Water Research

The selection of appropriate viral indicators for evaluating wastewater treatment performance remains challenging because candidate markers have rarely been compared systematically within a unified analytical framework. Here, we collected influent and effluent samples monthly for one year from two wastewater treatment plants in Japan and conducted, to our knowledge, the first comprehensive comparison of 19 viral targets and one protozoan target using high-throughput quantitative PCR. Pepper mild mottle virus (PMMoV) was consistently detected at high concentrations, showed limited seasonal variability, and exhibited an approximately 1.0 log10 reduction, comparable to those observed for pathogenic viruses. In contrast, Carjivirus, formerly known as crAssphage, was present at the highest concentrations but showed significantly greater reduction than pathogenic viruses. Tomato brown rugose fruit virus (ToBRFV), despite its high abundance and emerging recognition as a potential marker, exhibited pronounced seasonal fluctuations. Other Tobamovirus species, such as cucumber green mottle mosaic virus and tobacco mild green mosaic virus, exhibited similar removal but lower prevalence compared with PMMoV. Overall, PMMoV demonstrated the most balanced performance in terms of abundance, stability, and removal behavior, supporting its use as a robust indicator for monitoring virus removal in wastewater treatment.

Wastewater treatment plants (WWTPs) are known reservoirs of antibiotic resistance genes (ARGs). Non-antibiotic compounds such as antidepressants may further promote ARG acquisition through horizontal gene transfer (HGT). Desvenlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI) listed on the EU Surface Water Watch Lists, is among the most frequently detected antidepressants in WWTP effluents, yet its role in HGT has not been examined. Here, we detected desvenlafaxine at the highest concentrations among four antidepressants monitored across three municipal WWTPs in western New York. Using Acinetobacter baylyi ADP1 as a model recipient in natural transformation assays (n = 6), we found that desvenlafaxine significantly increased transformation frequency at 10 mg/L (1.74 {+/-} 0.33-fold) and 50 mg/L (1.49 {+/-} 0.19-fold; Padj < 0.05). Effects were independent of reactive oxygen species or membrane permeability stress, consistent with its very low toxicity (IC20 ~1353 mg/L). Instead, desvenlafaxine induced dose-dependent increases in membrane fluidity and shifts to less negative zeta potentials, suggesting that electrostatic interactions between its cationic amine group and the negatively charged membrane reduce surface repulsion and facilitate plasmid proximity during uptake. Non-targeted proteomics revealed a biphasic response: at 10 mg/L, competence-associated proteins (PilB, ComM) were upregulated and STRING analysis identified networks linked to membrane transport, transcriptional regulation, and envelope remodeling, while no connected network was recovered at 50 mg/L. Electron microscopy confirmed higher pili frequency at both doses. Together, these findings reveal an overlooked role of this non-antibiotic pharmaceutical in promoting ARG spread from wastewater environments.

The 18S rRNA gene has emerged as the primary molecular marker for amplicon-based characterization of microalgal communities, including in wastewater treatment systems, yet trade-offs between short- and long-read approaches remain poorly defined. We systematically compared V8-V9 short-read sequencing (Illumina MiSeq), full-length long-read sequencing with ss5ss3 primers (PacBio Sequel II), and computationally extracted V8-V9 regions from long-read data. Both in silico and in vitro analyses confirmed V8-V9 captured broader taxonomic coverage than ss5ss3, though partial reference sequences and taxonomic mis-annotations biased in silico assessments. Long-reads taxonomic advantage was database-dependent, constrained by SILVA databases genus-level curation but fully realized when paired with the species-level-curated and eukaryotes-focused PR{superscript 2} (Protist Ribosomal Reference) database. Long-read sequencing uniquely identified amplicon sequence variants (ASVs) assigned to key phosphorus assimilators (Scenedesmus obliquus, Desmodesmus sp., and Acutodesmus sp.) at species level during successful phosphorus removal in a full-scale microalgal cultivation system, while V8-V9 short-read sequencing revealed ASVs assigned to algal-predatory (Leptophryidae) and bacterivorous (Choanoflagellata and Rhogostoma-lineage) protists when performance declined, suggesting grazing pressure on the phosphorus-removing community. Although both approaches performed comparably for operational monitoring, these complementary strengths support short-read sequencing for routine community profiling and long-read sequencing for detailed functional investigations of Chlorophyta.

Accelerating the development of enzymatic degradation of polyesters such as poly(ethylene terephthalate) (PET) and poly(butylene terephthalate) (PBT) requires a rapid and parallelizable detection method. We developed a protein-based biosensor for the fast and accurate quantification of the PET and PBT degradation product, terephthalate (TPA), which we named TPAsense. Engineering TPAsense required overcoming low thermal stability and aggregation of the initial construct by introducing stabilizing mutations without disrupting the binding affinity to TPA. The sensor performance was validated by screening for the PBT degrading activity of a Leaf-branch Compost Cutinase (LCC) mutant library and comparing with liquid chromatography data. TPAsense detects nanomolar concentrations of TPA enabling shorter incubation times for screening workflows. In addition, a comparative analysis of PETase and PBTase kinetics was performed with TPAsense. Finally, we demonstrated the detection of PET microplastic in samples from a wastewater treatment plant by combining the biosensor and a PETase. TPAsense offers a platform to accelerate PETase and PBTase development for plastic waste recycling and detection of microplastic in the environment.

Influenza A viruses (IAV) remain a persistent One Health threat, and whole-genome sequencing from wastewater offers a promising surveillance tool. However, IAV is at low abundance in wastewater, making it difficult to sequence. We benchmarked four targeted enrichment methods suited for whole-genome sequencing including custom and off-the-shelf amplicon and probe-based methods. Our custom HA tiled-amplicon panel was sensitive, fast, and cost-effective, making it suitable for monitoring low-abundance seasonal variants of known subtypes. However, its reliance on conserved and intact primer-binding sites limited primer design to fewer subtypes. A previously published universal amplicon method targeted all IAV subtypes, but it performed poorly in wastewater due to its reliance on intact genome segments. Probe-capture methods were resilient to RNA degradation and mismatches, potentially enabling broader surveillance and detection of emerging strains. However, probes were costly, labor-intensive, and less sensitive than tiled-amplicon. When testing compatibility of sequencing methods with upstream virus concentration and extraction methods, ultrafiltration-based virus concentration outperformed large-volume direct extraction with all four sequencing methods. This set of benchmarking comparisons and custom panels provides needed information for the translation of IAV genomic sequencing into a routine component of wastewater surveillance.

Isoprenoid quinones are ubiquitous redox lipids that mediate electron transfer in various cellular processes across all domains of life. These molecules also serve as taxonomic and metabolic markers, facilitating the characterisation of microbial communities. However, their structural diversity and extreme hydrophobicity are challenging for comprehensive detection and quantification in complex biological matrices. In this study, we present a semi-quantitative HPLC-MS/MS method that enables the sensitive analysis of the widest range of quinones reported to date. Using a 16-quinone standard mixture, we optimised separation within a 14-minute HPLC gradient and achieved femtomole-level sensitivity in targeted analyses. When applied to sewage sludges sampled weekly over three weeks, our method detected 57 distinct quinones, revealing stage-specific quinone profiles that reflect shifts in bacterial communities during wastewater treatment. This rapid and sensitive workflow provides a robust tool for accurate quinone profiling in complex samples, opening avenues for the discovery of novel quinones through untargeted approaches. By pushing the boundaries of quinone profiling, our method holds significant promise for advancing microbial ecology, environmental monitoring, and biotechnological applications.

Enteroviruses (EVs) are ubiquitous contaminants of surface waters, where they can remain infectious for long periods of time. Most methods used for EV monitoring are unable to distinguish between infectious and non-infectious particles or between EV types. Because different types exhibit both distinct environmental persistence and health implications, there is a need for type-resolved infectivity measurements. Here we developed Integrated Cell Culture-Nanopore Sequencing (ICC-NanoporeSeq), a method combining short-term cell culture amplification with Nanopore sequencing of the VP1 gene. The ICC approach was adapted from a previously described ICC-RTqPCR protocol, while the NanoporeSeq workflow was derived from a clinical EV typing protocol and optimized for environmentally circulating EV types. Using samples containing known concentrations of ten EV types, the NanoporeSeq method accurately and reproducibly recovered the original proportions of all EV types after correction of biases. Furthermore, type-specific calibration curves generated with ICC-NanoporeSeq enabled quantification of the infectious concentrations of six EV types, allowing a simultaneous and type-resolved assessment of infectivity in mixed samples. Overall, ICC-NanoporeSeq provides a scalable approach for the parallel analysis of multiple EV types. Compared with the predecessor ICC-RTqPCR method, it eliminates the need for multiple type-specific PCR primers and can therefore be readily expanded to include additional EV types. IMPORTANCECurrent methods used to detect EVs in environmental samples generally measure viral genome copies without determining whether viruses remain infectious, limiting their use in public health risk assessment or water quality monitoring. At the same time, available infectivity assays are often labor-intensive and cannot distinguish between different EV types. Here, we developed ICC-NanoporeSeq, a method combining cell culture and Nanopore sequencing to simultaneously quantify the infectious concentrations of multiple EV types in samples containing mixed EV populations. The method provides an efficient and scalable approach for studying EVs in complex environmental matrices. ICC-NanoporeSeq has potential applications in wastewater-based epidemiology, environmental surveillance, and disinfection studies, where understanding the persistence of different EV types simultaneously is crucial.

Mass gatherings pose significant public health risks by facilitating the spread of infectious diseases. While wastewater-based surveillance (WBS) has been widely used to monitor pathogens in high-income settings, its use as a practical, multi-pathogen surveillance tool during mass gatherings in low- and middle-income countries remains limited. This study aimed to assess the operational feasibility, epidemiological significance, and public health utility of multi-pathogen WBS during the African Nations Championship (CHAN) football tournament in Uganda. Wastewater surveillance was conducted at Mandela National Stadium during eight match days in August 2025. Moore swabs were deployed at 38 manholes receiving wastewater from different toilet facilities across the stadium to capture representative wastewater samples. Samples were processed using Nanotrap(R) microbiome virus particles to concentrate pathogens, followed by nucleic acid extraction. Samples were analyzed for multiple enteric and respiratory pathogens, including Mpox, using quantitative PCR (qPCR). Descriptive analyses were performed to characterize pathogen detection patterns, positivity rates, and temporal distribution across surveillance sites. A total of 304 wastewater samples were collected and analyzed, of which 259 (85.2%) tested positive for at least one pathogen. Multiple pathogens were consistently detected across sampling days, with enteric pathogens predominating, particularly Shigella spp. (53.6%), Rotavirus A (35.9%) and Enterovirus (32.2%). The mpox virus was also detected in a notable proportion of samples (28.6%) across several sampling days. Respiratory pathogens, including SARS-CoV-2 (11.8%) and Influenza B (8.2%), were identified intermittently at lower frequencies. Pathogen diversity varied over time, with up to eight pathogens detected on a single day, and co-detection of multiple pathogens observed in the majority of positive samples. Cq value distributions further demonstrated variability in detected signal patterns across pathogens. Surveillance findings informed real-time public health interventions, including sanitation reinforcement, intensified hygiene promotion, environmental disinfection, and targeted risk communication, strengthened syndromic surveillance with on-site triage, and targeted environmental health assessments of food handling and wastewater infrastructure. These findings demonstrate the operational feasibility and public health utility of integrating multi-pathogen wastewater-based surveillance into mass-gathering preparedness and response frameworks in low-resource settings. By capturing diverse pathogen signals and informing targeted interventions during the CHAN football tournament, WBS can provide actionable population-level insights that can support outbreak preparedness and response. Scaling WBS within national preparedness systems could strengthen epidemic intelligence, enhance early warning capacity, and support data-driven public health decision-making during future mass gatherings and emerging infectious disease threats.

Respiratory Syncytial Virus (RSV) is responsible for a substantial health burden worldwide, particularly among children and older adults. In 2023, novel immunoprophylactic interventions for RSV were approved, underscoring the need to monitor circulating RSV lineages and detect mutations that could compromise intervention effectiveness. Here, we implemented wastewater-based genomic RSV surveillance by integrating digital PCR and amplicon-based sequencing within Switzerland's national wastewater monitoring program. We tracked RSV subtypes and individual mutations across the 2024-2025 peak season in six Swiss cities. RSV-A and RSV-B co-circulated nationwide, and both exhibited similar epidemiological dynamics estimated from their subtype-specific effective reproduction numbers. No previously reported F protein mutations relevant to prophylaxis efficacy were identified. Genetic diversity analysis of wastewater-derived sequences reflected patterns previously reported in clinical data, with higher diversity in RSV-A than RSV-B and greater variability in the G compared to the F gene. These findings demonstrate the potential of wastewater-based RSV surveillance for monitoring RSV dynamics and diversity and establish a national baseline for RSV evolution during the first season following vaccine implementation in Switzerland.

Favoured by global changes, freshwater cyanobacterial harmful blooms generate major ecological, economical and public health challenges. Microcystis, one of the most widespread cyanobacterial genera, grows within a phycosphere where specialised interactions with its microbiome occur, and are suspected to influence bloom appearance and its potential toxicity. Using a combination of metagenomic, metabolomic and metabolic modelling, we characterised the phycospheres of twelve Microcystis strains isolated from a French pond. The distribution of metabolic reactions within Microcystis was consistent with their genospecies, whereas the metabolic landscape at the community level diverged from cyanobacterial phylogeny indicating functional decoupling between cyanobacteria and their associated microbiomes. Phycosphere-associated bacteria substantially expand the metabolic repertoire of the system, while maintaining functional redundancy within and across communities. On the other hand, metabolomic profiles were largely driven by cyanobacterial metabolic outputs. Metabolic modelling, together with the identification of toxic specialised metabolites produced by specific biosynthetic gene clusters, further highlighted differences in metabolic potential among phycospheres. Together, these findings deepen the understanding of Microcystis phycosphere functioning, demonstrate the value of multi-omics systems biology approaches, and underscore the ecological relevance of interspecies and inter-phycosphere metabolic interactions as a structuring process in bloom-associated microbiomes.

Wastewater and environmental monitoring (WEM) was a critical public health surveillance tool for SARS-CoV-2 surveillance during the COVID-19 Pandemic. However, substantial methodological heterogeneity across laboratories continues to challenge the interpretation and thus compromise the actionability of resulting WEM measurements. This study quantifies interlaboratory concordance in SARS-CoV-2 WEM measurements using influent wastewater samples collected between September 2021 and January 2024 at a single wastewater treatment facility within the Ontario Wastewater Surveillance Initiative, analyzed independently by 12 laboratories using their routine methods. In the absence of a known true viral concentration, interlaboratory WEM measurements were evaluated against a facility-specific longitudinal benchmark derived from routine surveillance at the source facility and correlated to clinical surveillance metrics. Concordance was assessed across four WEM measurement units commonly used in practice: SARS-CoV-2 copies/mL, SARS-CoV-2 copies/copies of PMMoV, and their standardized counterpart wastewater viral activity level (WVAL) units of WVAL-standardized SARS-CoV-2 copies/mL and WVAL-standardized SARS-CoV-2 copies/copies of PMMoV. Measurements in each unit were analyzed using complementary analytical frameworks, including categorical concordance metrics, principal component analysis, and linear mixed-effects modelling. Across the study period, interlaboratory measurements consistently captured benchmark temporal dynamics, including major peaks and periods of low activity, but showed substantial variation in magnitude and public-health interpretation across laboratory methods. Concordance was strongest during epidemiological extremes and deteriorated during transitional periods, increasing the risk of misclassification with potentially implications for public health decision-making. To explore potential laboratory methodological drivers of agreement, associations between the benchmark concordance and the laboratory-specific concentration, extraction, and RT-qPCR analytical steps were assessed using Fishers exact tests, alongside extracted-mass threshold analyses. No single methodological factor showed a statistically significant association with benchmark concordance in this study; however, several parameters, including RNA template volume, total RT-qPCR reaction volume, and extracted mass of analyzed settled solids, may warrant further investigation in future studies.

Extracellular vesicles (EVs) are evolutionarily conserved mediators of intercellular communication released by cells into biological fluids and the extracellular environment. Despite their growing relevance in biomedical and veterinary research, knowledge on EVs in marine bivalves remains limited. The aim of this study was to optimize tailored protocols for EV isolation from the hemolymph of the Manila clam (Ruditapes philippinarum) based on density gradient ultracentrifugation (dgUC) or size exclusion chromatography (SEC). EV-enriched fractions were identified through nanoparticle tracking analysis, protein quantification, transmission electron microscopy, and cryo-electron microscopy. Both methods successfully isolated small EVs (<200 nm). While dgUC yielded higher-purity preparations, SEC provided a higher recovery rate and compatibility with downstream metabolomic analyses. Metabolomics performed on SEC fractions and on hemolymph, revealed that EV-enriched fractions possessed a distinct metabolic signature including enrichment in metabolites associated with nucleotide metabolism, glycolysis, redox regulation, and energy metabolism. Furthermore, we performed a pilot investigation into the presence of EVs released into conditioned water by Manila clams. Using tangential flow filtration and ultrafiltration, EVs were successfully concentrated from water samples and characterized by nanoparticle tracking analysis, CONAN assay, atomic force microscopy, and electron microscopy. Our findings demonstrate the feasibility of isolating EVs both from Manila clam hemolymph and from conditioned water, providing the first evidence of water-derived EV recovery in aquatic animals. Although further methodological refinement is needed to improve the purity of EVs isolated from water, and additional characterization studies are required to better define the molecular composition of clam-derived EVs, these results establish a foundation for future investigations into the role of EVs in bivalve biology and their potential application as minimally invasive biomarkers for aquaculture, environmental monitoring, and ecosystem health assessment.

Background Following the end of a public health emergency of international concern, divergence emerged between reported coronavirus disease 2019 (COVID-19) cases and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater. Exploring viral, clinical, patient, and surveillance-related factors underlying this divergence, we developed models to predict clinically confirmed infections, hospitalizations, and severe cases. Methods In this observational study, we analyzed ~2 years of data from January 2022 in Kanagawa Prefecture, Japan, assessing associations between wastewater SARS-CoV-2 RNA concentrations and confirmed, hospitalized, and severe cases, adjusting for wave and variant effects. Findings Our models based on wastewater viral RNA concentrations showed high predictive accuracy (R^2 = 0.8199-0.9961), closely tracking confirmed, hospitalized, and severe cases. Models derived from earlier waves were applied to subsequent waves with residual correction based on prior prediction errors and maintained good predictive performance (root mean square error = 0.0665-0.2065). Divergence between wastewater viral RNA concentrations and reported cases was not explained by changes in viral shedding. Declines in patients' healthcare-seeking behavior and testing were associated with trends in confirmed cases, whereas milder clinical presentation was associated with severe case trends. The lineages XBB.1.9.2 and BA.2.86 were identified as candidates associated with reduced virulence. Interpretation By incorporating understanding of viral, clinical, and surveillance-related mechanisms, wastewater surveillance may enable prediction of case trends approximately one week earlier than official reporting and inform healthcare capacity planning.

Wastewater-based epidemiology provides a scalable, noninvasive framework for population-level infectious disease monitoring, but traditional assays limit detection breadth and genomic insight. To address these constraints, we conducted targeted hybrid capture virome sequencing across 15 Texas cities over three years, from 2023 to 2025, generating [~]3 billion viral reads and identifying more than 900 strains across 374 species. Comprehensive temporal and spatial analysis revealed that the wastewater virome exhibits strong, predictable seasonal patterns, which grouped into three dominant seasonal clusters encompassing human, animal, and plant pathogens. Correlation network analysis revealed numerous positive co-occurrence patterns, including seasonal viral pairings, suggesting that the virome functions as a structured and interconnected ecological system. Leveraging this structure, we developed machine learning models using site-specific historical data to forecast individual viral species one month in advance. Of the 159 species modeled, approximately half achieved prediction performance of Pearsons Correlation Coefficient R{superscript 2} [&ge;] 0.50, and many exceeded R{superscript 2} [&ge;] 0.75. Classification models accurately inferred the month and season of sample collection (AUROC > 0.85 and > 0.95, respectively). Predictive features frequently included other viruses and temporal indicators, highlighting networked, seasonal virome dynamics. Sentinel pathogens (e.g., Norovirus, SARS-CoV-2) could be forecast accurately even with limited historical data. Together, these findings demonstrate that the wastewater virome is highly seasonal, interconnected, and forecastable, providing a foundation for proactive, metagenomics-based monitoring and early outbreak detection.

Wastewater treatment plants act as convergence zones for antibiotic residues, antibiotic-resistant bacteria (ARB), and antimicrobial resistance genes (ARGs), yet conventional processes are not designed to mitigate resistance dissemination from their effluents. While chemical disinfectants are generally effective, soluble quaternary ammonium compounds (QACs) can generate subinhibitory exposure gradients that promote resistance selection and co-selection both during treatment and after release into receiving waters. Here, we evaluate a contact-restricted alternative: benzyldimethyldodecyl ammonium chloride (BDMDAC) immobilised onto hydroxyapatite microparticles as a reusable and retainable post-treatment polishing strategy. Across single-strain assays, treated wastewater exposure, and experimental community evolution, immobilised BDMDAC-functionalised particles (BDMDAC-FPs) achieved concentration-dependent antimicrobial activity without detectable biocide leaching. Optimal exposure (200 mg/L, 4 h) resulted in a ~5.5 log reduction in total bacterial abundance and removal of clinically relevant ARGs. Antimicrobial efficacy was retained after one reuse cycle, supporting operational stability. Plasmid-borne QAC ARGs did not confer protection, and no enrichment of qac-associated or non-QAC ARGs was observed. Conjugation assays demonstrated suppression of horizontal gene transfer even under suboptimal exposure, and mobility-associated markers remained stable or declined during long-term community incubation. Collectively, the data support a contact-restricted mechanism in which antimicrobial pressure is spatially confined to the particle interface, generating high local lethality while limiting diffuse subinhibitory exposure. This spatial confinement decouples antimicrobial efficacy from classical disinfectant-driven resistance selection and mobility amplification. Immobilised BDMDAC-FPs therefore provide a mechanistically distinct and evolution-conscious framework for wastewater polishing technologies.

Sewer biofilms represent dynamic interfaces for exchange of bacteria and antibiotic resistance genes between biofilms and the overlying wastewater. Using inline, biofilm reactors, the movement of bacteria and 16S rRNA and carbapenemase genes (blaKPC, blaVIM, blaNDM, blaOXA-48-like, and blaIMP) between wastewater and sewer biofilms was investigated. Established, complex biofilms without these {beta}-lactamase (bla) genes, absorbed resistant bacteria within two minutes of exposure to high concentrations of resistant cultures in lab settings. Carbapenem-resistant organisms from these high-concentration source biofilms transferred to downstream biofilms over 60 minutes of representative sewer shear flows. Mass balances of bacteria and genes in biofilms versus wastewater under representative shear flow showed that biofilms exposed to resistant cultures contributed more to the wastewater than to the downstream biofilms. In field studies, established, complex biofilms without target carbapenem-resistant bacteria and genes from wastewater within hours and then stabilized between 2 to 15 days, not varying by more than 0.5 MPN/cm2 or 0.5 log gene copies (GC)/cm2. In contrast, metagenomic profiles of the bacterial community species continued to change up to 21 days. Established biofilms with resistant bacteria and genes exposed to tertiary-treated wastewater without target carbapenemase genes or meropenem antibiotics did not lose resistant genes or bacteria over nine days of exposure (i.e., < 1 log GC/cm2 reduction). Results show that sewer biofilms contribute to the resistance-gene signal found in sewer wastewater by absorbing and releasing bacteria and genes. Consideration of sewer biofilm dynamics is essential for more accurately interpreting wastewater bacterial concentrations in wastewater-based epidemiology studies. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/726639v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@19f6ce0org.highwire.dtl.DTLVardef@1a507c8org.highwire.dtl.DTLVardef@1a2013dorg.highwire.dtl.DTLVardef@ff8613_HPS_FORMAT_FIGEXP M_FIG C_FIG

Mass gatherings pose a concern for public health because they are associated with dense crowds, increased social interaction, and travel, all of which can facilitate the rapid transmission of infectious diseases. Wastewater and environmental surveillance (WES) were used for pathogen monitoring during the 2024 NFL Annual Player Selection Meeting (the Draft) in Detroit, MI, an event that drew an estimated 775,000 attendees. Wastewater and environmental samples were queried for respiratory viruses and clinically relevant antimicrobial resistance genes (ARG). WES did not detect an increase in the concentration of monitored respiratory viruses (SARS-CoV-2, IAV, IBV, and RSV) associated with the 2024 NFL Draft. In contrast, WES detected a transient increase in carbapenemase targets in wastewater, primarily driven by a fourfold increase in blaOXA-48. Resistome structure in wastewater was dominated by sampling site characteristics rather than changes associated with the event. The Draft weekend coincided with rainfall-driven combined sewer overflow (CSO), potentially allowing the dissemination of ARG to the environment. In surface waters receiving wastewater effluent, an increase in detection frequency and normalized concentrations for multiple ARG were observed following the Draft. WES provided an overview of pathogen prevalence before, during, and after a large-scale gathering, showing how it can warn of emerging health risks in near real time.

Mobile laboratories (MLs), whether vehicle mounted or portable, provide a versatile platform for on-site wastewater and environmental surveillance (WES) of pathogens, particularly in remote locations with limited laboratory infrastructure. However, molecular workflows intended for ML deployment require careful optimization to account for locally available equipment, consumables, infrastructure, workforce capacity, and operational constraints. In this study, we optimized an integrated ML workflow combining Oxford Nanopore Technologies (ONT) for shotgun metagenomics, multiplex metabarcoding for community level microbial analysis, and Biomeme based qPCR for targeted pathogen analysis. To further explore the potential of metagenomics for resistome assessment, we evaluated two whole metagenome enrichment approaches for their ability to improve detection of antimicrobial resistance genes. We introduce and validate a novel ONT based strategy for multiplexed sequencing small subunit (SSU) rRNA amplicon sequencing, enabling simultaneous profiling of bacteria, archaea, and microeukaryotes in complex microbial communities with multiplex metabarcoding. Sample pretreatment and nucleic acid (NA) extraction in this ML workflow were optimized using a combination chemical mechanical lysis approach followed by magnetic bead based NA purification. Workflow performance was verified using a mock community (ZymoBIOMICS Microbial Community Standard, Zymo Research, USA) and wastewater samples spiked with inactivated Mpox virus (MPXV), demonstrating accurate taxonomic representation and sensitive MPXV detection. Comparison with a commercial ZymoBIO bead beating kit for sediment sample showed ML NA extraction performed comparably. The time efficient multiplex metabarcoding workflow enabled simultaneous profiling of bacterial, archaeal, and eukaryotic diversity and produced results more concordant with qPCR based pathogen detection than the REPLIg Cell Whole Genome Amplification (WGA) & Whole Transcriptome amplification (WTA). The protocol for Mpox virus genome characterization was successfully validated for whole genome sequencing (WES) based detection and incorporated into the standard ML workflow. Across both high and low biomass environmental matrices, the Multiple Displacement Amplification (MDA) based metagenomic workflow, combined with the ML NA extraction procedure, reliably reproduced the expected composition of the Microbial Community Standard. Collectively, the integration of ONT technology with MDA metagenomics and mobile qPCR workflows provides an effective One Health approach for pathogen surveillance and outbreak response across heterogeneous environmental settings, which was later further enhanced by an offline bioinformatic and visualization pipeline enabling near real time detection of pathogens and AMR thus early risk assessment.

Metagenomic sequencing is increasingly applied to wastewater to characterize the diversity, dynamics, and relative abundance of human and animal viruses. Among these sequencing approaches are those that enrich viral nucleic acids from the wastewater matrix, aiming to increase the viral read fraction for analysis. However, the feasibility of scaling targeted viral sequencing to diverse sewersheds across large geographic scales is currently unknown. In this study, we apply hybrid capture metagenomic sequencing to nearly 450 weekly wastewater samples collected during the respiratory virus season in the United States and evaluate sequencing performance for generating public health-relevant data. Analysis of data from 15 wastewater treatment plants demonstrates that our approach enabled efficient capture of pathogens of interest, achieving a median viral read fraction over 19%. Importantly, relative abundance estimates of common pathogens correlated with direct quantification of viral targets using RT-ddPCR. Together, our results demonstrate that hybrid capture sequencing of wastewater is a viable tool to monitor both common and rare pathogens across geographically diverse sewersheds.

Antimicrobial resistance alone is a serious threat to global public health, but even more so when multidrug-resistant bacteria harbour heavy metal resistance genes, as these can drive co-selection of antibiotics and produce biofilms. The presence of such genes, combined with the bacterias ability to form biofilms, is strongly linked to treatment failure, persistent infections, and reduced therapeutic options. Here, we used the resazurin-crystal violet combination assay to screen a representative cohort of whole-genome sequenced Escherichia coli isolates (n=20) obtained from wastewater surveillance. The specific biofilm formation (SBF) index was used to grade the intensity of biofilm formation as strong, moderate, weak, and non-biofilm producers. Correlation analysis was used to test the association between the intensity of biofilm formation and genotypic features. The SBF index revealed that most of the wastewater E. coli isolates (n=13/20) were weak/non-biofilm producers, four isolates produced moderate biofilms, and three isolates (ST1434: A-O18ab:H55; ST401:A-H25; and ST399:A-O19:H12) produced strong biofilms. The diversity of virulence factors was similar in most of the isolates, except for the three isolates, which had fewer abundant virulence factors. The correlation analysis showed that there was no association between the expression of virulence genes and the formation of strong biofilms by the isolates (p > 0.05). Drug resistance profile was not correlated with higher biofilm production (p > 0.05), as 68.8% (n=11/16) of multi-drug resistant (MDR) and 50% (n=2/4) of non-MDR isolates had weak or no biofilm formation. Similarly, the SBF index was not associated with the number of plasmids in each of the E. coli genomes (p = 0.334). However, there was a positive association between the presence of two or more heavy metal resistance genes (HMRGs) and the strong biofilm formation in our isolates (p = 0.002). Our findings revealed the low occurrence of strong biofilm producers among wastewater E. coli isolates. Further studies are needed to evaluate the impact of the presence of HMRGs and their direct or indirect contribution to enhancing biofilm production and persistence in environmental reservoirs.