Validated predictive modelling of the environmental resistome.
Validated predictive modelling of the environmental resistome.
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DOI：
10.1038/ismej.2014.237
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发表时间：
2015-06
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
多重耐药细菌对公众健康构成重大威胁。环境在耐药性感染和人类风险总体上升中的作用在很大程度上是未知的。这项研究旨在评估泰晤士河流域的抗虫水平的驱动因素，模拟关键的生物，空间和化学变量，并为未来的风险评估建立预测模型。在2011年和2012年的四个时间点，从泰晤士河流域的13个地点采集了沉积物样本。对样本进行1类整合子流行率和第三代头孢菌素耐药菌计数分析。1类整合子流行率被验证为抗生素耐药性的分子标志物;耐药性水平显示出显著的地理空间和时间变化。在每个采样点的阻力水平的主要解释变量是周围的污水处理厂的数量，接近，大小和类型。模型1显示，处理植物占49.5%的抗性水平的方差。其他影响因素是不同的周围土地覆盖类型（例如，中性草原），时间模式和以前的降雨量的程度;当建模所有变量的模型（模型2）可以解释82.9%的电阻水平的变化，在整个流域。化学分析与处理厂出水的关键指标相关，并根据水质参数（污染物和宏观和微观营养水平）生成模型（模型3）。模型2在独立站点上进行β测试，并解释了整合子患病率的78%以上的变化，显示出显著的预测能力。我们相信本研究中的所有模型都是非常有用的工具，可以为缓解策略提供信息并确定优先顺序，以减少环境阻力。
Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.
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影响因子：
10.4
作者：
Ashbolt NJ;Amézquita A;Backhaus T;Borriello P;Brandt KK;Collignon P;Coors A;Finley R;Gaze WH;Heberer T;Lawrence JR;Larsson DG;McEwen SA;Ryan JJ;Schönfeld J;Silley P;Snape JR;Van den Eede C;Topp E
通讯作者：
Topp E
影响因子：
2.8
作者：
De Feo, G.;De Gisi, S.;Galasso, M.
通讯作者：
Galasso, M.
影响因子：
12.8
作者：
Tacao, Marta;Moura, Alexandra;Henriques, Isabel
通讯作者：
Henriques, Isabel
影响因子：
4.9
作者：
Gaze, WH;Abdouslam, N;Wellington, EMH
通讯作者：
Wellington, EMH
影响因子：
3.3
作者：
Amos, G. C. A.;Zhang, L.;Wellington, E. M.
通讯作者：
Wellington, E. M.