Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Abstract

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g., siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Furthermore, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K . pneumoniae strains that possessed some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) ( N = 16) or cKp ( N = 33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid’s Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression, the area under the curve for biomarker count was 0.962 ( P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49), respectively. These findings can be used to inform the identification of hvKp. IMPORTANCE Hypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all five of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which, in turn, would assist in optimizing patient care.

Phenotypic and genomic analysis of bacteria from war wounds in Dnipro, Ukraine

“…MRSN 122313 was susceptible to 27 of the 28 antibiotics but belonged to the well-known hypervirulence lineage ST23 (KL1), had a Kleborate score 14 of 5, and carried all five virulence markers that best predict a hypervirulent phenotype ( iucA , iroB , peg-344 , rmpA and rmpA2 ). 15 In contrast, the ST392 (KL27) MRSN 122289 was an ESBL-producer ( bla CTX-M-15 ), was susceptible to 19 of the 28 antibiotics tested, had a Kleborate score of 1, and only carried the ybt16 gene (encoding a yersiniabactin siderophore) on a chromosomal ICE Kp12 . This lineage is globally distributed and has been linked to infections in humans and companion animals.…”

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…These models coupled with logistic regression have been successfully used for identification of strong resistance predictors in Klebsiella pneumoniae. Such models may guide diagnosis and treatment selection in clinical practice [115]. Analysis of genomic data with the implementation of machine learning has allowed predictoin of resistance to polymyxins in Klebsiella pneumoniae [116].…”

Detection of carbapenemase producing Acinetobacter baumannii ST19 from Georgia and Ukraine carrying blaOXA-23, blaOXA-72, and/or blaNDM-5, December 2019 to June 2023

“…Long-read sequencing was performed on seven representative isolates from SL1, -2 and -3 ( Table ), as previously described [ 7 ]. Based on rep typing [ 8 ], circularised plasmids belonged to two groups, R3 and RP, except for three cryptic plasmids that carried no resistance genes ( Table ).…”