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).
2011-04-08 | GSE26358 | GEO
Project description:Whole genome sequencing of linezolid resistant Staphylococcus epidermidis
Project description:Coordinated protein-coding sequence transcriptional responses of Staphylococcus aureus to antimicrobial exposure are well described but little is known of the role of bacterial non-coding, small RNAs (sRNAs) in these responses. Here we used RNAseq to investigate the sRNA response of the epidemic multiresistant hospital ST239 S. Aureus strain JKD6009 and its vancomycin-intermediate clinical derivative, JKD6008, after exposure to four antibiotics representing the major classes of antimicrobials used to treat methicillin-resistant S. Aureus infections. These agents included vancomycin, linezolid, ceftobiprole, and tigecycline. We identified 410 potential sRNAs (sRNAs) and then compared global sRNA and mRNA expression profiles at 2 and 6 hours, without antibiotic exposure, and after exposure to 0.5 x MIC for each antibiotic, for both JKD6009 (VSSA), and JKD6008 (VISA). Two strains were used (JKD6009, vancomycin-susceptible S. Aureus; JKD6008, in vivo derived vancomycin-intermediate S. Aureus). The complete JKD6008 genome seqeuce was used as the reference. Two time points, 2 hours and 6 hours after culture in Mueller Hinton broth. Strains were exposed to no antibiotic, or 0.5 x MIC for 10 mins for the following antibiotics; vancomycin, linezolid, ceftobiprole, tigecycline. RNA isolation procedures enriched for mRNA or sRNA. The 40 cDNA libraries were sequenced using a whole flowcell (8 lanes) in an Illumina genome analyzer GAII for 36 cycles. Data was analyzed using the BioConductor package limma, and by applying non-negative matrix factorization to determine the impact of antibiotic exposure on the sRNA and mRNA transcriptional profiles.
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.