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

Assessing the Occurrence of Host-Specific Faecal Indicator Markers in Water Systems as a Function of Water, Sanitation and Hygiene Practices: A Case Study in Rural Communities of Vhembe District Municipality, South Africa

In settings where humans and animals closely coexist, the introduction of faecal material into unprotected water sources significantly increases the risk of contracting diarrhoeal and zoonotic waterborne diseases. The data were gathered from a survey conducted through interviews at randomly sampled villages; additionally, water samples were collected in randomly selected households and their associated feeder catchments. Molecular techniques were used, specifically qPCR, to run host-specific Bacteroides microbial source tracking (MST) assays for human, cattle, pig, chicken and dog faecal contamination. Unexpectedly, the qPCR assays revealed dogs to be the most prevalent (40.65%) depositor of faecal matter in unprotected surface water, followed by humans (40.63%); this finding was contradictory to survey findings indicating cattle as the leading source. At the household level, dogs (16.67%) and chickens (15.28%) played prominent roles, as was expected. Reflecting on some of the basic daily practices in households, nearly 89.00% of the population was found to store water due to erratic supply, in contrast to 93.23% using an improved water source. Additionally, a significant association was found between water, sanitation and hygiene (WASH) variables and the occurrence of MST markers after performing a bivariate linear regression. However, the inconsistency between the MST results and household surveys suggests pervasive sanitation issues, even in households without domesticated animals.

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.

Molecular microbiological approaches reduce ambiguity about the sources of faecal pollution and identify microbial hazards within an urbanised coastal environment

Urbanised beaches are regularly impacted by faecal pollution, but management actions to resolve the causes of contamination are often obfuscated by the inability of standard Faecal Indicator Bacteria (FIB) analyses to discriminate sources of faecal material or detect other microbial hazards, including antibiotic resistance genes (ARGs). We aimed to determine the causes, spatial extent, and point sources of faecal contamination within Rose Bay, a highly urbanised beach within Sydney, Australia's largest city, using molecular microbiological approaches. Sampling was performed across a network of transects originating at 9 stormwater drains located on Rose Bay beach over the course of a significant (67.5 mm) rainfall event, whereby samples were taken 6 days prior to any rain, on the day of initial rainfall (3.8 mm), three days later after 43 mm of rain and then four days after any rain. Quantitative PCR (qPCR) was used to target marker genes from bacteria (i.e., Lachnospiraceae and Bacteroides) that have been demonstrated to be specific to human faeces (sewage), along with gene sequences from Heliobacter and Bacteriodes that are specific to bird and dog faeces respectively, and ARGs (sulI, tetA, qnrS, dfrA1 and vanB). 16S rRNA gene amplicon sequencing was also used to discriminate microbial signatures of faecal contamination. Prior to the rain event, low FIB levels (mean: 2.4 CFU/100 ml) were accompanied by generally low levels of the human and animal faecal markers, with the exception of one transect, potentially indicative of a dry weather sewage leak. Following 43 mm of rain, levels of both human faecal markers increased significantly in stormwater drain and seawater samples, with highest levels of these markers pinpointing several stormwater drains as sources of sewage contamination. During this time, sewage contamination was observed up to 1000 m from shore and was significantly and positively correlated with often highly elevated levels of the ARGs dfrA1, qnrS, sulI and vanB. Significantly elevated levels of the dog faecal marker in stormwater drains at this time also indicated that rainfall led to increased input of dog faecal material from the surrounding catchment. Using 16S rRNA gene amplicon sequencing, several indicator taxa for stormwater contamination such as Arcobacter spp. and Comamonadaceae spp. were identified and the Bayesian SourceTracker tool was used to model the relative impact of specific stormwater drains on the surrounding environment, revealing a heterogeneous contribution of discrete stormwater drains during different periods of the rainfall event, with the microbial signature of one particular drain contributing up to 50% of bacterial community in the seawater directly adjacent. By applying a suite of molecular microbiological approaches, we have precisely pinpointed the causes and point-sources of faecal contamination and other associated microbiological hazards (e.g., ARGs) at an urbanised beach, which has helped to identify the most suitable locations for targeted management of water quality at the beach.

Microbiomes in drinking water treatment and distribution: A meta-analysis from source to tap

A meta-analysis of existing and available Illumina 16S rRNA datasets from drinking water source, treatment and drinking water distribution systems (DWDS) were collated to compare changes in abundance and diversity throughout. Samples from bulk water and biofilm were used to assess principles governing microbial community assembly and the value of amplicon sequencing to water utilities. Individual phyla relationships were explored to identify competitive or synergistic factors governing DWDS microbiomes. The relative importance of stochasticity in the assembly of the DWDS microbiome was considered to identify the significance of source and treatment in determining communities in DWDS. Treatment of water significantly reduces overall species abundance and richness, with chlorination of water providing the most impact to individual taxa relationships. The assembly of microbial communities in the bulk water of the source, primary treatment process and DWDS is governed by more stochastic processes, as is the DWDS biofilm. DWDS biofilm is significantly different from bulk water in terms of local contribution to beta diversity, type and abundance of taxa present. Water immediately post chlorination has a more deterministic microbial assembly, highlighting the significance of this process in changing the microbiome, although elevated levels of stochasticity in DWDS samples suggest that this may not be the case at customer taps. 16S rRNA sequencing is becoming more routine, and may have several uses for water utilities, including: detection and risk assessment of potential pathogens such as those within the genera of Legionella and Mycobacterium; assessing the risk of nitrification in DWDS; providing improved indicators of process performance and monitoring for significant changes in the microbial community to detect contamination. Combining this with quantitative methods like flow cytometry will allow a greater depth of understanding of the DWDS microbiome.

Locating illicit discharges in storm sewers in urban areas using multi-parameter source tracking: Field validation of a toolbox composite index to prioritize high risk areas

In urban areas served by separate sewerage systems, illicit connections to the storm drain system from residences or commercial establishments are frequent whether these misconnections were made accidentally or deliberately. As a result, untreated and contaminated wastewater enters into storm sewers leading to pollution of receiving waters and non-compliance with water quality standards. Typical procedures for detecting illicit connections to the storm sewer system are time consuming and expensive, especially in a highly urbanised area. In this study, we investigated the use of human wastewater micropollutants WWMPs (caffeine, theophylline, and carbamazepine) and advanced DNA molecular markers (human specific Bacteroides HF183 and mitochondrial DNA) as anthropogenic tracers in order to assist identifying potential cross connections. Water samples from storm outfalls and storm sewer pipes in three urban subcatchments were collected in dry weather from 2013 to 2018. All samples contained various concentrations of these markers especially HF183, caffeine and theophylline, suggesting that the storm pipe system studied is widely contaminated by sanitary sewers. None of the traditional indicators or markers tested is sufficient alone to determine the origin of fecal pollution. In a highly urbanised area, the combination of at least three specific human markers was needed in order to locate the residential section with likely misconnections. The human specific Bacteroides HF183, and theophylline appeared to be the most effective markers (along with E. coli) of crossconnections, whereas carbamazepine can provide an indication of contamination through sanitary sewer exfiltration. A composite sewer cross-connection index was developed, and eight misconnected houses were identified and corrected. The index approach enables the reduction of false positives that could lead to expensive interventions to identify cross-connected households. The results show the multiparameter source tracking toolbox as an effective method to identify sewer cross connections for sustainable storm water management.

Human Fecal Contamination Corresponds to Changes in the Freshwater Bacterial Communities of a Large River Basin

Microbial water quality is generally monitored by culturable fecal indicator bacteria (FIB), which are intended to signal human health risk due to fecal pollution. However, FIB have limited utility in most urbanized watersheds as they do not discriminate among fecal pollution sources, tend to make up a small fraction of the total microbial community, and do not inform on pollution impacts on the native ecosystem. To move beyond these limitations, we assessed entire bacterial communities and investigated how bacterial diversity relates to traditional ecological and human health-relevant water quality indicators throughout the Milwaukee River Basin. Samples were collected from 16 sites on 5 days during the summer, including both wet and dry weather events, and were processed by 16S rRNA gene amplicon sequencing. Historical water quality at each sampling location, as opposed to upstream land use, was associated significantly with bacterial community alpha diversity. Source partitioning the sequence data was important for determining water quality relationships. Sewage-associated bacterial sequences were detected in all samples, and the relative abundance of sewage sequences was strongly associated with the human Bacteroides fecal marker. From this relationship, we developed a preliminary threshold for human sewage pollution when using bacterial community sequence data. Certain abundant freshwater bacterial sequences were also associated with human fecal pollution, suggesting their possible utility in water quality monitoring. This study sheds light on how bacterial community analysis can be used to supplement current water quality monitoring techniques to better understand interactions between ecological water quality and human health indicators. IMPORTANCE Surface waters in highly developed mixed-use watersheds are frequently impacted by a wide variety of pollutants, leading to a range of impairments that must be monitored and remediated. With advancing technologies, microbial community sequencing may soon become a feasible method for routine evaluation of the ecological quality and human health risk of a water body. In this study, we partnered with a local citizen science organization to evaluate the utility of microbial community sequencing for identifying pollution sources and ecological impairments in a large mixed-use watershed. We show that changes in microbial community diversity and composition are indicative of both long-term ecological impairments and short-term fecal pollution impacts. By source partitioning the sequence data, we also estimate a threshold target for human sewage pollution, which may be useful as a starting point for future development of sequencing-based water quality monitoring techniques.

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.

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.

The Use of Ribosomal RNA as a Microbial Source Tracking Target Highlights the Assay Host-Specificity Requirement in Water Quality Assessments

For microbial source tracking (MST), the 16S ribosomal RNA genes (rDNA) of host-specific bacteria and mitochondrial DNA (mtDNA) of animal species, known to cause fecal contamination of water, have been commonly used as molecular targets. However, low levels of contamination might remain undetected by using these DNA-based qPCR assays. The high copy numbers of ribosomal RNA (rRNA) could offer a solution for such applications of MST. This study compared the performance of eight MST assays: GenBac3 (general Bacteroidales), HF183 (human), BacCan (dog), Rum-2-Bac (ruminant), Pig-2-Bac (swine), Gull4 (gull), GFD, and Av4143 (birds) between rRNA-based and rDNA-based approaches. Three mtDNA-based approaches were tested: DogND5, SheepCytB, and HorseCytB. A total of 151 animal fecal samples and eight municipal sewage samples from four regions of Finland were collected for the marker evaluation. The usability of these markers was tested by using a total of 95 surface water samples with an unknown pollution load. Overall, the performance (specificity, sensitivity, and accuracy) of mtDNA-based assays was excellent (95–100%), but these markers were very seldom detected from the tested surface water samples. The rRNA template increased the sensitivity of assays in comparison to the rDNA template. All rRNA-based assays (except Av4143) had more than 80% sensitivity. In contrast, only half (HF183, Rum-2-Bac, Pig-2-Bac, and Gull4) of rDNA-based assays reached this value. For markers targeted to bird feces, the use of the rRNA-based assay increased or at least did not change the performance. Regarding specificity, all the assays had >95% specificity with a DNA template, except the BacCan assay (71%). While using the RNA template for the assays, HF183 and BacCan exhibited only a low level of specificity (54 and 55%, respectively). Further, the HF183 assay amplified from multiple non-targeted animal fecal samples with the RNA template and the marker showed cross-amplification with the DNA template as well. This study recommends using the rRNA-based approach for MST assays targeting bird fecal contamination. In the case of mammal-specific MST assays, the use of the rRNA template increases the sensitivity but may reduce the specificity and accuracy of the assay. The finding of increased sensitivity calls for a further need to develop better rRNA-based approaches to reach the required assay performance.

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.

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.

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.

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.

Airborne microbial biodiversity and seasonality in Northern and Southern Sweden

Microorganisms are essential constituents of ecosystems. To improve our understanding of how various factors shape microbial diversity and composition in nature it is important to study how microorganisms vary in space and time. Factors shaping microbial communities in ground level air have been surveyed in a limited number of studies, indicating that geographic location, season and local climate influence the microbial communities. However, few have surveyed more than one location, at high latitude or continuously over more than a year. We surveyed the airborne microbial communities over two full consecutive years in Kiruna, in the Arctic boreal zone, and Ljungbyhed, in the Southern nemoral zone of Sweden, by using a unique collection of archived air filters. We mapped both geographic and seasonal differences in bacterial and fungal communities and evaluated environmental factors that may contribute to these differences and found that location, season and weather influence the airborne communities. Location had stronger influence on the bacterial community composition compared to season, while location and season had equal influence on the fungal community composition. However, the airborne bacterial and fungal diversity showed overall the same trend over the seasons, regardless of location, with a peak during the warmer parts of the year, except for the fungal seasonal trend in Ljungbyhed, which fluctuated more within season. Interestingly, the diversity and evenness of the airborne communities were generally lower in Ljungbyhed. In addition, both bacterial and fungal communities varied significantly within and between locations, where orders like Rhizobiales, Rhodospirillales and Agaricales dominated in Kiruna, whereas Bacillales, Clostridiales and Sordariales dominated in Ljungbyhed. These differences are a likely reflection of the landscape surrounding the sampling sites where the landscape in Ljungbyhed is more homogenous and predominantly characterized by artificial and agricultural surroundings. Our results further indicate that local landscape, as well as seasonal variation, shapes microbial communities in air.

Influences of Ingredients and Bakers on the Bacteria and Fungi in Sourdough Starters and Bread

Sourdough starters are naturally occurring microbial communities in which the environment, ingredients, and bakers are potential sources of microorganisms. The relative importance of these pools remains unknown. Here, bakers from two continents used a standardized recipe and ingredients to make starters that were then baked into breads. We characterized the fungi and bacteria associated with the starters, bakers' hands, and ingredients using 16S and internal transcribed spacer (ITS) rRNA gene amplicon sequencing and then measured dough acidity and bread flavor. Starter communities were much less uniform than expected, and this variation manifested in the flavor of the bread. Starter communities were most similar to those found in flour but shared some species with the bakers' skin. While humans likely contribute microorganisms to the starters, the reverse also appears to be true. This bidirectional exchange of microorganisms between starters and bakers highlights the importance of microbial diversity on bodies and in our environments as it relates to foods.IMPORTANCE Sourdough starters are complex communities of yeast and bacteria which confer characteristic flavor and texture to sourdough bread. The microbes present in starters can be sourced from ingredients or the baking environment and are typically consistent over time. Herein, we show that even when the recipe and ingredients for starter and bread are identical, different bakers around the globe produce highly diverse starters which then alter bread acidity and flavor. Much of the starter microbial community comes from bread flour, but the diversity is also associated with differences in the microbial community on the hands of bakers. These results indicate that bakers may be a source for yeast and bacteria in their breads and/or that bakers' jobs are reflected in their skin microbiome.

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.

Quantifying the contribution of microbial immigration in engineered water systems

Immigration is a process that can influence the assembly of microbial communities in natural and engineered environments. However, it remains challenging to quantitatively evaluate the contribution of this process to the microbial diversity and function in the receiving ecosystems. Currently used methods, i.e., counting shared microbial species, microbial source tracking, and neutral community model, rely on abundance profile to reveal the extent of overlapping between the upstream and downstream communities. Thus, they cannot suggest the quantitative contribution of immigrants to the downstream community function because activities of individual immigrants are not considered after entering the receiving environment. This limitation can be overcome by using an approach that couples a mass balance model with high-throughput DNA sequencing, i.e., ecogenomics-based mass balance. It calculates the net growth rate of individual microbial immigrants and partitions the entire community into active populations that contribute to the community function and inactive ones that carry minimal function. Linking activities of immigrants to their abundance further provides quantification of the contribution from an upstream environment to the downstream community. Considering only active populations can improve the accuracy of identifying key environmental parameters dictating process performance using methods such as machine learning.

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.

A comparative assessment of conventional and molecular methods, including MinION nanopore sequencing, for surveying water quality

Nucleic acid based techniques, such as quantitative PCR (qPCR) and next generation sequencing (NGS), provide new insights into microbial water quality, but considerable uncertainty remains around their correct interpretation. We demonstrate, for different water sources in informal settlements in the Kathmandu Valley, Nepal, significant Spearman rank correlations between conventional and molecular microbiology methods that indicate faecal contamination. At family and genera level, 16S rRNA amplicon sequencing results obtained with the low-cost, portable next generation sequencer MinION from Oxford Nanopore Technologies had significant Spearman rank correlations with Illumina MiSeq sequencing results. However, method validation by amplicon sequencing of a MOCK microbial community revealed the need to ascertain MinION sequencing results for putative pathogens at species level with complementary qPCR assays. Vibrio cholerae hazards were poorly associated with plate count faecal coliforms, but flagged up by the MinION screening method, and confirmed by a qPCR assay. Plate counting methods remain important to assess viability of faecal coliforms in disinfected water sources. We outline a systematic approach for data collection and interpretation of such complementary results. In the Kathmandu Valley, there is high variability of water quality from different sources, including for treated water samples, illustrating the importance of disinfection at the point of use.

A comparative assessment of conventional and molecular methods, including MinION nanopore sequencing, for surveying water quality

Nucleic acid based techniques, such as quantitative PCR (qPCR) and next generation sequencing (NGS), provide new insights into microbial water quality, but considerable uncertainty remains around their correct interpretation. We demonstrate, for different water sources in informal settlements in the Kathmandu Valley, Nepal, significant Spearman rank correlations between conventional and molecular microbiology methods that indicate faecal contamination. At family and genera level, 16S rRNA amplicon sequencing results obtained with the low-cost, portable next generation sequencer MinION from Oxford Nanopore Technologies had significant Spearman rank correlations with Illumina MiSeq sequencing results. However, method validation by amplicon sequencing of a MOCK microbial community revealed the need to ascertain MinION sequencing results for putative pathogens at species level with complementary qPCR assays. Vibrio cholerae hazards were poorly associated with plate count faecal coliforms, but flagged up by the MinION screening method, and confirmed by a qPCR assay. Plate counting methods remain important to assess viability of faecal coliforms in disinfected water sources. We outline a systematic approach for data collection and interpretation of such complementary results. In the Kathmandu Valley, there is high variability of water quality from different sources, including for treated water samples, illustrating the importance of disinfection at the point of use.

Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process

BACKGROUND

Ubiquitous in natural and engineered ecosystems, microbial immigration is one of the mechanisms shaping community assemblage. However, quantifying immigration impact remains challenging especially at individual population level. The activities of immigrants in the receiving community are often inadequately considered, leading to potential bias in identifying the relationship between community composition and environmental parameters.

RESULTS

This study quantified microbial immigration from an upstream full-scale anaerobic reactor to downstream activated sludge reactors. A mass balance was applied to 16S rRNA gene amplicon sequencing data to calculate the net growth rates of individual populations in the activated sludge reactors. Among the 1178 observed operational taxonomic units (OTUs), 582 had a positive growth rate, including all the populations with abundance > 0.1%. These active populations collectively accounted for 99% of the total sequences in activated sludge. The remaining 596 OTUs with a growth rate ≤ 0 were classified as inactive populations. All the abundant populations in the upstream anaerobic reactor were inactive in the activated sludge process, indicating a negligible immigration impact. We used a supervised learning regressor to predict environmental parameters based on community composition and compared the prediction accuracy based on either the entire community or the active populations. Temperature was the most predictable parameter, and the prediction accuracy was improved when only active populations were used to train the regressor.

CONCLUSIONS

Calculating growth rate of individual microbial populations in the downstream system provides an effective approach to determine microbial activity and quantify immigration impact. For the studied biological process, a marginal immigration impact was observed, likely due to the significant differences in the growth environments between the upstream and downstream processes. Excluding inactive populations as a result of immigration further enhanced the prediction of key environmental parameters affecting process performance.

Relationships between chemical and microbial faecal source tracking markers in urban river water and sediments during and post-discharge of human sewage

This study explores the relationships between faecal source tracking (FST) markers (quantitative Polymerase Chain Reaction (qPCR) markers and steroids), microbial indicators, the faecal ageing ratio of atypical colonies/total coliforms (AC/TC) and potential human pathogens (Giardia, Cryptosporidium and Campylobacter). Faecal source PCR markers tested were GenBac3, HumM3, HumBac (HF183-Bac708R); Bifidobacterium adolescentis, wildfowl and canine-associated markers. Sediment and water samples from the Avon River were collected during and post-discharge of untreated human sewage inputs, following a series of earthquakes, which severely damaged the Christchurch sewerage system. Significant, positive Spearman Ranks (r_s) correlations were observed between human-associated qPCR markers and steroid FST markers and Escherichia coli and F-specific RNA bacteriophage (r_s 0.57 to 0.84, p < 0.001) in water samples. These human source indicative FST markers demonstrated that they were also effective predictors of potentially pathogenic protozoa in water (r_s 0.43-0.74, p ≤ 0.002), but correlated less well with Campylobacter. Human-associated qPCR and steroid markers showed significant, substantial agreement between the two FST methods (Cohen's kappa, 0.78, p = 0.023), suggesting that water managers could be confident in the results using either method under these contamination conditions. Low levels of fluorescent whitening agents (FWA) (mean 0.06 μg/L, range 0.01-0.40 μg/L) were observed in water throughout the study, but steroids and FWA appeared to be retained in river sediments, months after continuous sewage discharges had ceased. No relationship was observed between chemical FST markers in sediments and the overlying water, and few correlations in sediment between chemical FST markers and target microorganisms. The low values observed for the faecal ageing ratio, AC/TC in water, were significantly, negatively correlated with increasing pathogen detection. This study provides support for the use of the AC/TC ratio, and qPCR and steroid FST markers as indicators of health risks associated with the discharge of raw human sewage into a freshwater system.

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.

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.

Impact of nutrient addition on diversity and fate of fecal bacteria

Understanding the variations in the microorganisms associated with human fecal pollution in different types of water is necessary to manage water quality and predict human health risks. Using an Illumina sequencing method, we investigated variations in the fecal bacteria originating from fresh human feces and their decay trends in nutrient-supplemented water and natural river water. Nutrient addition contributed to the growth of heterotrophic bacteria like Comamonadaceae, Cytophagaceae, and Sphingobacteriaceae, but led to lower concentrations for Bacteroidaceae, Lachnospiraceae, and Ruminococcaceae. This result suggests that the utilization of nutrients by high-activity bacteria may suppress other bacteria via depletion of the available nutrient resources. As we did not observe proliferation of Bacteroidales, Lactobacillales, Clostridiales, or Ruminococcaceae in either supplemented or river water, we consider these groups suitable for use as indicators to determine the level of fecal pollution. Moreover, we tested the persistence of Bacteroidales markers, including general-Bacteroidales marker GenBac and human-specific Bacteroidales marker qHS601, by quantitative PCR. We observed similar trends in the decay of the Bacteroidales markers GenBac and qHS601 in the nutrient-supplemented water and natural river water, and the high R² values of the GenBac (R²_{nutrient-supplemented} = 0.93, R²_{natural river} = 0.81) and qHS601 (R²_{nutrient-supplemented} = 0.93, R²_{natural river} = 0.91) suggests they are a good fit for the first-order decay model. We also found stronger correlations between the markers and potential pathogenic anaerobes in the different types of water, demonstrating the validity of the use of GenBac and qHS601 from Bacteroidales for the identification of human-associated pollution sources.

Fecal pollution: new trends and challenges in microbial source tracking using next-generation sequencing

In this minireview, we expand upon traditional microbial source tracking (MST) methods by discussing two recently developed, next-generation-sequencing (NGS)-based MST approaches to identify sources of fecal pollution in recreational waters. One method defines operational taxonomic units (OTUs) that are specific to a fecal source, e.g., humans and animals or shared among multiple fecal sources to determine the magnitude and likely source association of fecal pollution. The other method uses SourceTracker, a program using a Bayesian algorithm, to determine which OTUs have contributed to an environmental community based on the composition of microbial communities in multiple fecal sources. Contemporary NGS-based MST tools offer a promising avenue to rapidly characterize fecal source contributions for water monitoring and remediation efforts at a broader and more efficient scale than previous molecular MST methods. However, both NGS methods require optimized sequence processing methodologies (e.g. quality filtering and clustering algorithms) and are influenced by primer selection for amplicon sequencing. Therefore, care must be taken when extrapolating data or combining datasets. Furthermore, traditional limitations of library-dependent MST methods, including differential decay of source material in environmental waters and spatiotemporal variation in source communities, remain to be fully understood. Nevertheless, increasing use of these methods, as well as expanding fecal taxon libraries representative of source communities, will help improve the accuracy of these methods and provide promising tools for future MST investigations.

Fecal contamination in shallow temperate estuarine lagoon: Source of the pollution and environmental factors

In inner coastal waters such as lagoons, which are very turbid and rich in suspended particles, the survival of fecal pollution microorganisms may find favorable environments. In order to better characterize the sources and dynamics of fecal pollution in a strongly turbid environment, in situ observations were made in the Curonian Lagoon. A combination of traditional monitoring and molecular methods were used. To monitor the water quality, the indicator Escherichia coli (EC) was selected as a proxy for fecal contamination. E. coli concentration correlated with environmental parameters as pH, oxygen and turbidity. The main pollution sources are the sewage outlets in the lagoon area, while the pollution coming via rivers did not play a significant role. Still the human associated E. coli consisted only of 0 up to 20% of analyzed isolates, and did not correlate with the E. coli concentrations in the study sites. The role of birds, especially for potentially virulent E. coli may be underestimated in the lagoon.

Quantifying the Relative Contributions of Environmental Sources to the Microbial Community in an Urban Stream under Dry and Wet Weather Conditions

Investigating sources of microbial contamination in urban streams, especially when there are no contributions from combined sewer overflows or sewage effluent discharges, can be challenging. The objectives of this study were to identify the sources of microbes in an urban stream and quantify their relative contributions to the microbial community in the stream under dry and wet weather conditions. A microbial source tracking method relying on the 16S rRNA gene was used to investigate the microbial communities in water samples of an urban stream (i.e., from 11 dry and 6 wet weather events), as well as in streambed sediment, soils, street sweepings, sanitary sewage, an upstream lake, and feces of animals and birds collected between 2013 and 2015. The results showed that the Escherichia coli levels in the stream were significantly higher in wet weather flow than in dry weather flow. The upstream lake contributed approximately 93% of the microbes in dry weather flows. Water discharged from storm drain outfalls was the biggest source of microbes in wet weather flows, with a median contribution of approximately 90% in the rising limb and peak flow and about 75% in the declining limb of storms. Furthermore, about 70 to 75% of the microbes in the storm drain outfall water came from materials washed off from the street surfaces in the watershed. Fecal samples did not appear to contribute substantially to the microbes in environmental samples. The results highlight the significance of street surfaces in contributing microbial loads to urban streams under wet weather conditions.IMPORTANCE Identifying the sources of microbial contamination is important for developing best management practices to protect the water quality of urban streams for recreational uses. This study collected a large number of water samples from an urban stream under both dry and wet weather conditions and provided quantitative information on the relative contributions of various environmental compartments to the overall microbial contamination in the stream under the two weather conditions. The watershed in this study represents urban watersheds where no dominant fecal sources are consistently present. The findings highlight the importance of reducing the direct contribution of microbes from street surfaces in the watershed to urban streams under wet weather conditions. The methods and findings from this study are expected to be useful to stormwater managers and regulatory agencies.

Precipitation influences pathogenic bacteria and antibiotic resistance gene abundance in storm drain outfalls in coastal sub-tropical waters

Stormwater contamination can threaten the health of aquatic ecosystems and human exposed to runoff via nutrient and pathogen influxes. In this study, the concentrations of 11 bacterial pathogens and 47 antibiotic resistance genes (ARGs) were determined by using high-throughput microfluidic qPCR (MFQPCR) in several storm drain outfalls (SDOs) during dry and wet weather in Tampa Bay, Florida, USA. Data generated in this study were also compared with the levels of fecal indicator bacteria (FIB) and sewage-associated molecular markers (i.e., Bacteroides HF183 and crAssphage markers) in same SDOs collected in a recent study (Ahmed et al., 2018). Concentration of FIB, sewage-associated markers, bacterial pathogens and many ARGs in water samples were relatively high and SDOs may be potentially hotspots for microbial contamination in Tampa Bay. Mean concentrations of culturable E. coli and Enterococcus spp. were tenfold higher in wet compared to dry weather. The majority of microbiological contaminants followed this trend. E. coli eaeA, encoding the virulence factor intimin, was correlated with levels of 20 ARGs, and was more frequently detected in wet weather than dry weather samples. The bla_KPC gene associated with carbapenem resistant Enterobacteriaceae and the beta-lactam resistant gene (bla_NPS) were only detected in wet weather samples. Frequency of integron genes Intl2 and Intl3 detection increased by 42% in wet weather samples. Culturable E. coli and Enterococcus spp. significantly correlated with 19 of 47 (40%) ARG tested. Sewage-associated markers crAssphage and HF183 significantly correlated (p < 0.05) with the following ARGs: intl1, sul1, tet(M), ampC, mexB, and tet(W). The presence of sewage-associated marker genes along with ARGs associated with sewage suggested that aging sewage infrastructure contributed to contaminant loading in the Bay. Further research should focus on collecting spatial and temporal data on the microbiological contaminants especially viruses in SDOs.

Decay of sewage-associated bacterial communities in fresh and marine environmental waters and sediment

Understanding the microbial quality of recreational waters is critical to effectively managing human health risks. In recent years, the development of new molecular methods has provided scientists with alternatives to the use of culture-based fecal indicator methods for investigating sewage contamination in recreational waters. Before these methods can be formalized into guidelines, however, we must investigate their utility, including strengths and weaknesses in different environmental media. In this study, we investigated the decay of sewage-associated bacterial communities in water and sediment from three recreational areas in Southeast Queensland, Australia. Outdoor mesocosms with water and sediment samples from two marine and one freshwater sites were inoculated with untreated sewage and sampled on days 0, 1, 4, 8, 14, 28, and 50. Amplicon sequencing was performed on the DNA extracted from water and sediment samples, and SourceTracker was used to determine the decay of sewage-associated bacterial communities and how they change following a contamination event. No sewage-associated operational taxonomic units (OTUs) were detected in water and sediment samples after day 4; however, the bacterial communities remained changed from their background measures, prior to sewage amendment. Following untreated sewage inoculation, the mesocosm that had the most diverse starting bacterial community recovered to about 60% of its initial community composition, whereas the least diverse bacterial community only recovered to about 30% of its initial community composition. This suggests that a more diverse bacterial community may play an important role in water quality outcomes after sewage contamination events. Further investigation into potential links between bacterial communities and measures of fecal indicators, pathogens, and microbial source tracking (MST) markers is warranted and may provide insight for recreational water decision-makers.

Tracking antibiotic resistance gene pollution from different sources using machine-learning classification

BACKGROUND

Antimicrobial resistance (AMR) has been a worldwide public health concern. Current widespread AMR pollution has posed a big challenge in accurately disentangling source-sink relationship, which has been further confounded by point and non-point sources, as well as endogenous and exogenous cross-reactivity under complicated environmental conditions. Because of insufficient capability in identifying source-sink relationship within a quantitative framework, traditional antibiotic resistance gene (ARG) signatures-based source-tracking methods would hardly be a practical solution.

RESULTS

By combining broad-spectrum ARG profiling with machine-learning classification SourceTracker, here we present a novel way to address the question in the era of high-throughput sequencing. Its potential in extensive application was firstly validated by 656 global-scale samples covering diverse environmental types (e.g., human/animal gut, wastewater, soil, ocean) and broad geographical regions (e.g., China, USA, Europe, Peru). Its potential and limitations in source prediction as well as effect of parameter adjustment were then rigorously evaluated by artificial configurations with representative source proportions. When applying SourceTracker in region-specific analysis, excellent performance was achieved by ARG profiles in two sample types with obvious different source compositions, i.e., influent and effluent of wastewater treatment plant. Two environmental metagenomic datasets of anthropogenic interference gradient further supported its potential in practical application. To complement general-profile-based source tracking in distinguishing continuous gradient pollution, a few generalist and specialist indicator ARGs across ecotypes were identified in this study.

CONCLUSION

We demonstrated for the first time that the developed source-tracking platform when coupling with proper experiment design and efficient metagenomic analysis tools will have significant implications for assessing AMR pollution. Following predicted source contribution status, risk ranking of different sources in ARG dissemination will be possible, thereby paving the way for establishing priority in mitigating ARG spread and designing effective control strategies.

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

The efficacy of SourceTracker software to attribute contamination from a variety of fecal sources spiked into ambient freshwater samples was investigated. Double-blinded samples spiked with ≤5 different sources (0.025-10% vol/vol) were evaluated against fecal taxon libraries characterized by next-generation amplicon sequencing. Three libraries, including an initial library (17 nonlocal sources), a blinded source library (5 local sources), and a composite library (local and nonlocal sources), were used with SourceTracker. SourceTracker's predictions of fecal compositions in samples were made, in part, based on distributions of taxa within abundant genera identified as discriminatory by discriminant analyses but also using a large percentage of low abundance taxa. The initial library showed poor ability to characterize blinded samples, but, using local sources, SourceTracker showed 91% accuracy (31/34) at identifying the presence of source contamination, with two false positives for sewage and one for horse. Furthermore, sink predictions of source contamination were positively correlated (Spearman's ρ ≥ 0.88, P < 0.001) with spiked source volumes. Using the composite library did not significantly affect sink predictions ( P > 0.79) compared to those made using the local sources alone. Results of this study indicate that geographically associated fecal samples are required for SourceTracker to assign host sources accurately.

Toward Forensic Uses of Microbial Source Tracking

The science of microbial source tracking has allowed researchers and watershed managers to go beyond general indicators of fecal pollution in water such as coliforms and enterococci, and to move toward an understanding of specific contributors to water quality issues. The premise of microbial source tracking is that characteristics of microorganisms that are strongly associated with particular host species can be used to trace fecal pollution to particular animal species (including humans) or groups, e.g., ruminants or birds. Microbial source tracking methods are practiced largely in the realm of research, and none are approved for regulatory uses on a federal level. Their application in the conventional sense of forensics, i.e., to investigate a crime, has been limited, but as some of these methods become standardized and recognized in a regulatory context, they will doubtless play a larger role in applications such as total maximum daily load assessment, investigations of sewage spills, and contamination from agricultural practices.

Regional Assessment of Human Fecal Contamination in Southern California Coastal Drainages

Host-associated genetic markers that allow for fecal source identification have been used extensively as a diagnostic tool to determine fecal sources within watersheds, but have not been used in routine monitoring to prioritize remediation actions among watersheds. Here, we present a regional assessment of human marker prevalence among drainages that discharge to the U.S. southern California coast. Approximately 50 samples were analyzed for the HF183 human marker from each of 22 southern California coastal drainages under summer dry weather conditions, and another 50 samples were targeted from each of 23 drainages during wet weather. The HF183 marker was ubiquitous, detected in all but two sites in dry weather and at all sites during wet weather. However, there was considerable difference in the extent of human fecal contamination among sites. Similar site ranking was produced regardless of whether the assessment was based on frequency of HF183 detection or site average HF183 concentration. However, site ranking differed greatly between dry and wet weather. Site ranking also differed greatly when based on enterococci, which do not distinguish between pollution sources, vs. HF183, which distinguishes higher risk human fecal sources from other sources, indicating the additional value of the human-associated marker as a routine monitoring tool.

A High-Throughput DNA-Sequencing Approach for Determining Sources of Fecal Bacteria in a Lake Superior Estuary

Current microbial source-tracking (MST) methods, employed to determine sources of fecal contamination in waterways, use molecular markers targeting host-associated bacteria in animal or human feces. However, there is a lack of knowledge about fecal microbiome composition in several animals and imperfect marker specificity and sensitivity. To overcome these issues, a community-based MST method has been developed. Here, we describe a study done in the Lake Superior-Saint Louis River estuary using SourceTracker, a program that calculates the source contribution to an environment. High-throughput DNA sequencing of microbiota from a diverse collection of fecal samples obtained from 11 types of animals (wild, agricultural, and domesticated) and treated effluent (n = 233) was used to generate a fecal library to perform community-based MST. Analysis of 319 fecal and environmental samples revealed that the community compositions in water and fecal samples were significantly different, allowing for the determination of the presence of fecal inputs and identification of specific sources. SourceTracker results indicated that fecal bacterial inputs into the Lake Superior estuary were primarily attributed to wastewater effluent and, to a lesser extent, geese and gull wastes. These results suggest that a community-based MST method may be another useful tool for determining sources of aquatic fecal bacteria.

Next-generation sequencing and culture-based techniques offer complementary insights into fungi and prokaryotes in beach sands

A next-generation sequencing (NGS) approach, in conjunction with culture-based methods, was used to examine fungal and prokaryotic communities for the presence of potential pathogens in beach sands throughout Portugal. Culture-based fungal enumeration revealed low and variable concentrations of the species targeted (yeasts and dermatophytes), which were underrepresented in the community characterized by NGS targeting the ITS1 region. Conversely, NGS indicated that the potentially pathogenic species Purpureocillium liliacinum comprised nearly the entire fungal community. Culturable fecal indicator bacterial concentrations were low throughout the study and unrelated to communities characterized by NGS. Notably, the prokaryotic communities characterized revealed a considerable abundance of archaea. Results highlight differences in communities between methods in beach sand monitoring but indicate the techniques offer complementary insights. Thus, there is a need to leverage culture-based methods with NGS methods, using a toolbox approach, to determine appropriate targets and metrics for beach sand monitoring to adequately protect public health.

Multiple lines of evidence to identify sewage as the cause of water quality impairment in an urbanized tropical watershed

Indicator bacteria, which are conventionally used to evaluate recreational water quality, can originate from various non-human enteric and extra-enteric sources, hence they may not be indicative of human health risk nor do they provide information on the sources of contamination. In this study we utilized traditional (enterococci and Escherichia coli) and alternative (Clostridium perfringens) indicator bacteria, F⁺-specific coliphage, molecular markers for microorganisms associated with human sewage (human-associated Bacteroides and polyomaviruses), and microbial community analysis tools (16S rRNA gene fragment amplicon sequencing), to identify and evaluate human sewage-related impact in the Manoa watershed in Honolulu, Hawaii. Elevated concentrations of enterococci (geometric mean ranging from 1604 to 2575 CFU 100 mL^-1) and C. perfringens (45-77 CFU 100 mL^-1) indicated impairment of the urbanized section of the stream, while indicator bacteria concentrations decreased downstream in the tidally influenced Ala Wai Canal. The threshold values triggering water quality violation notifications in Hawaii were exceeded in 33.3-75.0% of samples collected at sites in the urbanized section of Manoa Stream, but were not exceeded in any of the samples collected at an upstream site located in a forested area. Correlation between indicator bacteria concentrations and rainfall amounts was weak to moderate but significant (E. coli R = 0.251, P = 0.009; enterococci R = 0.369, P < 0.001; C. perfringens R = 0.343, P < 0.001), while concentrations of human fecal-associated molecular markers were not significantly correlated with rainfall (human-associated Bacteroides, R = 0.131, P = 0.256; human-associated polyomaviruses, R = 0.213, P = 0.464). Presence of human sewage was confirmed by detection of human-associated Bacteroides and human polyomavirus in the urbanized section of Manoa Stream (83.3-100% and 41.7-66.7% positive samples respectively). It was further confirmed by microbial community analyses which suggested that an average 2.4-3.4% of the total bacterial population in this section was associated with sewage. Microbial community profiles were significantly influenced by rainfall (R² = 0.4390, P < 0.001), pH (R² = 0.3077, P = 0.006), salinity (R² = 0.2614, P = 0.038), and conductivity (R² = 0.2676, P = 0.031). Although microbial diversity fluctuated throughout the watershed, it was lower in the impaired section. Leaking sewer systems and illegal cross-connections are implicated in the impairment of the watershed, hence both the sewer and the storm water lines should be routinely inspected. Collectively, our data suggest that information derived from the analysis of microbial communities complements current marker-based microbial source tracking techniques and environmental monitoring programs.

A Community Multi-Omics Approach towards the Assessment of Surface Water Quality in an Urban River System

A multi-omics approach was applied to an urban river system (the Brisbane River (BR), Queensland, Australia) in order to investigate surface water quality and characterize the bacterial population with respect to water contaminants. To do this, bacterial metagenomic amplicon-sequencing using Illumina next-generation sequencing (NGS) of the V5-V6 hypervariable regions of the 16S rRNA gene and untargeted community metabolomics using gas chromatography coupled with mass spectrometry (GC-MS) were utilized. The multi-omics data, in combination with fecal indicator bacteria (FIB) counts, trace metal concentrations (by inductively coupled plasma mass spectrometry (ICP-MS)) and in-situ water quality measurements collected from various locations along the BR were then used to assess the health of the river ecosystem. Sites sampled represented the transition from less affected (upstream) to polluted (downstream) environments along the BR. Chemometric analysis of the combined datasets indicated a clear separation between the sampled environments. Burkholderiales and Cyanobacteria were common key factors for differentiation of pristine waters. Increased sugar alcohol and short-chain fatty acid production was observed by Actinomycetales and Rhodospirillaceae that are known to form biofilms in urban polluted and brackish waters. Results from this study indicate that a multi-omics approach enables a deep understanding of the health of an aquatic ecosystem, providing insight into the bacterial diversity present and the metabolic output of the population when exposed to environmental contaminants.

Amplicon-based taxonomic characterization of bacteria in urban and peri-urban roof-harvested rainwater stored in tanks

Overall, 26% of Australian households use rainwater tanks as a source of potable and nonpotable water. Limited information is available on the total bacterial communities in tank water. Therefore, identification of dominant bacterial communities, diversity, and their distribution is important in understanding the microbial quality of tank water. In this study, the abundance and diversity of bacterial communities in 88 tank water samples collected from the urban areas of Brisbane (n=44) and the peri-urban center of Currumbin (n=44) in Southeast Queensland, Australia were determined using amplicon-based Illumina next-generation sequencing. In addition, the SourceTracker program was used to identify the sources of fecal contamination in tank water samples. Sequence reads were also analyzed to detect potential bacterial pathogenic genera in the tank water samples collected. Differences in sample coverage, alpha diversity, and richness did not differ significantly between the Brisbane and Currumbin tank water samples. Comamonadaceae and Planctomycetaceae were the most abundant families in all tank water samples. Curvibacter was the most abundant genus in all tank water samples. SourceTracker revealed that around 34% (Brisbane) and 43% (Currumbin) of tank water samples had a signature for bird fecal contamination. The potential opportunistic pathogenic genera including Burkholderia, Chromobacterium, Clostridium, Legionella, Mycobacterium, Nocardia, and Pseudomonas were most prevalent in tank water samples. Next-generation sequencing can be used as an initial screening tool to identify a wide array of potential pathogenic genera in tank water samples followed by quantifying specific pathogen(s) of interest using more sensitive molecular assays such as quantitative PCR (qPCR).

Complete Microbiota Engraftment Is Not Essential for Recovery from Recurrent Clostridium difficile Infection following Fecal Microbiota Transplantation

Bacterial communities from subjects treated for recurrent Clostridium difficile infection (rCDI) by fecal microbiota transplantation (FMT), using either heterologous donor stool samples or autologous stool samples, were characterized by Illumina next-generation sequencing. As previously reported, the success of heterologous FMT (90%) was superior to that of autologous FMT (43%) (P = 0.019), and post-FMT intestinal bacterial communities differed significantly between treatment arms (P < 0.001). Subjects cured by autologous FMT typically had greater abundances of the Clostridium XIVa clade and Holdemania bacteria prior to treatment, and the relative abundances of these groups increased significantly after FMT compared to heterologous FMT and pre-FMT samples. The typical shift to post-FMT, donor-like assemblages, featuring high relative abundances of genera within the Bacteroidetes and Firmicutes phyla, was not observed in the autologous FMT subjects. Autologous FMT patient bacterial communities were significantly different in composition than those for heterologous FMT patients and donors (P < 0.001). The SourceTracker program, which employs a Bayesian algorithm to determine source contributions to sink communities, showed that patients initially treated by heterologous FMT had significantly higher percentages of engraftment (i.e., similarity to donor communities, mean value of 74%) compared to those who suffered recurrence following autologous FMT (1%) (P ≤ 0.013). The findings of this study suggest that complete donor engraftment may be not necessary if functionally critical taxa are present in subjects following antibiotic therapy.

This study provides a detailed characterization of fecal bacterial communities in subjects who participated in a previously published randomized clinical trial to treat recurrent C. difficile infection (rCDI). Bacterial communities were characterized to determine differences between subjects who received fecal bacteria either from healthy donor stool samples or their own stool samples as “placebo” in order to determine which groups of bacteria were most important in achieving a cure. The results of this study suggested that bacteria associated with secondary bile acid metabolism could potentially provide resistance to infection and that complete transfer of healthy donor microorganisms was not necessary to resolve CDI following unsuccessful antibiotic treatment.

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.

Evidence of Avian and Possum Fecal Contamination in Rainwater Tanks as Determined by Microbial Source Tracking Approaches

Avian and possum fecal droppings may negatively impact roof-harvested rainwater (RHRW) water quality due to the presence of zoonotic pathogens. This study was aimed at evaluating the performance characteristics of a possum feces-associated (PSM) marker by screening 210 fecal and wastewater samples from possums (n = 20) and a range of nonpossum hosts (n = 190) in Southeast Queensland, Australia. The host sensitivity and specificity of the PSM marker were 0.90 and 0.95 (maximum value, 1.00), respectively. The mean concentrations of the GFD marker in possum fecal DNA samples (8.8 × 10(7) gene copies per g of feces) were two orders of magnitude higher than those in the nonpossum fecal DNA samples (5.0 × 10(5) gene copies per g of feces). The host sensitivity, specificity, and concentrations of the avian feces-associated GFD marker were reported in our recent study (W. Ahmed, V. J. Harwood, K. Nguyen, S. Young, K. Hamilton, and S. Toze, Water Res 88:613-622, 2016, http://dx.doi.org/10.1016/j.watres.2015.10.050). The utility of the GFD and PSM markers was evaluated by testing a large number of tank water samples (n = 134) from the Brisbane and Currumbin areas. GFD and PSM markers were detected in 39 of 134 (29%) and 11 of 134 (8%) tank water samples, respectively. The GFD marker concentrations in PCR-positive samples ranged from 3.7 × 10(2) to 8.5 × 10(5) gene copies per liter, whereas the concentrations of the PSM marker ranged from 2.0 × 10(3) to 6.8 × 10(3) gene copies per liter of water. The results of this study suggest the presence of fecal contamination in tank water samples from avian and possum hosts. This study has established an association between the degradation of microbial tank water quality and avian and possum feces. Based on the results, we recommend disinfection of tank water, especially for tanks designated for potable use.

IMPORTANCE

The use of roof-harvested rainwater (RHRW) for domestic purposes is a globally accepted practice. The presence of pathogens in rainwater tanks has been reported by several studies, supporting the necessity for the management of potential health risks. The sources of fecal pollution in rainwater tanks are unknown. However, the application of microbial source tracking (MST) markers has the potential to identify the sources of fecal contamination in a rainwater tank. In this study, we provide evidence of avian and possum fecal contamination in tank water samples using molecular markers. This study established a potential link between the degradation of the microbial quality of tank water and avian and possum feces.

Regional Similarities and Consistent Patterns of Local Variation in Beach Sand Bacterial Communities throughout the Northern Hemisphere

Recent characterization of the bacterial community structure in beach sands has revealed patterns of biogeography similar to those observed in aquatic environments. Studies to date, however, have mainly focused on subtidal sediments from marine beaches. Here, we investigate the bacterial diversity, using Illumina-based sequencing of the V5-V6 region of the 16S rRNA gene, at 11 beaches representing those next to the Great Lakes, Florida, and the Pacific Ocean. The alpha diversity differed significantly among regions (P< 0.0001), while the within-region diversity was more similar. The beta diversity also differed by region (P< 0.001), where freshwater sands had significantly higher abundances of taxa within the Actinobacteria, Betaproteobacteria, and Verrucomicrobia than marine environments. In contrast, marine sands harbored greater abundances of Gamma proteobacteria and Planctomycetes, and those from Florida had more Delta proteobacteria and Firmicutes Marine beaches had significantly different phylogenetic community structures (P ≤ 0.018), but freshwater and Florida beaches showed fewer within-region phylogenetic differences. Furthermore, regionally distinct patterns in taxonomic variation were observed in backshore sands, which had communities distinct from those in nearshore sands (P< 0.001). Sample depth minimally influenced the community composition. The results of this study reveal distinct bacterial community structures in sand on a broad geographic scale but moderate regional similarity and suggest that local variation is primarily related to the distance from the shoreline. This study offers a novel comparison of the bacterial communities in freshwater and marine beach sands and provides an important basis for future comparisons and analyses to elucidate factors affecting microbial ecology in this underexplored environment.

IMPORTANCE

This study presents a large-scale geographic characterization of the bacterial communities present in beach sands. While previous studies have evaluated how environmental factors influence bacterial community composition, few have evaluated bacterial communities in freshwater sands. Furthermore, the use of a consistent methodology to characterize bacterial communities here allowed a novel comparison of communities across geographic regions. We reveal that while the community composition in sands at individual beaches is distinct, beach sands within the same region harbor similar assemblages of bacteria and these assemblages differ greatly between regions. In addition, moisture, associated with distance from the shoreline, strongly influences the bacteria present in sands and more strongly influences the bacteria present than sample depth does. Thus, the data presented here offer an important basis for a broader characterization of the ecology of bacteria in sands, which may also be relevant to public health and resource management initiatives.