Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1004049 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Recurrent epidemics of methicillin-resistant Staphylococcus aureus (MRSA) have illustrated that the effectiveness of antibiotics in clinical application is rapidly fading. A feasible approach is to combine natural products with existing antibiotics to achieve an antibacterial effect. In this molecular docking study, we found that theaflavin (TF) preferentially binds the allosteric site of penicillin-binding protein 2a (PBP2a), inducing the PBP2a active site to open, which is convenient for β-lactam antibiotics to treat MRSA infection, instead of directly exerting antibacterial activity at the active site. Subsequent TMT-labeled proteomics analysis showed that TF treatment did not significantly change the landscape of the Staphylococcus aureus (S. aureus) USA300 proteome.Checkerboard dilution tests and kill curve assays were performed to validate the synergistic effect of TF and ceftiofur, and the fractional inhibitory concentration index (FICI) was 0.1875.Our findings provide a potential therapeutic strategy to combine existing antibiotics with natural products to resolve the prevalent infections of multidrug-resistant pathogens.
2022-11-09 | PXD037668 | Pride
Project description:M13 bacteriophage random peptide phage display libraries selected for binding to serum antibodies
| PRJNA196935 | ENA
Project description:Cationic antimicrobial peptides (CAPs) are promising novel alternatives to conventional antibacterial agents, but the overlap in resistance mechanisms between small-molecule antibiotics and CAPs is unknown. Does evolution of antibiotic resistance decrease (cross-resistance) or increase (collateral sensitivity) susceptibility to CAPs? We systematically addressed this issue by studying the susceptibilities of a comprehensive set of antibiotic resistant Escherichia coli strains towards 24 antimicrobial peptides. Strikingly, antibiotic resistant bacteria frequently showed collateral sensitivity to CAPs, while cross-resistance was relatively rare. We identified clinically relevant multidrug resistance mutations that simultaneously elevate susceptibility to certain CAPs. Transcriptome and chemogenomic analysis revealed that such mutations frequently alter the lipopolysaccharide composition of the outer cell membrane and thereby increase the killing efficiency of membrane-interacting antimicrobial peptides. Furthermore, we identified CAP-antibiotic combinations that rescue the activity of existing antibiotics and slow down the evolution of resistance to antibiotics. Our work provides a proof of principle for the development of peptide based antibiotic adjuvants that enhance antibiotic action and block evolution of resistance.
2018-03-29 | GSE96706 | GEO
Project description:The lantibiotic mersacidin is an antimicrobial peptide of 20 amino acids that is produced by Bacillus sp. strain HIL Y-85,54728. Lantibiotics are antibiotics containing nonproteinogenic amino acids like lanthionine and/or 3-methyllanthionine. Mersacidin interferes with cell wall biosynthesis by targeting the peptidoglycan precursor lipid II and inhibits the growth of MRSA and other gram-positive bacteria. Therefore, mersacidin could be a lead substance for the development of new antibacterial agents. The clinical VISA isolates S. aureus 137/93A and its spontaneous mutant S. aureus 137/93G show reduced sensitivity towards mersacidin. This phenotype could not be traced to factors being responsible for vancomycinresistance (i.e. the thickened cell wall). Here, we focussed on the comparative transcriptome analysis of S. aureus 137/93A and S. aureus SG511 (sensitive strain) via full genome S. aureus microarrays to identify genes involved in the reduced sensitivity towards mersacidin.
2009-01-17 | GSE12016 | GEO
Project description:The human gut is colonized by trillions of microorganisms that influence human health and disease through the metabolism of xenobiotics, including therapeutic drugs and antibiotics. The diversity and metabolic potential of the human gut microbiome have been extensively characterized, but it remains unclear which microorganisms are active and which perturbations can influence this activity. Here, we use flow cytometry, 16S rRNA gene sequencing, and metatranscriptomics to demonstrate that the human gut contains distinctive subsets of active and damaged microorganisms, primarily composed of Firmicutes, which display marked temporal variation. Short-term exposure to a panel of xenobiotics resulted in significant changes in the physiology and gene expression of this active microbiome. Xenobiotic-responsive genes were found across multiple bacterial phyla, encoding novel candidate proteins for antibiotic resistance, drug metabolism, and stress response. These results demonstrate the power of moving beyond DNA-based measurements of microbial communities to better understand their physiology and metabolism. RNA-Seq analysis of the human gut microbiome during exposure to antibiotics and therapeutic drugs.
2012-10-30 | E-GEOD-38151 | biostudies-arrayexpress
Project description:Display technologies, e.g., phage, ribosome, mRNA, bacterial, and yeast-display, combine high content peptide libraries with appropriate screening strategies to identify functional peptide sequences. Construction of large peptide library and display-screen system in intact mammalian cells will facilitate the development of peptide therapeutics targeting transmembrane proteins. Our previous work established linear-double-stranded DNAs (ldsDNAs) as innovative biological parts to implement AND gate genetic circuits in mammalian cell line. In the current study, we employ ldsDNA with terminal NNK degenerate codons as AND gate input to build highly diverse peptide library in mammalian cells. Only PCR reaction and cell transfection experiments are needed to construct the library. High-throughput sequencing (HTS) results reveal that our new strategy could generate peptide library with both amino acid sequence and peptide length diversities. Our work establishes ldsDNA as biological parts for building highly diverse peptide library in mammalian cells, which shows great application potential in developing therapeutic peptides targeting transmembrane proteins.
2020-07-19 | GSE134671 | GEO
Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci.
Model Type: Predictive machine learning model.
Model Relevance: Prediction of Human cytotoxicity endpoints.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos42ez
2024-05-13 | MODEL2405130002 | BioModels
Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci.
Model Type: Predictive machine learning model.
Model Relevance: The model predicts the probability of growth inhibition.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos18ie
2024-05-08 | MODEL2405080002 | BioModels