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 and 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.
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
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.
], 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
], 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.