MALDI-TOF MS, based on the microbial identification of characteristic protein fingerprints of bacteria, usually takes only a few minutes to rapidly identify the species of different microorganisms, greatly shortening the detection time and improving the diagnostic efficiency of infectious diseases. Anaerobic bacteria are hard to isolate and culture by conventional approach, and MALDI-TOF MS provides a useful technology for its identification. In this study, we conducted a meta-analysis to analyze the differences of independent research results by addressing heterogeneity between studies, potentially providing new insights into the identification of anaerobic bacteria by MALDI-TOF MS [50,51].
According to inclusion and exclusion criteria, 28 anaerobic genera were included, which assessed critically by two available MALDI-TOF MS systems. It is all known that anaerobes are more difficulty to identify than aerobes in clinical lab [52]. However, using MALDI-TOF MS, the overall identification accuracy of anaerobic bacteria at genus level was 92% (95% CI of 0.90 to 0.93) in 28 included articles with 6685 various anaerobes isolates. These results indicated that MALDI-TOF MS was a qualified method for the 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 rates of 18 anaerobic genera (Bacteroides spp., Lactobacillus spp., Parabacteroides spp., Clostridium spp, ect.) were more than 80%, the correct rates of identification of 6 anaerobic genera(Fusobacterium spp., Eggerthella spp., Actinobaculum spp., Atopobium spp., Anaerococcus spp., Flavonifracter spp.,) were between 60% and 80%, while the other 4 anaerobic genera (Eubacterium spp.,Bilophila spp.,Butyricimonas spp. and Porphyromonas spp.) identification rates were relative lower (less than 60%). 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 the limit of uncommon anaerobes species spectra in commercial reference libraries is also part of reasons. Therefore, it is increasing 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 six months [53].
In this study, we analyzed two commonly used identification systems, MALDI biotyper and VITEK MS. In order to compare the same anaerobic genus between two systems, we focused analyzing 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. Figure 2 and Figure 3 showed the overall identification rates of the specimens with two identification systems, MALDI biotyper and VITEK MS, respectively. The identification capacities of the two systems in Table 2 and forest plot (Figure 2 and Figure 3) was different. The summary identification rate of MALDI biotyper in Table 2 was higher than the VITEK MS, while 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 accounted for a large proportion, most of them were not included in the relevant database as previously described, which led to the decrease of overall identification rate. This is consistent with the data presented in the forest plot.
In addition to instrument itself, the identification correct ratio of anaerobic bacteria is also related to the system paired database. As shown in Table 3, one third of the 28 studies displayed identification errors, most of them were correct genus and wrong species, and some of them were wrong genera. These results might attribute to the similarity protein composition of species, which makes the differentiation of the quality peak difficult, and makes it difficult for MALDI-TOF MS to correctly identify the strain. The similarity of the protein structure leaded to incorrect identification results, which were not only found in anaerobic bacteria, but also in other genera. Prod’ hom et al. [8] indicated that the lower identification scores for MALDI-TOF MS of Streptococcus spp. and Staphylococcus spp. might be related to interspecies correlation and bacterial cell wall composition. Tomoyuki Yunoki et al. also believed that the identification accuracy of MALDI-TOF MS was relatively low among the less common isolated species [28]. Therefore, updating the existing information and completing the database of difficultly identified organisms (such as Fusobacterium spp. and Porphyromonas micra) were useful to improve the identification accuracy of MALDI-TOF MS.
In conclusion, MALDI-TOF MS has shown a high degree of accuracy in anaerobic identification in current meta-analysis, although it still lacks in the identification of rare anaerobic species. As a brand new technology, MALDI-TOF MS is not only widely used in the clinical diagnosis of pathogenic diseases, but also developed for other applications, such as strain typing [54], detection of virulence factors [55] and evaluation of drug resistance [56]. In addition, the direct identification of pathogenic bacteria in blood culture [57] and urine [58] is also one of the research hotspots. Therefore, it is necessary to analyze the comprehensive ability of this technique in clinical microbiology diagnosis in the future.