Fitness Costs of Antibiotic Resistance Impede the Evolution of Resistance to Other Antibiotics

Chowdhury, Farhan R.; Findlay, Brandon L.

doi:10.1021/acsinfecdis.3c00156

Cited by 10 publications

(3 citation statements)

References 77 publications

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“…The effect of resistance on virulence is unresolved and may depend on the strain, the number and nature of resistance genes, and whether resistance genes on mobile elements are linked with virulence genes ( 33 , 34 ). However, in some cases, antimicrobial resistance may decrease virulence ( 35 – 38 ). Furthermore, and more importantly, there are numerous examples of MDR- and XDR-cKp strains acquiring only a portion of the hvKp-specific virulence plasmid with the resultant pathotype designation of hvKp in the absence of their virulence potential assessed in valid infection models.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Russo,

Alvarado,

Davies

et al. 2024

mBio

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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.

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Section: Discussionmentioning

confidence: 99%

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Russo,

Alvarado,

Davies

et al. 2024

mBio

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“…The effect of resistance on virulence is unresolved and may depend on the strain, the number and nature of resistance genes, and whether resistance genes on mobile elements are linked with virulence genes (32,33). However, in some cases antimicrobial resistance may decrease virulence (34)(35)(36)(37). Further, and more importantly, there are numerous examples of MDR-and XDR-cKp strains acquiring only a portion of the hvKp-specific virulence plasmid with the resultant pathotype designation of hvKp in the absence of their virulence potential assessed in valid infection models.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of hypervirulent and classicalKlebsiella pneumoniaewith acquired drug resistance

Russo,

Alvarado,

Davies

et al. 2023

Preprint

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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 0.955 (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 88% (14/16), a specificity of 94% (31/33), and an overall accuracy of 92% (45/49). 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.

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“…As a thorny problem threatening the public health worldwide, infectious diseases induced by pathogenic bacteria, especially these concerned with the drug-resistance bacteria attributed to the drug-resistance bacteria, has now been regarded as the paradigms gearing considerable research attention. [1][2][3] The drug-resistance bacteria, which is mainly derived from the misuse of antibiotics, have considerably exacerbated the difficulty for the treating of disease related to the bacterial infections. [4,5] The widespread spread of resistance genes make some bacterial strains lose sensitivity to antibiotics on the shelf, greatly increasing the difficulty of treating infection-related diseases, such as the respiratory infections and digestive tract infections.…”

Section: Introductionmentioning

confidence: 99%

Alder‐Longo Method Derived Cationic Porphyrin‐Based Porous Polymer as Physical Antibacterial Agent for the Bacteria‐Infected Wound Synergy Therapy

Li,

Fu,

Chu

et al. 2024

ChemistrySelect

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The widespread of drug‐resistant genes has greatly exacerbated the difficulty for the effectively tre atment of diseases associated with bacterial infections. The ineffectiveness of antibiotics poses great challenges to the development of new antimicrobial agents, especially those does not cause bacterial resistance. Herein, a cation porous organic polymer (CPOP) denoted as BDPP, was facilely prepared via the Alder‐Longo method skipping the tedious porphyrin‐bearing monomer synthesis procedure. The repeated porphyrin units endowed excellent photoactivity with the porous skeletons, and the inherent cations significantly improved the binding ability of BDPP with the bacterial cell membrane. As a result, the as‐synthesized POPs could be used as a physical broad spectrum sterilization agent, amalgamating the cation and photothermal therapy to exert synergistic amplified antibacterial effects. In vivo assay demonstrated BDPP with good biocompatibility, could also significantly promote the healing of wounds with bacteria‐infection. This work paves a facile way for the rational construction of material‐based antibacterial agent for the bacteria inactivation.

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Fitness Costs of Antibiotic Resistance Impede the Evolution of Resistance to Other Antibiotics

Cited by 10 publications

References 77 publications

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Differentiation of hypervirulent and classicalKlebsiella pneumoniaewith acquired drug resistance

Alder‐Longo Method Derived Cationic Porphyrin‐Based Porous Polymer as Physical Antibacterial Agent for the Bacteria‐Infected Wound Synergy Therapy

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