Ciprofloxacin resistance in Klebsiella pneumoniae: phenotype prediction from genotype and global distribution of resistance determinants

Tsang, Kara K., Amke, Iana, Aanensen, David M., Abdulahi, Jabir, Aiken, Alexander and Hassan, Brekhna 2026. Ciprofloxacin resistance in Klebsiella pneumoniae: phenotype prediction from genotype and global distribution of resistance determinants. Genome Medicine 10.1186/s13073-026-01785-w

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Abstract

Background Ciprofloxacin resistant Klebsiella pneumoniae is common or emerging in many geographies, and knowledge of local resistance rates is important for empirical therapy. While K. pneumoniae ciprofloxacin resistance determinants are known, there is a lack of systematic data on the effect of these determinants, alone and in combination, and there are no publicly accessible tools for predicting ciprofloxacin resistance from whole genome sequence data. Methods The KlebNET-GSP AMR Genotype-Phenotype Group aggregated a matched genotype-phenotype dataset of n = 12,167 K. pneumoniae species complex (KpSC) isolates from 27 countries between 2001 and 2021. We developed a rule-based classifier to predict ciprofloxacin resistance by categorizing the number of mutations in the quinolone resistance determining regions of gyrA and parC and the number of plasmid-mediated quinolone resistance genes, including the presence/absence of aac(6')-Ib-cr (which can acetylate ciprofloxacin). Predictive performance was assessed using the discovery dataset, with the reassessment of discrepant and concordant isolates; and validated using externally contributed datasets (n = 7,030 KpSC isolates). Results The rule-based classifier predicted R vs S/I with categorical agreement, sensitivity, and specificity > 96%, and major/very major error rates < 4%. Performance was similar across diverse KpSC sources (human, animal, other), species, and intra-species lineages. External validation of the classifier yielded overall 90.18% categorical agreement [95% confidence interval (CI), 89.49–90.88%], 8.74% major errors [95% CI, 7.41–10.06%], 4.19% very major errors [95% CI, 3.60–4.77%], and 10.14% minor errors [95% CI, 9.44–10.85%]. We implemented the classifier in Kleborate, a command-line tool that is integrated into the Pathogenwatch web platform. Using this to assess the global distribution of ciprofloxacin resistance determinants inKpSC genomes available in Pathogenwatch (n = 31,319, from 109 countries between years 2000–2023), we observed a significant positive association between national quinolone consumption rates and predicted ciprofloxacin resistance (R2 = 0.20, p = 0.004). Conclusion Ciprofloxacin resistance phenotypes can be reasonably predicted from genotype, which is sufficient for informing surveillance. However, unexplained resistance remains and accuracy is insufficient for clinical applications. We demonstrate the value of aggregating genotype-phenotype data to explore resistance mechanisms and develop predictors, but highlight complexities in combining phenotype data from different assays and standards.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Medicine
Additional Information:	Full author list can be seen at https://doi.org/10.1186/s13073-026-01785-w
Publisher:	BioMed Central
Date of First Compliant Deposit:	5 November 2026
Date of Acceptance:	28 September 2026
Last Modified:	05 Nov 2026 12:45
URI:	https://orca.cardiff.ac.uk/id/eprint/190694

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