Ontology highlight
ABSTRACT:
PROVIDER: PRJNA665848 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13037900_1.fastq.gz | Fastqsanger.gz | |||
| SRR13037900_2.fastq.gz | Fastqsanger.gz | |||
| SRR13037901_1.fastq.gz | Fastqsanger.gz | |||
| SRR13037901_2.fastq.gz | Fastqsanger.gz | |||
| SRR13037902_1.fastq.gz | Fastqsanger.gz |
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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.
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Project description:Metabolic mutations induce antibiotic resistance
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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.
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Project description:Bacterial evolution of antibiotic resistance frequently has deleterious side effects on microbial growth, virulence, and susceptibility to other antimicrobial agents. However, it is unclear how these trade-offs could be utilized for manipulating antibiotic resistance in the clinic, not least because the underlying molecular mechanisms are poorly understood. Using laboratory evolution, we demonstrate that clinically relevant resistance mutations in Escherichia coli constitutively rewire a large fraction of the transcriptome in a repeatable and stereotypic manner. Strikingly, lineages adapted to functionally distinct antibiotics and having no resistance mutations in common show a wide range of parallel gene expression changes that alter oxidative stress response, iron homeostasis, and the composition of the bacterial outer membrane and cell surface. These common physiological alterations are associated with changes in cell morphology and enhanced sensitivity to antimicrobial peptides. Finally, the constitutive transcriptomic changes induced by resistance mutations are largely distinct from those induced by antibiotic stresses in the wild-type. This indicates a limited role for genetic assimilation of the induced antibiotic stress response during resistance evolution. Our work suggests that diverse resistance mutations converge on similar global transcriptomic states that shape genetic susceptibility to antimicrobial compounds.
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Project description:Antibiotic resistance is a growing threat, underscoring the need to understand the underlying mechanisms. Aminoglycosides kill bacteria by disrupting translation fidelity, leading to the synthesis of aberrant proteins. Surprisingly, mutations in fusA, a gene encoding translation elongation factor G (EF-G), frequently confer resistance, even though EF-G neither participates in mRNA decoding nor blocks aminoglycoside binding. Here, we show that EF-G resistance variants selectively slow ribosome movement along mRNA when aminoglycoside is bound. This delay increases the chance that the drug dissociates before misreading occurs. Over several elongation cycles, this selective silencing of drug-bound ribosomes prevents error clusters formation, preserving proteome and membrane integrity. As a result, fusA mutations confer resistance early in treatment by preventing self-promoted aminoglycoside uptake. Translation on drug-free ribosomes remains sufficiently rapid to sustain near-normal bacterial growth. This previously unrecognized resistance mechanism—selective silencing of corrupted targets—reveals a novel antibiotic resistance strategy with potential therapeutic implications.
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Project description:Heinemann2005 - Genome-scale reconstruction
of Staphylococcus aureus (iMH551)
This model is described in the article:
In silico genome-scale
reconstruction and validation of the Staphylococcus aureus
metabolic network.
Heinemann M, Kümmel A,
Ruinatscha R, Panke S.
Biotechnol. Bioeng. 2005 Dec; 92(7):
850-864
Abstract:
A genome-scale metabolic model of the Gram-positive,
facultative anaerobic opportunistic pathogen Staphylococcus
aureus N315 was constructed based on current genomic data,
literature, and physiological information. The model comprises
774 metabolic processes representing approximately 23% of all
protein-coding regions. The model was extensively validated
against experimental observations and it correctly predicted
main physiological properties of the wild-type strain, such as
aerobic and anaerobic respiration and fermentation. Due to the
frequent involvement of S. aureus in hospital-acquired
bacterial infections combined with its increasing antibiotic
resistance, we also investigated the clinically relevant
phenotype of small colony variants and found that the model
predictions agreed with recent findings of proteome analyses.
This indicates that the model is useful in assisting future
experiments to elucidate the interrelationship of bacterial
metabolism and resistance. To help directing future studies for
novel chemotherapeutic targets, we conducted a large-scale in
silico gene deletion study that identified 158 essential
intracellular reactions. A more detailed analysis showed that
the biosynthesis of glycans and lipids is rather rigid with
respect to circumventing gene deletions, which should make
these areas particularly interesting for antibiotic
development. The combination of this stoichiometric model with
transcriptomic and proteomic data should allow a new quality in
the analysis of clinically relevant organisms and a more
rationalized system-level search for novel drug targets.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180072.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2015-07-30 | MODEL1507180072 | BioModels
Project description:In this translational research study, Formalin-Fixed-Paraffin-Embedded (FFPE) tumor tissue blocks from patients with early-stage (II-III) colorectal cancer will be assessed for a comprehensive cancer gene panel from NIPD Genetics (https://www.nipd.com/) targeting regions in 37 clinically relevant cancer genes.
The colorectal cancer panel includes an extended list of clinically relevant genes, designed to target clinically actionable and clinically significant mutations that will provide physicians with genetic information regarding a) prediction of the patient’s response to targeted therapy, b) prognosis, that is, prediction of clinical outcome, c) diagnosis and molecular classification of colorectal cancer.
| 2375757 | ecrin-mdr-crc
Project description:Understanding antibiotic resistance is essential for combating drug-resistant pathogens. Many bacteria horizontally acquire enzymes that modify antibiotics or their targets to prevent interaction, but when resistance emerges without such modifications, the underlying mechanisms often remain unknown. Here, we describe how mutations in the fusA gene, which encodes translation elongation factor G (EF-G), drive resistance against aminoglycoside antibiotics. fusA resistance mutations, found throughout EF-G, confer resistance without disrupting AGA binding to the ribosome. EF-G resistance variants selectively slow down translocation on aminoglycoside-bound ribosomes until the drug dissociates. This slowdown prevents antibiotic-induced incorporation of consecutive incorrect amino acids (error clusters) into proteins, preserving the proteome and membrane integrity, and limiting drug uptake. In contrast, translocation on antibiotic-free ribosomes remains sufficiently rapid to support near-normal cell growth with or without antibiotic. This previously unrecognized resistance concept—selective silencing of corrupted targets—offers new insights into antibiotic resistance mechanisms beyond traditional paradigms.
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