Microbial source tracking leverages a wide range of approaches designed to trace the origins of fecal contamination in aquatic environments. Although source tracking methods are typically employed within the laboratory setting, computational techniques can be leveraged to advance microbial source tracking methodology. Herein, we present a logic regression-based supervised learning approach for the discovery of source-informative genetic markers within intergenic regions across the Escherichia coli genome that can be used for source tracking. With just single intergenic loci, logic regression was able to identify highly source-specific (i.e., exceeding 97.00%) biomarkers for a wide range of host and niche sources, with sensitivities reaching as high as 30.00%-50.00% for certain source categories, including pig, sheep, mouse, and wastewater, depending on the specific intergenic locus analyzed. Restricting the source range to reflect the most prominent zoonotic sources of E. coli transmission (i.e., bovine, chicken, human, and pig) allowed for the generation of informative biomarkers for all host categories, with specificities of at least 90.00% and sensitivities between 12.50% and 70.00%, using the sequence data from key intergenic regions, including emrKY-evgAS, ibsB-(mdtABCD-baeSR), ompC-rcsDB, and yedS-yedR, that appear to be involved in antibiotic resistance. Remarkably, we were able to use this approach to classify 48 out of 113 river water E. coli isolates collected in Northwestern Sweden as either beaver, human, or reindeer in origin with a high degree of consensus-thus highlighting the potential of logic regression modeling as a novel approach for augmenting current source tracking efforts.IMPORTANCEThe presence of microbial contaminants, particularly from fecal sources, within water poses a serious risk to public health. The health and economic burden of waterborne pathogens can be substantial-as such, the ability to detect and identify the sources of fecal contamination in environmental waters is crucial for the control of waterborne diseases. This can be accomplished through microbial source tracking, which involves the use of various laboratory techniques to trace the origins of microbial pollution in the environment. Building on current source tracking methodology, we describe a novel workflow that uses logic regression, a supervised machine learning method, to discover genetic markers in Escherichia coli, a common fecal indicator bacterium, that can be used for source tracking efforts. Importantly, our research provides an example of how the rise in prominence of machine learning algorithms can be applied to improve upon current microbial source tracking methodology.

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

Since stormwater conveys a variety of contaminants into water bodies, green infrastructure (GI) is increasingly being adopted as an on-site treatment solution in addition to controlling peak flows. The purpose of this study was to identify differences in microbial water quality of stormwater in watersheds retrofitted with GI vs. those without GI. Considering stormwater is recently recognized as a contributor to the antibiotic resistance (AR) threat, another goal of this study was to characterize changes in the microbiome and collection of AR genes (resistome) of urban stormwater with season, rainfall characteristics, and fecal contamination. MinION long-read sequencing was used to analyze stormwater microbiome and resistome from watersheds with and without GI in Columbus, Ohio, United States, over 18 months. We characterized fecal contamination in stormwater via culturing Escherichia coli and with molecular microbial source tracking (MST) to identify sources of fecal contamination. Overall, season and storm event (rainfall) characteristics had the strongest relationships with changes in the stormwater microbiome and resistome. We found no significant differences in microbial water quality or the microbiome of stormwater in watersheds with and without GI implemented. However, there were differences between the communities of microorganisms hosting antibiotic resistance genes (ARGs) in stormwater from watersheds with and without GI, indicating the potential sensitivity of AR bacteria to treatment. Stormwater was contaminated with high concentrations of human-associated fecal bacterial genes, and the ARG host bacterial community had considerable similarities to human feces/wastewater. We also identified 15 potential pathogens hosting ARGs in these stormwater resistome, including vancomycin-resistant Enterococcus faecium (VRE) and multidrug-resistant Pseudomonas aeruginosa. In summary, urban stormwater is highly contaminated and has a great potential to spread AR and microbial hazards to nearby environments. This study presents the most comprehensive analysis of stormwater microbiome and resistome to date, which is crucial to understanding the potential microbial risk from this matrix. This information can be used to guide future public health policy, stormwater reuse programs, and urban runoff treatment initiatives.

No single microbial source tracking (MST) marker can be applied to determine the sources of fecal pollution in all water types. This study aimed to validate a high-throughput quantitative polymerase chain reaction (HT-qPCR) method for the simultaneous detection of multiple MST markers. A total of 26 fecal-source samples that had been previously collected from human sewage (n = 6) and ruminant (n = 3), dog (n = 6), pig (n = 6), chicken (n = 3), and duck (n = 2) feces in the Kathmandu Valley, Nepal, were used to validate 10 host-specific MST markers, i.e., Bacteroidales (BacHum, gyrB, BacR, and Pig2Bac), mitochondrial DNA (mtDNA) (swine, bovine, and Dog-mtDNA), and viral (human adenovirus, porcine adenovirus, and chicken/turkey parvovirus) markers, via HT-qPCR. Only Dog-mtDNA showed 100 % accuracy. All the tested bacterial markers showed a sensitivity of 100 %. Nine of the 10 markers were further used to identify fecal contamination in groundwater sources (n = 54), tanker filling stations (n = 14), drinking water treatment plants (n = 5), and river water samples (n = 6). The human-specific Bacteroidales marker BacHum and ruminant-specific Bacteroidales marker BacR was detected at a high ratio in river water samples (83 % and 100 %, respectively). The results of HT-qPCR were in agreement with the standard qPCR. The comparable performances of HT-qPCR and standard qPCR as well as the successful detection of MST markers in the fecal-source and water samples demonstrated the potential applicability of these markers for detecting fecal contamination sources via HT-qPCR.

Bioretention cells (BRCs) control stormwater flow on-site during precipitation, reducing runoff and improving water quality through chemical, physical, and biological processes. While BRCs are effective in these aspects, they provide habitats for wildlife and may face microbial hazards from fecal shedding, posing a potential threat to human health and the nearby environment. However, limited knowledge exists regarding the ability to control microbial hazards (e.g., beyond using typical indicator bacteria) through stormwater biofiltration. Therefore, the purpose of this study is to characterize changes in the bacterial community of urban stormwater undergoing bioretention treatment, with the goal of assessing the public health implications of these green infrastructure solutions. Samples from BRC inflow and outflow in Columbus, Ohio, were collected post-heavy storms from October 2021 to March 2022. Conventional culture-based E. coli monitoring and microbial source tracking (MST) were conducted to identify major fecal contamination extent and its sources (i.e., human, canine, avian, and ruminant). Droplet digital polymerase chain reaction (ddPCR) was utilized to quantify the level of host-associated fecal contamination in addition to three antibiotic resistant genes (ARGs): tetracycline resistance gene (tetQ), sulfonamide resistance gene (sul1), and Klebsiella pneumoniae carbapenemase resistance gene (blaKPC). Subsequently, 16S rRNA gene sequencing was conducted to characterize bacterial community differences between stormwater BRC inflow and outflow. Untreated urban stormwater reflects anthropogenic contamination, suggesting it as a potential source of contamination to waterbodies and urban environments. When comparing inlet and outlet BRC samples, urban stormwater treated via biofiltration did not increase microbial hazards, and changes in bacterial taxa and alpha diversity were negligible. Beta diversity results reveal a significant shift in bacterial community structure, while simultaneously enhancing the water quality (i.e., reduction of metals, total suspended solids, total nitrogen) of urban stormwater. Significant correlations were found between the bacterial community diversity of urban stormwater with fecal contamination (e.g. dog) and ARG (sul1), rainfall intensity, and water quality (hardness, total phosphorous). The study concludes that bioretention technology can sustainably maintain urban microbial water quality without posing additional public health risks, making it a viable green infrastructure solution for heavy rainfall events exacerbated by climate change.

Pollution from domestic on-site wastewater treatment systems (OWTS) is a significant contaminant pressure in many rural catchments. However, due to their design, and dispersed proliferation, it is difficult to assess their impact. Water testing methodologies employ bacterial culturing methods and chemical analysis which may lose resolution and/or specificity being confounded by diffuse agricultural sources within a rural environment. In this study, we successfully assessed the applicability of Pepper Mild Mottle Virus (PMMoV) as a human faecal source tracker for deficient on-site wastewater treatment systems. The transport of PMMoV was first studied in the effluent of a 30 cm deep soil column which was dosed for 510 days with primary influent from a conventional septic system. The removal of PMMoV through the 30 cm deep soil column was quantified with a 5-day seeding trial employing primary influent mixed with PMMoV sourced from Tabasco pepper product ®. The trial was then carried out at field scale with the seeding solution dosed into an operational percolation trench receiving septic tank effluent which had been instrumented for porewater sampling. Samples were taken at depths of 10 cm, 30 cm, and 50 cm across the length of the trench at distances of 1 m, 7.5 m, and 17.5 m from the inlet of the trench. PMMoV was detected on all days of the trial, with a peak concentration of 1 × 106 found at the rear of the trench on day 2 of the seeding trial. Finally, to assess the effectiveness of PPMoV as a microbial source tracking tool from a water receptor perspective, three rural catchments with high densities of OWTSs were sampled and analysed for hourly variations in biological parameters which included total coliforms, Escherichia coli, PMMoV, and chemical parameters total organic carbon, total nitrogen, and total carbon. PMMoV was detected in all river samples over a 24-hour period, thereby indicating its suitability as a tracer of human wastewater effluent in such environments with multiple diffuse sources.

Fecal bacteria in surface water may indicate threats to human health. Our hypothesis is that village settlements in tropical rural areas are major hotspots of fecal contamination because of the number of domestic animals usually roaming in the alleys and the lack of fecal matter treatment before entering the river network. By jointly monitoring the dynamics of Escherichia coli and of seven stanol compounds during four flood events (July-August 2016) at the outlet of a ditch draining sewage and surface runoff out of a village of Northern Lao PDR, our objectives were (1) to assess the range of E. coli concentration in the surface runoff washing off from a village settlement and (2) to identify the major contributory sources of fecal contamination using stanol compounds during flood events. E. coli pulses ranged from 4.7 × 104 to 3.2 × 106 most probable number (MPN) 100 mL-1, with particle-attached E. coli ranging from 83 to 100%. Major contributory feces sources were chickens and humans (about 66 and 29%, respectively), with the highest percentage switching from the human pole to the chicken pole during flood events. Concentrations indicate a severe fecal contamination of surface water during flood events and suggest that villages may be considered as major hotspots of fecal contamination pulses into the river network and thus as point sources in hydrological models.

Manila Bay, a multipurpose body of water located around Metro Manila, Philippines, is progressively deteriorating because of massive pollution. Reports have shown that the bay and its aquatic resources (i.e., seafood) are contaminated with fecal matter and enteric pathogens, posing a threat to public health and industry. This problem raises the need for a microbial source tracking methodology as a part of the rehabilitation efforts in the bay. Bivalve mollusks cultivated in water can serve as sentinel species to detect fecal pollution and can complement water monitoring. With the use of polymerase chain reaction and DNA sequence analysis, this study detected Cryptosporidium spp. in Asian green mussels (Perna viridis) cultivated and harvested in Manila Bay and sold in Bulungan Seafood Market, Parañaque, Philippines, from 2019 to 2021 with an overall occurrence of 8.77% (n = 57). The analysis of the 18S rDNA segment revealed three genotypes from Cryptosporidium-positive samples, namely, Cryptosporidium sp. rat genotype IV (60%), C. galli (20%), and C. meleagridis (20%). These findings suggest fecal pollution in bivalve cultivation sites coming from sewage, nonpoint, and agricultural sources. The presence of C. meleagridis, the third most common cause of human cryptosporidiosis, in mussels poses a threat to human health. Thus, there is a need to establish routine detection and source tracking of Cryptosporidium spp. in Manila Bay and to educate seafood consumers on food safety.

This research investigated the in-situ decay rates of four human wastewater-associated markers (Bacteroides HF183 (HF183), Lachnospiraceae Lachno3 (Lachno3), cross-assembling phage (crAssphage), pepper mild mottle virus (PMMoV) and three enteric viruses (human adenovirus 40/41 (HAdV 40/41), enterovirus (EV) and human norovirus GII (HNoV GII) in two estuarine water environments (Davidson Park (DP) and Hen and Chicken Bay (HCB) in temperate Sydney, NSW, Australia, employing qPCR and RT-qPCR assays. The study also aimed to compare decay rates observed in mesocosms with previously published laboratory microcosms, providing insights into the persistence of markers and viruses in estuarine environments. Results indicated varying decay rates between DP and HCB mesocosms, with HF183 exhibiting relatively faster decay rates compared to other markers and enteric viruses in sunlight and dark mesocosms. In DP mesocosms, HF183 decayed the fastest, contrasting with PMMoV, which exhibited the slowest. Sunlight induced higher decay rates for all markers and viruses in DP mesocosms. In HCB sunlight mesocosms, HF183 nucleic acid decayed most rapidly compared to other markers and enteric viruses. In dark mesocosms, crAssphage showed the fastest decay, while PMMoV decayed at the slowest rate in both sunlight and dark mesocosms. Comparisons with laboratory microcosms revealed faster decay of markers and enteric viruses in laboratory microcosms than the mesocosms, except for crAssphage and HAdV 40/41 in dark, and PMMoV in sunlight mesocosms. The study concludes that decay rates of markers and enteric viruses vary between estuarine mesocosms, emphasizing the impact of sunlight exposure, which was potentially influenced by the elevated turbidity at HCB estuarine waters. The generated decay rates contribute valuable insights for establishing site-specific risk-based thresholds of human wastewater-associated markers.

Sandboxes in public play spaces afford a crucial opportunity for urban children to engage in naturalistic play that fosters development of cognitive, social, and motor skills. As open pits, sandboxes in New York City public playgrounds are potentially exposed to fecal inputs from various sources, including wild and domestic animals. A longitudinal study of thirteen sandboxes located in public playgrounds on the east side of Manhattan reveals ubiquity of the fecal indicator bacteria enterococci and Escherichia coli through all seasons. The highest concentrations of bacteria occur in surface sand (n = 42; mean enterococci 230 MPN/g and E. coli 182 MPN/g dry weight), with significantly lower levels at depths below the surface (n = 35; mean enterococci 21 MPN/g and E. coli 12 MPN/g dry weight), a stratification consistent with fecal loading at the surface. Generalized linear mixed models indicate that sand depth (surface vs. underlayers) is the most influential variable affecting bacterial levels (P <0.001 for both enterococci and E. coli), followed by sampling season (P <0.001 for both). Bacterial concentrations do not vary significantly as a function of playground location or ZIP code within the study area. Children\'s exposure while playing in sandboxes likely reaches 105 enterococci and 104E. coli in a typical play period. Microbial source tracking to identify fecal hosts reveals dog, bird, and human biomarkers in low concentrations. Open sandbox microcosms installed at ground level in the urban environment of Manhattan are fouled by enterococci and E. coli within two weeks, while adjacent closed microcosms exhibit no fecal contamination over a 33-day sampling period. Collectively, our results indicate that increasing the frequency of sand refills and covering sandboxes during times of disuse would be straightforward management strategies to mitigate fecal contamination in playground sandboxes.