The University of Minnesota pathway prediction system: predicting metabolic logic

Unveiling intricate transformation pathways of emerging contaminants during wastewater treatment processes through simplified network analysis

As the key stage for purifying wastewater, elimination of emerging contaminants (ECs) is found to be fairly low in wastewater treatment plants (WWTPs). However, less knowledge is obtained regarding the transformation pathways between various chemical structures of ECs under different treatment processes. This study unveiled the transformation pathways of ECs with different structures in 15 WWTPs distributed across China by simplified network analysis (SNA) we proposed. After treatment, the molecular weight of the whole component of wastewater decreased and the hydrophilicity increased. There are significant differences in the structure of eliminated, consistent and formed pollutants. Amino acids, peptides, and analogues (AAPAs) were detected most frequently and most removable. Benzenoids were refractory. Triazoles were often produced. The high-frequency reactions in different WWTPs were similar, (de)methylation and dehydration occurred most frequently. Different biological treatment processes performed similarly, while some advanced treatment processes differed, such as a significant increase of -13.976 (2HO reaction) paired mass distances (PMDs) in the chlorine alone process. Further, the common structural transformation was uncovered. 4 anti-hypertensive drugs, including irbesartan, valsartan, olmesartan, and losartan, were identified, along with 22 transformation products (TPs) of them. OH2 and H2O PMDs occurred most frequently and in 80.81 % of the parent-transformation product pairs, the intensity of the product was higher than parent in effluents, whose risk should be considered in future assessment activity. Together our results provide a macrography perspective on the transformation processes of ECs in WWTPs. In the future, selectively adopting wastewater treatment technology according to structures is conductive for eliminating recalcitrant ECs in WWTPs.

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry

Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.

Suspect and Nontarget Screening Reveal the Underestimated Risks of Antibiotic Transformation Products in Wastewater Treatment Plant Effluents

Antibiotics are anthropogenic contaminants with a global presence and of deep concern in aquatic environments, while less is known about the occurrence and risks of their transformation products (TPs). Herein, we developed a comprehensive suspect and nontarget screening workflow based on high-resolution mass spectrometry to identify unknown antibiotic TPs in wastewater treatment plant effluents. We identified 211 compounds (35 parent antibiotics and 176 TPs) at confidence levels of ≥3 and 107 TPs originated from macrolides. TPs were quantified by 17 TPs standards and semiquantified by the predicted response factors and accounted for 55.6-95.1% (76.7% on average) of the total concentrations of parents and TPs. 22.2%, 63.1%, and 18.8% of the identified TPs were estimated to be more persistent, mobile, and toxic than their parent antibiotics, respectively. Further ecological risk assessment based on concentrations and toxicity to aquatic organisms revealed that the cumulative risks of TPs were generally higher than those of parents. Despite the newly formed N-oxide TPs, the tertiary treatment process (mainly ozonation) could decrease the averaged 20.3% of concentrations and 36.2% of the risks of antibiotic-related compounds. This study highlights the necessity to include antibiotic TPs in environmental scrutiny and risk assessment of antibiotics in different aquatic environments.

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

Recent advances in assessment methods and mechanism of microbe-mediated chlorpyrifos remediation

Chlorpyrifos (CP) is one of the Organophosphorus pesticides (OPs) primarily used in agriculture to safeguard crops from pests and diseases. The pervasive use of chlorpyrifos is hazardous to humans and the environment as it inhibits the receptor for acetylcholinesterase activity, leading to abnormalities linked to the central nervous system. Hence, there is an ardent need to develop an effective and sustainable approach to the on-site degradation of chlorpyrifos. The role of microbes in the remediation of pesticides is considered the most effective and eco-friendly approach, as they have strong degradative potential due to their gene and enzymes naturally adapted to these sites. Several reports have previously been published on exploring the role of microbes in the degradation of CP. However, detection of CP as an environmental contaminant is an essential prerequisite for developing an efficient microbial-mediated biodegradation method with less harmful intermediates. Most of the articles published to date discuss the fate and impact of CP in the environment along with its degradation mechanism but still fail to discuss the analytical portion. This review is focused on the latest developments in the field of bioremediation of CP along with its physicochemical properties, toxicity, fate, and conventional (UV-Visible spectrophotometer, FTIR, NMR, GC-MS, etc) and advanced detection methods (Biosensors and immunochromatography-based methods) from different environmental samples. Apart from it, this review explores the role of metagenomics, system biology, in-silico tools, and genetic engineering in facilitating the bioremediation of CP. One of the objectives of this review is to educate policymakers with scientific data that will enable the development of appropriate strategies to reduce pesticide exposure and the harmful health impacts on both Human and other environmental components. Moreover, this review provides up-to-date developments related to the sustainable remediation of CP.

Development of a PFAS reaction library: identifying plausible transformation pathways in environmental and biological systems

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are used in many consumer applications due to their stain repellency, surfactant properties, ability to form water-proof coatings and use in fire suppression. The production, application, transport, use and disposal of PFAS and PFAS-treated products have resulted in their wide-spread occurrence in environmental and biological systems. Concern over exposure to PFAS and their transformation products and metabolites has necessitated the development of tools to predict the transformation of PFAS in environmental systems and metabolism in biological systems. We have developed reaction libraries for predicting transformation products and metabolites in a variety of environmental and biological reaction systems. These reaction libraries are based on generalized reaction schemes that encode the process science of PFAS reported in the peer-reviewed literature. The PFAS reaction libraries will be executed through the Chemical Transformation Simulator, a web-based tool that is available to the public. These reaction libraries are intended for predicting the environmental transformation and metabolism of PFAS only.

BioTransformer 3.0-a web server for accurately predicting metabolic transformation products

BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

Identifying Functional Groups that Determine Rates of Micropollutant Biotransformations Performed by Wastewater Microbial Communities

The goal of this research was to identify functional groups that determine rates of micropollutant (MP) biotransformations performed by wastewater microbial communities. To meet this goal, we performed a series of incubation experiments seeded with four independent wastewater microbial communities and spiked them with a mixture of 40 structurally diverse MPs. We collected samples over time and used high-resolution mass spectrometry to estimate biotransformation rate constants for each MP in each experiment and to propose structures of 46 biotransformation products. We then developed random forest models to classify the biotransformation rate constants based on the presence of specific functional groups or observed biotransformations. We extracted classification importance metrics from each random forest model and compared them across wastewater microbial communities. Our analysis revealed 30 functional groups that we define as either biotransformation promoters, biotransformation inhibitors, structural features that can be biotransformed based on uncharacterized features of the wastewater microbial community, or structural features that are not rate-determining. Our experimental data and analysis provide novel insights into MP biotransformations that can be used to more accurately predict MP biotransformations or to inform the design of new chemical products that may be more readily biodegradable during wastewater treatment.

Computational Approaches for the Prediction of Environmental Transformation Products: Chlorination of Steroidal Enones

There is growing interest in the fate and effects of transformation products generated from emerging pollutant classes, and new tools that help predict the products most likely to form will aid in risk assessment. Here, using a family of structurally related steroids (enones, dienones, and trienones), we evaluate the use of density functional theory to help predict products from reaction with chlorine, a common chemical disinfectant. For steroidal dienones (e.g., dienogest) and trienones (e.g., 17β-trenbolone), computational data support that reactions proceed through spontaneous C4 chlorination to yield 4-chloro derivatives for trienones and, after further reaction, 9,10-epoxide structures for dienones. For testosterone, a simple steroidal enone, in silico predictions suggest that C4 chlorination is still most likely, but slow at environmentally relevant conditions. Predictions were then assessed through laboratory chlorination reactions (0.5-5 mg Cl₂/L) with product characterization via HRMS and NMR, which confirmed near exclusive 4-chloro and 9,10-epoxide products for most trienones and all dienones, respectively. Also consistent with computational expectations, testosterone was effectively unreactive at these same chlorine levels, although products consistent with in silico predictions were observed at higher concentrations (in excess of 500 mg Cl₂/L). Although slight deviations from in silico predictions were observed for steroids with electron-rich substituents (e.g., C17 allyl-substituted altrenogest), this work highlights the potential for computational approaches to improve our understanding of transformation products generated from emerging pollutant classes.

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

Using computer tools for the evaluation of biodegradability, toxicity, and activity on the AT1 receptor of degradation products identified in the removal of valsartan by using photo-electro-Fenton process

This work deals with the theoretical approach of biodegradability, lipophilicity, and physiological activity of VAL and four degradation products (DPs) detected after 20 min of the photo-electro-Fenton (PEF) process. The biodegradability calculation, taking into account the change in the theoretical oxygen demand, showed that the four DPs had a more negative value than VAL, indicating that they are more susceptible to oxidation. However, these results do not imply more accessible biotransformation pathways than VAL, as observed using the EAWAG-BBD program, through which neutral biotransformation pathway prediction for VAL and DPs was made. Subsequently, by calculating the theoretical lipophilicity of the molecules (log P), the theoretical toxicity of the DPs was proposed, where the DPs had log P values between 1 and 3, lower values than those of VAL (log P = 4), indicating that DPs could be less toxic than the original compound (VAL). Both results suggest that VAL degradation (by photo-electro-Fenton process proposed) yields a positive effect on the environment. Finally, when molecular dynamic simulations were carried out, it was observed that DP1, DP2, and DP3 maintained similar interactions to those of VAL with the binding site of the AT1R. DP4 did not show any interaction. These results indicated that, despite the presence of DPs, generated after 20 min of the treatment, they could not exert a physiological activity in any organism the same way that does VAL.

Development of the Organonitrogen Biodegradation Database: Teaching Bioinformatics and Collaborative Skills to Undergraduates during a Pandemic

Physical distancing and inaccessibility to laboratory facilities created an opportunity to transition undergraduate research experiences to remote, digital platforms, adding another level of pedagogy to their training. Basic bioinformatics skills together with critical analysis of scientific literature are essential for addressing research questions in modern biology. The work presented here describes a fully online, collaborative research experience created to allow undergraduate students to learn those skills. The research experience was focused on the development and implementation of the Organonitrogen Biodegradation Database (ONDB, z.umn.edu/ondb). The ONDB was developed to catalog information about the cost, chemical properties, and biodegradation potential of commonly used organonitrogen compounds. A cross-institutional team of undergraduate researchers worked in collaboration with two faculty members and a postdoctoral fellow to develop the database. Students carried out extensive online literature searches and used a biodegradation prediction website to research and represent the microbial catabolism of different organonitrogen compounds. Participants employed computational tools such as R, Shiny, and flexdashboard to construct the database pages and interactive web interface for the ONDB. Worksheets and forms were created to encourage other students and researchers to gather information about organonitrogen compounds and expand the database. Student progress was evaluated through biweekly project meetings, presentations, and a final reflection. The ONDB undergraduate research experience provided a platform for students to learn bioinformatics skills while simultaneously developing a teaching and research tool for others.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Organic micropollutant removal in full-scale rapid sand filters used for drinking water treatment in The Netherlands and Belgium

Biological treatment processes have the potential to remove organic micropollutants (OMPs) during water treatment. The OMP removal capacity of conventional drinking water treatment processes such as rapid sand filters (RSFs), however, has not been studied in detail. We investigated OMP removal and transformation product (TP) formation in seven full-scale RSFs all treating surface water, using high-resolution mass spectrometry based quantitative suspect and non-target screening (NTS). Additionally, we studied the microbial communities with 16S rRNA gene amplicon sequencing (NGS) in both influent and effluent waters as well as the filter medium, and integrated these data to comprehensively assess the processes that affect OMP removal. In the RSF influent, 9 to 30 of the 127 target OMPs were detected. The removal efficiencies ranged from 0 to 93%. A data-driven workflow was established to monitor TPs, based on the combination of NTS feature intensity profiles between influent and effluent samples and the prediction of biotic TPs. The workflow identified 10 TPs, including molecular structure. Microbial community composition analysis showed similar community composition in the influent and effluent of most RSFs, but different from the filter medium, implying that specific microorganisms proliferate in the RSFs. Some of these are able to perform typical processes in water treatment such as nitrification and iron oxidation. However, there was no clear relationship between OMP removal efficiency and microbial community composition. The innovative combination of quantitative analyses, NTS and NGS allowed to characterize real scale biological water treatments, emphasizing the potential of bio-stimulation applications in drinking water treatment.

A deep learning architecture for metabolic pathway prediction

MOTIVATION

Understanding the mechanisms and structural mappings between molecules and pathway classes are critical for design of reaction predictors for synthesizing new molecules. This article studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compound participates. We apply a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier. In contrast to previously applied machine learning methods for this problem, our framework automatically extracts relevant shape features directly from input SMILES representations, which are atom-bond specifications of chemical structures composing the molecules.

RESULTS

Our method is capable of correctly predicting the respective metabolic pathway class of 95.16% of tested compounds, whereas competing methods only achieve an accuracy of 84.92% or less. Furthermore, our framework extends to the task of classification of compounds having mixed membership in multiple pathway classes. Our prediction accuracy for this multi-label task is 97.61%. We analyze the relative importance of various global physicochemical features to the pathway class prediction problem and show that simple linear/logistic regression models can predict the values of these global features from the shape features extracted using our framework.

AVAILABILITY AND IMPLEMENTATION

https://github.com/baranwa2/MetabolicPathwayPrediction.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ASAPP: Architectural Similarity-Based Automated Pathway Prediction System and Its Application in Host-Pathogen Interactions

The significance of metabolic pathway prediction is to envision the viable unknown transformations that can occur provided the appropriate enzymes are present. It can facilitate the prediction of the consequences of host-pathogen interactions. In this article, we have proposed a new algorithm Architectural Similarity-based Automated Pathway Prediction (ASAPP) to predict metabolic pathways based on the structural similarity among the metabolites. ASAPP takes two-dimensional structure and molecular weight of metabolites as input, and generates a list of probable transformations without the knowledge of any externally established reactions, with an accuracy of 85.09 percent. ASAPP has also been applied to predict the outcome of pathogen liberated toxins on the carbohydrate and lipid pathways of the hosts. We have analyzed the disruption of host pathways in the presence of toxins, and have found that some metabolites in Glycolysis and the TCA cycle have a high chance of being the breakpoints in the pathway. The tool is available at http://asapp.droppages.com/.

Capturing the oxic transformation of iopromide - A useful tool for an improved characterization of predominant redox conditions and the removal of trace organic compounds in biofiltration systems?

The biological degradation of many trace organic compounds has been reported to be strongly redox dependent. The traditional characterization of redox conditions using the succession of inorganic electron acceptors such as dissolved oxygen and nitrate falls short in accurately describing the critical transition state between oxic and suboxic conditions. Novel monitoring strategies using intrinsic redox tracers might be suitable to close that gap. This study investigated the potential use of the successive biological transformation of the iodinated contrast medium iopromide as an intrinsic tracer of prevailing redox conditions in biofiltration systems. Iopromide degradation in biofiltration systems was monitored by quantifying twelve known biological transformation products formed under oxic conditions. A novel dimensionless parameter (T_IOP) was introduced as a measure for the successive transformation of iopromide. A strong correlation between the consumption of dissolved oxygen and iopromide transformation emphasized the importance of general microbial activity on iopromide degradation. However, results disproved a direct correlation between oxic (>1 mg/L O₂) and suboxic (<1 mg/L O₂) conditions and the degree of iopromide transformation. Results indicated that besides redox conditions also the availability of biodegradable organic substrate affects the degree of iopromide transformation. Similar behavior was found for the compounds gabapentin and benzotriazole, while the oxic degradation of metoprolol remained stable under varying substrate conditions.

The environmental fate of synthetic organic chemicals

This article focuses on the routes of transport and abiotic processes involved in the environmental transformation of synthetic organic chemicals and how molecular structure controls the products and lifetimes of several important classes of organic chemicals. The chapter also discusses the current methods to reliably determine the rates and products of degradation of new chemicals based on combinations of chemical structure and environmental processes as well as use of laboratory and field measurements. Methods are also discussed for use of structure activity relations for this purpose.

BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification

Background

A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.

Results

To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.

Conclusion

BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0324-5) contains supplementary material, which is available to authorized users.

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia toward sustainable production of the second generation of biofuels, with methyl ethyl ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methylbutanenitrile for further study. We reconstructed 3 679 610 novel biosynthetic pathways toward these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18 622 were found to be the most biologically feasible ones on the basis of thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.

Study of the in vitro and in vivo metabolism of the tryptamine 5-MeO-MiPT using human liver microsomes and real case samples

The synthetic tryptamine 5-methoxy-N-methyl-N-isopropyltryptamine (5-MeO-MiPT) has recently been abused as a hallucinogenic drug in Germany and Switzerland. This study presents a case of 5-MeO-MiPT intoxication and the structural elucidation of metabolites in pooled human liver microsomes (pHLM), blood, and urine. Microsomal incubation experiments were performed using pHLM to detect and identify in vitro metabolites. In August 2016, the police encountered a naked man, agitated and with aggressive behavior on the street. Blood and urine samples were taken at the hospital and his premises were searched. The obtained blood and urine samples were analyzed for in vivo metabolites of 5-MeO-MiPT using liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS). The confiscated pills and powder samples were qualitatively analyzed using Fourier transform infrared (FTIR), gas chromatography-mass spectrometry (GC-MS), LC-HRMS/MS, and nuclear magnetic resonance (NMR). 5-MeO-MiPT was identified in 2 of the seized powder samples. General unknown screening detected cocaine, cocaethylene, methylphenidate, ritalinic acid, and 5-MeO-MiPT in urine. Seven different in vitro phase I metabolites of 5-MeO-MiPT were identified. In the forensic case samples, 4 phase I metabolites could be identified in blood and 7 in urine. The 5 most abundant metabolites were formed by demethylation and hydroxylation of the parent compound. 5-MeO-MiPT concentrations in the blood and urine sample were found to be 160 ng/mL and 3380 ng/mL, respectively. Based on the results of this study we recommend metabolites 5-methoxy-N-isopropyltryptamine (5-MeO-NiPT), 5-hydroxy-N-methyl-N-isopropyltryptamine (5-OH-MiPT), 5-methoxy-N-methyl-N-isopropyltryptamine-N-oxide (5-MeO-MiPT-N-oxide), and hydroxy-5-methoxy-N-methyl-N-isopropyltryptamine (OH-5-MeO-MiPT) as biomarkers for the development of new methods for the detection of 5-MeO-MiPT consumption, as they have been present in both blood and urine samples.

Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data

Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules, e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity offered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.

ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies

Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework BNICE.ch along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database ( http://lcsb-databases.epfl.ch/atlas/ ) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.

Plant Assimilation Kinetics and Metabolism of 2-Mercaptobenzothiazole Tire Rubber Vulcanizers by Arabidopsis

2-Mercaptobenzothiazole (MBT) is a tire rubber vulcanizer found in potential sources of reclaimed water where it may come in contact with vegetation. In this work, we quantified the plant assimilation kinetics of MBT using Arabidopsis under hydroponic conditions. MBT depletion kinetics in the hydroponic medium with plants were second order (t1/2 = 0.52 to 2.4 h) and significantly greater than any abiotic losses (>18 times faster; p = 0.0056). MBT depletion rate was related to the initial exposure concentration with higher rates at greater concentrations from 1.6 μg/L to 147 μg/L until a potentially inhibitory level (1973 μg/L) lowered the assimilation rate. 9.8% of the initial MBT mass spike was present in the plants after 3 h and decreased through time. In-source LC-MS/MS fragmentation revealed that MBT was converted by Arabidopsis seedlings to multiple conjugated-MBT metabolites of differential polarity that accumulate in both the plant tissue and hydroponic medium; metabolite representation evolved temporally. Multiple novel MBT-derived plant metabolites were detected via LC-QTOF-MS analysis; proposed transformation products include glucose and amino acid conjugated MBT metabolites. Elucidating plant transformation products of trace organic contaminants has broad implications for water reuse because plant assimilation could be employed advantageously in engineered natural treatment systems, and plant metabolites in food crops could present an unintended exposure route to consumers.

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

MOTIVATION

Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes.

RESULTS

In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate-product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate-product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate-product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters.

AVAILABILITY AND IMPLEMENTATION

CONTACT

: maskot@bio.titech.ac.jp.

Systematic Exploration of Biotransformation Reactions of Amine-Containing Micropollutants in Activated Sludge

The main removal process for polar organic micropollutants during activated sludge treatment is biotransformation, which often leads to the formation of stable transformation products (TPs). Because the analysis of TPs is challenging, the use of pathway prediction systems can help by generating a list of suspected TPs. To complete and refine pathway prediction, comprehensive biotransformation studies for compounds exhibiting pertinent functional groups under environmentally relevant conditions are needed. Because many polar organic micropollutants present in wastewater contain one or several amine functional groups, we systematically explored amine biotransformation by conducting experiments with 19 compounds that contained 25 structurally diverse primary, secondary, and tertiary amine moieties. The identification of 144 TP candidates and the structure elucidation of 101 of these resulted in a comprehensive view on initial amine biotransformation reactions. The reactions with the highest relevance were N-oxidation, N-dealkylation, N-acetylation, and N-succinylation. Whereas many of the observed reactions were similar to those known for the mammalian metabolism of amine-containing xenobiotics, some N-acylation reactions were not previously described. In general, different reactions at the amine functional group occurred in parallel. Finally, recommendations on how these findings can be implemented to improve microbial pathway prediction of amine-containing micropollutants are given.

A weight-of-evidence approach to assess chemicals: case study on the assessment of persistence of 4,6-substituted phenolic benzotriazoles in the environment

BACKGROUND

One important purpose of the European REACH Regulation (EC No. 1907/2006) is to promote the use of alternative methods for assessment of hazards of substances in order to avoid animal testing. Experience with environmental hazard assessment under REACH shows that efficient alternative methods are needed in order to assess chemicals when standard test data are missing. One such assessment method is the weight-of-evidence (WoE) approach. In this study, the WoE approach was used to assess the persistence of certain phenolic benzotriazoles, a group of substances including also such of very high concern (SVHC).

RESULTS

For phenolic benzotriazoles, assessment of the environmental persistence is challenging as standard information, i.e. simulation tests on biodegradation are not available. Thus, the WoE approach was used: overall information resulting from many sources was considered, and individual uncertainties of each source analysed separately. In a second step, all information was aggregated giving an overall picture of persistence to assess the degradability of the phenolic benzotriazoles under consideration although the reliability of individual sources was incomplete.

CONCLUSIONS

Overall, the evidence suggesting that phenolic benzotriazoles are very persistent in the environment is unambiguous. This was demonstrated by a WoE approach considering the prerequisites of REACH by combining several limited information sources. The combination enabled a clear overall assessment which can be reliably used for SVHC identification. Finally, it is recommended to include WoE approaches as an important tool in future environmental risk assessments.

Computational methods to identify metabolic sub-networks based on metabolomic profiles

Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.

Root Uptake of Pharmaceuticals and Personal Care Product Ingredients

Crops irrigated with reclaimed wastewater or grown in biosolids-amended soils may take up pharmaceuticals and personal care product ingredients (PPCPs) through their roots. The uptake pathways followed by PPCPs and the propensity for these compounds to bioaccumulate in food crops are still not well understood. In this critical review, we discuss processes expected to influence root uptake of PPCPs, evaluate current literature on uptake of PPCPs, assess models for predicting plant uptake of these compounds, and provide recommendations for future research, highlighting processes warranting study that hold promise for improving mechanistic understanding of plant uptake of PPCPs. We find that many processes that are expected to influence PPCP uptake and accumulation have received little study, particularly rhizosphere interactions, in planta transformations, and physicochemical properties beyond lipophilicity (as measured by Kow). Data gaps and discrepancies in methodology and reporting have so far hindered development of models that accurately predict plant uptake of PPCPs. Topics warranting investigation in future research include the influence of rhizosphere processes on uptake, determining mechanisms of uptake and accumulation, in planta transformations, the effects of PPCPs on plants, and the development of predictive models.

Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments

MOTIVATION

Recent advances in mass spectrometry and related metabolomics technologies have enabled the rapid and comprehensive analysis of numerous metabolites. However, biosynthetic and biodegradation pathways are only known for a small portion of metabolites, with most metabolic pathways remaining uncharacterized.

RESULTS

In this study, we developed a novel method for supervised de novo metabolic pathway reconstruction with an improved graph alignment-based approach in the reaction-filling framework. We proposed a novel chemical graph alignment algorithm, which we called PACHA (Pairwise Chemical Aligner), to detect the regioisomer-sensitive connectivities between the aligned substructures of two compounds. Unlike other existing graph alignment methods, PACHA can efficiently detect only one common subgraph between two compounds. Our results show that the proposed method outperforms previous descriptor-based methods or existing graph alignment-based methods in the enzymatic reaction-likeness prediction for isomer-enriched reactions. It is also useful for reaction annotation that assigns potential reaction characteristics such as EC (Enzyme Commission) numbers and PIERO (Enzymatic Reaction Ontology for Partial Information) terms to substrate-product pairs. Finally, we conducted a comprehensive enzymatic reaction-likeness prediction for all possible uncharacterized compound pairs, suggesting potential metabolic pathways for newly predicted substrate-product pairs.

PROXIMAL: a method for Prediction of Xenobiotic Metabolism

Background

Contamination of the environment with bioactive chemicals has emerged as a potential public health risk. These substances that may cause distress or disease in humans can be found in air, water and food supplies. An open question is whether these chemicals transform into potentially more active or toxic derivatives via xenobiotic metabolizing enzymes expressed in the body. We present a new prediction tool, which we call PROXIMAL (Prediction of Xenobiotic Metabolism) for identifying possible transformation products of xenobiotic chemicals in the liver. Using reaction data from DrugBank and KEGG, PROXIMAL builds look-up tables that catalog the sites and types of structural modifications performed by Phase I and Phase II enzymes. Given a compound of interest, PROXIMAL searches for substructures that match the sites cataloged in the look-up tables, applies the corresponding modifications to generate a panel of possible transformation products, and ranks the products based on the activity and abundance of the enzymes involved.

Results

PROXIMAL generates transformations that are specific for the chemical of interest by analyzing the chemical’s substructures. We evaluate the accuracy of PROXIMAL’s predictions through case studies on two environmental chemicals with suspected endocrine disrupting activity, bisphenol A (BPA) and 4-chlorobiphenyl (PCB3). Comparisons with published reports confirm 5 out of 7 and 17 out of 26 of the predicted derivatives for BPA and PCB3, respectively. We also compare biotransformation predictions generated by PROXIMAL with those generated by METEOR and Metaprint2D-react, two other prediction tools.

Conclusions

PROXIMAL can predict transformations of chemicals that contain substructures recognizable by human liver enzymes. It also has the ability to rank the predicted metabolites based on the activity and abundance of enzymes involved in xenobiotic transformation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0241-4) contains supplementary material, which is available to authorized users.

enviPath--The environmental contaminant biotransformation pathway resource

The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.

Evaluation of the persistence of transformation products from ozonation of trace organic compounds - a critical review

Ozonation is an efficient treatment system to reduce the concentration of trace organic compounds (TrOCs) from technical aquatic systems such as drinking water, wastewater and industrial water, etc. Although it is well established that ozonation generally improves the removal of organic matter in biological post-treatment, little is known about the biodegradability of individual transformation products resulting from ozonation of TrOCs. This publication provides a qualified assessment of the persistence of ozone-induced transformation products based on a review of published product studies and an evaluation of the biodegradability of transformation products with the biodegradability probability program (BIOWIN) and the University of Minnesota Pathway Prediction System (UM-PPS). The oxidation of TrOCs containing the four major ozone-reactive sites (olefins, amines, aromatics and sulfur-containing compounds) follows well described reaction pathways leading to characteristic transformation products. Assessment of biodegradability revealed a high sensitivity to the formed products and hence the ozone-reactive site present in the target compound. Based on BIOWIN, efficient removal can be expected for products from cleavage of olefin groups and aromatic rings. In contrast, estimations and literature indicate that hydroxylamines and N-oxides, the major products from ozonation of secondary and tertiary amines are not necessarily better removed in biological post-treatment. According to UM-PPS, degradation of these products might even occur via reformation of the corresponding amine. Some product studies with sulfide-containing TrOCs showed a stoichiometric formation of sulfoxides from oxygen transfer reactions. However, conclusions on the fate of transformation products in biological post-treatment cannot be drawn based on BIOWIN and UM-PPS.

Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach

MOTIVATION

Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale.

RESULTS

In this article, we develop a novel method to predict the multistep reaction sequences for de novo reconstruction of metabolic pathways in the reaction-filling framework. We propose a supervised approach to learn what we refer to as 'multistep reaction sequence likeness', i.e. whether a compound-compound pair is possibly converted to each other by a sequence of enzymatic reactions. In the algorithm, we propose a recursive procedure of using step-specific classifiers to predict the intermediate compounds in the multistep reaction sequences, based on chemical substructure fingerprints/descriptors of compounds. We further demonstrate the usefulness of our proposed method on the prediction of enzymatic reaction networks from a metabolome-scale compound set and discuss characteristic features of the extracted chemical substructure transformation patterns in multistep reaction sequences. Our comprehensively predicted reaction networks help to fill the metabolic gap and to infer new reaction sequences in metabolic pathways.

AVAILABILITY AND IMPLEMENTATION

Materials are available for free at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2014/

Reducing the discharge of micropollutants in the aquatic environment: the benefits of upgrading wastewater treatment plants

Micropollutants (MPs) as individual compounds or in complex mixtures are relevant for water quality and may trigger unwanted ecological effects. MPs originate from different point and diffuse sources and enter water bodies via different flow paths. Effluents from conventional wastewater treatment plants (WWTPs), in which various MPs are not or not completely removed, is one major source. To improve the water quality and avoid potential negative ecological effects by micropollutants, various measures to reduce the discharge should be taken. In this feature we discuss one of these measures; the benefits of upgrading WWTPs toward reduced MP loads and toxicities from wastewater effluents, using the recently decided Swiss strategy as an example. Based on (i) full-scale case studies using ozonation or powder activated carbon treatment, showing substantial reduction of MP discharges and concomitant reduced toxicities, (ii) social and political acceptance, (iii) technical feasibility and sufficient cost-effectiveness, the Swiss authorities recently decided to implement additional wastewater treatment steps as mitigation strategy to improve water quality. Since MPs are of growing global concern, the concepts and considerations behind the Swiss strategy are explained in this feature, which could be of use for other countries as well. It should be realized that upgrading WWTPs is not the only solution to reduce the discharge of MPs entering the environment, but is part of a broader, multipronged mitigation strategy.

Aerobic soil biotransformation of 6:2 fluorotelomer iodide

6:2 FTI [F(CF2)6CH2CH2I] is a principal industrial raw material used to manufacture 6:2 FTOH [F(CF2)6CH2CH2OH] and 6:2 FTOH-based products and could enter aerobic environments from possible industrial emissions where it is manufactured. This is the first study to assess 6:2 FTI aerobic soil biotransformation, quantify transformation products, and elucidate its biotransformation pathways. 6:2 FTI biotransformation led to 6:2 FTOH as a key intermediate, which was subsequently biotransformed to other significant transformation products, including PFPeA [F(CF2)4COOH, 20 mol % at day 91], 5:3 acid [F(CF2)5CH2CH2COOH, 16 mol %], PFHxA [F(CF2)5COOH, 3.8 mol %], and 4:3 acid [F(CF2)4CH2CH2COOH, 3.0 mol %]. 6:2 FTI biotransformation also led to a significant level of PFHpA [F(CF2)6COOH, 16 mol % at day 91], perhaps via another putative intermediate, 6:2 FTUI [F(CF2)6CH ═ CHI], whose molecular identity and further biotransformation were not verified because of the lack of an authentic standard. Total recovery of the aforementioned per- and polyfluorocarboxylates accounted for 59 mol % of initially applied 6:2 FTI by day 91, in comparison to 56 mol % when soil was dosed with 6:2 FTOH, which did not lead to PFHpA. Thus, were 6:2 FTI to be released from its manufacture and undergo soil microbial biotransformation, it could form PFPeA, PFHpA, PFHxA, 5:3 acid, and 4:3 acid in the environment.

An algorithm for rapid computational construction of metabolic networks: a cholesterol biosynthesis example

Alternative pathways of metabolic networks represent the escape routes that can reduce drug efficacy and can cause severe adverse effects. In this paper we introduce a mathematical algorithm and a coding system for rapid computational construction of metabolic networks. The initial data for the algorithm are the source substrate code and the enzyme/metabolite interaction tables. The major strength of the algorithm is the adaptive coding system of the enzyme-substrate interactions. A reverse application of the algorithm is also possible, when optimisation algorithm is used to compute the enzyme/metabolite rules from the reference network structure. The coding system is user-defined and must be adapted to the studied problem. The algorithm is most effective for computation of networks that consist of metabolites with similar molecular structures. The computation of the cholesterol biosynthesis metabolic network suggests that 89 intermediates can theoretically be formed between lanosterol and cholesterol, only 20 are presently considered as cholesterol intermediates. Alternative metabolites may represent links with other metabolic networks both as precursors and metabolites of cholesterol. A possible cholesterol-by-pass pathway to bile acids metabolism through cholestanol is suggested.

Simulation of chemical metabolism for fate and hazard assessment. IV. Computer-based derivation of metabolic simulators from documented metabolism maps

Computer simulation of xenobiotic metabolism and degradation is usually performed proceeding from a set of expert-developed rules modelling the actual enzyme-driven chemical reactions. With the accumulation of extensive metabolic pathway data, the analysis required to derive such chemical reaction patterns has become more objective, but also more convoluted and demanding. Herein we report on our computer-based approach for the analysis of metabolic maps, leading to the construction of reaction rules statistically suitable for simulation purposes. It is based on the set of so-called bare transformations which encompass all unique reaction patterns as obtained by a heuristically enhanced maximum common subgraph algorithm. The bare transformations guarantee that no existing metabolite is missed in simulation at the expense of an enormous amount of false positive predictions. They are rendered more selective by correlating the generated true and false positives to the locations of typical chemical functional groups in the potential reactants. The approach and its results are illustrated for a metabolic map collection of 15 cycloalkanes.

GC-MS(n) and LC-MS/MS couplings for the identification of degradation products resulting from the ozonation treatment of Acetochlor

The degradation of the chloracetamide herbicide acetochlor has been studied under simulated ozonation treatment plant conditions. The degradation of acetochlor included the formation of several degradation products that were identified using GC/ion-trap mass spectrometry with EI and CI and HPLC/electrospray-QqTOF mass spectrometry. Thirteen ozonation products of acetochlor have been identified. Ozonation of the deuterated herbicide combined to MS(n) and high-resolution mass measurement allowed effective characterization of the degradation products. At the exception of one of them, the product B (2-chloro-2', ethyl-6', methyl-acetanilide), none of the identified degradation products has been already reported in the literature. Post-ozonation kinetics studies revealed that the concentrations of most degradation products evolved noticeably with time, particularly during the first hours following the ozonation treatment. This raises concerns about the fate of degradation products in the effluents of treatment plants and suggests the need for a better control on these products if their toxicity was demonstrated.

Efficient bio-deodorization of aniline vapor in a biotrickling filter: metabolic mineralization and bacterial community analysis

A biotrickling filter inoculated with commercial mixed microorganisms B350 was employed to treat N-containing odorous vapor - aniline. Results indicated no aniline could be detected when empty bed residence time (EBRT) was larger than 110s at inlet concentration of 0.30 g m(-3). The variation of inlet concentration did not change removal efficiencies when concentration is less than 0.21 g m(-3) at fixed EBRT 110s. Biodegradation mechanism of aniline was tentatively proposed based on identified intermediates and predicted biodegradation pathway as well as final mineralized products. Aniline was firstly biodegraded to catechol, and then to levulinic acid and subsequently to succinic acid. Finally, about 62% aniline carbon was completely mineralized to CO(2), while about 91% aniline nitrogen was converted into ammonia and nitrate. Bacterial community in biotrickling filter was found that at least seven bands microbes were identified for high efficiencies of bioreactor at stable state. In all, biotrickling filter seeded with B350 would be a better choice for the purification odorous gas containing high concentration aniline.

Concentrations and composition profiles of benzotriazole UV stabilizers in municipal sewage sludge in China

The environmental contamination and fate of benzotriazole UV stabilizers (BZTs) have received increasing attention due to their large production volume and wide usage in various consumer and industrial products. In the present work, 60 municipal sewage sludge samples from wastewater treatment plants (WWTPs) in 33 cities in China were collected to investigate the occurrence and distribution of 9 frequently used BZTs. The most dominant analogue was 2-[3,5-bis(1-methyl-1-phenylethyl)-2-hydroxyphenyl]benzotriazole (UV-234) at a median concentration of 116 ng/g (dry weight) and accounted on average for 27.2% of total BZTs. The abundance was successively followed by 2-(2-hydroxy-5-tert-octylphenyl)benzotriazole (UV-329, average 24.3%), 2-(2-hydroxy-3-tert-butyl-5-methylphenyl)-5-chlorobenzotriazole (UV-326, average 22.2%), 2-(3,5-di-tert-amyl-2-hydroxyphenyl)benzotriazole (UV-328, average 17.7%), and 2-(2-hydroxy-5-methylphenyl)benzotriazole (UV-P, average 6.6%), with median concentrations of 66.8, 67.8, 57.3, and 20.6 ng/g, respectively. 5-Chloro-2-(3,5-di-tert-butyl-2-hydroxyphenyl)benzotriazole (UV-327) and 2-(3-sec-butyl-5-tert-butyl-2-hydroxyphenyl)benzotriazole (UV-350) had low detection frequency, while 2-(3,5-di-tert-butyl-2-hydroxyphenyl)benzotriazole (UV-320) and 2-(5-tert-butyl-2-hydroxyphenyl)benzotriazole (UV-PS) were not detectable in any sample. To our knowledge, this is the first study reporting the occurrence of UV-234, UV-329, and UV-350 in sewage sludge in China. Significant correlations were found among the BZT concentrations and also with a WWTP characteristic (daily treatment volume). Furthermore, results from degradation prediction and multimedia fate simulation based on a quantitative structure-property relationship (QSPR) model at screening level also implied that the commercial BZT chemicals and their plausible transformation products might be persistent in the environment.

Use of the University of Minnesota Biocatalysis/Biodegradation Database for study of microbial degradation

Microorganisms are ubiquitous on earth and have diverse metabolic transformative capabilities important for environmental biodegradation of chemicals that helps maintain ecosystem and human health. Microbial biodegradative metabolism is the main focus of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD). UM-BBD data has also been used to develop a computational metabolic pathway prediction system that can be applied to chemicals for which biodegradation data is currently lacking. The UM-Pathway Prediction System (UM-PPS) relies on metabolic rules that are based on organic functional groups and predicts plausible biodegradative metabolism. The predictions are useful to environmental chemists that look for metabolic intermediates, for regulators looking for potential toxic products, for microbiologists seeking to understand microbial biodegradation, and others with a wide-range of interests.

Bioremediation approaches for organic pollutants: a critical perspective

Due to human activities to a greater extent and natural processes to some extent, a large number of organic chemical substances such as petroleum hydrocarbons, halogenated and nitroaromatic compounds, phthalate esters, solvents and pesticides pollute the soil and aquatic environments. Remediation of these polluted sites following the conventional engineering approaches based on physicochemical methods is both technically and economically challenging. Bioremediation that involves the capabilities of microorganisms in the removal of pollutants is the most promising, relatively efficient and cost-effective technology. However, the current bioremediation approaches suffer from a number of limitations which include the poor capabilities of microbial communities in the field, lesser bioavailability of contaminants on spatial and temporal scales, and absence of bench-mark values for efficacy testing of bioremediation for their widespread application in the field. The restoration of all natural functions of some polluted soils remains impractical and, hence, the application of the principle of function-directed remediation may be sufficient to minimize the risks of persistence and spreading of pollutants. This review selectively examines and provides a critical view on the knowledge gaps and limitations in field application strategies, approaches such as composting, electrobioremediation and microbe-assisted phytoremediation, and the use of probes and assays for monitoring and testing the efficacy of bioremediation of polluted sites.

Simulation of chemical metabolism for fate and hazard assessment. II CATALOGIC simulation of abiotic and microbial degradation

The unprecedented pollution of the environment by xenobiotic compounds has provoked the need to understand the biodegradation potential of chemicals. Mechanistic understanding of microbial degradation is a premise for adequate modelling of the environmental fate of chemicals. The aim of the present paper is to describe abiotic and biotic models implemented in CATALOGIC software. A brief overview of the specificities of abiotic and microbial degradation is provided followed by detailed descriptions of models built in our laboratory during the last decade. These are principally new models based on unique mathematical formalism already described in the first paper of this series, which accounts more adequately than currently available approaches the multipathway metabolic logic in prokaryotes. Based on simulated pathways of degradation, the models are able to predict quantities of transformation products, biological oxygen demand (BOD), carbon dioxide (CO(2)) production, and primary and ultimate half-lives. Interpretation of the applicability domain of models is also discussed.

Simulation of chemical metabolism for fate and hazard assessment. I: approach for simulating metabolism

Information regarding the metabolism of xenobiotic chemicals plays a central role in regulatory risk assessments. In regulatory programmes where metabolism studies are required, the studies of metabolic pathways are often incomplete and the identification of activated metabolites and important degradation products are limited by analytical methods. Because so many more new chemicals are being produced than can be assessed for potential hazards, setting assessment priorities among the thousands of untested chemicals requires methods for predictive hazard identification which can be derived directly from chemical structure and their likely metabolites. In a series of papers we are sharing our experience in the computerized management of metabolic data and the development of simulators of metabolism for predicting the environmental fate and (eco)toxicity of chemicals. The first paper of the series presents a knowledge-based formalism for the computer simulation of non-intermediary metabolism for untested chemicals, with an emphasis on qualitative and quantitative aspects of modelling metabolism.