Accounting for Bacterial Overlap Between Raw Water Communities and Contaminating Sources Improves the Accuracy of Signature-Based Microbial Source Tracking

Abstract

Microbial source tracking (MST) analysis is essential to identifying and mitigating the fecal pollution of water resources. The signature-based MST method uses a library of sequences to identify contaminants based on operational taxonomic units (OTUs) that are unique to a certain source. However, no clear guidelines for how to incorporate OTU overlap or natural variation in the raw water bacterial community into MST analyses exist. We investigated how the inclusion of bacterial overlap between sources in the library affects source prediction accuracy. To achieve this, large-scale sampling – including feces from seven species, raw sewage, and raw water samples from water treatment plants – was followed by 16S rRNA amplicon sequencing. The MST library was defined using three settings: (i) no raw water communities represented; (ii) raw water communities selected through clustering analysis; and (iii) local water communities collected across consecutive years. The results suggest that incorporating either the local background or representative bacterial composition improves MST analyses, as the results were positively correlated to measured levels of fecal indicator bacteria and the accuracy at which OTUs were assigned to the correct contamination source increased fourfold. Using the proportion of OTUs with high source origin probability, underpinning a contaminating signal, is a solid foundation in a framework for further deciphering and comparing contaminating signals derived in signature-based MST approaches. In conclusion, incorporating background bacterial composition of water in MST can improve mitigation efforts for minimizing the spread of pathogenic and antibiotic resistant bacteria into essential freshwater resources.

FORENSIC: an Online Platform for Fecal Source Identification

“…The interactive version of this figure is available at https://forensic.sfs.uwm.edu/result/example . Data from Hägglund et al and Hu et al ( 16 , 63 ). See Table S5 in the supplemental material for the full list of the individual predictions.…”

“…Fecal predictions using the global classifiers of the sewage, animal fecal, and environmental samples from Hägglund et al ( 16 ) and Hu et al ( 63 ). Download .…”

“…The ever-decreasing cost of sequencing and the development of advanced computational tools has made high-throughput sequencing one of the most efficient ways to study bacterial communities, and it shows promise for microbial source tracking applications (8,9). Recent studies have used 16S rRNA gene amplicons to successfully characterize human and animal fecal sources in surface waters (10)(11)(12)(13)(14)(15)(16). These studies rely on the detection of fecal community signatures shaped by coevolutionary dynamics between hosts and their gut microbiota (17)(18)(19)(20).…”

“…The interactive version of this figure is available at https://forensic.sfs.uwm.edu/result/example. Data from Hägglund et al and Hu et al(16,63). See…”

Impact of coagulation–ultrafiltration on long-term pipe biofilm dynamics in a full-scale chloraminated drinking water distribution system

“…A Bayesian signature based microbial source tracking method (SourceTracker 39 ) was utilized using default settings with the exception of a rarefaction depth set to 10 000. The source library included 68 biofilm communities, five raw-water communities and seven negative control communities (to account for possible contamination 40 ).…”