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Yonsei Medical Journal logoLink to Yonsei Medical Journal
. 2022 Nov 17;63(12):1138–1143. doi: 10.3349/ymj.2022.0271

Identification of Fusobacterium Species Using Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry by Updating ASTA CoreDB

Shin Young Yun 1, Yunhee Lee 1, Juwon Hong 2, Dong-Chan Kim 2, Hyukmin Lee 1,✉, Dongeun Yong 1, Yun Kyong Lim 3, Joong-Ki Kook 3,✉, Kyungwon Lee 1,4
PMCID: PMC9760896  PMID: 36444550

Abstract

Purpose

Fusobacterium species can cause infections, and associations with cancer are being increasingly reported. As their clinical significance differs, accurate identification of individual species is important. However, matrix-assisted laser desorption/ionization-time of flight mass spectrometry has not been found to be effective in identifying Fusobacterium species in previous studies. In this study, we aimed to improve the accuracy and efficacy of identifying Fusobacterium species in clinical laboratories.

Materials and Methods

In total, 229 Fusobacterium isolates were included in this study. All isolates were identified at the species level based on nucleotide sequences of the 16S ribosomal RNA gene and/or DNA-dependent RNA polymerase β-subunit gene (rpoB). Where necessary, isolates were identified based on whole genome sequences. Among them, 47 isolates were used for updating the ASTA database, and 182 isolates were used for the validation of Fusobacterium spp. identification.

Results

Fusobacterium isolates used for validation (182/182) were correctly identified at the genus level, and most (180/182) were correctly identified at the species level using the ASTA MicroIDSys system. Most of the F. nucleatum isolates (74/75) were correctly identified at the subspecies level.

Conclusion

The updated ASTA MicroIDSys system can identify nine species of Fusobacterium and four subspecies of F. nucleatum in good agreement. This tool can be routinely used in clinical microbiology laboratories to identify Fusobacterium species and serve as a springboard for future research.

Keywords: Fusobacterium, Fusobacterium nucleatum, Fusobacterium necrophorum, spectrometry, mass, matrix-assisted laser desorption-ionization

INTRODUCTION

Fusobacterium spp. are gram negative, rod shaped, obligate anaerobic bacteria and are difficult to culture and require long incubation times.1,2,3 Generally, Fusobacterium spp. act as the normal flora of the female genital tract, gastrointestinal tract, and oral cavity. They can, however, cause infections, and their association with cancer is being increasingly reported.4,5,6,7 Fusobacterium necrophorum can cause acute tonsillitis, peritonsillar abscess4,6,8 and Lemierre’s disease, accompanied by internal jugular thrombophlebitis and septic emboli.9 In addition, Fusobacterium nucleatum might be associated with the development of colorectal cancer, and a high abundance of F. nucleatum in colorectal tumors has been shown to be associated with poor overall survival.10,11 Other studies have reported molecular features of F. nucleatum to be associated with colorectal carcinogenesis.12,13 F. nucleatum is classified into four subspecies: animalis, nucleatum, polymorphum, and vincentii, and each subspecies might have different pathogenicity.14,15,16 These species show different biofilm forming ability in vitro,17,18 and associations with colorectal cancer at the subspecies level are being studied.19 F. nucleatum subsp. animalis may also be associated with inflammatory bowel disease,20 and F. nucleatum subsp. nucleatum is frequently isolated in periodontal disease.14 Other relatively rare species, including Fusobacterium gonidiaformans, Fusobacterium hwasookii, and Fusobacterium mortiferum, have been isolated from infections at other sites.21,22,23 Recently, researchers have suggested that non-nucleatum Fusobacterium may be associated with colorectal cancer.24 Therefore, easy and accurate identification of these organisms at the subspecies and species levels is important in clinical laboratories.

Recently, whole genome sequence comparison methods, such as genome-to-genome distance or average nucleotide identity analysis, and 16S ribosomal RNA gene (16S rDNA) nucleotide comparison methods have been used as gold standards for the classification of bacteria at the species or subspecies level.25,26 Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (MS) is presently the most widely used tool for identifying microorganisms in clinical microbiology laboratories. The accuracy of identification using MALDI-TOF MS depends on the quality of its database. However, Bruker Biotyper (Bruker Daltonics, Bremen, Germany) includes ten Fusobacterium species, and VITEK MS (bioMérieux, Marcy l’Etoile, France), which is also one of the most widely used MALDI-TOF systems, includes only four Fusobacterium species. As such, both MALDI-TOF MS methods have been found to be insufficient in identifying variable Fusobacterium species in previous studies.27,28,29,30,31 In one study using the Bruker Biotyper, only 20% (1/5) of Fusobacterium species were identified at the genus level, and 75% (3/4) of F. nucleatum isolates were not identified.31 In another study using the Bruker Biotyper, only 25% (3/12) of Fusobacterium species were identified at the species level, with a score above 1.8, and 16.7% (2/12) were not correctly identified at both the genus and species level.30 A previous study of F. nucleatum using VITEK MS showed correct identification of 82.4% (28/34) of isolates at the subspecies level.28

The ASTA MicroIDSys system (ASTA Inc., Suwon, South Korea) is a MALDI-TOF MS system developed in 2014. The database includes only six Fusobacterium species: Fusobacterium canifelinum, F. mortiferum, F. necrophorum, F. nucleatum, Fusobacterium ulcerans, and Fusobacterium varium. In addition, the database is not able to differentiate between the four subspecies of F. nucleatum. In this study, to improve the accuracy and efficacy of identifying Fusobacterium species in clinical laboratories, we updated the ASTA database using seven Fusobacterium species (total 47 isolates) and four F. nucleatum subspecies (total 25 isolates) (Table 1). In addition, we evaluated the function of the ASTA MicroIDSys system in identifying Fusobacterium isolates at the species or subspecies level.

Table 1. Number of Species used for Database Upgrade and Analysis.

Species The number of isolates
Total Used for database upgrade Used for validation
F. gonidiaformans 3 1 2
F. hwasookii 6 3 3
F. mortiferum 14 0 14
F. necrophorum 25 0 25
F. nucleatum 100 25 75
F. nucleatum subsp. animalis 31 7 24
F. nucleatum subsp. nucleatum 19 6 13
F. nucleatum subsp. polymorphum 31 7 24
F. nucleatum subsp. vincentii 19 5 14
F. periodonticum 11 3 8
F. pseudoperiodonticum 6 5 1
F. ulcerans 11 5 6
F. varium 53 5 48
Total 229 47 182
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No strain was used for both upgrading and validation simultaneously.

MATERIALS AND METHODS

Bacterial isolates

A total of 229 Fusobacterium isolates belonged to nine species and four subspecies of F. nucleatum (Table 1). Of these, 171 were collected at Yonsei University Severance Hospital in Seoul from January 2006 to January 2021, and 53 were obtained from Korean Collection for Oral Microbiology of Chosun University in Gwangju, Korea. Five isolates of ATCC, one F. nucleatum subsp. animalis (ATCC 51191), two F. nucleatum subsp. nucleatum (ATCC 25586 and ATCC23726), one F. nucleatum subsp. polymorphum (ATCC10953), and one F. nucleatum subsp. vincentii (ATCC49256) were also included. The collected isolates were stored in skimmed milk at -80℃ before analysis. The selection of isolates to be used for validation and database upgrade were randomly determined. During the confirmation of identification by 16S rDNA sequencing, F. mortiferum and F. necrophorum seemed to be well identified empirically; therefore, we only conducted validation.

Molecular identification method

All clinical isolates from Severance Hospital were identified at the species level by sequencing of 16S rDNA, and rpoB genes were also analyzed for subspecies identification of F. nucleatum. A k-mer based method for the identification of all F. gonidiaformans, F. hwasookii, Fusobacterium periodonticum and Fusobacterium pseudoperiodonticum isolates was performed using whole genome sequencing.32 In addition, it was performed on some F. nucleatum isolates that showed poor 16S rDNA and rpoB gene sequencing results. All strains from Korean Collection for Oral Microbiology (KCOM, Gwangju, Korea) were isolated from the oral cavities of a Korean population and identified at the species level based on whole-genome sequences.22,33

The generation of a local database–combined mass spectrum (CMS) creation

To generate a local database, 47 Fusobacterium isolates were used for CMS creation. A bacterial isolate colony was directly placed on the MALDI target plate and allowed to dry, after which 1.5 µL of 70% formic acid was applied to the sample and air dried. The matrix solution (1.5 µL) was composed of 10 g/L α-cyano-4-hydroxycinnamic acid (Sigma-Aldrich, St. Louis, MO, USA) saturated in 2.5% trifluoroacetic acid (Sigma-Aldrich) and 50% acetonitrile (Merck, Darmstadt, Germany). Each isolate yielded 20 spectra. According to ASTA’s recommendations, a single CMS was created using 20 isolated spectra. Each spectrum was created using 1200 laser shots and analyzed using a mass m/z of 2000–20000. The ASTA MicroID Standard (AMS) was used for the calibration (ASTA Inc., Suwon, Korea). Previously, six Fusobacterium sp. species with 68 CMS have been used to identify clinical isolates. Following this update, 11 Fusobacterium spp. and 117 CMS were used. A new version of the ASTA CoreDB was created using the newly updated CMS (ver.1.27.04).

Four and six CMS were obtained to evaluate the performance of differentiating between F. periodonticum and F. pseudoperiodonticum, respectively. The resulting spectrum data were composed of mass bins of relative intensity (%) (Supplementary Fig. 1, only online). We could compare the percentages of intensity signals and mass-to-charge ratios (m/z) of protein components by binning of the spectra. To visualize the difference more clearly, a heat map of cross-matched data was created, as well as principal component analysis (PCA) (Supplementary Fig. 1B and C, only online). The distance in the heat map was based on the Spearman correlation value. Closely related spectra are marked in hot colors, and the unrelated spectra are indicated by cold colors. In the PCA plot, species CMS are described by two vectors, the first principal component (Dim1) and the other crossing it at right angles (Dim2). The domain of each species is illustrated by a colored ellipse (confidence level 99.9%).

Validation of the upgraded ASTA MicroIDSys database

After upgrading the database, the remaining 182 isolates were used for validation of the ASTA MicroIDSys database according to the manufacturer’s instructions, which was prescribed in the CMS creation method. Target samples were analyzed using MicroIDSys and control software (ver. 3.1.4). The acceptable criteria for the identification score was 140 or more, which is identical to the manufacturer’s recommendations. Each isolate was tested in duplicate. There was a total of 27 cases in which repeated tests were inconsistent. Twenty-five of them were “un-identified” in the first of the two tests, with accurate results coming from the second. All cases of un-identified results were caused by poor-quality spectra, and the most likely reason for this is that the target spot preparation was suboptimal: either too much or too little organism was applied or matrix solution was not added.34 The remaining two cases were identified accurately in one test, but with different species in the other test. These two cases were considered to be incorrectly identified in consideration of the possibility of inaccurate results.

RESULTS

All Fusobacterium species (100%, 182/182) were correctly identified at the genus level, and most (98.90%, 180/182) were correctly identified by the ASTA MicroIDSys system at the species level (Table 2). F. gonidiaformans (100%, 2/2), F. hwasookii (100%, 3/3), F. mortiferum (100%, 14/14), F. necrophorum (100%, 25/25), F. pseudoperiodonticum (100%, 1/1), F. ulcerans (100%, 6/6), and F. varium (100%, 48/48) were correctly identified at the species level.

Table 2. Comparison of Identification Results of the 182 Fusobacterium Isolates by ASTA MicroIDSys and Reference Method.

Species (no. isolates) Correct identification to % agreement at species level*
Genus level Species level Subspecies level*
F. gonidiaformans (2) 0 2 - 100
F. hwasookii (3) 0 3 - 100
F. mortiferum (14) 0 14 - 100
F. necrophorum (25) 0 25 - 100
F. nucleatum (75) 1 74 - 98.67
F. nucleatum subsp. animalis* (24) 1 0 23* 95.83*
F. nucleatum subsp. nucleatum* (13) 0 0 13* 100*
F. nucleatum subsp. polymorphum* (24) 0 0 24* 100*
F. nucleatum subsp. vincentii* (14) 0 0 14* 100*
F. periodonticum (8) 1 7 - 87.5
F. pseudoperiodonticum (1) 0 1 - 100
F. ulcerans (6) 0 6 - 100
F. varium (48) 0 48 - 100
Total (182) 2 180 - 98.90
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Reference methods consisted of 16S rDNA sequencing for species identification, rpoB gene sequencing for subspecies identification in F. nucleatum, and whole genome sequencing.

*The identification to the subspecies level was indicated only for F. nucleatum. In this cases, %agreement was calculated at the subspecies level.

Most of the F. nucleatum isolates (98.67%, 74/75) were correctly identified at the subspecies level. F. nucleatum subsp. nucleatum (100%, 13/13), F. nucleatum subsp. polymorphum (100%, 24/24), and F. nucleatum subsp. vincentii (100%, 14/14) were correctly identified at the subspecies level. Of the 24 F. nucleatum subsp. animalis isolates, 23 were correctly identified at the subspecies level (95.83%, 23/24), and one isolate was identified as F. canifelinum.

Although F. periodonticum and F. pseudoperiodonticum were difficult to differentiate using 16S rDNA and rpoB sequencing,33 the protein spectra of F. periodonticum and F. pseudoperiodonticum showed sharp differences (Supplementary Fig. 1, only online). In the heat map, distance values were derived by subtracting the Spearman correlation value from one and were 0.0–0.6 among F. periodonticum, 0.0–0.8 among F. pseudoperiodonticum, and 0.5–1.0 between F. periodonticum and F. pseudoperiodonticum (Supplementary Fig. 1B, only online). In the PCA plot, two species could be divided clearly by the first principal component. Unfortunately, the first principal component had more than hundreds of factors contributing in various proportions, and thus, we could not derive a single protein element that separated the two species (Supplementary Fig. 1C, only online). Finally, ASTA MicroIDSys could correctly identified F. periodonticum and F. pseudoperiodonticum (88.89%, 8/9) at the species level (Table 2). Only one F. periodonticum isolate was identified as F. pseudoperiodonticum.

DISCUSSION

Fusobacterium nucleatum shows genetic variability, has four subspecies, and seems to be reclassified to the species level.35 As such it is important to identify Fusobacterium species correctly, especially at the subspecies level for F. nucleatum. MALDI-TOF MS is easy to use, inexpensive, and requires less than a minute for microorganism identification.31,36,37 However, the quality of the supporting database can influence on the performance of the identification, and well-defined microorganisms are required for improvement of the database. To date, frequently isolated species could be identified with the ASTA MicroIDSys, but relatively rare species could not be identified. To solve this problem, we collected as many Fusobacterium species from clinical samples as possible and upgraded the ASTA MicroIDSys database. F. canifelinum, F. mortiferum, F. necrophorum, F. nucleatum, F. ulcerans, and F. varium were identified using the ASTA database. However, F. ulcerans and F. varium were sometimes not correctly identified as others, and thus, these species were also included in the updated list. All isolates in this study were confirmed by molecular testing, 16S rDNA sequencing, rpoB sequencing, and whole genome sequencing. All F. gonidiaformans, F. hwasookii, F. periodonticum, and F. pseudoperiodonticum isolates were confirmed by whole genome sequencing.

Molecular methods are more accurate for bacterial identification than MALDI-TOF MS, but they are time-consuming and expensive; therefore, laboratories cannot use them for routine bacterial identification methods. Finally, we developed and validated an ASTA database to identify Fusobacterium species in clinical laboratories. 16S rDNA and rpoB are well-conserved and useful regions for bacterial identification, but some species show nearly 100% similarity of these regions. Even in those cases, proteomic analysis like MALDI-TOF MS can be a useful identification method, because expressed proteins are coded by various genes.

Two isolates were identified to the genus level. One isolate of F. nucleatum subsp. animalis was identified as F. canifelinum and one isolate of F. periodonticum was identified as F. pseudoperiodonticum. All the isolates were correctly identified using repeated tests with ASTA MicroIDSys. These discrepancies may be caused by the similarity of the spectra, for which adding more spectral information may be helpful. The other isolates of the same species were correctly identified.

F. periodonticum and F. pseudoperiodonticum are difficult to distinguish in clinical laboratories. The distinction between the two species was described by mass binning, a heat map, and PCA plotting. Interestingly, nearly all F. periodonticum and F. pseudoperiodonticum were correctly identified at the species level using the ASTA MicroIDSys. Overall, same species were more closely related than the other species. In a PCA plot, the two species could be divided clearly by the first principal component. Each ellipse contains 99.9% of all samples drawn under Gaussian distribution. Thus, we could see the differences in the protein spectra of the two species, and these differences seemed to allow ASTA MicroIDSys to differentiate between the two species.

The ASTA MALDI-TOF database is based on spectra obtained by analyzing various isolates received through official procedures in large hospitals and institutions in Korea, as well as clinical isolates from foreign authorized institutions. ASTA’s protein spectrum database is constructed by collecting 20 to 30 spectra per isolate for each isolate. Only spectra that pass verification, checking the relationship with the spectra of the existing database, are added to the new database. Therefore, the accuracy of microbial identification is very high.38,39 In addition, it has a faster laser speed of >1000 Hz, compared to other equipment, and the plate loading time is shorter than 1.5 minutes. It can identify 96 isolates in about 20 minutes. Also, the sample preparation kit developed by the company is also provided to the users, making it is possible to maintain uniformity of various conditions until the sample spectrum is obtained. Lastly, based on the artificial intelligence machine learning data processing process developed by the ASTA software team, a self-developed database is established that can accurately distinguish isolates at not only the genus and species level but also the subspecies level. Recently, simple microbial identification and a database that can distinguish between resistant and susceptible isolates for specific antibiotics are being developed.

A limitation of this study was that rare species of Fusobacterium were not included in the updated list, such as F. canifelinum, F. equinum, and F. gastrosuis. Secondly, we were unable to collect clinical isolates of F. canifelinum. Thirdly, the identification data of previous database was not available, and we could not compare the data before and after upgrading database. Finally, the numbers of some species were too small. For example, only one isolate of F. pseudoperiodonticum was used for validation, and further investigation using additional isolates is needed.

However, ASTA MicroIDsys could identify nine species of Fusobacterium and four subspecies of F. nucleatum, which may be associated with colorectal cancer, with good agreement after upgrading the ASTA MicroIDSys database. A previous report on F. nucleatum subspecies identification by MALDI-TOF MS showed only 82.4% (28/34) of isolates were correctly identified at the subspecies level 28, and a better identification ability (98.67%) was shown in this study. In addition, F. periodonticum and F. pseudoperiodonticum are known to be differentiated only by the analysis of whole genome sequences, but we confirmed differences in their protein spectra and proved the identification ability of ASTA MicroIDSys. This allows clinical laboratories to identify them more easily and rapidly.

In conclusion, the updated ASTA MicroIDSys can identify Fusobacterium species in good agreement. Therefore, this tool can be routinely used in clinical microbiology laboratories to identify Fusobacterium species, and it can serve as a springboard for future research including F. nucleatum and colorectal cancer.

Footnotes

The authors have no potential conflicts of interest to disclose.

AUTHOR CONTRIBUTIONS:
  • Conceptualization: Kyungwon Lee.
  • Data curation: Joong-Ki Kook, Yun Kyong Lim, and Hyukmin Lee.
  • Formal analysis: Joong-Ki Kook, Yun Kyong Lim, and Hyukmin Lee.
  • Investigation: Yunhee Lee.
  • Methodology: Kyungwon Lee and Hyukmin Lee.
  • Project administration: Kyungwon Lee, Hyukmin Lee, and Joong-Ki Kook.
  • Resources: Hyukmin Lee.
  • Software: Dong-Chan Kim and Juwon Hong.
  • Supervision: Kyungwon Lee, Hyukmin Lee, and Joong-Ki Kook.
  • Validation: Yunhee Lee.
  • Visualization: Shin Young Yun.
  • Writing—original draft: Shin Young Yun.
  • Writing—review & editing: Kyungwon Lee, Hyukmin Lee, Dong-Chan Kim, Juwon Hong, Joong-Ki Kook, and Dongeun Yong.
  • Approval of final manuscript: all authors.

SUPPLEMENTARY MATERIAL

Supplementary Fig. 1

Proteomic comparison of F. periodonticum and F. pseudoperiodonticum by (A) mass bin analysis, (B) heat map, and (C) principal component analysis (PCA) using ASTA MicroIDSys. Four and six combined mass spectrum (CMS) were obtained to evaluate the performance of differentiating between F. periodonticum and F. pseudoperiodonticum, respectively. The resulting spectrum data were composed of mass bins of relative intensity (%). We could compare the percentage of intensity signals and mass-to-charge ratios (m/z) of protein components by binning of the spectra. To visualize the difference more clearly, a heat map of cross-matched data was created, along with a PCA plot. The distance in the heat map was based on the Spearman correlation value. Closely related spectra are marked in hot colors, and unrelated spectra are in cold colors. In the PCA plot, species CMS were described by two vectors, the first principal component (Dim1) and the other crossing it at right angles (Dim2). The domains of each species are illustrated by colored ellipses (confidence level 99.9%). The first principal component had more than hundreds of factors contributing in various proportions, and thus, we could not derive a single protein element that separated the two species.

ymj-63-1138-s001.pdf (408.8KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Fig. 1

Proteomic comparison of F. periodonticum and F. pseudoperiodonticum by (A) mass bin analysis, (B) heat map, and (C) principal component analysis (PCA) using ASTA MicroIDSys. Four and six combined mass spectrum (CMS) were obtained to evaluate the performance of differentiating between F. periodonticum and F. pseudoperiodonticum, respectively. The resulting spectrum data were composed of mass bins of relative intensity (%). We could compare the percentage of intensity signals and mass-to-charge ratios (m/z) of protein components by binning of the spectra. To visualize the difference more clearly, a heat map of cross-matched data was created, along with a PCA plot. The distance in the heat map was based on the Spearman correlation value. Closely related spectra are marked in hot colors, and unrelated spectra are in cold colors. In the PCA plot, species CMS were described by two vectors, the first principal component (Dim1) and the other crossing it at right angles (Dim2). The domains of each species are illustrated by colored ellipses (confidence level 99.9%). The first principal component had more than hundreds of factors contributing in various proportions, and thus, we could not derive a single protein element that separated the two species.

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