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This report demonstrates the applicability of a combination of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) and chemometrics for rapid and reliable identification of vegetative cells of the causative agent of anthrax,Bacillus anthracis. Bacillus cultures were prepared under standardized conditions and inactivated according to a recently developed MS-compatible inactivation protocol for highly pathogenic microorganisms. MALDI-TOF MS was then employed to collect spectra from the microbial samples and to build up a database of bacterial reference spectra. This database comprised mass peak profiles of 374 strains fromBacillus and related genera, among them 102 strains ofB. anthracis and 121 strains ofB. cereus . The information contained in the database was investigated by means of visual inspection of gel view representations, univariatet tests for biomarker identification, unsupervised hierarchical clustering, and artificial neural networks (ANNs). Analysis of gel views and independentt tests suggestedB. anthracis - andB. cereus group-specific signals. For example, mass spectra ofB. anthracis exhibited discriminating biomarkers at 4,606, 5,413, and 6,679 Da. A systematic search in proteomic databases allowed tentative assignment of some of the biomarkers to ribosomal protein or small acid-soluble proteins. Multivariate pattern analysis by unsupervised hierarchical cluster analysis further revealed a subproteome-based taxonomy of the genusBacillus . Superior classification accuracy was achieved when supervised ANNs were employed. For the identification ofB. anthracis , independent validation of optimized ANN models yielded a diagnostic sensitivity of 100% and a specificity of 100%.

applied and environmental microbiology

Identification of <i>Bacillus anthracis</i> by Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Artificial Neural Networks

Identification of Bacillus anthracis by Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Artificial Neural Networks