Project description:We sequenced mRNA from three independent biological replicates of Staphylococcus epidermidis biofilms with different proportion of dormant cells. Whole trancriptome analysis of Staphylococcus epidermidis biofilms with prevented and induced dormancy.
Project description:We used the next generation sequencing method to profile gene expression changes in cutaneous T effectors isolated from mice with early colonization with Staphylococcus aureus or Staphylococcus epidermidis
Project description:The study aims to identify genes associated with Linezolid resistance. Linezolid resistant strains were compared to a Linezolid sensitive reference strain in the presence of linezolid and absence of linezolid (mock).
Project description:We report the application of single cell RNA sequencing technology for high-throughput profiling of nasal microbiome Staphylococcus epidermidis in human nasal epithelial cells.
Project description: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.
