Kildare Beverly J, Leutenegger Christian M, McSwain Belinda S, Bambic Dustin G, Rajal Veronica B, Wuertz Stefan
Department of Civil and Environmental Engineering, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA.
Water Res. 2007 Aug;41(16):3701-15. doi: 10.1016/j.watres.2007.06.037. Epub 2007 Jun 21.
We report the design and validation of new TaqMan((R)) assays for microbial source tracking based on the amplification of fecal 16S rRNA marker sequences from uncultured cells of the order Bacteroidales. The assays were developed for the detection and enumeration of non-point source input of fecal pollution to watersheds. The quantitative "universal"Bacteroidales assay BacUni-UCD detected all tested stool samples from human volunteers (18 out of 18), cat (7 out of 7), dog (8 out of 8), seagull (10/10), cow (8/8), horse (8/8), and wastewater effluent (14/14). The human assay BacHum-UCD discriminated fully between human and cow stool samples but did not detect all stool samples from human volunteers (12/18). In addition, there was 12.5% detection of dog stool (1/8), but no cross-reactivity with cat, horse, or seagull fecal samples. In contrast, all wastewater samples were positive for the BacHum-UCD marker, supporting its designation as 100% sensitive for mixed-human source identification. The cow-specific assay BacCow-UCD fully discriminated between cow and human stool samples. There was 38% detection of horse stool (3/8), but no cross-specificity with any of the other animal stool samples tested. The dog assay BacCan-UCD discriminated fully between dog and cow stool or seagull guano samples and detected 62.5% stool samples from dogs (5/8). There was some cross-reactivity with 22.2% detection of human stool (4/18), 14.3% detection of cat stool (1/7), and 28.6% detection of wastewater samples (4/14). After validation using stool samples, single-blind tests were used to further demonstrate the efficacy of the developed markers; all assays were sensitive, reproducible, and accurate in the quantification of mixed fecal sources present in aqueous samples. Finally, the new assays were compared with previously published sequences, which showed the new methodologies to be more specific and sensitive. Using Bayes' Theorem, we calculated the conditional probability that the four assays would correctly identify general and host-specific fecal pollution in a specific watershed in California for which 73 water samples had been analyzed. Such an approach allows for a direct comparison of the efficacy of different MST methods, including those based on library-dependent methodologies. For the universal marker BacUni-UCD, the probability that fecal pollution is present when the marker is detected was 1.00; the probability that host-specific pollution is present was 0.98, 0.84, and 0.89 for the human assay HF160F, the cow assay BacCow-UCD, and the dog assay BacCan-UCD, respectively. The application of these markers should provide meaningful information to assist with efforts to identify and control sources of fecal pollution to impaired watersheds.
我们报告了基于从拟杆菌目未培养细胞中扩增粪便16S rRNA标记序列的新型TaqMan((R))微生物源追踪检测方法的设计与验证。这些检测方法是为检测和计数粪便污染对流域的非点源输入而开发的。定量“通用”拟杆菌目检测方法BacUni-UCD检测出了所有来自人类志愿者（18/18）、猫（7/7）、狗（8/8）、海鸥（10/10）、牛（8/8）、马（8/8）的测试粪便样本以及废水排放样本（14/14）。人类检测方法BacHum-UCD能完全区分人类和牛的粪便样本，但未检测出所有人类志愿者的粪便样本（12/18）。此外，对狗粪便样本的检测率为12.5%（1/8），但与猫、马或海鸥粪便样本无交叉反应。相比之下，所有废水样本的BacHum-UCD标记均为阳性，这支持了其在混合人类源识别方面100%敏感的认定。牛特异性检测方法BacCow-UCD能完全区分牛和人类粪便样本。对马粪便样本的检测率为38%（3/8），但与其他任何测试动物粪便样本均无交叉特异性。狗检测方法BacCan-UCD能完全区分狗和牛的粪便或海鸥的鸟粪样本，检测出了62.5%的狗粪便样本（5/8）。与人类粪便样本的交叉反应率为22.2%（4/18），与猫粪便样本的交叉反应率为14.3%（1/7），与废水样本的交叉反应率为28.6%（4/14）。在使用粪便样本进行验证后，采用单盲测试进一步证明了所开发标记的有效性；所有检测方法在定量水样中存在的混合粪便源时均灵敏、可重复且准确。最后，将新检测方法与先前发表的序列进行了比较，结果表明新方法更具特异性和敏感性。利用贝叶斯定理，我们计算了这四种检测方法在加利福尼亚一个特定流域中正确识别一般和宿主特异性粪便污染的条件概率，该流域已分析了73个水样。这种方法允许直接比较不同微生物源追踪方法的有效性，包括基于文库依赖方法的那些方法。对于通用标记BacUni-UCD，检测到该标记时存在粪便污染的概率为1.00；对于人类检测方法HF160F、牛检测方法BacCow-UCD和狗检测方法BacCan-UCD，检测到宿主特异性污染的概率分别为0.98、0.84和0.89。这些标记的应用应能提供有意义的信息，以协助识别和控制受损流域粪便污染的来源。
