Water Research

Antibiotic resistance genes (ARGs) circulating among clinically relevant bacteria pose serious challenges to public health. Given the ancient and environmental bacterial origins of ARGs, a better understanding of the carriers of ARGs beyond the clinically most relevant species is urgently needed for more farsighted resistance monitoring and intervention measures. While the risks of emerging ARGs from environmental sources have been recognized, the identification bottlenecks stem from the limitations of shotgun metagenomic sequencing and bioinformatic methods. Here, we used long-read metagenomic sequencing and bacteria-specific methylation profiles to re-establish the links between established (well-described) or latent (absent in databases) ARGs and their bacterial and genetic contexts in wastewater. The base modification data produced by PacBio SMRT sequencing was analyzed by an in-house pipeline utilizing position weight matrices and UMAP visualizations. The approach was validated by a synthetic community with known bacterial composition. Our analysis revealed several previously unreported ARGs and their hosts with varying risk levels defined by their potential as emerging public health threats. For instance, Arcobacter, as one of the prevalent taxa in influent wastewater, was shown to carry a latent beta-lactamase gene with high predicted mobility potential. Of the other emerging beta-lactamases, we provided a real-life example of ongoing pdif module-mediated genetic reshuffling of the blaMCA gene occurring at least within Acinetobacter hosts in our samples. Additionally, we identified Simplicispira, Phycisphaerae, and environmental groups of the Bacteroidales order as the carriers of established, clinically important ARGs. These findings support the intermediate host roles of strictly environmental bacteria for the further dissemination of mobilized ARGs, highlighting the importance of exploring the uncultivated, or non-pathogenic, carriers of ARGs for the early detection of newly arising ARGs and mobility mechanisms.

Antimicrobial resistance (AMR) is a growing global public health threat projected to cause up to 10 million deaths annually by 2050 if no immediate action is taken. While misuse and overuse of antibiotics are the main drivers of increasing AMR, the eco-evolutionary dynamics of AMR in the environment - particularly across the urban-rural continuum - remain poorly understood. Using shotgun sequencing, we investigated urban, farm, and rural water sources in the Berlin-Brandenburg region to explore the distinctness or overlap of their antibiotic resistance gene (ARG) profiles and the potential impact of wastewater treatment plants (WWTP). ARGs were identified using multiple databases and five bioinformatic tools, combining sequence-based alignment and deep learning approaches. This multi-tool approach allowed for the detection of up to 18 AMR classes--more than any single tool alone. The multi-tool screening approach for ARGs, combined with the ABRicate algorithm, was superior to all single ARG tools and databases, detecting more AMR classes, allowing for biocide and metal resistance detection, while less sensitive for detection of aminocoumarin resistance genes. ARG diversity was higher in urban lake sediments, urban waters, and wastewater compared to rural lake sediments and water. Among all environments, urban lake water showed the highest overall ARG abundance, second only to wastewater, and this pattern held across all AMR classes, except for aminoglycoside resistance, which was most prevalent in rural lake sediments. The WWTP was unable to remove the circulating pool of ARGs, despite a decrease in unique ARGs in the outflow.

Antimicrobial resistance (AMR) is a growing problem, with annual deaths set to pass 10 million by 2050 if current trends continue. Wastewater surveillance has been proposed as a strategy to understand population-level resistance, and water reclamation facilities (WRFs) have been identified as a control point for environmental dissemination of resistant bacteria. Understanding dynamics of AMR across WRFs requires advanced molecular tools that elucidate host bacteria, especially for mobile resistance carried on plasmids. To that end, influent, activated sludge, and effluent were collected from three WRFs in North Carolina, Texas, and California during three weeks of Spring 2024. Samples were analyzed using Hi-C proximity ligation sequencing to identify the AMR host range for chromosomal and plasmid-based resistance. A total of 1,868 hits for 244 unique resistance genes were observed, with seven resistance genes identified in all samples. Resistance genes were more likely to be carried on a microbial plasmid in influent, but more likely to be in a chromosome in activated sludge. Seventeen total microbial hosts for resistance genes were identified in effluent, suggesting WRF effluents may be sources of resistant bacteria to receiving surface waters. A high proportion of all identified host relationships were confined to just four bacterial families. Hi-C contact mapping is a critical tool to more fully describe the AMR host range in complex matrices, particularly for plasmid-based resistance genes. ImportanceAntimicrobial resistance (AMR) threatens modern medicine. Water reclamation facilities receive a complex mixture of antibiotics and rely on active microbial communities for treatment, thereby acting as critical systems to prevent environmental spread of resistance. However, AMR dynamics are difficult to discern in complex wastewater environments due to antibiotic resistance genes (ARGs) being frequently carried on mobile pieces of DNA that are difficult to link to specific bacteria using conventional shotgun sequencing. Novel proximity ligation sample preparation techniques like Hi-C physically link co-located sequences of DNA before shotgun sequencing. This allows sequencing to elucidate the bacterial hosts for both stable and mobile ARGs. In the current study, Hi-C sequencing was carried out on influent, activated sludge, and effluent collected from water reclamation facilities in California, Texas, and North Carolina to assess the resistome host range across treatment. 5 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/26346186v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1e4620eorg.highwire.dtl.DTLVardef@e1c3a7org.highwire.dtl.DTLVardef@1f40964org.highwire.dtl.DTLVardef@94b886_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Wastewater-based epidemiology (WBE) is an effective surveillance approach for monitoring viruses of public health relevance at the community level, complementing clinical surveillance systems. Molecular methods such as PCR/qPCR are widely used for targeted detection, while next-generation sequencing (NGS) with targeted enrichment panels has emerged as a complementary strategy for broader viral detection and genomic characterization. This study comparatively evaluated conventional PCR/qPCR and a targeted enrichment whole-genome sequencing Viral Surveillance Panel (VSP, Illumina) for virus detection in wastewater. Fifty-six wastewater samples collected between 2017 and 2023 from a wastewater treatment plant in Cordoba, Argentina, were concentrated by polyethylene glycol precipitation and pooled by season and year, reaching a total of 14 pools. Each pool was analyzed in parallel by PCR/qPCR for eight human viruses of public health importance and by the VSP, targeting 66 viral species, sequenced on a NovaSeq 6000 platform, and analyzed with the DRAGEN pipeline. Detection frequencies for each virus using PCR/qPCR and VSP were: RoV A 100%/14.3%; NoV 100%/14.3%; AiV 50%/42.9%; SARS-CoV-2 14.3%/0%; HAV 42.9%/0%; HEV 14.3%/0%; JCPyV 35.7%/85.7%; BKPyV 28.6%/71.4%, respectively. In addition, VSP detected the genomes of Astrovirus (71.4%), Salivirus (21.4%), Coxsackie A (14.3%), Rotavirus C (14.3%), and Merkel Cell virus (7.1%), and enable the recovery of 16 near complete genomes (coverage > 92.5%) of AiV, JCPyV, BKPyV, Salivirus and Astrovirus. PCR/qPCR and targeted enrichment NGS provide complementary information wastewater viral surveillance. Their combined application improves virus detection and genomic characterization, reinforcing the value of integrated approaches in environmental virology and public health monitoring.

The World Health Organization (WHO) has designated carbapenemase-producing Klebsiella pneumoniae (CP-KP) as a critical priority pathogen due to its increasing importance for human health. As wastewater-based surveillance (WBS) is discussed as a complementary tool for classical systems regarding hazard forecasting and early-warning, we designed a "wet-lab to genomics" workflow to target CP-KP in raw influent wastewater samples to support method development processes across different scientific disciplines. The CP-KP screening workflow was set up based on membrane filtration, selective chromogenic media for selective cultivation and the modified carbapenem inactivation method (mCIM) for confirming carbapenemase-production using 33 samples from four different wastewater treatment plants in North-Eastern Germany. All samples tested positive for CP-KP, with concentrations ranging between 102 and 104 colony-forming units (cfu) per 100 ml across the sample set. As a result, 320 isolates belonged to the Klebsiella, Enterobacter, Citrobacter (KEC)- group, with the majority being identified as KP (n= 297; 93%), including n= 253 (79%) verified CP-KP. Genotypic characterization of CP-KP by PCR revealed the predominance of blaOXA-48-related genes (n= 83) among isolates from all WWTPs. As quality parameters, colony counts for viable Escherichia coli (EC) were employed as a proxy for valid wastewater samples and extended-spectrum beta-lactamase-producing E. coli (ESBL-EC) as indicator for AMR, with cfu/100 ml ranges from 10 to 10 and 102 to 10, respectively. To verify the screening outcome, a subset of 58 CP-KP from two WWTPs were subjected to whole genome sequencing (WGS). As a result, eight different sequence types (STs), i.e., ST147 and ST273 (both: clonal group 147), ST258, ST35, ST15, ST37, ST307, and ST485 were identified. These include clinically relevant STs clustering closest with fecal isolates from Germany when compared with Pathogenwatch-database entries. Moreover, WGS data enabled the identification of antibiotic resistance genes (ARGs), and the detection of closely related isolates within the WWTP dataset.

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 pose challenges 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 optimized 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. HighlightsO_LIuHPLC-Orbitrap method for the semi-quantitative profiling of isoprenoid quinones C_LIO_LIAnalysis of the widest range of isoprenoid quinones to date C_LIO_LIFemtomole-level sensitivity in just 14 minutes of chromatographic separation C_LIO_LIDetection of 57 quinones in complex wastewater sludge matrices C_LIO_LIMost comprehensive set of quinone standards including microbially-purified quinones C_LI

Cyanobacterial harmful algal blooms (cyanoHABs) are a major ecological and public health concern, commonly monitored for hepatotoxic microcystins and cylindrospermopsins and neurotoxic anatoxins and saxitoxins. However, the broader suite of bioactive metabolites produced during blooms remains under characterized. Here, we interrogated a chromatography fraction library generated from a cyanoHAB in Muskegon, Michigan. From this library, we isolated two new micropeptins (1 and 2), including an analog bearing a bishomologated tyrosine residue, and we confirmed the structure of ferintoic acid C (3). Structures were established using complementary spectrometric and spectroscopic methods. To expand chemical space coverage beyond isolated compounds, we analyzed LC-MS/MS data using the GNPS2 Analysis Hub query language for product ion searching, enabling annotation of cyanopeptide classes and class-specific modifications across the fraction set, which provided a practical and user-friendly approach for identifying cyanopeptide classes. One of the new micropeptins (1) exhibited moderate inhibition of neutrophil elastase, consistent with roles in ecological interactions and potential relevance to human exposure. Analysis of field samples from ongoing Lake Erie blooms showed recurring micropeptins but no evidence of microcystins. Together, these results challenge microcystin-centric assessments of bloom hazard and support expanded monitoring of non-microcystin cyanopeptides. SYNOPSISRoutine cyanoHAB monitoring targets few regulated toxins; we reveal abundant, under characterized cyanopeptides and enable rapid class-level annotation across datasets with a new LC-MS/MS analysis pipeline. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/704577v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1849d1eorg.highwire.dtl.DTLVardef@16729a8org.highwire.dtl.DTLVardef@1dffe58org.highwire.dtl.DTLVardef@b36a52_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Microbially mediated Mn() oxidation plays a critical role in regulating the global Mn() cycle and represents an environmentally friendly strategy for remediation Mn() contaminated waters. This study presents the first demonstration that Achromobacter pulmonis ss21, a bacterium isolated from Baiyangdian Lake, exhibits the excellent capacity to oxidze Mn(). The Mn() oxidation efficiency of ss21 reached 98.82% and 97.05% for 200 and 400 mg/L Mn(), respectively. Transcriptome analysis revealed that direct Mn() oxidation was catalyzed by genes encoding copper resistance system multicopper oxidase (HV701_RS04390), LLM-type flavin oxidoreductase (HV701_RS19365) and quinone oxidoreductase (HV701_RS24690), which regulate extracellular electron transfer for continuous Mn() oxidation. In addition, thioredoxin (HV701_RS19360) and glutathione peroxidase (HV701_RS19445) genes maintained intracellular redox homeostasis, ensuring stable and efficient direct Mn() oxidation under high Mn() stress. Moreover, genes (iscU, hscA, fliS, HV701_RS03300, and HV701_RS06395) associated with metabolic support, motility, and transcriptional regulation supported indirect Mn() oxidation. Metabolomics analysis revealed the upregulation of L-Tyrosine, L-Isoleucine, glutamic acid, Gln-His-His, Flavin Adenine Dinucleotide (FAD), xanthine related to ss21 Mn() oxidation, which corresponded to the direct Mn() oxidation genes. This study provides a comprehensive understanding of the molecular mechanisms of biological Mn() oxidation by Achromobacter sp. and highlights its potential application in the bioremediation of Mn contaminated aquatic environments under high metal stress conditions. IMPORTANCEMicrobially mediated Mn() oxidation is a fundamental process in global biogeochemical cycles and offers a sustainable pathway for remediating heavy metal-polluted waters. Achromobacter pulmonis ss21 showed excellent performance in Mn() oxidation. The highly efficient removal of Mn() was achieved through oxidoreductase catalysis, regulation of extracellular electron transfer, maintenance of redox homeostasis, and ensurance of stable and efficient Mn() oxidation under high Mn() stress. Moreover, the Mn() oxidation was supported by metabolites, which prevented irreversible protein damage from oxidative stress induced by high Mn() concentration, alleviating oxidative stress, and stimulating the production of ROS. These findings expand the known diversity of Mn() oxidizing bacteria and offer valuable information for the molecular mechanisms of biological Mn() oxidation.

Hydrazine is an industrially valuable product. Strikingly, hydrazine is also a key intermediate in the energy metabolism of anaerobic ammonium-oxidizing (anammox) bacteria, where it is formed by hydrazine synthase. To study the molecular mechanism and activity of isolated hydrazine synthase, a sensitive, relatively fast and easy method to quantify hydrazine is needed. However, reported methods such as colorimetric assays, MALDI-TOF MS, and enzymatic conversion of hydrazine to dinitrogen gas, are either insensitive or laborious. In this study, we describe the validation and application of a fast and simple liquid chromatography-mass spectrometry (LC-MS) method to reproducibly quantify hydrazine produced by anammox hydrazine synthase. Hydrazine was derivatized with benzaldehyde, and directly injected onto a C18 column coupled to a Q-TOF MS. To increase assay performance, 15N2-hydrazine was included as internal standard. The response ratio of hydrazine was linearly proportional to the hydrazine concentration from 0.05-1 {micro}M with an average correlation coefficient of 0.9925. Intra- and inter-day accuracy lay between 88-113% and 95-105%, respectively. Intra- and inter-day precision (RSD, %) [≤] 11%. Hydrazine and derivatized hydrazine were stable when stored at -70{degrees}C or in the autosampler. We successfully applied the LC-MS method to determine hydrazine production by isolated hydrazine synthase and within cell lysate of anammox bacteria.

Granulation is a complex microbial-aggregation process essential for forming aerobic granular sludge (AGS) and other microbial granules used in wastewater treatment. However, the biological mechanisms that drive granule formation remain poorly understood. Cyclic-di-GMP (c-di-GMP) is a well-established second messenger that regulates biofilm formation, suggesting it may be used to enhance microbial granulation. Mycobacterium smegmatis, a nonpathogenic model bacterium for Mycobacterium tuberculosis, naturally forms granules. Because M. smegmatis carries a single c-di-GMP modulating gene, dcpA, that encodes an enzyme with both diguanylate cyclase (DGC) and phosphodiesterase (PDE) activities, it offers a unique opportunity to examine the role of c-di-GMP in granulation. Here, we generated and studied two engineered M. smegmatis strains overexpressing dcpA or dcpA{Delta}EAL, the latter of which is defective in PDE activity. Using these engineered strains, we examined different forms of biofilm growth, cell morphology, plastic surface adhesion, granulation, and settleability. Results of sludge volume index and microscopy indicated that the aggregates of M. smegmatis were granules rather than flocs, and the settleability of the granules was particularly robust when the cells were grown in a carbon rich medium known to promote granulation. Engineered strains sustained stable granulation more effectively than the wildtype under low concentration Tween-80 treatment, which was used to induce dispersion. These results suggest that overproduction of DcpA and thus the modulated level of intracellular c-di-GMP enhances granulation and promotes granule persistence in M. smegmatis. Our study further demonstrates that M. smegmatis is a useful model for elucidating biological mechanisms underlying granulation, which could be leveraged to improve granular technologies for wastewater treatment.

Propionic acid (HPr) accumulation is a major indicator of anaerobic digestion (AD) dysfunction, yet the relative contributions of acidity, undissociated HPr, and propionate ions (Pr-) to process inhibition remain poorly understood. We investigated these effects in mesophilic batch AD microcosms fed with municipal sewage sludge, using a comparative design involving HPr, sodium propionate (NaPr), NaCl, and HCl treatments across two series of experiments. While 20 mM HPr caused a 22% reduction in the maximal methane production rate, 81 mM HPr led to complete inhibition, with the initial pH dropping to 5.1. By contrast, 81 mM NaPr reduced methane production rate by only 40%, and 81 mM NaCl caused no inhibition, demonstrating that acidity is the dominant inhibitory factor, with Pr- exerting a secondary concentration-dependent effect. 16S rRNA gene amplicon sequencing revealed strong, compound-specific shifts in microbial community composition, affecting key functional groups including syntrophs and methanogenic archaea. The proportion of methanogens dropped from 2-3% in control reactors to less than 0.2% under 81 mM HPr, consistent with the observed methane production inhibition. Under HPr81, over 100 ASVs were differentially abundant compared to controls, a pattern largely shared with HCl-treated reactors, further confirming the predominant role of acidity. The number of differentially abundant ASVs was negatively correlated with methane production rates (R{superscript 2} = 0.97), underscoring the link between community reshaping and process impairment. These results provide a unifying framework for propionate inhibition in AD and suggest that microbial community profiling could serve as an early warning tool for process imbalance detection.

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.

Freshwater biofilms host diverse microbial eukaryotic communities that are central to ecosystem functioning and serve as key indicators of water quality. Molecular biomonitoring approaches based on environmental DNA (eDNA) sequencing are increasingly used to characterise these communities, offering scalable alternatives to traditional microscopy-based assessments. Understanding how DNA sequencing methods influence the observed community composition and diversity is essential for ensuring accurate ecological interpretation. Here, we compared short-read Illumina and long-read Pacific Biosciences sequencing of the 18S rRNA gene, alongside a trimmed long-read dataset (restricted to the Illumina-primed region), to evaluate how read length and sequencing platform affect community profiling in river biofilms from seven English rivers sampled across three timepoints. Distinct community patterns were observed between the sequencing approaches, with PERMANOVA revealing significant differences in beta diversity (p = 0.001) and modest effect sizes (R2 = 3.8-8.3%). While the long and trimmed datasets produced nearly identical community structures, both diverged strongly from the short-read data, suggesting that short-read sequencing captures a systematically different subset of taxa than long-read sequencing. Long-read sequencing significantly improved taxonomic resolution of the 18S rRNA gene, particularly at the genus and species levels, enabling detection of lineages that were unresolvable in short-read data. However, comparisons of paired long- and trimmed-read ASVs indicated that trimming can increase taxonomic mismatches at finer ranks, likely due to reduced sequence length rather than sequencing platform bias. Collectively, our results demonstrate that sequencing strategy significantly influences inferred community composition and taxonomic precision. Long-read sequencing provides a more robust representation of community diversity, whereas trimmed analyses reveal how shorter amplicons may contribute to misidentification. These findings emphasise the importance of considering read length when interpreting eDNA-based assessments using the 18S rRNA gene and support the adoption of long-read sequencing for high-resolution biomonitoring applications.

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} [≥] 0.50, and many exceeded R{superscript 2} [≥] 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.

Global climate change and nutrient pollution from agricultural systems increase cyanobacterial bloom and toxin release events, and will pose a significant concern for public health and safety over the coming decades. Many in situ studies have focused on environmental, chemical, and microbial community changes and their impact on cyanobacterial bloom frequency and toxicity. However, fine-scale genetic differences in the genomes of bloom-forming cyanobacteria may also impact the quantity and types of toxins produced. Metagenomic approaches allow resolution of strain- and nucleotide-level changes within microbial communities and can improve our understanding of the factors that affect cyanobacterial bloom dynamics and toxicity. Here, we conduct a metagenomic analysis of the bloom-forming cyanobacterial genus Microcystis across a 10-month lake time series from the Valle de Bravo Reservoir, to assess how within-genus genotype-level changes are linked to intracellular toxin production and extracellular toxin release, as well as how single nucleotide variation may affect the types of microcystin toxins produced. Our results demonstrate that the abundances of both toxigenic and non-toxigenic Microcystis genotypes are significantly related to microcystin toxin concentrations. In addition to these genome-wide ("strain"-level) associations, specific single nucleotide variants show strong associations with chemical variants of microcystin in the environment. Our work highlights the importance of fine-scale analysis of microbial community composition for understanding cyanobacterial bloom dynamics and toxin production.