MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models

Moretti, Sébastien; Tran, Van Du T.; Mehl, Florence; Ibberson, Mark; Pagni, Marco

doi:10.1093/nar/gkaa992

Cited by 107 publications

(108 citation statements)

References 44 publications

(25 reference statements)

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“…We performed further refinement from literature and by using the human metabolic networks in the HumanCyc database (Trupp et al, 2010) and Recon3D ( Brunk et al, 2018 ). Model checks were done with MEMOTE, and refinements were performed with MetaNetX 4.2, BiGG, ChEBI, MetaCyc, and PubChem databases ( Caspi et al, 2014 ; Hastings et al, 2016 ; Norsigian et al, 2020 ; Kim et al, 2021 ; Moretti et al, 2021 ). The new GEM contains 4,660 genes, 3,614 reactions, and 4,052 metabolites and conforms to the minimum standardised content for a newly published GEM based on recently published community standards ( Carey et al, 2020 ); 100% of the metabolites in ( i HsaEC21) have a human-readable descriptive name, 100% have an inchi key, 100% of metabolite annotation conformity with the BiGG database and in MetaNetX, Kyoto Encyclopedia of Genes and Genomes (KEGG), ChEBI, ModelSEED, HMDb, or MetaCyc ( Caspi et al, 2014 ; Hastings et al, 2016 ; Wishart et al, 2018 ; Norsigian et al, 2020 ; Kanehisa et al, 2021 ; Kim et al, 2021 ; Moretti et al, 2021 ; Seaver et al, 2021 ); 100% of the metabolites have a charge and chemical formula with a charge balance of 75.3% ( ).…”

Section: Resultsmentioning

confidence: 99%

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

et al. 2021

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The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus’ pathogenic mechanism and support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first, in its healthy state. We then demonstrate how the entry of SARS-CoV-2 into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. A new computational method that predicts 20 unique reactions as drug targets from our models and provides a platform for future studies on viral entry inhibition, immune regulation, and drug optimisation strategies. The model is available in BioModels (https://www.ebi.ac.uk/biomodels/MODEL2007210001) and the software tool, findCPcli, that implements the computational method is available at https://github.com/findCP/findCPcli.

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Section: Resultsmentioning

confidence: 99%

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

et al. 2021

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“…The UniProt SPARQL endpoint ( ) allows users to perform complex queries on UniProt RDF data and to combine UniProt RDF data in real time with RDF data from other resources providing SPARQL endpoints, through so-called “federated queries”. Resources that provide SPARQL endpoints that may be of particular interest in natural product research and that are highly complementary to UniProt, include Rhea [ 23 , 24 ], the Integrated Database of Small Molecules (IDSM) [ 29 ], which supports chemical similarity and chemical substructure searches over ChEBI and other chemical structure databases, the OMA [ 30 ] and OrthoDB [ 31 ] resources of orthologous groups, and the MetaNetX resource of genome-scale metabolic models [ 32 ]. A tutorial for querying these resources with SPARQL is available at .…”

Section: Resultsmentioning

confidence: 99%

Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB

et al. 2021

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The UniProt Knowledgebase UniProtKB is a comprehensive, high-quality, and freely accessible resource of protein sequences and functional annotation that covers genomes and proteomes from tens of thousands of taxa, including a broad range of plants and microorganisms producing natural products of medical, nutritional, and agronomical interest. Here we describe work that enhances the utility of UniProtKB as a support for both the study of natural products and for their discovery. The foundation of this work is an improved representation of natural product metabolism in UniProtKB using Rhea, an expert-curated knowledgebase of biochemical reactions, that is built on the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Knowledge of natural products and precursors is captured in ChEBI, enzyme-catalyzed reactions in Rhea, and enzymes in UniProtKB/Swiss-Prot, thereby linking chemical structure data directly to protein knowledge. We provide a practical demonstration of how users can search UniProtKB for protein knowledge relevant to natural products through interactive or programmatic queries using metabolite names and synonyms, chemical identifiers, chemical classes, and chemical structures and show how to federate UniProtKB with other data and knowledge resources and tools using semantic web technologies such as RDF and SPARQL. All UniProtKB data are freely available for download in a broad range of formats for users to further mine or exploit as an annotation source, to enrich other natural product datasets and databases.

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“…The annotations were added in accordance with the MIRIAM guidelines 47 . After adding the BiGG-IDs to the model, ModelPolisher was used for further annotations of the model’s reactions and metabolites for references to other databases, such as KEGG, MetaNetX 48 , or MetaCyc 44 .…”

Section: Methodsmentioning

confidence: 99%

An updated genome-scale metabolic network reconstruction ofPseudomonas aeruginosaPA14 to characterize mucin-driven shifts in bacterial metabolism

Payne

Renz

Dunphy

et al. 2021

Preprint

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Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also providing a novel insight about an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing novel biological insights.

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MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models

Cited by 107 publications

References 44 publications

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB

An updated genome-scale metabolic network reconstruction ofPseudomonas aeruginosaPA14 to characterize mucin-driven shifts in bacterial metabolism

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