Renz, A., Dräger, A. Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus. npj Syst Biol Appl 7, 30 (2021). https://doi.org/10.1038/s41540-021-00188-4
Renz, A., Dräger, A. Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus. npj Syst Biol Appl 7, 30 (2021). https://doi.org/10.1038/s41540-021-00188-4
Renz, A., Dräger, A. Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus. npj Syst Biol Appl 7, 30 (2021). https://doi.org/10.1038/s41540-021-00188-4
Renz, A., Dräger, A. Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus. npj Syst Biol Appl 7, 30 (2021). https://doi.org/10.1038/s41540-021-00188-4
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 Mᴇᴍᴏᴛᴇ, 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.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
