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CefiderocolFinder: a tool for detecting genetic adaptations implicated in cefiderocol resistance.
Bryan van den Brand1, Daan W Notermans2, Nelianne J Verkaik3
1Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands; Department of Medical Microbiology and Infection Control, University of Groningen, University Medical Center Groningen, The Netherlands.
A new bioinformatics tool, CefiderocolFinder, rapidly identifies genetic adaptations conferring cefiderocol resistance in multidrug-resistant microorganisms (MDRO). This aids in understanding treatment failures and improving clinical decisions for difficult-to-treat infections.
Area of Science:
- Microbiology and Infectious Diseases
- Bioinformatics and Computational Biology
- Antimicrobial Resistance
Background:
- Cefiderocol is a critical last-resort antibiotic for multidrug-resistant microorganism (MDRO) infections.
- Genetic determinants of cefiderocol resistance are complex and challenging to identify.
- Rapid detection of resistance mechanisms is crucial for effective treatment strategies.
Purpose of the Study:
- To develop and validate CefiderocolFinder, a bioinformatics pipeline for detecting genetic adaptations associated with cefiderocol resistance.
- To analyze whole genome sequencing (WGS) data for rapid identification of resistance determinants.
- To correlate genetic findings with antimicrobial susceptibility testing (AST) results.
Main Methods:
- CefiderocolFinder was developed in Python, incorporating alignment, variant calling, annotation, and filtering steps.
- The pipeline analyzes short-read WGS data from key pathogens like *Escherichia coli*, *Klebsiella pneumoniae*, *Pseudomonas aeruginosa*, and *Acinetobacter baumannii*.
- Validation was performed using WGS datasets with known cefiderocol AST results.
Main Results:
- Six unique genetic adaptations linked to increased cefiderocol minimum inhibitory concentrations (MICs) were identified in MDRO isolates.
- Loss-of-function mutations were observed in genes such as *cirA*, *oprD*, *ompC*, *ompF*, *acrR*, and *ftsI*.
- CefiderocolFinder detected resistance-associated adaptations in 75% of *E. coli* and 35% of *P. aeruginosa* isolates, with lower prevalence in *K. pneumoniae* and none in *A. baumannii*.
Conclusions:
- CefiderocolFinder provides valuable genetic context to phenotypical AST results, particularly in cases of technical uncertainty.
- The tool can inform clinicians about specific genetic resistance mechanisms in *E. coli*, improving treatment guidance.
- CefiderocolFinder enhances the prediction of cefiderocol resistance for *K. pneumoniae* and *P. aeruginosa*.
- The CefiderocolFinder pipeline is openly accessible for research and clinical application.
