RESUMO
BACKGROUND: Quantitating the contribution of phenotype-responsible elements in hypervirulent Klebsiella pneumoniae is needed. METHODS: Isogenic mutants of four hypervirulent clinical isolates that produced K1 (ST23), K2 (ST86), K20 (ST1544), or K54 (ST29) capsules (mean 2.2 log10 LD50 (range 1.5-2.9)) were created to measure the effects on LD50 in a murine model of the hypervirulence-associated plasmid (pVir), iucA, prmpA, prmpA2 (truncated), irp2, and clbBC. FINDINGS: Curing pVir had the greatest increase in survival (mean LD50 to 7.6 (range 7.0-9.0, p ≤ 0.0001), a dosage comparable to classical K. pneumoniae. Results also showed increased mean LD50s for ΔprmpA (5.9, p ≤ 0.0001), ΔiucA (3.6, p ≤ 0.0001), Δirp2 (3.4), ΔrmpAΔiucA (6.3, p ≤ 0.0001), and ΔpVirΔirp2 (8.7, p ≤ 0.0001). Notably ΔpVir had an additional mean LD50 increase of 1.3 compared to the pVir-encoded ΔprmpAΔiucA (p ≤ 0.01), suggesting presence of additional pVir-virulence genes. Truncated pRmpA2 did not contribute to virulence. Odd ratios in the absence of pVir/yersiniabactin, pVir, pRmpA/aerobactin, pRmpA, aerobactin, yersiniabactin, and colibactin demonstrated a 250-fold, 67-fold, 20-fold, 16.7-fold, 9.6-fold, and 1.7-fold decrease in lethality respectively. INTERPRETATION: These data can guide countermeasure development. FUNDING: This work was supported by NIH R21 AI123558-01 and 1R21AI141826-01A1 (Dr. Russo) and the Department of Veterans Affairs VA Merit Review (I01 BX004677-01) (Dr. Russo). This study was also partially funded by the U.S. Defense Health Program (DHP) Operations and Maintenance.
RESUMO
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.IMPORTANCEHypervirulent 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.
Assuntos
Infecções por Klebsiella , Klebsiella pneumoniae , Humanos , Animais , Camundongos , Infecções por Klebsiella/epidemiologia , Biomarcadores , Antibacterianos/farmacologia , Antibacterianos/uso terapêutico , SideróforosRESUMO
Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combination 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. Further, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combination 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 5 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.