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Record W4391822583 · doi:10.1128/msphere.00789-23

Dynamics and quantitative contribution of the aminoglycoside 6′- <i>N</i> -acetyltransferase type Ib to amikacin resistance

2024· article· en· W4391822583 on OpenAlexafffund
Ophélie d’Udekem d’Acoz, Fong Hue, Tianyi Ye, Louise Wang, Maxime Leroux, Lucila Rajngewerc, Tung Tran, Kimberly Phan, María Soledad Ramirez, Walter Reisner, Marcelo E. Tolmasky, Rodrigo Reyes‐Lamothe

Bibliographic record

VenuemSphere · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Institute on Minority Health and Health DisparitiesCanadian Institutes of Health ResearchGovernment of CanadaNational Institutes of HealthCanada Foundation for InnovationCanada Research Chairs
KeywordsAmikacinAminoglycosideAcetyltransferaseN-acetyltransferaseDynamics (music)ChemistryType (biology)BiologyBiochemistryPhysicsAntibioticsEcology

Abstract

fetched live from OpenAlex

ABSTRACT Aminoglycosides are essential components in the available armamentarium to treat bacterial infections. The surge and rapid dissemination of resistance genes strongly reduce their efficiency, compromising public health. Among the multitude of modifying enzymes that confer resistance to aminoglycosides, the aminoglycoside 6′- N -acetyltransferase type Ib [AAC(6′)-Ib] is the most prevalent and relevant in the clinical setting as it can inactivate numerous aminoglycosides, such as amikacin. Although the mechanism of action, structure, and biochemical properties of the AAC(6′)-Ib protein have been extensively studied, the contribution of the intracellular milieu to its activity remains unclear. In this work, we used a fluorescent-based system to quantify the number of AAC(6′)-Ib per cell in Escherichia coli , and we modulated this copy number with the CRISPR interference method. These tools were then used to correlate enzyme concentrations with amikacin resistance levels. Our results show that resistance to amikacin increases linearly with a higher concentration of AAC(6′)-Ib until it reaches a plateau at a specific protein concentration. In vivo imaging of this protein shows that it diffuses freely within the cytoplasm of the cell, but it tends to form inclusion bodies at higher concentrations in rich culture media. Addition of a chelating agent completely dissolves these aggregates and partially prevents the plateau in the resistance level, suggesting that AAC(6′)-Ib aggregation lowers resistance to amikacin. These results provide the first step in understanding the cellular impact of each AAC(6′)-Ib molecule on aminoglycoside resistance. They also highlight the importance of studying its dynamic behavior within the cell. IMPORTANCE Antibiotic resistance is a growing threat to human health. Understanding antibiotic resistance mechanisms can serve as foundation for developing innovative treatment strategies to counter this threat. While numerous studies clarified the genetics and dissemination of resistance genes and explored biochemical and structural features of resistance enzymes, their molecular dynamics and individual contribution to resistance within the cellular context remain unknown. Here, we examined this relationship modulating expression levels of aminoglycoside 6′- N -acetyltransferase type Ib, an enzyme of clinical relevance. We show a linear correlation between copy number of the enzyme per cell and amikacin resistance levels up to a threshold where resistance plateaus. We propose that at concentrations below the threshold, the enzyme diffuses freely in the cytoplasm but aggregates at the cell poles at concentrations over the threshold. This research opens promising avenues for studying enzyme solubility’s impact on resistance, creating opportunities for future approaches to counter resistance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.280
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2024
Admission routes2
Has abstractyes

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