知名学者讲学计划第二十七期——Genomics-guided discovery of new antibiotics killing superbug

发布时间:2019-11-19 | 浏览次数:

报告题目:Genomics-guided discovery of new antibiotics killing superbug
主讲嘉宾:Yongxin Li(香港大学)
主持人:李 猛 教授
时间:2019年11月23日周六15:30
地点:汇元楼103会议室 

 

报告摘要:
Microbe produces diverse natural products such as signal molecules and antibiotics that mediate communication and competition in a complex microbiota. These genetically encoded small molecules are the vast unexplored source for drug discovery. In the genomics era of natural products, the exponentially growing metagenome data provides us a good chance to mine unexplored microbiome for drug discovery. Our lab aims to establish omics-guided discovery approach for targeted discovery of natural product with chemical novelty and biological importance, by the combined use of big data genome mining, metabolomics, and bioassay. In this talk, I will share with you our genomics-guided discovery of novel antibiotics.
The worldwide prevalence of infections caused by antibiotic-resistant bacteria poses a serious threat to public health due to the limited therapeutic alternatives. The inevitable resistance of antibiotics not only highlight the importance of elucidating emerging resistance mechanisms but also urgent the need for new antibiotics killing the antibiotics resistance superbugs. To tackle the antibiotics resistance crisis, we describe a big data genome mining for targeted discovery of new antibiotics. By applying this approach to 5,585 complete bacterial genomes, we demonstrate a novel mechanism of resistance toward nonribosomal peptide antibiotics that are based on hydrolytic cleavage by D-stereospecific peptidases1. This finding not only provides a potential early indicator of antibiotic resistance to peptide antibiotics1 but also offers a guideline to the design of new drugs in tackling superbugs2.Additionally, we analyze thousands of bacterial genomes to investigate their capacity for biosynthesis of cationic nonribosomal peptides with activity against Gram-negative bacteria. With the aid of this big data genome mining approach, we identified two novel compounds (brevicidine and laterocidine) showing bactericidal activities against antibiotic-resistant Gram-negative pathogens. The two peptides show efficacy against E. coli in a mouse thigh infection model, with an apparently low risk of resistance3. These findings may contribute to the discovery and development of Gram-negative antibiotics. 

 

嘉宾简介:
香港大学,PI,  博导,助理教授。研究方向: 借助大数据智能分析与宏基因组挖掘技术挖掘海洋微生物群落中蕴藏的活性天然产物潜能,运用合成生物学策略实现不可培养微生物的重要活性天然产物的发现与生物制造。在Nature Chemical Biology等国际期刊发表SCI论文25篇。