Faculty of Biotechnology, College of Applied Life Sciences, SARI, Jeju National University, Jeju, 63243, Republic of Korea.
Subtropical/tropical Organism Gene Bank, Jeju National University, Jeju, 63243, Republic of Korea.
Environ Microbiol. 2018 Sep;20(9):3132-3140. doi: 10.1111/1462-2920.14281. Epub 2018 Aug 5.
In this minireview, we expand upon traditional microbial source tracking (MST) methods by discussing two recently developed, next-generation-sequencing (NGS)-based MST approaches to identify sources of fecal pollution in recreational waters. One method defines operational taxonomic units (OTUs) that are specific to a fecal source, e.g., humans and animals or shared among multiple fecal sources to determine the magnitude and likely source association of fecal pollution. The other method uses SourceTracker, a program using a Bayesian algorithm, to determine which OTUs have contributed to an environmental community based on the composition of microbial communities in multiple fecal sources. Contemporary NGS-based MST tools offer a promising avenue to rapidly characterize fecal source contributions for water monitoring and remediation efforts at a broader and more efficient scale than previous molecular MST methods. However, both NGS methods require optimized sequence processing methodologies (e.g. quality filtering and clustering algorithms) and are influenced by primer selection for amplicon sequencing. Therefore, care must be taken when extrapolating data or combining datasets. Furthermore, traditional limitations of library-dependent MST methods, including differential decay of source material in environmental waters and spatiotemporal variation in source communities, remain to be fully understood. Nevertheless, increasing use of these methods, as well as expanding fecal taxon libraries representative of source communities, will help improve the accuracy of these methods and provide promising tools for future MST investigations.
在这篇小型综述中，我们通过讨论两种新开发的基于下一代测序（NGS）的 MST 方法，扩展了传统的微生物源追踪（MST）方法，以识别娱乐水中粪便污染的来源。一种方法定义了特定于粪便来源（例如人类和动物）的操作分类单元（OTUs），或在多个粪便来源之间共享，以确定粪便污染的程度和可能的来源关联。另一种方法使用 SourceTracker，这是一种基于贝叶斯算法的程序，根据多个粪便来源中微生物群落的组成，确定哪些 OTUs 对环境群落有贡献。与传统的分子 MST 方法相比，基于当代 NGS 的 MST 工具提供了一种很有前途的方法，可以快速描述粪便来源对水监测和修复工作的贡献，并且规模更大、效率更高。然而，这两种 NGS 方法都需要优化序列处理方法（例如质量过滤和聚类算法），并且受到扩增子测序引物选择的影响。因此，在推断数据或组合数据集时必须谨慎。此外，源材料在环境水中的差异衰减和源群落的时空变化等传统库依赖性 MST 方法的局限性仍有待充分理解。然而，越来越多地使用这些方法，以及扩大代表源群落的粪便分类单元库，将有助于提高这些方法的准确性，并为未来的 MST 研究提供有前途的工具。
