Identification of Biomarkers for Differentiation ofHypervirulent Klebsiella pneumoniae from ClassicalK. pneumoniae

Thomas A. Russo,a,b,c Ruth Olson,a,c Chi-Tai Fang,d,e Nicole Stoesser,f,g Mark Miller,h Ulrike MacDonald,a,c Alan Hutson,i

Jason H. Barker,j Ricardo M. La Hoz,k James R. Johnson,l for the Hypervirulent Klebsiella pneumoniae InvestigatorGroup (HVKPIG)

aDepartment of Medicine, University at Buffalo, State University of New York, Buffalo, New York, USAbDepartment of Microbiology and Immunology, The Witebsky Center for Microbial Pathogenesis, University atBuffalo, State University of New York, Buffalo, New York, USA

cVeterans Administration Western New York Healthcare System, Buffalo, New York, USAdInstitute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, TaiwaneDepartment of Internal Medicine, National Taiwan University Hospital, Taipei, TaiwanfNuffield Department of Medicine, University of Oxford, Oxford, United KingdomgModernising Medical Microbiology Consortium, University of Oxford, Oxford, United KingdomhDepartment of Medicine, McGill University, Montreal, CanadaiDepartment of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, New York, USAj Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USAkDepartment of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USAlMinneapolis VA Health Care System and University of Minnesota, Minneapolis, Minnesota, USA

ABSTRACT A hypervirulent Klebsiella pneumoniae (hvKp) pathotype is undergoingglobal dissemination. In contrast to the usual health care-associated epidemiology ofclassical K. pneumoniae (cKp) infections, hvKp causes tissue-invasive infections inotherwise healthy individuals from the community, often involving multiple sites.An accurate test to identify hvKp strains is needed for improved patient care andepidemiologic studies. To fill this knowledge gap, clinical criteria or randomblood isolates from North American and United Kingdom strain collections wereused to assemble hvKp-rich (n � 85) and cKp-rich (n � 90) strain cohorts, re-spectively. The isolates were then assessed for multiple candidate biomarkers hy-pothesized to accurately differentiate the two cohorts. The genes peg-344, iroB,iucA, plasmid-borne rmpA gene (prmpA), and prmpA2 all demonstrated �0.95 di-agnostic accuracy for identifying strains in the hvKp-rich cohort. Next, to validatethis epidemiological analysis, all strains were assessed experimentally in a murinesepsis model. peg-344, iroB, iucA, prmpA, and prmpA2 were all associated with ahazard ratio of �25 for severe illness or death, additionally supporting their utilityfor identifying hvKp strains. Quantitative siderophore production of �30 �g/ml alsostrongly predicted strains as members of the hvKp-rich cohort (accuracy, 0.96) andexhibited a hazard ratio of 31.7 for severe illness or death. The string test, a widelyused marker for hvKp strains, performed less well, achieving an accuracy of only0.90. Last, using the most accurate biomarkers to define hvKp, prevalence stud-ies were performed on two Western strain collections. These data strongly sup-port the utility of several laboratory markers for identifying hvKp strains with ahigh degree of accuracy.

KEYWORDS biomarkers, classical Klebsiella pneumoniae, diagnosis, diagnostic test,hypervirulent Klebsiella pneumoniae

Received 9 May 2018 Returned formodification 31 May 2018 Accepted 12 June2018

Accepted manuscript posted online 20June 2018

Citation Russo TA, Olson R, Fang C-T, StoesserN, Miller M, MacDonald U, Hutson A, Barker JH,La Hoz RM, Johnson JR, for the HypervirulentKlebsiella pneumoniae Investigator Group(HVKPIG). 2018. Identification of biomarkers fordifferentiation of hypervirulent Klebsiellapneumoniae from classical K. pneumoniae. J ClinMicrobiol 56:e00776-18. https://doi.org/10.1128/JCM.00776-18.

Editor Daniel J. Diekema, University of IowaCollege of Medicine

Copyright © 2018 American Society forMicrobiology. All Rights Reserved.

Address correspondence to Thomas A. Russo,trusso@buffalo.edu.

For a commentary on this article, see https://doi.org/10.1128/JCM.00959-18.

BACTERIOLOGY

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Currently, most infections due to Klebsiella pneumoniae in North America and Europeare 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 theirpropensity for acquiring plasmids containing numerous antimicrobial resistance deter-minants, which makes treatment challenging (1–3).

In the mid-1980s and 1990s, reports from Taiwan described a unique clinical syndromeof community-acquired, tissue-invasive K. pneumoniae infection in otherwise healthy indi-viduals that often presented in multiple sites or subsequently spread (metastaticspread) (4, 5). These cases included pyogenic liver abscess in the absence of biliary tractdisease, abscesses at nonhepatic sites, pneumonia, endophthalmitis, meningitis, andnecrotizing fasciitis (6, 7). To distinguish this pathotype from cKp, the designationhypervirulent K. pneumoniae (hvKp) has been used, and an increasing number of suchcases are being reported worldwide (7–9).

Reports on putative hvKp infection have primarily used clinical features and/or apositive string test (using an inoculation loop to generate a viscous string �5 mm inlength from a bacterial colony) (10) as the case definition. However, the correspon-dence between the string test and clinical features observed with hvKp infection isvariable, 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% havebeen reported (10, 13). This poor specificity is especially problematic in low-prevalenceareas.

Clinical criteria to identify hvKp strains and the associated infections also areproblematical. A conservative clinical definition that requires the occurrence of acommunity-acquired, tissue-invasive infection in an otherwise healthy host precludesrecognition of hvKp infection in patients who are immunocompromised or in a healthcare setting and is inapplicable to strain collections that lack clinical data. An accuratediagnostic test that can differentiate between hvKp and cKp strains is needed foroptimal clinical care and infection control efforts, epidemiological surveillance for hvKpinfections and the associated antimicrobial resistance trends, and diverse other re-search studies (e.g., to define patient cohorts for treatment trials or studies to deter-mine if there is a genetic susceptibility to hvKp infection).

To date, most putative hvKp strains, as defined primarily by a positive stringtest, have been antimicrobial susceptible (7). However, the acquisition of extensive orpan-antimicrobial resistance has the potential to create the ultimate superbug. This wasaccomplished experimentally by the introduction of a K. pneumoniae carbapenemase(KPC)-producing plasmid into an hvKp strain (14) and is now being observed in theclinical venue, with hvKp acquiring genes that encode extended-spectrum �-lactamasesand carbapenemases (15–18). Further, an extensively drug resistant (XDR) cKp strainthat 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 virulenceplasmid (19–22) or within chromosomal islands (23). We hypothesized that some ofthese genes (and/or their associated phenotypes) would be accurate markers for hvKpstrains. Therefore, we evaluated several genotypic and phenotypic biomarkers for theirability to accurately differentiate putative hvKp from cKp strains (based on an epide-miological analysis and experimental virulence in a murine infection model) andidentified several such markers.

MATERIALS AND METHODSDevelopment 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 thatthe inclusion of any bacterial genotypic or phenotypic information in the definition of strain cohortscould introduce bias.

The criterion for inclusion of a strain in the hvKp-rich cohort was isolation from a healthy, ambulatorypatient 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 fromdeidentified 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), pneu-

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TABLE 1 Characteristics of the hvKP-rich and cKP-rich strains

Strain designation(capsule type)a

Site of strainisolation

Primary site ofinfectionb

Metastatic or additionalsite(s) Geographic location

hvKP-rich cohorthvKP1 (K2) Liver Liver Spleen Buffalo, NY, USAhvKP2 (K1) Blood Eye None Buffalo, NY, USAhvKP3 (K2) Blood Liver None Minneapolis, MN, USAhvKP4 (K1) Liver Liver Eye New York, New York, USAhvKP5 (K2) Blood Lung None Iowa City, IA, USAhvKP6 (K1) Blood Liver None Niagara Falls, NY, USAhvKP7 (K1) Liver Liver None Wake Forest, NC, USAhvKP8 (K1) Blood Liver None San Francisco, CA, USAhvKP9 (K1) Blood Liver Epidural space, psoas muscle Boston, MA, USAhvKP10 (K1) Liver Liver Leg, cerebellum Taipei, TaiwanhvKP11 (K1) Blood Arm Supraclavicular region Taipei, TaiwanhvKP12 (K2) Blood Back Buttock, thigh Taipei, TaiwanhvKP13 (K1) Blood Iliopsoas Thigh Taipei, TaiwanhvKP14 (K1) Blood Lung Leg Taipei, TaiwanhvKP15 (K1) Blood Liver Leg, kidney Taipei, TaiwanhvKP16 (K54) Blood Tonsil Deep neck Taipei, TaiwanhvKP17 (K54) Blood Urinary tract Leg Taipei, TaiwanhvKP18 (K20) Blood Urinary tract Leg Taipei, TaiwanhvKP19 (K1) Liver Liver None San Diego, CA, USAhvKP20 (K2) Blood Liver Lung, pleural space San Diego, CA, USAhvKP21 (K1) Blood Liver None San Diego, CA, USAhvKP22 (K1) Blood Liver None Yakima, WA, USAhvKP23 (K2) Blood Liver Soft tissue, brain Takoma Park, MD, USAhvKP24 (K20) Blood Liver None St. Paul, MN, USAhvKP25 (K1) Liver Liver None Wake Forest, NC, USAhvKP26 (K1) Liver Liver Eye Taipei, TaiwanhvKP27 (K1) Blood Liver Eye Taipei, TaiwanhvKP28 (K1) Blood Liver Eye, lung, testis Taipei, TaiwanhvKP29 (K1) Blood Liver Eye, meninges, lung Taipei, TaiwanhvKP30 (K1) Blood Liver Eye Taipei, TaiwanhvKP31 (K1) Blood Liver Eye Taipei, TaiwanhvKP32 (K1) Blood Liver Eye, lumbar spine Taipei, TaiwanhvKP33 (K1) Liver Liver Eye Taipei, TaiwanhvKP34 (K1) Liver Liver Eye Taipei, TaiwanhvKP35 (K1) Blood Liver Eye Taipei, TaiwanhvKP36 (K1) Blood Liver Eye, cervical spine Taipei, TaiwanhvKP37 (K1) Blood Liver Meninges, lung Taipei, TaiwanhvKP38 (K1) Blood Liver Meninges, cervical spine Taipei, TaiwanhvKP39 (K1) Blood Liver Eye, brain Taipei, TaiwanhvKP40 (K2) Liver Liver Eye Taipei, TaiwanhvKP41 (K2) Blood Liver Meninges Taipei, TaiwanhvKP42 (K1) Blood Liver Meninges, brain Taipei, TaiwanhvKP43 (K54) Blood Liver Meninges Taipei, TaiwanhvKP44 (K1) Blood Liver None Taipei, TaiwanhvKP45 (K1) Blood Liver None Taipei, TaiwanhvKP46 (K1) Blood Liver None Taipei, TaiwanhvKP47 (K1) Blood Liver None Taipei, TaiwanhvKP48 (K1) Liver Liver None Taipei, TaiwanhvKP49 (K1) Blood Liver None Taipei, TaiwanhvKP50 (K1) Liver Liver None Taipei, TaiwanhvKP51 (K1) Blood Liver None Taipei, TaiwanhvKP52 (K1) Blood Liver None Taipei, TaiwanhvKP53 (K1) Blood Liver None Taipei, TaiwanhvKP54 (K1) Blood Liver None Taipei, TaiwanhvKP55 (K1) Blood Liver None Taipei, TaiwanhvKP56 (K1) Liver Liver None Taipei, TaiwanhvKP57 (K1) Liver Liver None Taipei, TaiwanhvKP58 (K1) Blood Liver None Taipei, TaiwanhvKP59 (K1) Blood Liver None Taipei, TaiwanhvKP60 (K2) Blood Liver None Taipei, TaiwanhvKP61 (K2) Liver Liver None Taipei, TaiwanhvKP62 (K2) Blood Liver None Taipei, TaiwanhvKP63 (K2) Liver Liver None Taipei, Taiwan

(Continued on next page)

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monia (1), endophthalmitis (1), tonsillar abscess (1), and osteomyelitis (1). Two or more additional sitesof 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).

Since most K. pneumoniae infections in North America and the United Kingdom presumably are dueto cKp strains, the cKp-rich strain cohort (n � 90) was generated from randomly chosen, deidentifiedblood isolates from (number per site) Montreal, Canada (40), Buffalo, NY (25), and Oxford, UnitedKingdom (25) (Table 1). Blood isolates were chosen since such strains were likely to represent the morevirulent extreme of the pathogenesis spectrum. However, since information regarding clinical manifes-tations and host characteristics was unavailable, a limitation of this cohort is the potential presence ofhvKp strains.

TABLE 1 (Continued)

Strain designation(capsule type)a

Site of strainisolation

Primary site ofinfectionb

Metastatic or additionalsite(s) Geographic location

hvKP64 (K2) Liver Liver None Taipei, TaiwanhvKP65 (K2) Blood Liver None Taipei, TaiwanhvKP66 (K2) Blood Liver None Taipei, TaiwanhvKP67 (K5) Blood Liver None Taipei, TaiwanhvKP68 (K5) Blood Liver None Taipei, TaiwanhvKP69 (K20) Liver Liver None Taipei, TaiwanhvKP70 (K20) Blood Liver None Taipei, TaiwanhvKP71 (K54) Liver Liver None Taipei, TaiwanhvKP72 (K54) Liver Liver None Taipei, TaiwanhvKP73 (K54) Blood Liver None Taipei, TaiwanhvKP74 (K57) Blood Liver None Taipei, TaiwanhvKP75 (NT) Blood Liver None Taipei, TaiwanhvKP76 (NT) Liver Liver None Taipei, TaiwanhvKP77 (NT) Blood Liver None Taipei, TaiwanhvKP78 (NT) Blood Liver None Taipei, TaiwanhvKP79 (K2) Bone Femur Ulna Dallas, TX, USAhvKP80 (K1) Liver Liver Lung Ft. Lauderdale, FL, USAhvKP81 (K57) Blood Liver None Taipei, TaiwanhvKP82 (K54) Blood Liver None Taipei, TaiwanhvKP83 (K2) Blood Liver None Taipei, TaiwanhvKP84 (K5) Blood Liver None Taipei, TaiwanhvKP85 (K5) Liver Liver None Buffalo, NY, USA

cKP-rich cohortcKP1-8 (NT) Blood Montreal, QC, CanadacKP9-15 (NT) Blood Oxford, England, UKcKP16-18 (K2) Blood Oxford, England, UKcKP19-21 (NT) Blood Oxford, England, UKcKP22 (K57) Blood Buffalo, NY, USAcKP23-29 (NT) Blood Buffalo, NY, USAcKP30-40 (NT) Blood Montreal, QC, CanadacKP41 (K54) Blood Montreal, QC, CanadacKP42-55 (NT) Blood Montreal, QC, CanadacKP56 (K54) Blood Montreal, QC, CanadacKP57 (K20) Blood Montreal, QC, CanadacKP58-61 (NT) Blood Montreal, QC, CanadacKP62 (K1) Blood Oxford, England, UKcKP63-65 (NT) Blood Oxford, England, UKcKP66 (K20) Blood Oxford, England, UKcKP67-70 (NT) Blood Oxford, England, UKcKP71 (K2) Blood Oxford, England, UKcKP72 (NT) Blood Oxford, England, UKcKP73 (K57) Blood Buffalo, NY, USAcKP74-75 (NT) Blood Buffalo, NY, USAcKP76 (K57) Blood Buffalo, NY, USAcKP77-78 (NT) Blood Buffalo, NY, USAcKP79 (K2) Blood Buffalo, NY, USAcKP80-82 (NT) Blood Buffalo, NY, USAcKP83 (K2) Blood Buffalo, NY, USAcKP84-89 (NT) Blood Buffalo, NY, USAcKP90 (NT) Blood Oxford, ENG, UK

aNT, not a K1, K2, K5, K20, K54, or K57 capsule type.bProbable.

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Genotypic biomarkers chosen for evaluation. Study isolates were assessed for the presence of 10genes 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, andthe chromosomal gene rmpA (crmpA) (regulators of the mucoid phenotype via increased capsuleproduction), and peg-344 (putative transporter) (24–27). Also included were genes associated epidemi-ologically with putative hvKp strains, namely, terB (tellurite resistance), iroB (salmochelin siderophorebiosynthesis), and irp2 (yersiniabactin siderophore biosynthesis) (22, 28–30). The virulence plasmid-located genes peg-1631 (hypothetical protein) and peg-589 (putative carboxymuconolactone decarbox-ylase family) were studied. Capsular types K1, K2, K5, K20, K54, and K57, which have been consideredmarkers for hvKp strains (7, 12), also were assessed.

Phenotypic biomarkers chosen for study. Two hypothetically discriminatory phenotypic traits wereevaluated. These included (i) the string test, due to its wide range of reported sensitivity and specificityvalues and its lack of rigorous assessment using well-defined strain cohorts, and (ii) qualitative andquantitative siderophore production, due to suspected greater siderophore production by hvKp strainsthan 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 typeswas performed as described previously (4, 31). PCR-based detection of the other candidate biomarkergenes used (per reaction) 5 �l of 2� TaqFrogga Mix (Frogga Bio, North York, Canada), 0.75 �l of forwardprimer, 0.75 �l of reverse primer (20 pmol/�l primer stock), 1 �l of genomic DNA (50 ng/�l), and 2.5 �lof water. PCR was performed using an Applied Biosystems GeneAmp PCR system 9700 instrument withthe following cycling conditions: step 1, 95.0°C for 2 min; step 2, 95.0°C for 30 s; step 3, primer-specificannealing 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 initialdevelopment of PCR assays for biomarkers, amplification products were confirmed to contain the correctDNA sequence. Subsequently, a gene was considered present if the band of the predicted size wasdetected. Specific primers, PCR conditions, and product sizes are listed in Table S1 in the supplementalmaterial.

Siderophore assays. For the qualitative plate siderophore production assay, Kings B agar platescontaining chrome azurol S dye (CAS) were prepared as described previously (32). Cells of each test strainwere lifted from an overnight-growth agar plate using a 10-�l pipette tip, stabbed into the Kings B agar,and incubated at 37°C. After overnight growth, formation of an opaque golden-yellow zone around thecolony indicted high-level siderophore production (Fig. S1).

For the qualitative solution siderophore production assay, test strains were grown individuallyovernight 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. Equalvolumes of the diluted supernatant and a 98% siderophore assay solution (27) were placed into a wellin a flat-bottom 96-well plate. After incubation in the dark for 30 min, development of an orangeappearance was scored as positive, and no change in the baseline purple/blue appearance was scoredas negative (Fig. S2).

For the quantitative siderophore production assay, which also used culture supernatants from strainsgrown 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 studieswere reviewed and approved by the University at Buffalo, State University of New York (SUNY), and theVeterans Administration Institutional Animal Care Committee. This study was done in strict accordancewith the recommendations in the Guide for the Care and Use of Laboratory Animals endorsed by the NationalInstitutes 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 strainsbeing 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 thehvKp-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 thegroup survived a given challenge inoculum. For the cKp-rich cohort a challenge inoculum of 2 � 103 to5 � 103 CFU was initially used for all strains, with sequential challenges of 3 � 105 to 5 � 105 CFU forsome strains and 3 � 107 to 6 � 107 CFU for all strains if all animals in the group survived a givenchallenge inoculum.

Statistical analysis. The associations of the dichotomous genotypic and phenotypic biomarkers withstrain cohort were examined by logistic regression and were described using the odds ratios (ORs) andthe corresponding 95% confidence intervals (CIs). The performance of the binary genotypic andphenotypic biomarkers in distinguishing between the hvKp-rich and cKp-rich cohorts was summarizedby the marker’s estimated diagnostic accuracy (number of correctly identified cases divided by the totalnumber of cases), sensitivity, and specificity. Combinations of biomarkers were examined using receiveroperating 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 wasmodeled using a proportional odds model (35). If no deaths occurred with the 107 CFU dose, theobservation 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 assesscombinations of factors associated with severe illness or death by dose levels. The predictive value of thesingle continuous variable (quantitative siderophore production) was examined using an ROC curve withthe estimated diagnostic accuracy given by the area under the ROC curve.

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RESULTSMultiple biomarkers, including peg-344, iroB, iucA, prmpA, and prmpA2, differ-

entiate the hvKp-rich and cKp-rich strain cohorts with high accuracy. This epide-miological analysis relied on clinical syndromes observed in infected humans to definethe strain cohorts that were compared to identify discriminating biomarkers. Five ofthese biomarkers, all of which were genes, achieved a diagnostic accuracy of �0.95 fordifferentiating 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; speci-ficity, 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; specificity0.97). In contrast, none of the studied phenotypic tests achieved a �0.95 accuracy; thequalitative siderophore solution assay had the best performance characteristics (accu-racy, 0.93; sensitivity, 0.91; specificity, 0.96) (Tables 2; see also Table S2 in the supple-mental 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 anal-ysis indicated that the combination of peg-344 and iucA marginally increased accuracyto 0.98 (sensitivity, 0.94; specificity, 1.0). Biomarker data for individual strains andhvKp-rich and cKp-rich strain cohort totals are listed in Table S2.

Multiple biomarkers, including peg-344, iroB, iucA, prmpA, prmpA2, and peg-589, accurately predict mortality in a murine sepsis model. Although murineinfection models mimic human infections imperfectly, it is logical to expect that hvKpstrains would be more lethal for mice than cKp strains. Therefore, all strains wereassessed 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 challengeinoculum (dose)-dependent fashion was calculated.

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 assay0.93 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 assay0.83 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)aPP, primer pair (see Table S1 in the supplemental material for details); SP, siderophore production.bND, not done (unable to calculate).

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Although the rank order differed slightly, the results of this analysis concurredclosely with those of the epidemiological analysis. That is, seven biomarkers (six genesand one phenotypic trait) were associated individually with a �25-fold-greater likeli-hood 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 sidero-phore production assay (33.6), and prmpA2 (31.2) (Table 3 and Fig. 1A). Based onstepwise multivariate regression, no two-marker combination outperformed peg-344for predicting severe illness or death. Thus, these markers strongly predict a hyperviru-lent phenotype, additionally supporting their utility for identifying hvKp strains.

Quantitative siderophore levels are highly predictive of both epidemiologicaland experimental virulence. Quantitative siderophore production, the single contin-uous variable studied, was examined using an ROC curve. According to ROC curveanalysis and box plots (Fig. 2A and B), a quantitative siderophore concentration of �30�g/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, siderophoreconcentrations of �30 �g/ml were associated with an HR of 31.7 for severe illness ordeath, compared to strains with concentrations of �30 �g/ml (Fig. 1B).

Prevalence of hvKp in K. pneumoniae blood isolates from Canada and theUnited Kingdom. The prevalence of hvKp as defined by the presence of the four mostaccurate biomarkers (peg-344, iroB, iucA, and prmpA) was assessed among 179 K.pneumoniae blood isolates (110 from Montreal, Canada, isolated from March 2009 toFebruary 2013, and 69 from Oxford, United Kingdom, isolated from January 2008 toApril 2011). The lack of associated clinical data enabled an unbiased assessment. Usingthese criteria, the prevalences of hvKp were 0.9% (1/110) and 5.8% (4/69) among theMontreal and Oxford isolates, respectively. All of these strains were assessed experi-mentally for virulence in the murine sepsis model and caused severe illness or deathwith a challenge dose of only 103 CFU.

DISCUSSION

This study used epidemiological and experimental lines of evidence to identifyseveral laboratory markers for identifying hvKp strains with a high degree of accuracy.The epidemiological analysis compared clinically defined hvKp-rich and cKp-rich straincohorts for the presence of selected biomarkers. peg-344, iroB, iucA, prmpA, and prmpA2all identified an isolate to be a member of the hvKp-rich strain cohort with an accuracyof �0.95. The second line of evidence compared clinically defined hvKp-rich and

TABLE 3 Hazard ratio of severe illness or death in outbred CD1 mice as a function ofgenotypic 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)aPP, primer pair (see Table S1 in the supplemental material for details); SP, siderophore production.

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cKp-rich strain cohorts in a murine sepsis model in which hvKp strains were predictedto be more lethal than cKp strains. peg-344, iroB, iucA, prmpA, and prmpA2 were allassociated with a hazard ratio of �25 for severe illness or death, additionally supportingtheir utility for identifying hvKp strains.

These findings are predicted to be generalizable. Although most isolates in thehvKp-rich strain cohort were from Taiwanese patients, 22% were from patients indiverse U.S. locations. Further, the hvKp-rich strain cohort (n � 85) consisted of capsuletypes 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 unsur-prising since these genes likely are linked on the hvKp virulence plasmid, which isresponsible, at least in part, for the hypervirulent phenotype. The advantage of geneticbiomarkers is that a rapid nucleic acid amplification test can be developed and, if FDAapproved, 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 rapiddiagnostic test.

Our screen for peg-344, iroB, iucA, and prmpA documented a low prevalence of hvKp

FIG 1 The survival of CD1 mice as a function of dose after subcutaneous challenge. Outbred CD1 miceunderwent subcutaneous challenge with all strains from the hvKp-rich cohort (n � 85) and the cKp-richcohort (n � 90). Animals were monitored for up to 14 days for the development of severe illness (inextremis state) or death. (A) Mice challenged with strains that possessed peg-344 had a 64-fold increasein the hazard ratio of severe illness or death compared to the levels in strains that did not. (B) Micechallenged with strains that produced �30 �g/ml of siderophores had a 31.7-fold increase in the hazardratio of severe illness or death compared to strains that produced �30 �g/ml.

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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 identifiedas hvKp were true positives. However, the most recent isolates in these collections werecollected more than 5 years ago. The biomarkers identified in this study can be used todetermine prevalence rates in different geographic locations, sites of infection, andtime periods or even in real time (to identify possible temporal trends).

Another important study finding was that the string test, which currently is usedwidely to identify hvKp strains, performed suboptimally. Its accuracy, specificity, andsensitivity of 0.90, 0.89, and 0.91, respectively, were inferior to values for several of thegenotypic biomarkers evaluated and for siderophore production. Our data suggest thatthe string test should not be used as a definitive diagnostic test for hvKp, particularlyin low-prevalence areas, where its performance characteristics will lead to substantiallymore erroneous classifications than with the other more accurate markers identified inthis 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 befluid. It would therefore seem logical that the best genetic marker for hvKp strainswould be one that clearly contributes to the hypervirulence phenotype. Here, we

FIG 2 A quantitative siderophore concentration of �30 �g/ml is strongly predictive of strains thatbelong to the hvKp-rich versus the cKp-rich cohort. A quantitative measurement of total siderophoreproduction 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 thatcorresponds to a concentration of 30 �g/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�g/ml.

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demonstrated that total siderophore production strongly correlated with in vivo viru-lence (Fig. 2). Aerobactin has previously been shown to be the dominant siderophoreproduced by hvKp strains (26, 27) and to be the critical siderophore that enhancesvirulence ex vivo and in vivo (26). Since aerobactin is a critical mediator of virulence, itis likely to be a durable biomarker among hvKp strains. We found iucA, one of the genesin the iuc operon encoding aerobactin, to be one of the most accurate genetic markersfor differentiating between hvKp and cKp strains; it may therefore represent a stablegenetic 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 andlead to improved outcomes. Specifically, accurate identification of hvKp would allowmore rapid consideration of possible unrecognized sites of infection, which oftenmanifest as occult abscesses (37). Such infectious foci are likely to require drainage,extended antimicrobial therapy, and, potentially, site-directed treatment (e.g., withmeningitis, brain or prostatic abscesses, or endophthalmitis [vitrectomy or intravitrealantibiotics]) (5). Since the hypermucoviscosity of hvKp interferes with definitive percu-taneous drainage by clogging the catheter (38), identification of hvKp could alert theclinician to consider using a larger-gauge catheter and more frequent catheter irriga-tion. The association of hvKp with relapse (39–42), perhaps due to its hypermucovis-cous phenotype and biofilm formation (43), suggests that hvKp infections may requireprolonged treatment to maximize cure rates and minimize relapse. The ability todifferentiate cKp from hvKp would enable the generation of controlled data to addressthis and other clinical issues.

We along with others (41) have established that close contacts of infected patientsmay also be colonized by the infecting hvKp strain, subsequently leading to infection.Presently, it is unclear whether empirical prophylactic therapy (as recommended forinvasive meningococcal disease [44]), prophylaxis of colonized individuals (either ofwhich may select for antimicrobial-resistant strains and negatively impact outcome), orobservation alone is the most appropriate course of action. However, the availability ofa test that can reliably identify hvKp would facilitate studies designed to fill thisknowledge gap.

The ability to accurately identify hvKp also would facilitate epidemiologic surveil-lance by researchers or public health laboratories. Antimicrobial resistance in hvKpstrains is increasingly prevalent (14, 16), and molecular epidemiologic studies areneeded to track the global spread of hvKp strains and emerging resistance trends.Further, although hvKp infections occur in all ethnic groups, even those infections thatare acquired in Western countries commonly involve Asians and, to a lesser degree,Hispanics (8). A discriminating test for hvKp is needed to more accurately definehigh-risk host groups and could be used for genetic investigations of host suscep-tibility.

This study has some limitations. First, the hvKp-rich cohort was limited to isolatesfrom healthy, community-dwelling ambulatory patients, whereas patients with comor-bidities, 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 healthcare-associated hvKp infection. Second, the study population is relatively small fordiagnostic test validation. However, the predictive power of the best markers wasextraordinarily strong. This is likely due to the identified biomarkers’ linkage on avirulence plasmid or chromosomal virulence island that endows K. pneumoniae withthe hypervirulent phenotype. Third, the studied hvKp-rich and cKp-rich strains cohortswere contaminated by a few misclassified strains; the clinical criteria used to definehvKp-rich cohort was imperfect due to an absence of standardization, and other thanblood isolates, clinical data were unavailable for the cKp-rich strain cohort. A post hocanalysis supported this consideration and suggested that the estimated accuracy ofbiomarkers was likely an underestimate due to the presence of some probable cKpstrains within the hvKp-rich cohort (strains hvKp77 and hvKp78) and of hvKp strainspresent within the cKp-rich cohort (strains cKp17, cKp18, cKp57, and cKp62) based on

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the presence or absence of biomarkers and their performance in the murine sepsismodel (see Table S2 in the supplemental material). Last, all clinical tests need to beinterpreted within the context of pretest probability. In low-prevalence regions, theabsence of the most accurate biomarkers identified in this study would effectivelyrule out that an isolate is hypervirulent. However, a positive result might overpredicthypervirulence and could require additional support. Nonetheless, from a clinicalvantage point, it would be desirable to overcall an isolate as hypervirulent.

In summary, this study identified several biomarkers that are highly accurate inidentifying hvKp strains, as defined both epidemiologically and experimentally basedon clinically defined strain cohorts and experimentally in a murine sepsis model. Thesemarkers should enable a variety of important epidemiologic surveillance studies on theprevalence of hvKp in various populations and on emerging antimicrobial resistance,which could limit treatment options. Additionally, rapid recognition of hvKp in theclinical setting has the potential to improve patient care. Further validation of thesedifferentiating markers should be undertaken in well-characterized patient cohorts witha larger number of strains.

SUPPLEMENTAL MATERIAL

Supplemental material for this article may be found at https://doi.org/10.1128/JCM.00776-18.

SUPPLEMENTAL FILE 1, PDF file, 2.5 MB.

ACKNOWLEDGMENTSThis 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 andPrevention cooperative agreement number 1 U50 CK000477 (J.H.B.). The funders hadno role in study design, data collection and analysis, decision to publish, or preparationof the manuscript.

Members of the Hypervirulent Klebsiella pneumoniae investigator group are thefollowing: 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; DavidE. 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; JaroslawZola, Buffalo, NY, USA.

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