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      Approaches for completing metabolic networks through metabolite damage and repair discovery

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          Abstract

          Metabolites are prone to damage, either via enzymatic side reactions, which collectively form the underground metabolism, or via spontaneous chemical reactions. The resulting non-canonical metabolites that can be toxic, are mended by dedicated “metabolite repair enzymes.” Deficiencies in the latter can cause severe disease in humans, whereas inclusion of repair enzymes in metabolically engineered systems can improve the production yield of value-added chemicals. The metabolite damage and repair loops are typically not yet included in metabolic reconstructions and it is likely that many remain to be discovered. Here, we review strategies and associated challenges for unveiling non-canonical metabolites and metabolite repair enzymes, including systematic approaches based on high-resolution mass spectrometry, metabolome-wide side-activity prediction, as well as high-throughput substrate and phenotypic screens.

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          Highlights

          • Underground metabolism can generate useful, useless, or toxic metabolites.

          • Damaged metabolites can be produced non-enzymatically or owing to enzyme promiscuity.

          • Metabolite repair enzymes eliminate damaged metabolites or pre-empt their formation.

          • Tools to discover underground pathways could be used to reveal hidden repair systems.

          • Metabolite repair is crucial to account for in disease and metabolic engineering.

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          Most cited references75

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          Current status and applications of genome-scale metabolic models

          Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases. Electronic supplementary material The online version of this article (10.1186/s13059-019-1730-3) contains supplementary material, which is available to authorized users.
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            The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes

            The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.
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              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.
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                Author and article information

                Contributors
                Journal
                Curr Opin Syst Biol
                Curr Opin Syst Biol
                Current Opinion in Systems Biology
                Elsevier Ltd
                2452-3100
                1 December 2021
                December 2021
                : 28
                : None
                Affiliations
                [1]Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
                Author notes
                []Corresponding author: Linster, Carole L carole.linster@ 123456uni.lu
                [a]

                Co-first authors.

                Article
                S2452-3100(21)00073-1 100379
                10.1016/j.coisb.2021.100379
                8669784
                6130e4e4-ad79-4f08-a7bc-841de2578fb9
                © 2021 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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                Categories
                Article

                metabolite repair enzymes,underground metabolism,non-canonical metabolites,untargeted metabolomics

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