Using next generation sequencing to reveal human impact on aquatic reservoirs of antibiotic resistant bacteria at the catchment scale

首席医疗官Dame Sally Davies将抗生素耐药性的威胁与气候变化和全球恐怖主义构成的威胁进行了比较。细菌对抗生素的耐药性已经存在了数亿年，因为它进化到对抗细菌和真菌产生的抗生素。耐药性要么是通过突变，要么是通过从其他细菌吸收DNA而产生的，这些细菌甚至可能不是密切相关的。这种水平抗性基因转移是临床对抗感染所面临的最重要问题之一。临床病原菌所吸收的新型耐药基因来源于环境细菌，一旦进入人类病原菌甚至无害的共生菌中，就会被临床使用抗生素所选择。然而，对于这些基因被动员到人类相关细菌的条件或位置，或者这些耐药基因的人类传播途径是什么，人们知之甚少。越来越多的证据表明，在农业中使用抗生素导致临床中看到的耐药性增加，然而，对农场动物耐药性进化的研究远远少于对人类的研究，因此可用的证据较少。关于自然环境中耐药细菌的储存库，特别是被人类或动物粪便严重污染的地方，所知就更少了。每天有110亿升废水排入英国河流；关键的是，这种治疗并没有显著减少耐药细菌的数量。每年有数百万吨动物粪便被扩散到农田，为更广泛的环境提供了额外的抗性生物输入。我们之前的工作表明，使用一种标记基因，可以预测沉积物中抗生素抗性基因的水平，在干净和脏的沉积物之间变化高达1000倍。我们的数据还显示，废水处理厂对这种影响负有大部分责任（约50%），30%与河流附近土地的弥漫性污染有关。该联盟产生的其他数据表明，人类对这些耐药生物的环境储存库存在真正的暴露风险，仅在英格兰和威尔士，每年就有数百万人因休闲利用沿海水域而暴露。这个项目将首次使用尖端的高通量DNA测序技术和计算分析来增加我们对人类活动的理解，这些活动导致泰晤士河流域的抗生素耐药细菌水平上升。将在一年内的三个时间点，在40个采样点确定3000多种不同抗性基因的丰度和特性，一式三份，以捕捉季节性和流量的影响。我们还将测量一系列抗生素残留、金属和营养物质。我们会在整个集水区使用有关污水处理厂类型、规模、位置和土地用途的图形资讯系统数据。这些数据将共同用于建立一个模型，该模型将揭示在流域规模上抗性基因丰度和多样性的主要驱动因素。我们还将确定与不同污染源相关的新型分子标记，这些标记可以用作污染源跟踪目标。我们的目标是分析能够降低耐药细菌水平的具体缓解战略的影响，这将使人们能够估计耐药水平的降低，从而为政策和监管目标提供信息。将开发一种翻译工具，用于监测从DNA序列分析和建模工作中鉴定出的最重要的标记基因。这将是一种负担得起的测试，将有助于确定人类健康风险评估的关键因素。

The threat of antibiotic resistance has been compared to that posed by climate change and global terrorism by the Chief medical Officer Dame Sally Davies. Bacterial resistance to antibiotics has existed for hundreds of millions of years, as it evolved to combat antibiotics produced by bacteria and fungi. Resistance is conferred either by mutation or by uptake of DNA from other bacteria which may not even be closely related. This horizontal resistance gene transfer is one of the most important issues facing the fight against infection in the clinic. Novel resistance genes that are taken up by clinical pathogens originate in environmental bacteria, and once in human pathogens or even harmless commensal bacteria, will be selected for by clinical use of antibiotics. However, little is known about the conditions under or locations in which these genes are mobilised into human associated bacteria, or what the human exposure routes for transmission of these resistance genes are. Increasing evidence suggests that the use of antibiotics in agriculture contributes to the increase in resistance seen in the clinic, however much less research has focused on evolution of resistance in farm animals than in humans so less evidence is available. Even less is known regarding reservoirs of resistant bacteria in the natural environment, particularly locations heavily polluted by human or animal waste. 11 billion litres of waste water are discharged into UK rivers every day; critically much of this treatment does not significantly reduce numbers of resistant bacteria. Millions of tons of animal faecal wastes are spread to agricultural land every year, providing additional inputs of resistant organisms into the wider environment. Our previous work has shown that the use of a marker gene, which is predictive of levels of antibiotic resistance genes in sediments, varies by up to 1000 times between clean and dirty sediments. Our data also shows that waste water treatment plants are responsible for the majority of this effect (about 50%), and 30% is associated with diffuse pollution from land adjacent to the river. Other data generated by the consortium suggests that there are real human exposure risks to these environmental reservoirs of resistant organisms, with several million exposure events occurring each year in England and Wales through recreational use of coastal waters alone.This project will, for the first time, use cutting edge high through put DNA sequencing technologies and computational analyses to increase our understanding of the human activities that drive increased levels of antibiotic resistant bacteria across the River Thames catchment. Abundance and identity of over 3000 different resistance genes will be determined at 40 sampling sites, in triplicate at three time points over one year, to capture impacts of seasonality and flow. We will also measure a range of antibiotic residues, metals and nutrients. We will use graphical information system data on waste water treatment plant type, size and location and land use throughout the catchment. Together this data will be used to produce a model which will reveal the main drivers of resistance gene abundance and diversity at the catchment scale. We will also identify novel molecular markers associated with different sources of pollution that can be used as source tracking targets. We aim to analyse the effects of specific mitigation strategies that are able to reduce levels of resistant bacteria, this will enable estimates of reduction in resistance levels that can inform policy and regulatory targets.A translational tool will be developed for surveillance of the most important marker genes identified from the DNA sequence analyses and modelling work. This will be an affordable test that will help identify key factors for human health risk assessment.