ACS ES&T Water

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.

Wastewater surveillance (WWS) is established as a vital tool for monitoring polio and SARS-CoV-2 with potential to improve surveillance for many other infectious diseases. This study evaluated the feasibility of detecting measles virus (MeV) RNA in wastewater as part of a national WS preparedness trial in Brisbane, Australia, from March to June 2025. Composite and passive sampling methods were employed in parallel at three wastewater treatment plants serving populations between 230,000 and 584,000. Nucleic acids were extracted and analyzed using RT-qPCR targeting MeV N and M genes to distinguish wild-type and vaccine strains. MeV RNA were detected in both 24-hour composite and passive samples on May 26 to 27, 2025 from the largest catchment of 584,000 which also included an international airport. No measles cases were reported in this city or region within 4 weeks of the WS detections. These were confirmed as vaccine-derived measles virus (MeVV) strain via specific RT-qPCR assay. Extraction recoveries varied (11.5% to 70.5%), with passive sampling showing higher efficiency. This is the first report of use of passive samples for detection of MeV. These findings are consistent with other studies reporting WWS results of both MeVV genotype A and wild type genotype B and/or D. It demonstrates the potential for sensitive MeV WWS with rapid differentiation of MeVV from wild type MeV shedding, including in airport transport hubs and with different sample types. Use of WWS could strengthen measles surveillance by enabling rapid detection of MeV RNA and supporting outbreak preparedness and response. This requires optimised methods which are specific to or differentiate wild-type MeV from MeVV. Furthermore, the successful detection of MeV using passive sampling in this study highlights its potential for deployment in diverse global contexts which may include non-sewered settings.

Background: Beaches are popular summertime destinations in Canada. However, they can be affected by specific fecal pollution sources, increasing the risk of recreational water illness. Objectives: This study was conducted to determine the risks of acute gastrointestinal illness (AGI) among Canadian beachgoers and to evaluate the influence of different fecal indicator bacteria (FIB) and other water quality measures on assessing these risks. Methods: In a prospective cohort design, beachgoers were recruited at sites across Canada from 2023 to 2025. Sociodemographic characteristics and exposures were determined through an on-site survey, with a 7-day follow-up survey to determine risks of AGI. Bayesian mixed-effects logistic regression models were fitted to evaluate the effects of an ordinal water contact variable (no contact, minimal contact, body immersion, and swallowed water) on the incident risk of AGI, with an interaction included for water quality indicators. The levels of six FIB and water quality measures were assessed: Escherichia coli, enterococci DNA, three microbial source tracking DNA markers (human HF183/BacR287, human mitochondria, seagull Gull4), and turbidity. Results: A total of 4085 participants were recruited, with 67.6% completing the follow-up survey. The overall incident risk of AGI was 2.6%. Both swallowing water and body immersion increased AGI risks compared to no water contact: median of 20 excess cases (95% Credible Interval [CrI]: 4, 64) and 5 excess cases (95% CrI: 1, 19) of AGI predicted per 1000 beachgoers, respectively. Escherichia coli and seagull DNA marker levels were associated with AGI among those who had water contact, particularly among those who reported swallowing water. Discussion: While the overall burden of AGI due to beach water contact in Canada was low, increased risks are associated with E. coli levels particularly among those who swallow water. This could be related to fecal contamination from seagulls. However, there is substantial uncertainty in the predicted effect sizes.

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.

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.

Antimicrobial resistance (AMR) is a significant and growing threat to human, plant and animal health, the global economy, and food security. The One Health approach to AMR recognises the role of the environment in the evolution, emergence, and dissemination of AMR. In part, this is due to anthropogenic pollution that releases AMR organisms alongside cocktails of compounds that may select for AMR in situ, which then pose an exposure risk to humans and animals. This has spurred growing interest from cross-sectoral stakeholders in environmental risk assessment (ERA) of antibiotics, with regards to their selective potential. Many different experimental and modelling approaches have been used to determine the lowest concentration of an antibiotic that may select for AMR. Debates continue regarding which individual approach, if any, may be best for determining concentrations of antibiotics that may select for AMR, for ERA purposes. This paper contributes to this ongoing discourse by refining and using a previously published method SELECT (SELection Endpoints in Communities of bacTeria) to rapidly generate predicted no effect concentrations for resistance (PNECRs) for 32 antibiotics on the premise that reduction in growth of complex community of bacteria correlates with selection for AMR resistance genes. The database of PNECRs of antibiotics presented here is the largest generated using a single experimental, empirical approach that will aid future efforts towards creating a standardised test. PNECR data were used to conduct ERAs using measured environmental concentrations of antibiotics to rank antibiotics by potential selection risk in different environments. The experimental approach and statistical code have been made open access, with online tutorials available to facilitate other laboratories using the SELECT 2.0 method. Finally, we discuss the limitations of this approach and how these could be addressed in future studies.

Wastewater sequencing is an increasingly valuable tool in tracking the spread of infectious disease agents across space and time in areas of dense human settlement. Among pathogens that can be readily detected by this approach is influenza A, which follows predictable patterns of prevalence through the winter months in North America. Here, we leverage routine surveillance of a municipal wastewater treatment plant in Northern California to describe an atypical, off-season spike in influenza A concentrations that rivals that of the winter respiratory virus season. Drawing upon metagenomic data generated through hybrid-capture sequencing, we assemble and subsequently characterize fragments of divergent influenza genomes that appear to derive predominantly from the avian H16 clade. These strains exhibit close evolutionary relationships to influenza isolated from migratory shorebirds, hinting at potential host species and mechanisms of geographic spread. Analysis of read abundances suggest that these avian strains dominate the pool of influenza circulating during the summer months, when typical human-infecting strains are essentially absent. Together, our results expand the value of wastewater sequencing to encompass sensitive tracking of outbreaks within animals in interface regions where human settlement abuts wildlands, increasing overall pandemic preparedness.

Reducing the cost and environmental impact of cell culture media is an important goal for cultivated meat, the process of generating meat in vitro using proliferating animal cells. While prior approaches have demonstrated the use of microbial lysates to replace expensive animal-based fetal bovine serum (FBS) in media, these formulations still rely on large quantities of growth factors such as fibroblast-like growth factor 2 (FGF2). Here, we demonstrate the use of FGF2-expressing Vibrio natriegens to create whole-cell lysates that replace both FBS and FGF2 in cell culture media for cultivated meat applications. This medium, named "VN40FGF", supports rapid proliferation of immortalized bovine muscle satellite cells (iBSCs) in the absence of supplemented FGF2. Cells grown in VN40FGF maintain phenotype and differentiation capacity. We also demonstrate that engineered V. natriegens can grow in spent cell culture media, further improving sustainability and economics, and reducing potential eutrophication concerns associated with waste disposal. Our approach combines multiple strategies for reducing the total number of media inputs, demonstrating opportunities for more economical and sustainable cell culture, especially for cultivated meats.

A compact, in-house developed ultraviolet germicidal irradiation (UVGI) system adaptable to static, mobile, or robotic platforms was developed for the effective sterilization of bacteria and fungi using a wireless mode of operation. Under controlled laboratory conditions, its efficacy was evaluated against three representative biofilm-forming pathogens: Bacillus subtilis (Gram-positive, spore-forming, motile bacterium), Escherichia coli K12 (Gram-negative, non-spore-forming, non-motile bacterium), and Candida albicans M-207 (multi-drug-resistant, clinical yeast isolate). Microbial viability following UVGI exposure was assessed using colony-forming unit (CFU) and MTT assays, and morphological alterations were characterized by scanning electron microscopy (SEM). Cultures were exposed to UV-C radiation at distances of 1-5 m for 15-90 min. CFU assay demonstrated 100% kill of all tested organisms at 1 m and 15 min, corresponding to doses of 600.3, 576 & 697.5 mJ/cm{superscript 2}. Although MTT assays indicated residual metabolic activity under the same conditions, CFU results confirmed that surviving cells were unable to proliferate, highlighting the robustness of UV treatment for long-term inactivation. SEM confirmed distinct morphological alterations such as complete destruction of extracellular matrix & reduction in number of cells indicating cell death with increase in UV dose as compared to controls. A dose & time-dependent inactivation of biofilm-forming bacteria & fungi was observed on exposure to UVGI. Therefore, this pilot study validates the effectiveness of the newly developed UVGI surface sterilizer against biofilm-forming bacterial and fungal pathogens. Overall, the system demonstrates proof-of-concept efficacy under laboratory conditions and holds strong potential for future development and validation in hospitals and other contaminated public spaces. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/715580v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@150cefcorg.highwire.dtl.DTLVardef@450831org.highwire.dtl.DTLVardef@1cfd6borg.highwire.dtl.DTLVardef@1419ba8_HPS_FORMAT_FIGEXP M_FIG C_FIG IMPORTANCEMicroorganisms that form biofilms on surfaces are difficult to eliminate and contribute to the spread of infections in healthcare and indoor environments. There is a need for practical, easy-to-use disinfection technologies that can effectively reduce such contamination. In this study, we developed a compact, in-house, wireless UV-C disinfection system designed for flexible operation across different surface types. The system was evaluated under controlled laboratory conditions using representative biofilm-forming bacterial and fungal pathogens. Our findings show that the system can effectively reduce microbial contamination, demonstrating proof-of-concept efficacy. This work highlights the potential of accessible, non-chemical UV-based technologies and supports their further validation for applications in real-world disinfection settings.

As toxicology shifts towards non-animal testing, quantitative models are essential to predict adverse health effects from molecular or cellular perturbations. Quantitative Adverse Outcome Pathways (qAOPs) represent such models, building on mechanistic knowledge and quantifying the Key Event Relationships (KERs) described in AOPs. Despite the recognized need, the number of qAOPs remains limited. Bayesian-based approaches are often chosen for developing qAOP for their flexibility, but most use discretized variables, limiting their predictive power. In addition, these models are mainly built from newly generated data, underexploiting the large amount of information available. This study successfully leverages data from public literature and presents an innovative framework based on continuous variables to develop a Bayesian-based quantitative model for a central KER towards liver fibrosis. The model predicts the probability of the expression fold change for two key markers of hepatic stellate cell activation (aSMA and COL1A1), given the effects on tissue injury, using in vitro data from 9 chemicals. We propose a newly developed workflow to assist in knowledge identification, organization, and extraction from scientific literature and chemical databases. Based on in vitro data and in vivo information from the Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) database, we estimate a biologically relevant range in COL1A1 fold change that indicates an activated state of stellate cells and high liver fibrosis odds ratios. Our study provides a case example of integrating published data and continuous variables to build a Bayesian-based model, which constitutes an essential step for predicting liver fibrosis from in vitro data.

Indo-Pacific humpback dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides) are increasingly threatened across their native range, yet the relative influence of multiple stressors in shaping their population dynamics remains unclear. Current conservation strategies for both species are limited by incomplete data and limited assessment of affecting factors. Here, we integrated eDNA metabarcoding with Joint Species Distribution Modeling (JSDM) to assess how environmental gradients, pollution, and trophic associations interactively influence cetacean distributions in Hong Kong waters. We show that degraded water quality and intensified human activity negatively associated with cetacean occurrence, with clear species-specific responses to different stressors. S. chinensis covaried most strongly with Secchi disc depth, and presence of vessels, while N. phocaenoides was negatively associated with nitrate nitrogen and microbial pollution (sewage). The JSDM variance partitioning analysis highlighted that the occurrence of S. chinensis was primarily associated with anthropogenic disturbances (30.04%), followed by water physical properties (26.63%), whereas N. phocaenoides was more strongly associated with physical (40.9%) and anthropogenic disturbances (35.2%). By integrating eDNA and JSDM, our approach provides fine-scale diagnostics of species-specific vulnerabilities, supporting adaptive conservation strategies and guiding the realignment of protected areas to mitigate biodiversity loss in urbanized marine ecosystems. Environmental ImplicationOur study demonstrates that hazardous water pollutants, including microbial contamination, nutrient enrichment, and chemical stressors, vessel pressure, show strong, species-specific impacts on resident cetaceans in Hong Kong. By integrating eDNA metabarcoding with joint species distribution models, we provide a diagnostic framework that directly links pollutant profiles to ecological risk. These findings highlight that conventional conservation strategies overlooking pollution drivers are insufficient for marine megafauna persistence. Our approach offers an early-warning system for monitoring hazardous pollutants in coastal ecosystems and supports adaptive management strategies to mitigate biodiversity loss in urbanized seascapes.

IntroductionDrowning risk begins with water exposure, yet population-water relationships have rarely been quantified at scale using environmental measures. This study explored whether satellite-derived data was associated with subnational drowning mortality and whether associations differed by country income level. MethodsWe linked Global Burden of Disease (GBD 2021) age-standardised drowning mortality rates to satellite-derived exposures for 212 subnational regions across 12 countries (2006-2021; 3,392 region-years). Exposures were extracted via Google Earth Engine and standardised. Gamma-log generalised linear mixed models included region random intercepts and year fixed effects. Income-stratified models were estimated separately. Supplementary models assessed maritime vessel activity. ResultsNear-water population percentage was the strongest correlate of drowning (IRR 1.40; 95% CI 1.33-1.47). Permanent water coverage was protective (IRR 0.80; 0.73-0.88), as were nighttime lights (IRR 0.96; 0.95-0.97) and hot days [≥]30{degrees}C (IRR 0.95; 0.92-0.99). Mean temperature (IRR 1.17; 1.11-1.23) and precipitation (IRR 1.03; 1.01-1.04) were positively associated. Near-water effects were consistent across income strata (LIC 1.25; MIC 1.31; HIC 1.24), while other predictors showed weak or inconsistent within-strata associations. Vessel activity was modestly associated with drowning in Global Fishing Watch models (IRR 1.05; 1.01-1.09) but not in Synthetic Aperture Radar models. DiscussionSatellite-derived indicators can characterise drowning risk at scale, with population proximity to water emerging as a robust cross-context correlate. Protective associations for permanent water suggest landscape configuration may shape risk beyond proximity alone, highlighting geospatial datas value for targeting prevention where surveillance is limited.

2,4,6-Trinitrotoluene (TNT) is a recalcitrant and pervasive environmental pollutant. Although different environmental microbes have demonstrated their ability to degrade or transform TNT, the underlying genetic basis and cellular machinery remain unclear. In this study, we investigated bacterial strategies in response to TNT exposure in Pantoea sp. MT58 and P. putida KT2440 using proteomics and random barcode transposon-site sequencing (RB-TnSeq). Pantoea sp. MT58 was found to utilize TNT as a sole nitrogen source, whereas P. putida KT2440 exhibited only stress tolerance without assimilation. Pantoea sp. MT58 encodes multiple putative nitroreductases that were upregulated, yet deletion of these genes did not affect growth on TNT, revealing pathway redundancy. Furthermore, fitness profiling provided no evidence for genes involved in the canonical Meisenheimer-complex pathway associated with nitrite release. Instead, the data are most consistent with a sequential nitro-group reduction route in which nitrogen is ultimately recovered as ammonium, with nitrogen routed through the GS-GOGAT pathway with purine and urea pools as the candidate buffering architecture for TNT mineralization. Conversely, P. putida KT2440 relied on Ttg/RND efflux pumps and toluene tolerance proteins for survival without nitrogen assimilation from TNT. This work distinguishes routes for productive nitrogen assimilation from those involved in nitroaromatic tolerance, expanding the mechanistic understanding of anthropogenic compound metabolism to inform future bioremediation efforts.

Traditionally, studies have explored the impacts of individual water chemistry parameters on the persistence of Mycobacterium spp. and Legionella spp. in isolation with the underlying assumption that these associations are likely monotonic in nature. Yet chemical and microbiological changes are complex, and associations are likely highly combinatorial. In this study, we use interpretable machine learning models to disentangle the integrative and nonlinear associations between water chemistry and occurrence/abundance of Mycobacterium spp. and Legionella spp. Seasonal data from source water, point-of-entry and distribution systems of eight full-scale drinking water systems demonstrated that shifts in overall water chemistry were associated with the changes in microbial abundance during treatment and distribution. Machine learning models indicated moderate predictive ability of integrated water chemistry towards Legionella spp. abundance and towards the occurrence of both Legionella spp. and Mycobacterium spp., whereas predictive performance for Mycobacterium spp. abundance was limited. The association between nitrate and Legionella spp. abundance was disinfectant regimes dependent, while dissolved organic carbon exhibited a concentration dependent response type (i.e., positive and negative association). In chloraminated systems, Legionella spp. abundance was positively associated with ammonia and nitrate, highlighting the critical role of nitrification. Here, it appears that pH likely influences the initial colonization of Legionella spp. while ammonia governs its abundance in drinking water. Overall, this study demonstrates that integrated water chemistry and parameter-specific nonlinear effects collectively explain persistence of Mycobacterium spp. and Legionella spp. in drinking water systems.

Neem-derived biopesticides are increasingly applied in agriculture and have been tested in aquaculture research, yet their effects on non-target aquatic invertebrates remain insufficiently characterized. We evaluated the effect of neem extract on the brine shrimp Artemia franciscana using an integrated ecotoxicological approach combining phenotypic, transcriptomic, and histological analyses. Juvenile A. franciscana exhibited dose-dependent mortality and sublethal abnormalities, with a 24 h median lethal concentration of 292.48 mg/L (95% confidence interval, 257.75- 331.89) for mortality and a median effective concentration of 146.36 mg/L (95% confidence interval, 113.04- 189.50) for the combined endpoint "abnormal + dead". In adults, males showed greater mortality than females after extended exposure. High-throughput RNA sequencing revealed broad treatment-associated differences in transcript abundance, with juveniles displaying downregulation of detoxification enzymes and chitin biosynthesis genes, alongside enrichment of immune- and cuticle-related gene ontologies. Adults showed transcriptional signatures of stress, including upregulation of heat shock proteins and cytoskeletal components, and suppression of genes involved in energy metabolism. Chitin precursor enzymes were selectively downregulated in males, and altered carbohydrate metabolism was observed in females. Histological analyses revealed structural deterioration of the brood sac cuticle and reduced ovarian area in treated females, consistent with transcriptomic evidence of impaired exoskeletal and reproductive processes. Overall, neem exposure was associated with phenotypic, histological, and transcriptomic changes in A. franciscana. These results support the use of combined transcriptomic and histopathological endpoints to characterize responses to plant-derived biopesticides in aquatic arthropods.

The use of field water for laboratory rearing of mosquitoes could offer a better representation of the natural aquatic environment than laboratory tap or deionised water. For logistical reasons, such water may be stored in the laboratory environment for an extended period, but its stability is poorly documented. This study evaluated the influence of laboratory storage conditions on the kinetics of physicochemical parameters of breeding water collected from a field habitat. To capture within-habitat variability, water was collected from multiple spatial points from a breeding site and transferred into plastic containers for storage under laboratory conditions. Water physicochemical parameters were measured in the field to establish baseline readings, while laboratory measurements were done at 2-3-day intervals over 2 months to evaluate temporal changes. A linear mixed-effects model was fitted to evaluate the determinants of changes in physicochemical parameters under laboratory storage. Most parameters exhibited high stability; however, water temperature increased significantly by an average of [~]1.5 (p= 0.046) relative to the field. Water pH demonstrated a long-term rise over the 2-month storage period with a transient, significant dip of 0.71 units after a week of storage (p< 0.001). Overall, LMM analyses revealed that ambient relative humidity was the strongest statistical predictor of change in all water parameters except pH (p< 0.05). Ambient temperature correlated positively with water temperature and ammonium nitrogen (NH4-N) (p<0.002), and negatively with dissolved oxygen (p< 0.002). These results indicate that stored field water is highly sensitive to the laboratory microclimate. Specifically, water temperature, pH, and NH4-N serve as candidate indicators for storage-related physicochemical drift. We recommend the rigorous standardisation of insectary humidity and temperature, and monitoring of water parameters, which are likely relevant for bioassay reproducibility.

Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. The mutational signatures of mutagens in normal tissues offer opportunities to detect such exposures and survey them at population level. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that normal kidney cell genomes report an extensive repertoire of somatic mutational signatures. Microdissection of kidney structures revealed that proximal tubules exhibit higher mutation rates than other components of the nephron and most normal cell types despite low cell division rates. This is explained by marked enrichment of mutational signatures due to known exogenous carcinogenic mutagens including the plant-derived aristolochic acids, as well as several signatures of unknown causes including an unknown agent prevalent in Japan (SBS12), and signatures of uncertain origins (SBS40b and SBS40c). The results suggest the existence of multiple, common, systemically circulating mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

Microbial communities play a central role in compost-bedded pack (CBP) systems by driving organic matter decomposition and nutrient cycling. The objective of this study was to characterize and compare the bacterial community structure of CBP from two dairy farms in Cordoba, Argentina, using 16S rRNA gene sequencing. Two CBP systems were evaluated: Martin Bono (MB; 30 months in operation) and Angela Teresa (AT; 20 months). The MB system was established on natural soil without bedding addition and included concrete feed alleys, whereas AT was initiated with peanut shell bedding and lacked concrete alleys. In both systems, compost was tilled twice daily. Two samples per farm were collected at a depth of 30 cm during winter 2019. Raw Illumina reads were processed using the DADA2 pipeline, including quality filtering, error modeling, denoising, and chimera removal. A total of four samples yielded 2,503 amplicon sequence variants (ASVs), with approximately 76% of reads retained after filtering and chimera removal, indicating high-quality sequencing data. Taxonomic analysis revealed that bacterial communities in both systems were dominated by phyla typically associated with compost environments, including Actinobacteriota, Proteobacteria, and Firmicutes. Differences in relative abundance between systems suggested shifts in community composition associated with management conditions.

The East Asian region, known for its high levels of human and fishery activities, experiences serious plastic pollution in the marine environment, especially in seawater and along coastlines. Wharf roaches (Ligia spp.) collected from the coast of western Japan frequently ingest expanded polystyrene (EPS), which is then excreted as microplastic through their feces. However, the impact of EPS exposure and ingestion on the gut microbiome of wharf roaches remains unclear. Thus, this study aimed to investigate the effects of EPS ingestion on the gut microbiota of wharf roaches by examining their gut microbiota and gene expression. The expression levels of more than 400 genes, including those associated with xenobiotic metabolism, and the abundance of gut microbial community were altered. Microbial analysis revealed that at least five archaeal types, two to four bacterial types, three to seven eukaryotic types, and three viral types were involved in a correlation network composed of strong associations. Among them, Haloquadratum, Halalkalicoccus, and Methanospirillum (archaea); Volvox (eukaryote); and Varicellovirus and T4-like viruses showed significantly increased abundance. Furthermore, covariance structure analysis indicated that the viruses and methanogens played key causal roles as characteristic factors related to EPS administration. In conclusion, EPS disrupts the intestinal environment of wharf roaches and serves as a potential material for viral activation and methane production. Building on our previous field study that identified wharf roaches as potential indicators of coastal EPS pollution, this study provides novel insights into the ecological impacts of EPS ingestion and consequences of plastic pollution.

Wastewater surveillance is increasingly used for antimicrobial resistance (AMR) monitoring in urban environments, but low-resource settings often lack a piped sewerage system. Instead, coprophagous flies--flies that ingest feces--may serve as composite samplers for monitoring fecal wastes present in terrestrial environments. We evaluated whether the class 1 integron-integrase gene intI1 was associated with genetic markers of AMR and fecal source tracking markers (FST) in coprophagous flies collected from latrine entrances and food preparation areas in low-income urban Maputo, Mozambique. We quantified intI1, an enteric 16S rRNA target (for normalization), three FST markers, and 30 ARG targets using qPCR. We normalized concentrations of intI1 and each target to enteric 16S rRNA. We fit linear mixed models with a random intercept for housing compound to estimate within-fly associations between log10 relative abundance of intI1 and log10 relative abundance of each target with and without adjustment for fly taxonomic group, capture location, and standardized fly mass. We also modeled per-fly unique ARG count (i.e., number of ARG targets detected) using Poisson regression. Of 188 flies assayed, 176 passed internal controls; intI1 and enteric 16S rRNA were detected in 95% and 96% of flies, respectively. Higher relative abundance of intI1 was positively associated with ARG and FST targets, with the strongest associations observed for sulfonamide-(sul1: {beta} = 0.87; 95% CI: 0.81, 0.94; sul2: {beta} = 0.81; 95% CI: 0.73, 0.89), tetracycline- (tetA: {beta} = 0.78; 95% CI: 0.70, 0.85; tetB: {beta} = 0.69; 95% CI: 0.60, 0.79), and trimethoprim-related (dfrA17: {beta} = 0.78; 95% CI: 0.70, 0.86) genes. Associations with FST markers were weaker (i.e., human mtDNA: {beta} = 0.46; 95% CI: 0.37, 0.55; human-associated Bacteroides: {beta} = 0.34; 95% CI: 0.25, 0.43). Higher relative abundance of intI1 was also associated with a greater number of ARGs detected: each 10-fold increase in intI1 was associated with an 8% higher expected unique ARG count (aRR=1.08, 95% CI: 1.04-1.12). These findings support the need for further research across different settings exploring intI1 carried by coprophagous flies as a potential standardized screening target for AMR surveillance in unsewered terrestrial environments.