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Tracking the primary sources of fecal pollution in a tropical watershed in a one-year study.

ABSTRACT: A study was conducted to determine the primary sources of fecal pollution in a subtropical watershed using host-specific assays developed in temperate regions. Water samples (n = 534) from 10 different sites along the Rio Grande de Arecibo (RGA) watershed were collected mostly on a weekly basis (54 sampling events) during 13 months. DNA extracts from water samples were used in PCR assays to determine the occurrence of fecal bacteria (Bacteroidales, Clostridium coccoides, and enterococci) and human-, cattle-, swine-, and chicken-specific fecal sources. Feces from 12 different animals (n = 340) and wastewater treatment samples (n = 16) were analyzed to determine the specificity and distribution of host-specific assays. The human-specific assay (HF183) was found to be highly specific, as it did not cross-react with nontarget samples. The cattle marker (CF128) cross-reacted to some extent with swine, chicken, and turkeys and was present in 64% of the cattle samples tested. The swine assays showed poor host specificity, while the three chicken assays showed poor host distribution. Differences in the detection of host-specific markers were noted per site. While human and cattle assays showed moderate average detection rates throughout the watershed, areas impacted by wastewater treatment plants and cattle exhibited the highest prevalence of these markers. When conditional probability for positive signals was determined for each of the markers, the results indicated higher confidence levels for the human assay and lower levels for all the other assays. Overall, the results from this study suggest that additional assays are needed, particularly to track cattle, chicken, and swine fecal pollution sources in the RGA watershed. The results also suggest that the geographic stability of genetic markers needs to be determined prior to conducting applied source tracking studies in tropical settings.

SUBMITTER: Toledo-Hernandez C

PROVIDER: S-EPMC3591936 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Microbial contamination of recreation waters is a major concern globally, with pollutants originating from many sources, including human and other animal wastes often introduced during storm events. Fecal contamination is traditionally monitored by employing culture methods targeting fecal indicator bacteria (FIB), namely E. coli and enterococci, which provides only limited information of a few microbial taxa and no information on their sources. Host-associated qPCR and metagenomic DNA sequencing are complementary methods for FIB monitoring that can provide enhanced understanding of microbial communities and sources of fecal pollution. Whole metagenome sequencing (WMS), quantitative real-time PCR (qPCR), and culture-based FIB tests were performed in an urban watershed before and after a rainfall event to determine the feasibility and application of employing a multi-assay approach for examining microbial content of ambient source waters. Cultivated E. coli and enterococci enumeration confirmed presence of fecal contamination in all samples exceeding local single sample recreational water quality thresholds (E. coli, 410 MPN/100 mL; enterococci, 107 MPN/100 mL) following a rainfall. Test results obtained with qPCR showed concentrations of E. coli, enterococci, and human-associated genetic markers increased after rainfall by 1.52-, 1.26-, and 1.11-fold log10 copies per 100 mL, respectively. Taxonomic analysis of the surface water microbiome and detection of antibiotic resistance genes, general FIB, and human-associated microorganisms were also employed. Results showed that fecal contamination from multiple sources (human, avian, dog, and ruminant), as well as FIB, enteric microorganisms, and antibiotic resistance genes increased demonstrably after a storm event. In summary, the addition of qPCR and WMS to traditional surrogate techniques may provide enhanced characterization and improved understanding of microbial pollution sources in ambient waters.

Project description:BACKGROUND:Antimicrobial resistance (AMR) has been a worldwide public health concern. Current widespread AMR pollution has posed a big challenge in accurately disentangling source-sink relationship, which has been further confounded by point and non-point sources, as well as endogenous and exogenous cross-reactivity under complicated environmental conditions. Because of insufficient capability in identifying source-sink relationship within a quantitative framework, traditional antibiotic resistance gene (ARG) signatures-based source-tracking methods would hardly be a practical solution. RESULTS:By combining broad-spectrum ARG profiling with machine-learning classification SourceTracker, here we present a novel way to address the question in the era of high-throughput sequencing. Its potential in extensive application was firstly validated by 656 global-scale samples covering diverse environmental types (e.g., human/animal gut, wastewater, soil, ocean) and broad geographical regions (e.g., China, USA, Europe, Peru). Its potential and limitations in source prediction as well as effect of parameter adjustment were then rigorously evaluated by artificial configurations with representative source proportions. When applying SourceTracker in region-specific analysis, excellent performance was achieved by ARG profiles in two sample types with obvious different source compositions, i.e., influent and effluent of wastewater treatment plant. Two environmental metagenomic datasets of anthropogenic interference gradient further supported its potential in practical application. To complement general-profile-based source tracking in distinguishing continuous gradient pollution, a few generalist and specialist indicator ARGs across ecotypes were identified in this study. CONCLUSION:We demonstrated for the first time that the developed source-tracking platform when coupling with proper experiment design and efficient metagenomic analysis tools will have significant implications for assessing AMR pollution. Following predicted source contribution status, risk ranking of different sources in ARG dissemination will be possible, thereby paving the way for establishing priority in mitigating ARG spread and designing effective control strategies.

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Tracking the primary sources of fecal pollution in a tropical watershed in a one-year study.

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