Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus

Abstract Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight...

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Autores principales:	Alina Renz, Andreas Dräger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/717ead0249a247c2abe50c8c58728643
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spelling	oai:doaj.org-article:717ead0249a247c2abe50c8c587286432021-12-02T16:10:34ZCurating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus10.1038/s41540-021-00188-42056-7189https://doaj.org/article/717ead0249a247c2abe50c8c587286432021-06-01T00:00:00Zhttps://doi.org/10.1038/s41540-021-00188-4https://doaj.org/toc/2056-7189Abstract Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.Alina RenzAndreas DrägerNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Alina Renz Andreas Dräger Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus
description	Abstract Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.
format	article
author	Alina Renz Andreas Dräger
author_facet	Alina Renz Andreas Dräger
author_sort	Alina Renz
title	Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus
title_short	Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus
title_full	Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus
title_fullStr	Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus
title_full_unstemmed	Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus
title_sort	curating and comparing 114 strain-specific genome-scale metabolic models of staphylococcus aureus
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/717ead0249a247c2abe50c8c58728643
work_keys_str_mv	AT alinarenz curatingandcomparing114strainspecificgenomescalemetabolicmodelsofstaphylococcusaureus AT andreasdrager curatingandcomparing114strainspecificgenomescalemetabolicmodelsofstaphylococcusaureus
_version_	1718384416383827968

Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus

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