Ontology highlight
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PROVIDER: PRJNA1252076 | 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:Chemotherapy resistance is a major obstacle to curing cancer patients. Combination drug regimens have shown promise as a method to overcome resistance; however, to date only some cancers have been cured with this method. Collateral sensitivity – the phenomenon whereby resistance to one drug is co-occurrent with sensitivity to a second drug – has been gaining traction as a promising new concept to guide rational design of combination regimens. Here we evolved over 100 subclones of the Eµ-Myc; p19ARF -/- cell line to be resistant to one of four classical chemotherapy agents: doxorubicin, vincristine, paclitaxel, and cisplatin. We then surveyed collateral responses to acquisition of resistance to these agents. Although numerous collateral sensitivities have been documented for antibiotics and targeted cancer therapies, we observed only one collateral sensitivity: half of cell lines that acquired resistance to paclitaxel also acquired a collateral sensitivity to verapamil. However, we found that the mechanism of this collateral sensitivity was unrelated to the mechanism of paclitaxel resistance. Interestingly, we observed heterogeneity in the phenotypic response to acquisition of resistance to most of the drugs we tested, most notably for paclitaxel, suggesting the existence of multiple different states of resistance. Surprisingly, this phenotypic heterogeneity in paclitaxel resistant cell lines was unrelated to transcriptomic heterogeneity among those cell lines. These features of phenotypic and transcriptomic heterogeneity must be taken into account in future studies of treated tumor subclones and in design of chemotherapy combinations.
2022-03-23 | GSE166425 | GEO
Project description:Ceftazidime-avibactam use selects multidrug-resistance and prevents designing collateral sensitivity-based therapies against Pseudomonas aeruginosa
| PRJEB85283 | ENA
Project description:Tigecycline is a broad-spectrum active intravenous antibiotic that is also active against methicillin-resistant staphylococcus aureus. In Phase 3 and 4 clinical trials, increased all-cause mortality was observed in patients treated with tigecycline compared to patients in the control group. The reason for the increase is not yet clear. In this study, we found tigecycline could cause abnormal coagulation in tumor patients, especially in patients with hematological malignancies. The main manifestations were decreased fibrinogen and prolonged activated prothrombin time (APTT), thrombin time (TT) and D-dimer. In addition, functional studies have found that tigecycline could inhibit platelet adhesion and aggregation, and the patient's coagulation function could gradually recover after discontinuation. Gene sequencing results suggested that tigecycline could significantly regulate the expression of genes related to platelet function pathways, and could increase the incidence of single nucleotide polymorphisms and the number of alternative splices in CHO cells with tigecycline treatment. Abnormal platelet function and low numbers are common in patients with hematological malignancies. Our study could explain the mechanism of abnormal coagulation caused by tigecycline. At the same time, a warning should be given when doctors applied tigecycline to cure infections in tumor patients.
2022-06-01 | GSE198830 | GEO
Project description:Tigecycline, a protein translation inhibitor, is a treatment of last resort for infections caused by the opportunistic multidrug resistant human pathogen Acinetobacter baumannii. However, strains resistant to tigecycline were reported not long after its clinical introduction. Translation inhibitor antibiotics perturb ribosome function and induce the reduction of (p)ppGpp, an alarmone involved in the stringent response that negatively modulates ribosome production. Through RNA sequencing, this study revealed a significant reduction in the transcription of genes in citric acid cycle and cell respiration, suggesting tigecycline inhibits or slows down bacterial growth. Our results indicated that the drug-induced reduction of (p)ppGpp level promoted the production but diminished the degradation of ribosomes, which mitigates the translational inhibition effect by tigecycline. The reduction of (p)ppGpp also led to a decrease of transcription coupled nucleotide excision repair which likely increases the chances of development of tigecycline resistant mutants. Increased expression of genes linked to horizontal gene transfer were also observed. The most upregulated gene, rtcB, involving in RNA repair, is either a direct tigecycline stress response or is in response to the transcription de-repression of a toxin-antitoxin system. The most down-regulated genes encode two b-lactamases, which is a possible by-product of tigecycline-induced reduction in transcription of genes associated with peptidoglycan biogenesis. This transcriptomics study provides a global genetic view of why A. baumannii is able to rapidly develop tigecycline resistance.
2020-08-03 | GSE131451 | GEO
Project description:Collateral sensitivity of antibiotic resistant bacteria to antimicrobial peptides
| PRJNA379428 | ENA