Application of SourceTracker for Accurate Identification of Fecal Pollution in Recreational Freshwater: A Double-Blinded Study

Community coalescence under variable hydrochemical conditions of the Chesapeake Bay shaped bacterial diversity and functional traits

Community coalescence related to bacterial mixing events regulates community characteristics and affects the health of estuary ecosystems. At present, bacterial coalescence and its driving factors are still unclear. The present study used a dataset from the Chesapeake Bay (2017) to address how bacterial community coalescence in response to variable hydrochemistry in estuarine ecosystems. We determined that variable hydrochemistry promoted the deterioration of water quality. Temperature, orthophosphate, dissolved oxygen, chlorophyll a, Secchi disk depth, and dissolved organic phosphorus were the key environmental factors driving community coalescence. Bacteria with high tolerance to environmental change were the primary taxa accumulated in community coalescence, and the significance of deterministic processes to communities was revealed. Community coalescence was significantly correlated with the pathways of metabolism and organismal systems, and promoted the co-occurrence of antibiotic resistance and virulence factor genes. Briefly, community coalescence under variable hydrochemical conditions shaped bacterial diversity and functional traits, to optimise strategies for energy acquisition and lay the foundation for alleviating environmental pressures. However, potential pathogenic bacteria in community coalescence may be harmful to human health and environmental safety. The present study provides a scientific reference for ecological management of estuaries.

New insight into identifying sediment phosphorus sources in multi-source polluted urban river: Effect of environmental-induced microbial community succession on stability of microbial source tracking results

Identifying sediment phosphorus sources, the key to control eutrophication, is hindered in multi-source polluted urban rivers by the lack of appropriate methods and data resolution. Community-based microbial source tracking (MST) offers new insight, but the bacterial communities could be affected by environmental fluctuations during the migration with sediments, which might induce instability of MST results. Therefore, the effects of environmental-induced community succession on the stability of MST were compared in this study. Liangxi River, a highly eutrophic urban river, was selected as the study area where sediment phosphorus sources are difficult to track because of multi-source pollution and complicated hydrodynamic conditions. Spearman correlation analysis (P < 0.05) was conducted to recognize a close relationship between sediment, bacterial communities and phosphorus, verifying the feasibility of MST for identify sediment phosphorus sources. Two distinct microbial community fingerprints were constructed based on whether excluded 113 vulnerable species, which were identified by analyzing the differences of microorganisms across a concentration gradient of exogenous phosphorus input in microbial environmental response experiment. Because of the lower unknown proportion and relative standard deviations, MST results were more stable and reliable when based on the fingerprints excluding species vulnerable to phosphorus. This study presents a novel insight on how to identify sediment phosphorus sources in multi-source polluted urban river, and would help to develop preferential control strategies for eutrophication management.

Metagenomic analysis of microbial communities and antibiotic resistant genes in the Tijuana river, and potential sources

The Tijuana River is a transborder river that flows northwest across the border from Baja California in Mexico into Southern California before discharging into the Pacific Ocean. The river is frequently contaminated with raw sewage due to inadequate sanitary infrastructure in Tijuana. To assess the type and degree of microbial contamination, water samples were collected monthly from a near-border and an estuarine site from August 2020 until May 2021. A portion of each sample was used for epifluorescent microscopy and DNA was extracted directly from the rest for shotgun metagenomic sequencing. After sequence quality checking and processing, we used the rapid taxonomic identifier tool Kaiju to characterize the microbial diversity of the metagenomes and matched the sequences against the Comprehensive Antibiotic Resistance Database (CARD) to examine antimicrobial resistance genes (ARGs). Bacterial and viral-like particle (VLP) abundance was consistently higher in the near-border samples than in the estuarine samples, while alpha diversity (within sample biodiversity) was higher in estuarine samples. Beta-diversity analysis found clear compositional separation between samples from the two sites, and the near-border samples were more dissimilar to one another than were the estuarine sites. Near-border samples were dominated by fecal-associated bacteria and bacteria associated with sewage sludge, while estuarine sites were dominated by marine bacteria. ARGs were more abundant at the near-border site, but were also readily detectable in the estuarine samples, and the most abundant ARGs had multi-resistance to beta-lactam antibiotics. SourceTracker analysis identified human feces and sewage sludge to be the largest contributors to the near-border samples, while marine waters dominated estuarine samples except for two sewage overflow dates with high fecal contamination. Overall, our research determined human sewage microbes to be common in the Tijuana River, and the prevalence of ARGs confirms the importance of planned infrastructure treatment upgrades for environmental health.

Persistence of location-specific microbial signatures on boats during voyages

Objects collect microorganisms from their surroundings and develop a microbial "fingerprint" that may be useful for determining an object's past location (provenance). It may be possible to use ubiquitous microorganisms for forensics or as environmental sensors. Here, we use microbial communities in the Chesapeake Bay region to demonstrate the use of natural microorganisms as biological sensors to determine the past location of boats. The microbiomes of two boats and of the open water were sampled as these vessels traveled from the Port of Baltimore to the Port of Norfolk, and back to Baltimore. 16S rRNA sequencing was performed to identify microorganisms. Differential abundance and machine learning analyses were utilized to identify microbial signatures and predicted probabilities which were used to determine the vessel's previous location. The work presented here provides a better understanding of how microbes in aquatic systems can be leveraged as utility for object biosensors.

Metagenomics-assembled analysis revealed the characteristics of antibiotic resistome and community coalescence in the soils irrigated with different irrigation materials

Antibiotic resistance has received widespread attention in recent years. Soil irrigation and fertilization are routine agricultural practices, but also lead to the spread of antibiotic resistance genes (ARGs) in soil-crop system such as via resistome coalescence. Despite community coalescence being ubiquitous and important in natural ecosystems, little research has been done to investigate resistome coalescence during soil irrigation activities. In this study, the characteristics of antibiotic resistome and community coalescence in the soils irrigated with different irrigation materials (wastewater, wastewater-river water, and wastewater-manure) have been revealed by utilizing microcosm experiments and high-throughput sequencing-based metagenomic assembly approaches. Results showed irrigation and coalescence changed soil quality and resistome. Totally, 789 unique ARGs were identified in the irrigation system, including some emerging ARGs. The abundance and diversity of ARGs increased in the coalesced soils, mainly due to the newly imported ARGs from irrigation materials. Relatively, the soils irrigated with wastewater and manure showed higher level of ARGs. Irrigation with the mixtures containing river water caused greater loss of indigenous taxa, while the community structure of mixing treatment with manure changed more dramatically. Interestingly, the succession of community in coalesced soils was influenced by transient competition for resources and ecological niche width, and the highest abundance and diversity of microorganisms and ARGs were found in the initial phase of coalescence, followed by a gradual succession towards the original community. With increasement of wastewater in the irrigation materials, the soil community showed a stepwise change rather than linear change. Notably, natural deposit of irrigation materials reduced their impacts on the ARGs in the coalesced soils. Findings provide new insights into the resistome coalescence during agricultural practices for reducing the spread risks of ARGs.

Anthropogenic activity remains the main contributor to fecal pollution in managed tropical watersheds as unraveled by PhyloChip microarray-based microbial source tracking

The spread of disease by enteric pathogens associated with fecal contamination is a major concern for the management of urban watersheds. So far, the relative contribution of natural and anthropogenic sources to fecal pollution in managed tropical watersheds remains poorly evaluated. In this study, the microbiomes of water samples collected from managed watersheds in Singapore were elicited using the PhyloChip, a dense 16S rRNA gene-based DNA microarray, and fecal impairment was inferred using a machine-learning classification algorithm (SourceTracker). The predicted contribution of wildlife fecal sources to environmental samples was generally negligible (< 0.01 ± 0.01), indicating a low likelihood of fecal impairment from natural sources. However, sewage showed considerably higher contribution (0.09 ± 0.05) to microbial communities in a subset of watershed samples from canals and rivers, suggesting persistent impairment of certain areas by anthropogenic activity although being managed. Interestingly, the contribution of sewage microbial communities showed decreasing trends from canals/rivers to the connected reservoirs, indicating meaningful auto-mitigation of fecal pollution in canals and rivers. Notably, exclusion of locally derived fecal samples and source categories from the training data set impaired the predictive performance of the classification algorithm despite a high degree of similarity in the phylogenetic composition of microbiomes in biologically similar but geographically distinct sources.

Municipal separate storm sewer system (MS4) dry weather flows and potential flow sources as assessed by conventional and advanced bacterial analyses

Municipal separate storm sewer systems (MS4s) function in urbanized areas to convey flows during both wet weather (i.e., stormwater) and dry weather (i.e., urban runoff as well as subsurface sources of flow) to receiving waters. While urban stormwater is known to contain microbial and chemical pollutants, MS4 dry weather flows, or non-stormwater discharges (NSWDs), are much less studied, although they are also known to contain pollutants, especially when these flows include raw sewage. In addition, some natural NSWDs (e.g., from groundwater infiltrating MS4 pipes) are critical for aquatic habitat protection. Thus, it is important to distinguish NSWD sources to prevent non-natural flows while retaining natural waters (i.e., groundwater). Here, MS4 dry weather flows were assessed by analyzing water samples from MS4 outfalls across multiple watersheds and water provider service areas in south Orange County, CA; potential NSWD sources including sewage, recycled water, potable water, and groundwater were sampled and analyzed for their likely contributions to overall NSWDs. Geochemical and microbiological water quality indicators, as well as bacterial communities, differed across NSWDs, yet water quality within most locations did not vary significantly diurnally or by sampling date. Meanwhile, NSWD source waters had distinctly different bacterial taxa abundances and specific bacterial genera. Shared geochemical and microbial characteristics of certain sources and outfall flows suggested the contributions of sources to outfall flows. The average proportions by sources contributing to MS4 outfalls were further estimated by SourceTracker and FEAST, respectively. The results of this study highlight the use of multiple tools when assessing chemical and microbiological water quality to predict sources of NSWDs contributing to urban MS4 flows during dry weather. This information can be used to support management actions to reduce unnatural and high risk sources of dry weather drainage while preserving natural sources important to environmental health in downstream receiving waters.

Exploring the Bacterial Community in Aged Fecal Sources from Dairy Cows: Impacts on Fecal Source Tracking

(1) Background: This paper discusses the impact of agricultural activities on stream health, particularly in relation to dairy cow fecal pollution. The study explores the fecal microbiome of cattle and the potential ecological implications of aging fecal pollution on waterways. (2) Methods: The study examines changes in the bacterial community available for mobilization from in-situ decomposing cowpats and the effects of simulated rainfall. The microbiome of individual cowpats was monitored over 5.5 months. We used 16S rRNA metagenomics and machine learning software, FEAST (Fast Expectation-mAximization for microbial Source Tracking), for bacterial and fecal source assignments. (3) Results: The phyla Bacillota and Bacteroidota are dominant in the fecal microbiota of fresh cow feces but shift to Pseudomonodota, Actinomycetota, and environmental Bacteroidota in aged cowpats. Potential impacts of these bacterial community shifts on inputs to local agricultural streams are discussed in relation to water quality monitoring and aging sources of fecal contamination. We identified taxon orders that are potential indicators of fresh cattle sources (Oscillospirales and Bacteroidales) and aged sources (Peptostreptococcales-Tissierellales) in water bodies. (4) The paper highlights that bacterial metagenomic profiling can inform our understanding of the ecology of microbial communities in aquatic environments and the potential impacts of agricultural activities on ecosystem health.

Ecological dynamics imposes fundamental challenges in community-based microbial source tracking

Quantifying the contributions of possible environmental sources ("sources") to a specific microbial community ("sink") is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far-reaching applications in pollution control, public health, and forensics. MST methods generally fall into two categories: target-based methods (focusing on the detection of source-specific indicator species or chemicals); and community-based methods (using community structure to measure similarity between sink samples and potential source environments). As next-generation sequencing becomes a standard community-assessment method in microbiology, numerous community-based computational methods, referred to as MST solvers hereafter have been developed and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider microbial interactions and priority effects in microbial communities. Here, we revisit the performance of several representative MST solvers. We show compelling evidence that solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. In particular, we clearly demonstrate that the presence of either microbial interactions or priority effects will render the MST problem mathematically unsolvable for MST solvers. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solvers fail to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solvers fail to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in MST. Interpretation of results of existing MST solvers should be done cautiously.

Microbial source tracking identifies sources of contamination for a river flowing into the Yellow Sea

The excessive input of nutrients into rivers can lead to contamination and eutrophication, which poses a threat to the health of aquatic ecosystems. It is crucial to identify the sources of contaminants to develop effective management plans for eutrophication. However, traditional methods for identifying pollution sources have been insufficient, making it difficult to manage river health effectively. High-throughput sequencing offers a novel method for microbial community source tracking, which can help identify dominant pollution sources in rivers. The Wanggang River was selected for study, as it has suffered accelerated eutrophication due to considerable nutrient input from riparian pollutants. The present study identified the dominant microbial communities in the Wanggang River basin, including Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Verrucomicrobia, and Firmicutes. The Source Tracker machine-learning classification system was used to create source-specific microbial community fingerprints to determine the primary sources of contaminants in the basin, with agricultural fertilizer being identified as the main pollutant source. By identifying the microbial communities of potential pollution sources, the study determined the contributing pollutant sources in several major sections of the Wanggang River, including industry, urban land, pond culture, and livestock land. These findings can be used to improve the identification of pollution sources in specific environments and develop effective pollution management plans for polluted river water.

Time-scale analysis of the long-term variability of human gut microbiota characteristics in Chinese individuals

Studying the dynamics and stability of the human gut microbiota over time is important for exploring their relationship with human health and developing treatment strategies for putative microbiome-related ailments. Here, we collected stool samples from seven healthy Chinese subjects at 1-month intervals between 2016 and 2020. Sequencing and bioinformatics analyses revealed that the bacteria in the collected fecal samples fluctuated over time, and the extent of these changes increased over time. Further, the average shared proportion value obtained using Sourcetracker2 was 63.5% for samples collected from the same individual in the preceding month, and over a 3-year period, this value decreased to 40.7%. Furthermore, the proportion of different bacteria in the gut microbiota of the different subjects fluctuated to varying degrees. Therefore, our results suggested that it is important to consider the effect of time on gut microbiota composition when it is used to evaluate health. Our study opens up a new field of microbiota research, considering not just the instantaneous microbiota, but also the change of the gut microbiota over time.

Evaluating a potential model to analyze the function of the gut microbiota of the giant panda

To contribute to the conservation of endangered animals, the utilization of model systems is critical to analyze the function of their gut microbiota. In this study, the results of a fecal microbial transplantation (FMT) experiment with germ-free (GF) mice receiving giant panda or horse fecal microbiota showed a clear clustering by donor microbial communities in GF mice, which was consistent with the results of blood metabolites from these mice. At the genus level, FMT re-established approximately 9% of the giant panda donor microbiota in GF mice compared to about 32% for the horse donor microbiota. In line with this, the difference between the panda donor microbiota and panda-mice microbiota on whole-community level was significantly larger than that between the horse donor microbiota and the horse-mice microbiota. These results were consistent with source tracking analysis that found a significantly higher retention rate of the horse donor microbiota (30.9%) than the giant panda donor microbiota (4.0%) in GF mice where the microbiota remained stable after FMT. Further analyzes indicated that the possible reason for the low retention rate of the panda donor microbiota in GF mice was a low relative abundance of Clostridiaceae in the panda donor microbiota. Our results indicate that the donor microbiota has a large effect on GF mice microbiota after FMT.

Storm promotes the dissemination of antibiotic resistome in an urban lagoon through enhancing bio-interactions

Antibiotic-resistance genes (ARGs) and resistant bacteria (ARB) are abundant in stormwater that could cause serious infections, posing a potential threat to public health. However, there is no inference about how stormwater contributes to ARG profiles as well as the dynamic interplay between ARGs and bacteria via vertical gene transfer (VGT) or horizontal gene transfer (HGT) in urban water ecosystems. In this study, the distribution of ARGs, their host communities, and the source and community assembly process of ARGs were investigated in Yundang Lagoon (China) via high-throughput quantitative PCR, 16S rRNA gene amplicon sequencing, and application of SourceTracker before, after and recovering from an extreme precipitation event (132.1 mm). The abundance of ARGs and mobile genetic elements (MGEs) was the highest one day after precipitation and then decreased 2 days after precipitation and so on. Based on SourceTracker and NMDS analysis, the ARG and bacterial communities in lagoon surface water from one day after precipitation were mainly contributed by the wastewater treatment plant (WWTP) influent and effluent. However, the contribution of WWTP to ARG communities was minor 11 days after the precipitation, suggesting that the storm promoted the ARG levels by introducing the input of ARGs, MGEs, and ARB from point and non-point sources, such as sewer overflow and land-applied manure. Based on a novel microbial network analysis framework, the contribution of positive biological interactions between ARGs and MGEs or bacteria was the highest one day after precipitation, indicating a promoted VGT and HGT for ARG dissemination. The microbial networks deconstructed 11 days after precipitation, suggesting the stormwater practices (e.g., tide gate opening, diversion channels, and pumping) alleviated the spread of ARGs. These results advanced our understanding of the distribution and transport of ARGs associated with their source in urban stormwater runoff.

Assessing multiple fecal sources to surf zone waters of two recreational beaches by bacterial community analysis

Fecal sources to recreational surf zone waters should be identified to protect public health. While watershed origins of human and other fecal sources are often discoverable by quantitative polymerase chain reaction (qPCR) of fecal markers using spatially stratified samples, similarly assessing wastewater treatment plant (WWTP) outfall and other offshore contributions to surf zones is challenged by individual marker fate and transport. Here, bacterial communities were assessed for relatedness between all hypothesized fecal sources and surf zone waters for two urban California recreational beaches, by sequencing genes encoding 16S rRNA and analyzing data using SourceTracker and FEAST. Ambient marine bacterial communities dominated the surf zone, while fecal (human, dog, or gull) or wastewater (sewage or treated WWTP effluent) bacterial communities were present at low proportions and those from recycled water were absent. Based on the relative abundances of bacterial genera specifically associated with human feces, the abundances of HF183 in bacterial community sequences, and FEAST and SourceTracker results when benchmarked to HF183, the major sources of HF183 to surf zone waters were human feces and treated WWTP effluent. While surf zone sequence proportions from human sources (feces, sewage and treated WWTP effluent) appeared uncorrelated to previously obtained qPCR HF183 results, the proportions of human fecal and potential human pathogen sequences in surf zone waters were elevated when there were more swimmers (i.e. during weekday afternoons, holidays and busy weekends, and race events), thus confirming previously-published qPCR-based conclusions that bather shedding contributed low levels of human fecal contamination. Here, bacterial community sequencing also showed evidence that treated WWTP effluent from an offshore outfall was entering the surf zone, thereby resolving a prior uncertainty. Thus, bacterial community sequencing not only confirms qPCR HF183-based human marker detections, but further allows for confirming fecal sources for which individual marker quantification results can be equivocal.

Tracking bacterial pollution at a marine wastewater outfall site - A case study from Norway

Coastal marine environments are increasingly affected by anthropogenic impacts, such as the release of sewage at outfall sites and agricultural run-off. Fecal pollution introduced to the sea through these activities poses risks of spreading microbial diseases and disseminating antibiotic resistant bacteria and their genes. The study area of this research, Bore beach, is situated between two such point sources, an outfall site where treated sewage is released 1 km off the coast and a stream that carries run-off from an agricultural area to the northern end of the beach. In order to investigate whether and to what extent fecal contamination from the sewage outfall reached the beach, we used microbial source tracking, based on whole community analysis. Samples were collected from sea water at varying distances from the sewage outfall site and along the beach, as well as from the sewage effluent and the stream. Amplicon sequencing of 16S rRNA genes from all the collected samples was carried out at two time points (June and September). In addition, the seawater at the sewage outfall site and the sewage effluent were subject to shotgun metagenomics. To estimate the contribution of the sewage effluent and the stream to the microbial communities at Bore beach, we employed SourceTracker2, a program that uses a Bayesian algorithm to perform such quantification. The SourceTracker2 results suggested that the sewage effluent is likely to spread fecal contamination towards the beach to a greater extent than anticipated based on the prevailing sea current. The estimated mixing proportions of sewage at the near-beach site (P4) were 0.22 and 0.035% in June and September, respectively. This was somewhat below that stream's contribution in June (0.028%) and 10-fold higher than the stream's contribution in September (0.004%). Our analysis identified a sewage signal in all the tested seawater samples.

New insights into identifying sediment phosphorus sources in river-lake coupled system: A framework for optimizing microbial community fingerprints

Identifying sediment phosphorus sources in river-lake coupled system is a question in developing preferential control strategies for phosphorus. As sediments adsorbed phosphorus and microbes would be transported with changing hydrodynamic, the phosphorus source-specific microbial community fingerprints shed light on determining the major sediment phosphorus sources. However, the identification of microbial community fingerprints is a challenge because both microbial succession and hydrological characteristics of river-lake systems would affect the stability of fingerprints. Therefore, this study provided a framework for optimizing phosphorus source-specific microbial community fingerprints, and attempted to identify the major sources of sediment phosphorus in river-lake coupled ecosystem. Meiliang Lake is one of the highly eutrophic area in Taihu Lake, where the sediments, bacterial communities, and phosphorus had a close relationship. Through analyzing the connectivity of microbes along water continuum, a microbial fingerprints candidate database was constructed. The phosphorus-related bacterial communities were screened and optimized by comparing the difference of predicted results between upstream and downstream, forming the stable microbial community fingerprints which consisted of Bacteroidia, Bacilli, Clostridia, and other species at the class level. SourceTracker results that based on the optimized phosphorus source-specific microbial community fingerprints indicated that the major sediment phosphorus sources to Meiliang Lake were Liangxi River, Wujingang River, and Donghuandi River, with the relative standard deviations ranging from 2.59% to 27.56%. The accuracy of phosphorus source apportionments was further confirmed based on the composite pollution index and hydrodynamic condition. This study put forward suggestions on how to improve the stability of microbial community fingerprints, and would help to improve the understanding of applying microbial source tracking method to identify the sources of abiotic pollution like sediment phosphorus.

Resistome and mobilome in surface runoff from manured soil as affected by setback distance

Land application of livestock manure introduces antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) into the soil environment. The objectives of this study were to examine the changes of resistome and mobilome in runoff and soil as a function of setback distance, i.e., the distance between manured soil and surface water, and to quantify the contributions of manure and background soil to the ARGs and MGEs in surface runoff. The resistome and mobilome in runoff and soil from a field-scale plot study were characterized using a high throughput quantitative polymerase chain reaction (HT-qPCR) array. It was estimated that a setback distance of ~40 m is required to reduce the total abundance of ARGs and MGEs in runoff from amended plots to that in control runoff. The resistome and mobilome of the soil in the setback region was not affected by manure-borne ARGs and MGEs. SourceTracker analyses revealed that background soil gradually became the predominant source of the ARGs and MGEs in runoff as setback distance increased. The results demonstrate how manure-borne ARGs and MGEs dissipated in agricultural runoff with increasing setback distance and had limited impacts on the resistome and mobilome of soil within the setback region.

Antimicrobial Resistance in Rivers: A Review of the Genes Detected and New Challenges

River ecosystems are very important parts of the water cycle and an excellent habitat, food, and drinking water source for many organisms, including humans. Antibiotics are emerging contaminants which can enter rivers from various sources. Several antibiotics and their related antibiotic resistance genes (ARGs) have been detected in these ecosystems by various research programs and could constitute a substantial problem. The presence of antibiotics and other resistance cofactors can boost the development of ARGs in the chromosomes or mobile genetic elements of natural bacteria in rivers. The ARGs in environmental bacteria can also be transferred to clinically important pathogens. However, antibiotics and their resistance genes are both not currently monitored by national or international authorities responsible for controlling the quality of water bodies. For example, they are not included in the contaminant list in the European Water Framework Directive or in the US list of Water-Quality Benchmarks for Contaminants. Although ARGs are naturally present in the environment, very few studies have focused on non-impacted rivers to assess the background ARG levels in rivers, which could provide some useful indications for future environmental regulation and legislation. The present study reviews the antibiotics and associated ARGs most commonly measured and detected in rivers, including the primary analysis tools used for their assessment. In addition, other factors that could enhance antibiotic resistance, such as the effects of chemical mixtures, the effects of climate change, and the potential effects of the coronavirus disease 2019 pandemic, are discussed. Environ Toxicol Chem 2022;41:687-714. © 2022 SETAC.

The Impact of Extreme Weather Events on Bacterial Communities and Opportunistic Pathogens in a Drinking Water Treatment Plant

Drinking water treatment processes are highly effective at improving water quality, but pathogens can still persist in treated water, especially after extreme weather events. To identify how extreme weather events affected bacterial populations in source and treated water, water samples were collected from the Yangtze River Delta area and a local full-scale drinking water treatment plant. Bacterial community structure and the occurrence of pathogens were investigated in samples using 16S rRNA sequencing and qPCR techniques. In this study, the results show that intense rainfall can significantly increase levels of bacteria and opportunistic pathogens in river and drinking water treatment processes (p < 0.05); in particular, the relative abundance of Cyanobacteria increased after a super typhoon event (p < 0.05). The biological activated carbon (BAC) tank was identified as a potential pathogen reservoir and was responsible for 52 ± 6% of the bacteria released downstream, according to Bayesian-based SourceTracker analysis. Our results provide an insight into the challenges faced by maintaining finished water quality under changing weather conditions.

Effect of temperature on the persistence of fecal bacteria in ambient anaerobic digestion systems treating swine manure

This study aimed to explore the effect of temperature on the persistence of fecal bacteria by multiple approaches in ambient anaerobic digestion systems treating swine manure. Both lab-scale (15 °C, 20 °C, and 25 °C) and field (26 °C on average) studies were conducted by high-throughput sequencing and culture-based methods. A community-wide Bayesian SourceTracker method was used to identify and estimate the fecal bacterial proportion in anaerobic effluent. High proportional contributions of fecal bacteria were observed in effluent at 15 °C (73%) and 20 °C (75%), while less was found at 25 °C (19%). This was further verified by a field study (23%) and an anaerobic reactor study at 37 °C (0.01%). To explore the potential reasons for differences in fecal bacterial proportions, bacterial taxa were divided into "lost" and "survivor" taxa in manure waste by LEfSe. The "survivor" taxa abundance was positively correlated with SourceTracker proportion (r = 0.913, P = 0.001), but negatively correlated with temperature (r = -0.826, P = 0.006). In addition, biomarkers in effluent were divided into "enriched" and "de novo" taxa. "Enriched" taxa, including acidogenic and acetogenic bacteria, were found at all temperatures, whereas taxa related to organic degradation were multiplied "de novo" at 25 °C. Variation partition analysis showed that temperature could explain 30% of variations in effluent bacterial community. Moreover, coliforms isolated from the manure and effluents at 15 °C and 20 °C were also phylogenetically related. This study provided comprehensive insight into the impact of temperature on the persistence of fecal bacteria in anaerobic effluent, with temperatures over 25 °C recommended to reduce fecal pollution.

A Combined Digital PCR and Next Generation DNA-Sequencing Based Approach for Tracking Nearshore Pollutant Dynamics Along the Southwest United States/Mexico Border

Ocean currents, multiple fecal bacteria input sources, and jurisdictional boundaries can complicate pollution source tracking and associated mitigation and management efforts within the nearshore coastal environment. In this study, multiple microbial source tracking tools were employed to characterize the impact and reach of an ocean wastewater treatment facility discharge in Mexico northward along the coast and across the Southwest United States- Mexico Border. Water samples were evaluated for fecal indicator bacteria (FIB), Enterococcus by culture-based methods, and human-associated genetic marker (HF183) and Enterococcus by droplet digital polymerase chain reaction (ddPCR). In addition, 16S rRNA gene sequence analysis was performed and the SourceTracker algorithm was used to characterize the bacterial community of the wastewater treatment plume and its contribution to beach waters. Sampling dates were chosen based on ocean conditions associated with northern currents. Evidence of a gradient in human fecal pollution that extended north from the wastewater discharge across the United States/Mexico border from the point source was observed using human-associated genetic markers and microbial community analysis. The spatial extent of fecal contamination observed was largely dependent on swell and ocean conditions. These findings demonstrate the utility of a combination of molecular tools for understanding and tracking specific pollutant sources in dynamic coastal water environments.

Microbial Diversity Profiling of Gut Microbiota of Macropus giganteus Using Three Hypervariable Regions of the Bacterial 16S rRNA

Animal faecal contamination of surface waters poses a human health risk, as they may contain pathogenic bacteria or viruses. Of the numerous animal species residing along surface waterways in Australia, macropod species are a top contributor to wild animals’ faecal pollution load. We characterised the gut microbiota of 30 native Australian Eastern Grey Kangaroos from six geographical regions (five kangaroos from each region) within South East Queensland in order to establish their bacterial diversity and identify potential novel species-specific bacteria for the rapid detection of faecal contamination of surface waters by these animals. Using three hypervariable regions (HVRs) of the 16S rRNA gene (i.e., V1–V3, V3–V4, and V5–V6), for their effectiveness in delineating the gut microbial diversity, faecal samples from each region were pooled and microbial genomic DNA was extracted, sequenced, and analysed. Results indicated that V1-V3 yielded a higher taxa richness due to its larger target region (~480 bp); however, higher levels of unassigned taxa were observed using the V1-V3 region. In contrast, the V3–V4 HVR (~569 bp) attained a higher likelihood of a taxonomic hit identity to the bacterial species level, with a 5-fold decrease in unassigned taxa. There were distinct dissimilarities in beta diversity between the regions, with the V1-V3 region displaying the highest number of unique taxa (n = 42), followed by V3–V4 (n = 11) and V5–V6 (n = 8). Variations in the gut microbial diversity profiles of kangaroos from different regions were also observed, which indicates that environmental factors may impact the microbial development and, thus, the composition of the gut microbiome of these animals.

Limited Bacterial Removal in Full-Scale Stormwater Biofilters as Evidenced by Community Sequencing Analysis

In urban areas, untreated stormwater runoff can pollute downstream surface waters. To intercept and treat runoff, low-impact or "green infrastructure" approaches such as using biofilters are adopted. Yet, actual biofilter pollutant removal is poorly understood; removal is often studied in laboratory columns, with variable removal of viable and culturable microbial cell numbers including pathogens. Here, to assess bacterial pollutant removal in full-scale planted biofilters, stormwater was applied, unspiked or spiked with untreated sewage, in simulated storm events under transient flow conditions, during which biofilter influents versus effluents were compared. Based on microbial biomass, sequences of bacterial community genes encoding 16S rRNA, and gene copies of the human fecal marker HF183 and of the Enterococcus spp. marker Entero1A, removal of bacterial pollutants in biofilters was limited. Dominant bacterial taxa were similar for influent versus effluent aqueous samples within each inflow treatment of either spiked or unspiked stormwater. Bacterial pollutants in soil were gradually washed out, albeit incompletely, during simulated storm flushing events. In post-storm biofilter soil cores, retained influent bacteria were concentrated in the top layers (0-10 cm), indicating that the removal of bacterial pollutants was spatially limited to surface soils. To the extent that plant-associated processes are responsible for this spatial pattern, treatment performance might be enhanced by biofilter designs that maximize influent contact with the rhizosphere.

Synergistic Application of Molecular Markers and Community-Based Microbial Source Tracking Methods for Identification of Fecal Pollution in River Water During Dry and Wet Seasons

It is important to track fecal sources from humans and animals that negatively influence the water quality of rural rivers and human health. In this study, microbial source tracking (MST) methods using molecular markers and the community-based FEAST (fast expectation–maximization microbial source tracking) program were synergistically applied to distinguish the fecal contributions of multiple sources in a rural river located in Beijing, China. The performance of eight markers were evaluated using 133 fecal samples based on real-time quantitative (qPCR) technique. Among them, six markers, including universal (BacUni), human-associated (HF183-1 and BacH), swine-associated (Pig-2-Bac), ruminant-associated (Rum-2-Bac), and avian-associated (AV4143) markers, performed well in the study. A total of 96 water samples from the river and outfalls showed a coordinated composition of fecal pollution, which revealed that outfall water might be a potential input of the Fsq River. In the FEAST program, bacterial 16S rRNA genes of 58 fecal and 12 water samples were sequenced to build the “source” library and “sink,” respectively. The relative contribution (<4.01% of sequence reads) of each source (i.e., human, swine, bovine, or sheep) was calculated based on simultaneous screening of the operational taxonomic units (OTUs) of sources and sinks, which indicated that community-based MST methods could be promising tools for identifying fecal sources from a more comprehensive perspective. Results of the qPCR assays indicated that fecal contamination from human was dominant during dry weather and that fecal sources from swine and ruminant were more prevalent in samples during the wet season than in those during the dry season, which were consistent with the findings predicted by the FEAST program using a very small sample size. Information from the study could be valuable for the development of improved regulation policies to reduce the levels of fecal contamination in rural rivers.

Trace volatile compounds in the air of domestic waste landfill site: Identification, olfactory effect and cancer risk

Landfill sites are regarded as sources of volatile compounds (VOCs) and odors emitted to the atmosphere. Surface emissions of VOCs and odors were investigated in a rural domestic waste landfill site located in southwest China. A total of 76 chemical compounds belonging to 3 chemical families were identified and quantified. The total number of VOCs (TVOC) detected ranged from 18.1 to 806.3 mg/m³, while odorous gases and greenhouse gases ranged from 0.4 to 21.2 and 0-100.5 mg/m³, respectively. High emissions were found in the air surrounding the leachate storage pool (LSP) and dumping area (DPA). The dominant species of VOCs were hexaldehyde, m-xylene, propylene oxide, acetophenone, and 2-butanone. The traceability analysis showed that the odors and VOCs diffused to the downwind boundary mainly came from the DPA and LSP. According to the olfactory effect analysis and cancer risk assessment, the main odor-causing gaseous pollutants were hydrogen sulfide, propionic acid, styrene, and 2-pentanone, while benzene, trichlorethylene, and 1,3-butadiene were the dominant carcinogens. This study provides new insights into the emission characteristics, olfactory effects, and cancer risks of VOCs and odors emitted from rural domestic solid waste landfill sites.

Microbiome Sample Comparison and Search: From Pair-Wise Calculations to Model-Based Matching

A huge quantity of microbiome samples have been accumulated, and more are yet to come from all niches around the globe. With the accumulation of data, there is an urgent need for comparisons and searches of microbiome samples among thousands of millions of samples in a fast and accurate manner. However, it is a very difficult computational challenge to identify similar samples, as well as identify their likely origins, among such a grand pool of samples from all around the world. Currently, several approaches have already been proposed for such a challenge, based on either distance calculation, unsupervised algorithms, or supervised algorithms. These methods have advantages and disadvantages for the different settings of comparisons and searches, and their results are also drastically different. In this review, we systematically compared distance-based, unsupervised, and supervised methods for microbiome sample comparison and search. Firstly, we assessed their accuracy and efficiency, both in theory and in practice. Then we described the scenarios in which one or multiple methods were applicable for sample searches. Thirdly, we provided several applications for microbiome sample comparisons and searches, and provided suggestions on the choice of methods. Finally, we provided several perspectives for the future development of microbiome sample comparison and search, including deep learning technologies for tracking the sources of microbiome samples.

Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine

The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal source of geographical local and foreign samples were examined herein, by applying support vector machine (SVM) algorithm. Random forest (RF) and Adaboost were applied for comparison as well. Discriminatory sequences were selected from Clostridiale, Bacteroidales, or Lactobacillales bacterial groups using extremely randomized trees (ExtraTrees). 1.51-12.64% of the unique sequences in the original library composed the representative markers, and they contributed 70% of the discrepancies between source microbiomes. The overall accuracy of the SVM model and the RF model on local samples was 96.08% and 98.04%, respectively, higher than that of the Adaboost (90.20%). As for the non-local samples, the SVM assigned most of the fecal samples into the correct category while several false-positive judgments occurred in closely related groups. The results in this paper suggested that the SVM was a time-saving and accurate method for fecal source tracking in contaminated water body with the potential capability of executing tasks based on geographically unassociated samples, and underlined the necessity of qPCR analysis for accurate detection of human source pollution.

Comparative decay of culturable faecal indicator bacteria, microbial source tracking marker genes, and enteric pathogens in laboratory microcosms that mimic a sub-tropical environment

Enteric pathogens can be present in drinking water catchments due to several point and non-point sources of faecal contamination. Pathogen and contaminant signatures will decay due to environmental stresses, such as temperature, Ultra Violet (UV) radiation, salinity, and predation. In this study, we determined the decay of the culturable faecal indicator bacterium (FIB) Escherichia coli (E. coli), two sewage-associated marker genes (Bacteroides HF183 and crAssphage CPQ_056), and enteric pathogens (Campylobacter spp., human adenovirus 40/41, and Cryptosporidium parvum) in two freshwater laboratory microcosms using culture-based, quantitative PCR (qPCR) and vital dye (determine the fraction of viable Cryptosporidium oocysts) assays. Freshwater samples from the Lake Wappa and Lake Wivenhoe (Australia) were seeded with untreated sewage and C. parvum oocysts, and their declining concentrations were measured over a 28-day period. Moreover, 16S rRNA amplicon sequencing was also undertaken to determine the change/shift in sewage-associated bacterial communities using SourceTracker. Overall, culturable E. coli and the HF183 marker gene decayed significantly (p < 0.05) faster than did the qPCR measured enteric pathogens suggesting that the absence of culturable FIB or qPCR HF183 in water samples may not indicate the absence of pathogens. The decay of crAssphage was similar to that of HAdV 40/41 and other pathogens tested, suggesting crAssphage may be a better surrogate for enteric viruses in sub-tropical catchment waters. The decay rates were greater at 25 °C compared to 15 °C, suggesting that FIB and pathogens persist longer in the winter season compared to summer. Overall decay rates of the tested microorganisms in this microcosm study suggest that sub-tropical conditions, especially temperature, have a negative impact on the persistence of tested microorganisms. Sewage-associated bacterial communities also showed similar patterns. Based on the results, which showed differences in simulated summer and winter temperatures for pathogen decay, corresponding management options and treatment need to be adjusted accordingly to minimize human health risks effectively.

Microbial Source Tracking in the Love Creek Watershed, Delaware (USA)

Fecal contamination of waterways in Delaware pose an ongoing problem for environmental and public health. For monitoring efforts, Enterococcus has been widely adopted by the state to indicate the presence of fecal matter from warm-blooded animals and to establish Primary and Secondary Contact Recreation criteria. In this study, we examined sites within the Love Creek watershed, a tributary of the Rehoboth bay, using next-generation sequencing and SourceTracker to determine sources of potential fecal contamination and compared to bacterial communities to chemical and nutrient concentrations. Microbial community from fecal samples of ten different types of animals and one human sample were used to generate a fecal library for community-based microbial source tracking. Orthophosphate and total dissolved solids were among the major factors associated with community composition. SourceTracker analysis of the monthly samples from the Love Creek watershed indicated the majority of the microbial community were attributed to "unknown" sources, i.e. wildlife. Those that attribute to known sources were primarily domestic animals, i.e. cat and dog. These results suggest that at the state level these methods are capable of giving the start for source tracking as a means to understanding bacterial contamination.

Shared bacterial communities between soil, stored drinking water, and hands in rural Bangladeshi households

Understanding household-level transmission pathways of fecal pathogens can provide insight for developing effective strategies to reduce diarrheal illness in low- and middle-income countries. We applied whole bacterial community analysis to investigate pathways of bacterial transmission in 50 rural Bangladeshi households. SourceTracker was used to quantify the shared microbial community in household reservoirs (stored drinking water, soil, and hands) and estimate the percentage of fecal-associated bacteria from child and mothers' feces in these reservoirs. Among the reservoirs studied, most bacterial transfer occurred between mothers' and children's hands and between mothers' hands and stored water. The relative percentage of human fecal-associated bacteria in all household reservoirs was low. We also quantified the number of identical amplicon sequence variants within and between individual households to assess bacterial community exchange in the domestic environment. Intra-household sharing of bacteria between mothers' and children's hands and between hands and soil was significantly greater than inter-household sharing.

Source tracking of antibiotic resistance genes in the environment - Challenges, progress, and prospects

Antibiotic resistance has become a global public health concern, rendering common infections untreatable. Given the widespread occurrence, increasing attention is being turned toward environmental pathways that potentially contribute to antibiotic resistance gene (ARG) dissemination outside the clinical realm. Studies during the past decade have clearly proved the increased ARG pollution trend along with gradient of anthropogenic interference, mainly through marker-ARG detection by PCR-based approaches. However, accurate source-tracking has been always confounded by various factors in previous studies, such as autochthonous ARG level, spatiotemporal variability and environmental resistome complexity, as well as inherent method limitation. The rapidly developed metagenomics profiles ARG occurrence within the sample-wide genomic context, opening a new avenue for source tracking of environmental ARG pollution. Coupling with machine-learning classification, it has been demonstrated the potential of metagenomic ARG profiles in unambiguously assigning source contribution. Through identifying indicator ARG and recovering ARG-host genomes, metagenomics-based analysis will further increase the resolution and accuracy of source tracking. In this review, challenges and progresses in source-tracking studies on environmental ARG pollution will be discussed, with specific focus on recent metagenomics-guide approaches. We propose an integrative metagenomics-based framework, in which coordinated efforts on experimental design and metagenomic analysis will assist in realizing the ultimate goal of robust source-tracking in environmental ARG pollution.

Increased inheritance of structure and function of bacterial communities and pathogen propagation in plastisphere along a river with increasing antibiotics pollution gradient

Plastic debris provides a stable substrate and novel ecological niche for microorganisms in the aquatic environment, which was referred to as "Plastisphere". Little is known about distribution patterns and responses of ecological function and structure of microbial communities in the plastisphere along rivers which usually have antibiotics pollution gradient. In this study, the differences in the community structure between the plastisphere and the planktonic bacteria, and their spatial variation of the community structure and function along a river with increased antibiotics pollution gradient was investigated at the watershed scale. The diversity of bacteria colonized on most plastic debris was higher than in surrounding water. Plastic debris could accumulate a higher abundance of some potential pathogens than surrounding water even at high antibiotics concentrations. The source tracking results showed that downstream plastisphere inherited much higher proportions of bacterial taxa from upstream than planktonic bacteria. About 92.3-99.7% of bacteria communities in downstream water were not from upstream but from the input of downstream human activities. On the contrary, high proportions of bacterial taxa in downstream plastisphere were closely connected to upstream. The plastisphere possesses higher ecological functional diversity than the planktonic bacteria. Seventy nine functional groups across plastisphere were predicted using functional annotation of prokaryotic taxa and only 65 functional groups were found in the planktonic bacteria. Plastisphere also acts as hotspot for biogeochemical cycling of nutrients such as N and S. Intensive human activities of urban and downstream agriculture and aquaculture had great effects on microbial community structure and functional groups of the Urumqi River. Pastisphere communities are much more resistant to human disturbance than planktonic bacteria. Compared to surrounding water, plastisphere increased inheritance from upstream microbial structure and function and also increased survival and propagation of pathogens in the downstream water with high concentrations of antibiotics.

Microbial Indicators of Fecal Pollution: Recent Progress and Challenges in Assessing Water Quality

PURPOSE OF REVIEW

Fecal contamination of water is a major public health concern. This review summarizes recent developments and advancements in water quality indicators of fecal contamination.

RECENT FINDINGS

This review highlights a number of trends. First, fecal indicators continue to be a valuable tool to assess water quality and have expanded to include indicators able to detect sources of fecal contamination in water. Second, molecular methods, particularly PCR-based methods, have advanced considerably in their selected targets and rigor, but have added complexity that may prohibit adoption for routine monitoring activities at this time. Third, risk modeling is beginning to better connect indicators and human health risks, with the accuracy of assessments currently tied to the timing and conditions where risk is measured. Research has advanced although challenges remain for the effective use of both traditional and alternative fecal indicators for risk characterization, source attribution and apportionment, and impact evaluation.

Effect of Quantitative Polymerase Chain Reaction Data Analysis Using Sample Amplification Efficiency on Microbial Source Tracking Assay Performance and Source Attribution

The widely used microbial source tracking (MST) technique, quantitative polymerase chain reaction (qPCR), quantifies host-specific gene abundance in polluted water to identify and prioritize contamination sources. This study characterized the effects of a qPCR data analysis using the sample PCR efficiencies (the LinRegPCR model) on gene abundance and compared them with the standard curve-based method (the mixed model). Five qPCR assays were evaluated: the universal GenBac3, human-specific HF183/BFDrev and CPQ_056, swine-specific Pig-2-Bac, and cattle-specific Bac3qPCR assays. The LinRegPCR model increased the low-copy amplification, especially in the HF183/BFDrev assay, thus lowering the specificity to 0.34. Up to 1.41 log₁₀ copies/g and 0.41 log₁₀ copies/100 mL differences were observed for composite fecal and sewage samples (n = 147) by the LinRegPCR approach, corresponding to an 18.2% increase and 6.4% decrease, respectively. Freshwater samples (n = 48) demonstrated a maximum of 1.95 log₁₀ copies/100 mL difference between the two models. Identical attributing sources by both models were shown in 54.55% of environmental samples; meanwhile, the LinRegPCR approach improved the inability to identify sources by the mixed model in 29.55% of the samples. This study emphasizes the need for a standardized data analysis protocol for qPCR MST assays for interlaboratory consistency and comparability.

Source identification of phosphorus in the river-lake interconnected system using microbial community fingerprints

Phosphorus, one of the primary limiting factors for eutrophication, plays a crucial role in the ecology health of aquatic ecosystems. However, understanding phosphorus bioavailability and source contributions in contaminated lake sediments which could help develop effective eutrophication management plans is limited largely due to the lack of appropriate methods in large catchments with a complex arrangement of sources. Based on the significant relationships between sediment, phosphorus and microbial community, source-specific microbial community fingerprints formed by machine-learning classification SourceTracker might shed light on determining dominant phosphorus sources in the river-lake systems in the era of high-throughput sequencing. This study was conducted in Dongting Lake that suffered accelerated eutrophication due to considerable phosphorus input from the inflow-rivers. The results of phosphorus fractionation according to the Standards, Measurements and Testing harmonized procedure indicated that sediments in the central lake had a higher concentration of non-apatite inorganic phosphorus (Mann-Whitney U test), which deserves greater attention on the risk of phosphorus release. The significant relationships between phosphorus fractionations, sediment and bacterial community were established with the spearman correlation and network analysis. SourceTracker analysis indicated that the major inflow-rivers of phosphorus sources to Dongting Lake were the Songzi, Miluo, and Xinqiang Rivers. The effects of sediment diffusion distance on phosphorus source apportionment were further confirmed. Taken together, our results contribute to an improved understanding of phosphorus fractionations and source contributions in the river-lake systems and its potential impact to eutrophication management plans.

Surf zone microbiological water quality following emergency beach nourishment using sediments from a catastrophic debris flow

Urban disaster response requires disposal of complex wastes. This study regards a case wherein high intensity rainfall fell over a remote mountainous area previously burned by wildfire, generating debris flows that devastated a downstream town. Sanitary sewers and homes with septic systems were damaged, releasing human waste into the debris flow field. Contaminated sediments, with their high fecal indicator bacteria (FIB) concentrations, were cleared from public rights-of-way and creek channels by local authorities, then disposed onto distant Goleta Beach for beach nourishment, causing immediate surf zone microbiological water quality exceedances. To determine potential public health threats, disposed sediments and surf zone waters were sampled and analyzed-relative to reference samples of mountain soil and raw sewage-for FIB, pathogens, human (HF183) and other host- (Gull2 TaqMan, and DogBact) associated DNA-based fecal markers, and bacterial community 16S rRNA gene sequences. Approximately 20% of disposed sediment samples contained the HF183 marker; sequencing suggested that all samples were contaminated by sewage. In an initial sediment disposal period, surf zone waters harbored intestinal bacterial sequences that were shared with disposed sediments and sewage. Yet surf zone bacterial communities returned to mostly marine clades within weeks. Taken together, multiple conventional and DNA-based analyses informed this forensic assessment of human waste contamination. In the future, similar analyses could be used earlier in disaster response to guide sediment disposal decisions towards continuously protecting beachgoer health.

Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics

Background

Microbial source tracking methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach uses deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an extensive diversity of studies, from beach contamination to human behavior.

Methods

Here, we demonstrate a novel application of SourceTracker to work with metagenomic datasets and tested this approach using sink samples from a study of coastal marine environments. Source environment metagenomes were obtained from metagenomics studies of gut, freshwater, marine, sand and soil environments. As part of this effort, we implemented features for determining the stability of source proportion estimates, including precision visualizations for performance optimization, and performed domain-specific source-tracking analyses (i.e., Bacteria, Archaea, Eukaryota and viruses). We also applied SourceTracker to metagenomic libraries generated from samples collected from the International Space Station (ISS).

Results

SourceTracker proved highly effective at predicting the composition of known sources using shotgun metagenomic libraries. In addition, we showed that different taxonomic domains sometimes presented highly divergent pictures of environmental source origins for both the coastal marine and ISS samples. These findings indicated that applying SourceTracker to separate domains may provide a deeper understanding of the microbial origins of complex, mixed-source environments, and further suggested that certain domains may be preferable for tracking specific sources of contamination.

FORENSIC: an Online Platform for Fecal Source Identification

Sewage overflows, agricultural runoff, and stormwater discharges introduce fecal pollution into surface waters. Distinguishing these sources is critical for evaluating water quality and formulating remediation strategies. With the falling costs of sequencing, microbial community-based water quality assessment tools are under development. However, their application is limited by the need to build reference libraries, which requires extensive sampling of sources and bioinformatic expertise. Here, we introduce FORest Enteric Source IdentifiCation (FORENSIC; https://forensic.sfs.uwm.edu/), an online, library-independent source tracking platform based on random forest classification and 16S rRNA gene amplicon sequences to identify in environmental samples common fecal contamination sources, including humans, domestic pets, and agricultural animals. FORENSIC relies on a broad reference signature database of Bacteroidales and Clostridiales, two predominant bacterial groups that have coevolved with their hosts. As a result, these groups demonstrate cohesive and reliable assemblage patterns within mammalian species or among species sharing the same diet/physiology. We created a scalable and extensible platform that we tested for global applicability using samples collected in distant geographic locations. This Web application offers a fast and intuitive approach for fecal source identification, particularly in sewage-contaminated waters.IMPORTANCE FORENSIC is an online platform to identify sources of fecal pollution without the need to create reference libraries. FORENSIC is based on the ability of random forest classification to extract cohesive source microbial signatures to create classifiers despite individual variability and to detect the signatures in environmental samples. We primarily focused on defining sewage signals, which are associated with a high human health risk in polluted waters. To test for fecal contamination sources, the platform only requires paired-end reads targeting the V4 or V6 regions of the 16S rRNA gene. We demonstrated that we could use V4V5 reads trimmed to the V4 positions to generate the reference signature. The systematic workflow we describe to create and validate the signatures could be applied to many disciplines. With the increasing gap between advancing technology and practical applications, this platform makes sequence-based water quality assessments accessible to the public health and water resource communities.

Network analysis infers the wilt pathogen invasion associated with non-detrimental bacteria

The microbiota colonizing the root endophytic compartment and surrounding rhizosphere soils contribute to plant growth and health. However, the key members of plant soil and endophytic microbial communities involved in inhibiting or assisting pathogen invasion remain elusive. By utilizing 16S high-throughput sequencing and a molecular ecological network (MEN) approach, we systematically studied the interactions within bacterial communities in plant endophytic compartments (stem and root) and the surrounding soil (bulk and rhizosphere) during bacterial wilt invasion. The endophytic communities were found to be strongly influenced by pathogen invasion according to analysis of microbial diversity and community structure and composition. Endophytic communities of the infected plants were primarily derived from soil communities, as assessed by the SourceTracker program, but with rare migration from soil communities to endophytic communities observed in healthy plants. Soil and endophytic microbiomes from infected plants showed modular topology and greater complexity in network analysis, and a higher number of interactions than those in healthy plants. Furthermore, interactions among microbial members revealed that pathogenic Ralstonia members were positively correlated with several bacterial genera, including Delftia, Stenotrophomonas, Bacillus, Clostridium XlVa, Fontibacillus, Acidovorax, Herminiimonas, and three unclassified bacterial genera, in infected plant roots. Our findings indicated that the pathogen invasion in the rhizosphere and endophytic compartments may be highly associated with bacteria that are normally not detrimental, and sometimes even beneficial, to plants.

Compositional and temporal stability of fecal taxon libraries for use with SourceTracker in sub-tropical catchments

Characterization of microbial communities using high-throughput amplicon sequencing is an emerging approach for microbial source tracking of fecal pollution. This study used SourceTracker software to examine temporal and geographical variability of fecal bacterial community profiles to identify pollutant sources in three freshwater catchments in sub-tropical Australia. Fecal bacterial communities from 10 animal species, humans, and composite wastewater samples from six sewage treatment plants were characterized and compared to freshwater samples using Illumina amplicon sequencing of the V5-V6 regions of the 16S rRNA gene. Source contributions were calculated in SourceTracker using new fecal taxon libraries as well as previously generated libraries to determine the effects of geographic and temporal variability on source assignments. SourceTracker determined 16S rRNA bacterial communites within freshwater samples, shared taxonomic similarities to that of wastewater at low levels (typically <3%). SourceTraker also predicted occasional fecal detection of deer and flying fox sources in the water samples. No significant differences in source contributions were observed within sequences from current and previously characterized fecal samples (P ≥ 0.107). However, significant differences were observed between previously characterized and newly characterized source communities (ANOSIM P ≤ 0.001), which shared <15% community composition. Results suggest temporal instability of fecal taxon libraries among tested sources and highlight continual evaluation of community-based MST using confirmatory qPCR analyses of marker genes.

Next-generation sequencing reveals fecal contamination and potentially pathogenic bacteria in a major inflow river of Taihu Lake

Taihu Lake is one of the largest freshwater lakes in China and serves as an important source for drinking water. This lake is suffering from eutrophication, cyanobacterial blooms and fecal pollution, and the inflow Tiaoxi River is one of the main contributors. The goal here was to characterize the bacterial community structure of Tiaoxi River water by next-generation sequencing (NGS), paying attention to bacteria that are either fecal-associated or pathogenic, and to examine the relationship between environmental parameters and bacterial community structure. Water samples collected from 15 locations in three seasons, and fecal samples collected from different hosts and wastewater samples were used for bacterial community analysis. The phyla Proteobacteria, Actinobacteria, Bacteroidetes, and Cyanobacteria were predominant in most of the water samples tested. In fecal samples, Bacteroidetes, Firmicutes, and Proteobacteria were abundant, while wastewater samples were dominated by Proteobacteria, Bacteroidetes, Acidobacteria, and Chloroflexi. The cluster analysis and principal coordinate analysis indicated that bacterial community structure was significantly different between water, fecal and sewage samples. Shared OTUs between water samples and chicken, pig, and human fecal samples ranged from 4.5 to 9.8% indicating the presence of avian, pig and human fecal contamination in Tiaoxi River. At genus level, five bacterial genera of fecal origin and sequences of seven potential pathogens were detected in many locations and their presence was correlated well with the land use pattern. The sequencing data revealed that Faecalibacterium could be a potential target for human-associated microbial source-tracking qPCR assays. Our results suggest that pH, conductivity, and temperature were the main environmental factors in shaping the bacterial community based on redundancy analysis. Overall, NGS is a valuable tool for preliminary investigation of environmental samples to identify the potential human health risk, providing specific information about fecal and potentially pathogenic bacteria that can be followed up by specific methods.

Sources and drivers of contamination along an urban tropical river (Ciliwung, Indonesia): Insights from microbial DNA, isotopes and water chemistry

Wastewater treatment infrastructure is lacking in many developing countries, often resulting in high loads of contaminants discharged to urban rivers. In these countries, targeted pollution mitigation requires an understanding of where, how and when contaminants enter water bodies. Here we report on contamination of the Ciliwung River, a dynamic, tropical system flowing through the Jakarta metropolitan area (Indonesia). We measured a set of isotopic, chemical and microbial tracers in representative water and contamination sources, as well as longitudinally within the river, to assess the spatial and temporal variations in contaminant levels in and pathways to the river. In the dry season, we observed a tight coupling between locally recharged groundwater sources and the river, whereas in the wet season, one single water source originating from the fractured headwaters predominantly contributed to river flow. Yet, the flushing of upstream waters in the wet season did not always lead to the dilution of contaminants downstream. We delineated several contamination hotspots along the river, particularly active during the wet season due to higher hydrological connectivity between sources and the river. These hotspots may originate from septic tank leakage, as supported by metal ratios and dominant microbial communities, although we could not rule out other potential sources such as urban runoff or sediment resuspension. Bayesian source tracking on the whole microbial community proved useful in outlining processes that conventional tracers did not capture, such as the occurrence of a localised domestic contamination in the upper catchment, and the inflow of agricultural runoff all along the river profile during the wet season. Our study emphasises the role of rivers as biogeochemical reactors that constantly process and transform contaminants and microbial communities. We also demonstrate the value of using isotopic, chemical and microbial tools together to trace the movement of water and contaminants through urban rivers.

Durable Long-Term Bacterial Engraftment following Encapsulated Fecal Microbiota Transplantation To Treat Clostridium difficile Infection

Fecal microbiota transplantation (FMT) has become a common rescue therapy for recurrent Clostridium difficile infection, and encapsulated delivery (cFMT) of healthy donor microbiota shows similar clinical efficacy as more traditional routes of administration. In this study, we characterized long-term patterns of bacterial engraftment in a cohort of 18 patients, who received capsules from one of three donors, up to 409 days post-FMT. Bacterial communities were characterized using Illumina sequencing of the V5-V6 hypervariable regions of the 16S rRNA gene, and engraftment was determined by using the Bayesian algorithm SourceTracker. All patients recovered clinically and were free of C. difficile infection following cFMT. The majority of patients (61%) showed high levels of engraftment after the first week following FMT, which were sustained throughout the year. A small subset, 22%, experienced a decline in donor engraftment after approximately 1 month, and a few patients (17%), two of whom were taking metformin, showed delayed and low levels of donor engraftment. Members of the genera Bacteroides, Parabacteroides, and Faecalibacterium were significantly and positively correlated with donor similarity (ρ = 0.237 to 0.373, P ≤ 0.017). Furthermore, throughout the year, patient fecal communities showed significant separation based on the donor fecal microbiota that they received (P < 0.001). Results of this study, which characterize long-term engraftment following cFMT, suggest that numerical donor similarity is not strictly related to clinical outcome and identify a persistent donor-specific effect on patient fecal microbial communities. Furthermore, results suggest that members of the Bacteroidetes may be important targets to improve engraftment via cFMT.IMPORTANCE Recurrent Clostridium difficile infection (rCDI) is the most common cause of hospital- and community-acquired diarrheal infection associated with antibiotic use. Fecal microbiota transplantation (FMT), a treatment that involves administration of fecal bacteria from a healthy donor to a recipient patient, is a highly effective rescue therapy for rCDI that is increasingly being incorporated into standard clinical practice. Encapsulated, freeze-dried preparations of fecal microbiota, administered orally, offer the simplest and most convenient route of FMT delivery for patients (cFMT). In this study, we evaluated the extent of bacterial engraftment following cFMT and the duration of donor bacterial persistence. All patients studied recovered clinically but showed differing patterns in long-term microbial community similarity to the donor that were associated with members of the bacterial group Bacteroidetes, previously shown to be prominent contributors to rCDI resistance. Results highlight long-lasting, donor-specific effects on recipient patient microbiota and reveal potential bacterial targets to improve cFMT engraftment.

Microbial Source Tracking Using 16S rRNA Amplicon Sequencing Identifies Evidence of Widespread Contamination from Young Children's Feces in an Urban Slum of Nairobi, Kenya

Child exposure to fecal contamination remains common in low- and middle-income countries after sanitation interventions. Unsafe disposal of children's feces may contribute to this continued exposure, but its relative importance to domestic fecal contamination is not well understood. To address this gap, we interviewed and collected environmental samples (drinking water, caregiver hands, child hands, surfaces, soil, open drainage ditches, standing water, streams) from 40 households in Kibera, an urban slum in Nairobi, Kenya. To track young children's feces (<3 years old) separately from other human-associated fecal sources, we validated distance-based and Bayesian (SourceTracker) microbial source tracking methods using amplicon-based sequencing of the 16S rRNA gene. Contamination by young children's feces could be identified and distinguished separately from older child/adult feces with high sensitivity and specificity in water and soil. Among environmental samples, young children's feces were almost always identified as the dominant source of human fecal contamination inside households (hands, surfaces) whereas older children/adult feces were often identified as the dominant source outside households (standing water, streams, soil). Markers for young children's feces were also detected in standing water and streams, and markers for both fecal sources were equally likely to be dominant in open ditches. These results establish motivation for sanitation interventions that directly address child feces management.

New Insights into Sediment Transport in Interconnected River-Lake Systems Through Tracing Microorganisms

A growing awareness of the wider environmental significance of diffuse sediment pollution in interconnected river-lake systems has generated the need for reliable provenance information. Owing to their insufficient ability to distinguish between multiple sources, common sediment source apportionment methods would rarely be a practical solution. On the basis of the inseparable relationships between sediment and adsorbed microorganisms, community-based microbial source tracking may be a novel method of identifying dominant sediment sources in the era of high-throughput sequencing. Dongting Lake was selected as a study area as it receives considerable sediment import from its inflowing rivers during the flood season. This study was conducted to characterize the bacterial community composition of sediment samples from the inflow-river estuaries and quantify their sediment microbe contributions to the central lake. Metagenomic analysis revealed that the community compositions of source sediment samples were significantly different, allowing specific sources to be identified with the machine learning classification program SourceTracker. A modified analysis using SourceTracker found that the major contributors to three major lake districts were the Songzi, Zishui, and Xinqiang Rivers. The impacts of hydrodynamic conditions on source apportionment were further verified and suggested the practicability of this method to offer a systematic and comprehensive understanding of sediment sources, pathways, and transport dynamics. Finally, a novel framework for sediment source-tracking was established to develop effective sediment management and control strategies in river-lake systems.

Influence of Library Composition on SourceTracker Predictions for Community-Based Microbial Source Tracking

Community-based microbial source tracking (MST) utilizes high-throughput DNA sequencing to profile and compare the microbial communities in different fecal sources and environmental samples. SourceTracker, a program that compares a library of OTUs from fecal sources (i.e., sources) to those in environmental samples (i.e., sinks) in order to determine sources of fecal contamination, is an emerging tool for community-based MST studies. In this study, we investigated the ability of SourceTracker to determine sources of known fecal contamination in spiked, in situ mesocosms containing different source contributors. We also evaluated how SourceTracker results were impacted by accounting for autochthonous taxa present in the sink environment. While SourceTracker was able to predict most sources present in the in situ mesocosms, fecal source library composition substantially influenced the program's ability to predict source contributions. Moreover, prediction results were most reliable when the library contained only known sources, autochthonous taxa were accounted for and when source profiles had low intragroup variability. Although SourceTracker struggled to differentiate between sources with similar bacterial community structures, it was able to consistently identify abundant and expected sources, suggesting that the SourceTracker program can be a useful tool for community-based MST studies.

Fecal source identification using random forest

BACKGROUND

Clostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species. As a result, members of Clostridiales and Bacteroidales are particularly suitable for identifying sources of fecal contamination in environmental samples. However, a comprehensive evaluation of their predictive power and development of computational approaches is lacking. Given the global public health concern for waterborne disease, accurate identification of fecal pollution sources is essential for effective risk assessment and management. Here, we use random forest algorithm and 16S rRNA gene amplicon sequences assigned to Clostridiales and Bacteroidales to identify common fecal pollution sources. We benchmarked the accuracy, consistency, and sensitivity of our classification approach using fecal, environmental, and artificial in silico generated samples.

RESULTS

Clostridiales and Bacteroidales classifiers were composed mainly of sequences that displayed differential distributions (host-preferred) among sewage, cow, deer, pig, cat, and dog sources. Each classifier correctly identified human and individual animal sources in approximately 90% of the fecal and environmental samples tested. Misclassifications resulted mostly from false-positive identification of cat and dog fecal signatures in host animals not used to build the classifiers, suggesting characterization of additional animals would improve accuracy. Random forest predictions were highly reproducible, reflecting the consistency of the bacterial signatures within each of the animal and sewage sources. Using in silico generated samples, we could detect fecal bacterial signatures when the source dataset accounted for as little as ~ 0.5% of the assemblage, with ~ 0.04% of the sequences matching the classifiers. Finally, we developed a proxy to estimate proportions among sources, which allowed us to determine which sources contribute the most to observed fecal pollution.

CONCLUSION

Random forest classification with 16S rRNA gene amplicons offers a rapid, sensitive, and accurate solution for identifying host microbial signatures to detect human and animal fecal contamination in environmental samples.