Application of phylogenetic microarray analysis to discriminate sources of fecal pollution

Occurrence of recreational water quality monitoring general fecal indicator bacteria and fecal source identification genetic markers in gray seal scat

The number of gray seals (Halichoerus grypus) observed along the United States Northwest Atlantic region has been increasing for decades. These colonial animals often haul-out on beaches seasonally in numbers ranging from a few individuals to several thousands. While these larger aggregations are an important part of gray seal behavior, there is public concern that haul-outs could lead to large amounts of fecal waste in recreational areas, potentially resulting in beach closures. Yet, data to confirm whether these animals contribute to beach closures is lacking and minimal information is available on the occurrence of key water quality monitoring genetic markers in gray seal scat. This study evaluates the concentration of E. coli (EC23S857), enterococci (Entero1a), and fecal Bacteroidetes (GenBac3) as well as six fecal source identification genetic markers (HF183/BacR287, HumM2, CPQ_056, Rum2Bac, DG3, and GFD) measured by qPCR in 48 wild gray seal scat samples collected from two haul-out areas in Cape Cod (Massachusetts, U.S.A.). Findings indicate that FIB genetic markers are shed in gray seal scat at significantly different concentrations with the Entero1a genetic marker exhibiting the lowest average concentration (-0.73 log10 estimated mean copies per nanogram of DNA). In addition, systematic testing of scat samples demonstrated that qPCR assays targeting host-associated genetic markers indicative of human, ruminant, and canine fecal pollution sources remain highly specific in waters frequented by gray seals (>97 % specificity).

A fecal score approximation model for analysis of real-time quantitative PCR fecal source identification measurements

Numerous qPCR-based methods are available to estimate the concentration of fecal pollution sources in surface waters. However, qPCR fecal source identification data sets often include a high proportion of non-detections (reactions failing to attain a prespecified minimal signal intensity for detection) and measurements below the assay lower limit of quantification (minimal signal intensity required to estimate target concentration), making it challenging to interpret results in a quantitative manner while accounting for error. In response, a Bayesian statistic based Fecal Score (FS) approach was developed that estimates the weighted average concentration of a fecal source identification genetic marker across a defined group of samples, mathematically incorporating qPCR measurements from all samples. Yet, implementation is technically demanding and computationally intensive requiring specialized training, the use of expert software, and access to high performance computing. To address these limitations, this study reports a novel approximation model for FS determination based on a frequentist approach. The performance of the Bayesian and Frequentist models are compared using fecal source identification qPCR data representative of different 'censored' data scenarios from a recently published study focusing on the impact of stormwater discharge in urban streams. In addition, data set eligibility recommendations for the responsible use of these models are presented. Findings indicate that the Frequentist model can generate similar average concentrations and uncertainty estimates for FS, compared to the original Bayesian approach. The Frequentist model should make calculations less computationally and technically intensive, allowing for the development of easier to use data analysis tools for fecal source identification applications.

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.

Anthropogenic Intensity-Determined Assembly and Network Stability of Bacterioplankton Communities in the Le'an River

Bacterioplankton are essential components of riverine ecosystems. However, the mechanisms (deterministic or stochastic processes) and co-occurrence networks by which these communities respond to anthropogenic disturbances are not well understood. Here, we integrated niche-neutrality dynamic balancing and co-occurrence network analysis to investigate the dispersal dynamics of bacterioplankton communities along human activity intensity gradients. Results showed that the lower reaches (where intensity of human activity is high) had an increased composition of bacterioplankton communities which induced strong increases in bacterioplankton diversity. Human activity intensity changes influenced bacterioplankton community assembly via regulation of the deterministic-stochastic balance, with deterministic processes more important as human activity increases. Bacterioplankton molecular ecological network stability and robustness were higher on average in the upper reaches (where there is lower intensity of human activity), but a human activity intensity increase of about 10%/10% can reduce co-occurrence network stability of bacterioplankton communities by an average of 0.62%/0.42% in the dry and wet season, respectively. In addition, water chemistry (especially NO₃^–-N and Cl^–) contributed more to explaining community assembly (especially the composition) than geographic distance and land use in the dry season, while the bacterioplankton community (especially the bacterioplankton network) was more influenced by distance (especially the length of rivers and dendritic streams) and land use (especially forest regions) in the wet season. Our research provides a new perspective of community assembly in rivers and important insights into future research on environmental monitoring and classified management of aquatic ecosystems under the influence of human activity.

Modeling human pollution in water bodies using somatic coliphages and bacteriophages that infect Bacteroides thetaiotaomicron strain GA17

The ability to detect human fecal pollution in water is of great importance when assessing the associated health risks. Many microbial source tracking (MST) markers have been proposed to determine the origin of fecal pollution, but their application remains challenging. A range of factors, not yet sufficiently analyzed, may affect MST markers in the environment, such as dilution and inactivation processes. In this work, a statistical framework based on Monte Carlo simulations and non-linear regression was used to develop a classification procedure for use in MST studies. The predictive model tested uses only two parameters: somatic coliphages (SOMCPH), as an index of general fecal pollution, and human host-specific bacteriophages that infect Bacteroides thetaiotaomicron strain GA17 (GA17PH). Taking into account bacteriophage dilution and differential inactivation, the threshold concentration of SOMCPH was calculated to be around 500 PFU/100 mL for a limit of detection of 10 PFU/100 mL. However, this threshold can be lowered by increasing the analyzed volume sample, which in turn lowers the limit of detection. The resulting model is sufficiently accurate for application in practical cases involving MST and could be easily used with markers other than those tested here.

Next Generation High Throughput Sequencing to Assess Microbial Communities: An Application Based on Water Quality

Traditional techniques to identify different contaminants (biological or chemical) in the waters are slow, laborious, and can require specialized expertise. Hence, the rapid determination of water quality using more sensitive and reliable metagenomic based approaches attains special importance. Metagenomics deals with the study of genetic material that is recovered from microbial communities present in environmental samples. In traditional techniques cultivation-based methodologies were used to describe the diversity of microorganisms in environmental samples. It has failed to function as a robust marker because of limited taxonomic and phylogenetic implications. In this backdrop, high-throughput DNA sequencing approaches have proven very powerful in microbial source tracking because of investigating the full variety of genome-based analysis such as microbial genetic diversity and population structure played by them. Next generation sequencing technologies can reveal a greater proportion of microbial communities that have not been reported earlier by traditional techniques. The present review highlights the shift from traditional techniques for the basic study of community composition to next-generation sequencing (NGS) platforms and their potential applications to the biomonitoring of water quality in relation to human health.

Seasonal and spatial distribution and assembly processes of bacterioplankton communities in a subtropical urban river

The ecological functions of core and non-core bacteria are gradually being identified, yet little is known about their responses to environmental changes and assembly processes, especially in urban river ecosystems. Here, we investigated bacterioplankton communities over 1 year in an urban section of the Ganjiang River, China. The results revealed that the alpha- and beta-diversity of bacterioplankton communities had no significant spatial differences along the urbanization gradient, but they presented distinct seasonal variations. The bacterioplankton communities were comprised of a few core taxa (11.8%) and a large number of non-core taxa (88.2%), of which the non-core taxa were the most active component responsible for community dynamics. Most non-core taxa (76.84%) belonged to non-typical freshwater bacteria, implying that they are more likely to derive from allochthonous inputs than the core taxa. Variance partitioning analyses showed that air temperature, flow rate and water chemistry together explained 58.2 and 38.9% of the variations of the core taxa and non-core taxa, respectively. In addition, the relative importance of temperature and water chemistry on the bacterioplankton communities prevailed over that of flow rate alone. This means that deterministic processes and stochastic processes simultaneously control the bacterioplankton community assembly, with deterministic processes contributing more than stochastic processes.

Patterns in Benthic Microbial Community Structure Across Environmental Gradients in the Beaufort Sea Shelf and Slope

The paradigm of tight pelagic-benthic coupling in the Arctic suggests that current and future fluctuations in sea ice, primary production, and riverine input resulting from global climate change will have major impacts on benthic ecosystems. To understand how these changes will affect benthic ecosystem function, we must characterize diversity, spatial distribution, and community composition for all faunal components. Bacteria and archaea link the biotic and abiotic realms, playing important roles in organic matter (OM) decomposition, biogeochemical cycling, and contaminant degradation, yet sediment microbial communities have rarely been examined in the North American Arctic. Shifts in microbial community structure and composition occur with shifts in OM inputs and contaminant exposure, with implications for shifts in ecological function. Furthermore, the characterization of benthic microbial communities provides a foundation from which to build focused experimental research. We assessed diversity and community structure of benthic prokaryotes in the upper 1 cm of sediments in the southern Beaufort Sea (United States and Canada), and investigated environmental correlates of prokaryotic community structure over a broad spatial scale (spanning 1,229 km) at depths ranging from 17 to 1,200 m. Based on hierarchical clustering, we identified four prokaryotic assemblages from the 85 samples analyzed. Two were largely delineated by the markedly different environmental conditions in shallow shelf vs. upper continental slope sediments. A third assemblage was mainly comprised of operational taxonomic units (OTUs) shared between the shallow shelf and upper slope assemblages. The fourth assemblage corresponded to sediments receiving heavier OM loading, likely resulting in a shallower anoxic layer. These sites may also harbor microbial mats and/or methane seeps. Substructure within these assemblages generally reflected turnover along a longitudinal gradient, which may be related to the quantity and composition of OM deposited to the seafloor; bathymetry and the Mackenzie River were the two major factors influencing prokaryote distribution on this scale. In a broader geographical context, differences in prokaryotic community structure between the Beaufort Sea and Norwegian Arctic suggest that benthic microbes may reflect regional differences in the hydrography, biogeochemistry, and bathymetry of Arctic shelf systems.

Evaluation of a eukaryote phylogenetic microarray for environmental monitoring of marine sediments

Increased exploitation of resources in sensitive marine ecosystems emphasizes the importance of knowledge regarding ecological impacts. However, current bio-monitoring practices are limited in terms of target-organisms and temporal resolution. Hence, developing new technologies is vital for enhanced ecosystem understanding. In this study, we have applied a prototype version of a phylogenetic microarray to assess the eukaryote community structures of marine sediments from an area with ongoing oil and gas drilling activity. The results were compared with data from both sequencing (metabarcoding) and morphology-based monitoring to evaluate whether microarrays were capable of detecting ecosystem disturbances. A significant correlation between microarray data and chemical pollution indicators, as well as sequencing-based results, was demonstrated, and several potential indicator organisms for pollution-associated parameters were identified, among them a large fraction of microorganisms not covered by traditional morphology-based monitoring. This suggests that microarrays have a potential in future environmental monitoring.

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.

Validated predictive modelling of sulfonamide and beta-lactam resistance genes in landfill leachates

The spread of antimicrobial resistance via landfill leachates jeopardizes millions of people's health, which can be exacerbated due to the unclear quantitative relationships between leachate characteristics and occurrences of antibiotic resistance genes (ARGs). Here, in parallel with sampling raw leachates from a real landfill, we constructed a lab-scale landfill and collected its leachates for 260 days. All leachate samples were analyzed for the abundance of integrons, sulfonamide resistance (sulR; sul1 and sul2) and beta-lactams resistance (blaR; bla_OXA, bla_CTX-M, and bla_TEM) genes. The enrichment of sulR subtypes was closely associated with the integrons' prevalence during the landfilling process (0.65-0.75 log₁₀(copies/mL)), which can be explained by the multiple linear regression that contained intl1, pH, and nitrogen compounds as variables. The predicted abundance of sulR genes (6.06 ± 0.6 log₁₀(copies/mL)) was statistically the same as the observed value in raw leachates (P = 0.73). The abundance of blaR genes decreased from 5.0 to 2.5 log₁₀(copies/mL) during the experiment (P < 0.001); and a locally weighted regression of blaR genes with integrons, COD and total nitrogen accurately predicted blaR genes abundance in raw leachate (Bootstrap = 10,000, P = 0.67). The partial least squares path modelling (PLS-PM) showed that variations of blaR genes in the lab and raw leachates shared an identical pattern (PLS-PM, Bootstrap = 10,000, P > 0.05), which was influenced by integrons and environmental factors with the coefficients of -0.11 and 0.39, respectively. We believe the validated models are highly useful tools to streamline the strategies for monitoring and prediction of ARGs.

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

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

Elucidating Waterborne Pathogen Presence and Aiding Source Apportionment in an Impaired Stream

Fecal indicator bacteria (FIB) are the basis for water quality regulations and are considered proxies for waterborne pathogens when conducting human health risk assessments. The direct detection of pathogens in water and simultaneous identification of the source of fecal contamination are possible with microarrays, circumventing the drawbacks to FIB approaches. A multigene target microarray was used to assess the prevalence of waterborne pathogens in a fecally impaired mixed-use watershed. The results indicate that fecal coliforms have improved substantially in the watershed since its listing as a 303(d) impaired stream in 2002 and are now near United States recreational water criterion standards. However, waterborne pathogens are still prevalent in the watershed, as viruses (bocavirus, hepatitis E and A viruses, norovirus, and enterovirus G), bacteria (Campylobacter spp., Clostridium spp., enterohemorrhagic and enterotoxigenic Escherichia coli, uropathogenic E. coli, Enterococcus faecalis, Helicobacter spp., Salmonella spp., and Vibrio spp.), and eukaryotes (Acanthamoeba spp., Entamoeba histolytica, and Naegleria fowleri) were detected. A comparison of the stream microbial ecology with that of sewage, cattle, and swine fecal samples revealed that human sources of fecal contamination dominate in the watershed. The methodology presented is applicable to a wide range of impaired streams for the identification of human health risk due to waterborne pathogens and for the identification of areas for remediation efforts.IMPORTANCE The direct detection of waterborne pathogens in water overcomes many of the limitations of the fecal indicator paradigm. Furthermore, the identification of the source of fecal impairment aids in identifying areas for remediation efforts. Multitarget gene microarrays are shown to simultaneously identify waterborne pathogens and aid in determining the sources of impairment, enabling further focused investigations. This study shows the use of this methodology in a historically impaired watershed in which total maximum daily load reductions have been successfully implemented to reduce risk. The results suggest that while the fecal indicators have been reduced more than 96% and are nearing recreational water criterion levels, pathogens are still detectable in the watershed. Microbial source tracking results show that additional remediation efforts are needed to reduce the impact of human sewage in the watershed.

A human fecal contamination score for ranking recreational sites using the HF183/BacR287 quantitative real-time PCR method

Human fecal pollution of recreational waters remains a public health concern worldwide. As a result, there is a growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for water quality research and management. However, there are currently no standardized approaches for field implementation and interpretation of qPCR data. In this study, a standardized HF183/BacR287 qPCR method was combined with a water sampling strategy and a novel Bayesian weighted average approach to establish a human fecal contamination score (HFS) that can be used to prioritize sampling sites for remediation based on measured human waste levels. The HFS was then used to investigate 975 study design scenarios utilizing different combinations of sites with varying sampling intensities (daily to once per week) and number of qPCR replicates per sample (2-14 replicates). Findings demonstrate that site prioritization with HFS is feasible and that both sampling intensity and number of qPCR replicates influence reliability of HFS estimates. The novel data analysis strategy presented here provides a prescribed approach for the implementation and interpretation of human-associated HF183/BacR287 qPCR data with the goal of site prioritization based on human fecal pollution levels. In addition, information is provided for future users to customize study designs for optimal HFS performance.

Limited influence of primary treated sewage waters on bacterial abundance, production and community composition in coastal seawaters

The response of bacteria in terms of abundance, production and community structure to changes induced by the discharge of primary treated sewage waters was investigated combining microbiological, chemical and molecular tools. The primary treatment did not affect substantially the bacterial community structure in wastewaters and did not reduce the concentrations of fecal indicators. The spatial distribution of the sewage plume was governed by vertical stratification and currents. Bacterial abundance and production in the sea receiving waste waters depended predominantly on environmental conditions. In the waters with the highest concentration of fecal pollution indicators the bacterial community was characterized by allochthonous bacteria belonging to Epsilonproteobacteria, Firmicutes, Gammaproteobacteria and Bacteroidetes. The latter two taxa were also present in unpolluted waters but had a different structure, typical for oligotrophic environments. Although the impact of primary treated sewage waters was limited, a sanitary risk persisted due to the relevant presence of potentially pathogenic bacteria.

Bacterial community structure transformed after thermophilically composting human waste in Haiti

Recycling human waste for beneficial use has been practiced for millennia. Aerobic (thermophilic) composting of sewage sludge has been shown to reduce populations of opportunistically pathogenic bacteria and to inactivate both Ascaris eggs and culturable Escherichia coli in raw waste, but there is still a question about the fate of most fecal bacteria when raw material is composted directly. This study undertook a comprehensive microbial community analysis of composting material at various stages collected over 6 months at two composting facilities in Haiti. The fecal microbiota signal was monitored using a high-density DNA microarray (PhyloChip). Thermophilic composting altered the bacterial community structure of the starting material. Typical fecal bacteria classified in the following groups were present in at least half the starting material samples, yet were reduced below detection in finished compost: Prevotella and Erysipelotrichaceae (100% reduction of initial presence), Ruminococcaceae (98–99%), Lachnospiraceae (83–94%, primarily unclassified taxa remained), Escherichia and Shigella (100%). Opportunistic pathogens were reduced below the level of detection in the final product with the exception of Clostridium tetani, which could have survived in a spore state or been reintroduced late in the outdoor maturation process. Conversely, thermotolerant or thermophilic Actinomycetes and Firmicutes (e.g., Thermobifida, Bacillus, Geobacillus) typically found in compost increased substantially during the thermophilic stage. This community DNA-based assessment of the fate of human fecal microbiota during thermophilic composting will help optimize this process as a sanitation solution in areas where infrastructure and resources are limited.

Microbial source tracking in impaired watersheds using PhyloChip and machine-learning classification

Sources of fecal indicator bacteria are difficult to identify in watersheds that are impacted by a variety of non-point sources. We developed a molecular source tracking test using the PhyloChip microarray that detects and distinguishes fecal bacteria from humans, birds, ruminants, horses, pigs and dogs with a single test. The multiplexed assay targets 9001 different 25-mer fragments of 16S rRNA genes that are common to the bacterial community of each source type. Both random forests and SourceTracker were tested as discrimination tools, with SourceTracker classification producing superior specificity and sensitivity for all source types. Validation with 12 different mammalian sources in mixtures found 100% correct identification of the dominant source and 84-100% specificity. The test was applied to identify sources of fecal indicator bacteria in the Russian River watershed in California. We found widespread contamination by human sources during the wet season proximal to settlements with antiquated septic infrastructure and during the dry season at beaches during intense recreational activity. The test was more sensitive than common fecal indicator tests that failed to identify potential risks at these sites. Conversely, upstream beaches and numerous creeks with less reliance on onsite wastewater treatment contained no fecal signal from humans or other animals; however these waters did contain high counts of fecal indicator bacteria after rain. Microbial community analysis revealed that increased E. coli and enterococci at these locations did not co-occur with common fecal bacteria, but rather co-varied with copiotrophic bacteria that are common in freshwaters with high nutrient and carbon loading, suggesting runoff likely promoted the growth of environmental strains of E. coli and enterococci. These results indicate that machine-learning classification of PhyloChip microarray data can outperform conventional single marker tests that are used to assess health risks, and is an effective tool for distinguishing numerous fecal and environmental sources of pathogen indicators.

Bacterial community composition and structure in an Urban River impacted by different pollutant sources

Microbial communities in terrestrial fresh water are diverse and dynamic in composition due to different environmental factors. The goal of this study was to undertake a comprehensive analysis of bacterial composition along different rivers and creeks and correlate these to land-use practices and pollutant sources. Here we used 454 pyrosequencing to determine the total bacterial community composition, and bacterial communities that are potentially of fecal origin, and of relevance to water quality assessment. The results were analyzed using UniFrac coupled with principal coordinate analysis (PCoA) to compare diversity, abundance, and community composition. Detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) were used to correlate bacterial composition in streams and creeks to different environmental parameters impacting bacterial communities in the sediment and surface water within the watershed. Bacteria were dominated by the phyla Proteobacteria, Bacteroidetes, Acidobacteria, and Actinobacteria, with Bacteroidetes significantly (P<0.001) higher in all water samples than sediment, where as Acidobacteria and Actinobacteria where significantly higher (P<0.05) in all the sediment samples than surface water. Overall results, using the β diversity measures, coupled with PCoA and DCA showed that bacterial composition in sediment and surface water was significantly different (P<0.001). Also, there were differences in bacterial community composition between agricultural runoff and urban runoff based on parsimony tests using 454 pyrosequencing data. Fecal indicator bacteria in surface water along different creeks and channels were significantly correlated with pH (P<0.01), NO2 (P<0.03), and NH4N (P<0.005); and in the sediment with NO3 (P<0.015). Our results suggest that microbial community compositions were influenced by several environmental factors, and pH, NO2, and NH4 were the major environmental factors driving FIB in surface water based on CCA analysis, while NO3 was the only factor in sediment.

Contamination with bacterial zoonotic pathogen genes in U.S. streams influenced by varying types of animal agriculture

Animal waste, stream water, and streambed sediment from 19 small (<32km(2)) watersheds in 12U.S. states having either no major animal agriculture (control, n=4), or predominantly beef (n=4), dairy (n=3), swine (n=5), or poultry (n=3) were tested for: 1) cholesterol, coprostanol, estrone, and fecal indicator bacteria (FIB) concentrations, and 2) shiga-toxin producing and enterotoxigenic Escherichia coli, Salmonella, Campylobacter, and pathogenic and vancomycin-resistant enterococci by polymerase chain reaction (PCR) on enrichments, and/or direct quantitative PCR. Pathogen genes were most frequently detected in dairy wastes, followed by beef, swine and poultry wastes in that order; there was only one detection of an animal-source-specific pathogen gene (stx1) in any water or sediment sample in any control watershed. Post-rainfall pathogen gene numbers in stream water were significantly correlated with FIB, cholesterol and coprostanol concentrations, and were most highly correlated in dairy watershed samples collected from 3 different states. Although collected across multiple states and ecoregions, animal-waste gene profiles were distinctive via discriminant analysis. Stream water gene profiles could also be discriminated by the watershed animal type. Although pathogen genes were not abundant in stream water or streambed samples, PCR on enrichments indicated that many genes were from viable organisms, including several (shiga-toxin producing or enterotoxigenic E. coli, Salmonella, vancomycin-resistant enterococci) that could potentially affect either human or animal health. Pathogen gene numbers and types in stream water samples were influenced most by animal type, by local factors such as whether animals had stream access, and by the amount of local rainfall, and not by studied watershed soil or physical characteristics. Our results indicated that stream water in small agricultural U.S. watersheds was susceptible to pathogen gene inputs under typical agricultural practices and environmental conditions. Pathogen gene profiles may offer the potential to address both source of, and risks associated with, fecal pollution.

Epidemiologic evaluation of multiple alternate microbial water quality monitoring indicators at three California beaches

INTRODUCTION

Advances in molecular methods provide new opportunities for directly measuring pathogens or host-associated markers of fecal pollution instead of relying on fecal indicator bacteria (FIB) alone for beach water quality monitoring. Adoption of new indicators depends on identifying relationships between either the presence or concentration of the indicators and illness among swimmers. Here we present results from three epidemiologic studies in which a broad range of bacterial and viral indicators of fecal contamination were measured simultaneously by either culture or molecular methods along with Enterococcus to assess whether they provide better health risk prediction than current microbial indicators of recreational water quality.

METHODS

We conducted prospective cohort studies at three California beaches -- Avalon Bay (Avalon), Doheny State Beach (Doheny), Surfrider State Beach (Malibu) -- during the summers of 2007, 2008 and 2009. The studies enrolled 10,785 swimmers across the beaches and recorded each swimmer's water exposure. Water and sand samples were collected several times per day at multiple locations at each beach and analyzed for up to 41 target indicators using 67 different methodologies. Interviewers contacted participants by phone 10-14 days later and recorded symptoms of gastrointestinal illness occurring after their beach visit. Regression models were used to evaluate the association between water quality indicators and gastrointestinal illness among swimmers at each beach.

RESULTS

F+ coliphage (measured using EPA Method 1602) exhibited a stronger association with GI illness than did EPA Method 1600 at the two beaches where it was measured, while a molecular method, F+ RNA Coliphage Genotype II, was the only indicator significantly associated with GI illness at Malibu. MRSA, a known pathogen, had the strongest association with GI illness of any microbe measured at Avalon. There were two methods targeting human-associated fecal anaerobic bacteria that were more strongly associated with GI illness than EPA Method 1600, but only at Avalon. No indicator combinations consistently had a higher odds ratio than EPA Method 1600, but one composite indicator, based on the number of pathogens detected at a beach, was significantly associated with gastrointestinal illness at both Avalon and Doheny when freshwater flow was high.

DISCUSSION

While EPA Method1600 performed adequately at two beaches based on its consistency of association with gastrointestinal illness and the precision of its estimated associations, F+ coliphage measured by EPA Method 1602 had a stronger association with GI illness under high risk conditions at the two beaches where it was measured. One indicator, F+ Coliphage Genotype II was the only indicator significantly associated with GI illness at Malibu. Several indicators, particularly those targeting human associated bacteria, exhibited relationships with GI illness that were equal to or greater than that of EPA Method 1600 at Avalon, which has a focused human fecal source. Our results suggest that site-specific conditions at each beach determine which indicator or indicators best predict GI illness.

Data Acceptance Criteria for Standardized Human-Associated Fecal Source Identification Quantitative Real-Time PCR Methods

There is growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for water quality management. The transition from a research tool to a standardized protocol requires a high degree of confidence in data quality across laboratories. Data quality is typically determined through a series of specifications that ensure good experimental practice and the absence of bias in the results due to DNA isolation and amplification interferences. However, there is currently a lack of consensus on how best to evaluate and interpret human fecal source identification qPCR experiments. This is, in part, due to the lack of standardized protocols and information on interlaboratory variability under conditions for data acceptance. The aim of this study is to provide users and reviewers with a complete series of conditions for data acceptance derived from a multiple laboratory data set using standardized procedures. To establish these benchmarks, data from HF183/BacR287 and HumM2 human-associated qPCR methods were generated across 14 laboratories. Each laboratory followed a standardized protocol utilizing the same lot of reference DNA materials, DNA isolation kits, amplification reagents, and test samples to generate comparable data. After removal of outliers, a nested analysis of variance (ANOVA) was used to establish proficiency metrics that include lab-to-lab, replicate testing within a lab, and random error for amplification inhibition and sample processing controls. Other data acceptance measurements included extraneous DNA contamination assessments (no-template and extraction blank controls) and calibration model performance (correlation coefficient, amplification efficiency, and lower limit of quantification). To demonstrate the implementation of the proposed standardized protocols and data acceptance criteria, comparable data from two additional laboratories were reviewed. The data acceptance criteria proposed in this study should help scientists, managers, reviewers, and the public evaluate the technical quality of future findings against an established benchmark.

Ultrafiltration and Microarray for Detection of Microbial Source Tracking Marker and Pathogen Genes in Riverine and Marine Systems

Pathogen identification and microbial source tracking (MST) to identify sources of fecal pollution improve evaluation of water quality. They contribute to improved assessment of human health risks and remediation of pollution sources. An MST microarray was used to simultaneously detect genes for multiple pathogens and indicators of fecal pollution in freshwater, marine water, sewage-contaminated freshwater and marine water, and treated wastewater. Dead-end ultrafiltration (DEUF) was used to concentrate organisms from water samples, yielding a recovery efficiency of >95% for Escherichia coli and human polyomavirus. Whole-genome amplification (WGA) increased gene copies from ultrafiltered samples and increased the sensitivity of the microarray. Viruses (adenovirus, bocavirus, hepatitis A virus, and human polyomaviruses) were detected in sewage-contaminated samples. Pathogens such as Legionella pneumophila, Shigella flexneri, and Campylobacter fetus were detected along with genes conferring resistance to aminoglycosides, beta-lactams, and tetracycline. Nonmetric dimensional analysis of MST marker genes grouped sewage-spiked freshwater and marine samples with sewage and apart from other fecal sources. The sensitivity (percent true positives) of the microarray probes for gene targets anticipated in sewage was 51 to 57% and was lower than the specificity (percent true negatives; 79 to 81%). A linear relationship between gene copies determined by quantitative PCR and microarray fluorescence was found, indicating the semiquantitative nature of the MST microarray. These results indicate that ultrafiltration coupled with WGA provides sufficient nucleic acids for detection of viruses, bacteria, protozoa, and antibiotic resistance genes by the microarray in applications ranging from beach monitoring to risk assessment.

Assessment of Genetic Markers for Tracking the Sources of Human Wastewater Associated Escherichia coli in Environmental Waters

In this study, we have evaluated the performance characteristics (host-specificity and -sensitivity) of four human wastewater-associated Escherichia coli (E. coli) genetic markers (H8, H12, H14, and H24) in 10 target (human) and nontarget (cat, cattle, deer, dog, emu, goat, horse, kangaroo, and possum) host groups in Southeast Queensland, Australia. The overall host-sensitivity values of the tested markers in human wastewater samples were 1.0 (all human wastewater samples contained the E. coli genetic markers). The overall host-specificity values of these markers to differentiate between human and animal host groups were 0.94, 0.85, 0.72, and 0.57 for H8, H12, H24, and H14, respectively. Based on the higher host-specificity values, H8 and H12 markers were chosen for a validation environmental study. The prevalence of the H8 and H12 markers was determined among human wastewater E. coli isolates collected from a wastewater treatment plant (WWTP). Among the 97 isolates tested, 44 (45%) and 14 (14%) were positive for the H8 and H12 markers, respectively. A total of 307 E. coli isolates were tested from environmental water samples collected in Brisbane, of which 7% and 20% were also positive for the H8 and H12 markers, respectively. Based on our results, we recommend that these markers could be useful when it is important to identify the source(s) of E. coli (whether they originated from human wastewater or not) in environmental waters.

Toolbox Approaches Using Molecular Markers and 16S rRNA Gene Amplicon Data Sets for Identification of Fecal Pollution in Surface Water

In this study, host-associated molecular markers and bacterial 16S rRNA gene community analysis using high-throughput sequencing were used to identify the sources of fecal pollution in environmental waters in Brisbane, Australia. A total of 92 fecal and composite wastewater samples were collected from different host groups (cat, cattle, dog, horse, human, and kangaroo), and 18 water samples were collected from six sites (BR1 to BR6) along the Brisbane River in Queensland, Australia. Bacterial communities in the fecal, wastewater, and river water samples were sequenced. Water samples were also tested for the presence of bird-associated (GFD), cattle-associated (CowM3), horse-associated, and human-associated (HF183) molecular markers, to provide multiple lines of evidence regarding the possible presence of fecal pollution associated with specific hosts. Among the 18 water samples tested, 83%, 33%, 17%, and 17% were real-time PCR positive for the GFD, HF183, CowM3, and horse markers, respectively. Among the potential sources of fecal pollution in water samples from the river, DNA sequencing tended to show relatively small contributions from wastewater treatment plants (up to 13% of sequence reads). Contributions from other animal sources were rarely detected and were very small (<3% of sequence reads). Source contributions determined via sequence analysis versus detection of molecular markers showed variable agreement. A lack of relationships among fecal indicator bacteria, host-associated molecular markers, and 16S rRNA gene community analysis data was also observed. Nonetheless, we show that bacterial community and host-associated molecular marker analyses can be combined to identify potential sources of fecal pollution in an urban river. This study is a proof of concept, and based on the results, we recommend using bacterial community analysis (where possible) along with PCR detection or quantification of host-associated molecular markers to provide information on the sources of fecal pollution in waterways.

A novel microbial source tracking microarray for pathogen detection and fecal source identification in environmental systems

Pathogen detection and the identification of fecal contamination sources are challenging in environmental waters. Factors including pathogen diversity and ubiquity of fecal indicator bacteria hamper risk assessment and remediation of contamination sources. A custom microarray targeting pathogens (viruses, bacteria, protozoa), microbial source tracking (MST) markers, and antibiotic resistance genes was tested against DNA obtained from whole genome amplification (WGA) of RNA and DNA from sewage and animal (avian, cattle, poultry, and swine) feces. Perfect and mismatch probes established the specificity of the microarray in sewage, and fluorescence decrease of positive probes over a 1:10 dilution series demonstrated semiquantitative measurement. Pathogens, including norovirus, Campylobacter fetus, Helicobacter pylori, Salmonella enterica, and Giardia lamblia were detected in sewage, as well as MST markers and resistance genes to aminoglycosides, beta-lactams, and tetracycline. Sensitivity (percentage true positives) of MST results in sewage and animal waste samples (21-33%) was lower than specificity (83-90%, percentage of true negatives). Next generation DNA sequencing revealed two dominant bacterial families that were common to all sample types: Ruminococcaceae and Lachnospiraceae. Five dominant phyla and 15 dominant families comprised 97% and 74%, respectively, of sequences from all fecal sources. Phyla and families not represented on the microarray are possible candidates for inclusion in subsequent array designs.

Temporal stability of the microbial community in sewage-polluted seawater exposed to natural sunlight cycles and marine microbiota

Billions of gallons of untreated wastewater enter the coastal ocean each year. Once sewage microorganisms are in the marine environment, they are exposed to environmental stressors, such as sunlight and predation. Previous research has investigated the fate of individual sewage microorganisms in seawater but not the entire sewage microbial community. The present study used next-generation sequencing (NGS) to examine how the microbial community in sewage-impacted seawater changes over 48 h when exposed to natural sunlight cycles and marine microbiota. We compared the results from microcosms composed of unfiltered seawater (containing naturally occurring marine microbiota) and filtered seawater (containing no marine microbiota) to investigate the effect of marine microbiota. We also compared the results from microcosms that were exposed to natural sunlight cycles with those from microcosms kept in the dark to investigate the effect of sunlight. The microbial community composition and the relative abundance of operational taxonomic units (OTUs) changed over 48 h in all microcosms. Exposure to sunlight had a significant effect on both community composition and OTU abundance. The effect of marine microbiota, however, was minimal. The proportion of sewage-derived microorganisms present in the microcosms decreased rapidly within 48 h, and the decrease was the most pronounced in the presence of both sunlight and marine microbiota, where the proportion decreased from 85% to 3% of the total microbial community. The results from this study demonstrate the strong effect that sunlight has on microbial community composition, as measured by NGS, and the importance of considering temporal effects in future applications of NGS to identify microbial pollution sources.

Blautia and Prevotella sequences distinguish human and animal fecal pollution in Brazil surface waters

Untreated sewage discharges and limited agricultural manure management practices contribute to fecal pollution in rural Brazilian waterways. Most microbial source tracking studies have focused on Bacteroidales, and few have tested host-specific indicators in underdeveloped regions. Sequencing of sewage and human and animal feces with Illumina HiSeq revealed Prevotellaceae as the most abundant family in humans, with Lachnospiraceae and Ruminococcaceae also comprising a large proportion of the microbiome. These same families were also dominant in animals. Bacteroides, the genus containing the most commonly utilized human-specific marker in the United States was present in very low abundance. We used oligotyping to identify Prevotella and Blautia sequences that can distinguish human fecal contamination. Thirty-five of 61 Blautia oligotypes and 13 of 108 Prevotella oligotypes in humans were host-specific or highly abundant (i.e. host-preferred) compared to pig, dog, horse and cow sources. Certain human Prevotella and Blautia oligotypes increased more than an order of magnitude along a polluted river transect in rural Brazil, but traditional fecal indicator levels followed a steady or even decreasing trend. While both Prevotella and Blautia oligotypes distinguished human and animal fecal pollution in Brazil surface waters, Blautia appears to contain more discriminatory and globally applicable markers for tracking sources of fecal pollution.

Discovering new indicators of fecal pollution

Fecal pollution indicators are essential to identify and remediate contamination sources and protect public health. Historically, easily cultured facultative anaerobes such as fecal coliforms, Escherichia coli, or enterococci have been used but these indicators generally provide no information as to their source. More recently, molecular methods have targeted fecal anaerobes, which are much more abundant in humans and other mammals, and some strains appear to be associated with particular host sources. Next-generation sequencing and microbiome studies have created an unprecedented inventory of microbial communities associated with fecal sources, allowing reexamination of which taxonomic groups are best suited as informative indicators. The use of new computational methods, such as oligotyping coupled with well-established machine learning approaches, is providing new insights into patterns of host association. In this review we examine the basis for host-specificity and the rationale for using 16S rRNA gene targets for alternative indicators and highlight two taxonomic groups, Bacteroidales and Lachnospiraceae, which are rich in host-specific bacterial organisms. Finally, we discuss considerations for using alternative indicators for water quality assessments with a particular focus on detecting human sewage sources of contamination.

Characterization of fecal concentrations in human and other animal sources by physical, culture-based, and quantitative real-time PCR methods

The characteristics of fecal sources, and the ways in which they are measured, can profoundly influence the interpretation of which sources are contaminating a body of water. Although feces from various hosts are known to differ in mass and composition, it is not well understood how those differences compare across fecal sources and how differences depend on characterization methods. This study investigated how nine different fecal characterization methods provide different measures of fecal concentration in water, and how results varied across twelve different fecal pollution sources. Sources investigated included chicken, cow, deer, dog, goose, gull, horse, human, pig, pigeon, septage and sewage. A composite fecal slurry was prepared for each source by mixing feces from 6 to 22 individual samples with artificial freshwater. Fecal concentrations were estimated by physical (wet fecal mass added and total DNA mass extracted), culture-based (Escherichia coli and enterococci by membrane filtration and defined substrate), and quantitative real-time PCR (Bacteroidales, E. coli, and enterococci) characterization methods. The characteristics of each composite fecal slurry and the relationships between physical, culture-based and qPCR-based characteristics varied within and among different fecal sources. An in silico exercise was performed to assess how different characterization methods can impact identification of the dominant fecal pollution source in a mixed source sample. A comparison of simulated 10:90 mixtures based on enterococci by defined substrate predicted a source reversal in 27% of all possible combinations, while mixtures based on E. coli membrane filtration resulted in a reversal 29% of the time. This potential for disagreement in minor or dominant source identification based on different methods of measurement represents an important challenge for water quality managers and researchers.

Bacteroidales terminal restriction fragment length polymorphism (TRFLP) for fecal source differentiation in comparison to and in combination with universal bacteria TRFLP

Terminal restriction fragment length polymorphism (TRFLP) is an attractive community analysis method for microbial source tracking (MST) because it is accessible, relatively inexpensive, and can discern multiple fecal sources simultaneously. A new Bacteroidales TRFLP (Bac-TRFLP) method was developed and its source identification performance was evaluated by itself, in comparison to, and in combination with an existing universal bacterial TRFLP method in two laboratories. Sixty-four blind samples from 12 fecal sources (sewage, septage, human, dog, horse, cow, deer, pig, chicken, goose, pigeon, and gull) were used for evaluation. Bac- and Univ-TRFLP exhibited similarly high overall correct identification (>88% and >89%, respectively), excellent specificity regardless of fecal sources, variable sensitivity depending on the source, and stable performance across two laboratories. Compared to Univ-TRFLP, Bac-TRFLP had better sensitivity and specificity with horse, cow, and pig fecal sources but was not suited for certain avian sources such as goose, gull, and pigeon. Combining the general and more targeted TRFLP methods (Univ&Bac-TRFLP) achieved higher overall correct identification (>92%), higher sensitivity and specificity metrics, and higher reproducibility between laboratories. Our results suggest that the Bac-TRFLP and Univ&Bac-TRFLP methods are promising additions to the MST toolbox and warrant further evaluation and utilization in field MST applications.

Evaluation of molecular community analysis methods for discerning fecal sources and human waste

Molecular microbial community analyses provide information on thousands of microorganisms simultaneously, and integrate biotic and abiotic perturbations caused by fecal contamination entering water bodies. A few studies have explored community methods as emerging approaches for microbial source tracking (MST), however, an evaluation of the current state of this approach is lacking. Here, we utilized three types of community-based methods with 64 blind, single- or dual-source, challenge samples generated from 12 sources, including: humans (feces), sewage, septage, dogs, pigs, deer, horses, cows, chickens, gulls, pigeons, and geese. Each source was a composite from multiple donors from four representative geographical regions in California. Methods evaluated included terminal restriction fragment polymorphism (TRFLP), phylogenetic microarray (PhyloChip), and next generation (Illumina) sequencing. These methods correctly identified dominant (or sole) sources in over 90% of the challenge samples, and exhibited excellent specificity regardless of source, rarely detecting a source that was not present in the challenge sample. Sensitivity, however, varied with source and community analysis method. All three methods distinguished septage from human feces and sewage, and identified deer and horse with 100% sensitivity and 100% specificity. Method performance improved if the composition of blind dual-source reference samples were defined by DNA contribution of each single source within the mixture, instead of by Enterococcus colony forming units. Data analysis approach also influenced method performance, indicating the need to standardize data interpretation. Overall, results of this study indicate that community analysis methods hold great promise as they may be used to identify any source, and they are particularly useful for sources that currently do not have, and may never have, a source-specific single marker gene.

Performance of forty-one microbial source tracking methods: a twenty-seven lab evaluation study

The last decade has seen development of numerous new microbial source tracking (MST) methodologies, but many of these have been tested in just a few laboratories with a limited number of fecal samples. This method evaluation study examined the specificity and sensitivity of 41 MST methodologies by analyzing data generated in 27 laboratories. MST methodologies that targeted human, cow, ruminant, dog, gull, pig, horse, and sheep were tested against sewage, septage, human, cow, dog, deer, pig, chicken, pigeon, gull, horse, and goose fecal samples. Each laboratory received 64 blind samples containing a single source (singletons) or two sources (doubletons), as well as diluted singleton samples to assess method sensitivity. Laboratories utilized their own protocols when performing the methods and data were deposited in a central database before samples were unblinded. Between one and seven laboratories tested each method. The most sensitive and specific assays, based on an analysis of presence/absence of each marker in target and non-target fecal samples, were HF183 endpoint and HF183SYBR (human), CF193 and Rum2Bac (ruminant), CowM2 and CowM3 (cow), BacCan (dog), Gull2SYBR and LeeSeaGull (gull), PF163 and pigmtDNA (pig), HoF597 (horse), PhyloChip (pig, horse, chicken, deer), Universal 16S TRFLP (deer), and Bacteroidales 16S TRFLP (pig, horse, chicken, deer); all had sensitivity and specificity higher than 80% in all or the majority of laboratories. When the abundance of MST markers in target and non-target fecal samples was examined, some assays that performed well in the binary analysis were found to not be sensitive enough as median concentrations fell below a minimum abundance criterion (set at 50 copies per colony forming units of enterococci) in target fecal samples. Similarly, some assays that cross-reacted with non-target fecal sources in the binary analysis were found to perform well in a quantitative analysis because the cross-reaction occurred at very low levels. Based on a quantitative analysis, the best performing methods were HF183Taqman and BacH (human), Rum2Bac and BacR (ruminant), LeeSeaGull (gull), and Pig2Bac (pig); no cow or dog-specific assay met the quantitative specificity and sensitivity criteria. Some of the best performing assays in the study were run by just one laboratory so further testing of assay portability is needed. While this study evaluated the marker performance in defined samples, further field testing as well as development of frameworks for fecal source allocation and risk assessment are needed.

Comparison of the microbial community structures of untreated wastewaters from different geographic locales

Microbial sewage communities consist of a combination of human fecal microorganisms and nonfecal microorganisms, which may be residents of urban sewer infrastructure or flowthrough originating from gray water or rainwater inputs. Together, these different microorganism sources form an identifiable community structure that may serve as a signature for sewage discharges and as candidates for alternative indicators specific for human fecal pollution. However, the structure and variability of this community across geographic space remains uncharacterized. We used massively parallel 454 pyrosequencing of the V6 region in 16S rRNA genes to profile microbial communities from 13 untreated sewage influent samples collected from a wide range of geographic locations in the United States. We obtained a total of 380,175 high-quality sequences for sequence-based clustering, taxonomic analyses, and profile comparisons. The sewage profile included a discernible core human fecal signature made up of several abundant taxonomic groups within Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria. DNA sequences were also classified into fecal, sewage infrastructure (i.e., nonfecal), and transient groups based on data comparisons with fecal samples. Across all sewage samples, an estimated 12.1% of sequences were fecal in origin, while 81.4% were consistently associated with the sewage infrastructure. The composition of feces-derived operational taxonomic units remained congruent across all sewage samples regardless of geographic locale; however, the sewage infrastructure community composition varied among cities, with city latitude best explaining this variation. Together, these results suggest that untreated sewage microbial communities harbor a core group of fecal bacteria across geographically dispersed wastewater sewage lines and that ambient water quality indicators targeting these select core microorganisms may perform well across the United States.