Project description:The alarming rise of antimicrobial resistance in Mycobacterium tuberculosis coupled with the shortage of new antibiotics has made tuberculosis (TB) control a global health priority. Non-steroidal anti-inflammatory drugs (NSAIDs) inhibit the growth of multi-drug resistant isolates of M. tuberculosis. Repurposing NSAIDs, with known clinical properties and safety records, offers a direct route to clinical trials. Therefore we investigated the novel mechanisms of anti-mycobacterial action of the NSAID, carprofen. Integrative molecular and microbiological approaches revealed that carprofen, a bactericidal drug, inhibited bacterial drug efflux mechanisms. In addition, carprofen restricted mycobacterial biofilm-like growth, highlighting the requirement of efflux-mediated communicative systems for the formation of biofilms. Transcriptome profiling revealed that carprofen likely acts by inhibiting respiration through the disruption of membrane potential, which may explain why spontaneous drug-resistant mutants could not be raised due to the pleiotropic nature of carprofen’s anti-tubercular action. This immunomodulatory drug has the potential to reverse TB antimicrobial resistance by inhibiting drug efflux pumps and biofilm formation, and paves a new chemotherapeutic path for tackling tuberculosis.
Project description:Antimicrobial peptides (AMPs) are garnering attention as possible alternatives to antibiotics. Here, we describe the antimicrobial properties of epinecidin-1 against multi-drug resistant clinical isolates of P. aeruginosa (P. aeruginosa (R)) and P. aeruginosa from ATCC (P. aeruginosa (19660)) in vivo. The minimum inhibitory concentrations (MICs) of epinecidin-1 against P. aeruginosa (R) and P. aeruginosa (19660) were determined, and compared with those of imipenem. Epinecidin-1 was found to be highly effective at combating peritonitis infection caused by P. aeruginosa (R) or P. aeruginosa (19660) in mouse models, without inducing adverse behavioral effects, or liver or kidney toxicity. Taken together, our results indicate that epinecidin-1 enhances the survival rate of mice infected with the bacterial pathogen P. aeruginosa through both antimicrobial and immunomodulatory effects. RNA from mice treated with epinecidin-1 were individually compared to RNA from PBS control mice.
Project description:Antimicrobial peptides (AMPs) are garnering attention as possible alternatives to antibiotics. Here, we describe the antimicrobial properties of epinecidin-1 against multi-drug resistant clinical isolates of P. aeruginosa (P. aeruginosa (R)) and P. aeruginosa from ATCC (P. aeruginosa (19660)) in vivo. The minimum inhibitory concentrations (MICs) of epinecidin-1 against P. aeruginosa (R) and P. aeruginosa (19660) were determined, and compared with those of imipenem. Epinecidin-1 was found to be highly effective at combating peritonitis infection caused by P. aeruginosa (R) or P. aeruginosa (19660) in mouse models, without inducing adverse behavioral effects, or liver or kidney toxicity. Taken together, our results indicate that epinecidin-1 enhances the survival rate of mice infected with the bacterial pathogen P. aeruginosa through both antimicrobial and immunomodulatory effects.
Project description:The emergence and spread of carbapenem-resistant Klebsiella pneumoniae (CR-KPN) infections have worsened the current situation worldwide. Clinically, cotrimoxazole (CTX) and amikacin (AMI) are considered to be the preferred drugs in the treatment of (CR-KPN). But for now, the extensive use of cotrimoxazole (CTX) and amikacin (AMI) During the course of treatment leads to the emergence of cotrimoxazole- and amikacin-resistant infections, which is of great clinical concern. Previous evidence has shown that bacteria with reduced metabolism tend to be resistant to antibiotics, however, the mechanism remains unclear. In the present study, proteomics was performed on the sensitive, cotrimoxazole-resistant, amikacin-resistant and cotrimoxazole/amikacin-both-resistant KPN clinical isolates, and 2266 proteins were identified in total by liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) analysis. Further bioinformatic analysis showed down-regulation of tricarboxylic acid cycle pathway and up-regulation of alcohol metabolic or glutathione metabolism processes, which may contribute to ROS clearance and cell survival, in drug-resistant isolates. Finally, combined with minimum inhibitory concentration (MIC) of Amikacin and Cotrimoxazole on different KPN isolates, we identified nine proteins contributed mostly to such an alteration and the survival of bacteria under drug pressure, which could reveal novel mechanisms or pathways involved in drug resistance. These proteins and their pathways might be used as targets for the development of novel therapeutics against antimicrobial-resistant (AMR) infections.
Project description:In order to identify mechanisms of drug resistance to HER2-targeted therapy, we performed cDNA microarray analysis on drug naiive BT474 and drug resistant BT474 cells treated with lapatinib for 0, 10, and 20 hrs.
Project description:Transcriptional profiling of mycobacterium tuberculosis clinical isolates in China comparing extensively drug-resistant tuberculosis with drug sensitive one.
Project description:Transcriptional profiling of mycobacterium tuberculosis clinical isolates in China comparing extensively drug-resistant tuberculosis with drug sensitive one. The same condition experiment. The samples were from the different drug-resistant strains. Only one replicate.
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.
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.
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.