title: Rapid identification of oral Actinomyces species cultivated from subgingival biofilm by MALDI-TOF-MS.
abstract: BACKGROUND
  Actinomyces are a common part of the residential flora of the human intestinal tract, genitourinary system and skin. Isolation and identification of Actinomyces by conventional methods is often difficult and time consuming. In recent years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has become a rapid and simple method to identify bacteria.
  
  
  OBJECTIVE
  The present study evaluated a new in-house algorithm using MALDI-TOF-MS for rapid identification of different species of oral Actinomyces cultivated from subgingival biofilm.
  
  
  METHODS
  Eleven reference strains and 674 clinical strains were used in this study. All the strains were preliminarily identified using biochemical methods and then subjected to MALDI-TOF-MS analysis using both similarity-based analysis and classification methods (support vector machine [SVM]). The genotype of the reference strains and of 232 clinical strains was identified by sequence analysis of the 16S ribosomal RNA (rRNA).
  
  
  RESULTS
  The sequence analysis of the 16S rRNA gene of all references strains confirmed their previous identification. The MALDI-TOF-MS spectra obtained from the reference strains and the other clinical strains undoubtedly identified as Actinomyces by 16S rRNA sequencing were used to create the mass spectra reference database. Already a visual inspection of the mass spectra of different species reveals both similarities and differences. However, the differences between them are not large enough to allow a reliable differentiation by similarity analysis. Therefore, classification methods were applied as an alternative approach for differentiation and identification of Actinomyces at the species level. A cross-validation of the reference database representing 14 Actinomyces species yielded correct results for all species which were represented by more than two strains in the database.
  
  
  CONCLUSIONS
  Our results suggest that a combination of MALDI-TOF-MS with powerful classification algorithms, such as SVMs, provide a useful tool for the differentiation and identification of oral Actinomyces.
authors.affiliation: Institute for Medical Microbiology and Epidemiology of Infectious Diseases, University Hospital of Leipzig, Leipzig, Germany; catalinasuzana.stingu@medizin.uni-leipzig.de.
authors.forename: Catalina S
authors.initials: CS
authors.lastname: Stingu
authors.affiliation: Institute for Medical Microbiology and Epidemiology of Infectious Diseases, University Hospital of Leipzig, Leipzig, Germany.
authors.forename: Toralf
authors.initials: T
authors.lastname: Borgmann
authors.affiliation: Institute for Medical Microbiology and Epidemiology of Infectious Diseases, University Hospital of Leipzig, Leipzig, Germany.
authors.forename: Arne C
authors.initials: AC
authors.lastname: Rodloff
authors.affiliation: Institute for Medical Microbiology and Epidemiology of Infectious Diseases, University Hospital of Leipzig, Leipzig, Germany.
authors.forename: Paul
authors.initials: P
authors.lastname: Vielkind
authors.affiliation: Centre of Periodontology, Department for Cariology, Endodontology and Periodontology, University Hospital of Leipzig, Leipzig, Germany.
authors.forename: Holger
authors.initials: H
authors.lastname: Jentsch
authors.affiliation: Institute of Biochemistry, University of Leipzig, Leipzig, Germany.
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Schellenberger
authors.affiliation: Institute of Biochemistry, University of Leipzig, Leipzig, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Eschrich
keywords: MALDI-TOF-MS; oral Actinomyces; support vector machine