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Database of MALDI-TOF mass spectra for rapid and reliable identification of highly pathogenic bacteria

A team of researchers at the Robert Koch Institute (RKI) has published a database of MALDI-TOF mass spectra useful for rapid and reliable identification of highly pathogenic bacteria.

MALDI-TOF MS is a relatively new technology which has revolutionised the way microorganisms are identified. The method relies on the reproducible detection of microbial protein mass patterns obtained from whole cells, cell lysates, or crude bacterial extracts. Microbial MALDI-TOF mass spectra can be regarded as snapshots of the protein composition of the individual strains studied. The protein biomarkers are typically high-abundance proteins with housekeeping functions, such as ribosomal proteins. For microbial identification mass spectra are analysed by pattern-matching approaches by which mass spectra from the bacteria under study are matching against validated databases of microbial reference spectra. The availability of high-quality and comprehensive spectral databases has been considered of paramount importance for attaining accurate identification results.

To support diagnostics of highly pathogenic bacteria researchers at the RKI have compiled large databases with hundreds of experimental mass spectra from a large variety of microbial strains. Most of these strains are from highly pathogenic (biosafety level 3, BSL-3) bacteria such as Bacillus anthracis, Yersinia pestis, Francisella tularensis, and others, or from their close and more distant relatives. The complete RKI database has been recently published at ZENODO under the Creative Commons license (CC-BY-NC-SA, attribution, non-commercial, share-alike). This database can be used as a reference for the diagnostics of BSL-3 bacteria using proprietary and free software packages for MALDI-TOF MS-based microbial identification. It is hoped that the free database will be helpful to improve the diagnostics of highly pathogenic bacteria at institutions in and outside of Germany.

Date: 23.11.2016