Ontology highlight
ABSTRACT:
PROVIDER: PRJNA699319 | ENA |
REPOSITORIES: ENA
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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.
2022-02-16 | MODEL2007110001 | BioModels
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.
2022-02-16 | MODEL2007150001 | BioModels
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.
2022-02-16 | MODEL2007150002 | BioModels
Project description:Whole genome sequencing (WGS) of MCF-10A cells without or with NFIB overexpression cultured at 30 or 75 days and 5-fluoracil-resistant or oxaliplatin-resistant MCF-10A cells with NFIB overexpression cultured at 75 days to investigate whether the oncogenic potential of NFIB is linked to its function in replication initiation.
2023-08-22 | GSE201866 | GEO
Project description:Antimicrobial resistance (AMR) arises from complex genetic and regulatory changes, including single mutations, gene acquisitions or cumulative effects. Advancements in genomics and proteomics facilitate more comprehensive understanding of the mechanisms behind antimicrobial resistance. In this study, 74 clinically obtained Klebsiella pneumoniae isolates with increased meropenem and/or imipenem MICs were characterized by broth microdilution and PCR to check for the presence of carbapenemase genes. Subsequently, a representative subset of 15 isolates was selected for whole genome sequencing (WGS) by Illumina and Nanopore sequencing, and proteomic analysis by liquid chromatography-mass spectrometry (LC-MS/MS) to investigate the mechanisms underlying the differences in carbapenem susceptibility of Klebsiella pneumoniae isolates. Identical techniques were applied to characterize 4 mutants obtained after sequential meropenem exposure. We demonstrated that in clinically obtained isolates, increased copy numbers of blaOXA-48 containing plasmids, combined with OmpK36 loss, contributed to high carbapenem MICs without involvement of OmpK35 or other porins or efflux systems. In the meropenem exposed mutants, increased copy numbers of blaCTX-M-15 or blaOXA-48 containing plasmids, combined with OmpK36 loss was demonstrated. The OmpK36 loss resulted from the insertion of IS1 transposable elements or partial deletion of the ompK36 gene. Additionally, we identified two mutations, C59A and C58A, in the DNA coding the copA antisense RNA of IncFII plasmids and multiple mutations of an IncR plasmid, associated with increased plasmid copy numbers. This study demonstrates that by combining WGS and LC-MS/MS, the effect of genomic changes on protein expression related to antibiotic resistance and the mechanisms behind antibiotic resistance can be elucidated.
2025-06-06 | PXD061983 | Pride
Project description:Genome sequence analysis of antimicrobial-resistant bacteria
| PRJDB8858 | ENA