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

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 ( Kp SC) 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 Kp SC 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 Kp SC 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 in Kp SC 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 (R 2 = 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.

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Publication Details

Journal
Genome Medicine
Published
2026-10-27
DOI
https://doi.org/10.1186/s13073-026-01785-w
Primary Topic
Antibiotic Resistance in Bacteria
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article
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article

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

Nicholas Feasey, Margaret M. C. Lam, Benjamin P. Howden, Niclas Raffelsberger et al.
Genome Medicine
Antibiotic Resistance in Bacteria
article

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

Nicholas Feasey, Margaret M. C. Lam, Benjamin P. Howden, Niclas Raffelsberger, Maria Laura Ferrando, Refath Farzana, Federico Di Marco, Alicia Fajardo Lubián, Andrew G. McArthur, Jukka Corander, Val F. Lanza, Edward J. Feil, David Leslie Paterson, Nicola D’Alterio, Marisa Haenni, Appiah-Korang Labi, Odion O. Ikhimiukor, Gherard Batisti Biffignandi, Melese Hailu Legese, Vo Thi Trang Dai, Iruka N. Okeke, Yukino Gütlin, Stephen G. Baker, Oliver Pearse, Adriano de Souza Santos Monteiro, Thongpan Leangapichart, Brian M. Forde, Ebenezer Foster-Nyarko, Claire L. Gorrie, Aline Cuénod, Kara K. Tsang, Patrick Musicha, Le Nguyen Minh Hoa, Rafael Cantón, My H. Pham, Wataru Hayashi, Adane Mihret, Eva Heinz, Michael A. Bachman, Pilar Donado-Godoy, Giovanni Lorenzin, Samuel Lipworth, Bernd Neumann, Ilhem Boutiba-Ben Boubaker, Fernando Lázaro‐Perona, Teresa M. Coque, Amy J. Mathers, Taane Gregory Clark, Jonathan R. Iredell, Adrian Egli, P. Harris, Sylvain Brisse, Nubwa Medugu, Shizuo Kayama, Sophia David, Francesco Pomilio, Marit Andrea Klokkhammer Hetland, Mae Newton-Foot, Jennifer Cornick, Leah Wendy Roberts, Geetha Nagaraj, Alessandra Cornacchia, João Perdigão, Norikazu Kitamura, Håkon Pedersen Kaspersen, Alexandra Chiaverini, Sandra Reuter, Andriniaina Rakotondrasoa, Katherine Barry, Marta Corbella, Marta Hernández-García, Sally R. Partridge, Małgorzata Ligowska-Marzęta, Aasmund Fostervold, Anderson O. Oaikhena, Iren Høyland Löhr, Adam W. J. Jenney, Daniela María Cirillo, David M. Aanensen, Joice Neves Reis, K L Ravikumar, Luis G. C Pacheco, Alexander Aiken, Tommaso Giani, Fahad Khokhar, Nguyen Thi Hoa, Jenny Draper, Brekhna Hassan, Susana Campino, Dana Itani, Jabir Abdulahi, Le Thi Lien, Iana Amke, Emília Ma. M. de Andrade Belitardo, Chanté Fortuin, Annapaula Correia, Iván Bloise, S. Wesley Long, Carla Rodrigues
article en

Abstract

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 ( Kp SC) 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 Kp SC 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 Kp SC 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 in Kp SC 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 (R 2 = 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.

Genome Medicine
Good health and well-being
Openalex Percentile: Top 22%
Antibiotic Resistance in Bacteria
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