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BMC Infectious Diseases

, 19:941 | Cite as

Application of MALDI-TOF MS to rapid identification of anaerobic bacteria

  • Ying Li
  • Mingzhu Shan
  • Zuobin Zhu
  • Xuhua Mao
  • Mingju Yan
  • Ying Chen
  • Qiuju Zhu
  • Hongchun Li
  • Bing GuEmail author
Open Access
Research article
  • 43 Downloads
Part of the following topical collections:
  1. Bacterial and fungal diseases

Abstract

Background

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been rapidly developed and widely used as an analytical technique in clinical laboratories with high accuracy in microorganism identification.

Objective

To validate the efficacy of MALDI-TOF MS in identification of clinical pathogenic anaerobes.

Methods

Twenty-eight studies covering 6685 strains of anaerobic bacteria were included in this meta-analysis. Fixed-effects models based on the P-value and the I-squared were used for meta-analysis to consider the possibility of heterogeneity between studies. Statistical analyses were performed by using STATA 12.0.

Results

The identification accuracy of MALDI-TOF MS was 84% for species (I2 = 98.0%, P < 0.1), and 92% for genus (I2 = 96.6%, P < 0.1). Thereinto, the identification accuracy of Bacteroides was the highest at 96% with a 95% CI of 95–97%, followed by Lactobacillus spp., Parabacteroides spp., Clostridium spp., Propionibacterium spp., Prevotella spp., Veillonella spp. and Peptostreptococcus spp., and their correct identification rates were all above 90%, while the accuracy of rare anaerobic bacteria was relatively low. Meanwhile, the overall capabilities of two MALDI-TOF MS systems were different. The identification accuracy rate was 90% for VITEK MS vs. 86% for MALDI biotyper system.

Conclusions

Our research showed that MALDI-TOF-MS was satisfactory in genus identification of clinical pathogenic anaerobic bacteria. However, this method still suffers from different drawbacks in precise identification of rare anaerobe and species levels of common anaerobic bacteria.

Keywords

MALDI-TOF MS Anaerobes Bacteria identification 

Abbreviations

CI

Confidence interval

MALDI-TOF MS

Matrix-assisted laser desorption ionization-time of flight mass spectrometry

Background

Anaerobic bacteria exist as part of the normal flora in the human intestinal tract, oral cavity and urogenital tract [1], and can cause infectious diseases as a result of impairment to the microenvironment and/or immune system. Anaerobic infection can also be induced by deep wounds accompanied with facultative anaerobes and aerobic bacteria invasion. Invasive anaerobic infections are life threatening, and the mortality rate of anaerobic bacteremia is high as 40% [2]. Thus, the accurate and fast identification of anaerobic bacteria is pivotal to prompt antimicrobial treatments. Conventional anaerobe identification methods are cumbersome, time-consuming, and costly. It requires a long-term cultivation (not less than 24 h) to obtain enough inocula. In addition, the identification work is complex, including colony traits, colony morphology, and staining results. Meanwhile, it is difficult to identify rare or newly identified species by using conventional phenotyping methods and commercial kits [3]. Real-time, fast, high-throughput, high-sensitivity, high-selectivity, and low cost have been the goals pursued by analysts in modern analytical science.

The modern mass spectrometry technology enhances the understanding about the whole biological system through direct analysis of biological molecules such as proteins, lipids, carbohydrates and amino acids [4], which has been applied to the field of life science [5]. As an emerging technology, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been widely used in clinical microbial diagnosis in the past decade. It is gradually replacing the traditional identification methods [6, 7]. MALDI-TOF MS is a rapid mass spectrometry technology developed in the late 1980s with relatively high sensitivity to various samples types. It is a useful, fast and accurate tool for routine laboratory analysis and has been used to identify mycobacteria [8, 9], nocardia [10], yeasts [11, 12] and anaerobes [13, 14] isolated from solid media of clinical specimens. At present, there are few studies to evaluate the efficacy of identifying anaerobic bacteria by MALDI-TOF MS. The aim of the present meta-analysis is to determine the reliability and effectiveness of mass spectrometry as a routine diagnostic method for anaerobic bacteria by searching related publications in the literature.

Methods

Search strategy

The scientific literature was extensively searched using the MeSH terms “maldi-ms” and “anaerobic bacteria” to query the electronic database of Medline and Web of science (up to 1 April 2018). Selected articles contained studies involving the identification of anaerobes by MALDI-TOF MS. The references cited in these articles were examined to determine other articles. The meta-analysis was performed by referring to (when appropriate) the PRISMA guidelines [15]. EndNote X8 (Thomson Reuters) was used for literature management. We read the titles and abstracts of each searched publication and selected only those relevant articles for full-text reading. There are no restrictions on the language, publication status and geographical distribution of publications.

Inclusion and exclusion criteria

We set up the criteria for the inclusion and exclusion of the literature. The inclusion criteria were as follows: (1) the study objective: the clinical specimens were identified as anaerobic bacteria by reference methods (16S rRNA gene sequencing); (2) the study method: the identification of anaerobes by MALDI-TOF MS; (3) the research objective: the accuracy of MALDI-TOF MS identification of anaerobes. The exclusion criteria included the following aspects: review articles, reviews, case reports, scientific abstracts and lectures; common anaerobes with fewer than 10 strains of anaerobes and less than 5 uncommon anaerobes; direct identification of bacteria in the positive blood culture bottle; The target bacteria could not be extracted, and pathogenic microorganisms or industrial environmental microbes of plants or animals were identified.

The identification criteria of MALDI-TOF MS in the included studies were as follows: a score of ≥2.0 was considered an accurate species-level identification; ≥ 1.7 but < 2.0, a probable genus-level identification; an isolate with a score < 1.7 was considered as “unidentified”; and an isolate identified as another species or genus was considered to be “misidentification”.

Quality assessment

What is important in meta-analysis is whether heterogeneity exists in the included studies and the possible reasons for the existence of heterogeneity, because heterogeneity may lead to deviations in meta-analysis results - known as “mixed apples and oranges” [16]. The sources of heterogeneity can be divided into, different research designs, different experimental conditions, different definitions of exposure and/or outcomes, different measurement methods, and the existence of other interference factors, i.e. covariates. In addition, low-quality literature will bring significant heterogeneity [17]. The following modified criteria, referring to the quality assessment for studies of diagnostic accuracy (QUADAS) [18], were used to assess the quality of original studies: study design, category and geographical distribution of strains, blinded status, reference methods, threshold, strain source, and system database.

Assessment of publication bias and influence analysis

According to statistics, the studies of positive results are more likely to publish than those of negative results, but it could not really represent the overall study population. In fact, the samples may be less representative. This result is called “publication bias” in statistics [17]. Funnel diagrams are commonly used graphical tests to assess publication bias in meta-analysis [19]. Egger’ s linear regression test of funnel plot asymmetry at the genus level and Begg’s rank correlation (with continuity correction) showed that little publication bias was detected in this review (t = − 1.54 and P = 0.123 for Egger’ s Test; z = − 0.35 and P = 0.727 for Begg’s Test).

Results

Results of the systematic literature search

A total of 234 articles were retrieved from the electronic database. Additional four articles were identified through manual search, bibliographic search, and commentator suggestions. Finally, 28 studies were included according to the defined inclusion and exclusion criteria (Fig. 1). Countries and study periods included in all articles were shown in Table 1. The geographical distributions of the literature were Asia (5, 17.86%), Australia (1, 3.57%), South America (1, 3.57%), North America (4, 14.29%) and Europe (17, 60.71%), containing 24 cities in 14 countries.
Fig. 1

Flow diagram for selection of studies

Table 1

Geographical distributions and study periods of all included studies

Author (publication year)

Country

City

Period of the study

Lucia Sanchez Ramos (2018) [20]

Germany

Leipzig

NM

Mervi Gürsoy (2017) [21]

Finland

Turku

NM

Belén Rodríguez-Sánchez (2017) [22]

Spain

Madrid

January 2010 to August 2012.

A.C.M. Veloo (2016) [23]

The Netherlands

Groningen

NM

Tomoyuki Yunoki (2016) [24]

Japan

Kyoto

June 2013 to May 2014

Sung Jin Jo, M.D. (2015) [25]

Korea

Seoul

January to February 2015

NINA HANDAL (2014) [26]

Norway

Lørenskog

January 2009 to December 2013

Wonmok Lee, M.D. (2014) [27]

Korea

Seoul

2011

M.J. Barba (2014) [28]

Spain

A Coruña

2007–2014

Roy Chean (2014) [29]

Australia

Melbourne

2000–2010

Mariela S. Záratea (2014) [30]

Argentina

Ciudad Autónoma de Buenos Aires

NM

Yang Li (2014) [31]

China

Nanjing

NM

Yen-Michael S. Hsu (2014) [32]

USA

St. Louis

NM

Susanna K P Lau (2013) [33]

China

Hong Kong

NM

O. Garner (2013) [34]

USA

St. Louis

January 2012 to August 2012.

Melody Barreau(2013) [35]

France

Marseille

2010–2013

L. Coltella (2013) [7]

Italy

Rome

June 2010 to October 2011

Bryan H. Schmitt (2012) [36]

USA

Minnesota

2012

N. Wüppenhorst (2012) [37]

Germany

Freiburg

NM

Silvia Vega-Casta˜no (2012) [38]

Spain

Salamanca

NM

Rémi Fournier (2012) [39]

France

Lille

NM

M. Knoester (2012) [40]

The Netherlands

Leiden

January 2010 to February 2011

D. P. Fedorko (2012) [41]

USA

Bethesda

NM

Ulrik Stenz Justesen (2011) [14]

Denmark

Vejle

November 2007 to October 2010

Esther Culebras (2011) [42]

Spain

Madrid

2004–2006

Bernard La Scola (2011) [13]

France

Marseille

2009–2010

A. C. M. Veloo (2011) [43]

The Netherlands

Leiden

NM

A.C.M. Velooa (2011) [44]

The Netherlands

Groningen

NM

NM Not mentioned in the article

Bacterial isolates

After comprehensive and detailed data compilation, we collected 6685 (Additional file 1) strains of anaerobic bacteria. The most 4 common genera (> 500) in this article were Bacteroides spp. (1952), Clostridium spp. (1599), Propionibacterium spp. (611) and Prevotella spp. (509). A total of 5125 anaerobic bacteria were analyzed by MALDI biotyper, and VITEK MS analyzed a total of 1609 anaerobic bacteria. In addition, 49 anaerobic bacteria were analyzed by both MALDI-TOF MS systems.

Performance of the MS system

The overall statistical results of the meta-analysis at the genus and species levels identification were summarized using a forest plots of random-effects model (Figs. 2 and 3) [3, 13, 14, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]. Of these, 6008 (92%; 95% CI of 90 to 93%) were correctly identified at the genus level, while 5656 (84%; 95% CI of 81 to 87%) were correctly identified at the species level by MALDI-TOF MS using a random-effects model.
Fig. 2

Forest plot for the meta-analysis of the gross identification ratio at the genus level

Fig. 3

Forest plot for the meta-analysis of the gross identification ratio at the species level

The pooled identification results of MALDI-TOF MS by random-effects for all anaerobic genera were shown in Table 2. The overall correct identification ratio of MALDI-TOF MS to anaerobic bacteria ranged from 60 to 100% at the genus level and ranged from 51 to 100% at the species level. Significant heterogeneity was found both at the genus level (P < 0.001; I2 = 96.6%) and the species level (P < 0.001; I2 = 98.0%). Identification accuracy of Bacteroides spp. was the highest at 96% with a 95% CI of 95 to 97%. The higher proportion of anaerobic bacteria was Lactobacillus spp., Parabacteroides spp., Clostridium spp., Propionibacterium spp., Prevotella spp., Veillonella spp. and Peptostreptococcus spp. The correct identification rate was higher than 90%. Identification accuracy of Bifidobacterium spp., Solobacterium spp., Finegoldia spp., Capnocytophaga spp., Parvimonas spp., Peptoniphilus spp., Slackia spp., Actinomyces spp., Ruminococcus spp. and Tissierella spp. was similar with an overall correct identification ratio at 80%, followed by Fusobacterium spp., Eggerthella spp. with an identification proportion above 70%. Identification accuracy of Actinobaculum spp., Atopobium spp., Anaerococcus spp. and Flavonifracter spp. was similar with an overall correct identification ratio at 60%. The lowest performance of MALDI-TOF MS was in Eubacterium spp., Bilophila spp., Butyricimonas spp. and Porphyromonas spp. (50%). Multiple factors contributed to this result, including the category of strains, the proportion of common and unusual species, or the reference library version.
Table 2

Identification accuracy rate of all anaerobic genera

Genus

Proportion

95%CI

Weight%

Bacteroides

96%

95–97%

6.79

Lactobacillus

95%

89–102%

5.16

Parabacteroides

94%

87–101%

4.88

Clostridium

92%

90–93%

6.73

Propionibacterium

91%

89–93%

6.55

Prevotella

91%

88–93%

6.48

Veillonella

91%

85–197%

5.25

Peptostreptococcus

90%

85–95%

5.60

Bifidobacterium

89%

76–103%

2.60

Solobacterium

88%

73–104%

2.27

Finegoldia

87%

83–91%

5.96

Capnocytophaga

86%

60–112%

1.01

Parvimonas

86%

82–91%

5.78

Peptoniphilus

86%

81–91%

5.59

Slackia

83%

67–98%

2.24

Actinomyces

81%

74–89%

4.66

Ruminococcus

80%

45–115%

0.59

Tissierella

80%

45–115%

0.59

Fusobacterium

79%

74–84%

5.61

Eggerthella

74%

63–85%

3.39

Actinobaculum

68%

46–91%

1.26

Atopobium

68%

51–85%

1.92

Anaerococcus

64%

54–73%

3.92

Flavonifracter

63%

29–96%

0.64

Eubacterium

57%

20–94%

0.55

Bilophila

56%

23–88%

0.68

Butyricimonas

56%

23–88%

0.68

Porphyromonas

50%

36–64%

2.59

Subgroup meta-analyses

We selected the genera (sample number not smaller than 5) identified by MALDI biotyper and VITEK MS to compare the identification accuracy for the same genus of the two systems (Table 3). The identification accuracy rate of MALDI biotyper was higher than VITEK MS for Parabacteroides spp., Eggerthella spp., Peptostreptococcus spp., Parvimonas spp., Bacteroides spp., Clostridium spp. and Peptoniphilus spp., and the efficacy of the two systems were similar for Prevotella spp. and Actinomyces spp. However, the heterogeneity of MALDI biotyper was more significant. In addition, the correct rate of MALDI biotyper for some strains (such as Finegoldia spp. and Fusobacterium spp.) was lower than VITEK MS, and the heterogeneity of MALDI biotype was higher than the latter. To sum up, the results of Table 3 showed that the correct rate of MALDI biotyper identification of anaerobic bacteria was higher than that of VITEK MS, while the heterogeneity of the MALDI biotyper was more significant.
Table 3

Accuracy of MALDI-TOF MS identification

Genus

Numbera

MALDI biotyperb

Vitekb

Parabacteroides

42/6

100% (I2 = 0.0%, P > 0.01)

72% (I2 = 83.3%, P > 0.01)

Peptostreptococcus

41/91

100% (I2 = 0.0%, P > 0.01)

97% (I2 = 76.6%, P > 0.01)

Eggerthella

36/26

100% (I2 = 21.8%, P > 0.01)

77% (I2 = 0.0%, P > 0.01)

Parvimonas

205/7

100% (I2 = 35.5%, P > 0.01)

98% (I2 = 19.0%, P > 0.01)

Clostridium

779/820

98% (I2 = 63.5%, P < 0.01)

94% (I2 = 96.7%, P < 0.01)

Finegoldia

233/17

98% (I2 = 77.8%, P < 0.01)

99% (I2 = 2.0%, P > 0.01)

Prevotella

404/105

92% (I2 = 84.1%, P < 0.01)

92% (I2 = 0.0%, P > 0.01)

Bacteroides

1517/435

97% (I2 = 89.4%, P < 0.01)

96% (I2 = 74.8%, P < 0.01)

Fusobacterium

214/34

91% (I2 = 89.7%, P < 0.01)

92% (I2 = 84.8%, P = 0.01)

Propionibacterium

605/6

90% (I2 = 91.6%, P < 0.01)

100% (I2 = 0.0%, P > 0.01)

Peptoniphilus

41/91

85% (I2 = 92.4%, P < 0.01)

62% (I2 = 96.8%, P < 0.01)

Actinomyces

79/28

74% (I2 = 94.5%, P < 0.01)

74% (I2 = 0.0%, P > 0.01)

aThe left side of / is the number of samples of MALDI biotyper, and the right side of / is the number of samples of Vitek

bThe higher I-square values combined P value more than 0.1 mean the higher heterogeneity between those studies

In additional, the identification rate of anaerobic bacteria in European countries (species: 84%, genus: 88%) was lower than that in Asia (species: 84%, genus: 91%) and North America (species: 86%, genus: 94%). The protocol for the studies at different cities was the same. A total of 21 articles reported on the media, including anaerobic horse blood agar, chocolate agar, blood culture bottle, schaedler agar, bacteroides bile esculin agar, CDC anaerobic blood agar, brucella blood plates, columbia blood plates and blood plates. Among them, brucella and columbia blood plates were two most frequently used media (species 73% and genus 92%; species 73% and genus 75%).

It was worth noting that VITEK MS incorrectly identified Actinomyces georgiae as Capnocytophaga gingivalis, MALDI biotyper incorrectly identified Clostridium spp. as Enterococcus spp. (Table 4), and MALDI biotyper also incorrectly identified some rare anaerobic bacteria Mogibacterium timidum and Parvimonas micra as other bacteria, probably due to the lack of corresponding standard spectra in the database.
Table 4

Common misidentification pattern in these studies

Sequence identifcation

MALDI-TOF MS identifcation

System

Reference

Actinomyces georgiae

Capnocytophaga gingivalis

bioMérieux Vitek MS

[27]

Actinomyces graevenitzii

Actinomyces turicensis

Bruker MALDI Biotyper

[7]

Actinomyces meyeri

Actinomyces odontolyticus

Bruker MALDI Biotyper

[58]

Actinomyces viscosus

Actinomyces oris

Bruker MALDI Biotyper

[58]

Anaerococcus hydrogenalis

Bacteroides fragilis

Bruker MALDI Biotyper

[24]

Anaerococcus tetradius

Brevibacillus spp.

bioMérieux Vitek MS

[31]

Bacteroides cellulosilyticus

Bacteroides intestinalis

Bruker MALDI Biotyper

[3]

Bacteroides dorei

Bacteroides vulgatus

Bruker MALDI Biotyper

[28, 58]

Bacteroides faecis

Bacteroides thetaiotaomicron

Bruker MALDI Biotyper

[58]

Bacteroides faecis

Bacteroides thetaiotaomicron

bioMérieux Vitek MS

[27]

Bacteroides nordii

Bacteroides thetaiotaomicron

bioMérieux Vitek MS

[27]

Bacteroides vulgatus

Bacteroides eggerthii

bioMérieux Vitek MS

[31]

Clostridium baratii

Clostridium paraputrificum

bioMérieux Vitek MS

[20]

Clostridium beijerinckii

Clostridium butyricum

bioMérieux Vitek MS

[20]

Clostridium bifermentans

Clostridium sordellii

bioMérieux Vitek MS

[20]

Clostridium bolteae

Clostridium clostridioforme

bioMérieux Vitek MS

[27]

Clostridium butyricum

Clostridium beijerinckii

bioMérieux Vitek MS

[20]

Clostridium cadaveris

Clostridium sordellii

bioMérieux Vitek MS

[20]

Clostridium clostridioforme

Bacillus ssp.

bioMérieux Vitek MS

[20]

Clostridium difficile

Clostridium septicum

bioMérieux Vitek MS

[20]

Clostridium difficile

Enterococcus faecium

Bruker MALDI Biotyper

[24]

Clostridium histolyticum

C. sordellii/C. septicum

bioMérieux Vitek MS

[20]

Clostridium limosum

Clostridium tyrobutyricum

bioMérieux Vitek MS

[20]

Clostridium perfringens

Enterococcus faecalis

Bruker MALDI Biotyper

[24]

Clostridium sordellii

Clostridium bifermentans

bioMérieux Vitek MS

[20]

Clostridium sporogenes

C.difficile/ C.glycolicum

bioMérieux Vitek MS

[20]

Clostridium tetani

Clostridium septicum

bioMérieux Vitek MS

[20]

Fusobacterium nucleatum

Enterococcus faecalis

Bruker MALDI Biotyper

[24]

Fusobacterium nucleatum

Fusobacterium naviforme

Bruker MALDI Biotyper

[26]

Mogibacterium timidum

Clostridium halophilum

Bruker MALDI Biotyper

[58]

Parvimonas micra

Slackia exigua

Bruker MALDI Biotyper

[24]

Peptoniphilus Indolicus

Peptoniphilus harei

Bruker MALDI Biotyper

[28, 58]

Porphyromonas gulae

Porphyromonas gingivalis

Bruker MALDI Biotyper

[58]

Prevotella bivia

Streptococcus anginosus

Bruker MALDI Biotyper

[24]

Prevotella denticola

Bacteroides fragilis

Bruker MALDI Biotyper

[24]

Prevotella oralis

Prevotella nanciencis

Bruker MALDI Biotyper

[58]

Prevotella oris

Prevotella buccae

Bruker MALDI Biotyper

[24]

Veillonella dispar

Veillonella parvula

Bruker MALDI Biotyper

[7]

Veillonella dispar

Veillonella parvula

bioMérieux Vitek MS

[27]

Discussion

MALDI-TOF MS, based on the microbial identification of characteristic protein fingerprints of bacteria, usually takes only a few minutes to rapidly identify species of different microorganisms, thus greatly shortening the detection time and improving the diagnostic efficiency of infectious diseases. It is usually difficult to isolate and culture anaerobic bacteria by conventional approaches, and MALDI-TOF MS provides a useful technology for their identification. In this study, we conducted a meta-analysis to analyze the differences in independent research results by addressing heterogeneity between studies in an attempt to shed new light on the identification of anaerobic bacteria by MALDI-TOF MS [45, 46].

According to the inclusion and exclusion criteria, 28 anaerobic genera were included and assessed critically using two currently available MALDI-TOF MS systems. It is known that anaerobes are more difficult to be identified in clinical laboratories [47]. However, using MALDI-TOF MS, the overall identification accuracy of anaerobic bacteria was 92% (95% CI of 0.90 to 0.93) at the genus level in 28 included articles with 6685 various anaerobes isolates. These results indicate that MALDI-TOF MS is a qualified method for accurate and rapid identification of pathogenic anaerobes. At the same time, we noticed that the identification property of MALDI-TOF MS against common anaerobe isolate species was variable. Among them, the correct rate was more than 80% for 18 anaerobic genera (Bacteroides spp., Lactobacillus spp., Parabacteroides spp., Clostridium spp., ect.), 60–80% for 6 anaerobic genera (Fusobacterium spp., Eggerthella spp., Actinobaculum spp., Atopobium spp., Anaerococcus spp., Flavonifracter spp.,), and lower than 60% for the other 4 anaerobic genera (Eubacterium spp., Bilophila spp., Butyricimonas spp. and Porphyromonas spp.). The different identification correct rate might be due to the difficulty of obtaining satisfactory spectra from some species, such as Mogibacterium timidum or Actinomyces georgiae, and partly due to the limit of uncommon anaerobes species spectra in commercial reference libraries. Therefore, it is increasingly important to update the library of various anaerobic species, especially those lacking or poorly represented in the current version. Fortunately, commercial databases are constantly being improved and updated at intervals of about three to 6 months [48].

In this study, we analyzed two commonly used commercial MALDI-TOFMS systemstwo identification systems: the Bruker MALDI biotyper and the bioMérieux VITEK MS. To compare the same anaerobic genus between the two systems, we focused our attention on analysis of 12 out of 28 anaerobic bacteria genera included in both systems. Among them, Bacteroides spp., Clostridium spp., Propionibacterium spp. and Prevotella spp. were the predominant anaerobes. Figures 2 and 3 showed the overall identification rates of the specimens with two identification systems, MALDI biotyper and VITEK MS. The identification capacities of the two systems in Table 3 and forest plot (Figs. 2 and 3) was different. The overall identification rate of MALDI biotyper was higher than that of VITEK MS (Table 3), though the data in forest plot was opposite. It is supposed that low equipment cost leads to a wider range of MALDI biotyper applications. The rare anaerobic specimens identified by MALDI biotyper may account for a large proportion of the reason, for most of them were not included in the relevant database as previously described, which decreased of overall identification rate. This is consistent with the data presented in the forest plot.

In addition to the instrument, the identification correct ratio of anaerobic bacteria is also related to the system paired database. As shown in Table 4, one-third of the 28 studies displayed identification errors, most of which were correct genus and wrong species, and some of which were wrong genera. These results might attribute to the similarity protein composition of the species, which made the differentiation of the quality peak difficult, and made it difficult for MALDI-TOF MS to correctly identify the strain. The similarity of the protein structures led to the incorrect identification results in both anaerobic bacteria and other genera, such as Streptococcus spp. [49], Mycobacterium spp. [50], Enterococcus spp. [51] and yeast [52]. These result-related mistakes might be attributed to the similar protein compositions of the species, making the discrepancy of differentiation of the quality peak difficult, and making it difficult for MALDI-TOF MS to correctly identify the strains. Beyond this, the lower identification scores might be related to interspecies correlation and bacterial cell wall composition [6]. Therefore, updating the existing information and perfecting the database of difficultly identified organisms (such as Fusobacterium spp. and Porphyromonas spp.) are useful to improve the identification accuracy of MALDI-TOF MS.

Another point that cannot be ignored is the impact of the geographical distribution on the identification rate of MALDI-TOF MS. The anaerobic identification rate was slightly lower in European countries than that elsewhere. In our study, we give priority to the following reasons for this situation. The geographical distribution of the collected literature was related to the level of the economic development, given the high cost of the equipment. For this reason, the strains involved in this study reflected the situation in some developed countries rather the whole world; for instance, the technology is more advanced in Europe than that in other continents, where MALDI-TOF MS has been first applied to the field of microbial identification. In addition, MALDI-TOF MS was used to identify microorganisms with imperfect databases in some of the early studies, which is also the reason for the relatively low overall identification rate. Therefore, updating and perfecting the databases are essential for improving the identification rate. On the other hand, technological improvements should be made to reduce the cost of MALDI-TOF MS equipment as much as possible so that it can be applied in more countries and cities.

There are some limitations in our research. First, Table 3 does not list all anaerobes collected because some data of the species were discarded for statistical reasons. In additional, some articles only reported the results of MALDI-TOF MS identification of the isolates at the “species” level without providing the identification results at the “genus” level. If a specimen was not identified to “species”, it would be defined as being identified neither at the species level nor at the genus level. This may lead to the negligence of the strains identified at the “genus” level, thus underestimating the accuracy of MALDI-TOF MS in identifying anaerobes at the genus level. Despite these problems, MALDI-TOF MS is still widely used in bacterial identification and other fields such as strain typing [53], detection of virulence factors [54] and evaluation of drug resistance [55, 56, 57].

Conclusions

In conclusion, the current meta-analysis showed that MALDI-TOF MS has shown a high degree of accuracy in anaerobic identification, although there is a lack of data to define its effectiveness in identifying rare anaerobic species. As a novel technology, MALDI-TOF MS has been widely used in the clinical diagnosis of pathogenic diseases. Therefore, it is necessary to analyze the comprehensive ability of this technique in clinical and microbiology diagnosis in the future.

Notes

Acknowledgments

We are grateful for the MALDI-TOF MS provided by the Microbiology Laboratory of Xuzhou Medical University and the kind help provided by the laboratory teachers.

Authors’ contributions

Conceived and designed the study: BG, HCL. Searched and screened the literature: MZS, XHM, MJY. Analyzed the data: MZS, XHM. Wrote and revised the paper: YL, MZS, ZBZ, YC, QJZ. All authors read and approved the final version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 17KJB360014), Jiangsu Privincial Natural Science Foundation (BK20151154) and the National Natural Science Foundation of China (81871734, 81471994, 81902040). Jiangsu Privincial Medical Talent (ZDRCA2016053) and Six talent peaks project of Jiangsu Province (WSN-135) lead the design of each part of the study. The Natural Science Foundation of Jiangsu Province (Grants No BK20150209) and the Advanced health talent of six-one project of Jiangsu Province (LGY2016042) participated in revising manuscripts. The funding bodies had no role in the collection, analysis, and interpretation of data.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Supplementary material

12879_2019_4584_MOESM1_ESM.xlsx (50 kb)
Additional file 1. The details of 6685 strains.

References

  1. 1.
    Norin E. How normal is a “normal” flora in animal or man? Anaerobe. 2011;17(6):431–2.CrossRefGoogle Scholar
  2. 2.
    Kim J, Lee Y, Park Y, et al. Anaerobic bacteremia: impact of inappropriate therapy on mortality. Infect Chemother. 2016;48(2):91–8.CrossRefGoogle Scholar
  3. 3.
    Lau SK, Tang BS, Teng JL, et al. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry for identification of clinically significant bacteria that are difficult to identify in clinical laboratories. J Clin Pathol. 2014;67(4):361–6.CrossRefGoogle Scholar
  4. 4.
    Petrotchenko EV, Borchers CH. Modern mass spectrometry-based structural proteomics. Adv Protein Chem Struct Biol. 2014;95(2):193–213.CrossRefGoogle Scholar
  5. 5.
    Matsuo T, Seyama Y. Introduction to modern biological mass spectrometry. J Mass Spectrom. 2015;35(2):114–30.CrossRefGoogle Scholar
  6. 6.
    Prod'Hom G, Bizzini A, Durussel C, et al. Matrix-assisted laser desorption ionization-time of flight mass spectrometry for direct bacterial identification from positive blood culture pellets. J Clin Microbiol. 2010;48(4):1481–3.Google Scholar
  7. 7.
    Coltella L, Mancinelli L, Onori M, et al. Advancement in the routine identification of anaerobic bacteria by MALDI-TOF mass spectrometry. Eur J Clin Microbiol Infect Dis. 2013;32(9):1183–92.CrossRefGoogle Scholar
  8. 8.
    Buckwalter SP, Olson SL, Connelly BJ, et al. Evaluation of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of Mycobacterium species, Nocardia species, and other aerobic Actinomycetes. J Clin Microbiol. 2016;54(2):376.CrossRefGoogle Scholar
  9. 9.
    Saleeb PG, Drake SK, Murray PR, et al. Identification of Mycobacteria in solid-culture media by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol. 2011;49(5):1790–4.CrossRefGoogle Scholar
  10. 10.
    Segawa S, Nishimura M, Sogawa K, et al. Identification of Nocardia species using matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry. Clin Proteomics. 2015;12(1):6.CrossRefGoogle Scholar
  11. 11.
    Aslani N, Janbabaei G, Abastabar M, et al. Identification of uncommon oral yeasts from cancer patients by MALDI-TOF mass spectrometry. BMC Infect Dis. 2018;18(1):24.CrossRefGoogle Scholar
  12. 12.
    Veen SQ, Van CECJ, Kuijper EJ. High-throughput identification of bacteria and yeast by matrix-assisted laser desorption ionization-time of flight mass spectrometry in conventional medical microbiology laboratories. J Clin Microbiol. 2010;48(3):900–7.CrossRefGoogle Scholar
  13. 13.
    Scola BL, Fournier PE, Raoult D. Burden of emerging anaerobes in the MALDI-TOF and 16S rRNA gene sequencing era. Anaerobe. 2011;17(3):106–12.CrossRefGoogle Scholar
  14. 14.
    Justesen US, Holm A, Knudsen E, et al. Species identification of clinical isolates of anaerobic bacteria: a comparison of two matrix-assisted laser desorption ionization-time of flight mass spectrometry systems. J Clin Microbiol. 2011;49(12):4314–8.CrossRefGoogle Scholar
  15. 15.
    Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;6(10):e1–34.CrossRefGoogle Scholar
  16. 16.
    Georgia S, Simon S, Higgins JP. Obstacles and opportunities in meta-analysis of genetic association studies. Genet Med. 2005;7(1):13–20.CrossRefGoogle Scholar
  17. 17.
    Ioannidis JP. Interpretation of tests of heterogeneity and bias in meta-analysis. J Eval Clin Pract. 2010;14(5):951–7.CrossRefGoogle Scholar
  18. 18.
    Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36.CrossRefGoogle Scholar
  19. 19.
    Hunter JP, Saratzis A, Sutton AJ, et al. In meta-analyses of proportion studies, funnel plots were found to be an inaccurate method of assessing publication bias. J Clin Epidemiol. 2014;67(8):897–903.CrossRefGoogle Scholar
  20. 20.
    Sanchez Ramos L, Rodloff AC. Identification of Clostridium species using the VITEK® MS. Anaerobe. 2018;54:217–23.CrossRefGoogle Scholar
  21. 21.
    Gursoy M, Harju I, Matomaki J, et al. Performance of MALDI-TOF MS for identification of oral Prevotella species. Anaerobe. 2017;47:89–93.CrossRefGoogle Scholar
  22. 22.
    Rodríguez-Sánchez B, Alcalá L, Marín M, et al. Evaluation of MALDI-TOF MS (matrix-assisted laser desorption-ionization time-of-flight mass spectrometry) for routine identification of anaerobic bacteria. Anaerobe. 2016;42:101–7.CrossRefGoogle Scholar
  23. 23.
    Veloo AC, de Vries ED, Jean-Pierre H, et al. The optimization and validation of the Biotyper MALDI-TOF MS database for the identification of gram-positive anaerobic cocci. Clin Microbiol Infect. 2016;22(9):793–8.CrossRefGoogle Scholar
  24. 24.
    Yunoki T, Matsumura Y, Nakano S, et al. Genetic, phenotypic and matrix-assisted laser desorption ionization time-of-flight mass spectrometry-based identification of anaerobic bacteria and determination of their antimicrobial susceptibility at a University Hospital in Japan. J Infect Chemother. 2016;22(5):303–7.CrossRefGoogle Scholar
  25. 25.
    Jo SJ, Park KG, Han K, et al. Direct identification and antimicrobial susceptibility testing of Bacteria from positive blood culture bottles by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and the Vitek 2 system. Ann Lab Med. 2016;36(2):117–23.CrossRefGoogle Scholar
  26. 26.
    Handal N, Bakken Jørgensen S, Smith Tunsjø H, et al. Anaerobic blood culture isolates in a Norwegian university hospital: identification by MALDI-TOF MS vs 16S rRNA sequencing and antimicrobial susceptibility profiles. APMIS. 2015;123(9):749–58.CrossRefGoogle Scholar
  27. 27.
    Lee W, Kim M, Yong D, et al. Evaluation of VITEK mass spectrometry (MS), a matrix-assisted laser desorption ionization time-of-flight MS system for identification of anaerobic bacteria. Ann Lab Med. 2015;35(1):69–75.CrossRefGoogle Scholar
  28. 28.
    Barba MJ, Fernández A, Oviaño M, et al. Evaluation of MALDI-TOF mass spectrometry for identification of anaerobic bacteria. Anaerobe. 2014;30:126–8.CrossRefGoogle Scholar
  29. 29.
    Chean R, Kotsanas D, Francis MJ, et al. Comparing the identification of Clostridium spp. by two Matrix-Assisted Laser Desorption Ionization-Time of Flight (MALDI-TOF) mass spectrometry platforms to 16S rRNA PCR sequencing as a reference standard: a detailed analysis of age of culture and sample preparation. Anaerobe. 2014;30:85–9.CrossRefGoogle Scholar
  30. 30.
    Zárate MS, Romano V, Nievas J, et al. Utilidad de la espectrometría de masas MALDI-TOF en la identificación de bacterias anaerobias. Rev Argent Microbiol. 2014;46(2):98–102.PubMedGoogle Scholar
  31. 31.
    Li Y, Gu B, Liu G, et al. MALDI-TOF MS versus VITEK 2 ANC card for identification of anaerobic bacteria. J Thorac Dis. 2014;6(5):517–23.PubMedPubMedCentralGoogle Scholar
  32. 32.
    Hsu YM, Burnham CA. MALDI-TOF MS identification of anaerobic bacteria: assessment of pre-analytical variables and specimen preparation techniques. Diagn Microbiol Infect Dis. 2014;79(2):144–8.CrossRefGoogle Scholar
  33. 33.
    Lau SK, Tang BS, Teng JL, et al. Matrix-assisted laser desorption ionisation time-of-flight mass spectrometry for identification of clinically significant bacteria that are difficult to identify in clinical laboratories. J Clin Pathol. 2014;67(4):361–6.Google Scholar
  34. 34.
    Garner O, Mochon A, Branda J, et al. Multi-Centre evaluation of mass spectrometric identification of anaerobic bacteria using the VITEK® MS system. Clin Microbiol Infect. 2014;20(4):335–9.Google Scholar
  35. 35.
    Barreau M, Pagnier I, La Scola B. Improving the identification of anaerobes in the clinical microbiology laboratory through MALDI-TOF mass spectrometry. Anaerobe. 2013;22:123–5.Google Scholar
  36. 36.
    Schmitt BH, Cunningham SA, Dailey AL, et al. Identification of Anaerobic Bacteria by Bruker Biotyper Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry with On-Plate Formic Acid Preparation. J Clin Microbiol. 2013;51(3):782–6.Google Scholar
  37. 37.
    Wüppenhorst N, Consoir C, Lörch D, et al. Direct identification of bacteria from charcoal-containing blood culture bottles using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. Eur J Clin Microbiol Infect Dis. 2012;31(10):2843–50.Google Scholar
  38. 38.
    Vega-Castaño S, Ferreira L, González-Ávila M, et al. Eficacia de la espectrometría de masas MALDI-TOF en la identificación debacterias anaerobias. Enferm Infecc Microbiol Clin. 2012;30(10):597–601.Google Scholar
  39. 39.
    Fournier R, Wallet F, Grandbastien B, et al. Chemical extraction versus direct smear for MALDI-TOF mass spectrometry identification of anaerobic bacteria. Anaerobe. 2012;18(3):294–7.Google Scholar
  40. 40.
    Knoester M, van Veen SQ, Claas EC, et al. Routine identification of clinical isolates of anaerobic bacteria: matrix-assisted laser desorption ionization-time of flight mass spectrometry performs better than conventional identification methods. J Clin Microbiol. 2012;50(4):1504.Google Scholar
  41. 41.
    Fedorko DP, Drake SK, Stock F, et al. Identification of clinical isolates of anaerobic bacteria using matrix-assisted laser desorption ionization-time of flight mass spectrometry. Eur J Clin Microbiol Infect Dis. 2012;31(9):2257–62.Google Scholar
  42. 42.
    Culebras E, Rodriguez-Avial I, Betriu C, et al. Rapid identification of clinical isolates of Bacteroides species by matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry. Anaerobe. 2012;18(1):163–5.Google Scholar
  43. 43.
    Veloo AC, Erhard M, Welker M, et al. Identification of Gram-positive anaerobic cocci by MALDI-TOF mass spectrometry. Syst Appl Microbiol. 2011;34(1):58–62.Google Scholar
  44. 44.
    Veloo AC, Knoester M, Degener JE, et al. Comparison of two matrix-assisted laser desorption ionisation-time of flight mass spectrometry methods for the identification of clinically relevant anaerobic bacteria. Clin Microbiol Infect. 2011;17(10):1501–6.Google Scholar
  45. 45.
    Andreas W, Lukas S, Jette J, et al. MALDI-TOF MS in microbiological diagnostics-identification of microorganisms and beyond (mini review). Appl Microbiol Biotechnol. 2012;93(3):965–74.Google Scholar
  46. 46.
    Neelja S, Manish K, Kanaujia PK, et al. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Front Microbiol. 2015;6:791.Google Scholar
  47. 47.
    Marinach-Patrice C, Lethuillier A, Marly A, et al. Use of mass spectrometry to identify clinical Fusarium isolates. Clin Microbiol Infect. 2010;15(7):634–42.Google Scholar
  48. 48.
    Lavigne JP, Espinal P, Dunyach-Remy C, et al. Mass spectrometry: a revolution in clinical microbiology? Clin Chem Lab Med. 2013;51(2):257–70.Google Scholar
  49. 49.
    Fan WT, Qin TT, Bi RR, et al. Performance of the matrix-assisted laser desorption ionization time-of-flight mass spectrometry system for rapid identification of streptococci: a review. Eur J Clin Microbiol Infect Dis. 2017;36(6):1005–12.Google Scholar
  50. 50.
    Zingue D, Flaudrops C, Drancourt M. Direct matrix-assisted laser desorption ionisation time-of-flight mass spectrometry identification of mycobacteria from colonies. Eur J Clin Microbiol Infect Dis. 2016;35(12):1983–7.Google Scholar
  51. 51.
    Lallemand E, Arvieux C, Coiffier G, et al. Use of MALDI-TOF mass spectrometry after liquid enrichment (BD Bactec™) for rapid diagnosis of bone and joint infections. Res Microbiol. 2017;168(2):122–29.Google Scholar
  52. 52.
    Ling H, Yuan Z, Shen J, et al. Accuracy of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of clinical pathogenic fungi: a meta-analysis. J Clin Microbiol. 2014;52(7):2573–82.Google Scholar
  53. 53.
    Sauer S, Freiwald A, Maier T, et al. Classification and identification of bacteria by mass spectrometry and computational analysis. PLoS One. 2008;3(7):e2843.Google Scholar
  54. 54.
    Bittar F, Ouchenane Z, Smati F, et al. MALDI-TOF-MS for rapid detection of staphylococcal Panton-Valentine leukocidin. Int J Antimicrob Agents. 2009;34(5):467–70.Google Scholar
  55. 55.
    De Carolis E, Vella A, Florio AR, et al. Use of matrix-assisted laser desorption ionization-time of flight mass spectrometry for caspofungin susceptibility testing of Candida and Aspergillus species. J Clin Microbiol. 2012;50(7):2479–83.Google Scholar
  56. 56.
    Lata M, Sharma D, Deo N, et al. Proteomic analysis of ofloxacin-mono resistant Mycobacterium tuberculosis isolates. J Proteomics. 2015;127:114–21.Google Scholar
  57. 57.
    Sharma D, Lata M, Singh R, et al. Cytosolic Proteome Profiling of Aminoglycosides Resistant Mycobacterium tuberculosis Clinical Isolates Using MALDI-TOF/MS. Front Microbiol. 2016;7:1816.Google Scholar
  58. 58.
    Huang YL, Sun QL, Li JP, et al. Evaluation of an in-house MALDI-TOF MS rapid diagnostic method for direct identification of micro-organisms from blood cultures. J Med Microbiol. 2019;68(1):41–7.Google Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Ying Li
    • 1
  • Mingzhu Shan
    • 1
  • Zuobin Zhu
    • 2
  • Xuhua Mao
    • 3
  • Mingju Yan
    • 1
  • Ying Chen
    • 1
  • Qiuju Zhu
    • 4
  • Hongchun Li
    • 1
    • 5
  • Bing Gu
    • 1
    • 5
    Email author
  1. 1.School of Medical TechnologyXuzhou Medical UniversityXuzhouChina
  2. 2.Department of GeneticsXuzhou Medical UniversityXuzhouChina
  3. 3.Department of Clinical LaboratoryYixing People’s HospitalWuxiChina
  4. 4.Jiangsu Key Laboratory of Brain Disease BioinformationXuzhou Medical UniversityXuzhouChina
  5. 5.Department of Laboratory MedicineAffiliated Hospital of Xuzhou Medical UniversityXuzhouChina

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