Strain-level multidrug-resistant pathogenic bacteria in urban wastewater treatment plants: Transmission, source tracking and evolution.

Wastewater treatment plants (WWTPs) serve as reservoirs for various pathogens and play a pivotal role in safeguarding environmental safety and public health by mitigating pathogen release. Pathogenic bacteria, known for their potential to cause fatal infections, present a significant and emerging threat to global health and remain poorly understood regarding their origins and transmission in the environment. Using metagenomic approaches, we identified a total of 299 pathogens from three full-scale WWTPs. We comprehensively elucidated the occurrence, dissemination, and source tracking of the pathogens across the WWTPs, addressing deficiencies in traditional detection strategies. While indicator pathogens in current wastewater treatment systems such as Escherichia coli are effectively removed, specific drug-resistant pathogens, including Pseudomonas aeruginosa, Pseudomonas putida, and Aeromonas caviae, persist throughout the treatment process, challenging complete eradication efforts. The anoxic section plays a predominant role in controlling abundance but significantly contributes to downstream pathogen diversity. Additionally, evolution throughout the treatment process enhances pathogen diversity, except for upstream transmission, such as A. caviae str. WP8-S18-ESBL-04 and P. aeruginosa PAO1. Our findings highlight the necessity of expanding current biomonitoring indicators for wastewater treatment to optimize treatment strategies and mitigate the potential health risks posed by emerging pathogens. By addressing these research priorities, we can effectively mitigate risks and safeguard environmental safety and public health.

The use of E. coli phylogrouping and microbial source tracking (non-species specific, human-specific, bovine-specific bacteroidales markers) to elucidate hydro(geo)logical contamination mechanisms in southeastern Ontario, Canada.

In Ontario, monitoring, maintenance, and treatment of private drinking systems (e.g. wells) are the responsibility of the well owner. Fecal contamination of drinking water threatens public health, particularly in rural communities which are often fully reliant on unregulated private groundwater as a primary drinking water source. Private well users face a higher risk of acute gastrointestinal illness compared to those served by municipally operated systems (Murphy et al., 2016). Accordingly, the current study sought to characterize the fecal indicator, E. coli, isolated from southeastern Ontario private groundwater wells, including phylogroups and host source. Results were examined in the context of antecedent climate and local hydrogeological setting to elucidate likely contaminant sources and pathways. A total of 737 E. coli isolates from 260 private wells were assigned to phylogroups using the Clermont PCR phylotyping method, with likely host source determined using host-specific Bacteroidales 16S rRNA RT qPCR assays. Multivariate models were developed for the main E. coli phylogroups (A, B1, B2, and D) and all microbial source tracking markers. Models were coupled for interpretation where possible, based on associations between phylogroups and MST markers. Preferential subsurface flow, and to a lesser degree, overland flow, were likely mechanisms of contamination across all models. Distinct temporal associations were found based on the fecal source. Multiple models were developed and will be discussed, in an attempt to elucidate source-specific contamination mechanisms, in support of risk assessment and appropriate protective actions.

Microbial source tracking and identification of fecal contamination patterns in saltwater estuaries between base- and storm-flow events using dual genome-based approaches.

Fecal contamination from natural and anthropogenic sources poses significant threats to saltwater estuaries, particularly after storms or heavy rainfall. Monitoring fecal contamination is essential for protecting these vulnerable ecosystems having important ecological and economic values. In this study, we investigated the abundance, sources, and potential causes of fecal contamination at three marine and seven freshwater stations across Vaughn Bay (WA, USA), a shellfish growing district, during base- and storm-flow events. Additionally, we evaluated the performance of fecal indicator bacteria (FIB) quantification, optical brightener assessment, and qPCR analysis for fecal contamination quantification. We compared the effectiveness of qPCR-based microbial source tracking (MST), which targeted a broad range of hosts including, such as humans, birds, cows, horses, ruminants, dogs, and pigs, with sequencing-based MST in identifying fecal contamination sources. Both MST analysis approaches identified birds and humans as the primary sources of fecal contamination. For marine water stations, freshwater creeks VBU001, VBU002, and VB047, along with drain VB007, were identified as the main sources of human-derived fecal contamination in Vaughn Bay, based on Kendall's tau analysis (τ: 0.58-0.97). This information indicates that the septic systems in the catchment areas of these creeks and drains require further investigation to achieve effective fecal contamination control. Optical brightener, FIB enumeration and qPCR quantification results were generally higher during storm-flow events, although they showed poor correlation with each other (Pearson r < 0.40), likely due to physiological and phylogenetic differences among the target organisms of these methods. However, the sequencing-based method faces challenges in precise quantitative identification of differences in fecal contamination between base- and storm-flow events. Due to its high-throughput and cost-effectiveness, we recommend using sequencing-based analysis for large-scale identification of the primary sources of fecal contamination in water environments, followed by targeted qPCR quantification of MST markers for more precise assessments.

Campylobacter species, Salmonella serotypes and ribosomal RNA-based fecal source tracking in the Kokemäki River watershed.

Fecal contamination of surface water compromises the usability of surface water for drinking water production due to an increase in human health risks. In this study, we collected surface water samples for two years from the Kokemäki River (Finland). The downstream river stretch is used for feeding production of artificial ground water for a major drinking water treatment plant. The prevalence of Campylobacter species and Salmonella serotypes together with fecal source identifiers targeting general, human, gull, swine, and ruminant were evaluated at 16 sampling sites throughout the studied watershed. We detected Campylobacter spp. from all 16 sampling sites with Campylobacter jejuni and Campylobacter lari as the most detected species. Salmonella spp. was detected in 10 out of 16 sampling sites, with Salmonella Typhimurium being the most common serovar. Regarding spatial variation in the hygienic quality of surface water, the upstream area (urban proximity) and downstream area (agricultural proximity) had higher microbial loads than the middle section of the study area. Samples taken in fall and spring had higher microbial loads than summer and winter samples. The lower ratio of rRNA to rRNA-gene (rDNA) of studied microbes in the winter than in other seasons may indicate low metabolic activity of bacterial targets during winter. The number of gulls, swine, and cattle in the catchment area concorded with the number of fecal source identifiers in the surface water. Further, the prevalence of gull-specific source identifier agreed with the detection of C. coli, C. lari, and S. Typhimurim, whereas the prevalence of swine- and ruminant-specific source identifiers agreed with the detection of C. jejuni and C. coli. Thus, fecal source identifiers are shown to be important tools for monitoring zoonotic pathogens affecting microbial quality of surface water. Further, variation in fecal loads indicates such variation in health risks related to surface water use.

Faecal source apportionment using molecular methods: A proof of concept using the FEAST algorithm.

Faecal contamination of freshwater and marine environments represents a significant risk for public health, recreational activity and food safety, and tools for evaluating complex multi-source contamination remain largely in the development phase. We evaluated the efficacy of the Fast Expectation Maximization (FEAST) microbial source tracking (MST) algorithm to apportion sources of faecal contamination among four mammalian species of interest in coastal waters in New Zealand. Using 16S ribosomal DNA metabarcoding of faecal samples from cows, fur seals, and sheep, as well as human wastewater, we aimed to differentiate and quantify the contribution of these sources in mixed faecal samples. Multivariate analysis confirmed significant differences in the microbial communities associated with each mammalian source, with specific bacterial classes indicative of different sources. The FEAST algorithm was tested using mixed DNA and mixed faecal samples, and we found that the algorithm correctly assigned the dominant source from all samples, but underestimated the dominant source's proportional contribution. This underestimation suggests the need for further refinement and validation to ensure accurate source apportionment in environmental samples where the faecal signal is likely to be a minor component. Despite these limitations, the findings of our study, in combination with the evidence from others who have tested the FEAST algorithm in environmental settings, indicates that it represents an advance on existing tools for microbial source tracking and may become a useful addition to the toolbox for environmental management.

Origin of fecal contamination in lettuce and strawberries: From microbial indicators, molecular markers, and H. pylori.

Around 2 billion people utilize a water source contaminated with fecal-origin microorganisms, used for both human consumption and irrigation of crops. In Colombia, the water from the Bogotá River is employed for irrigating agricultural products, including raw-consumption foods like strawberries and lettuce. This poses a risk to the end consumer, as these foods are marketed as fresh products ready for direct consumption without undergoing any disinfection or cooking treatment. The aim of this study was to determine the origin of fecal contamination in strawberries and lettuce irrigated with surface waters from Cundinamarca, Colombia, using non-human and human molecular markers, along with Helicobacter pylori (H. pylori). A total of 50 samples were collected, 25 of strawberries and 25 of lettuce, taken from crops, markets, and supermarkets. Microbiological indicators (bacterial and viral) were detected through cultivation techniques, and Microbial Source Tracking (MST) markers and H. pylori were detected through PCR. The results of our study demonstrate the presence of Escherichia coli (E. coli) (12.5 %), Enterococcus (≥25 %), spores and vegetative forms of Spores of sulphite-reducing Clostridia (SRC) (≥37.5 %), coliphages (≥12.5 %), and Salmonella sp. (≥12.5 %), in both strawberries and lettuce. In the different samples analyzed, molecular markers were detected to differentiate the source of fecal contamination above 12.5 % (HF187, CF128, ADO and DEN) and H. pylori between 0 % and 25 %, highlighting deficiencies in the production chain. of food, and the risks they pose to food security. Highlighting deficiencies in the food production chain and the risks they pose to food safety.

Wastewater-based intestinal protozoa monitoring in Shanghai, China.

Intestinal protozoa Cryptosporidium spp., Giardia duodenalis, and Enterocytozoon bieneusi have been implicated in serious waterborne outbreaks worldwide. Wastewater-based epidemiology (WBE) is a promising approach for evaluating the disease prevalence in a catchment population in that it monitors the contamination level of the intestinal pathogens in wastewater. We collected 48 urban wastewater samples (24 from influents and 24 from effluents) from the Yangpu Wastewater Treatment Plant (YPWTP) in Shanghai, China. We identified Cryptosporidium spp., G. duodenalis, and E. bieneusi by nested polymerase chain reaction (PCR) amplification. Cryptosporidium hominis and subtype IdA14 were identified in two samples by analyzing the sequences of small subunit ribosomal RNA (SSU rRNA) and 60-kDa glycoprotein (gp60) genes, respectively. The G. duodenalis sub-assemblage AII (n = 8) and assemblage C (n = 4) in 12 samples were determined by analyzing triosephosphate isomerase (tpi) gene sequences. The E. bieneusi genotype A was identified in one sample by analyzing the sequence of the internal transcribed spacer (ITS) region of the rRNA gene. These findings suggest that improving wastewater treatment and monitoring the virility of pathogens in effluents is critical. We observed similar prevalence and genotypes/subtypes of the three intestinal protozoa in our wastewater samples as those reported in previous studies, providing evidence that WBE can be used as an effective epidemic management tool.IMPORTANCECryptosporidium spp., Giardia duodenalis, and Enterocytozoon bieneusi are common intestinal protozoa causing diarrhea. The infective oocysts, cysts, and spores released in feces can survive in different environments, including multiple types of water bodies. Humans can acquire these intestinal protozoan infections via the fecal-oral route as in waterborne transmission. Wastewater-based epidemiology can rapidly and reliably detect and monitor the emergence and spread of waterborne diseases. We detected Cryptosporidium spp., G. duodenalis, and E. bieneusi in a wastewater treatment plant in Shanghai, China, reflecting the occurrence and genetic characterizations of the three intestinal pathogens from community members served by the wastewater treatment plant.

Application of a microbial and pathogen source tracking toolbox to identify infrastructure problems in stormwater drainage networks: a case study.

Water scarcity and increasing urbanization are forcing municipalities to consider alternative water sources, such as stormwater, to fill in water supply gaps or address hydromodification of receiving urban streams. Mounting evidence suggests that stormwater is often contaminated with human feces, even in stormwater drainage systems separate from sanitary sewers. Pinpointing sources of human contamination in drainage networks is challenging given the diverse sources of fecal pollution that can impact these systems and the non-specificity of traditional fecal indicator bacteria (FIB) for identifying these host sources. As such, we used a toolbox approach that encompassed microbial source tracking (MST), FIB monitoring, and bacterial pathogen monitoring to investigate microbial contamination of stormwater in an urban municipality. We demonstrate that human sewage frequently contaminated stormwater (in >50% of routine samples), based on the presence of the human fecal marker HF183, and often exceeded microbial water quality criteria. Arcobacter butzleri, a pathogen of emerging concern, was also detected in >50% of routine samples, with 75% of these pathogen-positive samples also being positive for the human fecal marker HF183, suggesting human municipal sewage as the likely source for this pathogen. MST and FIB were used to track human fecal pollution in the drainage network to the most likely point source of contamination, for which a sewage cross-connection was identified and confirmed using tracer dyes. These results point to the ubiquitous presence of human sewage in stormwater and also provide municipalities with the tools to identify sources of anthropogenic contamination in storm drainage networks.IMPORTANCEWater scarcity, increased urbanization, and population growth are driving municipalities worldwide to consider stormwater as an alternative water source in urban environments. However, many studies suggest that stormwater is relatively poor in terms of microbial water quality, is frequently contaminated with human sewage, and therefore could represent a potential health risk depending on the type of exposure (e.g., irrigation of community gardens). Traditional monitoring of water quality based on fecal bacteria does not provide any information about the sources of fecal pollution contaminating stormwater (i.e., animals/human feces). Herein, we present a case study that uses fecal bacterial monitoring, microbial source tracking, and bacterial pathogen analysis to identify a cross-connection that contributed to human fecal intrusion into an urban stormwater network. This microbial toolbox approach can be useful for municipalities in identifying infrastructure problems in stormwater drainage networks to reduce risks associated with water reuse.

CrAss-like phages are suitable indicators of antibiotic resistance genes found in abundance in fecally polluted samples.

Antibiotic resistance genes (ARGs) have been extensively observed in bacterial DNA, and more recently, in phage particles from various water sources and food items. The pivotal role played by ARG transmission in the proliferation of antibiotic resistance and emergence of new resistant strains calls for a thorough understanding of the underlying mechanisms. The aim of this study was to assess the suitability of the prototypical p-crAssphage, a proposed indicator of human fecal contamination, and the recently isolated crAssBcn phages, both belonging to the Crassvirales group, as potential indicators of ARGs. These crAss-like phages were evaluated alongside specific ARGs (blaTEM, blaCTX-M-1, blaCTX-M-9, blaVIM, blaOXA-48, qnrA, qnrS, tetW and sul1) within the total DNA and phage DNA fractions in water and food samples containing different levels of fecal pollution. In samples with high fecal load (>103 CFU/g or ml of E. coli or somatic coliphages), such as wastewater and sludge, positive correlations were found between both types of crAss-like phages and ARGs in both DNA fractions. The strongest correlation was observed between sul1 and crAssBcn phages (rho = 0.90) in sludge samples, followed by blaCTX-M-9 and p-crAssphage (rho = 0.86) in sewage samples, both in the phage DNA fraction. The use of crAssphage and crAssBcn as indicators of ARGs, considered to be emerging environmental contaminants of anthropogenic origin, is supported by their close association with the human gut. Monitoring ARGs can help to mitigate their dissemination and prevent the emergence of new resistant bacterial strains, thus safeguarding public health.

Quantitative fecal pollution assessment with bacterial, viral, and molecular methods in small stream tributaries.

Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under 'wet' and 'dry' conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.

Stochastic processes drive divergence of bacterial and fungal communities in sympatric wild insect species despite sharing a common diet.

Arthropods harbor complex microbiota that play a pivotal role in host fitness. While multiple factors, like host species and diet, shape microbiota in arthropods, their impact on community assembly in wild insects remains largely unknown. In this study, we surveyed bacterial and fungal community assembly in nine sympatric wild insect species that share a common citrus fruit diet. Source tracking analysis suggested that these insects acquire some bacteria and fungi from the citrus fruit with varying degrees. Although sharing a common diet led to microbiota convergence, the diversity, composition, and network of both bacterial and fungal communities varied significantly among surveyed insect groups. Null model analysis indicated that stochastic processes, particularly dispersal limitation and drift, are primary drivers of structuring insect bacterial and fungal communities. Importantly, the influence of each community assembly process varied strongly depending on the host species. Thus, we proposed a speculative view that the host specificity of the microbiome and mycobiome assembly is widespread in wild insects despite sharing the same regional species pool. Overall, this research solidifies the importance of host species in shaping microbiomes and mycobiomes, providing novel insights into their assembly mechanisms in wild insects. IMPORTANCE: Since the microbiome has been shown to impact insect fitness, a mechanistic understanding of community assembly has potentially significant applications but remains largely unexplored. In this paper, we investigate bacterial and fungal community assembly in nine sympatric wild insect species that share a common diet. The main findings indicate that stochastic processes drive the divergence of microbiomes and mycobiomes in nine sympatric wild insect species. These findings offer novel insights into the assembly mechanisms of microbiomes and mycobiomes in wild insects.

Development of a novel crAss-like phage detection method with a broad spectrum for microbial source tracking.

CrAssphage has been recognized as the most abundant and human-specific bacteriophage in the human gut. Consequently, crAssphage has been used as a microbial source tracking (MST) marker to monitor human fecal contamination. Many crAss-like phages (CLPs) have been recently discovered, expanding the classification into the new order Crassvirales. This study aims to assess CLP prevalence in South Korea and develop a detection system for MST applications. Thirteen CLPs were identified in six human fecal samples and categorized into seven genera via metagenomic analysis. The major head protein (MHP) displayed increased sequence similarity within each genus. Eight PCR primer candidates, designed from MHP sequences, were evaluated in animal and human feces. CLPs were absent in animal feces except for those from raccoons, which hosted genera VI, VIIa, and VIIb. CLPs were detected in 91.52% (54/59) of humans, with genus VI (38 out of 59) showing the highest prevalence, nearly double that of p-crAssphage in genus I (22 out of 59). This study highlights genus VI as a potent MST marker, broadening the detection range for CLPs. Human-specific and selectively targeted MST markers can significantly impact hygiene regulations, lowering public health costs through their application in screening liver, sewage, wastewater, and various environmental samples.

Exploring seasonal variations, assembly dynamics, and relationships of bacterial communities in different habitats of marine ranching.

Offshore coastal marine ranching ecosystems provide habitat for diverse and active bacterial communities. In this study, 16S rRNA gene sequencing and multiple bioinformatics methods were applied to investigate assembly dynamics and relationships in different habitats. The higher number of edges in the water network, more balanced ratio of positive and negative links, and more keystone species included in the co-occurrence network of water. Stochastic processes dominated in shaping gut and sediment community assembly (R2 < 0.5), while water bacterial community assembly were dominated by deterministic processes (R2 > 0.5). Dissimilarity-overlap curve model indicated that the communities in different habitats have general dynamics and interspecific interaction (P < 0.001). Bacterial source-tracking analysis revealed that the gut was more similar to the sediment than the water bacterial communities. In summary, this study provides basic data for the ecological study of marine ranching through the study of bacterial community dynamics.

Fecal Microbiota Transplantation for Severe Infant Botulism, China.

Infant botulism in a 4-month-old boy in China who continued to excrete toxins for over a month despite antitoxin therapy was further treated with fecal microbiota transplantation. After treatment, we noted increased gut microbial diversity and altered fecal metabolites, which may help reduce intestinal pH and enhance anti-inflammatory capabilities.

Assessing the impact of inland navigation on the faecal pollution status of large rivers: A novel integrated field approach.

The contribution of ships to the microbial faecal pollution status of water bodies is largely unknown but frequently of human health concern. No methodology for a comprehensive and target-orientated system analysis was available so far. We developed a novel approach for integrated and multistage impact evaluation. The approach includes, i) theoretical faecal pollution source profiling (PSP, i.e., size and pollution capacity estimation from municipal vs. ship sewage disposal) for impact scenario estimation and hypothesis generation, ii) high-resolution field assessment of faecal pollution levels and chemo-physical water quality at the selected river reaches, using standardized faecal indicators (cultivation-based) and genetic microbial source tracking markers (qPCR-based), and iii) integrated statistical analyses of the observed faecal pollution and the number of ships assessed by satellite-based automated ship tracking (i.e., automated identification system, AIS) at local and regional scales. The new approach was realised at a 230 km long Danube River reach in Austria, enabling detailed understanding of the complex pollution characteristics (i.e., longitudinal/cross-sectional river and upstream/downstream docking area analysis). Faecal impact of navigation was demonstrated to be remarkably low at regional and local scale (despite a high local contamination capacity), indicating predominantly correct disposal practices during the investigated period. Nonetheless, faecal emissions were sensitively traceable, attributable to the ship category (discriminated types: cruise, passenger and freight ships) and individual vessels (docking time analysis) at one docking area by the link with AIS data. The new innovative and sensitive approach is transferrable to any water body worldwide with available ship-tracking data, supporting target-orientated monitoring and evidence-based management practices.

Benchmarking short and long read polishing tools for nanopore assemblies: achieving near-perfect genomes for outbreak isolates.

BACKGROUND: Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks. RESULTS: We tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct. CONCLUSIONS: Short reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers.

A case study showing highly traceable sources of bacteria on surfaces of university buildings.

University students predominantly spend their time indoors, where prolonged exposure raises the risk of contact with microorganisms of concern. However, our knowledge about the microbial community characteristics on university campus and their underpinnings is limited. To address it, we characterized bacterial communities from the surfaces of various built environments typical of a university campus, including cafeterias, classrooms, dormitories, offices, meeting rooms, and restrooms, in addition to human skin. The classrooms harbored the highest α-diversity, while the cafeterias had the lowest α-diversity. The bacterial community composition varied significantly across different building types. Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria were common phyla in university buildings, accounting for more than 90 % of total abundance. Staphylococcus aureus was the most abundant potential pathogen in classrooms, dormitories, offices, restrooms, and on human skin, indicating a potential risk for skin disease infections in these buildings. We further developed a new quantitative pathogenic risk assessment method according to the threat of pathogens to humans and found that classrooms exhibited the highest potential risk. The fast expectation-maximization algorithm identified 59 %-86 % of bacterial sources in buildings, with the human skin as the largest bacterial source for most buildings. As the sources of bacteria were highly traceable, we showed that homogeneous selection, dispersal limitation, and ecological drift were major ecological forces that drove community assembly. Our findings have important implications for predicting the distribution and sources of indoor dust bacterial communities on university campus.

Reduction in antimicrobial resistance in a watershed after closure of livestock farms.

Natural environments play a crucial role in transmission of antimicrobial resistance (AMR). Development of methods to manage antibiotic resistance genes (ARGs) in natural environments are usually limited to the laboratory or field scale, partially due to the complex dynamics of transmission between different environmental compartments. Here, we conducted a nine-year longitudinal profiling of ARGs at a watershed scale, and provide evidence that restrictions on livestock farms near water bodies significantly reduced riverine ARG abundance. Substantial reductions were revealed in the relative abundance of genes conferring resistance to aminoglycosides (42%), MLSB (36%), multidrug (55%), tetracyclines (53%), and other gene categories (59%). Additionally, improvements in water quality were observed, with distinct changes in concentrations of dissolved reactive phosphorus, ammonium, nitrite, pH, and dissolved oxygen. Antibiotic residues and other pharmaceuticals and personal care products (PPCPs) maintain at a similarly low level. Microbial source tracking demonstrates a significant decrease in swine fecal indicators, while human fecal pollution remains unchanged. These results suggest that the reduction in ARGs was due to a substantial reduction in input of antibiotic resistant bacteria and genes from animal excreta. Our findings highlight the watershed as a living laboratory for understanding the dynamics of AMR, and for evaluating the efficacy of environmental regulations, with implications for reducing environmental risks associated with AMR on a global scale.

Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking.

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.