The University of Minnesota Biocatalysis/Biodegradation Database: the first decade

Combining predictive and analytical methods to elucidate pharmaceutical biotransformation in activated sludge

While man-made chemicals in the environment are ubiquitous and a potential threat to human health and ecosystem integrity, the environmental fate of chemical contaminants such as pharmaceuticals is often poorly understood. Biodegradation processes driven by microbial communities convert chemicals into transformation products (TPs) that may themselves have adverse ecological effects. The detection of TPs formed during biodegradation has been continuously improved thanks to the development of TP prediction algorithms and analytical workflows. Here, we contribute to this advance by (i) reviewing past applications of TP identification workflows, (ii) applying an updated workflow for TP prediction to 42 pharmaceuticals in biodegradation experiments with activated sludge, and (iii) benchmarking 5 different pathway prediction models, comprising 4 prediction models trained on different datasets provided by enviPath, and the state-of-the-art EAWAG pathway prediction system. Using the updated workflow, we could tentatively identify 79 transformation products for 31 pharmaceutical compounds. Compared to previous works, we have further automatized several steps that were previously performed by hand. By benchmarking the enviPath prediction system on experimental data, we demonstrate the usefulness of the pathway prediction tool to generate suspect lists for screening, and we propose new avenues to improve their accuracy. Moreover, we provide a well-documented workflow that can be (i) readily applied to detect transformation products in activated sludge and (ii) potentially extended to other environmental studies.

Recent advances in plastic degradation - From microbial consortia-based methods to data sciences and computational biology driven approaches

The conventional methods of plastic waste management such as mechanical and chemical recycling, landfill complemented by incineration and pyrosis have limited scope. Thus, microbiological-based approaches by the application of microbial consortia or cocultures are appropriate, cost-effective, and eco-friendly to manage plastic wastes. Screening of novel plastic degrading microorganisms, the formulation of microbial consortia, and utilisation of their enzymes probably play a role in plastic waste management. The by-products of microbial degradation of plastic waste can be used as bio-energy sources, that aids in the development of cost-effective bio-digesters. The recent advancements in computational biology and bioinformatics play a vital role in understanding the molecular basis of enzymatic degradation of plastic polymers by microorganisms. Understanding the three-dimensional structures of plastic degrading enzymes and their metabolic pathways play a vital role in studying the microbial degradation of plastics. The present review highlights the scope of various microorganisms and their enzymes in plastic degradation. The review emphasizes the applications of co-cultures or microbial consortia-based approaches for the enhanced degradation of plastic polymers and the production of value-added end products that can be used as the prototypes of bioenergy sources. The review also provides a comprehensive outlook on the applications of data sciences, computational biology, and bioinformatics resources, and web-based tools towards the study of microbial degradation of plastic polymers.

Holistic evaluation of biodegradation pathway prediction: assessing multi-step reactions and intermediate products

The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules.

Recent Advanced Technologies for the Characterization of Xenobiotic-Degrading Microorganisms and Microbial Communities

Global environmental contamination with a complex mixture of xenobiotics has become a major environmental issue worldwide. Many xenobiotic compounds severely impact the environment due to their high toxicity, prolonged persistence, and limited biodegradability. Microbial-assisted degradation of xenobiotic compounds is considered to be the most effective and beneficial approach. Microorganisms have remarkable catabolic potential, with genes, enzymes, and degradation pathways implicated in the process of biodegradation. A number of microbes, including Alcaligenes, Cellulosimicrobium, Microbacterium, Micrococcus, Methanospirillum, Aeromonas, Sphingobium, Flavobacterium, Rhodococcus, Aspergillus, Penecillium, Trichoderma, Streptomyces, Rhodotorula, Candida, and Aureobasidium, have been isolated and characterized, and have shown exceptional biodegradation potential for a variety of xenobiotic contaminants from soil/water environments. Microorganisms potentially utilize xenobiotic contaminants as carbon or nitrogen sources to sustain their growth and metabolic activities. Diverse microbial populations survive in harsh contaminated environments, exhibiting a significant biodegradation potential to degrade and transform pollutants. However, the study of such microbial populations requires a more advanced and multifaceted approach. Currently, multiple advanced approaches, including metagenomics, proteomics, transcriptomics, and metabolomics, are successfully employed for the characterization of pollutant-degrading microorganisms, their metabolic machinery, novel proteins, and catabolic genes involved in the degradation process. These technologies are highly sophisticated, and efficient for obtaining information about the genetic diversity and community structures of microorganisms. Advanced molecular technologies used for the characterization of complex microbial communities give an in-depth understanding of their structural and functional aspects, and help to resolve issues related to the biodegradation potential of microorganisms. This review article discusses the biodegradation potential of microorganisms and provides insights into recent advances and omics approaches employed for the specific characterization of xenobiotic-degrading microorganisms from contaminated environments.

A comprehensive comparison of molecular feature representations for use in predictive modeling

Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples represented by vectors of values, and many methods for calculating molecular feature representations have been proposed. In this paper, we perform a comprehensive comparison of different molecular features, including traditional methods such as fingerprints and molecular descriptors, and recently proposed learnable representations based on neural networks. Feature representations are evaluated on 11 benchmark datasets, used for predicting properties and measures such as mutagenicity, melting points, activity, solubility, and IC50. Our experiments show that several molecular features work similarly well over all benchmark datasets. The ones that stand out most are Spectrophores, which give significantly worse performance than other features on most datasets. Molecular descriptors from the PaDEL library seem very well suited for predicting physical properties of molecules. Despite their simplicity, MACCS fingerprints performed very well overall. The results show that learnable representations achieve competitive performance compared to expert based representations. However, task-specific representations (graph convolutions and Weave methods) rarely offer any benefits, even though they are computationally more demanding. Lastly, combining different molecular feature representations typically does not give a noticeable improvement in performance compared to individual feature representations.

Investigation on phenol degradation capability of Scenedesmus regularis: influence of process parameters

Phenol removal from environmental solutions has attracted much attention due to phenol's high toxicity, even at low concentrations. This study aims to reveal the phenol biodegradation capacity of Scenedesmus regularis. Batch system parameters (pH, amount of algal cell, phenol concentration) on biodegradation were examined. After 24 h of treatment, 92.16, 94.50, 96.20, 80.53, 65.32, 52 and 40% of phenol were removed by Scenedesmus regularis in aqueous solutions containing 5, 10, 15, 20, 30, 40 and 50 mg/L of phenol, respectively. To describe the correlation between degradation rate and phenol concentration, the Michaelis-Menten kinetic equation was used where V_max and K_m are 0.82 mg phenol g algea^-1 h^-1 and 24.97 ppm, respectively. Phenol remediation ability of S.regularis can enable the usage of the spent biomass as biofuel feedstock and animal feed makes it a 'green' environmental sustainable process.

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.

Gene Editing and Systems Biology Tools for Pesticide Bioremediation: A Review

Bioremediation is the degradation potential of microorganisms to dissimilate the complex chemical compounds from the surrounding environment. The genetics and biochemistry of biodegradation processes in datasets opened the way of systems biology. Systemic biology aid the study of interacting parts involved in the system. The significant keys of system biology are biodegradation network, computational biology, and omics approaches. Biodegradation network consists of all the databases and datasets which aid in assisting the degradation and deterioration potential of microorganisms for bioremediation processes. This review deciphers the bio-degradation network, i.e., the databases and datasets (UM-BBD, PAN, PTID, etc.) aiding in assisting the degradation and deterioration potential of microorganisms for bioremediation processes, computational biology and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation experiments. Besides, the present review also describes the gene editing tools like CRISPR Cas, TALEN, and ZFNs which can possibly make design microbe with functional gene of interest for degradation of particular recalcitrant for improved bioremediation.

Removal of dichlorophenol by Chlorella pyrenoidosa through self-regulating mechanism in air-tight test environment

Microalgae are surprisingly efficient to remove pollutants in a hermetically closed environment, though its growth is inhibited in the absence of pollutants. The final pH, algal density, Chl-a content, and the removal efficiency of 2,4-dichlorophenol (2,4-DCP) by Chlorellar pyrenoidosa in a closed system were observed under different initial pH, lighting regimes, and various carbon sources. The optimal condition for 2,4-DCP removal was obtained, and adopted to observe the evolution of above items by domesticated and origin strains. The results showed that both respiration and photosynthesis participated in the degradation of 2,4-DCP, and caused the changes of pH. The photosynthesis seemed to increase the solution pH, while the respiration and the biodegradation of 2,4-DCP to decrease the solution pH. The domesticated strain achieved nearly 100% removal when initial concentrations of 2,4-DCP lower than 200 μg L^-1, due to providing a appropriate but narrow pH evolution range, mostly falling between 6.5 and 7.9. The research helps to understand the mechanism of biodegradation of chlorophenol compounds by green algae.

Elucidating the impact of microbial community biodiversity on pharmaceutical biotransformation during wastewater treatment

In addition to removing organics and other nutrients, the microorganisms in wastewater treatment plants (WWTPs) biotransform many pharmaceuticals present in wastewater. The objective of this study was to examine the relationship between pharmaceutical biotransformation and biodiversity in WWTP bioreactor microbial communities and identify taxa and functional genes that were strongly associated with biotransformation. Dilution-to-extinction of an activated sludge microbial community was performed to establish cultures with a gradient of microbial biodiversity. Batch experiments were performed using the dilution cultures to determine biotransformation extents of several environmentally relevant pharmaceuticals. With this approach, because the communities were all established from the same original community, and using sequencing of the 16S rRNA and metatranscriptome, we identified candidate taxa and genes whose activity and transcript abundances associated with the extent of individual pharmaceutical biotransformation and were lost across the biodiversity gradient. Metabolic genes such as dehydrogenases, amidases and monooxygenases were significantly associated with pharmaceutical biotransformation, and five genera were identified whose activity significantly associated with pharmaceutical biotransformation. Understanding how biotransformation relates to biodiversity will inform the design of biological WWTPs for enhanced removal of chemicals that negatively impact environmental health.

Identification of metabolic pathways using pathfinding approaches: a systematic review

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.

Bacteria from wheat and cucurbit plant roots metabolize PAHs and aromatic root exudates: Implications for rhizodegradation

The chemical interaction between plants and bacteria in the root zone can lead to soil decontamination. Bacteria that degrade polycyclic aromatic hydrocarbons (PAHs) have been isolated from the rhizospheres of plant species with varied biological traits; however, it is not known what phytochemicals promote contaminant degradation. One monocot and two dicotyledon plants were grown in PAH-contaminated soil from a manufactured gas plant (MGP) site. A phytotoxicity assay confirmed greater soil decontamination in rhizospheres when compared to bulk soil controls. Bacteria were isolated from plant roots (rhizobacteria) and selected for growth on anthracene and chrysene on PAH-amended plates. Rhizosphere isolates metabolized 3- and 4-ring PAHs and PAH catabolic intermediates in liquid incubations. Aromatic root exudate compounds, namely flavonoids and simple phenols, were also substrates for isolated rhizobacteria. In particular, the phenolic compounds-morin, caffeic acid, and protocatechuic acid-appear to be linked to bacterial degradation of 3- and 4-ring PAHs in the rhizosphere.

biochem4j: Integrated and extensible biochemical knowledge through graph databases

Biologists and biochemists have at their disposal a number of excellent, publicly available data resources such as UniProt, KEGG, and NCBI Taxonomy, which catalogue biological entities. Despite the usefulness of these resources, they remain fundamentally unconnected. While links may appear between entries across these databases, users are typically only able to follow such links by manual browsing or through specialised workflows. Although many of the resources provide web-service interfaces for computational access, performing federated queries across databases remains a non-trivial but essential activity in interdisciplinary systems and synthetic biology programmes. What is needed are integrated repositories to catalogue both biological entities and-crucially-the relationships between them. Such a resource should be extensible, such that newly discovered relationships-for example, those between novel, synthetic enzymes and non-natural products-can be added over time. With the introduction of graph databases, the barrier to the rapid generation, extension and querying of such a resource has been lowered considerably. With a particular focus on metabolic engineering as an illustrative application domain, biochem4j, freely available at http://biochem4j.org, is introduced to provide an integrated, queryable database that warehouses chemical, reaction, enzyme and taxonomic data from a range of reliable resources. The biochem4j framework establishes a starting point for the flexible integration and exploitation of an ever-wider range of biological data sources, from public databases to laboratory-specific experimental datasets, for the benefit of systems biologists, biosystems engineers and the wider community of molecular biologists and biological chemists.

Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures

Complex environmental mixtures consist of hundreds to thousands of unknown and unregulated organic compounds that may have toxicological relevance, including transformation products (TPs) of anthropogenic organic pollutants. Non-targeted analysis and suspect screening analysis offer analytical approaches for potentially identifying these toxic transformation products. However, additional tools and strategies are needed in order to reduce the number of chemicals of interest and focus analytical efforts on chemicals that may pose risks to humans and the environment. This brief review highlights recent developments in this field and suggests an integrated framework that incorporates complementary instrumental techniques, computational chemistry, and toxicity analysis, for prioritizing and identifying toxic TPs in the environment.

Electron Transport in a Dioxygenase-Ferredoxin Complex: Long Range Charge Coupling between the Rieske and Non-Heme Iron Center

Dioxygenase (dOx) utilizes stereospecific oxidation on aromatic molecules; consequently, dOx has potential applications in bioremediation and stereospecific oxidation synthesis. The reactive components of dOx comprise a Rieske structure Cys₂[2Fe-2S]His₂ and a non-heme reactive oxygen center (ROC). Between the Rieske structure and the ROC, a universally conserved Asp residue appears to bridge the two structures forming a Rieske-Asp-ROC triad, where the Asp is known to be essential for electron transfer processes. The Rieske and ROC share hydrogen bonds with Asp through their His ligands; suggesting an ideal network for electron transfer via the carboxyl side chain of Asp. Associated with the dOx is an itinerant charge carrying protein Ferredoxin (Fdx). Depending on the specific cognate, Fdx may also possess either the Rieske structure or a related structure known as 4-Cys-[2Fe-2S] (4-Cys). In this study, we extensively explore, at different levels of theory, the behavior of the individual components (Rieske and ROC) and their interaction together via the Asp using a variety of density function methods, basis sets, and a method known as Generalized Ionic Fragment Approach (GIFA) that permits setting up spin configurations manually. We also report results on the 4-Cys structure for comparison. The individual optimized structures are compared with observed spectroscopic data from the Rieske, 4-Cys and ROC structures (where information is available). The separate pieces are then combined together into a large Rieske-Asp-ROC (donor/bridge/acceptor) complex to estimate the overall coupling between individual components, based on changes to the partial charges. The results suggest that the partial charges are significantly altered when Asp bridges the Rieske and the ROC; hence, long range coupling through hydrogen bonding effects via the intercalated Asp bridge can drastically affect the partial charge distributions compared to the individual isolated structures. The results are consistent with a proton coupled electron transfer mechanism.

In silico exploratory study using structure-activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay

The mutagenic potential of chemicals is a cause of growing concern, due to the possible impact on human health. In this paper we have developed a knowledge-based approach, combining information from structure-activity relationship (SAR) and metabolic triggers generated from the metabolic fate of chemicals in biological systems for prediction of mutagenicity in vitro based on the Ames test and in vivo based on the rodent micronucleus assay. In the first part of the work, a model was developed, which comprises newly generated SAR rules and a set of metabolic triggers. These SAR rules and metabolic triggers were further externally validated to predict mutagenicity in vitro, with metabolic triggers being used only to predict mutagenicity of chemicals, which were predicted unknown, by SARpy. Hence, this model has a higher accuracy than the SAR model, with an accuracy of 89% for the training set and 75% for the external validation set. Subsequently, the results of the second part of this work enlist a set of metabolic triggers for prediction of mutagenicity in vivo, based on the rodent micronucleus assay. Finally, the results of the third part enlist a list of metabolic triggers to find similarities and differences in the mutagenic response of chemicals in vitro and in vivo.

LC-MS screening techniques for wastewater analysis and analytical data handling strategies: Sartans and their transformation products as an example

A large number of anthropogenic trace contaminants such as pharmaceuticals, their human metabolites and further transformation products (TPs) enter wastewater treatment plants on a daily basis. A mixture of known, expected, and unknown molecules are discharged into the receiving aquatic environment because only partial elimination occurs for many of these chemicals during physical, biological and chemical treatment processes. In this study, an array of LC-MS methods from three collaborating laboratories was applied to detect and identify anthropogenic trace contaminants and their TPs in different waters. Starting with theoretical predictions of TPs, an efficient workflow using the combination of target, suspected-target and non-target strategies for the identification of these TPs in the environment was developed. These techniques and strategies were applied to study anti-hypertensive drugs from the sartan group (i.e., candesartan, eprosartan, irbesartan, olmesartan, and valsartan). Degradation experiments were performed in lab-scale wastewater treatment plants, and a screening workflow including an inter-laboratory approach was used for the identification of transformation products in the effluent samples. Subsequently, newly identified compounds were successfully analyzed in effluents of real wastewater treatment plants and river waters.

Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches

Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements.

Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present.

Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches.

This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.

Building biological foundries for next-generation synthetic biology

Synthetic biology is an interdisciplinary field that takes top-down approaches to understand and engineer biological systems through design-build-test cycles. A number of advances in this relatively young field have greatly accelerated such engineering cycles. Specifically, various innovative tools were developed for in silico biosystems design, DNA de novo synthesis and assembly, construct verification, as well as metabolite analysis, which have laid a solid foundation for building biological foundries for rapid prototyping of improved or novel biosystems. This review summarizes the state-of-the-art technologies for synthetic biology and discusses the challenges to establish such biological foundries.

AromaDeg, a novel database for phylogenomics of aerobic bacterial degradation of aromatics

Understanding prokaryotic transformation of recalcitrant pollutants and the in-situ metabolic nets require the integration of massive amounts of biological data. Decades of biochemical studies together with novel next-generation sequencing data have exponentially increased information on aerobic aromatic degradation pathways. However, the majority of protein sequences in public databases have not been experimentally characterized and homology-based methods are still the most routinely used approach to assign protein function, allowing the propagation of misannotations. AromaDeg is a web-based resource targeting aerobic degradation of aromatics that comprises recently updated (September 2013) and manually curated databases constructed based on a phylogenomic approach. Grounded in phylogenetic analyses of protein sequences of key catabolic protein families and of proteins of documented function, AromaDeg allows query and data mining of novel genomic, metagenomic or metatranscriptomic data sets. Essentially, each query sequence that match a given protein family of AromaDeg is associated to a specific cluster of a given phylogenetic tree and further function annotation and/or substrate specificity may be inferred from the neighboring cluster members with experimentally validated function. This allows a detailed characterization of individual protein superfamilies as well as high-throughput functional classifications. Thus, AromaDeg addresses the deficiencies of homology-based protein function prediction, combining phylogenetic tree construction and integration of experimental data to obtain more accurate annotations of new biological data related to aerobic aromatic biodegradation pathways. We pursue in future the expansion of AromaDeg to other enzyme families involved in aromatic degradation and its regular update.

Database URL:http://aromadeg.siona.helmholtz-hzi.de

Association of biodiversity with the rates of micropollutant biotransformations among full-scale wastewater treatment plant communities

Biodiversities can differ substantially among different wastewater treatment plant (WWTP) communities. Whether differences in biodiversity translate into differences in the provision of particular ecosystem services, however, is under active debate. Theoretical considerations predict that WWTP communities with more biodiversity are more likely to contain strains that have positive effects on the rates of particular ecosystem functions, thus resulting in positive associations between those two variables. However, if WWTP communities were sufficiently biodiverse to nearly saturate the set of possible positive effects, then positive associations would not occur between biodiversity and the rates of particular ecosystem functions. To test these expectations, we measured the taxonomic biodiversity, functional biodiversity, and rates of 10 different micropollutant biotransformations for 10 full-scale WWTP communities. We have demonstrated that biodiversity is positively associated with the rates of specific, but not all, micropollutant biotransformations. Thus, one cannot assume whether or how biodiversity will associate with the rate of any particular micropollutant biotransformation. We have further demonstrated that the strongest positive association is between biodiversity and the collective rate of multiple micropollutant biotransformations. Thus, more biodiversity is likely required to maximize the collective rates of multiple micropollutant biotransformations than is required to maximize the rate of any individual micropollutant biotransformation. We finally provide evidence that the positive associations are stronger for rare micropollutant biotransformations than for common micropollutant biotransformations. Together, our results are consistent with the hypothesis that differences in biodiversity can indeed translate into differences in the provision of particular ecosystem services by full-scale WWTP communities.

Integration of bioinformatics to biodegradation

Bioinformatics and biodegradation are two primary scientific fields in applied microbiology and biotechnology. The present review describes development of various bioinformatics tools that may be applied in the field of biodegradation. Several databases, including the University of Minnesota Biocatalysis/Biodegradation database (UM-BBD), a database of biodegradative oxygenases (OxDBase), Biodegradation Network-Molecular Biology Database (Bionemo) MetaCyc, and BioCyc have been developed to enable access to information related to biochemistry and genetics of microbial degradation. In addition, several bioinformatics tools for predicting toxicity and biodegradation of chemicals have been developed. Furthermore, the whole genomes of several potential degrading bacteria have been sequenced and annotated using bioinformatics tools.

Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model

In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."

Stoichiometry and kinetic of the aerobic oxidation of phenolic compounds by activated sludge

The aerobic degradation of phenol (PH), catechol (CA), resorcinol (RE), pyrogallol (PY), and hydroquinone (HY) by phenol-acclimated activated sludge was investigated. A Haldane-type dependence of the respiration rate on PH, RE, and HY was observed; CA and PY exhibited a biphasic respiration pattern. According to the initial biodegradation rate, tested compounds were ordered as follows: CA>PH>>PYRE>HY. Also, they exhibited the following degree of toxicity to their own degradation: PY>>CARE>>PH>HY. Oxidation coefficients for PH, PY, RE, and HY were constant as a function of the consecutive additions of the compound. Conversely, an increase of YO/S from 1 to 1.5 molO2 molCA(-1) was observed during repeated additions of CA. The role of some enzymes involved in the aerobic degradation pathways of the tested compounds is discussed and related to the obtained results.

Chemoinformatics on Metabolic Pathways

Chemical genomics is one of the cutting-edge research areas in the post-genomic era, which requires a sophisticated integration of heterogeneous information, i.e., genomic and chemical information. Enzymes play key roles for dynamic behavior of living organisms, linking information in the chemical space and genomic space. In this chapter, the authors report our recent efforts in this area, including the development of a similarity measure between two chemical compounds, a prediction system of a plausible enzyme for a given substrate and product pair, and two different approaches to predict the fate of a given compound in a metabolic pathway. General problems and possible future directions are also discussed, in hope to attract more activities from many researchers in this research area.

Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis

Continued advances in metabolic engineering are increasing the number of small molecules being targeted for microbial production. Pathway yields and productivities, however, are often suboptimal, and strain improvement remains a persistent challenge given that the majority of small molecules are difficult to screen for and their biosynthesis does not improve host fitness. In this work, we have developed a generalized approach to screen or select for improved small-molecule biosynthesis using transcription factor-based biosensors. Using a tetracycline resistance gene 3' of a small-molecule inducible promoter, host antibiotic resistance, and hence growth rate, was coupled to either small-molecule concentration in the growth medium or a small-molecule production phenotype. Biosensors were constructed for two important chemical classes, dicarboxylic acids and alcohols, using transcription factor-promoter pairs derived from Pseudomonas putida, Thauera butanivorans, or E. coli. Transcription factors were selected for specific activation by either succinate, adipate, or 1-butanol, and we demonstrate product-dependent growth in E. coli using all three compounds. The 1-butanol biosensor was applied in a proof-of-principle liquid culture screen to optimize 1-butanol biosynthesis in engineered E. coli, identifying a pathway variant yielding a 35% increase in 1-butanol specific productivity through optimization of enzyme expression levels. Lastly, to demonstrate the capacity to select for enzymatic activity, the 1-butanol biosensor was applied as synthetic selection, coupling in vivo 1-butanol biosynthesis to E. coli fitness, and an 120-fold enrichment for a 1-butanol production phenotype was observed following a single round of positive selection.

Mathematical optimization applications in metabolic networks

Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

BACKGROUND

Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).

DESCRIPTION

MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.

CONCLUSIONS

The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).

Determination of biodegradability of phenolic compounds, characteristic to wastewater of the oil-shale chemical industry, on activated sludge by oxygen uptake measurement

The aim of this study was to investigate the biodegradation of phenol, o-cresol and p-cresol individually and as bi-substrate mixtures at low initial substrate concentrations. Activated sludge was taken from the Kohtla-Järve wastewater treatment plant, Estonia, which is also treating phenolic wastewater from the oil-shale chemical industry and is considered to be acclimated to the phenolic compounds. Respirometric data have been used for evaluation of the kinetic parameters describing the bio-oxidation of substrates. Activated sludge was able to degrade phenol and p-cresol faster than o-cresol, showing better affinity to p-cresol. However, at higher concentrations, phenol and p-cresol exhibited also an inhibitory effect to the microorganisms. The highest values for maximum rate of oxygen uptake (V(O2,max)) were obtained for the bi-substrate system of phenol--p-cresol among the mixtures containing both substrates at equal concentrations from 0.005 mM to 0.050 mM. Concerning the systems containing one substrate at 0.1 mM and the other substrate varied in the abovementioned range, the highest V(O2,max) values were found for phenol--o-cresol(0.1 mM). The interaction parameters indicated that phenol had a stronger inhibition effect on the biodegradation of p-cresol than p-cresol had on the biodegradation of phenol. However, the obtained interaction parameters for systems of phenol--o-cresol indicated that o-cresol had a stronger inhibition effect on the biodegradation of phenol, which in turn had a mild inhibition or even enhancing effect on the biodegradation of o-cresol. In the case of a 1:1 mixture, phenol and o-cresol had a similar mild inhibition effect on each other's biodegradation.

Integrated perspective for effective bioremediation

Identification of factors which can influence the natural attenuation process with available microbial genetic capacities can support the bioremediation which has been viewed as the safest procedure to combat with anthropogenic compounds in ecosystems. With the advent of molecular techniques, assimilatory capacity of an ecosystem can be defined with changing community dynamics, and if required, the essential genetic potential can be met through bioaugmentation. At the same time, intensification of microbial processes with nutrient balancing, expressing and enhancing the degradative capacities, could reduce the time frame of restoration of the ecosystem. The new concept of ecosystems biology has added greatly to conceptualize the networking of the evolving microbiota of the niche that helps in effective application of bioremediation tools to manage pollutants as additional carbon source.

MUCHA: multiple chemical alignment algorithm to identify building block substructures of orphan secondary metabolites

Background

In contrast to the increasing number of the successful genome projects, there still remain many orphan metabolites for which their synthesis processes are unknown. Metabolites, including these orphan metabolites, can be classified into groups that share the same core substructures, originated from the same biosynthetic pathways. It is known that many metabolites are synthesized by adding up building blocks to existing metabolites. Therefore, it is proposed that, for any given group of metabolites, finding the core substructure and the branched substructures can help predict their biosynthetic pathway. There already have been many reports on the multiple graph alignment techniques to find the conserved chemical substructures in relatively small molecules. However, they are optimized for ligand binding and are not suitable for metabolomic studies.

Results

We developed an efficient multiple graph alignment method named as MUCHA (Multiple Chemical Alignment), specialized for finding metabolic building blocks. This method showed the strength in finding metabolic building blocks with preserving the relative positions among the substructures, which is not achieved by simply applying the frequent graph mining techniques. Compared with the combined pairwise alignments, this proposed MUCHA method generally reduced computational costs with improving the quality of the alignment.

Conclusions

MUCHA successfully find building blocks of secondary metabolites, and has a potential to complement to other existing methods to reconstruct metabolic networks using reaction patterns.

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.

Integrating computational methods to retrofit enzymes to synthetic pathways

Microbial production of desired compounds provides an efficient framework for the development of renewable energy resources. To be competitive to traditional chemistry, one requirement is to utilize the full capacity of the microorganism to produce target compounds with high yields and turnover rates. We use integrated computational methods to generate and quantify the performance of novel biosynthetic routes that contain highly optimized catalysts. Engineering a novel reaction pathway entails addressing feasibility on multiple levels, which involves handling the complexity of large-scale biochemical networks while respecting the critical chemical phenomena at the atomistic scale. To pursue this multi-layer challenge, our strategy merges knowledge-based metabolic engineering methods with computational chemistry methods. By bridging multiple disciplines, we provide an integral computational framework that could accelerate the discovery and implementation of novel biosynthetic production routes. Using this approach, we have identified and optimized a novel biosynthetic route for the production of 3HP from pyruvate.

Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds

MOTIVATION

Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning.

RESULTS

We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment.

AVAILABILITY

The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.

Recent advances in environmental risk assessment of transformation products

When micropollutants degrade in the environment, they may form persistent and toxic transformation products, which should be accounted for in the environmental risk assessment of the parent compounds. Transformation products have become a topic of interest not only with regard to their formation in the environment, but also during advanced water treatment processes, where disinfection byproducts can form from benign precursors. In addition, environmental risk assessment of human and veterinary pharmaceuticals requires inclusion of human metabolites as most pharmaceuticals are not excreted into wastewater in their original form, but are extensively metabolized. All three areas have developed their independent approaches to assess the risk associated with transformation product formation including hazard identification, exposure assessment, hazard assessment including dose-response characterization, and risk characterization. This review provides an overview and defines a link among those areas, emphasizing commonalities and encouraging a common approach. We distinguish among approaches to assess transformation products of individual pollutants that are undergoing a particular transformation process, e.g., biotransformation or (photo)oxidation, and approaches with the goal of prioritizing transformation products in terms of their contribution to environmental risk. We classify existing approaches for transformation product assessment in degradation studies as exposure- or effect-driven. In the exposure-driven approach, transformation products are identified and quantified by chemical analysis followed by effect assessment. In the effect-driven approach, a reaction mixture undergoes toxicity testing. If the decrease in toxicity parallels the decrease of parent compound concentration, the transformation products are considered to be irrelevant, and only when toxicity increases or the decrease is not proportional to the parent compound concentration are the TPs identified. For prioritization of transformation products in terms of their contribution to overall environmental risk, we integrate existing research into a coherent model-based, risk-driven framework. In the proposed framework, read-across from data of the parent compound to the transformation products is emphasized, but limitations to this approach are also discussed. Most prominently, we demonstrate how effect data for parent compounds can be used in combination with analysis of toxicophore structures and bioconcentration potential to facilitate transformation product effect assessment.

In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival

Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli.

Fiber optic monooxygenase biosensor for toluene concentration measurement in aqueous samples

Measurements of pollutants such as toluene are critical for the characterization of contaminated sites and for the monitoring of remediation processes and wastewater treatment effluents. Fiber optic enzymatic biosensors have the potential to provide cost-effective, real time, continuous, in situ measurements. In this study, a fiber optic enzymatic biosensor was constructed and characterized for the measurement of toluene concentrations in aqueous solutions. The biological recognition element was toluene ortho-monooxygenase (TOM), expressed by Escherichia coli TG1 carrying pBS(Kan)TOM, while an optical fiber coated with an oxygen-sensitive ruthenium-based phosphorescent dye served as the transducer. Toluene was detected based on the enzymatic reaction catalyzed by TOM, which resulted in the consumption of oxygen and changes in the phosphorescence intensity. The biosensor was found to have a limit of detection of 3 μM, a linear signal range up to 100 μM, and a response time of 1 h. The performance was reproducible with different biosensors (RSD=7.4%, n=8). The biosensor activity declined with each measurement and with storage time, particularly at elevated temperatures. This activity loss could be partially reversed by exposure to formate, suggesting that NADH consumption was the primary factor limiting lifetime. This is the first report of an enzymatic toluene sensor and of an oxygenase-based biosensor. Since many oxygenases have been reported, the design concept of this oxygenase-based biosensor has the potential to broaden biosensor applications in environmental monitoring.

IGERS: inferring Gibbs energy changes of biochemical reactions from reaction similarities

Mathematical analysis and modeling of biochemical reaction networks requires knowledge of the permitted directionality of reactions and membrane transport processes. This information can be gathered from the standard Gibbs energy changes (DeltaG(0)) of reactions and the concentration ranges of their reactants. Currently, experimental DeltaG(0) values are not available for the vast majority of cellular biochemical processes. We propose what we believe to be a novel computational method to infer the unknown DeltaG(0) value of a reaction from the known DeltaG(0) value of the chemically most similar reaction. The chemical similarity of two arbitrary reactions is measured by the relative number (T) of co-occurring changes in the chemical attributes of their reactants. Testing our method across a validated reference set of 173 biochemical reactions with experimentally determined DeltaG(0) values, we found that a minimum reaction similarity of T = 0.6 is required to infer DeltaG(0) values with an error of <10 kJ/mol. Applying this criterion, our method allows us to assign DeltaG(0) values to 458 additional reactions of the BioPath database. We believe our approach permits us to minimize the number of DeltaG(0) measurements required for a full coverage of a given reaction network with reliable DeltaG(0) values.

Structure-based interpretation of biotransformation pathways of amide-containing compounds in sludge-seeded bioreactors

Partial microbial degradation of xenobiotic compounds in wastewater treatment plants (WWTPs) results in the formation of transformation products, which have been shown to be released and detectable in surface waters. Rule-based systems to predict the structures of microbial transformation products often fail to discriminate between alternate transformation pathways because structural influences on enzyme-catalyzed reactions in complex environmental systems are not well understood. The amide functional group is one such common substructure of xenobiotic compounds that may be transformed through alternate transformation pathways. The objective of this work was to generate a self-consistent set of biotransformation data for amide-containing compounds and to develop a metabolic logic that describes the preferred biotransformation pathways of these compounds as a function of structural and electronic descriptors. We generated transformation products of 30 amide-containing compounds in sludge-seeded bioreactors and identified them by means of HPLC-linear ion trap-orbitrap mass spectrometry. Observed biotransformation reactions included amide hydrolysis and N-dealkylation, hydroxylation, oxidation, ester hydrolysis, dehalogenation, nitro reduction, and glutathione conjugation. Structure-based interpretation of the results allowed for identification of preferences in biotransformation pathways of amides: primary amides hydrolyzed rapidly; secondary amides hydrolyzed at rates influenced by steric effects; tertiary amides were N-dealkylated unless specific structural moieties were present that supported other more readily enzyme-catalyzed reactions. The results allowed for the derivation of a metabolic logic that could be used to refine rule-based biotransformation pathway prediction systems to more specifically predict biotransformations of amide-containing compounds.