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Updated: Oct 31, 2025

A Tandem Liquid Chromatography–Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus
Published on: March 28, 2017
Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus
Alina Renz1,2,3, Andreas Dräger4,5,6,7
1Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany.
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.
Insights
This review analyzes 114 genome-scale metabolic models (GEMs) for Staphylococcus aureus, identifying their strengths and weaknesses. The findings guide researchers in selecting or combining GEMs to discover new antimicrobial drug targets against resistant strains.
Area of Science:
- Microbiology
- Systems Biology
- Computational Biology
Background:
- Staphylococcus aureus is a critical pathogen causing severe infections globally.
- Methicillin-resistant S. aureus (MRSA) and multi-drug resistant strains pose significant public health challenges.
- Novel antimicrobial and antistaphylococcal therapies are urgently needed.
Purpose of the Study:
- To provide a comprehensive overview of available genome-scale metabolic models (GEMs) for Staphylococcus aureus.
- To evaluate and compare the scope, quality, and capabilities of these GEMs.
- To guide researchers in selecting appropriate GEMs for identifying new antimicrobial drug targets.
Main Methods:
- Downloaded all 114 publicly available S. aureus GEMs.
- Assessed model scope (genes, metabolites, reactions).
- Performed quality control using MEMOTE and analyzed growth capabilities and model similarities.
Main Results:
- Characterized the diversity and features of 114 S. aureus GEMs.
- Identified strengths and limitations of individual models.
- Highlighted potential for combining models to enhance predictive power.
Conclusions:
- A curated overview of S. aureus GEMs is essential for effective use in research.
- Selecting the right GEM or combining models can accelerate the discovery of novel antimicrobial targets.
- This review serves as a guide for model-driven strategies against drug-resistant S. aureus.
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