The University of Minnesota pathway prediction system: predicting metabolic logic

Prediction of metabolic pathways from genome-scale metabolic networks

The analysis of a variety of data sets (transcriptome arrays, phylogenetic profiles, etc.) yields groups of functionally related genes. In order to determine their biological function, associated gene groups are often projected onto known pathways or tested for enrichment of known functions. However, these approaches are not flexible enough to deal with variations or novel pathways. During the last decade, we developed and refined an approach that predicts metabolic pathways from a global metabolic network encompassing all known reactions and their substrates/products, by extracting a subgraph connecting at best a set of seed nodes (compounds, reactions, enzymes or enzyme-coding genes). In this review, we summarize this work, while discussing the problems and pitfalls but also the advantages and applications of network-based metabolic pathway prediction.

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.

The University of Minnesota Pathway Prediction System: multi-level prediction and visualization

The University of Minnesota Pathway Prediction System (UM-PPS, http://umbbd.msi.umn.edu/predict/) is a rule-based system that predicts microbial catabolism of organic compounds. Currently, its knowledge base contains 250 biotransformation rules and five types of metabolic logic entities. The original UM-PPS predicted up to two prediction levels at a time. Users had to choose a predicted product to continue the prediction. This approach provided a limited view of prediction results and heavily relied on manual intervention. The new UM-PPS produces a multi-level prediction within an acceptable time frame, and allows users to view prediction alternatives much more easily as a directed acyclic graph.

Biotransformation of the antiviral drugs acyclovir and penciclovir in activated sludge treatment

The biotransformation of the two antiviral drugs, acyclovir (ACV) and penciclovir (PCV), was investigated in contact with activated sludge. Biodegradation kinetics were determined, and transformation products (TPs) were identified using Hybrid Linear Ion Trap- FT Mass Spectrometry (LTQ Orbitrap Velos) and 1D (1H NMR, 13C NMR) and 2D (1H,1H-COSY, 1H-(13)C-HSQC) NMR Spectroscopy. ACV and PCV rapidly dissipated in the activated sludge batch systems with half-lives of 5.3 and 3.4 h and first-order rate constants in relation to the amount of suspended solids (SS) of 4.9±0.1 L gss(-1) d(-1) and 7.6±0.3 L gss(-1) d(-1), respectively. For ACV only a single TP was found, whereas eight TPs were identified for PCV. Structural elucidation of TPs exhibited that transformation only took place at the side chain leaving the guanine moiety unaltered. The oxidation of the primary hydroxyl group in ACV resulted in the formation of carboxy-acyclovir (Carboxy-ACV). For PCV, transformation was more diverse with several enzymatic reactions taking place such as the oxidation of terminal hydroxyl groups and β-oxidation followed by acetate cleavage. Analysis of different environmental samples revealed the presence of Carboxy-ACV in surface and drinking water with concentrations up to 3200 ng L(-1) and 40 ng L(-1), respectively.

A framework for evaluating the contribution of transformation products to chemical persistence in the environment

The REACH legislation of the EU requires that transformation products be included in chemicals assessment for chemicals produced or imported in amounts exceeding 100 tones/year. However, including transformation products in assessments could be considered an intractable problem, particularly given the paucity of available data and the difficulty of predicting the most likely transformation route from the many possible products of a complex parent chemical (the so-called "combinatorial explosion" problem). Here, we present a scheme for identifying transformation products that substantially contribute to the joint persistence of a parent chemical and its substance family. Our scheme integrates methods for the prediction of biodegradation products, the estimation of physicochemical properties and degradation half-lives, and the calculation of a persistence metric, the joint persistence. We compare results from our scheme to 22 test cases with known transformation products. Our results highlight that the "combinatorial explosion" problem can be managed but that there is a serious need for better data for environmental half-lives of chemicals.

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.

In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene

Background

Bioremediation offers a promising pollution treatment method in the reduction and elimination of man-made compounds in the environment. Computational tools to predict novel biodegradation pathways for pollutants allow one to explore the capabilities of microorganisms in cleaning up the environment. However, given the wealth of novel pathways obtained using these prediction methods, it is necessary to evaluate their relative feasibility, particularly within the context of the cellular environment.

Results

We have utilized a computational framework called BNICE to generate novel biodegradation routes for 1,2,4-trichlorobenzene (1,2,4-TCB) and incorporated the pathways into a metabolic model for Pseudomonas putida. We studied the cellular feasibility of the pathways by applying metabolic flux analysis (MFA) and thermodynamic constraints. We found that the novel pathways generated by BNICE enabled the cell to produce more biomass than the known pathway. Evaluation of the flux distribution profiles revealed that several properties influenced biomass production: 1) reducing power required, 2) reactions required to generate biomass precursors, 3) oxygen utilization, and 4) thermodynamic topology of the pathway. Based on pathway analysis, MFA, and thermodynamic properties, we identified several promising pathways that can be engineered into a host organism to accomplish bioremediation.

Conclusions

This work was aimed at understanding how novel biodegradation pathways influence the existing metabolism of a host organism. We have identified attractive targets for metabolic engineers interested in constructing a microorganism that can be used for bioremediation. Through this work, computational tools are shown to be useful in the design and evaluation of novel xenobiotic biodegradation pathways, identifying cellularly feasible degradation routes.

Computational framework for predictive biodegradation

As increasing amounts of anthropogenic chemicals are released into the environment, it is vital to human health and the preservation of ecosystems to evaluate the fate of these chemicals in the environment. It is useful to predict whether a particular compound is biodegradable and if alternate routes can be engineered for compounds already known to be biodegradable. In this work, we describe a computational framework (called BNICE) that can be used for the prediction of novel biodegradation pathways of xenobiotics. The framework was applied to 4-chlorobiphenyl, phenanthrene, gamma-hexachlorocyclohexane, and 1,2,4-trichlorobenzene, compounds representing various classes of xenobiotics with known biodegradation routes. BNICE reproduced the proposed biodegradation routes found experimentally, and in addition, it expanded the biodegradation reaction networks through the generation of novel compounds and reactions. The novel reactions involved in the biodegradation of 1,2,4-trichlorobenzene were studied in depth, where pathway and thermodynamic analyses were performed. This work demonstrates that BNICE can be applied to generate novel pathways to degrade xenobiotic compounds that are thermodynamically feasible alternatives to known biodegradation routes and attractive targets for metabolic engineering.

In response to 'Can sugars be produced from fatty acids? A test case for pathway analysis tools'

MOTIVATION

In their article entitled 'Can sugars be produced from fatty acids? A test case for pathway analysis tools' de Figueiredo and co-authors assess the performance of three pathway prediction tools (METATOOL, PathFinding and Pathway Hunter Tool) using the synthesis of glucose-6-phosphate (G6P) from acetyl-CoA in humans as a test case. We think that this article is biased for three reasons: (i) the metabolic networks used as input for the respective tools were of very different sizes; (ii) the 'assessment' is restricted to two study cases; (iii) developers are inherently more skilled to use their own tools than those developed by other people. We extended the analyses led by de Figueiredo and clearly show that the apparent superior performance of their tool (METATOOL) is partly due to the differences in input network sizes. We also see a conceptual problem in the comparison of tools that serve different purposes. In our opinion, metabolic path finding and elementary mode analysis are answering different biological questions, and should be considered as complementary rather than competitive approaches.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The University of Minnesota Biocatalysis/Biodegradation Database: improving public access

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) began in 1995 and now contains information on almost 1200 compounds, over 800 enzymes, almost 1300 reactions and almost 500 microorganism entries. Besides these data, it includes a Biochemical Periodic Table (UM-BPT) and a rule-based Pathway Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) that predicts plausible pathways for microbial degradation of organic compounds. Currently, the UM-PPS contains 260 biotransformation rules derived from reactions found in the UM-BBD and scientific literature. Public access to UM-BBD data is increasing. UM-BBD compound data are now contributed to PubChem and ChemSpider, the public chemical databases. A new mirror website of the UM-BBD, UM-BPT and UM-PPS is being developed at ETH Zürich to improve speed and reliability of online access from anywhere in the world.

Identification of transformation products of organic contaminants in natural waters by computer-aided prediction and high-resolution mass spectrometry

Transformation products (TPs) of organic contaminants in aquatic environments are still rarely considered in water quality and chemical risk assessment, although they have been found in concentrations that are of concern. Since many different TPs can potentially be formed in the environment and analytical standards are typically lacking for these compounds, knowledge on the prevalence of TPs in aquatic environments is fragmentary. In this study, an efficient procedure was therefore developed to comprehensively screen for large numbers of potential TPs in environmental samples. It is based on a target list of plausible TPs that has been assembled using the University of Minnesota Pathway Prediction System (UM-PPS) for the computer-aided prediction of products of microbial metabolism and an extensive search for TPs reported in the scientific literature. The analytical procedure for screening of the compounds on the target list has been developed to allow for the detection of a broad range of compounds in complex environmental samples in the absence of commercially available reference standards. It includes solid phase extraction with broad enrichment efficiency, followed by liquid chromatography and tandem mass spectrometry with high mass resolution and accuracy. The identification of target TPs consisted of extracting the exact mass from the chromatogram, selecting peaks of sufficient intensity, checking the plausibility of the retention time, and interpreting mass spectra. The procedure was used to screen for TPs of 52 pesticides, biocides, and pharmaceuticals in seven representative surface water samples from different regions in Switzerland. Altogether, 19 TPs were identified, including both some well-known and commonly detected TPs, and some rarely reported ones (e.g., biotransformation products of the pharmaceuticals venlafaxine and verapamil, or of the pesticide azoxystrobin). Overall, the rather low number of TPs detected suggests that TPs may not pose a problem of unexpected magnitude for aquatic resources.

Metabolite and reaction inference based on enzyme specificities

Motivation: Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra.

Results: We have developed a system for metabolite and reaction inference based on enzyme specificities (MaRIboES). It employs structural and stereochemistry similarity measures and molecular fingerprints to generalize enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links.

Availability: Matlab and C++ code is freely available at https://gforge.nbic.nl/projects/mariboes/

Contact:d.deridder@tudelft.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

Genome-scale models of bacterial metabolism: reconstruction and applications

Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities.

Bionemo: molecular information on biodegradation metabolism

Bionemo (http://bionemo.bioinfo.cnio.es) stores manually curated information about proteins and genes directly implicated in the Biodegradation metabolism. When possible, the database includes information on sequence, domains and structures for proteins; and sequence, regulatory elements and transcription units for genes. Thus, Bionemo is a unique resource that complements other biodegradation databases such as the University of Minessota Biocatalysis/Biodegradation Database, or Metarouter, which focus more on the biochemical aspects of biodegradation than in the nature of the biomolecules carrying out the reactions. Bionemo has been built by manually associating sequences database entries to biodegradation reactions, using the information extracted from published articles. Information on transcription units and their regulation was also extracted from the literature for biodegradation genes, and linked to the underlying biochemical network. In its current version, Bionemo contains sequence information for 324 reactions and transcription regulation information for more than 100 promoters and 100 transcription factors. The information in the Bionemo database is available via a web server and the full database is also downloadable as a PostgresSQL dump. To facilitate the programmatic use of the information contained in the database, an object-oriented Perl API is also provided.