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评价用于区分粪便来源和人类粪便的分子群落分析方法。

Evaluation of molecular community analysis methods for discerning fecal sources and human waste.

机构信息

Southern California Coastal Water Research Project Authority, 3535 Harbor Blvd, Suite 110, Costa Mesa, CA 92626, USA.

出版信息

Water Res. 2013 Nov 15;47(18):6862-72. doi: 10.1016/j.watres.2013.02.061. Epub 2013 Jul 5.

Abstract

Molecular microbial community analyses provide information on thousands of microorganisms simultaneously, and integrate biotic and abiotic perturbations caused by fecal contamination entering water bodies. A few studies have explored community methods as emerging approaches for microbial source tracking (MST), however, an evaluation of the current state of this approach is lacking. Here, we utilized three types of community-based methods with 64 blind, single- or dual-source, challenge samples generated from 12 sources, including: humans (feces), sewage, septage, dogs, pigs, deer, horses, cows, chickens, gulls, pigeons, and geese. Each source was a composite from multiple donors from four representative geographical regions in California. Methods evaluated included terminal restriction fragment polymorphism (TRFLP), phylogenetic microarray (PhyloChip), and next generation (Illumina) sequencing. These methods correctly identified dominant (or sole) sources in over 90% of the challenge samples, and exhibited excellent specificity regardless of source, rarely detecting a source that was not present in the challenge sample. Sensitivity, however, varied with source and community analysis method. All three methods distinguished septage from human feces and sewage, and identified deer and horse with 100% sensitivity and 100% specificity. Method performance improved if the composition of blind dual-source reference samples were defined by DNA contribution of each single source within the mixture, instead of by Enterococcus colony forming units. Data analysis approach also influenced method performance, indicating the need to standardize data interpretation. Overall, results of this study indicate that community analysis methods hold great promise as they may be used to identify any source, and they are particularly useful for sources that currently do not have, and may never have, a source-specific single marker gene.

摘要

分子微生物群落分析可以同时提供数千种微生物的信息,并综合考虑因粪便污染进入水体而产生的生物和非生物扰动。有一些研究探索了群落方法作为微生物源追踪(MST)的新兴方法,但是,目前还缺乏对这种方法的评估。在这里,我们利用了三种基于群落的方法,对来自 12 个来源的 64 个盲样、单源或双源挑战样本进行了分析,这些来源包括:人类(粪便)、污水、化粪池、狗、猪、鹿、马、牛、鸡、海鸥、鸽子和鹅。每个来源均由加利福尼亚州四个代表性地理区域的多个供体组成的混合物。评估的方法包括末端限制性片段多态性(TRFLP)、系统发生微阵列(PhyloChip)和下一代(Illumina)测序。这些方法正确地识别了超过 90%的挑战样本中的主要(或唯一)来源,并且具有出色的特异性,无论来源如何,很少会检测到不存在于挑战样本中的来源。然而,灵敏度因来源和群落分析方法而异。所有三种方法都能将化粪池与人类粪便和污水区分开,并能以 100%的灵敏度和 100%的特异性识别鹿和马。如果通过混合物中每个单一来源的 DNA 贡献而不是通过肠球菌集落形成单位来定义盲样双源参考样本的组成,则三种方法的性能都会得到提高。数据分析方法也会影响方法的性能,这表明需要标准化数据解释。总体而言,这项研究的结果表明,群落分析方法具有很大的应用潜力,因为它们可以用于识别任何来源,而且对于目前没有或可能永远不会有特定来源的单一标记基因的来源特别有用。

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