Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

1Institut für Medizinische Mikrobiologie und Hygiene, Medizinische Fakultät Carl Gustav Carus, TU Dresden, 2Institut für Virologie, Medizinische Fakultät Carl Gustav Carus, TU Dresden
Published 7/11/2016
Immunology and Infection

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Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and molecular techniques (16S rRNA gene sequencing) permit the identification of rare bacterial pathogens in routine diagnostics. The goal of this protocol lies in the combination of both techniques which leads to more accurate and reliable data.

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Schröttner, P., Gunzer, F., Schüppel, J., Rudolph, W. W. Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS. J. Vis. Exp. (113), e53176, doi:10.3791/53176 (2016).


There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in immunocompromised patients. In most cases only few data, mostly published as case reports, are available which investigate the role of such pathogens as an infectious agent. Therefore, in order to clarify the pathogenic character of such microorganisms, it is necessary to conduct epidemiologic studies which include large numbers of these bacteria. The methods used in such a surveillance study have to meet the following criteria: the identification of the strains has to be accurate according to the valid nomenclature, they should be easy to handle (robustness), economical in routine diagnostics and they have to generate comparable results among different laboratories. Generally, there are three strategies for identifying bacterial strains in a routine setting: 1) phenotypic identification characterizing the biochemical and metabolic properties of the bacteria, 2) molecular techniques such as 16S rRNA gene sequencing and 3) mass spectrometry as a novel proteome based approach. Since mass spectrometry and molecular approaches are the most promising tools for identifying a large variety of bacterial species, these two methods are described. Advances, limitations and potential problems when using these techniques are discussed.


Secure identification of rare pathogens in routine diagnostics is hampered by the fact that classical cultural and biochemical methods are cumbersome and sometimes questionable. Furthermore, a diagnostic microbiology laboratory has to process a large number of pathogens, ranging from a few hundred to several thousands, daily, which requires the use of automated systems. In addition to the management of a high daily throughput, the precise identification of bacterial species is needed. This is warranted since they differ in their antimicrobial susceptibility pattern and therefore correct identification provides the clinician with essential information to choose appropriate antibiotics (e.g., Enterococcus spp., Acinetobacter spp.) 12,43.

Automated microbial identification systems (aMIS) apply standardized sets of enzymatic reactions to characterize the metabolic properties of bacterial isolates 13,15,16,26,27. Although the cartridges used in these systems utilize a large number of different biochemical reactions, e.g., 47 in the GN card of the aMIS used in this study 52, this strategy permits secure identification only for a limited set of bacteria. Furthermore, the database, an advanced expert system, is clearly focused on detection of relevant and highly relevant bacteria of medical importance 13,15,16,36. Two further systems, widely used in laboratories, also apply this biochemical approach for bacterial identification. Recent studies demonstrate a comparable identification accuracy between the aMIS used in this study and one of the competitors (93.7% and 93.0% respectively), while the 3rd aMIS has an identification accuracy of only 82.4% on species level 35. Such discrepancies may be explained by the quality of the underlying identification data references, the versions of kits and software, differences in metabolism and proficiency of technical personnel 35,36.

Two automated MALDI-TOF MS systems (MALDI-TOF microbial identification system, mMIS) are mainly used. These systems allow for detection of a large number of bacterial species based on their protein fingerprint mass spectra. For instance, the database of the mMIS used contains 6,000 reference spectra. Identification systems based on mass spectrometry offer fast and reliable detection of a great variety of microorganisms including rare pathogens 11,48,51. To date only a few direct comparisons are available between the mMIS used in this study and its competitor 19,33. According to Daek et al. both systems provide a similar high rate of identification accuracy, but the mMIS used in this study seems to be more reliable in species identification 19.

Similarly, molecular techniques addressing well conserved but also distinct genes (e.g., 16S rDNA or rpoB) permit a clear species identification 3,22,61. Among these, the 16S rDNA is the most widely used housekeeping gene because of its presence in all bacteria 34. Its function remains unchanged and finally, with roughly 1,500 bp, it is long enough to be suitable for bio-informatics 14,34. Many researchers regard 16S rRNA gene analysis as the "gold-standard" for bacterial identification 21. This is due to the fact that few laboratories use DNA-DNA hybridization techniques to date for identification of rare or new bacteria 14,34. Additionally, more and more databases are available which can be used for 16S rRNA gene analysis 50. However, it has to be taken into account that 16S rDNA based detection systems have a limited sensitivity compared to standard PCR protocols. Moreover, the molecular approach is sophisticated, time consuming and requires highly trained personnel as well as dedicated laboratory facilities and is, therefore, not easily implemented into routine diagnostics 55. Furthermore, it has been shown that the combination of at least two different methods of bacterial identification leads to highly accurate strain identification. The combination of MALDI-TOF MS and 16S rDNA sequencing permits the identification of large numbers of different bacterial species with high accuracy. Recently the combination of MALDI-TOF MS and 16S rRNA gene analysis was presented for bacterial identification studying epidemiological questions and rare pathogens 56.

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1. Extraction of Bacterial DNA

  1. Preparation of PBS Solution
    1. Weigh 1.65 g Na2HPO4 x 2H2O, 0.22 g NaH2PO4 x 2H2O and 8.80 g NaCl in a flask and fill with distilled water to a final volume of 1,000 ml. Adjust the pH to 7.4. For final use filter the solution through a bacteria-proof (0.22 µm) filter.
  2. DNA Extraction of Gram-negative Bacteria
    1. Streak the patient material on appropriate culture media (e.g., Columbia blood agar), identify and isolate potential pathogens.
    2. Prepare pure culture and perform Gram-staining in order to determine morphotype and to confirm purity and of the culture 49.
    3. Pick a single bacterial colony and transfer it with a 1 µl single use inoculation loop into a sterile 2.0 ml reaction tube which contains 1 ml PBS solution.
    4. Incubate this suspension in a thermomixer at 95 °C for 10 min. After incubation, let the bacterial extract (containing the dissolved DNA) cool to room temperature and either use it directly for amplification or store at -20 °C for further use (Figure 1a).
  3. DNA Extraction of Gram-positive Bacteria
    1. Streak the patient material on appropriate culture media (e.g., Columbia blood agar), identify and isolate potential pathogens. Perform Gram-staining in order to confirm morphotype of the culture.
    2. Prepare pure culture and perform Gram-staining in order to determine morphotype and to confirm purity of the culture.
    3. Pick a single bacterial colony with a sterile 1 µl inoculation loop and suspend it in 500 µl PBS solution in a 2.0 ml reaction tube.
    4. Add 500 µl glass-beads and transfer the tube to an oscillating homogenizer, operated at maximum frequency and amplitude (50 Hz), for 5 min. Use glass-beads of 1.0 mm diameter to disrupt the bacterial cell walls.
    5. Incubate this tube for 10 min at 95 °C and cool to room temperature. Use the extract either directly for the PCR reaction or store at -20 °C for further use.

2. 16S rDNA PCR

  1. Preparation of Amplification Primers
    1. Use the primers TPU1 (5´-AGA GTT TGA TCM TGG CTC AG-3' [M = A/C]) as forward primer and RTU4 (5´-TAC CAG GGT ATC TAA TCC TGT T-3´) as reverse primer 25. Prepare a primer stock solution with DNAse and RNAse free water, having a concentration of 100 pmol/µl (100 µM) and dilute it further to a working solution at 10 pmol/µl. Store the primer stock and working solution at -20 °C or use directly.
  2. Preparation of the PCR Reaction Mix
    1. Pipette 1 µl of each primer, 1 µl dNTP-mix (100 mM), 2.5 µl of the DNA extract, 5 µl 10x PCR buffer (containing 25 mM MgCl2), 0.25 µl Taq-polymerase (5 units/µl) and 39.25 µl DNAse- and RNAse free PCR water into a sterile 200 µl reaction tube.
  3. PCR Protocol
    1. Start the PCR program built as follows: First, an initial incubation step at 95 °C for 5 min which is necessary to activate the Taq-polymerase. Second, 35 amplification cycles, containing a 1 min. denaturation step at 95 °C, a 1 min. primer annealing step at 50 °C, and a 1.5 min primer extension step at 72 °C. Third, a final extension for 10 min at 72 °C. Finally, the PCR reaction is cooled to 4 °C.
      NOTE: Staphylococcus aureus and H2O serve as positive and negative control (Figure 1b).
    2. Place the tube containing the PCR reaction mix in the thermocycler and start the program.
  4. Purification of the PCR Product
    NOTE: In order to purify the PCR product, several protocols either enzyme based or with a silica adsorption matrix may be used. The single-step PCR cleanup used here utilizes two hydrolytic enzymatic reactions. While Exonuclease I (Exo I) removes single stranded DNA, shrimp alkaline phosphatase (SAP) hydrolyses unincorporated dNTPs The second procedure is based on a silica membrane adsorption technique with a high salt fast binding - wash - low salt elution cycle.
    1. Add 1.5 µl of an enzymatic mix containing both Exo I and SAP to 25 µl PCR product, mix thoroughly and transfer to a thermocycler applying a simple protocol of first 15 min at 37 °C followed by 15 min at 80 °C. Store the purified PCR product either at -20 °C or use it directly as target for labeling PCR.

3. DNA Agarose Gel Electrophoresis

  1. Preparation of the Electrophoresis Buffer (TBE-buffer)
    1. Titrate 54.0 g (445 mM) TRIS base, 27.5 g (445 mM) boric acid and 20 ml of a 0.5 M EDTA (10 mM) solution at pH 8.0 with NaOH in a flask and fill it with distilled water to a total volume of 1,000 ml. Then dilute this 5x buffer 1:10 with distilled water (0.5x TBE) for electrophoresis.
  2. Preparation of the Loading Buffer
    1. Mix 250 mg bromophenol blue, 250 mg xylene cyanol FF and 15 g of polysucrose (Materials Table) into a flask. Then fill it with 0.5x TBE buffer to a total volume of 100 ml. Aliquot the loading dye to 1 ml portions and store at -20 °C for further usage.
  3. Casting of the Agarose Gel
    1. Weigh 6 g standard electrophoresis agarose to 300 ml 0.5x TBE buffer (see section 3.1.1) in a flask, to prepare a 2% (w/v) agarose gel. Then put the agarose mixture in a microwave oven, heat it near-boiling point until the agarose is completely dissolved and the solution appears clear.
    2. Let the molten agarose cool sufficiently and add 10 µl of a 1% (w/v) ethidium bromide stock solution. Pour the solution into a cast and place a gel comb (allowing for 30 wells) in the cast. Remove the comb when the gel is completely solidified and put the gel into the electrophoresis chamber containing 0.5x TBE buffer.
      NOTE: Ethidium bromide is toxic and mutagenic. Therefore it should be handled with caution. It is advisable to use nitrile gloves. There are alternative intercalating nucleic acid stains which can be used as a non-toxic alternative.
  4. Agarose Gel Electrophoresis of the PCR products
    NOTE: In order to verify the correct size of the PCR amplicons and to estimate the DNA concentration the purified PCR product is separated in an agarose gel.
    1. Pipette 2 µl of the loading buffer into a PCR reaction tube and add 8 µl of the PCR product. Pipette this mix into the wells of the gel. Add 8 µl of a molecular-weight size marker (DNA ladder) in a separate well to estimate the size of the PCR product.
    2. Apply a constant voltage at 10 V/cm and stop the electrophoresis as soon as the bromophenol blue marker has reached about ¾ of the total length of the gel. Visualize the separated PCR fragments using an UV-transilluminator (wavelength 302 nm) and document them using a gel-documentation system. Estimate the quantity of DNA by visual comparison with the DNA ladder. Use approximately 10 ng as target for the labeling PCR.

4. Sanger Sequencing

  1. Cycle Sequencing PCR
    NOTE: Perform the PCR labeling in a 10 µl reaction using a commercial kit (Materials Table).
    1. Add 2 µl of 5x reaction mastermix, 2 µl of the DNA preparation, 1.5 µl of TPU1 or RTU4 working solution (10 pmol/µl) and 4.5 µl of DNAse and RNAse free water.
    2. Put this sequencing reaction mix in a thermocycler and run another PCR. In total, process 25 cycles consisting of a denaturation step (96 °C, 10 sec), an annealing step (45-60 °C, 5 sec) and an extension step (60 °C, 2 min). Finally, cool the reaction to 4 °C.
  2. Purification of the Sequencing Reaction
    1. Purify the labeling reaction mix using commercial spin columns for PCR purification (Materials Table).
    2. Load 10 µl of the sequencing reaction onto the pre-hydrated gel-filtration matrix and perform a centrifugation step at 750 x g for 3 min.
      NOTE: By the use of this procedure unincorporated dye terminators are retained in the gel matrix.
    3. Then dry the aqueous eluate in a vacuum concentrator. Centrifuge at 2,500 x g for 20 to 30 min at 40 °C to complete dryness.
    4. Add 10 µl of highly deionized formamide then denature at 90 °C for 2 min and cool the mixture on ice. Pipette 10 µl of the denatured samples on a 96 well µl plate. Store the dried and cleaned PCR product at -20 °C if the analysis is to be carried out at a later time.
  3. 16S rRNA Gene Sequencing and Analysis of the DNA Sequence
    NOTE: Perform sequence analysis on an automated sequencer (Materials Table). The sequencer used here is an automated fluorescence-based capillary electrophoresis system that analyzes 4 samples simultaneously (50 cm 4-capillary array) with a liquid polymer 67 (Figure 1c).
    1. Place the microtiter plate in the sequencer. Write a sample sheet (plate record) as laid out in 2, save it and start the sequencing run/analysis following the steps given in 1.
      NOTE: Duration of a sequence run is 2 hr and provides data from 800 to 1,000 bp.
    2. Open the sequencing analysis software (Materials Table). Sequence data are given as a text file (.seq) and a file containing the electropherogram including quality values (QV) (.ab1).
      NOTE: Base calling is automatically performed by the sequencing analysis software and the underlying electropherogram is checked visually (e.g., peak height, peak separation) before the sequence is used for further applications.
    3. Compare the stored DNA sequence file to the NCBI nr database using BLAST algorithm (


NOTE: The mass spectrometer uses a 337 nm N2 laser and is operated by a specific control software (Materials Table). Spectra are recorded in linear mode and a mass range between 2,000 to 20,000 Da is covered. Data interpretation and allocation of scores to the samples is carried out in real time by an analysis software (Materials Table) (Figure 1d).

  1. Preparation of Bacteria for MALDI-TOF MS Analysis
    1. Grow the bacteria of interest (e.g., Myroides odoratimimus) on Columbia blood agar containing 5% horse blood at 37 °C for 18 to 24 hr, depending on the bacterial strain (Figure 2a). Perform Gram-staining to verify the morphotype of the organism under investigation as well as purity of the culture.
    2. Use a wooden toothpick for transferring a single bacterial colony to a well of a 96-well steel target (Figure 2b and 2c). Spot 1 µl of 70 % formic acid on top of the air dried organism on the steel target and leave to dry for approximately 2-3 min.
    3. Then, overlay the spot with 1 µl matrix solution, containing 50 mg/ml CHCA (α-Cyano-4-hydroxycinnamic acid) in an organic solvent (50% acetonitrile, 2.5% trifluoroacetic acid, 47.5% H2O).
      NOTE: The acid overlay method (on plate preparation method) may be used to improve the quality of the mass spectra. It has been shown that for Gram-positive bacteria such an "acid attack" increases the score values of the measured spectra 45.
      NOTE: Alternatively an automated sample preparation system (Materials Table) may be used in which contact free spots of 1 µl 70% formic acid solution and, after a first drying cycle, 1 µl matrix solution are applied on the bacterial smear. The samples are then dried under standardized conditions at 60% relative humidity and are ready to use for analysis.
    4. Let the smear with matrix overlaid air-dry again for about 5 min in a hood This bacterial smear with the matrix applied is now stable for many hours.
      NOTE: Bacterial smears without matrix may not be stable due to protein degradation.
  2. Performing the MALDI-TOF MS Analysis
    1. Introduction of samples
      1. Open the control software of our mMIS (Materials Table).
      2. Aerate the sample loading port of the mass spectrometer by pressing the IN/OUT button of the instrument.
      3. Open the loading port after a click and insert the MALDI-TOF MS target plate with the dried bacterial smear plus matrix.
      4. Close the port and evacuate again. Observe vacuum and when it reaches 4.5 x 10-6 mbar start measurement.
    2. Sample Analysis
      1. Open the analysis software of our mMIS (Materials Table) and click the "File" menu. Select "New classification" and a new window "MALDI biotyper real time classification wizard" opens. Type the project name, for instance "Myroides measurement project_1, in the field "Project name" and press the button "New". Under the new dialog box named "New project" check project name and proceed by pressing the button "OK".
      2. In the "MALDI biotyper real time classification wizard" observe the "Analyte placement" window open. Select target positions (e.g., A1, A2, A3…), right-click any selected position (indicated by a blue square) and select "Add samples". In the automatically opened table view type in sample name, sample ID and optionally a comment. Click the button "Next" to proceed.
      3. In the "MALDI biotyper real time classification wizard" observe the "Selection of MALDI biotyper methods" window open. Select "Bruker Taxonomy" from "MSPs from taxonomy trees" and click "Next" to continue.
      4. In the "MALDI biotyper real time classification wizard" observe the "Project summary" window open. Check the inserted entries given above for the actual classification project. Start the classification run by pressing the button "Finish" (Figure 2d-f). Measurement starts automatically when the appropriate vacuum is reached.
      5. Follow the classification run until the measurement is successfully completed. View the results table in the "MALDI Biotyper Realtime Classification Project Myroides measurement project_1" open the menu "View" and click "Results".
      6. View the "Bruker Daltonics MALDI Biotyper Classification Results" as HTML file in the default web browser. Print and save the results.
    3. Measure a test standard daily prior to sample analysis to calibrate the instrument and perform instrument validation weekly (instrument self-test).
      NOTE: During measurement MALDI-TOF spectra are analyzed in real time by the analysis software (Materials Table). A list of ten species with their respective scores is given and is fed into the laboratory information system (LIS) for reporting.
    4. Critically evaluate the MALDI-TOF MS results (see representative results) and, if appropriate, include other tests, such as biochemical- and antibiotic susceptibility profiles or 16S rRNA gene sequencing and, if necessary, Gram-staining for assigning a bacterial species to the microbiological report.

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Representative Results

MALDI-TOF MS is a novel, fast and inexpensive method for microbiological routine diagnostics. Bacterial species identification by MALDI-TOF MS produces spectra mainly composed of ribosomal proteins but also other "very conserved proteins with house-keeping functions affected to a minimal extent by environmental conditions" 17.The database of this mMIS contains a large set of reference spectra and even bacteria which are rarely found in clinical isolates can be securely identified 7,56,57. Score values show the reliability of the identified species. Scores above 2.300 represent a highly probable species identification, a score between 2.000 and 2.300 indicates a secure species identification, a score between 1.700 and 2.000 stands for a probable species identification and a score below 1.700 is non-reliable. In the case of more than one species being identified with a score above 2.000, additional tests have to be applied and this is the reason why we have combined different methods such as 16S rDNA sequencing, API and techniques addressing the bacterial morpho- and/or phenotype such as Gram stain, motility etc. In the case where the score is below 1.700 the above mentioned tests have to be used to achieve reliable species identification. A combination of MALDI-TOF MS and 16S rDNA sequencing has been demonstrated recently 56-58. It should be pointed out that clear guidelines for species determination based on 16S rDNA sequence homologies are still lacking 22,61. For practical interpretation, homologies of ≥97%, as proposed by Stackebrandt and Göbel 61, are used 56. Advances in DNA sequencing allow the determination of whole bacterial genomes by next generation techniques at relatively low cost and, therefore, in principle identification of bacteria based on their genome sequence. This technique has already been used in genome based outbreak management or in epidemiology 9,29. However, the strongest limitation today is the lack of easy to use software packages for the end user 65.

Seven examples of rare occurring bacteria, isolated from patient samples during routine diagnostics and analyzed by both MALDI-TOF MS and 16s rRNA gene sequencing are listed in Table 1. MALDI-TOF spectra of strain #1 to #6 are shown in Figure 3 and a spectrum of strain #7, Sphingobacterium spiritivorum, is shown in Figure 1e. All isolates show high scores in MALDI-TOF MS analysis and 99 to 100% 16S rDNA sequence homologies. Thus, both techniques lead to secure genus and species identification. However, as pointed out in a recent publication on comparing identification methods of Myroides sp., the combination of MALDI-TOF MS and 16S rRNA gene sequencing led to more reliable results 56. These data illustrate that a single method used may not always be sufficient to achieve a reliable identification result. The combination of two independent methods leads to a higher accuracy and expands the commercial MALDI-TOF MS database with in-house entries resulted in an unequivocal species identification 56.

Strain #1 was identified as Chryseobacterium gleum (MALDI-TOF MS score 2.490, sequence homology 99%). Chryseobacteria are Gram-negative, non-fermenting rods and related to Myroides spp. Chryseobacterium spp. are regarded as emerging pathogens, associated with septicemia, pneumonia and urinary tract infections. The genus Chryseobacterium comprises a large number of species and the best studied species is Chryseobacterium indologenes. Similar to infections caused by Myroides sp., mostly immunocompromised patients are affected 6,10,60. Strain #2 was identified as Myroides odoratimimus (MALDI-TOF MS score 2.436, sequence homology 99%) and strain #3 as Myroides odoratus (MALDI-TOF MS score 2.237, sequence homology 99%) Myroides sp. are Gram-negative, nonfermenting rods which are associated with severe diseases such as sepsis, cellulitis or pneumonia. Mostly immunocompromised patients with underlying hematologic or oncologic diseases are affected 8,18,42. Strain #4 was identified as Sphingobacterium multivorum32,71 (MALDI-TOF MS score 2.092, sequence homology 99%).

The bacteria are Gram-negative non-fermenting rods, which may cause septicemia in immunocompromised patients 5,24,44,53. In addition, a case of fatal meningoencephalitis has been reported 70. The stains #5 (MALDI-TOF MS score 2.282, sequence homology 100%) and #6 (MALDI-TOF MS score 2.289, sequence homology 100%) were identified as Wohlfahrtiimonas chitiniclastica. These bacteria are short, non-motile, Gram-negative rods which were first isolated from larvae of the parasitic fly Wohlfahrtia magnifica and described in 2008 66. These zoonotic bacteria are regarded as emerging pathogens and causative agents for bacteremia, septicemia or soft tissue infections 4,40,64. Interestingly, most patients reported were either homeless and/or suffered from alcoholism and, therefore, the occurrence of these rare pathogens may be explained by the higher rate of ectoparasites in the homeless population 4,54. Strain #7, Sphingobacterium spiritivorum (MALDI-TOF MS score 2.269, sequence homology 100%), is a rare pathogen. It may cause infections in immunocompromised patients and first human isolates were described in 1982 31,71. A recent report identifies Sphingobacterium spiritivorum as causative agent for a fatal case of bacteremia and sepsis in a patient with acute myeloid leukemia 38.

Figure 1
Figure 1: Workflow for nucleic acid and/or mass spectrometry based detection of clinically relevant bacteria in the medical microbiology laboratory. Starting from a pure culture (a), target DNA is amplified in a thermocycler (b). PCR amplicons are sequenced in a four capillary sequencer using Sanger technology (c). Sequencing results are determined as percent homologies of query sequences to database entries by computer assisted comparison. A second, proteomics based method, uses mass spectrometry (d) and in the center a mass spectroscopic result of strain #1 in Table 1, Sphingobacterium spiritivorum, including its MALDI-TOF MS score, is given (e). A combination of mass spectrometry and 16S rRNA gene sequencing may be necessary, in order to achieve a more reliable and accurate species identification. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Analysis procedure using MALDI-TOF MS for identification of clinically relevant bacteria. An operator takes whole cell bacterial material (a) with a toothpick from a pure culture (b) and places bacterial smears on a steel target (c). The steel target is inserted into the loading port of the mass spectrometer. When the proper vacuum of 5 x 10-6 mbar is reached, the target will be moved to the ionization chamber. Using an N2-laser, ions are created by soft desorption which are then accelerated in an electrostatic field (d) and separated in the flight tube (e). The time of flight (TOF) needed for the ions to reach the detector of the flight tube (f) is directly related to their mass and forms the basis of subsequent calculations of mass peaks. Specific identification software (Materials Table) then assigns score values to the mass spectrum fingerprint of ionized bacterial proteins. Please click here to view a larger version of this figure.

Figure 3
Figure 3: MALDI-TOF spectra and assigned scores of rare pathogens. In this figure, representative MALDI-TOF spectra of the first six rare pathogens listed in Table 1 are shown. Species name and corresponding MALDI-TOF MS score are noted in each spectrum. Please click here to view a larger version of this figure.

Strain MALDI identification MALDI score 16s rDNA result BLAST homology
#1 Chryseobacterium gleum 2.211 Chryseobacterium gleum 99% identity
#2 Myroides odoratimimus 2.397 Myroides odoratimimus 99% identity
#3 Myroides odoratus 2.237 Myroides odoratus 99% identity
#4 Sphingobacterium multivorum 2.093 Sphingobacterium multivorum 99% identity
#5 Wohlfahrtiimonas chitiniclastica 2.282 Wohlfahrtiimonas chitiniclastica 100% identity
#6 Wohlfahrtiimonas chitiniclastica 2.289 Wohlfahrtiimonas chitiniclastica 100% identity
#7 Sphingobacterium spiritivorum 2.269 Sphingobacterium spiritivorum 100% identity

Table 1: Representative MALDI-TOFs results of rare occurring bacteria in comparison to 16s rRNA gene sequencing.

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Both MALDI-TOF MS and 16S rRNA gene sequencing offer the possibility to identify large numbers of different bacteria. MALDI-TOF MS is a fast and inexpensive method, which is easy to handle and large databases of bacterial mass spectra are available. For this reason, MALDI-TOF MS is a rapid, cost effective and reliable method to conduct screening studies focused on rare bacterial pathogens 17,20,39,51. In a prospective study comparing MALDI-TOF MS with other phenotypic identification methods, Seng et al. demonstrated cost effectiveness and speed of MALDI-TOF MS 59 and Tan et al. reported a reduction of reagent and labor costs for bacterial identification in their laboratory setting by 56.9% annually 62. Such cost reductions are supported by a note of Gaillot et al. 28

On the other hand, 16S rDNA sequencing is more time consuming, laborious and warrants specialized personnel to perform the analysis 34. Nevertheless, both approaches are suitable for routine diagnostics and it could be demonstrated that the combination of these two methods leads to a higher reliability and more secure identification accuracy, which is especially beneficial in case of doubtful results 56. Therefore, we propose the combination of both methods as the best approach to verify the identification of rare occurring bacteria in routine diagnostics. For reliable species identification, it is absolutely imperative to use pure bacterial cultures because mixed cultures prevent species determination. As a plausibility check, species identification obtained with either MALDI TOF MS or 16S rDNA sequencing has to be in accordance with the results from Gram-staining, biochemical characterization and the clinical presentation 49.

Mass spectrometry as an analytical tool for bacterial identification was first proposed in 1975 51. However, it took until the late 1980s to establish a practicable approach for protein analysis. The "soft desorption ionization" technology was introduced by Koichi Tanaka in 1985 63, who received the Nobel Prize for chemistry in 2002, allowing analysis of intact proteins. At the same time the matrix-assisted ionization time of flight mass spectrometry was introduced by Hillenkamp and Karas as they were the first to use an organic acid to analyze biomolecules 37 and this is the method which is still used today. Although the MALDI technology has been used sporadically 23,30,41, it took until 2004 when Bruker Daltonics introduced its microflex MALDI Biotyper system (Pittcon Conference & Expo, 2004, press release), that MALDI-TOF MS based microbial identification became a routine diagnostic technique 46. Recent approaches in MALDI-TOF MS techniques gave the opportunity to identify yeasts, detect multiresistant bacteria and perform antimicrobial susceptibility testing 17,51. Moreover, certain procedures offer the possibility to directly analyze bacteria from primary samples, such as urine or blood cultures 17,51.

Although the use of this system is principally easy, there are some pitfalls which may influence the results. The age of the microorganisms for instance affects the bacterial protein expression and therefore the reproducibility of the results. First, growth conditions may be a problem. As an example, enterobacteria such as Escherichia coli grow faster than non-fermenting bacteria (e.g., Pseudomonas aeruginosa) and consequently have to be analyzed earlier 17. Second, the matrix used for MALDI-TOF MS consists of small organic acid molecules that have a strong laser optical absorption for the wavelength of the laser used. Prior to analysis the matrix is added to the sample and both components undergo a crystallization process forming a solid solution.

This explains why changes in the matrix may affect the accuracy of the bacterial identification 17. Therefore, freshly prepared matrix solutions, not older than 7 days, should be used. Third, the medium from which bacteria are picked may influence the identification results 17,68. For instance, crystal violet, which is a component of MacConkey agar, interferes with mass spectra 17. Additionally, too many bacteria applied on the steel target will lead to lower scores and therefore affect the identification accuracy. Therefore, it is advisable to define clear criteria as to how an analysis is carried out. However, misleading results performed by MALDI-TOF MS are mostly caused by insufficient reference spectra contained in the database. (Currently the data base contains >6,000 entries.) This is especially still the case for the identification of anaerobic bacteria 17. However, additional spectra can be added by the user. MALDI-TOF MS library for instance contains 98 additional reference spectra of isolates which are not adequately addressed by the original database, such as Mycoplasma sp., Myroides sp., Legionella sp., Roseomonas sp., Comamonas sp. and Chryseobacterium sp. Consequently, additional reference spectra will lead to a higher identification accuracy 59,69. Finally, in some cases the spectra of different species are very similar. This can lead to misidentification in cases such as the discrimination of Escherichia coli and Shigella spp. or Streptococcus pneumoniae and Streptococcus mitis 17,51.

Sequencing of 16S rDNA and additional genes such as rpoB have simplified the molecular identification of rare or unknown bacteria. These genes are common in most bacteria and their individual function is identical. However, since they possess enough genetic variability to produce results which permit a differentiation on genus and species level, they can be used for bacterial identification 34. According to Stackebrandt and Göbel, homologies <97% represent different species 61. However, homologies >97% do not necessarily lead to a secure species identification 34,47. These uncertainties have several reasons.

The quality of the databases which are used to calculate the homologies is sometimes questionable 34. In cases where high homologies at the 16S rDNA level exist, uncertainties may result in species identification. A combination of different genetic regions may therefore lead to more secure results. Moreover, there are no general guidelines for the interpretation of 16S rDNA sequencing data 22,34,61. However, increasing the reliability of the existing databases will lead to a higher accuracy for bacterial identification using this molecular approach 34. Regarding the sequencing reaction we need to mention that the accuracy of the sequencing results mostly relies on the quality of the underlying PCR. Furthermore, since the results are being gained by comparing them to the entries listed in public databases, quality of sequence entries and maintenance of the data is of crucial importance.

In general, although both approaches, MALDI-TOF MS and 16S rRNA gene sequencing, have advantages they have also weaknesses. The combination of both methods, however, leads to a high accuracy for bacterial identification. For instance, in a previous study we could demonstrate that MALDI-TOF MS is able to distinguish between Myroides odoratimimus and Myroides odoratus 56. In a few cases the MALDI score suggested unreliable species identification, while the results obtained by 16S rDNA sequencing could confirm the species identity. Mass spectral fingerprints of these isolates were then created and introduced into our MALDI reference spectra database. Future research focusing on other rare bacteria may demonstrate the applicability of the proposed strategy.

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The authors declare that they have no competing financial interests.


The authors would like to thank Prof. Enno Jacobs for his continuing support.


Name Company Catalog Number Comments
CHROMASOLV, HPLC grade water, 1 L Sigma-Aldrich Chemie, München, Germany 270733
Tissue Lyser LT Qiagen, Hilden, Germany 85600 Oscillating homogenizer
Glass-beads 1.0 mm VWR International, Darmstadt, Germany 412-2917
Thermomixer 5436 Eppendorf, Hamburg, Germany 2050-100-05
QIAamp DNA Mini Kit (250) Qiagen, Hilden, Germany 51306
Taq PCR Core Kit (1,000 U) Qiagen, Hilden, Germany 201225
Forward Primer TPU1 (5´-AGA GTT TGA TCM TGG CTC AG-3’), Ulm, Germany  -
Reverse Primer RTU4 (5´-TAC CAG GGT ATC TAA TCC TGT T-3´), Ulm, Germany  -
Mastercycler  Eppendorf, Hamburg, Germany Thermocylcer
Reaction tube 1.5 ml SARSTEDT, Nümbrecht, Germany 72,692
Reaction tube 2 ml SARSTEDT, Nümbrecht, Germany 72,693,005
PCR 8er-CapStrips Biozym Scientific, Hessisch Oldendorf, Germany 711040X
PCR 8er-SoftStrips Biozym Scientific, Hessisch Oldendorf, Germany 711030X
Sharp R-ZV11  Sharp Electronics, Hamburg, Germany - Microwave
Titriplex III (EDTA Na2-salt dehydrate; 1 kg) Merck, Darmstadt, Germany 1084211000
SeaKem LE Agarose Biozym Scientific, Hessisch Oldendorf, Germany 849006
SmartLadder SF - 100 to 1,000 bp Eurogentec, Lüttich, Belgium MW-1800-04
Bromophenol blue (25 g) Sigma-Aldrich Chemie, München, Germany B0126
Xylene cyanol FF (10 g) Sigma-Aldrich Chemie, München, Germany X4126
ComPhor L Maxi  Biozym, Hessisch Oldendorf, Germany -
Ethidium bromide solution 1% (10 ml) Carl Roth, Karlsruhe, Germany 2218.1
Gel Doc 2000 Bio-Rad Laboratories, München, Germany - Gel-documentation system 
ExoSAP-IT (500 reactions) Affymetrix UK, Wooburn Green, High Wycombe, United Kingdom 78201
Buffer (10x) with EDTA  Life Technologies, Darmstadt, Germany 402824
BigDye Terminator Kit v1.1 Life Technologies, Darmstadt, Germany 4337450
Hi-Di formamide (25 ml) Life Technologies, Darmstadt, Germany 4311320
DyeEx 2.0 Spin Kit (250) Qiagen, Hilden, Germany 63206
3130 Genetic Analyzer Life Technologies, Darmstadt, Germany - Sequencer
MicroAmp optical 96-well reaction plate with barcode Life Technologies, Darmstadt, Germany 4306737
3130 Genetic Analyzer, plate base 96-well Life Technologies, Darmstadt, Germany 4317237
3130 Genetic Analyzer, plate retainer 96-well Life Technologies, Darmstadt, Germany 4317241
3130 Genetic Analyzer, well plate septa Life Technologies, Darmstadt, Germany 4315933
3130 Genetic Analyzer, POP-7 Polymer, 7 ml Life Technologies, Darmstadt, Germany 4352759
3130 Genetic Analyzer, 4-Capillary Array, 50 cm Life Technologies, Darmstadt, Germany 4333466
Sequencing Analysis Software 5.4 Life Technologies, Darmstadt, Germany -
microflex Bruker Daltonik, Bremen, Germany - Our mMIS
VITEK MS bioMérieux, Nürtingen, Germany  2nd mMIS
flexControl 3.4 Bruker Daltonik, Bremen, Germany - Device control software
Biotyper Realtime Classification 3.1 (RTC) Bruker Daltonik, Bremen, Germany - Analysis software
α-cyano-4-hydroxycinnamic acid, HCCA, 1 g Bruker Daltonik, Bremen, Germany 201344
Peptide Calibration Standard II Bruker Daltonik, Bremen, Germany 222570
MSP 96 target polished steel Bruker Daltonik, Bremen, Germany 8224989
peqGREEN peqlab  37-5010
MALDI Biotyper Galaxy  Bruker Daltonik, Bremen, Germany Part No. 1836007  Automated sample preparation system
Vitek 2  bioMérieux, Nürtingen, Germany  Our aMIS
MicroScan  Beckman Coulter, Krefeld, Germany 2nd aMIS
BD Phoenix™ Automated Microbiology System BD, Heidelberg, Germany 3rd aMIS
Staphylococcus aureus subsp. aureus Rosenbach (ATCC® 25923™) American Type Culture Collection (ATCC), LGC Standards, Molsheim, France Positive control for PCR 



  1. Applied Biosystems 3130/3130xl Genetic Analyzers - Getting Started Guide. Applied Biosystems | HITACHI. Foster City, CA 94404, USA. (2010).
  2. Ch. 5. Applied Biosystems 3130/3130xl Genetic Analyzers - Getting Started Guide. Applied Biosystems | HITACHI. Foster City, CA 94404, USA. 81-104 (2010).
  3. Adekambi, T., Drancourt, M., Raoult, D. The rpoB gene as a tool for clinical microbiologists. Trends Microbiol. 17, (1), 37-45 (2009).
  4. Almuzara, M. N., et al. First case of fulminant sepsis due to Wohlfahrtiimonas chitiniclastica. J.Clin.Microbiol. 49, (6), 2333-2335 (2011).
  5. Areekul, S., Vongsthongsri, U., Mookto, T., Chettanadee, S., Wilairatana, P. Sphingobacterium multivorum septicemia: a case report. J.Med.Assoc.Thai. 79, (6), 395-398 (1996).
  6. Aydin, T. T., et al. Chryseobacterium indologenes Septicemia in an Infant. Case Rep.Infect.Dis. 2014, 270521 (2014).
  7. Baillie, S., Ireland, K., Warwick, S., Wareham, D., Wilks, M. Matrix-assisted laser desorption/ionisation-time of flight mass spectrometry: rapid identification of bacteria isolated from patients with cystic fibrosis. Br.J.Biomed.Sci. 70, (4), 144-148 (2013).
  8. Benedetti, P., Rassu, M., Pavan, G., Sefton, A., Pellizzer, G. Septic shock, pneumonia, and soft tissue infection due to Myroides odoratimimus: report of a case and review of Myroides infections. Infection. 39, (2), 161-165 (2011).
  9. Bertelli, C., Greub, G. Rapid bacterial genome sequencing: methods and applications in clinical microbiology. Clin.Microbiol.Infect. 19, (9), 803-813 (2013).
  10. Bhuyar, G., Jain, S., Shah, H., Mehta, V. K. Urinary tract infection by Chryseobacterium indologenes. Indian J.Med.Microbiol. 30, (3), 370-372 (2012).
  11. Buchan, B. W., Ledeboer, N. A. Emerging technologies for the clinical microbiology laboratory. Clin.Microbiol.Rev. 27, (4), 783-822 (2014).
  12. Castillo-Rojas, G., et al. Comparison of Enterococcus faecium and Enterococcus faecalis Strains isolated from water and clinical samples: antimicrobial susceptibility and genetic relationships. PLoS ONE. 8, (4), e59491 (2013).
  13. Chatzigeorgiou, K. S., Sergentanis, T. N., Tsiodras, S., Hamodrakas, S. J., Bagos, P. G. Phoenix 100 versus Vitek 2 in the identification of gram-positive and gram-negative bacteria: a comprehensive meta-analysis. J.Clin.Microbiol. 49, (9), 3284-3291 (2011).
  14. Clarridge, J. E. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin.Microbiol.Rev. 17, (4), 840-862 (2004).
  15. Crowley, E., et al. Evaluation of the VITEK 2 Gram-negative (GN) microbial identification test card: collaborative study. J.AOAC Int. 95, (3), 778-785 (2012).
  16. Crowley, E., et al. Evaluation of the VITEK 2 gram positive (GP) microbial identification test card: collaborative study. J.AOAC Int. 95, (5), 1425-1432 (2012).
  17. Croxatto, A., Prod'hom, G., Greub, G. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol.Rev. 36, (2), 380-407 (2012).
  18. Crum-Cianflone, N. F., Matson, R. W., Ballon-Landa, G. Fatal case of necrotizing fasciitis due to Myroides odoratus. Infection. 42, (5), 931-935 (2014).
  19. Deak, E., et al. Comparison of the Vitek MS and Bruker Microflex LT MALDI-TOF MS platforms for routine identification of commonly isolated bacteria and yeast in the clinical microbiology laboratory. Diagn.Microbiol.Infect.Dis. 81, (1), 27-33 (2015).
  20. DeMarco, M. L., Ford, B. A. Beyond identification: emerging and future uses for MALDI-TOF mass spectrometry in the clinical microbiology laboratory. Clin.Lab.Med. 33, (3), 611-628 (2013).
  21. Deng, J., et al. Comparison of MALDI-TOF MS, gene sequencing and the Vitek 2 for identification of seventy-three clinical isolates of enteropathogens. J.Thorac.Dis. 6, (5), 539-544 (2014).
  22. Drancourt, M., Berger, P., Raoult, D. Systematic 16S rRNA gene sequencing of atypical clinical isolates identified 27 new bacterial species associated with humans. J.Clin.Microbiol. 42, (5), 2197-2202 (2004).
  23. Fenselau, C., Demirev, P. A. Characterization of intact microorganisms by MALDI mass spectrometry. Mass Spectrom.Rev. 20, (4), 157-171 (2001).
  24. Freney, J., et al. Septicemia caused by Sphingobacterium multivorum. J.Clin.Microbiol. 25, (6), 1126-1128 (1987).
  25. Funke, G., Frodl, R., Sommer, H. First comprehensively documented case of Paracoccus yeei infection in a human. J.Clin.Microbiol. 42, (7), 3366-3368 (2004).
  26. Funke, G., Funke-Kissling, P. Evaluation of the new VITEK 2 card for identification of clinically relevant gram-negative rods. J.Clin.Microbiol. 42, (9), 4067-4071 (2004).
  27. Funke, G., Funke-Kissling, P. Performance of the new VITEK 2 GP card for identification of medically relevant gram-positive cocci in a routine clinical laboratory. J.Clin.Microbiol. 43, (1), 84-88 (2005).
  28. Gaillot, O., et al. Cost-effectiveness of switch to matrix-assisted laser desorption ionization-time of flight mass spectrometry for routine bacterial identification. J.Clin.Microbiol. 49, (12), 4412 (2011).
  29. Gilchrist, C. A., Turner, S. D., Riley, M. F., Petri, W. A., Hewlett, E. L. Whole-genome sequencing in outbreak analysis. Clin.Microbiol.Rev. 28, (3), 541-563 (2015).
  30. Holland, R. D., et al. Rapid identification of intact whole bacteria based on spectral patterns using matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry. Rapid Commun.Mass Spectrom. 10, (10), 1227-1232 (1996).
  31. Holmes, B., Owen, R. J., Hollis, D. G. Flavobacterium spiritivorum, a new species isolated from human clinical specimens. Int.J.Syst.Bacteriol. 32, (2), 157-165 (1982).
  32. Holmes, B., Owen, R. J., Weaver, R. E. Flavobacterium multivorum, a new species isolated from human clinical specimens and previously known as group IIk, biotype 2. Int.J.Syst.Bacteriol. 31, (1), 21-34 (1981).
  33. Jamal, W., Albert, M., Rotimi, V. O. Real-time comparative evaluation of bioMerieux VITEK MS versus Bruker Microflex MS, two matrix-assisted laser desorption-ionization time-of-flight mass spectrometry systems, for identification of clinically significant bacteria. BMC Microbiol. 14, (1), 289 (2014).
  34. Janda, J. M., Abbott, S. L. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J.Clin.Microbiol. 45, (9), 2761-2764 (2007).
  35. Jin, W. Y., et al. Evaluation of VITEK 2, MicroScan, and Phoenix for identification of clinical isolates and reference strains. Diagn.Microbiol.Infect.Dis. 70, (4), 442-447 (2011).
  36. Jossart, M. F., Courcol, R. J. Evaluation of an automated system for identification of Enterobacteriaceae and nonfermenting bacilli. Eur.J.Clin.Microbiol.Infect.Dis. 18, (12), 902-907 (1999).
  37. Karas, M., Hillenkamp, F. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal.Chem. 60, (20), 2299-2301 (1988).
  38. Koh, Y. R., et al. The first Korean case of Sphingobacterium spiritivorum bacteremia in a patient with acute myeloid leukemia. Ann.Lab.Med. 33, (4), 283-287 (2013).
  39. Kok, J., Chen, S. C., Dwyer, D. E., Iredell, J. R. Current status of matrix-assisted laser desorption ionisation-time of flight mass spectrometry in the clinical microbiology laboratory. Pathology. 45, (1), 4-17 (2013).
  40. Koljalg, S., et al. First report of Wohlfahrtiimonas chitiniclastica from soft tissue and bone infection at an unusually high northern latitude. Folia Microbiol.(Praha). (2014).
  41. Krishnamurthy, T., Ross, P. L. Rapid identification of bacteria by direct matrix-assisted laser desorption/ionization mass spectrometric analysis of whole cells. Rapid Commun.Mass Spectrom. 10, (15), 1992-1996 (1996).
  42. Ktari, S., et al. Nosocomial outbreak of Myroides odoratimimus urinary tract infection in a Tunisian hospital. J.Hosp.Infect. 80, (1), 77-81 (2012).
  43. Lim, Y. M., Shin, K. S., Kim, J. Distinct antimicrobial resistance patterns and antimicrobial resistance-harboring genes according to genomic species of Acinetobacter isolates. J.Clin.Microbiol. 45, (3), 902-905 (2007).
  44. Marinella, M. A. Cellulitis and sepsis due to sphingobacterium. JAMA. 288, (16), 1985 (2002).
  45. McElvania, T. E., Shuey, S., Winkler, D. W., Butler, M. A., Burnham, C. A. Optimizing identification of clinically relevant Gram-positive organisms by use of the Bruker Biotyper matrix-assisted laser desorption ionization-time of flight mass spectrometry system. J Clin.Microbiol. 51, (5), 1421-1427 (2013).
  46. Mellmann, A., et al. Evaluation of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry in comparison to 16S rRNA gene sequencing for species identification of nonfermenting bacteria. J.Clin.Microbiol. 46, (6), 1946-1954 (2008).
  47. Mignard, S., Flandrois, J. P. 16S rRNA sequencing in routine bacterial identification: a 30-month experiment. J.Microbiol.Methods. 67, (3), 574-581 (2006).
  48. Nomura, F. Proteome-based bacterial identification using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS): A revolutionary shift in clinical diagnostic microbiology. Biochim.Biophys.Acta. (2014).
  49. Opota, O., Croxatto, A., Prod'hom, G., Greub, G. Blood culture-based diagnosis of bacteraemia: state of the art. Clin.Microbiol.Infect. 21, (4), 313-322 (2015).
  50. Patel, J. B. 16S rRNA gene sequencing for bacterial pathogen identification in the clinical laboratory. Mol.Diagn. 6, (4), 313-321 (2001).
  51. Patel, R. MALDI-TOF MS for the Diagnosis of Infectious Diseases. Clin.Chem. (2014).
  52. Pincus, D. H. Ch. 1. Encyclopedia of Rapid Microbiological Methods. Miller, M. J. PDA, DHI. Baltimore; River Grove. 1-32 (2005).
  53. Potvliege, C., et al. Flavobacterium multivorum septicemia in a hemodialyzed patient. J.Clin.Microbiol. 19, (4), 568-569 (1984).
  54. Rebaudet, S., Genot, S., Renvoise, A., Fournier, P. E., Stein, A. Wohlfahrtiimonas chitiniclastica bacteremia in homeless woman. Emerg.Infect.Dis. 15, (6), 985-987 (2009).
  55. Risch, M., et al. Comparison of MALDI TOF with conventional identification of clinically relevant bacteria. Swiss Med.Wkly. 140, 13095 (2010).
  56. Schröttner, P., Rudolph, W. W., Eing, B. R., Bertram, S., Gunzer, F. Comparison of VITEK2, MALDI-TOF MS, and 16S rDNA sequencing for identification of Myroides odoratus and Myroides odoratimimus. Diagn.Microbiol.Infect.Dis. 79, (2), 155-159 (2014).
  57. Schröttner, P., Rudolph, W. W., Taube, F., Gunzer, F. First report on the isolation of Aureimonas altamirensis from a patient with peritonitis. Int.J.Infect.Dis. 29, 71-73 (2014).
  58. Schröttner, P., et al. Actinobacillus equuli ssp. haemolyticus in a semi-occlusively treated horse bite wound in a 2-year-old girl. Ger.Med.Sci. 11, Doc14 (2013).
  59. Seng, P., et al. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin.Infect.Dis. 49, (4), 543-551 (2009).
  60. Shahul, H. A., Manu, M. K., Mohapatra, A. K., Chawla, K. Chryseobacterium indologenes pneumonia in a patient with non-Hodgkin's lymphoma. BMJ Case.Rep. 2014, (2014).
  61. Stackebrandt, E., Göbel, B. M. Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. Int.J.Syst.Bacteriol. 44, 846-849 (1994).
  62. Tan, K. E., et al. Prospective evaluation of a matrix-assisted laser desorption ionization-time of flight mass spectrometry system in a hospital clinical microbiology laboratory for identification of bacteria and yeasts: a bench-by-bench study for assessing the impact on time to identification and cost-effectiveness. J.Clin.Microbiol. 50, (10), 3301-3308 (2012).
  63. Tanaka, K. The origin of macromolecule ionization by laser irradiation (Nobel lecture). Angew.Chem.Int.Ed.Engl. 42, (33), 3860-3870 (2003).
  64. Thaiwong, T., Kettler, N. M., Lim, A., Dirkse, H., Kiupel, M. First report of emerging zoonotic pathogen Wohlfahrtiimonas chitiniclastica in the United States. J.Clin.Microbiol. 52, (6), 2245-2247 (2014).
  65. Török, M. E., Peacock, S. J. Rapid whole-genome sequencing of bacterial pathogens in the clinical microbiology laboratory--pipe dream or reality. J.Antimicrob.Chemother. 67, (10), 2307-2308 (2012).
  66. Toth, E. M., et al. Wohlfahrtiimonas chitiniclastica gen. nov., sp. nov., a new gammaproteobacterium isolated from Wohlfahrtia magnifica (Diptera: Sarcophagidae). Int.J.Syst.Evol.Microbiol. 58, (Pt 4) 976-981 (2008).
  67. Tristezza, M., Gerardi, C., Logrieco, A., Grieco, F. An optimized protocol for the production of interdelta markers in Saccharomyces cerevisiae by using capillary electrophoresis. J.Microbiol.Methods. 78, (3), 286-291 (2009).
  68. Valentine, N. B., Wahl, J. H., Kingsley, M. T., Wahl, K. L. Direct surface analysis of fungal species by matrix-assisted laser desorption/ionization mass spectrometry. Rapid Commun.Mass Spectrom. 16, (14), 1352-1357 (2002).
  69. van Veen, S. Q., Claas, E. C., Kuijper, E. J. 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. 48, (3), 900-907 (2010).
  70. Verma, R. K., Rawat, R., Singh, A., Singh, D. P., Verma, V. Sphingobacterium multivorum causing fatal meningoencephalitis: a rare case report. Int.J.Res.Med.Sci. 2, (4), 1710-1712 (2014).
  71. Yabuuchi, E., Kaneko, T., Yano, I., Moss, C. W., Miyoshi, N. Sphingobacterium gen. nov., Sphingobacterium spiritivorum comb. nov., Sphingobacterium multivorum comb. nov., Sphingobacterium mizutae sp. nov., and Flavobacterium indologenes sp. nov.: Glucose-nonfermenting Gram-negative rods in CDC groups IIK-2 and IIb. Int.J.Syst.Bacteriol. 33, (3), 580-598 (1983).



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