A hypervirulent Klebsiella pneumoniae (hvKp) pathotype is undergoing global dissemination. In contrast to the usual health care-associated epidemiology of classical K. pneumoniae (cKp) infections, hvKp causes tissue-invasive infections in otherwise healthy individuals from the community, often involving multiple sites.
KEYWORDS: biomarkers, classical Klebsiella pneumoniae, diagnosis, diagnostic test, hypervirulent Klebsiella pneumoniae
ABSTRACT
A hypervirulent Klebsiella pneumoniae (hvKp) pathotype is undergoing global dissemination. In contrast to the usual health care-associated epidemiology of classical K. pneumoniae (cKp) infections, hvKp causes tissue-invasive infections in otherwise healthy individuals from the community, often involving multiple sites. An accurate test to identify hvKp strains is needed for improved patient care and epidemiologic studies. To fill this knowledge gap, clinical criteria or random blood isolates from North American and United Kingdom strain collections were used to assemble hvKp-rich (n = 85) and cKp-rich (n = 90) strain cohorts, respectively. The isolates were then assessed for multiple candidate biomarkers hypothesized to accurately differentiate the two cohorts. The genes peg-344, iroB, iucA, plasmid-borne rmpA gene (prmpA), and prmpA2 all demonstrated >0.95 diagnostic accuracy for identifying strains in the hvKp-rich cohort. Next, to validate this epidemiological analysis, all strains were assessed experimentally in a murine sepsis model. peg-344, iroB, iucA, prmpA, and prmpA2 were all associated with a hazard ratio of >25 for severe illness or death, additionally supporting their utility for identifying hvKp strains. Quantitative siderophore production of ≥30 μg/ml also strongly predicted strains as members of the hvKp-rich cohort (accuracy, 0.96) and exhibited a hazard ratio of 31.7 for severe illness or death. The string test, a widely used marker for hvKp strains, performed less well, achieving an accuracy of only 0.90. Last, using the most accurate biomarkers to define hvKp, prevalence studies were performed on two Western strain collections. These data strongly support the utility of several laboratory markers for identifying hvKp strains with a high degree of accuracy.
INTRODUCTION
Currently, most infections due to Klebsiella pneumoniae in North America and Europe are due to classical K. pneumoniae (cKp) strains and occur primarily in hospitals and long-term-care facilities. cKp strains are of increasing clinical relevance due to their propensity for acquiring plasmids containing numerous antimicrobial resistance determinants, which makes treatment challenging (1–3).
In the mid-1980s and 1990s, reports from Taiwan described a unique clinical syndrome of community-acquired, tissue-invasive K. pneumoniae infection in otherwise healthy individuals that often presented in multiple sites or subsequently spread (metastatic spread) (4, 5). These cases included pyogenic liver abscess in the absence of biliary tract disease, abscesses at nonhepatic sites, pneumonia, endophthalmitis, meningitis, and necrotizing fasciitis (6, 7). To distinguish this pathotype from cKp, the designation hypervirulent K. pneumoniae (hvKp) has been used, and an increasing number of such cases are being reported worldwide (7–9).
Reports on putative hvKp infection have primarily used clinical features and/or a positive string test (using an inoculation loop to generate a viscous string >5 mm in length from a bacterial colony) (10) as the case definition. However, the correspondence between the string test and clinical features observed with hvKp infection is variable, from as low as 51% (11) to 79% (6), 90% (12), and 95% (13) and up to 98% (10). Conversely, among putative cKp isolates positive string test rates of 17% and 23% have been reported (10, 13). This poor specificity is especially problematic in low-prevalence areas.
Clinical criteria to identify hvKp strains and the associated infections also are problematical. A conservative clinical definition that requires the occurrence of a community-acquired, tissue-invasive infection in an otherwise healthy host precludes recognition of hvKp infection in patients who are immunocompromised or in a health care setting and is inapplicable to strain collections that lack clinical data. An accurate diagnostic test that can differentiate between hvKp and cKp strains is needed for optimal clinical care and infection control efforts, epidemiological surveillance for hvKp infections and the associated antimicrobial resistance trends, and diverse other research studies (e.g., to define patient cohorts for treatment trials or studies to determine if there is a genetic susceptibility to hvKp infection).
To date, most putative hvKp strains, as defined primarily by a positive string test, have been antimicrobial susceptible (7). However, the acquisition of extensive or pan-antimicrobial resistance has the potential to create the ultimate superbug. This was accomplished experimentally by the introduction of a K. pneumoniae carbapenemase (KPC)-producing plasmid into an hvKp strain (14) and is now being observed in the clinical venue, with hvKp acquiring genes that encode extended-spectrum β-lactamases and carbapenemases (15–18). Further, an extensively drug resistant (XDR) cKp strain that acquired part of an hvKp virulence plasmid caused a lethal nosocomial outbreak (19).
The hypervirulence of hvKp strains is mediated, in part, by genes on a large virulence plasmid (19–22) or within chromosomal islands (23). We hypothesized that some of these genes (and/or their associated phenotypes) would be accurate markers for hvKp strains. Therefore, we evaluated several genotypic and phenotypic biomarkers for their ability to accurately differentiate putative hvKp from cKp strains (based on an epidemiological analysis and experimental virulence in a murine infection model) and identified several such markers.
MATERIALS AND METHODS
Development of hvKp-rich and cKp-rich strain cohorts.
To identify a biomarker to differentiate hvKp from cKp strains, we chose to use clinical data to develop strain cohorts for evaluation, given that the inclusion of any bacterial genotypic or phenotypic information in the definition of strain cohorts could introduce bias.
The criterion for inclusion of a strain in the hvKp-rich cohort was isolation from a healthy, ambulatory patient with a clinical syndrome of tissue-invasive infection (e.g., hepatic and extrahepatic abscesses, necrotizing fasciitis, or endophthalmitis). The hvKp-rich cohort consisted of 85 strains isolated from deidentified patients from Taiwan and the United States (Table 1). The probable primary infections (number of cases) were hepatic abscess (76), necrotizing fasciitis (3), urinary tract infection (2), pneumonia (1), endophthalmitis (1), tonsillar abscess (1), and osteomyelitis (1). Two or more additional sites of infection were documented in 53% (45/85) of cases; these included (number of cases) endophthalmitis (13), meningitis (6), brain abscess (4), necrotizing fasciitis (10), pneumonia/empyema (5), epidural abscess (1), splenic abscess (1), psoas abscess (1), testicular abscess (1), osteomyelitis (4), and renal abscess (1).
TABLE 1.
Characteristics of the hvKP-rich and cKP-rich strains
| Strain designation (capsule type)a | Site of strain isolation | Primary site of infectionb | Metastatic or additional site(s) | Geographic location |
|---|---|---|---|---|
| hvKP-rich cohort | ||||
| hvKP1 (K2) | Liver | Liver | Spleen | Buffalo, NY, USA |
| hvKP2 (K1) | Blood | Eye | None | Buffalo, NY, USA |
| hvKP3 (K2) | Blood | Liver | None | Minneapolis, MN, USA |
| hvKP4 (K1) | Liver | Liver | Eye | New York, New York, USA |
| hvKP5 (K2) | Blood | Lung | None | Iowa City, IA, USA |
| hvKP6 (K1) | Blood | Liver | None | Niagara Falls, NY, USA |
| hvKP7 (K1) | Liver | Liver | None | Wake Forest, NC, USA |
| hvKP8 (K1) | Blood | Liver | None | San Francisco, CA, USA |
| hvKP9 (K1) | Blood | Liver | Epidural space, psoas muscle | Boston, MA, USA |
| hvKP10 (K1) | Liver | Liver | Leg, cerebellum | Taipei, Taiwan |
| hvKP11 (K1) | Blood | Arm | Supraclavicular region | Taipei, Taiwan |
| hvKP12 (K2) | Blood | Back | Buttock, thigh | Taipei, Taiwan |
| hvKP13 (K1) | Blood | Iliopsoas | Thigh | Taipei, Taiwan |
| hvKP14 (K1) | Blood | Lung | Leg | Taipei, Taiwan |
| hvKP15 (K1) | Blood | Liver | Leg, kidney | Taipei, Taiwan |
| hvKP16 (K54) | Blood | Tonsil | Deep neck | Taipei, Taiwan |
| hvKP17 (K54) | Blood | Urinary tract | Leg | Taipei, Taiwan |
| hvKP18 (K20) | Blood | Urinary tract | Leg | Taipei, Taiwan |
| hvKP19 (K1) | Liver | Liver | None | San Diego, CA, USA |
| hvKP20 (K2) | Blood | Liver | Lung, pleural space | San Diego, CA, USA |
| hvKP21 (K1) | Blood | Liver | None | San Diego, CA, USA |
| hvKP22 (K1) | Blood | Liver | None | Yakima, WA, USA |
| hvKP23 (K2) | Blood | Liver | Soft tissue, brain | Takoma Park, MD, USA |
| hvKP24 (K20) | Blood | Liver | None | St. Paul, MN, USA |
| hvKP25 (K1) | Liver | Liver | None | Wake Forest, NC, USA |
| hvKP26 (K1) | Liver | Liver | Eye | Taipei, Taiwan |
| hvKP27 (K1) | Blood | Liver | Eye | Taipei, Taiwan |
| hvKP28 (K1) | Blood | Liver | Eye, lung, testis | Taipei, Taiwan |
| hvKP29 (K1) | Blood | Liver | Eye, meninges, lung | Taipei, Taiwan |
| hvKP30 (K1) | Blood | Liver | Eye | Taipei, Taiwan |
| hvKP31 (K1) | Blood | Liver | Eye | Taipei, Taiwan |
| hvKP32 (K1) | Blood | Liver | Eye, lumbar spine | Taipei, Taiwan |
| hvKP33 (K1) | Liver | Liver | Eye | Taipei, Taiwan |
| hvKP34 (K1) | Liver | Liver | Eye | Taipei, Taiwan |
| hvKP35 (K1) | Blood | Liver | Eye | Taipei, Taiwan |
| hvKP36 (K1) | Blood | Liver | Eye, cervical spine | Taipei, Taiwan |
| hvKP37 (K1) | Blood | Liver | Meninges, lung | Taipei, Taiwan |
| hvKP38 (K1) | Blood | Liver | Meninges, cervical spine | Taipei, Taiwan |
| hvKP39 (K1) | Blood | Liver | Eye, brain | Taipei, Taiwan |
| hvKP40 (K2) | Liver | Liver | Eye | Taipei, Taiwan |
| hvKP41 (K2) | Blood | Liver | Meninges | Taipei, Taiwan |
| hvKP42 (K1) | Blood | Liver | Meninges, brain | Taipei, Taiwan |
| hvKP43 (K54) | Blood | Liver | Meninges | Taipei, Taiwan |
| hvKP44 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP45 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP46 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP47 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP48 (K1) | Liver | Liver | None | Taipei, Taiwan |
| hvKP49 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP50 (K1) | Liver | Liver | None | Taipei, Taiwan |
| hvKP51 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP52 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP53 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP54 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP55 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP56 (K1) | Liver | Liver | None | Taipei, Taiwan |
| hvKP57 (K1) | Liver | Liver | None | Taipei, Taiwan |
| hvKP58 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP59 (K1) | Blood | Liver | None | Taipei, Taiwan |
| hvKP60 (K2) | Blood | Liver | None | Taipei, Taiwan |
| hvKP61 (K2) | Liver | Liver | None | Taipei, Taiwan |
| hvKP62 (K2) | Blood | Liver | None | Taipei, Taiwan |
| hvKP63 (K2) | Liver | Liver | None | Taipei, Taiwan |
| hvKP64 (K2) | Liver | Liver | None | Taipei, Taiwan |
| hvKP65 (K2) | Blood | Liver | None | Taipei, Taiwan |
| hvKP66 (K2) | Blood | Liver | None | Taipei, Taiwan |
| hvKP67 (K5) | Blood | Liver | None | Taipei, Taiwan |
| hvKP68 (K5) | Blood | Liver | None | Taipei, Taiwan |
| hvKP69 (K20) | Liver | Liver | None | Taipei, Taiwan |
| hvKP70 (K20) | Blood | Liver | None | Taipei, Taiwan |
| hvKP71 (K54) | Liver | Liver | None | Taipei, Taiwan |
| hvKP72 (K54) | Liver | Liver | None | Taipei, Taiwan |
| hvKP73 (K54) | Blood | Liver | None | Taipei, Taiwan |
| hvKP74 (K57) | Blood | Liver | None | Taipei, Taiwan |
| hvKP75 (NT) | Blood | Liver | None | Taipei, Taiwan |
| hvKP76 (NT) | Liver | Liver | None | Taipei, Taiwan |
| hvKP77 (NT) | Blood | Liver | None | Taipei, Taiwan |
| hvKP78 (NT) | Blood | Liver | None | Taipei, Taiwan |
| hvKP79 (K2) | Bone | Femur | Ulna | Dallas, TX, USA |
| hvKP80 (K1) | Liver | Liver | Lung | Ft. Lauderdale, FL, USA |
| hvKP81 (K57) | Blood | Liver | None | Taipei, Taiwan |
| hvKP82 (K54) | Blood | Liver | None | Taipei, Taiwan |
| hvKP83 (K2) | Blood | Liver | None | Taipei, Taiwan |
| hvKP84 (K5) | Blood | Liver | None | Taipei, Taiwan |
| hvKP85 (K5) | Liver | Liver | None | Buffalo, NY, USA |
| cKP-rich cohort | ||||
| cKP1-8 (NT) | Blood | Montreal, QC, Canada | ||
| cKP9-15 (NT) | Blood | Oxford, England, UK | ||
| cKP16-18 (K2) | Blood | Oxford, England, UK | ||
| cKP19-21 (NT) | Blood | Oxford, England, UK | ||
| cKP22 (K57) | Blood | Buffalo, NY, USA | ||
| cKP23-29 (NT) | Blood | Buffalo, NY, USA | ||
| cKP30-40 (NT) | Blood | Montreal, QC, Canada | ||
| cKP41 (K54) | Blood | Montreal, QC, Canada | ||
| cKP42-55 (NT) | Blood | Montreal, QC, Canada | ||
| cKP56 (K54) | Blood | Montreal, QC, Canada | ||
| cKP57 (K20) | Blood | Montreal, QC, Canada | ||
| cKP58-61 (NT) | Blood | Montreal, QC, Canada | ||
| cKP62 (K1) | Blood | Oxford, England, UK | ||
| cKP63-65 (NT) | Blood | Oxford, England, UK | ||
| cKP66 (K20) | Blood | Oxford, England, UK | ||
| cKP67-70 (NT) | Blood | Oxford, England, UK | ||
| cKP71 (K2) | Blood | Oxford, England, UK | ||
| cKP72 (NT) | Blood | Oxford, England, UK | ||
| cKP73 (K57) | Blood | Buffalo, NY, USA | ||
| cKP74-75 (NT) | Blood | Buffalo, NY, USA | ||
| cKP76 (K57) | Blood | Buffalo, NY, USA | ||
| cKP77-78 (NT) | Blood | Buffalo, NY, USA | ||
| cKP79 (K2) | Blood | Buffalo, NY, USA | ||
| cKP80-82 (NT) | Blood | Buffalo, NY, USA | ||
| cKP83 (K2) | Blood | Buffalo, NY, USA | ||
| cKP84-89 (NT) | Blood | Buffalo, NY, USA | ||
| cKP90 (NT) | Blood | Oxford, ENG, UK |
NT, not a K1, K2, K5, K20, K54, or K57 capsule type.
Probable.
Since most K. pneumoniae infections in North America and the United Kingdom presumably are due to cKp strains, the cKp-rich strain cohort (n = 90) was generated from randomly chosen, deidentified blood isolates from (number per site) Montreal, Canada (40), Buffalo, NY (25), and Oxford, United Kingdom (25) (Table 1). Blood isolates were chosen since such strains were likely to represent the more virulent extreme of the pathogenesis spectrum. However, since information regarding clinical manifestations and host characteristics was unavailable, a limitation of this cohort is the potential presence of hvKp strains.
Genotypic biomarkers chosen for evaluation.
Study isolates were assessed for the presence of 10 genes and markers for capsule types. These included several genes located on the virulence plasmid, which have been shown experimentally to contribute to hypervirulence in in vivo infection models, namely, iucA (aerobactin siderophore biosynthesis), the plasmid-borne rmpA gene (prmpA), prmpA2, and the chromosomal gene rmpA (crmpA) (regulators of the mucoid phenotype via increased capsule production), and peg-344 (putative transporter) (24–27). Also included were genes associated epidemiologically with putative hvKp strains, namely, terB (tellurite resistance), iroB (salmochelin siderophore biosynthesis), and irp2 (yersiniabactin siderophore biosynthesis) (22, 28–30). The virulence plasmid-located genes peg-1631 (hypothetical protein) and peg-589 (putative carboxymuconolactone decarboxylase family) were studied. Capsular types K1, K2, K5, K20, K54, and K57, which have been considered markers for hvKp strains (7, 12), also were assessed.
Phenotypic biomarkers chosen for study.
Two hypothetically discriminatory phenotypic traits were evaluated. These included (i) the string test, due to its wide range of reported sensitivity and specificity values and its lack of rigorous assessment using well-defined strain cohorts, and (ii) qualitative and quantitative siderophore production, due to suspected greater siderophore production by hvKp strains than by cKp strains because they synthesize aerobactin (27), which contributes to hypervirulence (26).
PCR assays for biomarkers.
PCR-based detection of the K1, K2, K5, K20, K54, and K57 capsule types was performed as described previously (4, 31). PCR-based detection of the other candidate biomarker genes used (per reaction) 5 μl of 2× TaqFrogga Mix (Frogga Bio, North York, Canada), 0.75 μl of forward primer, 0.75 μl of reverse primer (20 pmol/μl primer stock), 1 μl of genomic DNA (50 ng/μl), and 2.5 μl of water. PCR was performed using an Applied Biosystems GeneAmp PCR system 9700 instrument with the following cycling conditions: step 1, 95.0°C for 2 min; step 2, 95.0°C for 30 s; step 3, primer-specific annealing temperature for 30 s; step 4, 72°C for 1 min; step 5, repeat steps 2 to 4 for 24 cycles; step 6, 72.0°C for 10 min; step 7, hold at 4°C. PCRs were resolved on a 2% agarose gel. As part of the initial development of PCR assays for biomarkers, amplification products were confirmed to contain the correct DNA sequence. Subsequently, a gene was considered present if the band of the predicted size was detected. Specific primers, PCR conditions, and product sizes are listed in Table S1 in the supplemental material.
Siderophore assays.
For the qualitative plate siderophore production assay, Kings B agar plates containing chrome azurol S dye (CAS) were prepared as described previously (32). Cells of each test strain were lifted from an overnight-growth agar plate using a 10-μl pipette tip, stabbed into the Kings B agar, and incubated at 37°C. After overnight growth, formation of an opaque golden-yellow zone around the colony indicted high-level siderophore production (Fig. S1).
For the qualitative solution siderophore production assay, test strains were grown individually overnight at 37°C in M9 iron-chelated M9 minimal medium containing Casamino Acids (c-M9-CA) (24). After centrifugation, supernatant was harvested, and an aliquot was diluted 5-fold in c-M9-CA. Equal volumes of the diluted supernatant and a 98% siderophore assay solution (27) were placed into a well in a flat-bottom 96-well plate. After incubation in the dark for 30 min, development of an orange appearance was scored as positive, and no change in the baseline purple/blue appearance was scored as negative (Fig. S2).
For the quantitative siderophore production assay, which also used culture supernatants from strains grown in c-M9-CA medium, methods were as described previously (27).
Murine sepsis model.
The murine sepsis model was as described previously (26, 33). Animal studies were reviewed and approved by the University at Buffalo, State University of New York (SUNY), and the Veterans Administration Institutional Animal Care Committee. This study was done in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals endorsed by the National Institutes of Health (34), and all efforts were made to minimize suffering. In brief, outbred male CD1 mice (18 to 22 g; n = 5 per group) were injected subcutaneously with various titers of the bacterial strains being assessed. Animals were monitored for up to 14 days for the development of the study endpoint, severe illness (in extremis state), or death, which was recorded as a dichotomous variable. For the hvKp-rich strain cohort, a challenge inoculum of 2 × 103 to 5 × 103 CFU was initially used for all strains, with sequential challenges of 3 × 105 to 5 × 105 CFU and 3 × 107 to 6 × 107 CFU if all animals in the group survived a given challenge inoculum. For the cKp-rich cohort a challenge inoculum of 2 × 103 to 5 × 103 CFU was initially used for all strains, with sequential challenges of 3 × 105 to 5 × 105 CFU for some strains and 3 × 107 to 6 × 107 CFU for all strains if all animals in the group survived a given challenge inoculum.
Statistical analysis.
The associations of the dichotomous genotypic and phenotypic biomarkers with strain cohort were examined by logistic regression and were described using the odds ratios (ORs) and the corresponding 95% confidence intervals (CIs). The performance of the binary genotypic and phenotypic biomarkers in distinguishing between the hvKp-rich and cKp-rich cohorts was summarized by the marker's estimated diagnostic accuracy (number of correctly identified cases divided by the total number of cases), sensitivity, and specificity. Combinations of biomarkers were examined using receiver operating characteristic (ROC) curves to identify the best classifier. The dose level (103, 105, or 107 CFU) needed to induce severe illness or death as a function of phenotypic and genotypic biomarkers was modeled using a proportional odds model (35). If no deaths occurred with the 107 CFU dose, the observation was considered censored at that dose level. The hazard ratio (HR) and corresponding 95% confidence interval were estimated in the univariate modeling. A stepwise model was used to assess combinations of factors associated with severe illness or death by dose levels. The predictive value of the single continuous variable (quantitative siderophore production) was examined using an ROC curve with the estimated diagnostic accuracy given by the area under the ROC curve.
RESULTS
Multiple biomarkers, including peg-344, iroB, iucA, prmpA, and prmpA2, differentiate the hvKp-rich and cKp-rich strain cohorts with high accuracy.
This epidemiological analysis relied on clinical syndromes observed in infected humans to define the strain cohorts that were compared to identify discriminating biomarkers. Five of these biomarkers, all of which were genes, achieved a diagnostic accuracy of ≥0.95 for differentiating the hvKp-rich and cKp-rich strain cohorts, as follows: peg-344 (accuracy, 0.97, sensitivity, 0.99, and specificity 0.96), iroB (accuracy, 0.97; sensitivity, 0.98; specificity, 0.96), iucA (accuracy, 0.96; sensitivity, 0.99; specificity, 0.94), prmpA (accuracy, 0.96; sensitivity, 0.98; specificity, 0.93), and prmpA2 (accuracy, 0.95; sensitivity, 0.93; specificity 0.97). In contrast, none of the studied phenotypic tests achieved a >0.95 accuracy; the qualitative siderophore solution assay had the best performance characteristics (accuracy, 0.93; sensitivity, 0.91; specificity, 0.96) (Tables 2; see also Table S2 in the supplemental material). The string test, which is widely used as a marker for hvKp strains, performed less well, achieving an accuracy of only 0.90 (sensitivity, 0.89; specificity, 0.91), as did the combination of the K1, K2, K5, K20, K54, and K57 capsule types (accuracy, 0.90; sensitivity, 0.93, specificity 0.88). Stepwise multivariate regression analysis indicated that the combination of peg-344 and iucA marginally increased accuracy to 0.98 (sensitivity, 0.94; specificity, 1.0). Biomarker data for individual strains and hvKp-rich and cKp-rich strain cohort totals are listed in Table S2.
TABLE 2.
Performance characteristics of the trait assessed as markers to identify hvKP
| Biomarkera | Accuracy | Sensitivity | Specificity | Odds ratio (95% CI) |
|---|---|---|---|---|
| peg-344-PP2 | 0.97 | 0.99 | 0.96 | 1,806.0 (197.8, 16,493.3) |
| peg-344-PP1 | 0.97 | 0.99 | 0.94 | 1,428.0 (163.4, 12,483.1) |
| iroB-PP1 | 0.97 | 0.98 | 0.96 | 892.3 (159.1, 5002.6) |
| iroB-PP2 | 0.97 | 0.98 | 0.96 | 892.3 (159.1, 5002.6) |
| iucA-PP2 | 0.96 | 0.98 | 0.94 | 705.5 (133.1, 3,738.3) |
| iucA-PP1 | 0.96 | 0.97 | 0.94 | 464.7 (107.6, 2,007.2) |
| prmpA | 0.96 | 0.98 | 0.93 | 581.0 (114.0, 2,961.8) |
| prmpA2 | 0.95 | 0.93 | 0.97 | 381.8 (92.4, 1,578.1) |
| peg-589-PP1 | 0.94 | 0.93 | 0.96 | 283.1 (77.0, 1040.3) |
| peg-589-PP2 | 0.94 | 0.93 | 0.96 | 283.1 (77.0, 1040.3) |
| Qualitative SP solution assay | 0.93 | 0.91 | 0.96 | 206.9 (59.9, 714.4) |
| String test | 0.90 | 0.89 | 0.91 | 86.6 (31.8, 235.8) |
| terB-PP2 | 0.90 | 0.88 | 0.92 | 79.8 (29.4, 216.4) |
| terB-PP1 | 0.89 | 0.87 | 0.92 | 69.0 (26.3, 180.7) |
| peg-1631-PP1 | 0.87 | 0.75 | 0.98 | 134.1 (30.4, 592.4) |
| peg-1631-PP2 | 0.85 | 0.74 | 0.97 | 83.0 (23.8, 289.6) |
| Qualitative SP plate assay | 0.83 | 0.72 | 0.93 | 35.6 (13.7, 92.3) |
| irp2 | 0.79 | 0.79 | 0.79 | 13.9 (6.7, 28.7) |
| crmpA | 0.54 | 0.09 | 0.99 | 9.3 (1.1, 75.6) |
| K1 | 0.77 | 0.55 | 0.98 | 110.1 (14.6, 827.1) |
| K2 | 0.57 | 0.20 | 0.93 | 3.5 (1.31, 9.36) |
| K5 | 0.52 | 0.05 | 1.0 | NDb |
| K20 | 0.51 | 0.05 | 0.97 | 2.17 (0.39, 12.18) |
| K54 | 0.53 | 0.08 | 0.98 | 3.95 (0.80, 19.57) |
| K57 | 0.51 | 0.02 | 0.97 | 0.70 (0.11, 4.29) |
| K1/K2 | 0.84 | 0.75 | 0.92 | 36.14 (14.47, 90.26) |
| All 6 capsule types | 0.90 | 0.93 | 0.88 | 94.56 (33.34, 268.19) |
PP, primer pair (see Table S1 in the supplemental material for details); SP, siderophore production.
ND, not done (unable to calculate).
Multiple biomarkers, including peg-344, iroB, iucA, prmpA, prmpA2, and peg-589, accurately predict mortality in a murine sepsis model.
Although murine infection models mimic human infections imperfectly, it is logical to expect that hvKp strains would be more lethal for mice than cKp strains. Therefore, all strains were assessed in a systemic infection model, with severe illness or death used as the endpoint. The probability that a given biomarker was associated with death in a challenge inoculum (dose)-dependent fashion was calculated.
Although the rank order differed slightly, the results of this analysis concurred closely with those of the epidemiological analysis. That is, seven biomarkers (six genes and one phenotypic trait) were associated individually with a >25-fold-greater likelihood of severe illness or death. These traits (and the associated HR) included peg-344 (64.0), iroB (59.2), prmpA (41.0), peg-589 (40.9), iucA (35.8), a qualitative solution siderophore production assay (33.6), and prmpA2 (31.2) (Table 3 and Fig. 1A). Based on stepwise multivariate regression, no two-marker combination outperformed peg-344 for predicting severe illness or death. Thus, these markers strongly predict a hypervirulent phenotype, additionally supporting their utility for identifying hvKp strains.
TABLE 3.
Hazard ratio of severe illness or death in outbred CD1 mice as a function of genotypic and phenotypic biomarkers
| Biomarkera | Hazard ratio (95% CI) |
|---|---|
| peg-344-PP2 | 64.0 (26.9, 152.3) |
| peg-344-PP1 | 51.7 (22.7, 118.1) |
| iroB-PP1 | 59.2 (25.8, 135.5) |
| iroB-PP2 | 59.2 (25.8, 135.5) |
| prmpA | 41.0 (19.1, 88.2) |
| peg-589-PP1 | 40.9 (20.0, 83.4) |
| peg-589-PP2 | 40.9 (20.0, 83.4) |
| iucA-PP2 | 35.8 (17.3, 74.2) |
| iucA-PP1 | 31.6 (15.8, 63.6) |
| Qualitative SP solution assay | 33.6 (17.3, 65.3) |
| prmpA2 | 31.2 (16.0, 60.7) |
| peg-1631-PP1 | 20.4 (11.5, 36.2) |
| peg-1631-PP2 | 15.4 (9.2, 25.7) |
| string test | 15.5 (8.8, 27.2) |
| terB-PP2 | 15.4 (8.9, 26.9) |
| terB-PP1 | 14.3 (8.3, 24.8) |
| Qualitative SP plate assay | 9.7 (6.0, 15.7) |
| irp2 | 7.8 (4.7, 12.9) |
| crmpA | 2.9 (1.4, 6.0) |
PP, primer pair (see Table S1 in the supplemental material for details); SP, siderophore production.
FIG 1.
The survival of CD1 mice as a function of dose after subcutaneous challenge. Outbred CD1 mice underwent subcutaneous challenge with all strains from the hvKp-rich cohort (n = 85) and the cKp-rich cohort (n = 90). Animals were monitored for up to 14 days for the development of severe illness (in extremis state) or death. (A) Mice challenged with strains that possessed peg-344 had a 64-fold increase in the hazard ratio of severe illness or death compared to the levels in strains that did not. (B) Mice challenged with strains that produced >30 μg/ml of siderophores had a 31.7-fold increase in the hazard ratio of severe illness or death compared to strains that produced <30 μg/ml.
Quantitative siderophore levels are highly predictive of both epidemiological and experimental virulence.
Quantitative siderophore production, the single continuous variable studied, was examined using an ROC curve. According to ROC curve analysis and box plots (Fig. 2A and B), a quantitative siderophore concentration of ≥30 μg/ml strongly predicted membership in the hvKp-rich strain cohort (accuracy, 0.96; sensitivity, 0.96; specificity, 0.94). Similarly, in the murine sepsis model, siderophore concentrations of ≥30 μg/ml were associated with an HR of 31.7 for severe illness or death, compared to strains with concentrations of <30 μg/ml (Fig. 1B).
FIG 2.
A quantitative siderophore concentration of >30 μg/ml is strongly predictive of strains that belong to the hvKp-rich versus the cKp-rich cohort. A quantitative measurement of total siderophore production was performed for all strains from the hvKp-rich cohort (n = 85) and the cKp-rich cohort (n = 90). (A) Data presented as a receiver operating characteristic curve. The point on the curve that corresponds to a concentration of 30 μg/ml is marked. (B) Data are presented as a box plot (minimum, first quartile, median, third quartile, and maximum). The horizontal line indicates a concentration of 30 μg/ml.
Prevalence of hvKp in K. pneumoniae blood isolates from Canada and the United Kingdom.
The prevalence of hvKp as defined by the presence of the four most accurate biomarkers (peg-344, iroB, iucA, and prmpA) was assessed among 179 K. pneumoniae blood isolates (110 from Montreal, Canada, isolated from March 2009 to February 2013, and 69 from Oxford, United Kingdom, isolated from January 2008 to April 2011). The lack of associated clinical data enabled an unbiased assessment. Using these criteria, the prevalences of hvKp were 0.9% (1/110) and 5.8% (4/69) among the Montreal and Oxford isolates, respectively. All of these strains were assessed experimentally for virulence in the murine sepsis model and caused severe illness or death with a challenge dose of only 103 CFU.
DISCUSSION
This study used epidemiological and experimental lines of evidence to identify several laboratory markers for identifying hvKp strains with a high degree of accuracy. The epidemiological analysis compared clinically defined hvKp-rich and cKp-rich strain cohorts for the presence of selected biomarkers. peg-344, iroB, iucA, prmpA, and prmpA2 all identified an isolate to be a member of the hvKp-rich strain cohort with an accuracy of >0.95. The second line of evidence compared clinically defined hvKp-rich and cKp-rich strain cohorts in a murine sepsis model in which hvKp strains were predicted to be more lethal than cKp strains. peg-344, iroB, iucA, prmpA, and prmpA2 were all associated with a hazard ratio of >25 for severe illness or death, additionally supporting their utility for identifying hvKp strains.
These findings are predicted to be generalizable. Although most isolates in the hvKp-rich strain cohort were from Taiwanese patients, 22% were from patients in diverse U.S. locations. Further, the hvKp-rich strain cohort (n = 85) consisted of capsule types representative of putative hvKp strains, including K1 (47 strains, or 55%), K2 (17, or 20%), K5 (4, or 5%), K20 (4, or 5%), K54 (7, or 8%), K57 (2, or 2%), and undefined (4, or 4%) (36).
The high degree of accuracy demonstrated for several genetic biomarkers is unsurprising since these genes likely are linked on the hvKp virulence plasmid, which is responsible, at least in part, for the hypervirulent phenotype. The advantage of genetic biomarkers is that a rapid nucleic acid amplification test can be developed and, if FDA approved, can be utilized by many clinical laboratories for patient care. Currently, peg-344 appears to be hvKp specific and, therefore, has potential utility as a rapid diagnostic test.
Our screen for peg-344, iroB, iucA, and prmpA documented a low prevalence of hvKp strains among K. pneumoniae blood isolates from Montreal, Canada (0.9%), and Oxford, United Kingdom (5.9%). Animal studies supported the finding that the strains identified as hvKp were true positives. However, the most recent isolates in these collections were collected more than 5 years ago. The biomarkers identified in this study can be used to determine prevalence rates in different geographic locations, sites of infection, and time periods or even in real time (to identify possible temporal trends).
Another important study finding was that the string test, which currently is used widely to identify hvKp strains, performed suboptimally. Its accuracy, specificity, and sensitivity of 0.90, 0.89, and 0.91, respectively, were inferior to values for several of the genotypic biomarkers evaluated and for siderophore production. Our data suggest that the string test should not be used as a definitive diagnostic test for hvKp, particularly in low-prevalence areas, where its performance characteristics will lead to substantially more erroneous classifications than with the other more accurate markers identified in this study. Quantitative siderophore production, which was a highly accurate predictor, cannot currently be measured in a straightforward fashion in diagnostic laboratories, but this could potentially be developed.
The genetic composition of pathogens, particularly the accessory genome, can be fluid. It would therefore seem logical that the best genetic marker for hvKp strains would be one that clearly contributes to the hypervirulence phenotype. Here, we demonstrated that total siderophore production strongly correlated with in vivo virulence (Fig. 2). Aerobactin has previously been shown to be the dominant siderophore produced by hvKp strains (26, 27) and to be the critical siderophore that enhances virulence ex vivo and in vivo (26). Since aerobactin is a critical mediator of virulence, it is likely to be a durable biomarker among hvKp strains. We found iucA, one of the genes in the iuc operon encoding aerobactin, to be one of the most accurate genetic markers for differentiating between hvKp and cKp strains; it may therefore represent a stable genetic marker. Further, the addition of peg-344 to iucA increased accuracy to 0.98.
Differentiation of hvKp from cKp strains could significantly impact patient care and lead to improved outcomes. Specifically, accurate identification of hvKp would allow more rapid consideration of possible unrecognized sites of infection, which often manifest as occult abscesses (37). Such infectious foci are likely to require drainage, extended antimicrobial therapy, and, potentially, site-directed treatment (e.g., with meningitis, brain or prostatic abscesses, or endophthalmitis [vitrectomy or intravitreal antibiotics]) (5). Since the hypermucoviscosity of hvKp interferes with definitive percutaneous drainage by clogging the catheter (38), identification of hvKp could alert the clinician to consider using a larger-gauge catheter and more frequent catheter irrigation. The association of hvKp with relapse (39–42), perhaps due to its hypermucoviscous phenotype and biofilm formation (43), suggests that hvKp infections may require prolonged treatment to maximize cure rates and minimize relapse. The ability to differentiate cKp from hvKp would enable the generation of controlled data to address this and other clinical issues.
We along with others (41) have established that close contacts of infected patients may also be colonized by the infecting hvKp strain, subsequently leading to infection. Presently, it is unclear whether empirical prophylactic therapy (as recommended for invasive meningococcal disease [44]), prophylaxis of colonized individuals (either of which may select for antimicrobial-resistant strains and negatively impact outcome), or observation alone is the most appropriate course of action. However, the availability of a test that can reliably identify hvKp would facilitate studies designed to fill this knowledge gap.
The ability to accurately identify hvKp also would facilitate epidemiologic surveillance by researchers or public health laboratories. Antimicrobial resistance in hvKp strains is increasingly prevalent (14, 16), and molecular epidemiologic studies are needed to track the global spread of hvKp strains and emerging resistance trends. Further, although hvKp infections occur in all ethnic groups, even those infections that are acquired in Western countries commonly involve Asians and, to a lesser degree, Hispanics (8). A discriminating test for hvKp is needed to more accurately define high-risk host groups and could be used for genetic investigations of host susceptibility.
This study has some limitations. First, the hvKp-rich cohort was limited to isolates from healthy, community-dwelling ambulatory patients, whereas patients with comorbidities, compromised immunity, barrier breakdown (e.g., wounds, endotracheal tubes, or intravascular devices), and health care contact also can develop hvKp infection (19). Nonetheless, our findings are likely to be applicable also to patients with health care-associated hvKp infection. Second, the study population is relatively small for diagnostic test validation. However, the predictive power of the best markers was extraordinarily strong. This is likely due to the identified biomarkers' linkage on a virulence plasmid or chromosomal virulence island that endows K. pneumoniae with the hypervirulent phenotype. Third, the studied hvKp-rich and cKp-rich strains cohorts were contaminated by a few misclassified strains; the clinical criteria used to define hvKp-rich cohort was imperfect due to an absence of standardization, and other than blood isolates, clinical data were unavailable for the cKp-rich strain cohort. A post hoc analysis supported this consideration and suggested that the estimated accuracy of biomarkers was likely an underestimate due to the presence of some probable cKp strains within the hvKp-rich cohort (strains hvKp77 and hvKp78) and of hvKp strains present within the cKp-rich cohort (strains cKp17, cKp18, cKp57, and cKp62) based on the presence or absence of biomarkers and their performance in the murine sepsis model (see Table S2 in the supplemental material). Last, all clinical tests need to be interpreted within the context of pretest probability. In low-prevalence regions, the absence of the most accurate biomarkers identified in this study would effectively rule out that an isolate is hypervirulent. However, a positive result might overpredict hypervirulence and could require additional support. Nonetheless, from a clinical vantage point, it would be desirable to overcall an isolate as hypervirulent.
In summary, this study identified several biomarkers that are highly accurate in identifying hvKp strains, as defined both epidemiologically and experimentally based on clinically defined strain cohorts and experimentally in a murine sepsis model. These markers should enable a variety of important epidemiologic surveillance studies on the prevalence of hvKp in various populations and on emerging antimicrobial resistance, which could limit treatment options. Additionally, rapid recognition of hvKp in the clinical setting has the potential to improve patient care. Further validation of these differentiating markers should be undertaken in well-characterized patient cohorts with a larger number of strains.
Supplementary Material
ACKNOWLEDGMENTS
This work was supported by NIH 1R21AI123558-01 (T.A.R. and A.H.), Department of Veterans Affairs VA Merit Review (1I01BX000984) (T.A.R.), the University of Oxford/Public Health England Clinical Lectureship (N.S.), the Centers for Disease Control and Prevention cooperative agreement number 1 U50 CK000477 (J.H.B.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Members of the Hypervirulent Klebsiella pneumoniae investigator group are the following: Martin Backer, Yakima, WA, USA; Rajinder Bajwa, Niagara Falls, NY, USA; Andrew T. Catanzaro, Washington, DC, USA; Derrick Crook, Oxford, United Kingdom; Kleper de Almeida, West Palm Beach, FL, USA; Joshua Fierer, San Diego, CA, USA; David E. Greenberg, Dallas, TX, USA; Michael Klevay, St. Paul, MN, USA; Payal Patel, Ann Arbor, MI, USA; Adam Ratner, New York, NY, USA; Jin-Town Wang, Taipei, Taiwan; Jaroslaw Zola, Buffalo, NY, USA.
Footnotes
For a commentary on this article, see https://doi.org/10.1128/JCM.00959-18.
Supplemental material for this article may be found at https://doi.org/10.1128/JCM.00776-18.
Contributor Information
Daniel J. Diekema, University of Iowa College of Medicine.
Collaborators: Martin Backer, Rajinder Bajwa, Andrew T. Catanzaro, Derrick Crook, Kleper de Almeda, Joshua Fierer, David E. Greenberg, Michael Klevay, Payal Patel, Adam Ratner, Jin-Town Wang, and Jaroslaw Zola
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