Environmental Science: Water Research & Technology

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

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.

Quaternary ammonium compounds (QACs) are high production volume biocidal compounds increasingly scrutinized for their potential to promote antimicrobial resistance spread. This study compared the release of QACs, QAC resistance indicator genes (qacE/qacE{Delta}1), and QAC tolerant bacteria from livestock and human waste streams into the environment. Five livestock farms with on-farm biogas plants (BGPs), a rural and an urban municipal wastewater treatment plant (WWTP) were studied in parallel. In WWTPs, <1% of incoming QACs were discharged with treated wastewater but 10-20% were transferred to sewage sludge. QAC concentrations in sewage sludge far exceeded those in raw and digested manure. The qacE/qacE{Delta}1 genes were detected in all samples with a higher relative abundance in solid than liquid samples. Relative abundances of QAC tolerant fast growing heterotrophic bacteria cultivated under high nutrient conditions at 37{degrees}C were higher in human than livestock waste streams. Providencia and Pseudomonas dominated the cultivated QAC tolerant bacteria in both systems but showed higher QAC tolerance when originating from human waste streams. Additionally, Enterobacteriaceae with higher QAC tolerance were cultivated from human waste streams. Most QAC tolerant strains carried antibiotic resistances without strong system differences. Only few strains carried the qacE/qacE{Delta}1 gene indicating that other mechanisms must be responsible for the increased QAC tolerance. In conclusion, QACs, qacE/qacE{Delta}1, and viable QAC tolerant bacteria including potential pathogenic bacteria were released from livestock and human waste streams into the environment with highest abundances in a post-pandemic sewage sludge sample. Highlights- QACs most abundant in human waste streams, especially biosolids - Higher relative abundance of QAC tolerant bacteria in human waste streams - Pseudomonas and Providencia dominated QAC tolerant bacteria in both waste streams - Enterobacteriaceae with higher QAC tolerance abundant in human waste streams - Most QAC tolerant strains carried additional antibiotic resistances Environmental implicationMunicipal wastewater treatment plants (WWTPs) and livestock farms are hotspots for antimicrobial resistance (AMR) propagation. We compared the simultaneous occurrence of quaternary ammonium compounds (QACs), resistance genes (RGs), QAC-tolerant bacteria, and their multidrug-resistance status in livestock and human waste streams. QACs, indicators of QAC tolerance and AMR occurred in both systems but were higher in WWTPs, especially sewage sludge. Our findings highlight the need for prudent disinfectant use and enhanced waste treatments to reduce the risks of spreading micropollutants, pathogens, and AMR via organic fertilizers or treated wastewater recycled in circular agricultural practice.

Numerous studies have shown that the abundance of antibiotic-resistant bacteria (ARBs) or antibiotic-resistance genes (ARGs) in soil increases after irrigation with wastewater. However, it is unclear whether this increase is due to the selection effects of pharmaceutical residues in the irrigation water or the continuous introduction of ARBs and ARGs with the wastewater. Further, it is unclear how the binding of antibiotics to natural colloids (1-1000 nm) affects their biological effects compared to truly dissolved substances (< 1 nm). We conducted competition experiments with resistant and susceptible Acinetobacter baylyi BD413 strains in wastewater, as well as in colloidal and truly dissolved extracts of soils irrigated with wastewater. Although the concentrations of our six target antibiotics were far below the measured minimum selective concentrations of the tested strains, we demonstrate that the resistant strain was favored in the wastewater and the colloidal extracts. In contrast, the truly dissolved fractions exhibited weaker and more variable selective effects. A non-targeted analysis revealed the presence of 82 additional substances in our extracts, including further antibiotics, pesticides, and different non-antibiotic drugs that may influence the selection of our resistant A. baylyi BD413 strain. Our findings suggest that antibiotic resistance is selected for in wastewater and wastewater-irrigated soils. This cannot be explained by antibiotic concentrations alone, but may also arise from the effects of complex mixtures of co-occurring contaminants, particularly those associated with colloidal particles.

Agricultural fields in the Mezquital Valley, Mexico, were irrigated with untreated wastewater over several decades. Following the construction of a wastewater treatment plant (WWTP) in Atotonilco de Tula, WWTP effluent is used for irrigation. To evaluate the effects of changed irrigation, a soil incubation experiment was performed. Soils of the Mezquital Valley long-term irrigated with untreated wastewater were irrigated with WWTP influent or effluent, both unspiked and spiked with antibiotics and biocidal compounds and incubated four weeks. We investigated the effects of shifted irrigation on the abundance of cultivable total heterotrophic and resistant bacteria (RB). Additionally, RB were cultivated from Coriandrum sativum (cilantro) sown in soil of the incubation experiment. While wastewater treatment significantly reduced the bacterial abundance in effluent, spiking increased RB abundance in both wastewater types including ciprofloxacin (CIP) RB. Before wastewater addition, all soils contained cultivable RB. Irrigation increased the relative abundance of RB cultivated on Mueller Hinton (MH) agar in Leptosols and Phaeozems, compared to soils prior to wastewater addition irrespective of the water type, but not in Vertisols, suggesting the soil type rather than water qualities influenced the RB abundance. Diverse CIP RB were cultivated including strains of 14 genera of three phyla. Among those, Achromobacter spp. strains related to potentially pathogenic A. spanius originating from soil were abundant in both leaves and roots of cilantro. Our results showed that the implementation of wastewater treatment does not reduce the abundance of cultivable RB in Mezquital Valley soils and cilantro plants. Health risk associated monitoring should include long-term persistent RB colonizing plants cultivated in wastewater irrigated soils.

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.

Fresh produce encounters pathogens at various stages of production and supply, with the harvesting process serving as one of these stages. To evaluate contamination associated with harvesting, we systematically swabbed zone 1 harvester surfaces and quantified Bacteroidales as a fecal biomarker using quantitative polymerase chain reaction (qPCR). Baseline contamination was dominated by non-detects, with occasional low-level detections (<25 copies/cm2) near the assay limit of detection (LoD). Detection occurred more frequently post-harvest (overall [~]4% pre-harvest and 10% post-harvest), while microbial loads remained low, indicating that harvesting primarily affected the likelihood of low-level contamination rather than increasing contamination abundance. Additionally, we developed and field-deployed a portable loop- mediated isothermal amplification (LAMP) assay for rapid harvester hygiene assessment and benchmarked its field performance against qPCR. Together, these results support a practical molecular tool for monitoring fecal contamination and informing cleaning and sanitization decisions.

Antibiotic residues exceeding selective concentrations for antibiotic-resistant bacteria have been detected in various environments, including manure, wastewater, and effluents from wastewater treatment plants. When these residues come into contact with soils, for instance, due to wastewater irrigation or fertilization with manure, they interact with soil constituents. Soil colloids (1-1000 nm), such as montmorillonite, have been observed to adsorb pharmaceuticals, including antibiotics. We investigated the effect of colloids on the bioavailability of ciprofloxacin and found, that added to bacterial growth medium, montmorillonite reduces, but does not completely prevent, the growth-inhibitory effect of the antibiotic. The bacteria were able to grow at up to roughly double the concentration of ciprofloxacin in the presence of montmorillonite. We show that the incomplete deactivation of ciprofloxacin was most probably caused by medium components that decreased the adsorption of ciprofloxacin to montmorillonite. We conclude that a selective potential of this highly active antibiotic in contaminated soils, which also contain nutrients enabling bacterial growth, cannot be ruled out. Environmental implicationAntibiotics such as ciprofloxacin are frequently detected in water bodies and soils due to wastewater irrigation or manure application. These residues raise concerns about environmental toxicity and antibiotic resistance. This study demonstrates that montmorillonite, a common clay mineral in soils, significantly reduces the antimicrobial efficacy of environmental ciprofloxacin concentrations by sorption. The findings reveal a natural attenuation mechanism that may influence the environmental fate and bioavailability of antibiotics. Understanding such interactions is critical for predicting antibiotic behavior in terrestrial systems and for designing more accurate environmental risk assessments.

Pathogen cross-contamination during food production is primarily controlled through environmental sanitation. However, sanitizer efficacy is often studied in bench-scale experiments that poorly approximate the fluid dynamics of sanitization and limits our understanding of commercial sanitization efficacy. This study paired computational fluid dynamics (CFD) estimates of shear stress with experimental measurements of Listeria innocua reduction on stainless steel following treatment with 100 ppm hypochlorite sanitizer. At the pilot-scale, sanitizer spray manually applied by researchers achieved a 2.6 {+/-} 0.4 log CFU/surface reduction; however, microbial reduction from manual operation of sanitizer spray equipment differed significantly between researchers (p < 0.05). Microbial reduction varied by location following stationary, bench-scale spray application of sanitizer for 3 s. The greatest reduction was at the point of sanitizer spray impingement (7.5 {+/-} 0.5 log CFU/surface) and directly adjacent to the impingement point (6.4 {+/-} 0.7 log CFU/surface) where shear stress was the highest. Significantly less microbial reduction (0.4 {+/-} 0.1 log CFU/surface) occurred where shear stress was lowest in the fluid-film of sanitizer running down from the impingement point (p < 0.05). Static submersion of inoculated coupons in sanitizer for 3 s resulted in a log reduction of 2.3 {+/-} 0.1 log CFU/surface. Discrepancies between bench-scale spraying, pilot-scale spraying, and submerged coupons demonstrate the need for sanitizer efficacy testing under realistic conditions to better estimate the risk reduction achieved through sanitation programs. IMPORTANCESanitation is critical for controlling pathogen cross-contamination during food production. These findings highlight the limitations of traditional approaches to sanitizer efficacy testing, not because they are invalid, but because they do not reflect the level of microbial reduction typically achieved in application. We demonstrate that these differences in outcomes are attributable to fluid dynamics and exposure, which are not well approximated in submerged coupon experiments. Accurate estimation of microbial reduction from sanitizer application is needed to guide food safety policy decisions. For example, overestimation of the risk reduction conferred by sanitizer treatment may result in food safety policies that neglect other sources of microbial reduction within sanitation programs.

Bacterial membrane vesicles (BMVs) have emerged as important contributors to the dissemination of antibiotic resistance genes (ARGs) in the environment. Here, we developed a high-performance immunomagnetic isolation method that improves the purity and selectivity of BMV recovery from wastewater, minimizes contamination from eDNA and viruses, and enables differentiation of BMVs originating from Gram-positive versus Gram-negative bacteria with minimal cross-reactivity. Using this approach, we found that ARGs such as the kanamycin resistance gene (kanR) was highly abundant in BMVs from both raw and treated wastewater, exhibited persistence following treatment, and retained the ability to generate antibiotic-resistant bacteria via transformation. Metagenomic sequencing further revealed that tetracycline resistance genes were the most abundant ARG class across all wastewater samples, while the composition of BMV-associated ARGs differed from the bulk ARG profile. These findings highlight the critical yet underrecognized role of BMVs in the spread of antimicrobial resistance and underscore the need to address BMV-mediated pathways within a One Health framework linking environmental and human health.

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.

Black Soldier Fly larvae (BSFL, Hermetia illucens) are highly effective for the bioconversion of food waste. However, their rearing process often produces substantial ammonia emissions, which are malodorous and environmentally concerning. We investigated the co-cultivation of BSFL with the sulfur-oxidizing bacterium Thiobacillus thioparus as a strategy to mitigate ammonia release. Importantly, under conditions where ammonia emissions were significantly reduced, neither larval growth nor bacterial viability was negatively affected. Furthermore, even when the initial bacterial inoculum was reduced to 3.3*105 CFU/g-food wastes, the bacterium rapidly recovered to functional levels and effectively controlled ammonia emissions. This indicates the absence of harmful interaction or nutrient competition between BSFL and T. thioparus. These findings suggest an efficient method for controlling ammonia in large-scale BSFL waste treatment. By reducing the required bacterial inoculum, this approach enables scalable microbial co-culturing with environmental and production benefits.

Wastewater-based surveillance (WBS) is increasingly used to monitor infectious disease dynamics, yet most evaluations focus on correlation or forecasting - neither of which directly assesses whether wastewater signals can identify the epidemiological events most relevant to public health decision-making. We argue that outbreak onset and epidemic peak detection are the operationally critical use cases of WBS, requiring a fundamentally different evaluation framework. We introduce a classification-based framework that treats WBS as an event-detection problem, defining outbreaks and peaks as discrete events, establishing detection intervals to account for timing uncertainty, and incorporating censoring and data completeness criteria for valid comparisons against imperfect clinical reference outcomes. Within this framework, we apply a Bayesian exponential growth model for outbreak detection - benchmarked against a standard reproductive number (Rt)-based method - and a rule-based algorithm for peak detection, evaluating performance via sensitivity and positive predictive value (PPV). Applied to county-level SARS-CoV-2 wastewater data from 281 U.S. counties (Biobot, 2021-2024), the exponential growth approach substantially outperforms the Rt-based baseline: sensitivity 0.82 and PPV 0.64 versus sensitivity 0.58 and PPV 0.19 for the best-performing Rt variant. Peak detection achieves sensitivity 0.84 and PPV 0.70 at the county level. Both peak and outbreak detection achieve strong and consistent performance against hospitalizations and deaths at the state level. Spatial aggregation yields a statistically significant improvement in peak detection PPV against a curated reference standard ($p < 0.001$), while outbreak detection improvements under aggregation are directionally consistent but not statistically significant. Wastewater leads case-defined outbreaks by 4-6 days but minimally leads epidemic peaks, consistent with wastewater approximating prevalence rather than incidence. These findings demonstrate that wastewater signals can reliably detect outbreak onset and epidemic peaks across spatial scales and clinical outcomes, and that the choice of detection method matters substantially in practice. The classification framework developed here provides a reusable and principled tool for evaluating any surveillance signal as an event-detection system, with direct relevance to how WBS is actually used in public health decision-making.

BackgroundCase-based infectious disease surveillance is subject to ascertainment bias when testing intensity varies across time and population subgroups. We previously developed a regression-based test adjustment methodology using Standardized Testing Ratios (STRs) to correct for differential testing patterns in COVID-19 surveillance data. Wastewater-based surveillance (WWS) measures viral burden in the community independently of diagnostic testing behavior, making it a valuable external validation tool for test-adjusted case estimates. MethodsWe analyzed 111 weeks of paired wastewater and case surveillance data from Ontario, Canada (July 19, 2020 to August 28, 2022). Wastewater SARS-CoV-2 signals from 107 sewersheds across 34 public health units were normalized within sewersheds and aggregated using population-weighted averages. We compared wastewater correlations with crude reported and test-adjusted case counts using Spearman rank correlations, linear regression, and negative binomial distributed lag nonlinear models (DLNM), stratified by epidemic period. ResultsTest-adjusted cases correlated substantially more strongly with wastewater signals than crude reported cases overall (Spearman {rho} = 0.849 vs. 0.679; linear R{superscript 2} = 0.609 vs. 0.191). The advantage of test adjustment was greatest during the Omicron wave, when population-level diagnostic testing contracted sharply following PCR eligibility restrictions ({rho} = 0.924 vs. 0.604; R{superscript 2} = 0.815 vs. 0.470). DLNM incorporating the wastewater signal explained substantially more variance in test-adjusted than crude reported cases (McFadden pseudo-R{superscript 2} 0.898 vs. 0.776), despite similar lag-response structure for both outcomes. ConclusionsWastewater surveillance provides compelling independent validation of a previously described test adjustment methodology for COVID-19 case surveillance. The agreement between wastewater signals and test-adjusted cases was strongest precisely when testing scarcity was most severe, supporting the use of test adjustment to recover accurate infection dynamics from case surveillance data during periods of changing testing access and policy.

Manual colony counting remains the rate-limiting, operator-dependent step in culture-based food microbiology quality control (QC). Automated colony analysis using machine learning (ML) offers the potential to standardise, accelerate, and improve the traceability of this process. However, systematic multi-method validation data for AI-based platforms against recognised international standards remain scarce. We conducted a prospective, multi-study validation of the Reshape Smart Incubator which is an automated imaging and ML-based colony analysis system, across eight ISO microbiological reference methods. In total, 887 plates were analysed, spanning qualitative (presence/absence) detection of Listeria spp. (ISO 11290-1) and Salmonella spp. (ISO 6579), and quantitative enumeration of total viable count (ISO 4833), Bacillus cereus (ISO 7932), Enterobacteriaceae (ISO 21528), coagulase-positive Staphylococci (ISO 6888), yeasts and moulds (ISO 21527), and lactic acid bacteria (ISO 15214). Automated results were benchmarked against the consensus of three or more trained technicians. The platform achieved 100% agreement with manual assessment for all both qualitative detection methods (ISO 11290-1, ISO 6579) with zero false positives and zero false negatives. For quantitative enumeration, agreement ranged from 92.97% (ISO 15214, n=122, using ISO-aligned {+/-}10%/>30 CFU thresholds) to 98.46% (ISO 21528, n=130). Where discrepancies occurred, they largely coincided with plates showing high inter-technician variability. Precision testing demonstrated a coefficient of variation of 5.88% and a mean standard deviation of 0.44 CFU for low-count plates. This study presents a comprehensive multi-ISO validation of an AI-based colony analysis system to date. The AI models demonstrated performance comparable to or exceeding that of trained human technicians across a broad range of microbiological targets, agar types, and colony morphologies, thereby supporting their use as a validated and traceable alternative to manual plate reading in accredited food microbiology quality control laboratories.

Riverine ecosystems particularly in industrialized environment are subjected to chronic press disturbances, resulting from the decadal release of synthetic organic compounds and other xenobiotics. While indigenous microbial communities are highly sensitive to such stressors, the resulting metabolic restructuring and functional reshaping of the microbiome, driven by these long-term anthropogenic pressures remains poorly characterized. In this study, a microbial ecology of Bhadar River flowing across the Jetpur Industrial Estate, (Jetpur) were studied. Using a cross-sectional comparative approach, soil/sediment samples were collected from the diverse polluted and non-polluted sites from the estate. The taxonomic profiling using 16S rRNA gene amplicon sequencing, taxo-phenomic shifts (through metaphenomics) was studied, while the functional potential of metabolic pathways was validated using high-resolution shot-gun metagenomic study. Due to prolong pollution, the samples were rich in sulphur (9809 to 12391 mg/L), where polluted samples were having elevated COD (2432 to 4150 mg/L) as well as BOD (1000 to 1420 mg/L) values, along with the presence of heavy metals (e.g., Fe, Mg). Results revealed a distinct taxonomic shift at both the bacterial and archaeal levels. In non-polluted sites Proteobacteria (33 to 57%) dominated along with Acidobacteria and Actinobacteria, with diverse genera like Alcaligenes and Serratia. Whereas, polluted sites exhibited marked increase in Bacteroidetes (13 to 29%), Firmicutes, and Synergistetes and genera like Alkalitalea, Mesotoga and Desulfomicrobium, reflecting anaerobic, fermentative, and sulfate-reducing phenotypes. The archaeal communities at polluted sites were dominated by Euryarchaeota (78 to 99%), specifically methanogenic genera of Methanosaeta and Methanocalculus, contrasting with the Methanomassiliicoccus dominance in non-polluted areas. The alpha-diversity was marginally higher in polluted sites (Shannon: 4.11 to 4.81 vs. 3.81 to 5.39 (non-polluted)), but beta-diversity underscored clear separation (94% variance explained by pollution). The shot-gun metagenomic analysis indicated a substantial enhancement in anaerobic metabolic capacities within the polluted microbiome, primarily in sulphur respiration (dissimilatory sulfate reduction), methanogenesis (elucidating biogenic pathways), along with nitrogen cycling (identifying key denitrification and ammonification genes). The polluted microbiome have developed the potential to metabolise/degrade complex aromatic compounds (pcaK for benzoate/protocatechuate transport) and heavy metal resistance. The strong positive co-occurrences among anaerobic phyla (Thermotogae, Synergistetes, Bacteroidetes) in polluted sites was established, indicating syntrophic interactions for xenobiotic metabolism. These findings provide a theoretical ecological model for perturbed industrial ecosystems, emphasizing the role of habitat selection in shaping microbial functional diversity and demonstrate the remarkable adaptation of autochthonous communities to persistent press disturbances.

Odd-chain carboxylates such as valerate and heptanoate are ecologically relevant metabolites and promising platform chemicals, yet the factors leading to their formation during secondary lactate fermentations remain poorly understood. Here, a continuous anaerobic bioreactor was operated for 297 days under mildly acidic conditions to evaluate how lactate:propionate molar ratios shape product spectrum in lactate fermentations. Valerate was the predominant odd-chain product under all conditions, reaching concentrations up to 110 mM, while heptanoate accumulated only at low levels (<10 mM). At low lactate concentrations (10-20 g/L), product selectivity strongly depended on the lactate:propionate ratio. When lactate:propionate ratios were around 1.2 mol/mol, odd-chain products were favored, whereas higher ratios (up to 4.8 mol/mol) shifted metabolism toward caproate and butyrate formation. However, this trend was not maintained at higher lactate concentrations (30-40 g/L; lactate not fully consumed), where odd-chain selectivities remained high even at lactate:propionate ratios of 4.8 mol/mol. Pathway analysis indicated that under high-lactate conditions up to 30% of lactate was redirected toward propionate and acetate formation, likely via the acrylate pathway. Microbial community analysis revealed a stable dominance of Caproiciproducens spp., that could be correlated to valerate production. Overall, this work provides mechanistic insights into the ecology of lactate fermentations and offers a framework for steering product selectivity in engineered anaerobic systems. HighlightsValerate was the dominant product, reaching up to 110 mM. Lactate:propionate ratios drive product selectivities. High lactate concentrations activated in situ propionate formation pathways. Caproiciproducens dominance was associated with sustained valerate production.

Chelating ion exchange resins are widely used to eliminate metal interferences in the analysis of ammonium (NH4+) in soil extraction solutions. However, their potential to co-adsorb NH4+ remains underexplored. Here, synthetic metal ion solutions containing 6-30 mg L-1 NH4+ and the metal cations Ca2+, Mg2+, Cu2+, Mn2+, and Zn2+ were treated with Amberlite IRC-748 resin. The resin efficiently removed Ca2+ (-42.2%), Mg2+ (-21.1%), Cu2+ (-99.9%), Mn2+ (-56.9%), and Zn2+ (-93.6%). However, NH4+ losses of 2.2-5.6% were observed, indicating concentration-dependent co-adsorption. While these losses may be acceptable for concentration measurements via routine assays such as photometric analysis, they may still affect the accuracy of high-precision N analyses that rely on quantitative NH4+ recovery. This highlights a methodological caveat for resin-treated samples, especially in low-NH4+ environments. We therefore recommend including recovery assessments and correction factors when using chelating resins to improve accuracy in NH4+ quantification.

Evidence supporting the use of airborne eDNA for biodiversity studies and ecosystem monitoring is growing. The promise of wide-area population dynamics data for downstream applications in targeted monitoring of pests and pathogens for agriculture and rare species for conservation is appealing; however, several technical challenges persist. Here, we focused on the development of a comprehensive dataset to facilitate assay development and accelerate the use of aerial sampling for species detection. Year-round metabarcoding data was generated using bacterial, fungal, plant, and arthropod primer sets and resulted in relative abundance estimates for 4,960 amplicon sequence variants (ASVs), 1,748 ASVs of which were assigned to a minimum taxonomic level of genus (bacteria, fungi, plants) or class (arthropods). Sequence diversity assessments and seasonal clustering based on presence/absence detection patterns were performed for individual ASVs, while discerning quantitative changes in seasonal abundance required grouping ASVs to at least the genus level. Examination of the technical aspects of metabarcoding suggested that the use of subsampling allows for consistent detection of genera with relative abundance values above 2 %, even when samples have varying sequencing depths. Sequencing depth was the primary determinate for detecting sporadic and/or rare ASVs. Sampler comparisons, common sources of variation, and the benefits of barcoding regional species to supplement the existing taxonomic databases were discussed. Insufficient knowledge of sampler coverage area for the different organism types was identified as a limitation to the deployment of aerial monitoring networks. Considerations for further aerial metabarcoding efforts are suggested based on our experimental findings. ImportanceOur study deals directly with the generation, analysis and limitations of airborne eDNA metabarcoding data for re-use by the broader environmental research community. This includes timing of seasonal detection for possible genera of interest across multiple kingdoms, including bacteria, fungi, plants and animals (specifically arthropods), and support for the generation of local databases to assess the current limitations of universal primers for species/genus taxonomic resolution. With regards to methodology, it continues to build upon established best practices for airborne eDNA collection in areas such as sub-sampling and sampling replicates, sampler type and sequencing depth. To accelerate possible uptake and application of the data, we provide the identified ASVs and their seasonal relative abundances as a resource.

Fermentation of C1 gases is an emerging technology where waste gases are bio converted into value-added products. This study navigates the gas fermentation potential of Gordonia rubripertincta to produce carotenoids. The crucial carbon monoxide dehydrogenase (CODH) enzyme, necessary for gas uptake by the microbe, was found to be present in G. rubripertincta through blastp on NCBI website. The organism was then used for gas fermentation experiments in a continuous stirred tank reactor (CSTR) in different modes of reactor operation resulting in the production of about 500 mg pigment/g WCW (wet cell weight). Two important reactor parameters, molybdenum content and pH, were optimized for enhanced carotenoid production. Overall, G. rubripertincta was observed to be an efficient candidate organism for C1 gas fermentation. KEY HIGHLIGHTSO_LIGordonia rubripertincta synthesises aerobic carbon monoxide dehydrogenase enzyme. C_LIO_LIIt is a potential gas fermenting microbe that gives carotenoids as product. C_LIO_LIThe gas uptake efficiency of the microbe is more in fed-batch discontinued mode. C_LIO_LIIn FB-D, the resultant carotenoids are 500+9 mg/g wet cell weight (WCW). C_LIO_LIMo/pH of 20 mg/7.0 resulted in highest carotenoids, i.e., 134+41 mg/g WCW. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/722808v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@8b1185org.highwire.dtl.DTLVardef@2b6f90org.highwire.dtl.DTLVardef@1a9697dorg.highwire.dtl.DTLVardef@14c9dc8_HPS_FORMAT_FIGEXP M_FIG C_FIG