Ontology highlight
ABSTRACT:
PROVIDER: PRJNA657169 | ENA |
REPOSITORIES: ENA
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Project description:Multi-drug resistant species that is isolated from the river nearby the hospital wastewater
| PRJNA689868 | ENA
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:<p>Traveler's diarrhea (TD) is caused by enterotoxigenic Escherichia coli (ETEC), other pathogenic gram-negative pathogens, norovirus and some parasites. Nevertheless, standard diagnostic methods fail to identify pathogens in more than 30% of TD patients, so it is predicted that new pathogens or groups of pathogens may be causative agents of disease. A comprehensive metagenomic study of the fecal microbiomes from 23 TD patients and seven healthy travelers was performed, all of which tested negative for the known etiologic agents of TD in standard tests. Metagenomic reads were assembled and the resulting contigs were subjected to semi-manual binning to assemble independent genomes from metagenomic pools. Taxonomic and functional annotations were conducted to assist identification of putative pathogens. We extracted 560 draft genomes, 320 of which were complete enough to be enough characterized as cellular genomes and 160 of which were bacteriophage genomes. We made predictions of the etiology of disease in individual subjects based on the properties and features of the recovered cellular genomes. Three subtypes of samples were observed. First were four patients with low diversity metagenomes that were predominated by one or more pathogenic E. coli strains. Annotation allowed prediction of pathogenic type in most cases. Second, five patients were co-infected with E. coli and other members of the Enterobacteriaceae, including antibiotic resistant Enterobacter, Klebsiella, and Citrobacter. Finally, several samples contained genomes that represented dark matter. In one of these samples we identified a TM7 genome that phylogenetically clustered with a strain isolated from wastewater and carries genes encoding potential virulence factors. We also observed a very high proportion of bacteriophage reads in some samples. The relative abundance of phage was significantly higher in healthy travelers when compared to TD patients. Our results highlight that assembly-based analysis revealed that diarrhea is often polymicrobial and includes members of the Enterobacteriaceae not normally associated with TD and have implicated a new member of the TM7 phylum as a potential player in diarrheal disease. </p>
| phs001352 | dbGaP
Project description:Multi drug resistant Pseudomonas aeruginosa
| PRJNA869185 | ENA