BioTechnology Institute and ‡Department of Soil, Water, and Climate, University of Minnesota , Saint Paul, Minnesota 55455, United States.
Natural Resources Research Institute and ∥Department of Civil Engineering, University of Minnesota Duluth , Duluth, Minnesota 55812, United States.
Environ Sci Technol. 2017 Aug 1;51(15):8263-8271. doi: 10.1021/acs.est.7b01353. Epub 2017 Jul 10.
Current microbial source-tracking (MST) methods, employed to determine sources of fecal contamination in waterways, use molecular markers targeting host-associated bacteria in animal or human feces. However, there is a lack of knowledge about fecal microbiome composition in several animals and imperfect marker specificity and sensitivity. To overcome these issues, a community-based MST method has been developed. Here, we describe a study done in the Lake Superior-Saint Louis River estuary using SourceTracker, a program that calculates the source contribution to an environment. High-throughput DNA sequencing of microbiota from a diverse collection of fecal samples obtained from 11 types of animals (wild, agricultural, and domesticated) and treated effluent (n = 233) was used to generate a fecal library to perform community-based MST. Analysis of 319 fecal and environmental samples revealed that the community compositions in water and fecal samples were significantly different, allowing for the determination of the presence of fecal inputs and identification of specific sources. SourceTracker results indicated that fecal bacterial inputs into the Lake Superior estuary were primarily attributed to wastewater effluent and, to a lesser extent, geese and gull wastes. These results suggest that a community-based MST method may be another useful tool for determining sources of aquatic fecal bacteria.
当前用于确定水道中粪便污染来源的微生物源追踪（MST）方法，使用针对动物或人类粪便中与宿主相关细菌的分子标记。然而，对于几种动物的粪便微生物组组成、标记特异性和敏感性不完美，我们知之甚少。为了克服这些问题，已经开发了一种基于群落的 MST 方法。在这里，我们描述了在苏必利尔湖-圣路易斯河河口进行的一项研究，该研究使用了 SourceTracker，这是一种计算环境中来源贡献的程序。从 11 种动物（野生、农业和家养）和处理废水（n = 233）获得的各种粪便样本的高通量 DNA 测序用于生成粪便库，以进行基于群落的 MST。对 319 个粪便和环境样本的分析表明，水样和粪便样本的群落组成有显著差异，这使得能够确定粪便输入的存在并识别特定来源。SourceTracker 的结果表明，粪便细菌输入苏必利尔湖河口主要归因于废水处理厂，其次是鹅和海鸥的粪便。这些结果表明，基于群落的 MST 方法可能是另一种确定水生粪便细菌来源的有用工具。
