Abstract
Chryseobacterium and Elizabethkingia species have recently emerged as causative agents in life-threatening infections in humans. We aimed to evaluate the rates at which four common microbial identification systems identify Chryseobacterium and Elizabethkingia species in clinical microbiology laboratories. Based on the results of 16S rRNA gene sequencing, a total of 114 consecutive bacteremic isolates, including 36 (31.6%) C. indologenes, 35 (30.7%) E. anophelis, 22 (19.3%) C. gleum, 13 (11.4%) E. meningoseptica, and other species, were included in this study. The overall concordance between each method and 16S rRNA gene sequencing when identifying Chryseobacterium and Elizabethkingia species was 42.1% for API/ID32, 41.2% for Phoenix 100 ID/AST, 43.9% for VITEK 2, and 42.1% for VITEK MS. Among the 22 C. gleum isolates, only one (4.8%) was correctly identified using VITEK 2 and Phoenix 100 ID/AST, and none were accurately recognized using API/ID32 or VITEK MS. Except for two isolates that were not identified using API/ID32, all E. anophelis isolates were misidentified by all four identification systems as E. meningoseptica. Our results show that these approaches have low accuracy when identifying Chryseobacterium and Elizabethkingia species. Hence, we recommend amending the discrimination rate of and adding non-claimed pathogens to databases of microbial identification systems.
Introduction
The genera Chryseobacterium and Elizabethkingia are aerobic, nonfermenting, nonmotile, catalase-positive, oxidase-positive, indole-positive, and gram-negative bacilli that are distributed in soil and water environments1,2. These microorganisms were recently been reported as agents that cause life-threatening pneumonia, bacteremia, meningitis, and neutropenic fever in humans, especially immunocompromised patients1–5.
Both Chryseobacterium and Elizabethkingia were historically derived from the genus Flavobacterium. To date, more than 90 species are included in the genus Chryseobacterium. Among these, C. indologenes is the most common cause of human infections1,5. Human infections caused by other Chryseobacterium species, such as C. gleum, are rarely reported6. Currently, the genus Elizabethkingia includes three species, including E. meningoseptica, E. miricola, and E. anophelis 7. Among these species, E. meningoseptica is the most well-known species that causes opportunistic infections in immunocompromised patients2–4. However, Elizabethkingia anophelis has recently emerged as a cause of life-threatening infections and has triggered several outbreaks of infections in Africa8, Singapore9, Hong Kong10, and the USA11,12.
Several methods have been developed to identify microorganisms. These include biochemical methods, 16S rRNA gene sequencing, multiple molecular marker sequencing, and protein fingerprinting techniques (for example, matrix-assisted laser desorption ionization–time of flight mass spectrometry; MALDI-TOF MS). Occasional studies have reported misidentifying Chryseobacterium and Elizabethkingia species when using conventional phenotypic identification systems and the VITEK 2 Automated Identification System (bioMérieux, Marcy l’Etoile, France)6,10,13. However, 16S rRNA gene sequencing has been shown to be a reliable method of identifying Chryseobacterium and Elizabethkingia species14,15. In this study, we used 16S rRNA sequencing to analyze Chryseobacterium and Elizabethkingia species isolated from patient blood samples. We compared the accuracies of the following four bacterial identification systems that are commonly used to identify Chryseobacterium and Elizabethkingia species: (1) API/ID32 Phenotyping Kits (bioMérieux, Marcy l’Etoile, France), (2) Phoenix 100 ID/AST Automated Microbiology System (Becton Dickinson Co., Sparks, MD, USA), (3) VITEK 2 Automated Identification System, and (4) VITEK MS MALDI-TOF MS System (bioMérieux, Marcy l’Etoile, France).
Results
Isolates and claimed microorganisms in databases of identification systems
A total of 114 consecutively non-repeated isolates that were initially identified as Chryseobacterium and Elizabethkingia species by a clinical microbiology laboratory were included in this study (Table 1). According to BLAST results based on 16S rRNA gene sequencing, 36 isolates were C. indologenes (31.6%), 35 were E. anophelis (30.7%), 22 were C. gleum (19.3%), 13 were E. meningoseptica (11.4%), 3 were C. culicis, 2 were C. bemardetii, 2 were Candidatus Chryseobacterium massilia, and 1 was E. miricola. The coverage rates of these Chryseobacterium and Elizabethkingia species that were obtained using the API/ID32, Phoenix 100 ID/AST, VITEK 2, and VITEK MS v2.0/v3.0 identification databases are shown in Table 1. Chryseobacterium indologenes and E. meningoseptica were claimed in the databases of all biochemical systems and MALDI-TOF MS systems. C. gleum was built into all systems except API/ID32. Elizabethkingia miricola was included only in the Phoenix 100 ID/AST database. However, these identification systems did not contain identification data for Candidatus C. massilia, C. bemardetii, C. culicis, and E. anophelis.
Table 1.
The manufacturers’ listed coverage of databases and identification of Chryseobacterium and Elizabethkingia species by API/ID32, Phoenix 100 ID/AST, VITEK 2, and VITEK MS.
| 16S rRNA sequence-based identification (no. of isolates) | Identification method (no. of isolates) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| API/ID32 v3.1 | Phoenix 100 ID/AST v5.51A | VITEK 2 v7.01 | VITEK MS Knowledge Base v2.0/v3.0 | |||||||||
| Strain name (no. of isolates) | Database Coverage | Correct rate (%) | Strain name (no. of isolates) | Database Coverage | Correct rate (%) | Strain name (no. of isolates) | Database Coverage | Correct rate (%) | Strain name (no. of isolates) | Database Coverage | Correct rate (%) | |
| Chryseobacterium species (65) | 55.4 | 53.8 | 56.9 | 53.8 | ||||||||
| Candidatus C. massilia (2) | C. indologenes (2) | No | 0 | Sphingomonas paucimobilis (2) | No | 0 | C. indologenes (1) Genus Brevundimonas (1) | No | 0 | C. indologenes (2) | No | 0 |
| C. bemardetii (2) | C. indologenes (2) | No | 0 | C. indologenes (1) Bergeyella zoohelcum (1) | No | 0 | C. indologenes (2) | No | 0 | Genus Chryseobacterium (1) No identification (1) | No | 0 |
| C. culicis (3) | E. meningoseptica (1) C. indologenes (2) | No | 0 | C. indologenes (2) Bergeyella zoohelcum (1) | No | 0 | C. indologenes (3) | No | 0 | C. indologenes (2) No identification (1) | No | 0 |
| C. gleum (22) | C. indologenes (18) Genus Chryseobacterium (1) No identification (3) | No | 0 | C. gleum (1) C. indologenes (14) Bergeyella zoohelcum (7) | Yes | 4.5 | C. gleum (1) C. indologenes (21) | Yes | 4.5 | C. indologenes (19) No identification (3) | Yes | 0 |
| C. indologenes (36) | C. indologenes (36) | Yes | 100 | C. indologenes (34) Bergeyella zoohelcum (2) | Yes | 94.4 | C. indologenes (36) | Yes | 100 | C. indologenes (35) No identification (1) | Yes | 97.2 |
| Elizabethkingia species (49) | 24.5 | 24.5 | 26.5 | 26.5 | ||||||||
| E. anopheles (35) | E. meningoseptica (26) No identification (2) | No | 0 | E. meningoseptica (35) | No | 0 | E. meningoseptica (35) | No | 0 | E. meningoseptica (35) | No | 0 |
| E. meningoseptica (13) | E. meningoseptica (12) No identification (1) | Yes | 92.3 | E. meningoseptica (12) Empedobacter brevis (1) | Yes | 92.3 | E. meningoseptica (13) | Yes | 100 | E. meningoseptica (13) | Yes | 100 |
| E. miricola (1) | E. meningoseptica (1) | No | 0 | E. meningoseptica (1) | Yes | 0 | E. meningoseptica (1) | No | 0 | E. meningoseptica (1) | No | 0 |
| Quality control strain | ||||||||||||
| C. indologenes BCRC 17271 | C. indologenes | Yes | 100 | C. indologenes | Yes | 100 | C. indologenes | Yes | 100 | C. indologenes | Yes | 100 |
| E. meningoseptica BCRC 10677 | E. meningoseptica | Yes | 100 | E. meningoseptica | Yes | 100 | E. meningoseptica | Yes | 100 | E. meningoseptica | Yes | 100 |
API/ID32 Phenotyping Kits
The API/ID32 system correctly identified all isolates of C. indologenes (36/36) and 92.3% (12/13) of E. meningoseptica isolates (Table 1). Eighteen of 22 C. gleum (81.8%) were misidentified as C. indologenes. Six of 7 rarely observed Chryseobacterium (including Candidatus C. massilia, C. bemardetii, and C. culicis) were recognized as C. indologenes, and the seventh was identified as E. meningoseptica. The majority (94.4%) of E. anophelis and E. miricola were identified as E. meningoseptica. The overall correct rates of identification of Chryseobacterium and Elizabethkingia species when using API/ID32 were 55.4% and 24.5%, respectively.
Phoenix 100 ID/AST Automated Microbiology System
Among the 36 isolates of C. indologenes, 34 (94.4%) were correctly identified. However, only 4.5% (1/22) of C. gleum were successfully diagnosed. No Candidatus C. massilia, C. bemardetii, and C. culicis were recognized (Table 1). The accuracy rate when identifying Chryseobacterium species was 53.8%. For the genus Elizabethkingia, 92.3% (12/13) of E. meningoseptica were accurately identified. However, the other two species, E. anophelis and E. miricola, were misdiagnosed as E. meningoseptica. The rate of successful identification of Elizabethkingia species was only 24.5% when using the Phoenix 100 ID/AST Automated Microbiology System.
VITEK 2 Automated Identification System
All C. indologenes (n = 36) were reliably identified when using VITEK 2 (Table 1). However, 4.5% (1/22) of C. gleum species were correctly identified, and the remaining 95.5% (21/22) were misidentified as C. indologenes. All C. bemardetii and C. culicis isolates were identified as C. indologenes. Of the Elizabethkingia species, all E. meningoseptica (13/13) were successfully identified, but the remaining three species were misidentified as E. meningoseptica. The accuracy of this method for identifying Chryseobacterium and Elizabethkingia species was 56.9% and 26.5%, respectively.
VITEK MS MALDI-TOF MS System
Of the Chryseobacterium species, 97.2% (35/36) of C. indologenes isolates were correctly identified. However, no Candidatus C. massilia, C. bemardetii, or C. culicis isolates were successfully recognized (Table 1). The overall rate at which this method correctly identified Chryseobacterium species was 53.8%. Of the Elizabethkingia species, all E. meningoseptica (13/13, 100%) were correctly identified. However, all E. anophelis and E. miricola were misidentified as E. meningoseptica. The accuracy rate of this method when identifying Elizabethkingia species was only 26.5%.
Discussion
In this study, we compared the accuracies of four commercial microbial identification systems to that of 16S rRNA gene sequencing for identifying Chryseobacterium and Elizabethkingia species. The overall concordance between each of these four commercial methods and 16S rRNA gene sequencing for identifying Chryseobacterium and Elizabethkingia species were as follows: API/ID32, 42.1%; Phoenix 100 ID/AST, 41.2%; VITEK 2, 43.9%; and VITEK MS, 42.1%. After taking the coverage of each database into account, the overall concordance between 16S rRNA gene sequencing and API/ID32, Phoenix 100 ID/AST, VITEK 2, and VITEK MS was 98%, 75.8%, 70.4%, and 67.6%, respectively.
Chryseobacterium gleum is rarely reported to cause infection in humans16,17. However, our data reveal that C. gleum accounts for 33.8% of Chryseobacterium bacteremia cases in humans. Lo et al.6 reported that 15 clinical isolates of C. gleum that were confirmed by 16S rRNA gene sequencing were misidentified by VITEK 2 as C. indologenes (14/15; 93.3%) and E. meningoseptica (1/15; 6.7%). When submitted to a Bruker Microflex LT MALDI-TOF MS System using Biotyper database 3.0 (Bruker Daltonics, Bremen, Germany), 2 (13.3%) and 13 (86.6%) of these 15 isolates were identified as C. gleum species and probable species, respectively. In our study, 81.8% of C. gleum isolates were misidentified as C. indologenes by all four commercial identification systems. Chryseobacterium gleum was included in the Phoenix 100 ID/AST, VITEK 2, and VITEK MS (v2.0 and v3.0) databases but not in the API/ID32 database. Among the 22 C. gleum isolates in our study, only 1 (4.5%) was correctly identified by VITEK 2 and Phoenix 100 ID/AST, and none were accurately recognized by VITEK MS. MALDI-TOF MS systems have become popular in clinical microbiology laboratories because they rapidly, highly accurately, and cost-effectively identify different microorganisms. However, despite the fact that C. gleum was included in the spectral database, none of the C. gleum isolates were accurately identified by VITEK MS. The four microbial identification systems used in our study are widely used by clinical microbiology laboratories all over the world. The inability of these techniques to distinguish C. gleum from C. indologenes may result from false impressions that have led to the notion that there is a low prevalence of C. gleum and an overestimation of the prevalence of C. indologenes infections in humans.
Recent studies have shown that E. anophelis is frequently misidentified as E. meningoseptica 8–10. Lau et al.10 reported 17 patients in Hong Kong who were diagnosed using 16S rRNA gene sequencing with infection with E. anophelis. However, all 17 E. anophelis isolates were recognized as E. meningoseptica by VITEK 2, and the Bruker MALDI-TOF MS Biotyper also failed to correctly identify E. anophelis 10. Similar to a report by Lau et al., Han et al.13 found in their study performed in South Korea that none of the tested 51 E. anophelis isolates was correctly identified by a Bruker MALDI-TOF MS Biotyper. A VITEK MS research-use-only (RUO) system coupled with a SARAMIS SuperSpectra database successfully identified all 51 E. anophelis isolates13, but this system is not available to clinical microbiology laboratories. In our study, 55.5% (27/49) of previously identified E. meningoseptica were revealed to be E. anophelis based on the results of 16S rRNA gene sequencing. Our results show that using VITEK MS with the v2.0 and v3.0 Knowledge Bases or any of the other three commonly used biochemical systems discussed here resulted in the failed identification of E. anophelis. We suggest that many previously reported E. meningoseptica infections might in fact have been identified as E. anophelis if they were analyzed using commercial identification systems. The prevalence of E. anophelis infections in humans could therefore be dramatically underestimated.
Conclusions
Being able to correctly identify microorganisms is extremely important in clinical practice and microbiologic research. However, the results of our study show that four microbial identification systems that are widely used in clinical microbiology laboratories are highly inaccurate when identifying Chryseobacterium and Elizabethkingia species. Specifically, the extremely low rates at which these methods identify the life-threatening pathogens C. gleum and E. anophelis may cause the prevalence of these species to be substantially underestimated. We recommend amending the method used to discriminate C. gleum from C. indologenes and adding a database for E. anophelis to the microbial identification systems discussed here.
Materials and Methods
Ethics and experimental biosafety statements
This study was approved by the Institutional Review Board of E-Da Hospital (EMRP-105–134). The need for patient informed consent was waived by the Institutional Review Board of E-Da Hospital because the retrospective analysis of routine blood cultures posed no more than a minimal risk of harm to the subjects. The experiments in this study were approved by the Institutional Biosafety Committee of E-Da Hospital. All experiments were performed in accordance with relevant guidelines and regulations.
Study design
An 11-year retrospective study was conducted at a 1,000-bed university-affiliated hospital that serves more than 2 million people in southern Taiwan. A clinical laboratory database was searched to identify blood cultures that were identified as containing Chryseobacterium and Elizabethkingia species between January 2005 and December 2015. The isolates were initially identified as Chryseobacterium and Elizabethkingia species by a clinical microbiology laboratory that first used API/ID32 Phenotyping Kits (2005–2013) and then used a VITEK MS MALDI-TOF MS System (2014–2015) after upgrading the microbial identification system. All isolates were stored as glycerol stocks at −80 °C until used. Chryseobacterium indologenes BCRC 17271 (ATCC 29897) and Elizabethkingia meningoseptica BCRC 10677 (ATCC 13253) were used as quality controls. The 16S rRNA gene sequencing method was considered the reference method for bacterial identification.
16S rRNA gene sequencing
Frozen bacterial glycerol stocks were thawed and subcultured on tryptic soy agar with 5% sheep blood (Becton Dickinson Co., Sparks, MD, USA) for further experiments. Total DNA was isolated from each sample using a Wizard Genomic DNA Purification Kit according to the manufacturer’s instructions (Promega, Madison, WI, USA). The primers used to amplify the internal fragments of the 16S rRNA gene were as described previously18. Purified polymerase chain reaction (PCR) was performed using a GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA, USA). The PCR products were sequenced using an Applied Biosystems 3730xl DNA Analyzer (Applied Biosystems, Foster City, CA, USA). The primers used to sequence 16S16S rRNA were 8f (5′-GGATCCAGACTTTGATYMTGGCTCAG-3′), 534r (5′-ATTACCGCGGCTGCTGG-3′), 534f (5′-CCAGCAGCCGCGGTAAT-3′), 968f (5′-AACGCGAAGAACCTTAC-3′), and 1512r (5′-GTGAAGCTTACGGYTAGCTTGTTACGACTT-3′)19. The sequences were reviewed and edited using Sequence Scanner v.1.0 (Applied Biosystems, Foster City, CA, USA). The obtained 16S rRNA sequences were compared to sequences in GenBank using the Basic Local Alignment Search Tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The results were considered valid if the homologous rate was ≥99%.
Identification of microorganisms using microbial identification systems
For re-identification, the thawed bacteria were inoculated on tryptic soy agar with 5% sheep blood after they were removed from the freezer. The plates were then incubated in a 5% CO2 atmosphere at 35 °C for 15 to 24 hours. All isolates were re-identified using API/ID32 Phenotyping Kits, Phoenix 100 ID/AST Automated Microbiology System, VITEK 2 Automated Identification System, and VITEK MS MALDI-TOF MS System. The isolates were identified according to each manufacturer’s instructions. For the API/ID32 Phenotyping Kits, an ID 32 GN card and database version 3.1 were used to identify microorganisms according to ATB Expression. The results obtained using the Phoenix 100 ID/AST System were analyzed using database version 5.51 A. A confidence level of ≥90% was defined as acceptable for the Phoenix System20. The identifications yielded by the VITEK 2 system were obtained using a GN ID card and database version 7.01. The quality of bacterial identification was assessed using VITEK 2 Advanced Expert System. The results were defined as acceptable at a confidence level of 96–99% (excellent identification) or 93–95% (very good quality)21. The mass spectral fingerprints generated by the VITEK MS System were analyzed using Knowledge Base v2.0 and repeatedly tested using Knowledge Base v3.0. A confidence value of ≥90% (reliable identification) or 85%–90% (acceptable identification) was regarded as a successful identification. A value was defined as no identification if the VITEK MS confidence value was <85%22.
Data Availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgements
This work was supported by grants EDPJ105082 from E-Da Hospital and MOST 105-2314-B-214-008 and 106-2314-B-214-009-MY2 from the Ministry of Science and Technology, Taiwan.
Author Contributions
All authors provided significant contributions, and all authors are in agreement regarding the content of the manuscript. Conception/design: Jiun-Nong Lin and Hsi-Hsun Lin; provision of study materials: Chung-Hsu Lai; collection and assembly of data: Jiun-Nong Lin, Chung-Hsu Lai, Chih-Hui Yang, Yi-Han Huang, and Hsiu-Fang Lin; data analysis and interpretation: all authors; manuscript writing: Jiun-Nong Lin and Chih-Hui Yang; and final approval of the manuscript: all authors.
Competing Interests
The authors declare that they have no competing interests.
Footnotes
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.