Environmental Science & Technology Letters

Sequencing-based wastewater surveillance is emerging as an important tool in pathogen-agnostic threat detection, potentially enabling early identification before capture through clinical surveillance systems. However, virus sequences of human pathogens are typically low in abundance in wastewater while much of the data is unclassifiable at the read level. This presents a challenge because genomes may not assemble well for novel pathogens of interest, but read-based methods cannot currently separate novel from previously seen unclassified sequences. Using ultra-deep untargeted sequencing of the wastewater RNA virome performed by the CASPER consortium (321 samples), we constructed a wastewater virus genome database (WVDB) with the goal of expanding the set of available high-quality non-redundant reference genomes. The first version of this database contains 21,015 near-complete viral genomes, of which the majority are ssRNA bacteriophage (79%). We additionally recovered genomes for putative plant and vertebrate-infecting viruses, human enteric viruses, and viruses whose host could not be predicted. Fewer than 4000 genomes had matches in previously published virus genome databases, and WVDB captured around one fifth of the reads that could not be classified by Kraken2. Further expansion of WVDB will provide a comprehensive resource of RNA virus genomes for characterization of viral diversity and dynamics in wastewater across space and time.

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.

Conventional air filtration relies on passive mechanical capture without pathogen inactivation, where viral reduction must be balanced with airflow and energy performance. We developed an Ablative Polymer Coated (APC) filtration system that converts passive filters into active pathogen-reducing surfaces while maintaining low airflow resistance. Unlike conventional approaches requiring denser, higher-resistance media, this strategy enhances biological performance at the filter surface without equivalent aerodynamic penalties. The coating incorporates benzalkonium chloride within a polyvinyl acetate/acrylate matrix for controlled ablative exposure. Performance was evaluated using transmission electron microscopy (TEM), aerosol challenge testing, and HVAC-scale filtration. Ablative exposure caused progressive structural disruption of MS2 bacteriophage, the SARS-CoV-2 simulant. In aerosol challenge testing, coated media achieved up to 99.997% viral filtration efficiency under respiratory airflow conditions. In HVAC (Heating, Ventilation, and Air Conditioning)-scale testing, coated filters achieved >85% viral filtration efficiency with minimal pressure-drop increase. Computational fluid dynamics modeling confirmed uniform airflow distribution without significant turbulence generation. Energy analysis suggested coated filters may reduce energy demand relative to conventional higher-resistance configurations while improving biological performance. These findings support ablative polymer-coated media as a strategy for reducing airborne viral burden without aerodynamic penalties of higher-efficiency passive filtration, suggesting an approach that complements rather than depends solely on tighter filter design.

Methods to concentrate wastewater samples are essential for sensitive environmental surveillance of infectious diseases. We defined six main principles used to concentrate viral pathogens in wastewater and performed a systematic review and meta-analysis of their performance. PubMed and Web of Science were searched on 31 January 2025 using terms wastewater, sewage, concentration methods and wastewater surveillance. We included all studies comparing [≥]2 concentration methods for virus detection. Our search identified 49 eligible studies published since 2013 across seven continents. We ranked the performance of evaluated methods in each study and generated an overall performance metric for each method principle by virus group (enveloped vs. non-enveloped) using Plackett-Luce analysis. Precipitation and filtration methods were the most studied, while magnetic bead-based and centrifugation were least studied. Magnetic bead-based methods were more effective for concentrating enveloped viruses (63% of pairwise comparisons), whereas flocculation performed better for non-enveloped viruses (60%). However, no single method strongly dominated and method rankings were variable between studies. This study provides evidence-based guidance for selecting wastewater concentration methods to support environmental surveillance of viral pathogens.

BackgroundContinuous, non-invasive viral surveillance is essential to monitor emerging pathogens and guide public health responses. Most environmental surveillance studies use targeted qPCR approaches, and comparisons between wastewater and indoor air surveillance remain limited. We aimed to compare the utility of emergency department indoor air and urban wastewater for tracking circulating viruses and resolving genomic information. MethodsWe conducted a matched-pair study comparing 19 weekly indoor air samples from the central ventilation exhaust shaft of an emergency department and 19 24-hour composite municipal wastewater samples in Leuven, Belgium, from December 2024 to April 2025. Both sample sets were processed using probe-based hybrid-capture viral metagenomics targeting over 3000 viral species, using influenza A as a clinically relevant test case. FindingsWastewater captured higher overall viral diversity (233 versus 106 species) and more complete genomes compared to indoor air, showing a relatively stable composition, mainly of enteric and animal-associated viruses. Indoor air demonstrated lower overall diversity but was enriched for respiratory viruses, including influenza A, coronaviruses, metapneumovirus, and respiratory syncytial virus, and more frequently achieved high genome coverage for these pathogens. Although both sample types permitted influenza A subtype characterization, influenza A genomes from wastewater were often less well covered. When coverage thresholds were met, indoor air supported targeted antiviral resistance-site screening for influenza A and RSV-A. InterpretationWastewater and indoor air generate distinct but complementary viromes. Wastewater acts as a diverse, population-level monitor for One-Health applications, whereas indoor air serves as a targeted, human-centric sentinel system facilitating further genomic characterization for respiratory viruses. FundingMustafa Karatas is supported by a Research Foundation Flanders (FWO) fundamental research scholarship (number: 11P7I24N). C.G., L.C., E.H., S.G. and E.A. acknowledge support from the DURABLE project. The DURABLE project has been co-funded by the European Union, under the EU4Health Programme (EU4H), project no. 101102733. Research in context Evidence before this studyWe searched PubMed for studies published between Jan 2000 and March 2024 using the terms "wastewater surveillance", "metagenomics", "indoor air", and "viral metagenomics". Previous studies have shown that wastewater surveillance can detect population-level viral circulation, and more recent work has explored indoor air sampling as a method for monitoring respiratory virus transmission. However, environmental metagenomic studies have largely examined these two sample types separately. Furthermore, most studies relied on untargeted sequencing approaches, which often yield fragmented genomes in these environments. To date, no study has systematically compared indoor air and wastewater using a comprehensive hybrid-capture viral metagenomics approach for virus surveillance. Added value of this studyWe conducted a matched comparison of indoor air from a hospital emergency department and municipal wastewater collected during the same weeks in Leuven, Belgium. We analyzed both sample types using an identical hybrid-capture viral metagenomics workflow targeting more than 3000 viral species. This design enabled a direct evaluation of how the two environmental surveillance lenses differ in viral diversity, genomic recovery, and epidemiological relevance. Wastewater captured broader viral diversity and a stable background dominated by enteric and animal-associated viruses, whereas indoor air captured more respiratory viruses and more frequently yielded high genome completeness for these pathogens. When genome coverage thresholds were met, indoor air data enabled influenza subtype identification and screening for antiviral resistance markers. Implications of all the available evidenceOur findings support a layered environmental surveillance strategy in which different environmental samples provide complementary information. Wastewater offers a stable, population-level view of viral circulation and captures broad viral diversity, including human and animal-associated viruses. Indoor air sampling in human-dominated settings provides a more direct signal of respiratory virus circulation and can yield genomes suitable for subtype and mutation-level characterization. Combining these approaches could strengthen metagenomic surveillance frameworks by improving the interpretation of environmental viral signals, supporting early detection of emerging pathogens, and helping distinguish human virus circulation from environmental or animal-derived detections.

Respiratory infections caused by bacterial pathogens like Mycobacterium tuberculosis and Bordetella pertussis have increased since the COVID-19 pandemic, yet clinical surveillance of both suffers from underreporting and delayed diagnoses. Wastewater monitoring is a valuable public health surveillance tool that can help fill gaps in clinical data yet has rarely been applied to respiratory bacterial pathogens despite evidence of bacterial shedding via excretion types that enter wastewater. In this study, we investigated the possibility for wastewater monitoring of two bacterial respiratory diseases, tuberculosis and pertussis, using two case studies of wastewater monitoring for M. tuberculosis and B. pertussis. We retrospectively measured concentrations of these pathogens in wastewater samples collected longitudinally from communities with and without known outbreaks of these diseases. We designed and validated a novel B. pertussis-specific assay for the NAD(P) gene; B. pertussis nucleic acids were detected sporadically in wastewater during an identified outbreak. We used a highly specific, established assay for M. tuberculosis nucleic acids, and found low concentrations of the marker in wastewater that were lag-correlated with clinical incidence rates 5 weeks later. Findings support the potential of wastewater monitoring for M. tuberculosis and B. pertussis to enable identification of communities with outbreaks of tuberculosis and pertussis and provide early warning for tuberculosis.

Hospital-acquired infections driven by ESKAPEE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) are highly prevalent. Premise plumbing, sinks and drains, seeds these organisms into patient environments via aerosolization and subsequent surface contamination. We measured viable ESKAPEE pathogens and overall microbial communities in and around sinks in two high-burden hospitals in La Paz, Bolivia, using culture and 16S rDNA sequencing. In a prospective observational study (May-August 2025), we collected 233 surface swabs and 39 air samples across four sink-related surface categories and in room air. Samples were plated on selective media for ESKAPEE identification and quantified as colony-forming units (CFU) normalized to 100 cm2 or 6000 L. DNA was extracted, and the full 16S rDNA gene was sequenced on PacBio Revio, analyzed via DADA2/QIIME2 and R. We detected viable presumptive ESKAPEE pathogens in 74.7% surface swabs and 74.4% air samples. Sink basins were most contaminated (mean 31CFU/100 cm2, 95 % CI16-46); concentrations declined with distance from the drain. Klebsiella/Enterobacter spp. showed the highest mean concentration across samples; S. aureus was most frequently detected (54.4% of samples). Hospital-specific differences were evident in culture positivity (Hospital A 85% vs. Hospital B 66.9%) and community composition (PERMANOVA P = 0.001; sample location explained 21.9% vs. 11.7% of variation). 16S profiling confirmed elevated relative abundances of Klebsiella, Enterococcus, and Enterobacter in basins relative to distant surfaces and air. The hospitals studied had high levels of ESKAPEE pathogens, underscoring the need for control measures.

Benzalkonium chloride (BAC) is widely used as a disinfectant in cleaning products and is frequently detected in indoor dust. In this study, we assessed dust samples, along with information on cleaning product use, from 24 pregnant participants. Dust samples were analyzed for BAC concentration and microbial tolerance. Different chain lengths of BAC (C12, C14, and C16) were quantified using LC-MS/MS, and bacterial isolates were tested for BAC tolerance using minimum inhibitory concentration (MIC) assays. BAC was ubiquitously detected, with C12 and C14 being dominant. Higher BAC concentrations were associated with reported disinfectant use and increased microbial tolerance. These findings suggest that indoor antimicrobial use may promote microbial resistance, highlighting potential exposure risks in indoor environments and the need for further investigation into health and ecological impacts.

The core biopolymers (DNA, RNA and proteins) are assembled from their monomers under conditions that avoid water. RNA is crucial for the Origin of Life. When cleaved from its polymerized state, RNA first transitions to nucleoside 2,3-cyclic phosphates. In the reverse direction, RNA polymerizes from 2,3-cyclic monomers in dry states, creating short oligomers that then can ligate on a template under aqueous, alkaline conditions. We studied the role of the counterions in polymerization of 2,3-cyclic nucleotides under geologically plausible settings. Through experiments and simulations, we demonstrate that the presence of ammonium and alkylammonium counterions greatly improves RNA polymerization. The otherwise less reactive cytidine containing monomers formed polyC sequences of up to heptamers; copolymers of AU, GC, or GCAU were detected up to hexamers. Our findings suggest three reasons for this: (1) (Alkyl)ammonium cations form hydrogen bonds with phosphates, (2) their alkaline pKa value can trigger general base catalysis, and (3) (alkyl)ammonium salts naturally form dry, anhydrous materials. The findings indicate that pyrolyzed organic tars creating ammonia-rich gas pockets in subsurface rocks could have enhanced the early evolution of RNA. TOC image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/713775v1_ufig1.gif" ALT="Figure 1"> View larger version (112K): org.highwire.dtl.DTLVardef@1adc431org.highwire.dtl.DTLVardef@12b8da0org.highwire.dtl.DTLVardef@5f187dorg.highwire.dtl.DTLVardef@140ed1a_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundIn September 2021, the U.S. Centers for Disease Control and Prevention (CDC) implemented the Traveler-based Genomic Surveillance (TGS) program, a surveillance system that leverages genomic sequencing of samples from international air travelers and aviation wastewater for early detection of infectious threats. MethodsDuring September 2021-August 2024, nasal samples were collected anonymously from volunteer international travelers arriving at eight U.S. airports. During February 2023- August 2024, aviation wastewater samples were collected from arriving flights. Nasal samples were pooled and sent to a laboratory for RT-PCR testing. Genomic sequencing was conducted for SARS-CoV-2 and respiratory, gastrointestinal (wastewater), and other pathogens of public health importance. FindingsNasal samples from 694,798 travelers were grouped into 67,308 pools and tested; 13,990 (20.8%) were positive for SARS-CoV-2. Over 80% (400/495) of airplane and 96{middle dot}6% (422/437) triturator (a wastewater collection point from multiple airplanes) samples were positive for SARS-CoV-2. Sequence results were made publicly available a median of 11 days (IQR 10- 13 days) after sample collection. Predominant SARS-CoV-2 variants changed over time. Positive tests for influenza virus and respiratory syncytial virus were high in December/January, and gastrointestinal viruses were detected in wastewater during all months. Monitoring was scaled in response to reported outbreaks of COVID-19 and Mycoplasma pneumoniae in China and clade 1 monkeypox virus in central Africa. InterpretationTraveler nasal and aviation wastewater sampling can provide critical early detection of infectious pathogens before widespread U.S. community transmission. The TGS program provides a model for integrated traveler-based genomic surveillance. FundingCDC Research in ContextO_ST_ABSEvidence before this projectC_ST_ABSWe searched PubMed for relevant studies published during December 1, 2020-August 31, 2024, using the terms "traveler surveillance", "wastewater monitoring", "SARS-CoV-2 genomics", and "airport-based surveillance", without language restrictions. Previous reports have shown the feasibility of using travelers as sentinel populations for disease surveillance. Modeling studies have proposed integrating genomic data into international travel surveillance systems to enhance early pathogen detection, and evidence from Australia, Canada, and the UK suggests such programs could be scalable and effective. Early pandemic-era wastewater surveillance, particularly aviation wastewater, demonstrated that air travel hubs can be used to monitor pathogen importation. Prior efforts largely focused on SARS-CoV-2, with limited integration of multi-pathogen surveillance or side-by-side comparisons of nasal and wastewater surveillance modalities. A limited number of public health reviews have examined the broader implications of airport-based surveillance, including novel methods like airplane wastewater testing. However, empirical data on sustained, large-scale implementation of these models especially outside of regulatory or mandatory testing frameworks have been sparse. Added value of this projectThis is the first real-world implementation and scale-up of an anonymized, multi-pathogen traveler-based surveillance system across multiple U.S. international airports. We developed a scalable framework that integrated nasal swab testing, airplane and airport wastewater sampling, with genomic sequencing into a unified pathogen surveillance platform. Unlike prior efforts which primarily focused on SARS-CoV-2, this program captured respiratory and gastrointestinal viruses simultaneously and tracked genomic variation in near-real time. The program transitioned from a pilot to a multi-modality national surveillance system in under four years, engaging nearly 700,000 international travelers, and nearly 1000 aviation wastewater samples. Our findings demonstrate the feasibility of rapidly adapting this infrastructure for emerging threats and underscores the importance of sentinel surveillance in addressing global sequencing blind spots. Implications of all the available evidenceThe successful scale-up and real-time application of the TGS program illustrates that traveler-based surveillance can serve as a critical global early warning tool. Data generated from this program have filled gaps in global pathogen tracking, informed public health responses to outbreaks, and demonstrated that surveillance of international travelers can be achieved without mandatory testing. The scalability, speed, and adaptability of the program offer a viable model for global replication, especially as routine surveillance capacities decline. Our findings suggest that integration of multi-modal, voluntary traveler surveillance including sequencing and wastewater-based epidemiology should be considered a core component of pandemic preparedness and response frameworks worldwide.

BackgroundBenzene, toluene, ethylbenzene, and xylene (BTEX) are volatile aromatic hydrocarbons that are widespread environmental pollutants arising from petroleum processing, fuel combustion, and other industrial activities. Persistent BTEX contamination poses substantial risks to human health and ecosystems, underscoring the need for effective long term remediation strategies. Microbial bioremediation is a promising and sustainable approach for BTEX removal, but development of these approaches requires accurate detection of the genes and pathways responsible for substrate specific degradation. Although profile hidden Markov model (HMM) databases are widely used for functional annotation, existing annotation resources lack the substrate-specific resolution needed to distinguish between closely-related BTEX-degrading enzymes with different catalytic specificities. ResultsWe developed BTEXgenie as a sensitive annotation tool that uses custom HMMs built from alignments of experimentally validated BTEX degradation proteins to identify genes involved in the initial steps of aerobic and anaerobic BTEX degradation. BTEXgenie improved detection of anaerobic BTEX degradation genes that were absent from KOfam annotations. In benchmarking against the KEGG KOfam HMM database, BTEXgenie achieved 17.73%higher overall sensitivity while maintaining comparable specificity at 97.02%across genes involved in BTEX degradation pathways. When applied to environmental metagenomes, BTEXgenie recovered pathway patterns consistent with reported site characteristics and known degradation potential. In addition to gene annotation, BTEXgenie supports downstream interpretation through KEGG pathway-based visualization of detected functions and Circos-based visualization of genomic hit distributions. ConclusionsBTEXgenie is a substrate-specific annotation tool built from custom HMMs for detecting genes involved in BTEX degradation. By integrating gene annotation with pathway and genome-level visualizations, BTEXgenie facilitates characterization of microbial BTEX degradation potential in environmental and comparative genomic studies.

Wastewater genomic surveillance provides an opportunity to detect human and animal influenza A virus (IAV). We aimed to implement an IAV genomic surveillance framework agnostic to subtype, which enables recovery of IAV from multiple hosts and estimation of proportions across subtypes. We conducted IAV genomic surveillance in wastewater during the 2024-2025 flu season at multiple sites in California and compared these data with available human clinical IAV sequences and test positivity. We applied a custom whole-genome, multi-host IAV probe enrichment panel and adapted our custom expectation-maximization (EM) algorithm to deconvolute IAV mixtures in wastewater and infer subtype relative abundances. Absolute IAV concentrations were quantified using RT-PCR-based assays. H5N1 wastewater and clinical sequences were further characterized by constructing a whole-genome maximum-likelihood phylogenetic tree. Finally, we performed variant analysis to examine amino acid substitutions detected in wastewater. Our IAV probe enrichment method and EM algorithm successfully enriched all eight segments of three circulating IAV subtypes and accurately estimated subclade relative abundances for mixed IAV samples. Seasonal human H1N1pdm09 and H3N2 were detected throughout the study period from both wastewater and clinical sequencing data, with H1N1 subclades 6B.1A.5a.2a.1 and 6B.1A.5a.2a co-circulating, and H3N2 dominated by subclade 3C.2a1b.2a.2a.3a.1. Wastewater surveillance consistently detected H5N1 clade 2.3.4.4b across three monitored wastewater sites, while clinical H5N1 detections, from anywhere in CA, were sporadic and rare. Whole-genome phylogenetic analysis revealed that wastewater H5N1 sequences clustered with reference sequences associated with dairy cow and avian infections, while all human clinical H5N1 sequences clustered exclusively with reference sequences associated with dairy cow infections. Amino acid substitutions were identified across viral segments, and no mutations associated with mammalian adaptation were observed from wastewater samples.

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.

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.

Understanding how airborne particulates disrupt the human alveolar barrier requires in vitro systems that accurately replicate its composition and function. We present a biodegradable lung alveoli-on-a-chip that reproduces the architecture and physiology of the human air-blood interface using a porous poly(lactic-co-glycolic acid) (PLGA) membrane positioned between epithelium and endothelium under air-liquid interface (ALI) culture. The membrane, fabricated by porogen-assisted nonsolvent-induced phase separation, exhibited >50 % porosity, [~]2 {micro}m thickness, and mechanical compliance over 100-fold higher than conventional Transwell inserts, closely resembling the native interstitium. During co-culture, gradual PLGA degradation was compensated by cell-secreted extracellular-matrix (ECM) proteins such as collagen IV and laminin, forming a self-remodeling barrier that maintained integrity for at least 11 days. The platform supported stable epithelial-endothelial co-culture, high transepithelial electrical resistance, and physiologically relevant permeability. To demonstrate its utility, the chip was used to assess pulmonary toxicity of four types of waste-combustion-derived particulates, including rubber, plastic bags, plastic bottles, and textile fibers, delivered apically under ALI conditions. All combustion products reduced cell viability, increased hydrogen-peroxide release, and elevated {gamma}-H2AX expression, indicating oxidative and genotoxic stress, while disrupting barrier permeability. Rubber combustion particles elicited the most severe toxicity, causing the greatest loss of viability, accumulation of reactive oxygen species, and formation of DNA double-strand breaks. Together, these results establish a biodegradable, ECM-remodeling lung alveoli-on-a-chip as a physiologically relevant platform for investigating source-specific particulate toxicity and alveolar-barrier pathophysiology. By bridging environmental exposure models with human-relevant lung biology, this system provides a quantitative and translatable tool for evaluating respiratory risks and therapeutic interventions.

Omics technologies are increasingly integrated into next-generation risk assessment, yet quantitative toxicogenomics outcomes remain highly dependent on analytical choices, motivating a systematic evaluation of how bioinformatics workflows influence hazard characterization and transcriptomic Points of Departure (tPOD). Here, we applied five independent transcriptomics pipelines to a shared dataset of RPTEC-TERT1 kidney cells exposed to cisplatin across multiple concentrations and timepoints, comparing effects of pre-processing, benchmark concentration modeling, and pathway-based interpretation strategies. Across workflows, substantial variability was observed in gene-level benchmark concentrations (BMCs), primarily driven by differences in normalization, filtering, and especially the modeling software used. Despite this variability, convergence increased at later timepoints as transcriptional responses strengthened, with 24 h consistently identified as the most sensitive timepoint at the gene level. Aggregation of gene-level BMCs into pathway-based metrics reduced variability but did not eliminate it, with pathway definition emerging as a major determinant of sensitivity estimates. Notably, distinct pathway resources showed minimal gene overlap, and smaller, biologically coherent gene sets (e.g., co-expression modules and biomarker panels) produced lower and less dispersed BMCs compared with broader pathway annotations. Furthermore, direct modeling of pathway activity scores yielded systematically different sensitivity estimates relative to median-based aggregation, with method-dependent conservativeness influenced by pathway coverage and response strength. Overall, our findings demonstrate that both analytical workflow design and pathway selection critically shape toxicogenomic-derived potency estimates, highlighting the need for harmonized, transparent methodologies to enable robust application of transcriptomics in chemical safety assessment and regulatory decision-making.

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.

Nano- and microplastics (NMPs), by-products of the fragmentation and degradation of plastic products, are ubiquitous environmental contaminants, yet their burden in pediatric immune tissues and functional consequences for developing immunity remain unknown. Here we report the first comprehensive characterization of NMPs in surgically excised pediatric tonsils (n = 30) using pyrolysis gas chromatography- mass spectrometry (Py-GC/MS), Nile Red fluorescence microscopy, and optical photothermal infrared (O-PTIR) spectroscopy. NMPs were detected in all specimens, with polystyrene, polyethylene, polyethylene terephthalate, and acrylonitrile butadiene styrene present in >90% of samples. To bridge clinical exposure data with mechanistic insight, we formulated a cryo-milled multi-polymer mixture reflecting the patient-derived polymer profile and challenged human lymphoid aggregate culture (HLAC) tonsil organoids at environmentally relevant concentrations. Multiplexed cytokine profiling of culture supernatants revealed a robust early inflammatory response at day 3, with significant upregulation of IL-6 (p = 0.011) and MIP-1{beta}/CCL4 (p = 0.011), followed by convergence toward control levels by day 14. Functional cytokine modules spanning immune, metabolic, structural, and growth factor pathways showed coordinated deviation from controls at day 3 post-exposure with subsequent normalization. Fluorescence-guided depth profiling demonstrated time-dependent penetration of 100 nm particles into organoid aggregates (70% tissue depth at day 3 versus 95% at day 14), and transmission electron microscopy revealed intracellular polyethylene within lymphocyte lysosomes. These findings establish pediatric tonsils as a sentinel tissue for NMP bioaccumulation and demonstrate that environmentally relevant polymer mixtures elicit transient but significant immunomodulatory responses in human lymphoid tissue, with implications for mucosal and systemic immune health in children. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/728317v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@19395f4org.highwire.dtl.DTLVardef@5a0380org.highwire.dtl.DTLVardef@19c0741org.highwire.dtl.DTLVardef@a052c5_HPS_FORMAT_FIGEXP M_FIG Structure: Translational pipeline from clinical tissue characterization to patient-informed preclinical modeling of nano-microplastic (NMP) exposure in pediatric lymphoid tissue. Pediatric tonsil tissue collected from clinically indicated tonsillectomies underwent tissue digestion for NMP characterization to identify NMP type and size distributions. In parallel, tonsil tissue was used to generate human lymphoid aggregate culture (HLAC) organoids that recapitulate the cellular complexity of the native tissue. These patient-derived organoids were then exposed to environmentally relevant compositions and concentrations of NMPs over time-course experiments, with longitudinal assessment of immunomodulatory responses including cytokine profiling and functional readouts. This bedside-to-bench approach establishes a physiologically relevant human system for investigating NMP-immune interactions, bridging clinical tissue analysis with mechanistic preclinical modeling to inform understanding of pediatric environmental exposures and their potential health impacts. C_FIG

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

IntroductionParticulate Matter (PM2.5) exposure contributes to the global disease burden, yet its monitoring remains sparse and uneven and is limited in many limited ground monitoring network settings. Road-traffic proxy indicators can provide indirect estimates of PM2.5 where measurements are limited but require context-specific validation. We evaluated three PM2.5 road-traffic related proxies:(I) population-Weighted Road Network Density (WRND), (ii) Euclidean (straight line) distance from highways (EH), and (iii) Euclidean distance from main roads (EM). MethodsWe validated proxies using high-resolution outdoor filtered PM2.5 personal exposure measurements collected over 1 year from 343 postpartum participants in The Gambia, Kenya, and Mozambique. Village-level spatial patterns for the PM2.5-proxy relationship were mapped using 5 km hexagonal aggregated tessellations. Proxy-PM2.5 associations were assessed using Spearman correlation, and predictive utility was tested using country-specific and global Random Forest (RF) models (3-fold cross-validation), reporting R2, RMSE, and feature importance ResultsSpatial mapping showed heterogeneous proxy-PM2.5 relationships across and within sites, with elevated PM2.5 occurring in both low- and high-proxy contests. WRND-PM2.5 correlations were weak overall and statistically significant only in Mozambique (r = 0.351; p = 0.005), with non-significant associations in Kenya (r = -0.041; p = 0.673) and The Gambia (r = -0.020; p = 0.909). EH-PM2.5 correlations were positive in The Gambia (r = 0.335; p = 0.053) and Mozambique (r = 0.292; p = 0.020) but negative and significant in Kenya (r = -0.224; p = 0.018).Single-variable RF models performed poorly across all countries (R2 < 0.45) and the Global model (R2=0.42). Combining proxies improved performance in Kenya (R2=0.52; RMSE=31.7{micro}g/m3) and Mozambique (R2=0.60; RMSE=8.9 {micro}g/m3), Global R2=0.46; RMSE=29.1 {micro}g/m3), although in The Gambia, the combined model (R2=0.53; RMSE=37.6 {micro}g/m3) did not exceed the best single-proxy model. ConclusionRoad-network proxies provide context-dependent signals of personal PM2.5 exposure, and predictive performance is strengthened when proxies are combined in a hybrid model.