Application of fast expectation-maximization microbial source tracking to discern fecal contamination in rivers exposed to low fecal inputs

Youfen Xu; Ganghua Han; Hongxun Zhang; Zhisheng Yu; Ruyin Liu

doi:10.1007/s12275-022-1651-9

Articles

Page Path: HOME > J. Microbiol > Volume 60(6); 2022 > Article

Journal Article
Application of fast expectation-maximization microbial source tracking to discern fecal contamination in rivers exposed to low fecal inputs: Youfen Xu ^1,2,3, Ganghua Han ^1,2,3, Hongxun Zhang ^1,2, Zhisheng Yu ^1,2, Ruyin Liu ^1,2,3; Journal of Microbiology 2022;60(6):594-601.
DOI: https://doi.org/10.1007/s12275-022-1651-9
Published online: April 18, 2022

¹College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, P. R. China, ²RCEES-IMCAS-UCAS Joint-Lab of Microbial Technology for Environmental Science, Beijing 100085, P. R. China, ³Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, P. R. China

Corresponding author: Ruyin Liu , Tel: +86-010-69672961,

Received: 17 December 2021 • Revised: 11 March 2022 • Accepted: 15 March 2022

prev next

394 Views
0 Download
9 Web of Science
9 Crossref
10 Scopus

Full Article

Download PDF

Abstract

Community-based microbial source tracking (MST) can be used to determine fecal contamination from multiple sources in the aquatic environment. However, there is little scientific information on its application potential in water environmental management. Here, we compared SourceTracker and Fast Expectation-maximization Microbial Source Tracking (FEAST) performances on environmental water bodies exposed to low fecal pollution and evaluated treatment effects of fecal pollution in the watershed utilizing community-based MST. Our results showed that FEAST overall outperformed SourceTracker in sensitivity and stability, and was able to discern multi-source fecal contamination (mainly chicken feces) in ambient water bodies exposed to low fecal inputs. Consistent with our previous PCR/qPCR-based MST assays, FEAST analysis indicates that fecal pollution has been significantly mitigated through comprehensive environmental treatment by the local government. This study suggests that FEAST can be a powerful tool for accurately evaluating the contribution of multi-source fecal contamination in environmental water, facilitating environmental management.

Keywords: microbial source tracking, FEAST, SourceTracker, environmental management

Figure & Data

References

Citations

Citations to this article as recorded by

Novel Microbial Engraftment Trajectories Following Microbiota Transplant Therapy in Ulcerative Colitis
Daphne Moutsoglou, Aneesh Syal, Sharon Lopez, Elizabeth C Nelson, Lulu Chen, Amanda J Kabage, Monika Fischer, Alexander Khoruts, Byron P Vaughn, Christopher Staley
Journal of Crohn's and Colitis.2025;[Epub] CrossRef
Integrating microbial community dynamics and emerging contaminants (ECs) for precisely quantifying the sources in groundwater affected by livestock farming
Kai Liu, Jinrong Qiu, Chih-Huang Weng, Zhongen Tang, Renchuan Fu, Xiaojun Lin, Xiujuan Wang, Na Liu, Jingwen Zeng
Journal of Hazardous Materials.2025; 494: 138691. CrossRef
SourceApp: A Novel Metagenomic Source Tracking Tool that can Distinguish between Fecal Microbiomes Using Genome-To-Source Associations Benchmarked Against Mixed Input Spike-In Mesocosms
Blake G. Lindner, Katherine E. Graham, Jacob R. Phaneuf, Janet K. Hatt, Konstantinos T. Konstantinidis
Environmental Science & Technology.2025; 59(19): 9507. CrossRef
A Practical Framework for Environmental Antibiotic Resistance Monitoring in Freshwater Ecosystems
Irene Beltrán de Heredia, Itziar Alkorta, Carlos Garbisu, Estilita Ruiz-Romera
Antibiotics.2025; 14(8): 840. CrossRef
Maternal–to–neonatal microbial transmission and impact of prenatal probiotics on neonatal gut development
Lulu Meng, Ge Fan, Haishan Xie, Kian Deng Tye, Lianyi Xia, Huijuan Luo, Xiaomei Tang, Ting Huang, Jiaxin Lin, Guangyu Ma, Xiaomin Xiao, Zhe Li
Journal of Translational Medicine.2025;[Epub] CrossRef
Faecal source apportionment using molecular methods: A proof of concept using the FEAST algorithm
Laura T. Kelly, Jack Sissons, Lucy Thompson, John K. Pearman
Water Research.2024; 266: 122365. CrossRef
Computational methods and challenges in analyzing intratumoral microbiome data
Qi Wang, Zhaoqian Liu, Anjun Ma, Zihai Li, Bingqiang Liu, Qin Ma
Trends in Microbiology.2023; 31(7): 707. CrossRef
Response and recovery mechanisms of river microorganisms to gradient concentrations of estrogen
Dan Qin, Yan Li, Nengwang Chen, Anyi Hu, Chang-Ping Yu
Frontiers in Microbiology.2023;[Epub] CrossRef
Improving the Identification of Fecal Contamination in Recreational Water through the Standardization and Normalization of Microbial Source Tracking
Megan N. Jamison, John J. Hart, David C. Szlag
ACS ES&T Water.2022; 2(12): 2305. CrossRef

Cite this Article

Cite this Article: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Application of fast expectation-maximization microbial source tracking to discern fecal contamination in rivers exposed to low fecal inputs

J. Microbiol. 2022;60(6):594-601. Published online April 18, 2022

DOI: https://doi.org/10.1007/s12275-022-1651-9

Related articles