WHO把产超广谱β-内酰胺酶肠杆菌被WHO列为对人类健康构成最大威胁的极为重要抗生素耐药病原体。我国是全球畜禽源细菌耐药性问题最严重的国家之一，在养殖动物、环境、动物源性食品等多种来源的大肠杆菌中检出介导对第三代头孢菌素甚至碳青霉烯类耐药基因的报道日益增多。虽然国内学者已经在该领域开展了大量工作，但由于研究方法和研究技术所限，难以对细菌耐药性的产生和跨介质、跨宿主传播开展全景式研究。随着下一代测序技术的快速发展，基于微生物基因组大数据分析平台为上述问题提供了可能。本课题拟基于国际上先进的微生物信息云平台，以2002-2019年养殖场来源的大肠杆菌为研究对象，利用测序大数据技术开展耐药菌的遗传演化基础研究，探明养殖场大肠杆菌头孢菌素耐药性的来源和范围、演变，解析耐药性广泛传播驱动机制及耐药性随食物链扩散到人群的风险，为细菌耐药性预警、耐药性风险评估与控制政策的制定提供理论基础和数据。

Escherichia coli producing extended spectrum beta-lactamases has been listed by WHO as an critical important antibiotic resistant pathogen that poses the greatest threat to human health. China is one of the countries with the most serious problem of drug resistance of livestock origin bacteria in the world. There are increasing reports on the detection of drug resistance genes mediating the third generation of cephalosporins and even carbapenems in E. coli from various sources such as farmed animals, environment and animal-derived food. There are increasing reports on the detection of genes conferring resistance to the third generation of cephalosporins and even carbapenems in E. coli from various sources such as farmed animals, environment and animal-derived food. Although domestic scholars have done a lot of work in this field, due to the limitations of research methods and technologies, it is difficult to conduct panoramic research on the source of bacterial drug resistance and cross-host transmission. With the rapid development of the next generation sequencing technology, the big data analysis platform based on microbial genome has provided possibilities for the above problems. The proposed research will explore the genetic evolution of cephalosporins resistant E. coli over time based on the next sequencing technology, include investigating the evolution and spread of cephalosporin resistance genes over time in farm animal from 2002 to 2019, analyze and further fully sequence the pandemic plasmids harboring ESBL gene and pandemic clonal lineage. With WGS we discover the novel resistance mechanism in resistant E. coli without genotype. We further We elucidate the risks of AMR in farm transfer via animal to human. With metagenome sequencing for samples from animal feces, farm and surroundings, we obtain data of genetic diversity of microbes and abundance of relevant resistance genes to evaluate the extent and range of AMR in farm diffusion to environment. To well address the above issue is largely relied on the "Cloud infrastructure for microbial bioinformatics，CLIMB". The results will be of important scientific and theoretical significance for advancing the prevention and control of drug resistance, reducing the use of antibiotics in livestock, and enhancing animal food safety in China.