Spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiome.
Spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiome.
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DOI：
10.1128/mbio.01135-14
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发表时间：
2014-05-27
期刊：
影响因子：
6.4
通讯作者：
Raskin L
中科院分区：
文献类型：
--
作者：
Pinto AJ;Schroeder J;Lunn M;Sloan W;Raskin L
细菌群落不断从饮用水处理厂通过饮用水分配系统迁移到我们的建筑环境中。了解分配系统中的细菌动力学对于确保向客户提供安全的饮用水至关重要。我们提出了一个为期15个月的调查，在饮用水系统的安阿伯，密歇根州的细菌群落动态。通过采样的水离开处理厂，在分配系统中的9个点，我们表明，距离衰减和分散性的细菌群落空间动态符合饮用水分配系统的布局。然而，在空间动态模式弱于时间趋势，表现出季节性循环与温度和水源水的使用模式，也表现出重现性的年度时间尺度上。时间趋势是由两个季节性的细菌集群组成的多个类群与不同的网络内的较大的饮用水细菌群落的协会。最后，我们表明，安阿伯数据集稳健地符合先前描述的种间占用丰度模型，该模型将一个分类单元的相对丰度与其检测频率联系起来。依靠这些见解，我们提出了饮用水系统中微生物管理的预测框架。此外，我们建议建立长期微生物观测站，收集高分辨率、空间分布、多年时间序列的群落组成和环境变量，以开发和测试预测框架。安全且符合法规的饮用水每升可能含有高达数百万的微生物，代表着影响公共卫生，水基础设施和水的美学质量的细菌，古细菌和真核生物的遗传多样性群体。预测饮用水微生物组动态的能力将确保更好地管理微生物污染风险。通过对饮用水细菌群落的时空调查，我们提出了对它们的时空群落动态的新见解，并建议将这些见解与饮用水系统微生物管理的预测框架联系起来。这种预测框架不仅有助于消除微生物风险，还有助于修改现有的水质监测工作，使其更具资源效率。此外，如果我们要充分预测有益操纵饮用水微生物组的风险和益处，那么微生物管理的预测框架将至关重要。
Bacterial communities migrate continuously from the drinking water treatment plant through the drinking water distribution system and into our built environment. Understanding bacterial dynamics in the distribution system is critical to ensuring that safe drinking water is being supplied to customers. We present a 15-month survey of bacterial community dynamics in the drinking water system of Ann Arbor, MI. By sampling the water leaving the treatment plant and at nine points in the distribution system, we show that the bacterial community spatial dynamics of distance decay and dispersivity conform to the layout of the drinking water distribution system. However, the patterns in spatial dynamics were weaker than those for the temporal trends, which exhibited seasonal cycling correlating with temperature and source water use patterns and also demonstrated reproducibility on an annual time scale. The temporal trends were driven by two seasonal bacterial clusters consisting of multiple taxa with different networks of association within the larger drinking water bacterial community. Finally, we show that the Ann Arbor data set robustly conforms to previously described interspecific occupancy abundance models that link the relative abundance of a taxon to the frequency of its detection. Relying on these insights, we propose a predictive framework for microbial management in drinking water systems. Further, we recommend that long-term microbial observatories that collect high-resolution, spatially distributed, multiyear time series of community composition and environmental variables be established to enable the development and testing of the predictive framework. Safe and regulation-compliant drinking water may contain up to millions of microorganisms per liter, representing phylogenetically diverse groups of bacteria, archaea, and eukarya that affect public health, water infrastructure, and the aesthetic quality of water. The ability to predict the dynamics of the drinking water microbiome will ensure that microbial contamination risks can be better managed. Through a spatial-temporal survey of drinking water bacterial communities, we present novel insights into their spatial and temporal community dynamics and recommend steps to link these insights in a predictive framework for microbial management of drinking water systems. Such a predictive framework will not only help to eliminate microbial risks but also help to modify existing water quality monitoring efforts and make them more resource efficient. Further, a predictive framework for microbial management will be critical if we are to fully anticipate the risks and benefits of the beneficial manipulation of the drinking water microbiome.