Escherichia coli
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA255254 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Chronic Pseudomonas aeruginosa infections evades antibiotic therapy and are associated with mortality in cystic fibrosis (CF) patients. We find that in vitro resistance evolution of P.aeruginosa towards clinically relevant antibiotics leads to phenotypic convergence towards distinct states. These states are associated with collateral sensitivity towards several antibiotic classes and encoded by mutations in antibiotic resistance genes, including transcriptional regulator nfxB. Longitudinal analysis of isolates from CF patients reveals similar and defined phenotypic states, which are associated with extinction of specific sub-lineages in patients. In depth investigation of chronic P.aeruginosa populations in a CF patient during antibiotic therapy revealed dramatic genotypic and phenotypic convergence. Notably, fluoroquinolone-resistant subpopulations harboring nfxB mutations were eradicated by antibiotic therapy as predicted by our in vitro data. This study supports the hypothesis that antibiotic treatment of chronic infections can be optimized by targeting phenotypic states associated with specific mutations to improve treatment success in chronic infections.
2018-10-23 | PXD007972 | Pride
Project description:Understanding constraints which shape antibiotic resistance is key for predicting and controlling drug resistance. Here, we performed high-throughput laboratory evolution of Escherichia coli. The transcriptome, resistance, and genomic profiles for the evolved strains in 48 environments were quantitatively analyzed. By analyzing the quantitative datasets through interpretable machine learning techniques, the emergence of low dimensional phenotypic states within the 192 strains was observed. Further analysis revealed the underlying biological processes responsible for the distinct states. We also report a novel constraint which leads to decelerated evolution. These findings bridge the genotypic, gene expression, and drug resistance space, and lead to a comprehensive understanding of constraints for antibiotic resistance.
2020-10-12 | GSE137348 | GEO
Project description:Pseudomonas aeruginosa is a predominant pathogen in chronic lung infections in individuals with cystic fibrosis (CF). Epidemic strains of P. aeruginosa, such as the Liverpool Epidemic Strain (LES), are capable of transferring between CF patients and have been associated with increased hospital visits and antibiotic treatments. Comparative genomics and phenotypic assays have shown that antibiotic resistance profiles differ among LES isolates and that genotype–phenotype associations are difficult to establish for resistance phenotypes in clinical isolates of P. aeruginosa based on these comparisons alone. We compared two LES isolates, LESlike1 and LESB58, and the common laboratory strain P. aeruginosa PAO1 using label-free quantitative proteomics to more accurately predict functional differences between strains. The proteomes of the LES isolates were found to be more similar to each other than to PAO1. However, we also observed a number of differences in the abundance of proteins involved in quorum sensing, virulence, and antibiotic resistance, including in the comparison of LESlike1 and LESB58. Specifically, the proteomic data revealed a higher abundance of proteins involved in polymyxin and aminoglycoside resistance in LESlike1. Minimum inhibitory concentration assays confirmed that LESlike1 has higher resistance to antibiotics from these classes. These findings provide an example of the ability of proteomic data to complement genotypic and phenotypic studies to understand resistance in clinical isolates.
2022-05-09 | PXD025827 | Pride
Project description:We employed a genome-wide microarray approach to obtain a profile of the transcriptional events in ciprofloxacin-treated EPEC shedding light on how ciprofloxacin affects EPEC transcriptional events and growth, aside from resistance mechanisms, and how this bacterium tolerates antibiotic stress.
2015-12-15 | GSE75988 | GEO
Project description:For finding new conditions that show maximum entropy and highest prediction interval, we bound the condition space to explore by making a list of top 98 conditions for MG1655 without genetic perturbations that use 13 most populated stresses (acidic, Ax, Bm, butanol, Cfs, cold, ethanol, heat, Mcn, Nx, hypoxia, osmotic, oxidative) or no stress and 7 most-used carbon sources (Glu 0.4%, Gly 0.4%, Lac 0.4%, Galactose, Arabinose 0.4%, Glucose 0.2%, and Alanine) for M9 medium. Among them, 13 conditions were in Ecomics dataset. For the 85 unexplored conditions, we identify the top 15 conditions that show maximum entropy and highest prediction interval in an adaptive fashion. That is, for each iteration, we find a condition in the list that shows maximum entropy and highest prediction interval from the model that was built from the training data. Since the maximum entropy quantification and prediction interval value are not at the same scale, we bound the two measures between zero and one by min-max normalization for 85 conditions. Then we supplement the predicted expression levels for that candidate condition and repeat the next iteration of the procedure, until we identify all 15 conditions. The initial training data is 2610 profiles of 178 transcription factors. Transcriptome profiling of 45 samples (3 replicates for each condition) for E. coli selected from optimal experiment design for genome-scale model.
2016-09-03 | E-GEOD-73671 | biostudies-arrayexpress
Project description:The rise of antimicrobial resistant pathogens calls for new antibacterial treatments, but potent new compounds are scarce. Development of new antibiotics is difficult, especially against Gram-negative bacteria, as here uptake is strongly hindered by the additional outer membrane. Most antimicrobial agents against Gram-negatives use the porin mediated pathway to cross the outer membrane, which limits the choice of an antibiotic, as it has to fit by size, charge and hydrophilicity. In E. coli, the major porins OmpF and OmpC are associated with antibiotic translocation and therefore also with unspecific antibiotic cross-resistance. In this regard, alternative uptake routes are of interest. We were interested in the uptake opportunities of the small, natural product antibiotic negamycin and thereby found new uptake pathways across the outer membrane of E. coli. Besides OmpF and OmpC, we investigated the role of the minor porins OmpN and ChiP in negamycin translocation. We detected an effect of OmpN and ChiP on negamycin susceptibility and confirmed passage by electrophysiological assays. The structure of OmpN was resolved in order to analyze the negamycin translocation mechanism by computational simulations. As abundancy of these minor porins was low in E. coli, their transcript levels were analyzed by RNA-Seq. Increased transcripts levels of ompN and chiP were observed upon negamycin treatment, hinting at a role in antibiotic uptake. These new, additional uptake pathways across the outer membrane of E. coli highlight the antibiotic potential of negamycin, especially as resistance development is low due to availability of multiple uptake routes at both the outer and inner membranes
2023-10-01 | GSE183363 | GEO
Project description:Comparison of the whole genome gene expression level of an amoxicillin resistant E. coli strain with the wildtype it was derived from. The process of amoxicillin adaptation of E. coli MG1655 wildtype cells is further descibed in van der Horst, M, J.M. Schuurmans, M. C. Smid, B. B. Koenders, and B. H. ter Kuile (2011) in Microb. Drug Resist. 17:141-147. Resistance to amoxicillin was induced in E. coli by growth in the presence of stepwise increasing antibiotic concentrations. To investigate consequences of the aquisition of amoxicillin resistance the transcriptomic profile of sensitive and resistant cells was compared in the absence and presence of sub-inhibitory (0.25xMIC) amoxicillin concentrations was compared.
2013-05-27 | GSE47221 | GEO
Project description:Antimicrobial resistance (AMR) is an increasing challenge for therapy and management of bacterial infections. Currently, antimicrobial resistance detection relies on phenotypic assays, which are performed independently of species identification. On the contrary, phenotypic prediction from molecular data using genomics is gaining interest in clinical microbiology and might become a serious alternative in the future. Although, in general protein analysis should be superior to genomics for phenotypic prediction, no untargeted proteomics workflow specifically related to AMR detection has been proposed so far. In this study, we present a universal proteomics workflow to detect the bacterial species and antimicrobial resistance related proteins in the absence of secondary antibiotic cultivation in less than 4 h from a primary culture. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms which resulted in a sensitivity of 92 % (100 % with vancomycin inference) and a specificity of 100 % with respect to AMR determinants. This proof-of concept study demonstrates the high potential of untargeted proteomics for clinical microbiology.
2021-11-25 | PXD022425 | Pride
Project description:Collateral Sensitivity Associated with Antibiotic Resistance Plasmids
| PRJNA644278 | ENA
Project description:Urinary tract infections (UTIs) represent a major burden across the population, although key facets of their pathogenesis challenge physicians and investigators alike. Escherichia coli epitomizes these obstacles: this Gram-negative bacterial species is the most prevalent agent of UTIs worldwide and can also colonize the urogenital tract in a phenomenon known as asymptomatic bacteriuria (ASB). Unfortunately, at the level of the organism, the relationship between symptomatic UTI and ASB is poorly defined, confounding our understanding of microbial pathogenesis and strategies for clinical management. Unlike diarrheagenic pathotypes of E. coli, the definition of uropathogenic E. coli (UPEC) remains phenomenologic, without conserved phenotypes and (known) genetic determinants that rigorously distinguish UTI- and ASB-associated strains. This manuscript provides a cross-disciplinary review of the current issues – from interrelated mechanistic and diagnostic perspectives – and describes new opportunities by which clinical resources can be leveraged to overcome molecular challenges. Specifically, we present our work harnessing a large collection of patient-derived isolates to identify features that do (and do not) distinguish UTI- from ASB-associated E. coli strains. Analyses of biofilm formation, previously reported to be higher in ASB strains, revealed extensive phenotypic heterogeneity that did not correlate with symptomatology. However, metabolomic experiments revealed distinct signatures between ASB and cystitis isolates, including species in the purine pathway (previously shown to be critical for intracellular survival during acute infection). Together, these studies demonstrate how large-scale, wild-type approaches can help dissect the physiology of colonization-versus-infection, suggesting that the molecular definition of UPEC may rest at the level of global bacterial metabolism.
2019-08-14 | ST001244 | MetabolomicsWorkbench