Experience with the MicroSeq D2 large-subunit ribosomal DNA sequencing kit for identification of commonly encountered, clinically important yeast species

Experience with a MicroSeq D2 large-subunit (LSU) ribosomal DNA (rDNA) sequencing kit for identification of yeast species commonly encountered in the mycology laboratory at Mayo Clinic is described here. A total of 131 isolates of yeasts recovered from clinical specimens were included in the study. Phenotypic methods used for initial identification included germ tube formation, urease production, microscopic morphological features on cornmeal agar, and an API 20C AUX system; all isolates were sequenced using a MicroSeq D2 LSU rDNA sequencing kit. Nucleic acid sequencing identified 93.9% of the isolates to the correct genus and species. A total of 100 of the isolates (representing 19 species of Candida) were sequenced, and 98% gave results concordant with identifications made by the API 20C AUX system; distance scores ranged from 0 to 1.88%, with an average value of 0.23%. Candida dubliniensis was not included in the MicroSeq database and was identified as Candida albicans. A total of 32 isolates representing 9 other genera (including Cryptococcus, Filobasidium, Kloeckera, Malassezia, Pichia, Sporidiobolus, Rhodotorula, Zygosaccharomyces, and Trichosporon) were included, and 81.3% showed concordant results when phenotypic and sequencing results were compared. Most discrepancies were attributed to the lack of inclusion of the species in the MicroSeq or API 20C AUX database. The MicroSeq D2 LSU rDNA sequencing kit appears to be accurate and useful for the identification of yeasts that might be seen in a clinical laboratory.     

Identification of coryneform bacterial isolates by ribosomal DNA sequence analysis

Identification of coryneform bacteria to the species level is important in certain circumstances for differentiating contamination and/or colonization from infection, which influences decisions regarding clinical intervention. However, methods currently used in clinical microbiology laboratories for the species identification of coryneform bacteria are often inadequate. We evaluated the MicroSeq 500 16S bacterial sequencing kit (Perkin-Elmer Biosystems, Foster City, Calif.), which is designed to sequence the first 527 bp of the 16S rRNA gene for bacterial identification, by using 52 coryneform gram-positive bacilli from clinical specimens isolated from January through June 1993 at the Mayo Clinic. Compared to conventional and supplemented phenotypic methods, MicroSeq provided concordant results for identification to the genus level for all isolates. At the species level, MicroSeq provided concordant results for 27 of 42 (64.3%) Corynebacterium isolates and 5 of 6 (83.3%) Corynebacterium-related isolates, respectively. Within the Corynebacterium genus, MicroSeq gave identical species-level identifications for the clinically significant Corynebacterium diphtheriae (4 of 4) and Corynebacterium jeikeium (8 of 8), but it identified only 50.0% (15 of 30) of other species (P < 0.01). Four isolates from the genera Arthrobacter, Brevibacterium, and Microbacterium, which could not be identified to the species level by conventional methods, were assigned a species-level identification by MicroSeq. The total elapsed time for running a MicroSeq identification was 15.5 to 18.5 h. These data demonstrate that the MicroSeq 500 16S bacterial sequencing kit provides a potentially powerful method for the definitive identification of clinical coryneform bacterium isolates.     

Experience with the MicroSeq D2 large-subunit ribosomal DNA sequencing kit for identification of filamentous fungi encountered in the clinical laboratory

Described herein is our experience with the MicroSeq D2 large-subunit rDNA sequencing kit for the identification of filamentous fungi encountered in the mycology laboratory at the Mayo Clinic. A total of 234 filamentous fungi recovered from clinical specimens were used in the evaluation. All were identified by using phenotypic characteristics as observed macroscopically and microscopically on any medium or a combination of media, which included Sabouraud's dextrose, inhibitory mold, cornmeal, Czapek-Dox, potato dextrose, and V8 juice agars; all isolates were sequenced using the MicroSeq D2 large-subunit rDNA sequencing kit. Of the of 234 isolates, 158 were correctly identified to the appropriate genus or genus and species by using nucleic acid sequencing. Sequences for 70 (29.9%) of the isolates (27 genera) were not included in the MicroSeq library. Of the 80 dematiaceous and 154 hyaline fungi sequenced, 65 and 51.2%, respectively, gave results concordant with those determined by phenotypic identification. Nucleic acid sequencing using the MicroSeq D2 large-subunit rDNA sequencing kit offers promise of being an accurate identification system; however, the associated library needs to include more of the clinically important genera and species.     

Identification of Mycobacterium spp. by using a commercial 16S ribosomal DNA sequencing kit and additional sequencing libraries

Current methods for identification of Mycobacterium spp. rely upon time-consuming phenotypic tests, mycolic acid analysis, and narrow-spectrum nucleic acid probes. Newer approaches include PCR and sequencing technologies. We evaluated the MicroSeq 500 16S ribosomal DNA (rDNA) bacterial sequencing kit (Applied Biosystems, Foster City, Calif.) for its ability to identify Mycobacterium isolates. The kit is based on PCR and sequencing of the first 500 bp of the bacterial rRNA gene. One hundred nineteen mycobacterial isolates (94 clinical isolates and 25 reference strains) were identified using traditional phenotypic methods and the MicroSeq system in conjunction with separate databases. The sequencing system gave 87% (104 of 119) concordant results when compared with traditional phenotypic methods. An independent laboratory using a separate database analyzed the sequences of the 15 discordant samples and confirmed the results. The use of 16S rDNA sequencing technology for identification of Mycobacterium spp. provides more rapid and more accurate characterization than do phenotypic methods. The MicroSeq 500 system simplifies the sequencing process but, in its present form, requires use of additional databases such as the Ribosomal Differentiation of Medical Microorganisms (RIDOM) to precisely identify subtypes of type strains and species not currently in the MicroSeq library.     

Use of the MicroSeq 500 16S rRNA gene-based sequencing for identification of bacterial isolates that commercial automated systems failed to identify correctly

Reliable automated identification and susceptibility testing of clinically relevant bacteria is an essential routine for microbiology laboratories, thus improving patient care. Examples of automated identification systems include the Phoenix (Becton Dickinson) and the VITEK 2 (bioMerieux). However, more and more frequently, microbiologists must isolate "difficult" strains that automated systems often fail to identify. An alternative approach could be the genetic identification of isolates; this is based on 16S rRNA gene sequencing and analysis. The aim of the present study was to evaluate the possible use of MicroSeq 500 (Applera) for sequencing the 16S rRNA gene to identify isolates whose identification is unobtainable by conventional systems. We analyzed 83 "difficult" clinical isolates: 25 gram-positive and 58 gram-negative strains that were contemporaneously identified by both systems--VITEK 2 and Phoenix--while genetic identification was performed by using the MicroSeq 500 system. The results showed that phenotypic identifications by VITEK 2 and Phoenix were remarkably similar: 74% for gram-negative strains (43 of 58) and 80% for gram-positive strains were concordant by both systems and also concordant with genetic characterization. The exceptions were the 15 gram-negative and 9 gram-positive isolates whose phenotypic identifications were contrasting or inconclusive. For these, the use of MicroSeq 500 was fundamental to achieving species identification. In clinical microbiology the use of MicroSeq 500, particularly for strains with ambiguous biochemical profiles (including slow-growing strains), identifies strains more easily than do conventional systems. Moreover, MicroSeq 500 is easy to use and cost-effective, making it applicable also in the clinical laboratory.     

Usefulness of the MicroSeq 500 16S rDNA bacterial identification system for identification of anaerobic Gram positive bacilli isolated from blood cultures

Using full 16S ribosomal RNA (rRNA) gene sequencing as the gold standard, 20 non-duplicating anaerobic Gram positive bacilli isolated from blood cultures were analysed by the MicroSeq 500 16S rDNA bacterial identification system. The MicroSeq system successfully identified 13 of the 20 isolates. Four and three isolates were misidentified at the genus and species level, respectively. Although the MicroSeq 500 16S rDNA bacterial identification system is better than three commercially available identification systems also evaluated, its database needs to be expanded for accurate identification of anaerobic Gram positive bacilli.     

Application of SmartGene IDNS software to partial 16S rRNA gene sequences for a diverse group of bacteria in a clinical laboratory

Laboratories often receive clinical isolates for bacterial identification that have ambiguous biochemical profiles by conventional testing. With the emergence of 16S rRNA gene sequencing as an identification tool, we evaluated the usefulness of SmartGene IDNS, a 16S rRNA sequence database and software program for microbial identification. Identification by conventional methods of a diverse group of bacterial clinical isolates was compared with gene sequences interrogated by the SmartGene and MicroSeq databases. Of 300 isolates, SmartGene identified 295 (98%) to the genus level and 262 (87%) to the species level, with 5 (2%) being inconclusive. MicroSeq identified 271 (90%) to the genus level and 223 (74%) to the species level, with 29 (10%) being inconclusive. SmartGene and MicroSeq agreed on the genus for 233 (78%) isolates and the species for 212 (71%) isolates. Conventional methods identified 291 (97%) isolates to the genus level and 208 (69%) to the species level, with 9 (3%) being inconclusive. SmartGene, MicroSeq, and conventional identifications agreed for 193 (64%) of the results. Twenty-seven microorganisms were not represented in MicroSeq, compared to only 2 not represented in SmartGene. Overall, SmartGene IDNS provides comprehensive and accurate identification of a diverse group of bacteria and has the added benefit of being a user-friendly program that can be modified to meet the unique needs of clinical laboratories.     

Sequence-based identification of Mycobacterium species using the MicroSeq 500 16S rDNA bacterial identification system

We evaluated the MicroSeq 500 16S rDNA Bacterial Sequencing Kit (PE Applied Biosystems), a 500-bp sequence-based identification system, for its ability to identify clinical Mycobacterium isolates. The organism identity was determined by comparing the 16S rDNA sequence to the MicroSeq database, which consists primarily of type strain sequences. A total of 113 isolates (18 different species), previously recovered and identified by routine methods from two clinical laboratories, were analyzed by the MicroSeq method. Isolates with discordant results were analyzed by hsp65 gene sequence analysis and in some cases repeat phenotypic identification, AccuProbe rRNA hybridization (Gen-Probe, Inc., San Diego, Calif.), or high-performance liquid chromatography of mycolic acids. For 93 (82%) isolates, the MicroSeq identity was concordant with the previously reported identity. For 18 (16%) isolates, the original identification was discordant with the MicroSeq identification. Of the 18 discrepant isolates, 7 (six unique sequences) were originally misidentified by phenotypic analysis or the AccuProbe assay but were correctly identified by the MicroSeq assay. Of the 18 discrepant isolates, 11 (seven unique sequences) were unusual species that were difficult to identify by phenotypic methods and, in all but one case, by molecular methods. The remaining two isolates (2%) failed definitive phenotypic identification, but the MicroSeq assay was able to definitively identify one of these isolates. The MicroSeq identification system is an accurate and rapid method for the identification of Mycobacterium spp.     

Usefulness of the MicroSeq 500 16S ribosomal DNA-based bacterial identification system for identification of clinically significant bacterial isolates with ambiguous biochemical profiles

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.     

Evaluation of RIDOM, MicroSeq, and Genbank services in the molecular identification of Nocardia species

The molecular identification of Nocardia species, when compared to phenotypic identification, has two primary advantages: rapid turn-around time and improved accuracy. The information content in the 5'-end of the 16S ribosomal RNA gene is sufficient for identification of most bacterial species. An evaluation was performed to demonstrate the quality of results provided by two specialized databases (RIDOM and MicroSeq 500 versions 1.1 and 1.4.3, library version 500-0125, respectively) and the more general GenBank database. In addition, these results were compared with phenotypic identifications. Partial 5'-16S rDNA sequences from 64 culture collection strains (DSM, CIP, JCM, and ATCC) were derived, in duplicate, independently in two laboratories. Furthermore, the sequences and the conventional identification results of 91 clinical Nocardia isolates were determined. With the exception of N. soli and N. cummidelens, all Nocardia type strains were distinguishable using 5'-16S rDNA sequencing. Assuming a normal distribution for the pairwise distances of all unique Nocardia sequences and choosing a reporting criterion of > or = 99.12% similarity for a "distinct species", a statistical error probability of 1.0% can be calculated. When the various databases were searched with the clinical isolate sequences RIDOM gave a perfect match in 71.4% of cases whereas MicroSeq yielded a perfect match in only 26.4%. The GenBank service gave a 100% similarity in 59.3% but in 70.4% of these cases the results obtained were not exclusive for a single Nocardia species. Conventional methods gave a correct identification in 59 cases, although most recent taxonomic changes were not taken into account. The RIDOM service (http://www.ridom-rdna.de/) is in the process of making available a comprehensive and high-quality database for bacterial identification purposes and provides excellent results for the majority of Nocardia isolates.     

Identification of clinical coryneform bacterial isolates: comparison of biochemical methods and sequence analysis of 16S rRNA and rpoB genes

We compared the relative levels of effectiveness of three commercial identification kits and three nucleic acid amplification tests for the identification of coryneform bacteria by testing 50 diverse isolates, including 12 well-characterized control strains and 38 organisms obtained from pediatric oncology patients at our institution. Between 33.3 and 75.0% of control strains were correctly identified to the species level by phenotypic systems or nucleic acid amplification assays. The most sensitive tests were the API Coryne system and amplification and sequencing of the 16S rRNA gene using primers optimized for coryneform bacteria, which correctly identified 9 of 12 control isolates to the species level, and all strains with a high-confidence call were correctly identified. Organisms not correctly identified were species not included in the test kit databases or not producing a pattern of reactions included in kit databases or which could not be differentiated among several genospecies based on reaction patterns. Nucleic acid amplification assays had limited abilities to identify some bacteria to the species level, and comparison of sequence homologies was complicated by the inclusion of allele sequences obtained from uncultivated and uncharacterized strains in databases. The utility of rpoB genotyping was limited by the small number of representative gene sequences that are currently available for comparison. The correlation between identifications produced by different classification systems was poor, particularly for clinical isolates.     

Evaluation of partial 16S ribosomal DNA sequencing for identification of nocardia species by using the MicroSeq 500 system with an expanded database

Identification of clinically significant nocardiae to the species level is important in patient diagnosis and treatment. A study was performed to evaluate Nocardia species identification obtained by partial 16S ribosomal DNA (rDNA) sequencing by the MicroSeq 500 system with an expanded database. The expanded portion of the database was developed from partial 5' 16S rDNA sequences derived from 28 reference strains (from the American Type Culture Collection and the Japanese Collection of Microorganisms). The expanded MicroSeq 500 system was compared to (i). conventional identification obtained from a combination of growth characteristics with biochemical and drug susceptibility tests; (ii). molecular techniques involving restriction enzyme analysis (REA) of portions of the 16S rRNA and 65-kDa heat shock protein genes; and (iii). when necessary, sequencing of a 999-bp fragment of the 16S rRNA gene. An unknown isolate was identified as a particular species if the sequence obtained by partial 16S rDNA sequencing by the expanded MicroSeq 500 system was 99.0% similar to that of the reference strain. Ninety-four nocardiae representing 10 separate species were isolated from patient specimens and examined by using the three different methods. Sequencing of partial 16S rDNA by the expanded MicroSeq 500 system resulted in only 72% agreement with conventional methods for species identification and 90% agreement with the alternative molecular methods. Molecular methods for identification of Nocardia species provide more accurate and rapid results than the conventional methods using biochemical and susceptibility testing. With an expanded database, the MicroSeq 500 system for partial 16S rDNA was able to correctly identify the human pathogens N. brasiliensis, N. cyriacigeorgica, N. farcinica, N. nova, N. otitidiscaviarum, and N. veterana.     

Comparison of Phenotypic and Genotypic Techniques for Identification of Unusual Aerobic Pathogenic Gram-Negative Bacilli

Rapid and accurate identification of bacterial pathogens is a fundamental goal of clinical microbiology, but one that is difficult or impossible for many slow-growing and fastidious organisms. We used identification systems based on cellular fatty acid profiles (Sherlock; MIDI, Inc., Newark, Del.), carbon source utilization (Microlog; Biolog, Inc., Hayward, Calif.), and 16S rRNA gene sequence (MicroSeq; Perkin-Elmer Applied Biosystems Division, Foster City, Calif.) to evaluate 72 unusual aerobic gram-negative bacilli isolated from clinical specimens at the Mayo Clinic. Compared to lengthy conventional methods, Sherlock, Microlog, and MicroSeq were able to identify 56 of 72 (77.8%), 63 of 72 (87.5%), and 70 of 72 (97.2%) isolates to the genus level (P = 0.002) and 44 to 65 (67.7%), 55 of 65 (84.6%), and 58 of 65 (89.2%) isolates to the species level (P = 0.005), respectively. Four Acinetobacter and three Bordetella isolates which could not be identified to the species level by conventional methods were identified by MicroSeq. In comparison to the full 16S rDNA sequences, the first 527 bp provided identical genus information for all 72 isolates and identical species information for 67 (93.1%) isolates. These data show that MicroSeq provides rapid, unambiguous identification of clinical bacterial isolates. The improved turnaround time provided by genotypic identification systems may translate into improved clinical outcomes.     

Internal transcribed spacer region sequence analysis using SmartGene IDNS software for the identification of unusual clinical yeast isolates

Rapid and accurate identification of clinically important yeasts is essential given their inherent differences in antifungal susceptibility. We implemented nucleic acid sequencing for those species that could not be identified by phenotypic methods. Internal Transcribed Spacer region 1 and 2 (ITS1 and ITS2) sequences were investigated using SmartGene IDNS software, an rDNA sequence database and analysis program for microbial identification (ID). Over a 2.5-year period, 2,938 specimens were evaluated. Most (94%) isolates were fully identified by conventional methods, with Candida species accounting for the majority of them. Of the 169 organisms that required molecular analysis, 79% were identified to species level, 19% to genus and 2% remained unresolved. Sequenced isolates encompassed 33 unique species of which approximately half (52%) were common pathogens with atypical biochemical profiles and the remainder were rarer yeast species. A significant proportion (33%) of sequenced organisms displayed elevated MICs to fluconazole. Our experience supports the use of molecular techniques as an adjunct to conventional methods for the identification of medically important yeasts. Susceptibility testing alone may provide valuable treatment information in situations where phenotypic assessments are inconclusive and molecular or proteomic testing is not readily available.     

Comparison of conventional and molecular methods for identification of aerobic catalase-negative gram-positive cocci in the clinical laboratory

Over a period of 18 months we have evaluated the use of 16S ribosomal DNA (rDNA) sequence analysis as a means of identifying aerobic catalase-negative gram-positive cocci in the clinical laboratory. A total of 171 clinically relevant strains were studied. The results of molecular analyses were compared with those obtained with a commercially available phenotypic identification system (API 20 Strep system; bioMérieux sa, Marcy l'Etoile, France). Phenotypic characterization identified 67 (39%) isolates to the species level and 32 (19%) to the genus level. Seventy-two (42%) isolates could not be discriminated at any taxonomic level. In comparison, 16S rDNA sequencing identified 138 (81%) isolates to the species level and 33 (19%) to the genus level. For 42 of 67 isolates assigned to a species with the API 20 Strep system, molecular analyses yielded discrepant results. Upon further analysis it was concluded that among the 42 isolates with discrepant results, 16S rDNA sequencing was correct for 32 isolates, the phenotypic identification was correct for 2 isolates, and the results for 8 isolates remained unresolved. We conclude that 16S rDNA sequencing is an effective means for the identification of aerobic catalase-negative gram-positive cocci. With the exception of Streptococcus pneumoniae and beta-hemolytic streptococci, we propose the use of 16S rDNA sequence analysis if adequate species identification is of concern.     

In silico analysis of 16S ribosomal RNA gene sequencing-based methods for identification of medically important anaerobic bacteria

This study is the first study that provides useful guidelines to clinical microbiologists and technicians on the usefulness of full 16S rRNA sequencing, 5'-end 527-bp 16S rRNA sequencing and the existing MicroSeq full and 500 16S rDNA bacterial identification system (MicroSeq, Perkin-Elmer Applied Biosystems Division, Foster City, California, USA) databases for the identification of all existing medically important anaerobic bacteria. Full and 527-bp 16S rRNA sequencing are able to identify 52-63% of 130 Gram-positive anaerobic rods, 72-73% of 86 Gram-negative anaerobic rods and 78% of 23 anaerobic cocci. The existing MicroSeq databases are able to identify only 19-25% of 130 Gram-positive anaerobic rods, 38% of 86 Gram-negative anaerobic rods and 39% of 23 anaerobic cocci. These represent only 45-46% of those that should be confidently identified by full and 527-bp 16S rRNA sequencing. To improve the usefulness of MicroSeq, bacterial species that should be confidently identified by full and/or 527-bp 16S rRNA sequencing but not included in the existing MicroSeq databases should be included.     

Ribosomal DNA sequencing for identification of aerobic gram-positive rods in the clinical laboratory (an 18-month evaluation)

We have evaluated over a period of 18 months the use of 16S ribosomal DNA (rDNA) sequence analysis as a means of identifying aerobic gram-positive rods in the clinical laboratory. Two collections of strains were studied: (i) 37 clinical strains of gram-positive rods well identified by phenotypic tests, and (ii) 136 clinical isolates difficult to identify by standard microbiological investigations, i.e., identification at the species level was impossible. Results of molecular analyses were compared with those of conventional phenotypic identification procedures. Good overall agreement between phenotypic and molecular identification procedures was found for the collection of 37 clinical strains well identified by conventional means. For the 136 clinical strains which were difficult to identify by standard microbiological investigations, phenotypic characterization identified 71 of 136 (52.2%) isolates at the genus level; 65 of 136 (47.8%) isolates could not be discriminated at any taxonomic level. In comparison, 16S rDNA sequencing identified 89 of 136 (65.4%) isolates at the species level, 43 of 136 (31.6%) isolates at the genus level, and 4 of 136 (2.9%) isolates at the family level. We conclude that (i) rDNA sequencing is an effective means for the identification of aerobic gram-positive rods which are difficult to identify by conventional techniques, and (ii) molecular identification procedures are not required for isolates well identified by phenotypic investigations.     

Multi-center evaluation of the VITEK® MS system for mass spectrometric identification of non-Enterobacteriaceae Gram-negative bacilli

Studies have demonstrated that matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a rapid, accurate method for the identification of clinically relevant bacteria. The purpose of this study was to evaluate the performance of the VITEK MS v2.0 system (bioMérieux) for the identification of the non-Enterobacteriaceae Gram-negative bacilli (NEGNB). This multi-center study tested 558 unique NEGNB clinical isolates, representing 18 genera and 33 species. Results obtained with the VITEK MS v2.0 were compared with reference 16S rRNA gene sequencing and when indicated recA sequencing and phenotypic analysis. VITEK MS v2.0 provided an identification for 92.5 % of the NEGNB isolates (516 out of 558). VITEK MS v2.0 correctly identified 90.9 % of NEGNB (507 out of 558), 77.8 % to species level and 13.1 % to genus level with multiple species. There were four isolates (0.7 %) incorrectly identified to genus level and five isolates (0.9 %), with one incorrect identification to species level. The remaining 42 isolates (7.5 %) were either reported as no identification (5.0 %) or called “mixed genera” (2.5 %) since two or more different genera were identified as possible identifications for the test organism. These findings demonstrate that the VITEK MS v2.0 system provides accurate results for the identification of a challenging and diverse group of Gram-negative bacteria.     

Performances of the Vitek MS matrix-assisted laser desorption ionization-time of flight mass spectrometry system for rapid identification of bacteria in routine clinical microbiology

Rapid and cost-effective matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)-based systems will replace conventional phenotypic methods for routine identification of bacteria. We report here the first evaluation of the new MALDI-TOF MS-based Vitek MS system in a large clinical microbiology laboratory. This system uses an original spectrum classifier algorithm and a specific database designed for the identification of clinically relevant species. We have tested 767 routine clinical isolates representative of 50 genera and 124 species. Vitek MS-based identifications were performed by means of a single deposit on a MALDI disposable target without any prior extraction step and compared with reference identifications obtained mainly with the VITEK2 phenotypic system; if the identifications were discordant, molecular techniques provided reference identifications. The Vitek MS system provided 96.2% correct identifications to the species level (86.7%), to the genus level (8.2%), or within a range of species belonging to different genera (1.3%). Conversely, 1.3% of isolates were misidentified and 2.5% were unidentified, partly because the species was not included in the database; a second deposit provided a successful identification for 0.8% of isolates unidentified with the first deposit. The Vitek MS system is a simple, convenient, and accurate method for routine bacterial identification with a single deposit, considering the high bacterial diversity studied and as evidenced by the low prevalence of species without correct identification. In addition to a second deposit in uncommon cases, expanding the spectral database is expected to further enhance performances.     

Comparison of genotypic and phenotypic methods for species-level identification of clinical isolates of coagulase-negative staphylococci

To compare commonly used phenotypic methods with genotypic identification methods 47 clinical isolates of coagulase-negative staphylococci (CONS), 10 CONS ATCC strains, and a Staphylococcus aureus clinical isolate were identified using the API Staph ID test, BD Phoenix Automated Microbiology System, and 16S rRNA gene and tuf gene sequencing. When necessary part of the sodA gene was sequenced for definitive identification. The results show that tuf gene sequencing is the best method for identification of CONS, but the API Staph ID test is a reasonably reliable phenotypic alternative. The performance of the BD Phoenix Automated Microbiology System for identification of CONS is poor. The present study also showed that although genotypic methods are clearly superior to phenotypic identifications, a drawback of sequence-based genotypic methods may be a lack of quality of deposited sequences in data banks. In particular, 16S rRNA gene sequencing suffers from the lack of high quality among sequences deposited in GenBank. Furthermore, genotypic identification based on 16S rRNA sequences has limited discriminating power for closely related Staphylococcus species. We propose partial sequencing of the tuf gene as a reliable and reproducible method for identification of CONS species.