基于毒力基因组合评价我国产志贺毒素大肠埃希菌潜在致病风险
Evaluation of the potential pathogenic risk of domestic Shiga toxin-producing Escherichia coli based on their virulence gene combination
张鑫昊 1王军 2崔生辉 3白莉4
作者信息
- 1. 国家食品安全风险评估中心,国家卫生健康委员会食品安全风险评估重点实验室,中国医学科学院创新单元(2019RU014),北京 100022;中国医学科学院北京协和医学院,群医学及公共卫生学院,北京 100730
- 2. 青岛农业大学食品科学与工程学院,山东青岛 266109
- 3. 中国食品药品检定研究院,北京 100050
- 4. 国家食品安全风险评估中心,国家卫生健康委员会食品安全风险评估重点实验室,中国医学科学院创新单元(2019RU014),北京 100022
- 折叠
摘要
目的 对我国不同来源产志贺毒素大肠埃希菌(STEC)分离株的致病潜力进行分级,为STEC的风险管理提供参考依据.方法 运用联合国粮食及农业组织和世界卫生组织于2018年联合发布的STEC与食品归因、表征和监测报告中提出的危害等级分级方法,对我国2018-2022年已发表的STEC数据进行分级分析.结果 分级结果显示,纳入研究的STEC菌株中72.9％为低危害菌株,26.0％为高危害菌株,1.1％为最高危害菌株.高危害菌株主要来自牛或牛肉食品(95.3％),仅有8株为最高危害菌株,分别来自牛和患者.结论 仅依据STEC菌株的血清型不足以作为菌株毒力评判标准,通过毒力基因组合对STEC感染风险进行分级是更可靠的方法.这一方法可为我国制定STEC监测和风险评估提供参考.
Abstract
Objective The pathogenic potential of Shiga toxin-producing Escherichia coli(STEC)isolated from different sources in China was classified to provide reference for risk management of STEC.Methods This study used hazard classification method,porposed by the Food and Agriculture Organization of the United Nations and the World Health Organization's report,what name is Shiga toxin-producing Escherichia coli(STEC)and food:attribution,characterization and monitoring.We used to classify and analyze the published STEC data in China from 2018 to 2022.Results STEC isolates were classified as low-risk(72.9％),high-hazard(26.0％)and highest-hazard(1.1％)strains.The high-hazard strains were mainly from cattle or beef food(95.3％),only 8 highest hazard strains were from cattle and patients.Conclusion The serotypes of STEC strains are not enough to evaluate the virulence of STEC strains.The virulence gene combination is a more reliable method to classify the risk of STEC infection.This method could provide reference for STEC surveillance and risk assessment in China.
关键词
产志贺毒素大肠埃希菌/毒力基因/食源性致病菌/危害等级分级Key words
Shiga toxin-producing Escherichia coli/virulence genes/foodborne pathogens/hazard classification引用本文复制引用
基金项目
国家食品安全风险评估中心高层次人才队伍建设项目()
国家食品安全风险评估中心青年科学基因项目(81402685)
出版年
2024
NETL
NSTL
万方数据