Abstract
Due to the inadequate automation in the amplification and sequencing procedures, the use of 16S rRNA gene sequence-based methods in clinical microbiology laboratories is largely limited to identification of strains that are difficult to identify by phenotypic methods. In this study, using conventional full-sequence 16S rRNA gene sequencing as the “gold standard,” we evaluated the usefulness of the MicroSeq 500 16S ribosomal DNA (rDNA)-based bacterial identification system, which involves amplification and sequencing of the first 527-bp fragment of the 16S rRNA genes of bacterial strains and analysis of the sequences using the database of the system, for identification of clinically significant bacterial isolates with ambiguous biochemical profiles. Among 37 clinically significant bacterial strains that showed ambiguous biochemical profiles, representing 37 nonduplicating aerobic gram-positive and gram-negative, anaerobic, and Mycobacterium species, the MicroSeq 500 16S rDNA-based bacterial identification system was successful in identifying 30 (81.1%) of them. Five (13.5%) isolates were misidentified at the genus level (Granulicatella adiacens was misidentified as Abiotrophia defectiva, Helcococcus kunzii was misidentified as Clostridium hastiforme, Olsenella uli was misidentified as Atopobium rimae, Leptotrichia buccalis was misidentified as Fusobacterium mortiferum, and Bergeyella zoohelcum was misidentified as Rimerella anatipestifer), and two (5.4%) were misidentified at the species level (Actinomyces odontolyticus was misidentified as Actinomyces meyeri and Arcobacter cryaerophilus was misidentified as Arcobacter butzleri). When the same 527-bp DNA sequences of these seven isolates were compared to the known 16S rRNA gene sequences in the GenBank, five yielded the correct identity, with good discrimination between the best and second best match sequences, meaning that the reason for misidentification in these five isolates was due to a lack of the 16S rRNA gene sequences of these bacteria in the database of the MicroSeq 500 16S rDNA-based bacterial identification system. In conclusion, the MicroSeq 500 16S rDNA-based bacterial identification system is useful for identification of most clinically important bacterial strains with ambiguous biochemical profiles, but the database of the MicroSeq 500 16S rDNA-based bacterial identification system has to be expanded in order to encompass the rarely encountered bacterial species and achieve better accuracy in bacterial identification.
Identification of bacteria in clinical microbiology laboratories is traditionally performed by isolation of the organisms and study of their phenotypic characteristics, including Gram staining, morphology, culture requirements, and biochemical reactions. However, these methods of bacterial identification have major drawbacks. First, they cannot be used for noncultivable organisms. Second, we are occasionally faced with organisms exhibiting biochemical characteristics that do not fit into patterns of any known genus and species. Third, identification of slow-growing organisms would be extremely slow and difficult.
Since the discovery of PCR and DNA sequencing, comparison of the gene sequences of bacterial species showed that the 16S rRNA gene is highly conserved within a species and among species of the same genus, and hence can be used as the new “gold standard” for identification of bacteria to the species level. Using this new standard, phylogenetic trees, based on base differences between species, are constructed, and bacteria are classified and reclassified into new genera (8). Recently we have reported the use of this technique for the identification to species level of bacterial strains that have posed problems of identification in our clinical microbiology laboratory, as well as the clinical impact of accurate identification of such isolates (1, 3, 4, 5, 6, 13, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25-29, 31; P. C. Y. Woo, J. H. C. Li, W. M. Tang, and K. Y. Yuen, Letter, N. Engl. J. Med. 345:842-843, 2001).
The MicroSeq 500 16S ribosomal DNA (rDNA)-based bacterial identification system (Perkin-Elmer Applied Biosystems Division, Foster City, Calif.) has been designed for rapid and accurate identification of bacterial pathogens. In this system, the first 527-bp fragment of the 16S rRNA gene of the bacterial strain is amplified, sequenced, and analyzed using the database of the system. It has been shown that the system is useful for the identification of aerobic pathogenic gram-negative bacilli, Mycobacterium species, and coryneform bacteria (9, 11, 12). However, due to the inadequate automation in the amplification and sequencing procedures, it is still very labor-intensive and not cost-effective to use this system for routine identification of all bacterial isolates in clinical microbiology laboratories. At the moment, the use of this system or other 16S rRNA gene sequence-based identification methods for bacterial identification in clinical microbiology laboratories is largely limited to the identification of strains that are difficult to identify by phenotypic methods. In this study, using DNA sequencing of the complete 16S rRNA gene as the gold standard, we evaluated the usefulness of this system in the identification of 37 clinically significant bacterial strains that showed ambiguous biochemical profiles. These strains represented 37 nonduplicating aerobic gram-positive and gram-negative, anaerobic, and Mycobacterium species. The potential for 16S rRNA gene sequencing for general use in clinical microbiology laboratories is also discussed.
MATERIALS AND METHODS
Bacterial strains.
The bacterial strains used in this study were isolated from patient specimens and obtained from the Clinical Microbiology Laboratory of Queen Mary Hospital in Hong Kong (1995 to 2001). Based on the Gram smear appearances, growth requirements, colonial morphologies, and the results of other simple phenotypic tests, such as motility, catalase, and cytochrome oxidase, appropriate strips or cards of the API system (bioMerieux Vitek, Hazelwood, Mo.) and Vitek system (bioMerieux Vitek) and/or additional conventional biochemical methods were used for identification of the bacterial strains (7). An ambiguous biochemical profile is defined as disagreement between the results provided by the API and Vitek systems or a biochemical profile that did not fit the typical profiles of known bacterial species (7). All bacterial strains that were clinically significant but showed ambiguous biochemical profiles were subject to conventional 16S rRNA gene sequencing. After excluding novel bacterial species, 37 strains, representing 37 nonduplicating aerobic gram-positive and gram-negative, anaerobic, and Mycobacterium species, were selected for DNA sequencing of the first 527-bp fragment of the 16S rRNA gene and analysis by the MicroSeq 16S rDNA-based bacterial identification system. Among the 37 strains, 24 (64.9%) were isolated from blood, four (10.8%) were isolated from stool, three (8.1%) were isolated from pus, two (5.4%) were isolated from biopsy specimens, one (2.7%) was isolated from bile, one (2.7%) was isolated from bronchoalveolar lavage, one (2.7%) was isolated from an intrauterine contraceptive device, and one (2.7%) was isolated from a cochlear implant.
Extraction of bacterial DNA.
Bacterial DNA extraction was modified from our previous published protocol (21). Briefly, 80 μl of NaOH (0.05 M) was added to 20 μl of bacterial cells suspended in distilled water, and the mixture was incubated at 60°C for 45 min, followed by addition of 6 μl of Tris-HCl (pH 7.0), achieving a final pH of 8.0. The resultant mixture was diluted 100-fold, and 5 μl of the diluted extract was used for PCR.
PCR, gel electrophoresis, and conventional 16S rRNA gene sequencing.
PCR amplification and DNA sequencing of the full 16S rRNA genes were performed according to our previous publications (1, 3, 4, 5, 6, 13, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25-29, 31; Woo et al., letter). Briefly, DNase I-treated distilled water and PCR master mix (which contains deoxynucleoside triphosphates [NTPs], PCR buffer, and Taq polymerase) were used in all PCRs by adding 1 U of DNase I (Pharmacia, Sweden) to 40 μl of distilled water or PCR master mix, incubating the mixture at 25°C for 15 min, and subsequently at 95°C for 10 min to inactivate the DNase I. The bacterial DNA extracts and control were amplified with 0.5 μM primers (Table 1) (Gibco BRL, Rockville, Md.). The PCR mixture (50 μl) contained bacterial DNA, PCR buffer (10 mM Tris-HCl [pH 8.3], 50 mM KCl, 2 mM MgCl2, 0.01% gelatin), a 200 μM concentration of each dNTP, and 1.0 U of Taq polymerase (Boehringer, Mannheim, Germany). The mixtures were amplified in 40 cycles of 94°C for 1 min, 55°C for 1 min, and 72°C for 2 min, with a final extension at 72°C for 10 min, in an automated thermal cycler (Perkin-Elmer Cetus, Gouda, The Netherlands). DNase I-treated distilled water was used as the negative control. 10 μl of each amplified product was electrophoresed in 1.0% (wt/vol) agarose gel, with a molecular size marker (Lambda DNA AvaII digest; Boehringer) in parallel. Electrophoresis in Tris-borate-EDTA buffer was performed at 100 V for 1.5 h. The gel was stained with ethidium bromide (0.5 μg/ml) for 15 min, rinsed, and photographed under UV light illumination.
TABLE 1.
PCR primers used for conventional 16S rRNA gene sequencing
| Strain no. | Primer
|
|
|---|---|---|
| Forward | Backward | |
| 1 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 2 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 3 | LPW81 5′-TGGCGAACGGGTGAGTAA-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 4 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 5 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 6 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 7 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 8 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 9 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 10 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 11 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 12 | LPW200 5′-GAGTTGCGAACGGGTGAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 13 | LPW398 5′-GGCGTGCTTACCACATG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 14 | LPW398 5′-GGCGTGCTTACCACATG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 15 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 16 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW325 5′-CGGATACCTTGTTACGACT-3′ |
| 17 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW282 5′-GCTTCGGGTGTYRCCAACTTTC-3′ |
| 18 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW205 5′-CTTGTTACGACTTCACCC-3′ |
| 19 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW325 5′-CGGATACCTTGTTACGACT-3′ |
| 20 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW325 5′-CGGATACCTTGTTACGACT-3′ |
| 21 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW325 5′-CGGATACCTTGTTACGACT-3′ |
| 22 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW325 5′-CGGATACCTTGTTACGACT-3′ |
| 23 | LPW81 5′-TGGCGAACGGGTGAGTAA-3′ | LPW324 5′-TTGTTACGACTTCACCCCA-3′ |
| 24 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 25 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 26 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 27 | LPW81 5′-TGGCGAACGGGTGAGTAA-3′ | LPW307 5′-TAGCGATTCCGACTTCAT-3′ |
| 28 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 29 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW182 5′-AGTCGCTGATTCCACTGTGG-3′ |
| 30 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW182 5′-AGTCGCTGATTCCACTGTGG-3′ |
| 31 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW182 5′-AGTCGCTGATTCCACTGTGG-3′ |
| 32 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 33 | LPW55 5′-AGTTTGATCCTGGCTCAG-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 34 | LPW81 5′-TGGCGAACGGGTGAGTAA-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 35 | LPW81 5′-TGGCGAACGGGTGAGTAA-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 36 | LPW81 5′-TGGCGAACGGGTGAGTAA-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
| 37 | LPW81 5′-TGGCGAACGGGTGAGTAA-3′ | LPW58 5′-AGGCCCGGGAACGTATTCAC-3′ |
The PCR products were gel purified using the QIAquick PCR purification kit (QIAgen, Hilden, Germany). Both strands of the PCR products were sequenced twice with an ABI 377 automated sequencer according to manufacturers' instructions (Perkin-Elmer Applied Biosystems Division), using the PCR primers and additional primers designed from the first round of sequencing results. The sequences of the PCR products were compared with known 16S rRNA gene sequences in the GenBank by multiple sequence alignment using the CLUSTAL W program (14).
PCR amplification and DNA sequencing of the first 527-bp fragment of the 16S rRNA gene and analysis by the MicroSeq 500 16S rDNA-based bacterial identification system.
Bacterial DNA extracts were amplified with 0.5 μM primers (005F and 531R). The PCR mixture (50 μl) contained bacterial DNA, PCR buffer (10 mM Tris-HCl [pH 8.3], 50 mM KCl, 3 mM MgCl2, 0.01% gelatin), a 200 μM concentration of each dNTP, and 1.0 U of Taq polymerase (Boehringer Mannheim, Germany). The mixtures were amplified in 30 cycles of 95°C for 30 s, 60°C for 30 s, and 72°C for 45 s, with a final extension at 72°C for 10 min, in an automated thermal cycler (Perkin-Elmer Cetus). The amplified products were purified and sequenced as described above. The DNA sequences were analyzed using the database provided by the MicroSeq 500 16S rDNA-based bacterial identification system.
RESULTS
Conventional 16S rRNA gene sequencing.
PCR of the 16S rRNA genes of the 37 isolates with ambiguous biochemical profiles showed bands at about 1,400 to 1,500 bp. For all 37 isolates, there was <1% difference between the 16S rRNA gene sequences of the isolates and the most closely matched sequence in the GenBank.
DNA sequencing of the first 527-bp fragment of the 16S rRNA gene and analysis by the MicroSeq 500 16S rDNA-based bacterial identification system.
PCR amplification of the first 527-bp fragments of the 16S rRNA genes of the 37 isolates showed bands at about 500 bp. Analysis of the 37 sequences using the MicroSeq 500 16S rDNA-based bacterial identification database showed that the identities of 30 (81.1%) strains were the same as those obtained by conventional 16S rRNA gene sequencing (Table 2). For the remaining seven (18.9%) sequences, five (13.5%) isolates were misidentified at the genus level (case 8, Granulicatella adiacens misidentified as Abiotrophia defectiva; case 12, Helcococcus kunzii misidentified as Clostridium hastiforme; case 19, Olsenella uli misidentified as Atopobium rimae; case 22, Leptotrichia buccalis misidentified as Fusobacterium mortiferum; and case 27, Bergeyella zoohelcum misidentified as Rimerella anatipestifer), whereas two (5.4%) were misidentified at the species level (case 17, Actinomyces odontolyticus misidentified as Actinomyces meyeri; case 32, Arcobacter cryaerophilus misidentified as Arcobacter butzleri).
TABLE 2.
Identification of clinical bacterial isolates with ambiguous biochemical profiles by conventional 16S rRNA gene sequencing, commercially available bacterial identification systems, and the Microseq 500 16S rDNA-based bacterial identification system
| Strain no. (reference) | Sexa/age (yr) of patient | Clinical specimen or specimen source | Identification by conventional 16S rRNA gene sequencing | Identification by commercially available bacterial identification system
|
Identification by MicroSeq 500 16S rDNA-based bacterial identification system
|
||||
|---|---|---|---|---|---|---|---|---|---|
| Vitek
|
API
|
Identity | Percentage difference between sequence of isolates and that of identified bacterium in database | ||||||
| Card used for identification | Identityc | Strips for identification | Identity | ||||||
| 1 (20) | F/43 | Blood | Staphylococcus aureus | GPI | 95% Staphylococcus simulans, 3% Staphylococcus haemolyticus | STAPH | 87% Staphylococcus aureus, 5% Staphylococcus warneri | Staphylococcus aureus | 0 |
| 2 | M/3 | Cochlear implant | Staphylococcus epidermidis | GPI | 53% Staphylococcus epidermidis, 27% Staphylococcus capitis | STAPH | 99% Staphylococcus chromogenes | Staphylococcus epidermidis | 0 |
| 3 | F/3 | Blood | Micrococcus luteus | GPI | 99.9% Micrococcus sp. | STAPH | 99% Staphylococcus auricularis | Micrococcus luteus | 0.2 |
| 4 (16) | F/69 | Blood | Streptococcus dysgalactiae | GPI | Unidentified | 20 STREP | 77% Streptococcus dysgalactiae | Streptococcus dysgalactiae | 0.6 |
| 5 (6) | M/81 | Blood | Streptococcus iniae | GPI | Unidentified | 20 STREP | 99% Streptococcus dysgalactiae | Streptococcus iniae | 0 |
| 6 | M/33 | Bronchoalveolar lavage | Streptococcus salivarius | GPI | Unidentified | 20 STREP | Unidentified | Streptococcus salivarius | 0.4 |
| 7 | F/31 | Blood | Streptococcus anginosus | GPI | Unidentified | 20 STREP | 70% Streptococcus intermedius, 30% Streptococcus anginosus | Streptococcus anginosus | 0 |
| 8 (19) | M/62 | Blood | Granulicatella adiacens | GPI | 98% Gemella morbillorum or Streptococcus agalactiae or Streptococcus acidominimus | 20 STREP | 80% Granulicatella adiacens, 16% Gemella morbillorum | Abiotrophia defectiva | 9.9 |
| 9 (19) | M/15 | Blood | Abiotrophia defectiva | GPI | Unidentified | 20 STREP | Unidentified | Abiotrophia defectiva | 1.1 |
| 10 | M/66 | Blood | Gemella morbillorum | GPI | 63% Gemella morbillorum or Streptococcus agalactiae or Streptococcus acidominimus, 22% Streptococcus pneumoniae | 20 STREP | 60% Leuconostoc sp., 28% Streptococcus mitis | Gemella morbillorum | 0.1 |
| 11 | F/41 | Blood | Gemella haemolysans | GPI | 68% Gemella morbillorum or Streptococcus agalactiae or Streptococcus acidominimus, 24% Streptococcus pneumoniae | 20 STREP | 95% Gemella haemolysans, 6% Gemella morbillorum | Gemella haemolysans | 0.6 |
| 12 | M/41 | Blood | Helcococcus kunzii | GPI | Unidentified | 20 STREP | 50% Gemella haemolysans, 30% Gemella morbillorum, 18% Abiotrophia adiacens | Clostridium hastiforme | 13.9 |
| 13 (4) | F/39 | Blood | Microbacterium sp. | GPI | Unidentified | CORYNE | 99% Aureobacterium or Corynebacterium aquaticum | Microbacterium liquefaciens | 0.8 |
| 14 | M/22 | Blood | Gordona terrae | GPI | Unidentified | CORYNE | 98% Rhodococcus sp. | Gordona terrae | 0 |
| 15 | M/38 | Pus swab | Tsukamurella sp. | GPI | Unidentified | CORYNE | 83% Rhodococcus sp., 12% Aureobacterium or Corynebacterium aquaticum | Tsukamurella pulmonis | 0.2 |
| 16 | M/71 | Blood | Clostridium ramosum | ANI | 86% Clostridium barati, 7% Lactobacillus catenaforme, 2% Clostridium paraputrificum | 20A | 97% Bifidobacterium sp. | Clostridium ramosum | 0 |
| 17 (18) | F/36 | Intrauterine contraceptive device | Actinomyces odontolyticus | ANI | 99% Propionibacterium granulosum | 20A | 78% Actinomyces meyeri or odontolyticus, 19% Actinomyces naeslundii | Actinomyces meyeri | 2.1 |
| 18 (15) | M/70 | Blood | Lactobacillus salivarius | ANI | Unidentified | 20A | 70% Actinomyces naeslundii, 30% Bifidobacterium sp. | Lactobacillus salivarius | |
| 19 | M/43 | Blood | Olsenella uli | ANI | 81% Propionibacterium granulosum, 11% Corynebacterium pseudotuberculosis | 20A | 43% Gemella morbillorum, 37% Lactobacillus fermentum, 10% Proionibacterium propionicum or avidum | Atopobium rimae | 11.1 |
| 20 | F/87 | Blood | Eggerthella lenta | ANI | 83% Corynebacterium jeikeium, 7% Clostridium hastiforme, 7% Clostridium histolyticum | 20A | 92% Eggerthella lenta, 8% Actinomyces viscosus | Eggerthella lenta | 0 |
| 21 | M/69 | Blood | Eubacterium tenue | ANI | 98% Clostridium difficile | 20A | 76% Clostridium bifermentens, 18% Clostridium cadaveris | Eubacterium tenue | 0 |
| 22 | F/70 | Blood | Leptotrichia buccalis | ANI | 99% Propionibacterium granulosum | 20A | 98% Bifidobacterium sp. | Fusobacterium mortiferum | 14.6 |
| 23 | M/36 | Blood | Neisseria elongata | NHI | 91% Kingella denitrificans | NH | 99% Neisseria gonorrhoeae | Neisseria elongata | 0 |
| 24 (28) | M/31 | Stool | Escherichia coli | GNI+ | 73% Salmonella arizonae, 17% Salmonella spp. | 20E | 76% Escherichia coli, 23% Salmonella arizonae | Escherichia coli | 0.1 |
| 25 | M/48 | Stool | Kluyvera ascorbata | GNI+ | 57% Kluyvera sp., 36% Enterobacter intermedius | 20E | 57% Enterobacter amnigenus, 24% Enterobacter intermedius, 19% Kluyvera sp. | Kluyvera ascorbata | 0.2 |
| 26 | M/60 | Bile | Shewanella alga | GNI+ | 76% nonfermenting gram-negative bacillus, 11% Comamonas acidovorans | 20NE | Unidentified | Shewanella alga | 0.4 |
| 27 | M/4 | Pus | Bergeyella zoohelcum | GNI+ | 53% nonfermenting gram-negative bacillus (asaccharolytic), 39% Myroides sp. | 20NE | 51.2% Psychrobacter phenylpyruvicus, 36.5% Bergeyella zoohelcum, 8.5% Methylobacterium mesophilicum | Riemerella anatipestifer | 7.4 |
| 28 (3) | M/32 | Blood | Haemophilus segnis | NHI | 56% Actinobacillus actinomycetemcomitans | NH | 58% Haemophilus aphrophilus or Haemophilus paraphrophilus | Haemophilus segnis | 0.2 |
| 29 (26) | M/66 | Blood | Campylobacter fetus | NDb | ND | ND | ND | Campylobacter fetus | 0 |
| 30 | M/9m | Stool | Campylobacter jejuni | ND | ND | ND | ND | Campylobacter jejuni or Campylobacter coli | 0 |
| 31 | F/41 | Stool | Campylobacter hyointestinalis | ND | ND | ND | ND | Campylobacter hyointestinalis | 0 |
| 32 (25) | M/7 | Blood | Arcobacter cryaerophilus | ND | ND | ND | ND | Arcobacter butzleri | 2.6 |
| 33 (5) | F/69 | Blood | Arcobacter butzleri | ND | ND | ND | ND | Arcobacter butzleri | 0 |
| 34 (27) | F/79 | Tissue biopsy | Mycobacterium chelonae | ND | ND | ND | ND | Mycobacterium chelonae or Mycobacterium abscessus | 0 |
| 35 (24) | F/9 | Blood | Mycobacterium neoaurum | ND | ND | ND | ND | Mycobacterium neoaurum | 0 |
| 36 (27) | M/62 | Tissue biopsy | Mycobacterium nonchromogenicum | ND | ND | ND | ND | Mycobacterium nonchromogenicum | 1.6 |
| 37 | M/77 | Pus | Mycoplasma hominis | ND | ND | ND | ND | Mycoplasma hominis | 0.1 |
M, male; F, female.
ND, not done.
Percent identity and organism name are given. In cases where identity with more than one organism was found, both or all are listed.
Identification by commercially available bacterial identification systems.
Phenotypic identification using API and Vitek systems were performed in 28 of the 37 isolates. Using full 16S rRNA gene sequencing as the gold standard, the API system correctly identified seven (25%) of the 28 isolates at >70% confidence, whereas the Vitek system only correctly identified one (3.6%) of the 28 isolates at >70% confidence (Table 2).
DISCUSSION
In this study, we showed that the MicroSeq 500 16S rDNA-based bacterial identification system is useful for identification of most clinically important bacterial strains with ambiguous biochemical profiles, and hence would be a useful substitution for conventional full-sequence 16S rRNA gene sequencing in identification of bacterial strains that pose problems in clinical microbiology laboratories. Using conventional 16S rRNA gene sequencing as the gold standard, the MicroSeq 500 16S rRNA bacterial identification system is able to identify 32 (86.5%) of the 37 (including 15 aerobic or facultative anaerobic gram-positive; 11 aerobic, microaerophilic, facultative anaerobic gram-negative; seven anaerobic; three mycobacterial; and one Mycoplasma) isolates with ambiguous biochemical profiles to the genus level, and is able to identify 30 (81.1%) of these 37 isolates to the species level.
The most important reason for failure of the MicroSeq 500 16S rDNA-based bacterial identification system in identifying a bacterium is a lack of the 16S rRNA gene sequence of the particular bacterium in the database. PCR amplification of all 37 isolates using 005F and 531R as PCR primers were successful, yielding specific bands at about 500 bp. Furthermore, DNA sequencing of the corresponding PCR products using the same oligonucleotides as sequencing primers posed no problems. When the sequences of the 527 bp were aligned to the database of the MicroSeq 500 16S rDNA-based bacterial identification system, seven (18.9%) of the isolates did not yield the correct identity. The 16S rRNA gene sequences of all the seven isolates were not included in the MicroSeq 500 16S rDNA-based bacterial identification system database, probably because they were expected to be rarely encountered. On the other hand, when the same 527-bp DNA sequences of these seven isolates were compared to the known 16S rRNA gene sequences in the GenBank, five yielded the correct identity, with good discrimination between the best and second best match sequences For the remaining two strains, only full 16S rRNA gene sequencing correctly identified them with good discrimination, indicating that the first 527-bp fragments of the 16S rRNA genes of these species were not discriminative enough. This discrepancy between using the GenBank database and the MicroSeq 500 16S rDNA-based bacterial identification system database suggests that the database of the latter has to be expanded in order to encompass the rarely encountered bacterial species and achieve better accuracy in identification of bacteria with ambiguous biochemical profiles. If this limitation of the MicroSeq 500 16S rDNA-based bacterial identification system database is overcome, it would be a better choice than full 16S rRNA gene sequencing in clinical microbiology laboratories, as it involves amplification and sequencing of only about 500 bp. Therefore it would be less time consuming and expensive than full 16S rRNA gene sequencing.
16S rRNA gene sequencing will continue to be the working gold standard for the identification of most bacteria, and better automation of such a technique may put it into routine use in large clinical microbiology laboratories, especially those serving tertiary centers, replacing the traditional phenotypic tests. Compared to phenotypic tests, 16S rRNA gene sequence-based identification schemes are superior in the identification of strains considered unidentifiable due to atypical biochemical profiles, slow-growing bacteria, rarely encountered bacterial species, and noncultivable strains. Furthermore, such a technique will be applicable to not only pyogenic bacteria but also other organisms such as mycobacteria (24, 27; Woo et al., letter), of which the identification is not routinely performed in most clinical microbiology laboratories because special expertise and equipment such as gas liquid chromatography are required. Modern technologies have made it possible to construct a high density of oligonucleotide arrays on a chip with oligonucleotides representing the 16S rRNA gene sequence of various bacteria. Such a design will facilitate automation of the annealing and detection of the PCR products of 16S rRNA gene amplification and avoid the step of sequencing the amplified PCR products. Hence, the turnaround time can be even shorter. Since amplification of the 16S rRNA gene takes only 4 to 6 h, and the annealing and detection of PCR product takes only another few hours, theoretically the identification can be completed within 1 day. However, at the moment, due to the inadequate automation of the DNA amplification and sequencing steps, it would not be cost-effective to use the MicroSeq 500 16S rDNA-based bacterial identification system for identification of all bacterial isolates in clinical microbiology laboratories. On the contrary, the use of this system for identification of clinically important bacteria with ambiguous biochemical profiles would be more cost-effective and the accuracy can be easily improved with expansion and regular updating of the database.
Despite the usefulness of 16S rRNA gene sequence-based identification in most circumstances, there are still problems in some situations that remain to be solved. These include sharing of similar 16S rRNA gene sequences among different bacterial species and too much variation of the 16S rRNA gene sequences among different strains within the same species. When two or more bacterial species such as Streptococcus pneumoniae, Streptococcus oralis, and Streptococcus mitis; Burkholderia pseudomallei and Burkholderia thailandensis; and some rapidly growing Mycobacterium species share similar 16S rRNA gene sequences, 16S rRNA gene sequence-based identification systems would be unable to differentiate the species. Additional sequencing systems based on other conserved gene sequences, such as groEL gene sequencing, has to be employed for the differentiation of these species (2, 10, 30). As for the problem of too much variation of the 16S rRNA gene sequences among different strains within the same species, such as in Enterobacter, Pantoea, and Leclercia, reclassification of these groups of bacteria may be necessary to achieve better identification using gene sequence-based bacterial identification systems. However, despite the impossibility to accurately assign a particular clinical isolate to a specific species, assigning the clinical isolate to a certain group can successfully assist the clinical management of the corresponding patient (23).
TABLE 3.
Analysis of DNA sequences of strains identified incorrectly using database of Microseq 500 16S rDNA bacterial identification system
| Strain no. | Identification by conventional 16S rRNA gene sequencing | Identification by DNA sequencing of first 527-bp fragment of 16S rRNA gene
|
||||
|---|---|---|---|---|---|---|
| Analysis by using database of MicroSeq 500 16S rDNA bacterial identification system | Analysis by using database of GenBank
|
|||||
| Best match | No. of base differences (%) between strain and best match | Second best match | No. of base differences (%) between strain and second best match | |||
| 8 | Granulicatella adiacens | Abiotrophia defectiva | Granulicatella adiacens | 0 (0) | Abiotrophia paraadiacens | 1 (0.2) |
| 12 | Helcococcus kunzii | Clostridium hastiforme | Helcococcus kunzii | 10 (1.9) | Sedimentibacter hydroxybenzoicus | 91 (17.3) |
| 17 | Actinomyces odontolyticus | Actinomyces meyeri | Actinomyces odontolyticus | 0 (0) | Actinomyces meyeri | 4 (0.8) |
| 19 | Olsenella uli | Atopobium rimae | Olsenella uli | 0 (0) | Olsenella profusa | 21 (4.0) |
| 22 | Leptotrichia buccalis | Fusobacterium mortiferum | Leptotrichia buccalis | 2 (0.4) | Streptobacillus moniliformis | 73 (13.6) |
| 27 | Bergeyella zoohelcum | Riemerella anatipestifer | Bergeyella zoohelcum | 2 (0.4) | Riemerella anatipestifer | 22 (4.2) |
| 32 | Arcobacter cryaerophilus | Arcobacter butzleri | Arcobacter cryaerophilus | 0 (0) | Arcobacter butzleri | 12 (2.3) |
Acknowledgments
This work is partly supported by the University Development Fund, University Research Grant Council, and the Committee for Research and Conference Grant, The University of Hong Kong.
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