Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education; College of Water Sciences, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, P. R. China.
Environ Sci Technol. 2021 Apr 20;55(8):4992-5000. doi: 10.1021/acs.est.1c00071. Epub 2021 Mar 14.
Recently, has been proposed as a human-specific marker for tracking fecal contamination. However, its performance has always been validated in a limited number of host samples, which may obscure our understanding of its utility. Furthermore, few studies have quantified confidence of fecal contamination when using . Here, we evaluate the performance and confidence of by analyzing a large panel of metagenomic data sets combined with Bayesian analyses. Results demonstrate that exhibits superior performance with high host sensitivity and specificity, indicating its suitability for tracking human fecal sources. With the marker, a high confidence (>90%) can be obtained and particularly, multiple samples with positive results provide a near certainty of confidence. The application of in the sediments of three Chinese urban rivers shows a high confidence of >97% of human fecal contamination, suggesting the serious challenge of sewage pollution in these environments. Additionally, significant correlation is observed between and antibiotic resistance genes (ARGs), expanding the utilization of for pollution management of ARGs. This study highlights the benefit of using metagenomic-based analysis for evaluating the performance and confidence of microbial source tracking markers in the coming era of big data with increasing resources in available metagenomic data.
最近， 被提议作为追踪粪便污染的人类特异性标记物。然而，其性能在有限数量的宿主样本中得到了验证，这可能会掩盖我们对其效用的理解。此外，很少有研究量化使用 时粪便污染的置信度。在这里，我们通过分析大量的宏基因组数据集并结合贝叶斯分析来评估 的性能和置信度。结果表明， 表现出优异的性能，具有较高的宿主敏感性和特异性，表明其适合追踪人类粪便来源。使用该标记物，可以获得高置信度（>90%），特别是多个阳性结果的样本可以提供接近确定的置信度。 在三条中国城市河流沉积物中的应用表明，人类粪便污染的置信度>97%，这表明这些环境中污水污染的严重挑战。此外， 与抗生素抗性基因（ARGs）之间存在显著相关性，这扩展了 用于 ARGs 污染管理的应用。本研究强调了在大数据时代，使用基于宏基因组的分析来评估微生物源追踪标记物的性能和置信度的好处，随着可用宏基因组数据的增加，这将成为未来的趋势。
