Ontology highlight
ABSTRACT:
PROVIDER: PRJNA757272 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR6747450.fastq.gz | Fastqsanger.gz | |||
| ERR6747452.fastq.gz | Fastqsanger.gz | |||
| ERR6747453.fastq.gz | Fastqsanger.gz | |||
| ERR6747454.fastq.gz | Fastqsanger.gz | |||
| ERR6747455.fastq.gz | Fastqsanger.gz |
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Project description:Although the relationship between phenotypic plasticity and evolutionary dynamics has attracted large interest, very little is known about the contribution of phenotypic plasticity to adaptive evolution. In this study, we analyzed phenotypic and genotypic changes in E. coli cells during adaptive evolution to ethanol stress. To quantify the phenotypic changes, transcriptome analyses were performed.
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Project description:Although the relationship between phenotypic plasticity and evolutionary dynamics has attracted large interest, very little is known about the contribution of phenotypic plasticity to adaptive evolution. In this study, we analyzed phenotypic and genotypic changes in E. coli cells during adaptive evolution to ethanol stress. To quantify the phenotypic changes, transcriptome analyses were performed. We previously obtained 6 independently evolved ethanol tolerant E. coli strains, strains A through F, by culturing cells under 5% ethanol stress for about 1000 generations and found a significantly larger growth rate than the parent strains (Horinouchi et al, 2010, PMID: 20955615). To elucidate the phenotypic changes that occurred during adaptive evolution, we quantified the time-series of the expression changes by microarray analysis. Starting from frozen stocks obtained at 6 time points (0, 384, 744, 1224, 1824 and 2496 hours) in laboratory evolution, cells were cultured under 5% ethanol stress, and mRNA samples were obtained in the exponential growth phase for microarray analysis.
2014-07-21 | E-GEOD-59050 | biostudies-arrayexpress
Project description:Genotypic and phenotypic antimicrobial resistance of Clostridioides difficile in the Irish population, 2022
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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:Genotypic and phenotypic characterization of antimicrobial resistant E.coli isolated from ready-to-eat food in Singapore based on disk diffusion, broth microdilution and whole genome sequencing
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Project description:Backtround:Cystic fibrosis (CF) is an inherited genetic disorder caused by the cystic fibrosis transmembrane conductance regulator(CFTR) gene mutation, producing sticky and thick mucosal fluids. This leads to an environment that facilitates the colonization ofopportunistic microorganisms, causing progressive acute and chronic lung infections.Rothia mucilaginosa, an oral commensal, isrelatively abundant in the lungs of CF patients. Recent studies have unveiled the anti-inflammatory effects ofR. mucilaginosausinginvitro3D lung epithelial cell culture andin vivomouse models. Apart from its probiotic effect,R. mucilaginosacan be pathogenic,resulting in severe infections. This dual nature highlights the bacterium’s complexity and diverse impact on health and disease.However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown.Results:To gain insights intoR. mucilaginosa’s cellular metabolism and genetic alterations, we developed the first manually curatedgenome-scale metabolic model,iRM23NL. Through growth kinetic experiments and high-throughput phenotypic microarray testings,we defined its complete catabolic phenome. Subsequently, we assessed the model’s effectiveness in accurately predicting growthbehaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that couldexpedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effecton metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laboriouslarge-scale screening of knock-outs and mutant transposon libraries. Finally, we examined the production levels of enterobactin undervarying nutritional conditions and suggested compounds that could alter its production.Conclusion:Overall,iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as avaluable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide the metabolic engineering totailorR. mucilaginosa’s metabolism for desired performance.Availability and implementation:Source code and model are freely accessible from GitHub and BioModels.
2024-04-10 | MODEL2310240001 | BioModels