Despite the tremendous efforts to improve river water quality, chemical contamination remains a significant issue. Besides well-known contaminants, in recent years, pollutants of industrial origin received increasing attention because of the huge knowledge gap regarding their occurrence, fate and environmental risks. Moreover, such pollutants often exhibit high concentration fluctuations over time, which makes them less predictable and measurable with classical short-time campaigns.
This study provides insights into the different sources of chemical contamination of the Rhine River based on temporal high-frequency LC-HRMS monitoring data from a single location. A newly developed prioritization strategy selected nearly 3000 substances as potentially major contaminants. A novel classification analysis based on temporal behavior identified 53 % of these compounds (accounting for 62 % of the time-integrated intensity recorded in the dataset) as originating from irregular emission sources. Irregular emissions can originate from industrial production cycles. After delimiting other potential irregular sources, we have strong evidence indicating that a considerable share of the irregular emissions likely comes from industrial activities. This finding is supported by the structural elucidation of sixteen irregularly emitted substances, for which the industrial origin was successfully confirmed. Those compounds include 3-chloro-5-(trifluoromethyl)pyridine-2-carboxylic acid and 4-(dimethylamino)-2,2-diphenylpentanenitrile. In addition, 40 other compounds exhibited temporal emission patterns similar to the sixteen industrial compounds, which strongly suggests a common contamination source. Finally, 100 top-ranking compounds were selected for further structural elucidation and emission reduction measures. The computational approach outlined within this study can be effectively applied in other large river catchments to identify unknown contaminants stemming from industrial sources.

Recent emphasis on the development of safe-and-sustainable-by-design chemicals highlights the need for methods facilitating the early assessment of persistence. Activated sludge experiments have been proposed as a time- and resource-efficient way to predict half-lives in simulation studies. Here, this persistence "read-across" approach was developed to be more broadly and robustly applicable. We evaluated 21 previously used reference plant protection products (PPPs) for their broader applicability in calibrating regression and classification models for predicting half-lives in soil (DT50_OECD307) and water-sediment systems (DT50_OECD308) based on their half-life in sludge and the organic carbon-water partition coefficient K_OC as predictors. The calibrated regression models showed satisfactory predictions of DT50_OECD307 for another 22 test PPPs. Performance was less satisfying for the prediction of DT50_OECD308 for 46 active pharmaceutical ingredients (APIs), suggesting a need for expanding the set of calibration substances and more experimental K_OC values. The classification models mostly correctly classified persistent and non-persistent test compounds for both PPPs and APIs, which is relevant for early-stage screening of persistence. Transformation products of the reference compounds in activated sludge samples were consistent with the reported degradation pathways in soil, particularly with respect to major aerobic, enzyme-catalyzed transformation reactions. Overall, "reading across" biotransformation in environmental compartments such as soils or sediments from experiments with activated sludge outperformed three widely used in silico approaches for estimating half-lives and hence has immediate potential to support early assessment of biodegradability when aiming to develop chemicals that are safe and sustainable by design.

Biotransformation assays conducted in activated sludge (AS) from wastewater treatment plants (WWTPs) offer various benefits, most notably the high microbial density and comparably high bioavailability of the chemicals, enabling short experimental times of 72 h. Moreover, rate constants determined in AS experiments have shown the potential to be used as predictors for half-lives in other environmental compartments, such as soil. Therefore, biotransformation experiments with AS could serve as a valuable basis for developing standardized, high-throughput persistence tests used for screening purposes, e.g., in a benign-by-design framework, if reproducibility of experimental outcomes can be ensured. Here, we tested protocols for the preservation of AS microbial communities using lyophilization or cryopreservation. Their preservation performance was evaluated for 36 representative micropollutants (MPs) in 72 h, lab-scale batch experiments, with fresh AS as a reference. Cryopreservation, using either DMSO or glycerol as a protective agent, preserved the biotransformation potential for most of the MPs (∼65%), showing significant deviations in biotransformation kinetics almost exclusively for amine-containing substances. Lyophilization, however, performed worse with over ∼89% of MPs exhibiting significantly decreased or enhanced biotransformation compared to fresh AS. We further demonstrate nonsignificant impacts of storage time and the possibility of using artificial instead of preserved native supernatant. Major shifts in community composition based on 16S rRNA gene sequencing results aligned with biotransformation outcomes. Overall, the results suggest that our optimized cryopreservation protocol holds promise to preserve the biotransformation potential of AS and, upon further refinement and testing, might effectively support long-term reproducibility in persistence assessment.

Assessing chemical environmental impacts is critical but challenging due to the time-consuming nature of experimental testing. Graph neural networks (GNNs) support superior prediction performance and mechanistic interpretation of (eco-)toxicity data, but face the risk of overfitting on the typically small experimental data sets. In contrast to purely data-driven approaches, we propose a mechanism-guided transfer learning strategy that is highly efficient and provides key insights into the underlying drivers of (eco-)toxicity. By leveraging the mechanistic link between baseline toxicity and toxicity toward nitrifiers, we pretrained a GNN on lipophilicity data (log P) and subsequently fine-tuned it on the limited data set of toxicity toward nitrifiers, achieving prediction performance comparable with pretraining on much larger but mechanistically less relevant data sets. Additionally, we enhanced GNN interpretability by adjusting multihead attentions after convolutional layers to identify key substructures, and quantified their contributions using a Shapley Value method adapted for graph-structured data with improved computational efficiency. The highlighted substructures aligned well with and effectively distinguished known structural alerts for baseline toxicity and specific modes of toxic action in nitrifiers. The proposed strategy will allow uncovering new structural alerts in other (eco)toxicity data, and thus foster new mechanistic insights to support chemical risk assessment and safe-by-design principles.

Background Environmental reservoirs of antibiotic resistance pose a threat to human and animal health. Aquatic biofilms impacted by wastewater effluent (WW) are known environmental reservoirs for antibiotic resistance; however, the relative importance of biotic factors and abiotic factors from WW on the abundance of antibiotic resistance genes (ARGs) within aquatic biofilms remains unclear. Additionally, experimental evidence is limited within complex aquatic microbial communities as to whether genes bearing low sequence similarity to validated reference ARGs are functional as ARGs.

Results To disentangle the effects of abiotic and biotic factors on ARG abundances, natural biofilms were previously grown in flume systems with different proportions of stream water and either ultrafiltered or non-ultrafiltered WW. In this study, we conducted deep shotgun metagenomic sequencing of 75 biofilm, stream, and WW samples from these flume systems and compared the taxonomic and functional microbiome and resistome composition. Statistical analysis revealed an alignment of the resistome and microbiome composition and a significant association with experimental treatment. Several ARG classes exhibited an increase in normalized metagenomic abundances in biofilms grown with increasing percentages of non-ultrafiltered WW. In contrast, sulfonamide and extended-spectrum beta-lactamase ARGs showed greater abundances in biofilms grown in ultrafiltered WW compared to non-ultrafiltered WW. Overall, our results pointed toward the dominance of biotic factors over abiotic factors in determining ARG abundances in WW-impacted stream biofilms and suggested gene family-specific mechanisms for ARGs that exhibited divergent abundance patterns. To investigate one of these specific ARG families experimentally, we biochemically characterized a new beta-lactamase from the Planctomycetota (Phycisphaeraceae). This beta-lactamase displayed activity in the cleavage of cephalosporin analog despite sharing a low sequence identity with known ARGs.

Conclusions This discovery of a functional planctomycete beta-lactamase ARG is noteworthy, not only because it was the first beta-lactamase to be biochemically characterized from this phylum, but also because it was not detected by standard homology-based ARG tools. In summary, this study conducted a metagenomic analysis of the relative importance of biotic and abiotic factors in the context of WW discharge and their impact on both known and new ARGs in aquatic biofilms.

Abwassereinträge aus der chemisch-pharmazeutischen Industrie wirken sich messbar auf die stoffliche Belastung der ARA-Abläufe aus und hinterlassen eine hochvariable, standortspezifische Signatur. Betriebsspezifische Messkampagnen helfen den Unternehmen, ihre Einträge adäquat zu erfassen und diese mit gezielten Massnahmen abzusenken.

Abstract: enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system.
Scientific contribution: The main scientific contributions are the development of a pathway prediction model applicable to diverse chemicals, a specialized evaluation method for holistic performance assessment, and a novel applicability domain algorithm for user-specific performance estimation. The introduction of two new data sets, and the creation of links to EC classes make enviPath a unique resource in microbial biotransformation research.

Micropollutants removal efficiency strongly vary across different aerobic wastewater treatment plants, resulting in their frequent detection in surface and ground waters. Seasonal temperature variation is a major factor influencing plant performance, but it is still unclear how prolonged periods of temperature change impact microbiome and micropollutant biotransformation. This work investigates the effect of long-term temperature variation on the microbial dynamics in an activated sludge system, and the impact on micropollutant biotransformation. Sequencing batch reactors were used as model system and 4–40 °C temperature range was studied. 16S rRNA amplicon sequencing showed that temperature drives microbial structure (gDNA) and activity (RNA), rather than time, and this was stronger below 15 °C and above 25 °C. The microbial community was richest and more diverse at 20 °C, while rarer and more specific taxa became predominant over time, at more extreme temperatures. This suggested that less abundant taxa might be responsible for maintaining the biotransformation capability in the activated sludge at extreme temperatures. Micropollutant biotransformation rates mostly deviated from the classic Arrhenius model below 15 °C and above 25 °C, indicating that prolonged exposure to temperature changes leads to temperature-induced taxonomic shifts, resulting in the emerging of different sets of biotransformation pathways over different temperature ranges.

Some bacteria can degrade organic micropollutants (OMPs) as primary carbon sources. Due to typically low OMP concentrations, these bacteria may benefit from supplemental assimilation of natural substrates present in the pool of dissolved organic matter (DOM). The biodegradability of such auxiliary substrates and the impacts on OMP removal are tightly linked to biotransformation pathways. Here, we aimed to elucidate the biodegradability and effect of different DOM constituents for the carbofuran degrader Novosphingobium sp. KN65.2, using a novel approach that combines pathway prediction, laboratory experiments, and fluorescence spectroscopy. Pathway prediction suggested that ring hydroxylation reactions catalysed by Rieske-type dioxygenases and flavin-dependent monooxygenases determine the transformability of the 11 aromatic compounds used as model DOM constituents. Our approach further identified two groups with distinct transformation mechanisms amongst the four growth-supporting compounds selected for mixed substrate biodegradation experiments with the pesticide carbofuran (Group 1: 4-hydroxybenzoic acid, 4-hydroxybenzaldehyde; Group 2: p-coumaric acid, ferulic acid). Carbofuran biodegradation kinetics were stable in the presence of both Group 1 and Group 2 auxiliary substrates. However, Group 2 substrates would be preferable for bioremediation processes, as they showed constant biodegradation kinetics under different experimental conditions (pre-growing KN65.2 on carbofuran vs. DOM constituent). Furthermore, Group 2 substrates were utilisable by KN65.2 in the presence of a competitor (Pseudomonas fluorescens sp. P17). Our study thus presents a simple and cost-efficient approach that reveals mechanistic insights into OMP-DOM biodegradation.

Trace organic contaminants (TrOCs) are omnipresent in wastewater treatment plants (WWTPs), yet, their removal during wastewater treatment is oftentimes incomplete and underlying biotransformation mechanisms are not fully understood. In this study, we elucidate how different factors, including pre-exposure levels and duration, influence microbial adaptation towards catabolic TrOC biodegradation and its potential role in biological wastewater treatment. Four sequencing batch reactors (SBRs) were operated in parallel in three succeeding phases, adding and removing a selection of 26 TrOCs at different concentration levels. After each phase of SBR operation, a series of batch experiments was conducted to monitor biotransformation kinetics of those same TrOCs across various spike concentrations. For half of our test TrOCs, we detected increased biotransformation in sludge pre-exposed to TrOC concentrations ≥5 µg L⁻¹ over a 30-day period, with most significant differences observed for the insect repellent DEET and the artificial sweetener saccharin. Accordingly, 16S rRNA amplicon sequencing revealed enrichment of taxa that have previously been linked to catabolic biodegradation of several test TrOCs, e.g., Bosea sp. and Shinella sp. for acesulfame degradation, and Pseudomonas sp. for caffeine, cyclamate, DEET, metformin, paracetamol, and isoproturon degradation. We further conducted shotgun metagenomics to query for gene products previously reported to be involved in the TrOCs’ biodegradation pathways. In the future, directed microbial adaptation may be a solution to improve bioremediation of TrOCs in contaminated environments or in WWTPs.