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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.
Objectives:
Cefiderocol is a novel last-resort cephalosporin antimicrobial increasingly used for difficult-to-treat infections by multidrug-resistant microorganisms, and is effective against carbapenem-resistant Enterobacterales and Pseudomonas species. Multiple chromosomally encoded genetic determinants have been implicated in cefiderocol resistance, including mutations, deletions and/or frameshifts. However, identification of these determinants remains labour-intensive and time-consuming. Therefore, we share CefiderocolFinder, a bioinformatics pipeline to detect 25 genetic adaptations implicated in cefiderocol resistance from short-read whole-genome sequencing (WGS) data.
Methods:
CefiderocolFinder was built using Python, supports WGS data of Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii, and contains alignment, variant calling, annotation and filtering steps. A short-read WGS dataset (n = 98) and a validation WGS dataset (n = 21) with cefiderocol antimicrobial susceptibility testing (AST) results were used to interpret and validate CefiderocolFinder.
Results:
Using CefiderocolFinder, with WGS data from 98 multidrug-resistant microorganisms collected from Ukrainian patients in 2022, six unique genetic adaptations were detected. These adaptations were associated with higher MICs in AST with cefiderocol. Loss-of-function mutations were found in the siderophore receptor cirA, the general porins oprD, ompC, ompF, negative regulator of the acrAB-tolC efflux operon acrR and a conservative in-frame insertion YRIN in ftsI encoding for penicillin-binding protein 3. The adaptations were identified in 12 of 16 E. coli (75%), 1 of 60 K. pneumoniae (1%), 6 of 17 P. aeruginosa (35%) and 0 of 5 A. baumannii (0%) isolates. CefiderocolFinder was validated using publicly available datasets.
Conclusions:
CefiderocolFinder provides context to corroborate phenotypical AST from WGS data, especially when the result is in an area of technical uncertainty. For E. coli, CefiderocolFinder can be a valuable tool for informing the clinician of specific genetic adaptations associated with resistance to cefiderocol, where for K. pneumoniae and P. aeruginosa the prediction of phenotypical resistance can be improved. CefiderocolFinder is available open access at http://github.com/Bryan-vd-Brand/CefiderocolFinder.
