Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1155305 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Whole genome sequencing of SYBARIS Aspergillus spp. known to be multi-drug resistant and difficult to treat. Aim of this experiment is to investigate the genetic basis of susceptibility to disease and elucidate molecular mechanisms of drug resistance in these strains.
2016-04-30 | E-SYBR-1 | biostudies-arrayexpress
Project description:Tuberculosis (TB) is one of the deadliest infectious disorders in the world. To effectively TB manage, an essential step is to gain insight into the lineage of Mycobacterium tuberculosis (MTB) strains and the distribution of drug resistance. Although the Campania region is declared a cluster area for the infection, to contribute to the effort to understand TB evolution and transmission, still poorly known, we have generated a dataset of 159 genomes of MTB strains, from Campania region collected during 2018-2021, obtained from the analysis of whole genome sequence data. The results show that the most frequent MTB lineage is the 4 according for 129 strains (81.11%). Regarding drug resistance, 139 strains (87.4%) were classified as multi susceptible, while the remaining 20 (12.58%) showed drug resistance. Among the drug-resistance strains, 8 were isoniazid-resistant MTB (HR-MTB), 7 were resistant only to one antibiotic (3 were resistant only to ethambutol and 3 isolate to streptomycin while one isolate showed resistance to fluoroquinolones), 4 multidrug-resistant MTB, while only one was classified as pre-extensively drug-resistant MTB (pre-XDR). This dataset expands the existing available knowledge on drug resistance and evolution of MTB, contributing to further TB-related genomics studies to improve the management of TB infection.
2023-11-17 | E-MTAB-13058 | biostudies-arrayexpress
Project description:Genomic investigation of extensively drug-resistant Klebsiella pneumoniae
| PRJNA787440 | ENA
Project description:Acinetobacter baumannii causes high mortality in ventilator-associated pneumonia patients and antibiotic treatment is compromised in multi-drug resistant strains resistant to beta-lactams, carbapenems, cephalosporins, polymyxins and tetracyclines. Among COVID-19 patients receiving ventilator support, multi-drug resistant A. baumannii secondary infection is associated with a two-fold increase in mortality. Here we investigated the use of the 8-hydroxyquinoline ionophore PBT2 to break resistance of A. baumannii to tetracycline class antibiotics.
2021-12-17 | GSE175535 | GEO
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:Antibiotic use can lead to expansion of multi-drug resistant pathobionts within the gut microbiome that can cause life-threatening infections. Selective alternatives to conventional antibiotics are in dire need. Here, we describe a Klebsiella PhageBank that enables the rapid design of antimicrobial bacteriophage cocktails to treat multi-drug resistant Klebsiella pneumoniae. Using a transposon library in carbapenem-resistant K. pneumoniae, we identified host factors required for phage infection in major Klebsiella phage families. Leveraging the diversity of the PhageBank and experimental evolution strategies, we formulated combinations of phages that minimize the occurrence of phage resistance in vitro. Optimized bacteriophage cocktails selectively suppressed the burden of multi-drug resistant K. pneumoniae in the mouse gut microbiome and drove bacterial populations to lose key virulence factors that act as phage receptors. Further, phage-mediated diversification of bacterial populations in the gut enabled co-evolution of phage variants with higher virulence and a broader host range. Altogether, the Klebsiella PhageBank represents a roadmap for both phage researchers and clinicians to enable phage therapy against a critical multidrug-resistant human pathogen.
2024-10-01 | GSE272297 | GEO