Fusion plasmid enhanced the endemic extensively drug resistant Klebsiella pneumoniae clone ST147 harbored blaOXA-48 to acquire the hypervirulence and cause fatal infection

Pathogenomics analysis of high-risk clone ST147 multidrug-resistant Klebsiella pneumoniae isolated from a patient in Egypt

BACKGROUND

The emergence of multi-drug-resistant Klebsiella pneumoniae (MDR-KP) represents a serious clinical health concern. Antibiotic resistance and virulence interactions play a significant role in the pathogenesis of K. pneumoniae infections. Therefore, tracking the clinical resistome and virulome through monitoring antibiotic resistance genes (ARG) and virulence factors in the bacterial genome using computational analysis tools is critical for predicting the next epidemic.

METHODS

In the current study, one hundred extended spectrum β-lactamase (ESBL)-producing clinical isolates were collected from Mansoura University Hospital, Egypt, in a six-month period from January to June 2022. One isolate was selected due to the high resistance phenotype, and the genetic features of MDR-KP recovered from hospitalized patient were investigated. Otherwise, the susceptibility to 25 antimicrobials was determined using the DL Antimicrobial Susceptibility Testing (AST) system. Whole genome sequencing (WGS) using Illumina NovaSeq 6000 was employed to provide genomic insights into K. pneumoniae WSF99 clinical isolate.

RESULTS

The isolate K. pneumoniae WSF99 was phenotypically resistant to the antibiotics under investigation via antibiotic susceptibility testing. WGS analysis revealed that WSF99 total genome length was 5.7 Mb with an estimated 5,718 protein-coding genes and a G + C content of 56.98 mol%. Additionally, the allelic profile of the WSF99 isolate was allocated to the high-risk clone ST147. Furthermore, diverse antibiotic resistance genes were determined in the genome that explain the high-level resistance phenotypes. Several β-lactamase genes, including blaCTX-M-15, blaTEM-1, blaTEM-12, blaSHV-11, blaSHV-67, and blaOXA-9, were detected in the WSF99 isolate. Moreover, a single carbapenemase gene, blaNDM-5, was predicted in the genome, positioned within a mobile cassette. In addition, other resistance genes were predicted in the genome including, aac(6')-Ib, aph(3')-VI, sul1, sul2, fosA, aadA, arr-2, qnrS1, tetA and tetC. Four plasmid replicons CoIRNAI, IncFIB(K), IncFIB(pQil), and IncR were predicted in the genome. The draft genome analysis revealed the occurrence of genetic mobile elements positioned around the ARGs, suggesting the ease of dissemination via horizontal gene transfer.

CONCLUSIONS

This study reports a comprehensive pathogenomic analysis of MDR-KP isolated from a hospitalized patient. These findings could be relevant for future studies investigating the diversity of antimicrobial resistance and virulence in Egypt.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

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.

ST218 Klebsiella pneumoniae became a high-risk clone for multidrug resistance and hypervirulence

BACKGROUND

The occurrence of multidrug-resistant and hypervirulent Klebsiella pneumoniae (MDR-hvKp) worldwide poses a great challenge for public health. Few studies have focused on ST218 MDR-hvKp.

METHODS

Retrospective genomic surveillance was conducted at the Peking University Third Hospital from 2017 and clinical information was obtained. To understand genomic and microbiological characteristics, antimicrobial susceptibility testing, plasmid conjugation and stability, biofilm formation, serum killing, growth curves and whole-genome sequencing were performed. We also assessed the clinical and microbiological characteristics of ST218 compared with ST23.

RESULTS

A total of eleven ST218 Kp isolates were included. The most common infection type was lower respiratory tract infection (72.7%, 8/11) in our hospital, whereas ST23 hvKp (72.7%, 8/11) was closely associated with bloodstream infection. Notably, nosocomial infections caused by ST218 (54.5%, 6/11) was slightly higher than ST23 (36.4%, 4/11). All of the ST218 and ST23 strains presented with the virulence genes combination of iucA + iroB + peg344 + rmpA + rmpA2. Interestingly, the virulence score of ST218 was lower than ST23, whereas one ST218 strain (pPEKP3107) exhibited resistance to carbapenems, cephalosporins, β-lactamase/inhibitors and quinolones and harbored an ~ 59-kb IncN type MDR plasmid carrying resistance genes including blaNDM-1, dfrA14 and qnrS1. Importantly, blaNDM-1 and qnrS1 were flanked with IS26 located within the plasmid that could successfully transfer into E. coli J53. Additionally, PEKP2044 harbored an ~ 41-kb resistance plasmid located within tetA indicating resistance to doxycycline.

CONCLUSION

The emergence of blaNDM-1 revealed that there is great potential for ST218 Kp to become a high-risk clone for MDR-hvKp, indicating the urgent need for enhanced genomic surveillance.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

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.

Whole-Genome Sequencing Revealed the Fusion Plasmids Capable of Transmission and Acquisition of Both Antimicrobial Resistance and Hypervirulence Determinants in Multidrug-Resistant Klebsiella pneumoniae Isolates

Klebsiella pneumoniae, a member of the Enterobacteriaceae family, has become a dangerous pathogen accountable for a large fraction of the various infectious diseases in both clinical and community settings. In general, the K. pneumoniae population has been divided into the so-called classical (cKp) and hypervirulent (hvKp) lineages. The former, usually developing in hospitals, can rapidly acquire resistance to a wide spectrum of antimicrobial drugs, while the latter is associated with more aggressive but less resistant infections, mostly in healthy humans. However, a growing number of reports in the last decade have confirmed the convergence of these two distinct lineages into superpathogen clones possessing the properties of both, and thus imposing a significant threat to public health worldwide. This process is associated with horizontal gene transfer, in which plasmid conjugation plays a very important role. Therefore, the investigation of plasmid structures and the ways plasmids spread within and between bacterial species will provide benefits in developing prevention measures against these powerful pathogens. In this work, we investigated clinical multidrug-resistant K. pneumoniae isolates using long- and short-read whole-genome sequencing, which allowed us to reveal fusion IncHI1B/IncFIB plasmids in ST512 isolates capable of simultaneously carrying hypervirulence (iucABCD, iutA, _prmpA, peg-344) and resistance determinants (armA, bla_NDM-1 and others), and to obtain insights into their formation and transmission mechanisms. Comprehensive phenotypic, genotypic and phylogenetic analysis of the isolates, as well as of their plasmid repertoire, was performed. The data obtained will facilitate epidemiological surveillance of high-risk K. pneumoniae clones and the development of prevention strategies against them.