Faculty of Biotechnology, College of Applied Life Sciences, SARI, Jeju National University, Jeju, 63243, Republic of Korea.
Research Institute for Basic Sciences (RIBS), Jeju National University, Jeju, 63243, Republic of Korea.
J Microbiol. 2021 Mar;59(3):259-269. doi: 10.1007/s12275-021-0668-9. Epub 2021 Feb 10.
The environment is under siege from a variety of pollution sources. Fecal pollution is especially harmful as it disperses pathogenic bacteria into waterways. Unraveling origins of mixed sources of fecal bacteria is difficult and microbial source tracking (MST) in complex environments is still a daunting task. Despite the challenges, the need for answers far outweighs the difficulties experienced. Advancements in qPCR and next generation sequencing (NGS) technologies have shifted the traditional culture-based MST approaches towards culture independent technologies, where community-based MST is becoming a method of choice. Metagenomic tools may be useful to overcome some of the limitations of community-based MST methods as they can give deep insight into identifying host specific fecal markers and their association with different environments. Adoption of machine learning (ML) algorithms, along with the metagenomic based MST approaches, will also provide a statistically robust and automated platform. To compliment that, ML-based approaches provide accurate optimization of resources. With the successful application of ML based models in disease prediction, outbreak investigation and medicine prescription, it would be possible that these methods would serve as a better surrogate of traditional MST approaches in future.
环境正受到各种污染源的围攻。粪便污染尤其有害，因为它会将致病菌散布到水道中。解析混合来源的粪便细菌的起源是困难的，复杂环境中的微生物源追踪（MST）仍然是一项艰巨的任务。尽管存在挑战，但答案的需求远远超过了所经历的困难。qPCR 和下一代测序 (NGS) 技术的进步已经将传统的基于培养的 MST 方法转向了基于无培养的技术，基于群落的 MST 正在成为一种首选方法。宏基因组工具可能有助于克服基于群落的 MST 方法的一些局限性，因为它们可以深入了解识别宿主特异性粪便标记物及其与不同环境的关联。采用机器学习 (ML) 算法以及基于宏基因组的 MST 方法，也将提供一个具有统计学稳健性和自动化的平台。作为补充，基于 ML 的方法可以准确地优化资源。随着基于 ML 的模型在疾病预测、疫情调查和药物处方中的成功应用，这些方法有可能在未来成为传统 MST 方法的更好替代品。
