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Appl Environ Microbiol. 2009 Nov;75(22):7229-42. doi: 10.1128/AEM.00857-09. Epub 2009 Sep 18.

Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks.

Author information

1
P25, Robert Koch-Institut, Nordufer 20, D-13353 Berlin, Germany. LaschP@rki.de

Abstract

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 from Bacillus and related genera, among them 102 strains of B. anthracis and 121 strains of B. cereus. The information contained in the database was investigated by means of visual inspection of gel view representations, univariate t tests for biomarker identification, unsupervised hierarchical clustering, and artificial neural networks (ANNs). Analysis of gel views and independent t tests suggested B. anthracis- and B. cereus group-specific signals. For example, mass spectra of B. 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 genus Bacillus. Superior classification accuracy was achieved when supervised ANNs were employed. For the identification of B. anthracis, independent validation of optimized ANN models yielded a diagnostic sensitivity of 100% and a specificity of 100%.

PMID:
19767470
PMCID:
PMC2786504
DOI:
10.1128/AEM.00857-09
[Indexed for MEDLINE]
Free PMC Article

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