Detecting early-warning biomarkers associated with heart-exosome genetic-signature for acute myocardial infarction: A source-tracking study of exosome.

The genetic information of plasma total-exosomes originating from tissues have already proven useful to assess the severity of coronary artery diseases (CAD). However, plasma total-exosomes include multiple sub-populations secreted by various tissues. Only analysing the genetic information of plasma total-exosomes is perturbed by exosomes derived from other organs except the heart. We aim to detect early-warning biomarkers associated with heart-exosome genetic-signatures for acute myocardial infarction (AMI) by a source-tracking analysis of plasma exosome. The source-tracking of AMI plasma total-exosomes was implemented by deconvolution algorithm. The final early-warning biomarkers associated with heart-exosome genetic-signatures for AMI was identified by integration with single-cell sequencing, weighted gene correction network and machine learning analyses. The correlation between biomarkers and clinical indicators was validated in impatient cohort. A nomogram was generated using early-warning biomarkers for predicting the CAD progression. The molecular subtypes landscape of AMI was detected by consensus clustering. A higher fraction of exosomes derived from spleen and blood cells was revealed in plasma exosomes, while a lower fraction of heart-exosomes was detected. The gene ontology revealed that heart-exosomes genetic-signatures was associated with the heart development, cardiac function and cardiac response to stress. We ultimately identified three genes associated with heart-exosomes defining early-warning biomarkers for AMI. The early-warning biomarkers mediated molecular clusters presented heterogeneous metabolism preference in AMI. Our study introduced three early-warning biomarkers associated with heart-exosome genetic-signatures, which reflected the genetic information of heart-exosomes carrying AMI signals and provided new insights for exosomes research in CAD progression and prevention.

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.

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.

Accounting for the microbial assembly of each process in wastewater treatment plants (WWTPs): study of four WWTPs receiving similar influent streams.

We evaluated a unique model in which four full-scale wastewater treatment plants (WWTPs) with the same treatment schematic and fed with similar influent wastewater were tracked over an 8-month period to determine whether the community assembly would differ in the activated sludge (AS) and sand filtration (SF) stages. For each WWTP, AS and SF achieved an average of 1-log10 (90%) and <0.02-log10 (5%) reduction of total cells, respectively. Despite the removal of cells, both AS and SF had a higher alpha and beta diversity compared to the influent microbial community. Using the Sloan neutral model, it was observed that AS and SF were individually dominated by different assembly processes. Specifically, microorganisms from influent to AS were predominantly determined by the selective niche process for all WWTPs, while the microbial community in the SF was relatively favored by a stochastic, random migration process, except two WWTPs. AS also contributed more to the final effluent microbial community compared with the SF. Given that each WWTP operates the AS independently and that there is a niche selection process driven mainly by the chemical oxygen demand concentration, operational taxonomic units unique to each of the WWTPs were also identified. The findings from this study indicate that each WWTP has its distinct microbial signature and could be used for source-tracking purposes.IMPORTANCEThis study provided a novel concept that microorganisms follow a niche assembly in the activated sludge (AS) tank and that the AS contributed more than the sand filtration process toward the final microbial signature that is unique to each treatment plant. This observation highlights the importance of understanding the microbial community selected by the AS stage, which could contribute toward source-tracking the effluent from different wastewater treatment plants.

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.

Quantitative microbial risk assessment for ingestion of antibiotic resistance genes from private wells contaminated by human and livestock fecal sources.

We used quantitative microbial risk assessment to estimate ingestion risk for intI1, erm(B), sul1, tet(A), tet(W), and tet(X) in private wells contaminated by human and/or livestock feces. Genes were quantified with five human-specific and six bovine-specific microbial source-tracking (MST) markers in 138 well-water samples from a rural Wisconsin county. Daily ingestion risk (probability of swallowing ≥1 gene) was based on daily water consumption and a Poisson exposure model. Calculations were stratified by MST source and soil depth over the aquifer where wells were drilled. Relative ingestion risk was estimated using wells with no MST detections and >6.1 m soil depth as a referent category. Daily ingestion risk varied from 0 to 8.8 × 10-1 by gene and fecal source (i.e., human or bovine). The estimated number of residents ingesting target genes from private wells varied from 910 (tet(A)) to 1,500 (intI1 and tet(X)) per day out of 12,000 total. Relative risk of tet(A) ingestion was significantly higher in wells with MST markers detected, including wells with ≤6.1 m soil depth contaminated by bovine markers (2.2 [90% CI: 1.1-4.7]), wells with >6.1 m soil depth contaminated by bovine markers (1.8 [1.002-3.9]), and wells with ≤6.1 m soil depth contaminated by bovine and human markers simultaneously (3.1 [1.7-6.5]). Antibiotic resistance genes (ARGs) were not necessarily present in viable microorganisms, and ingestion is not directly associated with infection. However, results illustrate relative contributions of human and livestock fecal sources to ARG exposure and highlight rural groundwater as a significant point of exposure.IMPORTANCEAntibiotic resistance is a global public health challenge with well-known environmental dimensions, but quantitative analyses of the roles played by various natural environments in transmission of antibiotic resistance are lacking, particularly for drinking water. This study assesses risk of ingestion for several antibiotic resistance genes (ARGs) and the class 1 integron gene (intI1) in drinking water from private wells in a rural area of northeast Wisconsin, United States. Results allow comparison of drinking water as an exposure route for antibiotic resistance relative to other routes like food and recreational water. They also enable a comparison of the importance of human versus livestock fecal sources in the study area. Our study demonstrates the previously unrecognized importance of untreated rural drinking water as an exposure route for antibiotic resistance and identifies bovine fecal material as an important exposure factor in the study setting.

Village Settlements in Mountainous Tropical Areas, Hotspots of Fecal Contamination as Evidenced by Escherichia coli and Stanol Concentrations in Stormwater Pulses.

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.

Mobile genetic elements affect the dissemination of antibiotic resistance genes (ARGs) of clinical importance in the environment.

The prevalence of antibiotic resistance genes (ARGs) in the environment is a quintessential One Health issue that threats both human and ecosystem health; however, the source and transmission of ARGs, especially clinically important ARGs (CLIARGs), in the environment have not yet been well studied. In the present study, shotgun metagenomic approaches were used to characterize the microbiome, resistome, and mobilome composition in human feces and six different environment sample types in South China. Overall, the resistome harbored 157 CLIARGs, with specific ARG hotspots (e.g., human feces, wastewater treatment plants, livestock manure and wastewater) excreting significantly higher abundance of CLIARGs compared with the natural environment. A redundancy analysis (RDA) was performed and revealed that the bacterial community compositions and mobile genetic elements (MGEs) explained 55.08% and 34.68% of the variations in ARG abundance, respectively, indicating that both bacterial community and MGEs are key contributors to the maintenance and dissemination of CLIARGs in the environment. The network analysis revealed non-random co-occurrence patterns between 200 bacterial genera and 147 CLIARGs, as well as between 135 MGEs and 123 CLIARGs. In addition to numerous co-shared CLIARGs among different sample types, the source tracking program based on the FEAST probabilistic model was used to estimate the relative contributions of the CLIARGs from potential sources to the natural environment. The source tracking analysis results delineated that mobilome, more than microbiome, contributed CLIARG transmission from those ARG hotspots into natural environment, and the MGEs in WWTPs seem to play the most significant role in the spread of CLIARGs to the natural environment (average contribution 32.9%-46.4%). Overall, this study demonstrated the distribution and dissemination of CLIARGs in the environment, and aimed to better inform strategies to control the spread of CLIARGs into the natural environment.

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

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

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.

Vertical stratification and seasonality of fecal indicator bacteria in New York City playground sandboxes.

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.

Epidemiological and Whole Genome Sequencing Analysis of Restaurant Salmonella Enteritidis Outbreak Associated with an Infected Food Handler in Jiangxi Province, China, 2023.

In China, Salmonella is one of the most frequent causes of bacterial gastroenteritis, and food handlers in restaurants as an important contaminated source were rarely reported. In May 2023, an outbreak of Salmonella enterica serovar Enteritidis infection in a restaurant in Jiangxi Province, China, was investigated. Cases were interviewed. Stool samples from cases, anal swabs from restaurant employees, suspicious raw food materials, and semifinished food were collected and examined. Pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS) were performed to determine the relatedness of the pathogen isolates. Antimicrobial resistance genes and virulence genes of isolates were analyzed by WGS. The antimicrobial profile of the isolates was detected by broth microdilution, which involved 20 different antibiotics. Among the 31 patrons, 26 showed gastrointestinal symptoms. Five Salmonella Enteritidis strains were isolated from patients (2), semifinished food (2), and food handler (1). The results of PFGE and single-nucleotide polymorphism showed that these five isolates were identical clones. These findings demonstrated that this outbreak was a restaurant Salmonella Enteritidis outbreak associated with an infected food handler. The rates of resistance to nalidixic acid and colistin and intermediate resistance to ciprofloxacin were 100%, 80%, and 100%, respectively. These outbreak isolates harbored point mutation gyrA p.D87G. The cause of inconsistency between the genotype and phenotype of resistance was deeply discussed. A total of 107 virulence genes were found in each isolate, with many being associated with Salmonella pathogenicity island (SPI)-1 and SPI-2. As an overlooked contamination source, infected food handlers can easily cause large-scale outbreaks. This outbreak highlighted that the government should enhance the training and supervision of food hygiene and safety for food handlers to prevent foodborne outbreaks.

Preliminary Characterization of an Active CMOS Pad Detector for Tracking and Dosimetry in HDR Brachytherapy.

We assessed the accuracy of a prototype radiation detector with a built in CMOS amplifier for use in dosimetry for high dose rate brachytherapy. The detectors were fabricated on two substrates of epitaxial high resistivity silicon. The radiation detection performance of prototypes has been tested by ion beam induced charge (IBIC) microscopy using a 5.5 MeV alpha particle microbeam. We also carried out the HDR Ir-192 radiation source tracking at different depths and angular dose dependence in a water equivalent phantom. The detectors show sensitivities spanning from (5.8 ± 0.021) × 10-8 to (3.6 ± 0.14) × 10-8 nC Gy-1 mCi-1 mm-2. The depth variation of the dose is within 5% with that calculated by TG-43. Higher discrepancies are recorded for 2 mm and 7 mm depths due to the scattering of secondary particles and the perturbation of the radiation field induced in the ceramic/golden package. Dwell positions and dwell time are reconstructed within ±1 mm and 20 ms, respectively. The prototype detectors provide an unprecedented sensitivity thanks to its monolithic amplification stage. Future investigation of this technology will include the optimisation of the packaging technique.

Occurrence, fate, and risk assessment of antibiotics in conventional and advanced drinking water treatment systems: From source to tap.

The occurrence and removal of 38 antibiotics from nine classes in two drinking water treatment plants (WTPs) were monitored monthly over one year to evaluate the efficiency of typical treatment processes, track the source of antibiotics in tap water and assess their potential risks to ecosystem and human health. In both source waters, 18 antibiotics were detected at least once, with average total antibiotic concentrations of 538.5 ng/L in WTP1 and 569.3 ng/L in WTP2. The coagulation/flocculation and sedimentation, sand filtration and granular activated carbon processes demonstrated limited removal efficiencies. Chlorination, on the other hand, effectively eliminated antibiotics by 48.7 ± 11.9%. Interestingly, negative removal was observed along the distribution system, resulting in a significant antibiotic presence in tap water, with average concentrations of 131.5 ng/L in WTP1 and 362.8 ng/L in WTP2. Source tracking analysis indicates that most antibiotics in tap water may originate from distribution system. The presence of antibiotics in raw water and tap water posed risks to the aquatic ecosystem. Untreated or partially treated raw water could pose a medium risk to infants under six months. Water parameters, for example, temperature, total nitrogen and total organic carbon, can serve as indicators to estimate antibiotic occurrence and associated risks. Furthermore, machine learning models were developed that successfully predicted risk levels using water quality parameters. Our study provides valuable insights into the occurrence, removal and risk of antibiotics in urban WTPs, contributing to the broader understanding of antibiotic pollution in water treatment systems.

Fecal Impairment Framework, A New Conceptual Framework for Assessing Fecal Contamination in Recreational Waters.

Fecal pollution of surface water is a pervasive problem that negatively affects waterbodies concerning both public health and ecological functions. Current assessment methods monitor fecal indicator bacteria (FIB) to identify pollution sources using culture-based quantification and microbial source tracking (MST). These types of information assist stakeholders in identifying likely sources of fecal pollution, prioritizing them for remediation, and choosing appropriate best management practices. While both culture-based quantification and MST are useful, they yield different kinds of information, potentially increasing uncertainty in prioritizing sources for management. This study presents a conceptual framework that takes separate human health risk estimates based on measured MST and E. coli concentrations as inputs and produces an estimate of the overall fecal impairment risk as its output. The proposed framework is intended to serve as a supplemental screening tool for existing monitoring programs to aid in identifying and prioritizing sites for remediation. In this study, we evaluated the framework by applying it to two primarily agricultural watersheds and several freshwater recreational beaches using existing routine monitoring data. Based on a combination of E. coli and MST results, the proposed fecal impairment framework identified four sites in the watersheds as candidates for remediation and identified temporal trends in the beach application. As these case studies demonstrate, the proposed fecal impairment framework is an easy-to-use and cost-effective supplemental screening tool that provides actionable information to managers using existing routine monitoring data, without requiring specialized expertize.

Assessment of plasmids for relating the 2020 Salmonella enterica serovar Newport onion outbreak to farms implicated by the outbreak investigation.

BACKGROUND: The Salmonella enterica serovar Newport red onion outbreak of 2020 was the largest foodborne outbreak of Salmonella in over a decade. The epidemiological investigation suggested two farms as the likely source of contamination. However, single nucleotide polymorphism (SNP) analysis of the whole genome sequencing data showed that none of the Salmonella isolates collected from the farm regions were linked to the clinical isolates-preventing the use of phylogenetics in source identification. Here, we explored an alternative method for analyzing the whole genome sequencing data driven by the hypothesis that if the outbreak strain had come from the farm regions, then the clinical isolates would disproportionately contain plasmids found in isolates from the farm regions due to horizontal transfer. RESULTS: SNP analysis confirmed that the clinical isolates formed a single, nearly-clonal clade with evidence for ancestry in California going back a decade. The clinical clade had a large core genome (4,399 genes) and a large and sparsely distributed accessory genome (2,577 genes, at least 64% on plasmids). At least 20 plasmid types occurred in the clinical clade, more than were found in the literature for Salmonella Newport. A small number of plasmids, 14 from 13 clinical isolates and 17 from 8 farm isolates, were found to be highly similar (> 95% identical)-indicating they might be related by horizontal transfer. Phylogenetic analysis was unable to determine the geographic origin, isolation source, or time of transfer of the plasmids, likely due to their promiscuous and transient nature. However, our resampling analysis suggested that observing a similar number and combination of highly similar plasmids in random samples of environmental Salmonella enterica within the NCBI Pathogen Detection database was unlikely, supporting a connection between the outbreak strain and the farms implicated by the epidemiological investigation. CONCLUSION: Horizontally transferred plasmids provided evidence for a connection between clinical isolates and the farms implicated as the source of the outbreak. Our case study suggests that such analyses might add a new dimension to source tracking investigations, but highlights the need for detailed and accurate metadata, more extensive environmental sampling, and a better understanding of plasmid molecular evolution.

Risk of Gastroenteritis from Swimming at a Wastewater-Impacted Tropical Beach Varies across Localized Scales.

Population growth and changing climate are expected to increase human exposure to pathogens in tropical coastal waters. We examined microbiological water quality in three rivers within 2.3 km of each other that impact a Costa Rican beach and in the ocean outside their plumes during the rainy and dry seasons. We performed quantitative microbial risk assessment (QMRA) to predict the risk of gastroenteritis associated with swimming and the amount of pathogen reduction needed to achieve safe conditions. Recreational water quality criteria based on enterococci were exceeded in >90% of river samples but in only 13% of ocean samples. Multivariate analysis grouped microbial observations by subwatershed and season in river samples but only by subwatershed in the ocean. The modeled median risk from all pathogens in river samples was between 0.345 and 0.577, 10-fold above the U.S. Environmental Protection Agency (U.S. EPA) benchmark of 0.036 (36 illnesses/1,000 swimmers). Norovirus genogroup I (NoVGI) contributed most to risk, but adenoviruses raised risk above the threshold in the two most urban subwatersheds. The risk was greater in the dry compared to the rainy season, due largely to the greater frequency of NoVGI detection (100% versus 41%). Viral log10 reduction needed to ensure safe swimming conditions varied by subwatershed and season and was greatest in the dry season (3.8 to 4.1 dry; 2.7 to 3.2 rainy). QMRA that accounts for seasonal and local variability of water quality contributes to understanding the complex influences of hydrology, land use, and environment on human health risk in tropical coastal areas and can contribute to improved beach management. IMPORTANCE This holistic investigation of sanitary water quality at a Costa Rican beach assessed microbial source tracking (MST) marker genes, pathogens, and indicators of sewage. Such studies are still rare in tropical climates. Quantitative microbial risk assessment (QMRA) found that rivers impacting the beach consistently exceeded the U.S. EPA risk threshold for gastroenteritis of 36/1,000 swimmers. The study improves upon many QMRA studies by measuring specific pathogens, rather than relying on surrogates (indicator organisms or MST markers) or estimating pathogen concentrations from the literature. By analyzing microbial levels and estimating the risk of gastrointestinal illness in each river, we were able to discern differences in pathogen levels and human health risks even though all rivers were highly polluted by wastewater and were located less than 2.5 km from one another. This variability on a localized scale has not, to our knowledge, previously been demonstrated.

The Tomato Brown Rugose Fruit Virus Movement Protein Gene Is a Novel Microbial Source Tracking Marker.

Microbial source tracking (MST) identifies sources of fecal contamination in the environment using host-associated fecal markers. While there are numerous bacterial MST markers that can be used herein, there are few such viral markers. Here, we designed and tested novel viral MST markers based on tomato brown rugose fruit virus (ToBRFV) genomes. We assembled eight nearly complete genomes of ToBRFV from wastewater and stool samples from the San Francisco Bay Area in the United States. Next, we developed two novel probe-based reverse transcription-PCR (RT-PCR) assays based on conserved regions of the ToBRFV genome and tested the markers' sensitivities and specificities using human and non-human animal stool as well as wastewater. The ToBRFV markers are sensitive and specific; in human stool and wastewater, they are more prevalent and abundant than a commonly used viral marker, the pepper mild mottle virus (PMMoV) coat protein (CP) gene. We used the assays to detect fecal contamination in urban stormwater samples and found that the ToBRFV markers matched cross-assembly phage (crAssphage), an established viral MST marker, in prevalence across samples. Taken together, these results indicate that ToBRFV is a promising viral human-associated MST marker. IMPORTANCE Human exposure to fecal contamination in the environment can cause transmission of infectious diseases. Microbial source tracking (MST) can identify sources of fecal contamination so that contamination can be remediated and human exposures can be reduced. MST requires the use of host-associated MST markers. Here, we designed and tested novel MST markers from genomes of tomato brown rugose fruit virus (ToBRFV). The markers are sensitive and specific to human stool and highly abundant in human stool and wastewater samples.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility.

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Antibiotic resistome in groundwater and its association with mountain springs and river.

The distribution of antibiotic resistance genes (ARGs) in water sources potentially threatens drinking water safety. However, the sources of antibiotic resistome in groundwater are still under-investigated. Here, we evaluated the profiles of antibiotic resistome in peri-urban groundwater and its associated water sources (river and mountain spring) to characterize the antibiotic resistome from natural water sources on groundwater resistome. A total of 261 antibiotic resistome were detected in groundwater, mountain spring, and river samples. The relative abundances of ARGs and mobile genetic elements (MGEs) were significantly higher in the river samples than in spring water and groundwater samples. The resistome profiles were similar between groundwater and spring water but differed from the river samples. According to source tracking results, the groundwater resistome was likely to be derived from springs (28.0%-50.0%) and rivers (28.6%-48.6%), which share the same trend for the source tracking of bacterial communities. Bacterial α-diversity, bacterial ß-diversity, and MGEs directly or indirectly affected the ARGs in groundwater samples. Although the abundance of groundwater resistome was not elevated by river and spring water, groundwater resistomes were diverse and may be derived from both river and spring water. We highlight the importance of groundwater resistome and its association with potential water sources, providing a better understanding and basis for the effective control of the ARG proliferation and dissemination in groundwater from exogenous water bodies in the future.