RESEARCH ARTICLE


Fastidious Gram-Negatives: Identification by the Vitek 2 Neisseria-Haemophilus Card and by Partial 16S rRNA Gene Sequencing Analysis



Ute Wolff Sönksen1, 2, Jens Jørgen Christensen2, *, Lisbeth Nielsen1, Annemarie Hesselbjerg2, Dennis Schrøder Hansen1, Brita Bruun1
1 Department of Clinical Microbiology, Hillerød Hospital, Hillerød, Denmark
2 Department of Microbiological Surveillance and Research, Statens Serum Institut, Copenhagen, Denmark

Article Information


Identifiers and Pagination:

Year: 2010
Volume: 4
First Page: 123
Last Page: 131
Publisher ID: TOMICROJ-4-123
DOI: 10.2174/1874285801004010123

Article History:

Received Date: 22/10/2010
Revision Received Date: 31/10/2010
Acceptance Date: 2/11/2010
Electronic publication date: 31/12/2010
Collection year: 2010

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Creative Commons License
© Sönksen et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Slagelse Hospital, Ingemannsvej 18, 4200 Slagelse, Denmark; Tel: +45 58559421; Fax: +45 58559410; E-mail: jejc@regionsjaelland.dk


Abstract

Taxonomy and identification of fastidious Gram negatives are evolving and challenging. We compared identifications achieved with the Vitek 2 Neisseria-Haemophilus (NH) card and partial 16S rRNA gene sequence (526 bp stretch) analysis with identifications obtained with extensive phenotypic characterization using 100 fastidious Gram negative bacteria. Seventy-five strains represented 21 of the 26 taxa included in the Vitek 2 NH database and 25 strains represented related species not included in the database. Of the 100 strains, 31 were the type strains of the species. Vitek 2 NH identification results: 48 of 75 database strains were correctly identified, 11 strains gave `low discrimination´, seven strains were unidentified, and nine strains were misidentified. Identification of 25 non-database strains resulted in 14 strains incorrectly identified as belonging to species in the database. Partial 16S rRNA gene sequence analysis results: For 76 strains phenotypic and sequencing identifications were identical, for 23 strains the sequencing identifications were either probable or possible, and for one strain only the genus was confirmed. Thus, the Vitek 2 NH system identifies most of the commonly occurring species included in the database. Some strains of rarely occurring species and strains of non-database species closely related to database species cause problems. Partial 16S rRNA gene sequence analysis performs well, but does not always suffice, additional phenotypical characterization being useful for final identification.

Keywords: Evaluation, fastidious Gram negatives, 16S rRNA gene sequencing, Vitek 2 NH.

INTRODUCTION

Fastidious Gram-negative bacteria comprise a number of different genera and species that may cause serious systemic infections. Their fastidious nature often makes identification a challenge in the routine microbiology laboratory, and their ability to cause invasive disease makes correct identification important. Their ability to cause endocarditis (HACEK group of bacteria: Haemophilus spp., Aggregatibacter spp, Cardiobacterium species, Eikenella corrodens, and Kingella kingae), animal-bite infections (e.g. Capnocytophaga spp., Neisseria weaveri, and Pasteurella spp.) and their role in abscess formation (E. corrodens, Aggregatibacter actinomycetemcomitans) illustrates their importance [1]. Other diagnostic challenges are that their taxonomy, including nomenclature [e.g. 2, 3], as well as the recognition of their etiologic possibilities, are continuously evolving; an example of the latter is the possibility of infective endocarditis when Neisseria elongata is isolated from blood cultures.

Conventional identification of fastidious Gram-negative bacteria is at times cumbersome, often requiring special media and phenotypic tests, plus specialist knowledge. As far as we know, there are only two fully automated identification systems for identification of fastidious Gram negative bacteria on the market, Vitek 2 Neisseria-Haemophilus (NH) (bioMérieux, Marcy L’Etoile, France) and Phoenix (Becton Dickinson, Cockneyville, MD, USA). BioMérieux has developed a card for the identification of 26 taxa of fastidious Gram-negatives, including Neisseria,Haemophilus, and the HACEK bacteria, for use in the Vitek 2 system. The card consists of 30 biochemical tests that are monitored up to 8 hours. The purpose of this study was to evaluate the utility of the Vitek 2 system in a clinical microbiology laboratory by comparing it with identification by conventional methods. In addition, it was attempted to assess the value of partial 16S rRNA gene sequence analysis (526 bp stretch) for identification within this group of bacteria.

MATERIALS AND METHODS

Bacterial Strains

The 100 bacterial strains examined in this study are shown in Tables 1a, 1b, and 2. They comprise clinical strains received for species identification or for research and monitoring purposes at the reference laboratories at Statens Serum Institut (SSI), supplemented with strains from various culture collections. The latter included 31 type strains, of which one is the type strain of Haemophilus paraphrophilus, now part of the new species Aggregatibacter aphrophilus [2], and another the type strain of Haemophilus pittmaniae, a new species comprising some haemolytic strains of Haemophilus [4]. The strains comprised two groups: a group of 75 strains representing 20 of the 26 taxa included in the Vitek 2 database (the three Campylobacter taxa plus Suttonella indologenes, Gardnerella vaginalis and Oligella urethralis were not included); and another group of 25 non-database strains representing 14 species of the same genera as those included in the database (Actinobacillus hominis, Moraxella spp. and Neisseria spp., i.a. animal bite species (Neisseria weaveri, Neisseria animaloris, and Neisseria zoodegmatis) plus Pasteurella spp.). The strains had been stored as either lyophilized or at - 70oC until the present study. All strains were sent from SSI under code numbers to the Clinical Microbiology Department at Hillerød, so that the investigators were blinded with respect to species identification.

Table 1a.

Identification Results Obtained By partial 16S rRNA Gene Sequence Analysis and by Vitek 2 NH Characterization of 45 Strains Included in the Vitek 2 NH Database


Strains Strain Designationsa,b 16S rRNA Gene Analysis Resultsc Difference Between Max Scoresd NCBI–BLAST Species Interpretation Vitek 2 NH Identificatione
A. ureae NCTC 10219T 893 / 879 (A. arthritidis) // 502 14 Probable Misidentified
A. ureae SSI: P 524 881 / 877 (A. arthritidis) // 498 4 Probable Correct
A. actinomycetemcomitans NCTC 9710T 755 / 704 (A. aphrophilus) // 425 51 Confirmed Low Discrimination
A. actinomycetemcomitans HK 666 830 / 731 (H. influenzae) // 463 99 Confirmed Correct
A. actinomycetemcomitans HK 1662 805 / 713 (H. segnis) // 448 92 Confirmed Correct
A. aphrophilus/paraphrophilus NCTC 5906T 762 / 739 (H. parainfluenzae) // 431 41 Confirmed Correct
A. aphrophilus/paraphrophilus SSI: P 536, AB 1635 706 / 673 (A. actinomycetemcomitans) // 407 33 Confirmed Correct
A. aphrophilus/paraphrophilus CCUG 14858T 710 / 675 (H. parainfluenzae) // 413 35 Confirmed Correct
A. aphrophilus/paraphrophilus CCUG 49494 818 / 789 (H. parainfluenzae) // 468 29 Confirmed Correct
A. segnis ATCC 33393T 856 / 740 (Pasteurella aerogenes) // 477 116 Confirmed Unidentified
A. segnis SSI: P 1292 847 / 782 (H. influenzae) // 497 65 Confirmed Low Discrimination
A. segnis SSI: P 1351 825 / 782 (H. influenzae) // 499 43 Confirmed Low Discrimination
C. canimorsus CCUG 19190 657 / 657 (C. cynodegmi) // 492 0 Probable Unidentified
C. canimorsus CCUG 19141 857 (C. cynodegmi) // 854 (C. canimorsus) // 494 -3 Possible Unidentified
C. canimorsus CCUG 19140 829 / 661 (C. cynodegmi) // 485 168 Confirmed Correct
C. canimorsus SSI: 4642/2006 838 / 751 (C. cynodegmi) // 488 87 Confirmed Misidentified
C. canimorsus SSI: 140/2006 587 / 533 (C. cynodegmi) // 365 54 Confirmed Misidentified
C. canimorsus SSI: 187/2006 856 / 780 (C. cynodegmi) // 485 76 Confirmed Misidentified
C. gingivalis CCUG 9715T 893 / 852 (C. granulosa) // 503 41 Confirmed Unidentified
C. ochracea SSI: 3435/04 859 / 852 (C. sputigena) // 491 7 Probable Correct
C. sputigena CCUG 9714T 785 / 765 (C. ochracea) // 481 18 Confirmed Unidentified
C. hominis CCUG 31207 841 / 753 (C. valvarum) // 496 88 Confirmed Correct
C. hominis CCUG 2711T 893 / 761 (C. valvarum) // 502 132 Confirmed Correct
C. hominis SSI: AB 2089 708 / 663 (C. valvarum) // 407 45 Confirmed Correct
E. corrodens ATCC 23834T 780 / 646 (N. denitrificans) // 436 134 Confirmed Correct
E. corrodens SSI: 13794/1992 623 / 541 (K. denitrificans) // 416 82 Confirmed Correct
E. corrodens SSI: 13897/1992 747 / 659 (K. denitrificans) // 425 88 Confirmed Correct
H. haemolyticus NCTC 10659T 856 / 856 (H. influenzae) // 500 0 Probable Misidentified
H. influenzae NCTC 8143T 832 / 736 (H. haemolyticus) // 462 96 Confirmed Correct
H. influenzae SSI: P 1227 756 / 680 (H. haemolyticus) // 418 76 Confirmed Correct
H. influenzae ATCC 49247 906 / 837 (H. haemolyticus) // 508 69 Confirmed Correct
H. parahaemolyticus NCTC 8479T 838 / 838 (A. pleuropneumoniae) // 486 0 Probable Correct
H. parahaemolyticus CCUG 48512 901 / 893 (A. pleuropneumoniae) // 499 8 Probable Misidentified
H. parainfluenzae NCTC 7857T 889 /841  (A. paraphrophilus) // 481 48 Confirmed Correct
H. parainfluenzae SSI: P 1538 870 / 756 (A. paraphrophilus) // 471 114 Confirmed Correct
H. parainfluenzae CCUG 49489 929 / 837 (A. paraphrophilus) // 445 92 Confirmed Correct
H. pittmaniaef CCUG 48703T 686 / 676 (H. parainfluenzae) // 400 10 Probable Low Discrimination
K. denitrificans    CCUG 6516T 838 / 838 (N. weaveri) // 469 0 Probable Low Discrimination
K. denitrificans CCUG 14999 883 / 767 (N. elongata) // 512 116 Confirmed Low Discrimination
K. kingae SSI: A 303T 886 / 762 (N. weaveri) // 495 114 Confirmed Unidentified
K. kingae CCUG 13025 879 / 758 (N. weaveri) // 499 111 Confirmed Unidentified
K. kingae SSI: 4541/05 904 / 785 (N. weaveri) // 509 119 Confirmed Correct
M. catarrhalis CCUG 353T 816 / 758 (M. canis) // 462 58 Confirmed Correct
M. catarrhalis SSI: RH 56295/84 805 / 747 (M. canis) // 460 58 Confirmed Correct
M. catarrhalis CCUG 11766 760 / 729 (M. nonliquefaciens) // 424 31 Confirmed Low Discrimination

a ATCC, American Type Culture Collection, Bethesda, Md., USA; CCUG, Culture Collection of the University of Göteborg, Sweden; HK, Mogens Kilian, Institute of Microbiology, Aarhus, Denmark; NCTC, National Collection of Type Cultures; SSI, Statens Serum Institut, Copenhagen, Denmark.

b T denotes type strain.

c Max score best taxon match / Max score next best taxon match (taxon of next best match) // base pairs examined. If best taxon match 16S identification is not identical to gold standard identification, the 16S rRNA identification is given before the first /

d Difference between best taxon match (most commonly same identification as gold standard identification) and next best match; in cases when gold standard identification was the same as next best match, the difference is negative.

e See Table 3 for details of Vitek 2 NH card examination.

f Formerly H.parahaemolyticus.

Table 1b.

Identification Results Obtained by Partial 16S rRNA Gene Sequence Analysis and by Vitek 2 NH Characterization of 30 Neisseria Species Strains Included in the Vitek 2 NH Database


Strains Strain Designationsa, b 16S rRNA Gene Analysis Resultsc Difference Between Max Scoresd NCBI –BLAST Species Interpretation Vitek 2 NH Identificatione
N. cinerea CCUG 2156T 904 (N. meningitidis)/ 897 (N. polysaccharea)/893 (N. cinerea) // 504 -11 Confirmed Correct
N. cinerea CCUG 346 758 / 753 (N. meningitidis / N. polysaccharea) // 420 5 Confirmed Correct
N. cinerea CCUG 5746 924 (N. meningitidis)/915( N. cinerea) // 515 -9 Confirmed Low Discrimination
N. elongata subsp.elongata CCUG 30802T 693 / 625 (N. subflavia / N. animalis) // 384 68 Confirmed Correct
N. elongata subsp.elongata CCUG 9686 879 / 774 (N. animalis) // 492 105 Confirmed Correct
N. elongata subsp.elongata SSI: AB 2895 909 / 798 (N. animalis) // 502 111 Confirmed Correct
N. gonorrhoeae CCUG 26876T 585 / 562 (N. cinerea) // 329 23 Confirmed Correct
N. gonorrhoeae SSI: 189/2006 904 / 850 (N. meningitidis) // 501 54 Confirmed Correct
N. gonorrhoeae SSI: 196/2006 915 / 861 (N. meningitidis) // 507 54 Confirmed Correct
N. gonorrhoeae SSI: 199/2006 805 / 760 (N. meningitidis) // 447 45 Confirmed Correct
N. gonorrhoeae SSI: 218/2006 823 / 773 (N. meningitidis) // 456 50 Confirmed Low Discrimination
N. gonorrhoeae SSI: 223/2006 921 / 866 (N. meningitidis) // 510 55 Confirmed Correct
N. gonorrhoeae SSI: 253/2006 854 / 800 (N. meningitidis) // 487 46 Confirmed Low Discrimination
N. gonorrhoeae, proAf neg. SSI: 177/2002 675 / 643 (N. cinerea) // 374 32 Confirmed Misidentified
N. gonorrhoeae, proA   neg. SSI: 67/2002 765 / 729 (N. meningitidis) // 428 36 Confirmed Misidentified
N. gonorrhoeae, proA   neg. SSI: 52/2002 904 / 850 (N. meningitidis) // 501 54 Confirmed Misidentified
N. lactamica CCUG 5853T 717 / 675 (N. polysaccharea) // 406 42 Confirmed Correct
N. lactamica SSI: BH 67320 794 / 791 (N. polysaccharea) // 518 3 Probable Correct
N. meningitidis CCUG 3269T 937 / 913 (N. polysaccharea) // 507 24 Confirmed Correct
N. meningitidis SSI: 17/2006 937 / 904 (N. cinerea) // 513 33 Confirmed Correct
N. meningitidis SSI: 18/2006 828 / 798 (N. polysaccharea) // 452 30 Confirmed Correct
N. meningitidis SSI: 19/2006 924 / 889 (N. cinerea) // 503 35 Confirmed Low Discrimination
N. meningitidis SSI: 20/2006 902 / 878 (N. polysaccharea) // 501 24 Confirmed Correct
N. meningitidis SSI: 21/2006 913 / 889 (N. polysaccharea) // 504 24 Confirmed Correct
N. meningitidis SSI: 50/2006 893 / 863 (N. cinerea) // 489 30 Confirmed Correct
N. meningitidis SSI: 23/2006 918 / 900 (N. cinerea) // 503 18 Confirmed Correct
N. meningitidis SSI: 60/2004 915 / 880 (N. cinerea) // 502 35 Confirmed Correct I
N. meningitidis SSI: 109/1998 913 / 888 (N. polysaccharea) // 501 25 Confirmed Correct
N. sicca CCUG 23929T 859 / 854 (N. pharyngis) // 483 5 Probable Correct I
N. sicca SSI: "19343" 884 / 845 (N. pharyngis) // 498 39 Confirmed Correct

a-e see Table 1a.

f proA: proline A arylamidase.

Table 2.

Identification Results Obtained by Partial 16S rRNA Gene Sequence Analysis and by Vitek 2 NH Characterization of 25 Strains not Included in the Vitek 2 NH Database


Strains Strain Designationsa, b 16S rRNA Gene Analysis Resultsc Differences Between Max Scoresd NCBI - BLAST Species Interpretation Vitek 2 NH Identification Vitek 2 NH Interpretation
A. hominis NCTC 11529T, SSI: P 578 865 / 852 (A. suis) // 496 13 Probable Low discrimination Correct genus not included
A. hominis SSI: P 575 836 / 816 (A. suis) // 495 20 Confirmed Misidentified Misidentified
A. hominis SSI: P 880 812 / 809 (A. suis et A. equuli) // 477 3 Probable Low discrimination No identification to genus level
M. non-liquefaciens ATCC 19975T 861 / 843 (M. lacunata) // 473 18 Confirmed Low discrimination Correct genus included
M. osloensis ATCC 19976T 746 / 601 (M. canis) // 413 145 Confirmed Low discrimination Correct genus not included
N. animaloris (CDC EF-4a) NCTC 12228T 778 / 765 (N. canis) // 413 13 Probable Misidentified Misidentified
N. animaloris (CDC EF-4a) CCUG 1976 865 / 859 (N. canis) // 413 6 Probable Unidentified Correct
N. animaloris (CDC EF-4a) SSI: P 669 855 / 836 (N. canis) // 455 19 Confirmed Unidentified Correct
N. flavescens ATCC 13120T 654 / 643 (N. flava) // 491 11 Probable Low discrimination Correct genus included
N. mucosa CCUG 26877T 806 (N. pharyngis)/ 791 (N. mucosa) // 499 -15 Possible Misidentified Misidentified
N. mucosa SSI: 10496/78 795 (N. pharyngis)/ 780 (N. mucosa) // 496 -15 Possible Misidentified Misidentified
N. pharyngis SSI: Piot 1268 822 (N. flavescens)/ 802 (N. subflava) // 504 Not givene Misidentified Misidentified Misidentified
N. polysaccharea CCUG 18030T 886 / 883 (N. meningitidis) // 505 3 Possible Low discrimination Correct genus included
N. weaveri SSI: 3667B/1997 802/708 (N. subflava) // 442 94 Confirmed Misidentified Misidentified
N. weaveri SSI: 4194/1998 889/778 (N. meningitidis) // 489 112 Confirmed Misidentified Misidentified
N. weaveri SSI: AB 2363 898/787 (N. meningitidis) // 494 111 Confirmed Low discrimination Correct genus included
N. zoodegmatis (CDC EF-4b) NCTC 12230T 836 / 782 (N. canis) // 476 54 Confirmed Misidentified Misidentified
N. zoodegmatis (CDC EF-4b) SSI: P 1168 834 / 810 (N. dentiae) // 498 24 Confirmed Misidentified Misidentified
N. zoodegmatis (CDC EF-4b) SSI: P 983 868 / 809 (N. canis) // 498 59 Confirmed Misidentified Misidentified
P. canis SSI: P 824 838 / 803 (P. dagmatis) // 501 35 Confirmed Misidentified Misidentified
P. dagmatis SSI: P 1533 857 / 839 (P. stomatis) // 501 18 Confirmed Misidentified Misidentified
P. multocida NCTC 10322T 854 / 776 (P. pneumotropica) // 495 78 Confirmed Misidentified Misidentified
P. multocida SSI: P 1367 892 / 816 (P. pneumotropica) // 497 76 Confirmed Misidentified Misidentified
P. multocida SSI: P 1320 838 / 762 (P. pneumotropica) // 469 76 Confirmed Unidentified Correct
P. stomatis SSI: P 716 796 / 774 (P. pneumotropica) // 455 22 Confirmed Unidentified Correct

a-e see Table 1a.

Table 3.

Vitek 2 NH Identification Results and Quality of Identification for all 100 Strains Included in the Study. No. of Strain(s) in Brackets


Strains (no. of Strains) Vitek 2 NH Results and Quality of Identification
A.  hominisa (3) H. influenzae, good (1); H. parahaemolyticus or A. aphrophilus/paraphrophilus, LD b (1); H. parahaemolyticus or A. aphrophilus/paraphrophilus or H. parainfluenzae, LD b (1)
A.  ureae (2) A. ureae, excellent (1); H. influenzae, excellent (1)
A.  actinomycetemcomitans (3) A. actinomycetemcomitans, excellent (2) A. actinomycetemcomitans or A. segnis, LDb (1);
A.  aphrophilus/paraphrophilus (4) A. aphrophilus/paraphrophilus, excellent (2), good (1), acceptable(1)
A.  segnis (3) A. segnis or H. parainfluenzae, LD b (1); H. influenzae or H. haemolyticus, LD b (1), unidentified (1)
C.  canimorsus (6) Capnocytophaga spp.,good (1); N. elongata, acceptable (3); unidentified (2)
C. gingivalis (1) Unidentified
C. ochracea (1) Capnocytophaga spp,excellent (1)
C. sputigena (1) unidentified
C. hominis (3) C. hominis, excellent (2), acceptable (1)
E. corrodens (3) E. corrodens, excellent(3)
H. haemolyticus (1) H. parainfluenzae, good (1)
H. influenzae (3) H. influenzae, excellent (3)
H. parahaemolyticus (2) H. parahaemolyticus, excellent (1); H. parainfluenzae, good (1)
H. parainfluenzae (3) H. parainfluenzae, excellent (2), very good (1)
H. pittmaniae c (1) A. aphrophilus/paraphrophilus or A. segnis, LD b (1)
K. denitrificans (2) K. denitrificans or N. cinerea, LDb (1); K.denitrificans or N. menigitidis, LDb (1)
K. kingae (3) K. kingae, acceptable (1); unidentified (2)
M. catarrhalis (3) M. catarrhalis, excellent (1), very good (1); N. cinerea or M. catarrhalis or N. meningitidis, LD b (1)
M. non-liquefaciens a (1) M. catarrhalis or N. gonorrhoeae,LD b (1)
M. osloensisa (1) Campylobacter fetusor Campylobactercoli, LD b (1)
N. animaloris (CDC EF-4a) a (3) N. elongata, acceptable (1); unidentified (2)
N. cinerea (3) N. cinerea, excellent (2); N. cinerea or K. denitrificans, LD b (1)
N. elongata ssp. elongata (3) N. elongata, excellent (2); N. elongata, acceptable (1)
N. flavescensa (1) N. elongata or K. denitrificans or N. cinerea, LD b (1)
N. gonorrhoeae, proA d positive (7) N. gonorrhoeae, excellent (5); N. cinerea or N. gonorrhoeae, LD b (1); N. gonorrhoeae or N. cinerea or N. elongata, LD b (1)
N. gonorrhoeae, proA negative (3) M. catarrhalis, excellent (3)
N. lactamica (2) N. lactamica, excellent (2)
N. meningitidis (10) N. meningitidis, excellent (9); N. meningitidis or N. sicca, LD b (1)
N. mucosa a (2) N. elongata, excellent (1); N. sicca excellent (1)
N. pharyngis a (1) N. sicca, excellent (1)
N. polysaccharea a (1) N. sicca or N. meningitidis LD b (1)
N. sicca (2) N. sicca, excellent (2)
N. weaveri a (3) N. elongata, excellent (2); N. cinerea or N. elongata or M. catarrhalis, LD b (1)
N. zoodegmatis (CDC EF-4b) a (3) N. elongata, excellent, (2); K. denitrificans, good (1)
P. canis a(1) H. parainfluenzae, good (1)
P. dagmatis a (1) H. parainfluenzae, good (1)
P. multocida a(3) H. influenzae, acceptable (1); H. parainfluenzae, acceptable (1); unidentified (1)
P. stomatisa (1) unidentified (1)

a Strains not included in the Vitek 2 NH database.

b LD: low discrimination.

c Formerly H. parahaemolyticus.

d ProA: proline A arylamidase.

The new validly published genus name Aggregatibacter [2] was used in the present study for the following species given as such in the Vitek database: Haemophilus actinomycetemcomitans, Haemophilus aphrophilus / paraphrophilus and Haemophilus segnis.

Identification of Strains

Conventional phenotypic identification comprised extensive characterization by the various reference laboratories at SSI according to conventional biochemical methods [1, 5, 6]. The final identification reached was considered to be the ‘gold standard’ with which identifications obtained by partial 16S rRNA gene sequence analysis and the Vitek 2 NH system were compared.

Partial 16S rRNA gene sequence analysis followed by blast examination was performed [7] using two amplification primers, BSF 8 and BSF 534, producing a 526 base pair (bp) fragment; these fragments were sequenced both ways. The edited sequences were compared to deposited sequences in the NCBI “bacteria” database (BLAST examination) and evaluated for the best and second best taxon matches taking into consideration the % identity (number of identical bases between the query and the subject sequence in the database), Maxscore bit (indication of alignment concordance) and E-values (indication of statistical significance of a given alignment). Thereby, the following results could be obtained by partial 16S rRNA gene sequencing/BLAST examination: 1) ‘confirmed’ (best species match was identical to the gold standard phenotypic identification with a distance in Maxscore bits to next best taxon match of > 15), 2) ‘probable’ (best species match was identical to the gold standard identification, but with a Maxscore bit difference to next best taxon match of < 15), 3) ‘possible’ (best species match was not identical to the gold standard identification, but the gold standard identification was among closely related taxons, which means a < 15 Maxscore bit difference to the best taxon match) or 4) ‘misidentified’ (if the conventional phenoptypis species identification was not listed among the closely related species/taxons).

Vitek 2 NH system testing was done by a microbiologist without expert knowledge of fastidious Gram-negative bacteria and was performed according to the manufacturer’s recommendations. Supplementary tests for strains identified with Low Discrimination were not done for two reasons: i) some of the supplementary tests were unavailable to us; and ii) results of these tests would tend to confuse identifications further since 25 % of the tested strains were not included in the Vitek 2 NH database.

Interpretation was done on the basis of results provided from the software (EX: excellent, VG: Very Good, GI: Good, AC: Acceptable, LD: Low Discrimination (between 2-3 identification choices), INC: Inconclusive (> 3 identification choices), and UNI: Unidentified (atypical biopattern)). The categories of results in the present study were defined as follows: (i) Correct identification was species identification identical to the ‘gold standard’ with the quality epithets EX, VG, GI and AC, except for the four Capnocytophaga species, where identification to the genus level was considered correct; (ii) Low discrimination (LD) between two or three species; (iii) Unidentified (included both INC and UNI); and (iv) Misidentification was identification with the epithets EX, VG, G and AC to a different species.

RESULTS

Partial 16S rRNA Gene Sequence Analysis Identifications

The identifications achieved by partial 16S rRNA gene sequence analysis of the 100 strains are shown in Tables 1a, 1b, and 2. Phenotypic and sequence analysis identifications to the species level were identical for 76 strains, resulting in ‘species confirmed’. For 23 strains sequence analysis identifications resulted in either ‘species probable’(n=16) or ‘species possible’ (n=7): 2 of 3 A. hominis strains, 2 of 6 C. canimorsus strains, 1 of 2 K. denitrificans strains, 1 of 2 N. lactamica strains, 1 of 2 N sicca strains and 2 of 3 N. animaloris strains; and all included strains of the following species: Actinobacillus ureae (2), C. ochracea (1), H. parahaemolyticus (2) and H. pittmanniae (1), N. cinerea (3), N. flavescens (1), N. mucosa (2) and N. polysaccharea (1). Only for the Neisseria pharyngis strain was the result of 16S rRNA gene sequence analysis in conflict with the conventional phenotypic identification, where the “gold standard” species was not among the listed taxon matches. Of the 24 strains where phenotypic and sequence analysis identifications were not identical, 12 were type strains.

Where several strains of the same species were examined, score bit differences among the different strains were about the same size for most of the species. However, for two of the six C. canimorsus strains (CCUG 19190 and CCUG 19141) the differences between first and second best taxon match were very small (0 and 3 respectively), while they were between 54 and 168 for the remaining four strains. The same applied to the two K. denitrificans strains (0 and 116). Remarkable variations in score bit differences between strains belonging to the same species were seen for all the strains of A. segnis,C. hominis, H. parainfluenzae and K. kingae. Of the 24 strains where the result was not ‘species confirmed’ by sequencing, 13 were Vitek database strains. Of these, seven were correctly identified by the Vitek 2 NH card.

Vitek Identifications of Vitek 2 NH Database Strains

Vitek 2 NH results for the 75 examined strains included in the Vitek 2 NH database are shown in Table 1a and 1b. Epithets of ‘acceptable’ or better were obtained for 57 (76%) of the strains. Of these, 48 (64%) were correctly identified, while 9 (12%) were misidentified. The risk of misidentification seems to be related to the epithets, as 4 of the 45 with ‘excellent’ identification, 0 of 2 with ‘very good’ identification, 2 of 4 with ‘good’ identification and 3 of 6 with ‘acceptable’ identification were misidentified (Table 3). The nine misidentified strains comprised three of nine Capnocytophaga strains identified as Neisseria elongata, three proline-arylamidase (proA) negative Neisseria gonorrhoeae identified as Moraxella catarrhalis, one each of Haemophilus haemolyticus (type strain) and H. parahaemolyticus, both identified as Haemophilus parainfluenzae, and one A. ureae identified as H. influenzae.

In 11 (15%) instances where ‘low discrimination’ between 2 or 3 species was obtained, the correct species was included among the suggested species for 9 strains (Table 3). For the 4 ‘low discrimination’ Neisseria spp., 3 were identified correctly to the genus level (Table 3). If one disregards the recent taxonomic changes within the genus Haemophilus (Materials and Methods), 4 of 4 ‘low discrimination’ strains of former and present Haemophilus spp. were identified correctly to the genus level (Table 3).

There were no strains where an ‘inconclusive’ result was obtained. Seven strains (9%) were unidentified: four of nine strains of Capnocytophaga spp., including the type strains of C. gingivalis and C. sputigena; two of three Kingella kingae strains, including the type strain; and the type strain of A. segnis.

Vitek Identification of Non-Vitek 2 NH Database Strains

Table 2 shows results for the 25 examined strains not included in the Vitek 2 NH database. Of these, 14 (56%) were identified with epithets of ‘excellent’ (7), ‘good’ (4), and ‘acceptable’ (3). All of these were by definition misidentified. Four strains were unidentified, which in this context is the correct result; and ‘Low discrimination’ was obtained for seven strains (Table 3).

DISCUSSION

Analysis (and comparison) of 16S rRNA gene sequences has revolutionized bacterial taxonomy and identification [9]. For strains difficult to identify by conventional phenotypic identification 16S rRNA gene sequencing is especially in focus [8]. Among the 100 strains studied, only a N. pharyngis strain obtained sequencing analysis results in conflict with the conventional phenotypic identification, as the “gold standard” species was not among the listed possible taxon matches. Importantly, the 16S rRNA gene sequence analysis results obtained did not result in misidentifications, but for 24 strains the need for further characterization was evident. This could consist of sequencing of longer bp stretches of the 16S rRNA gene, sequencing of other genes, or more extensive phenotypic characterization.

The obtained results thus illustrate both the strengths and weaknesses of the use of 16S rRNA gene sequence analysis for identification. There are, as yet, no generally accepted guidelines for correct genus and species identification, as it has not been possible to reach a consensus on threshold values like there is for DNA–DNA hybridization (Petti, 2007 [9], Stackebrandt & Goebel, 1994 [10], Janda & Abbott, 2007 [11]). In addition, different studies have identified groups of bacteria for which 16S rRNA gene sequences are less discriminative, as seen in this study for the 23 strains resulting in either species probable or possible.

Sequence divergence may vary considerably within genera and must ideally be assessed for each genus. We have attempted to elucidate the 16S rRNA gene sequence identification process by using standardized quantitative criteria for all the studied taxa (see Materials and Methods) and reporting the data in Tables 1 and 2 together with the species of the best and next best taxon match. This in order to document the 16S rRNA gene sequence identification process.

Great variation in score bit differences was seen within strains of A. segnis, C. canimorsus,C. hominis, H. parainfluenzae, K. denitrificans, and K. kingae. This might be an expression of great variation within the individual species, it may illustrate that taxonomic subgroups exist, or it could be caused by deposition of unvalidated sequences. Whether sequencing the whole 16S rRNA gene would have resulted in a confirmed species designation for the 23 probable and possible strains is not known. Of these 23 strains, 12 were type strains, six were culture collection strains and the remaining five were from well known reference laboratories.

Identification with the Vitek 2 NH card is, as with the whole Vitek 2 system, easy to handle. Correct identification (including Capnocytophaga to the genus level) was achieved for 48 of 75 (64%) strains in the Vitek 2 NH database, while 9 (12%) were misidentified. Identification problems, i.e. low discrimination and non- or misidentification of strains, were mainly connected with the Capnocytophaga spp., proA-negative N. gonnorhoeae, the haemolytic Haemophilus spp., the Kingella spp. and A. segnis. There were four misidentified strains with the epithet ‘excellent’, three gonococci and one A. ureae, which means that this epithet is not a guarantee of correct identification. It must, however, be borne in mind that the three misidentified gonococci were proA negative, a clone with this characteristic appearing most commonly in Scandinavia.

Our finding of 64% of correctly identified strains appears to be at variance with the findings of Valenza et al. [12], who found that 91% of their 188 strains were correctly identified without supplementary tests. This difference is most readily explained by differences in the qualitative and quantitative composition of the examined strains in the two studies. Valenza et al. examined no strains of proA-negative N. gonnorhoeae, H. haemolyticus, H. parahaemolyticus, A. ureae or A. segnis; and only one strain each of Capnocytophaga spp. and Kingella spp. This is in contrast to our nine strains of Capnocytophaga spp. and five strains of Kingella spp. However, these taxa represent some of the most difficult with regard to conventional identification, making it extra desirable that automatic identification results in reliable identifications. Disregarding these problematic strains, results of the two studies are similar. With regard to the 49 remaining strains in the present study we found no un- or misidentified strains compared to five unidentified and one misidentified strains among the 126 remaining strains in the study of Valenza et al.

Our results also appear to disagree with the recently published multicenter study by Rennie et al. [13], where 371 clinical strains were tested. They found 97% overall correct identification, including among the correctly identified strains 10% with low discrimination where the correct identification was among the suggested choices. Again, the variance is probably explained by the different quantitative composition of the strains examined in the two studies. Of the strains examined in the study of Rennie et al., 35% were ‘easy-to-identify’ H. influenzae and H. parainfluenzae, in contrast to only 6% in the present study. Also, their study did not comprise proA-negative N. gonorrhoeae. The conclusion drawn from the three studies is thus that the Vitek 2 system correctly identifies almost all strains of H. influenzae,H. parainfluenzae, C. hominis, E. corrodens, N. meningitidis and the four apathogenic Neisseria species included in the database.

As done previously by others [14, 15], we did not limit our study to strains included in the Vitek 2 database. This was done in order to evaluate the ability of the Vitek 2 NH card in a setting most closely emulating the diagnostic challenges in clinical microbiology laboratories. As seen under Results, 56% of these strains were erroneously ‘correctly identified’ with epithets of acceptable or better, half of them ‘excellent’. Only four strains were correctly found to be unidentified and seven showed ‘low discrimination’. This is not satisfactory.

In conclusion, the Vitek 2 NH card was found to be an easily used tool in the laboratory, being able to identify the most commonly occurring species in the database correctly. The system would benefit from including tests in the card that ensures that apparent “correct identifications” of bacteria not in the database kept at a minimum. And conversely, including tests that enable difficult bacteria such as Capnocytophaga and Kingella to be identified correctly.

CONFLICT OF INTEREST

Funding from external sources has not been received. None of the authors has any associations that can pose a conflict of interest.

ACKNOWLEDGEMENTS

Part of the results of this study were presented at the 18th European Congress of Clinical Microbiology and Infectious Diseases (Barcelona, Spain).

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