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Journal Article

Classification and identification of bacteria by mass spectrometry and computational analysis

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50512

Sauer,  Sascha
Nutrigenomics and Gene Regulation (Sascha Sauer), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50158

Freiwald,  Anja
Nutrigenomics and Gene Regulation (Sascha Sauer), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50397

Kube,  Michael
High Throughput Technologies, Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50488

Reinhardt,  Richard
High Throughput Technologies, Max Planck Institute for Molecular Genetics, Max Planck Society;

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journal.pone.0002843.pdf
(Any fulltext), 422KB

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Citation

Sauer, S., Freiwald, A., Maier, T., Kube, M., Reinhardt, R., Kostrzewa, M., et al. (2008). Classification and identification of bacteria by mass spectrometry and computational analysis. PLoS One, (7), e2843-e2843. doi:10.1371/journal.pone.0002843.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-7F71-5
Abstract
BACKGROUND: In general, the definite determination of bacterial species is a tedious process and requires extensive manual labour. Novel technologies for bacterial detection and analysis can therefore help microbiologists in minimising their efforts in developing a number of microbiological applications. METHODOLOGY: We present a robust, standardized procedure for automated bacterial analysis that is based on the detection of patterns of protein masses by MALDI mass spectrometry. We particularly applied the approach for classifying and identifying strains in species of the genus Erwinia. Many species of this genus are associated with disastrous plant diseases such as fire blight. Using our experimental procedure, we created a general bacterial mass spectra database that currently contains 2800 entries of bacteria of different genera. This database will be steadily expanded. To support users with a feasible analytical method, we developed and tested comprehensive software tools that are demonstrated herein. Furthermore, to gain additional analytical accuracy and reliability in the analysis we used genotyping of single nucleotide polymorphisms by mass spectrometry to unambiguously determine closely related strains that are difficult to distinguish by only relying on protein mass pattern detection. CONCLUSIONS: With the method for bacterial analysis, we could identify fire blight pathogens from a variety of biological sources. The method can be used for a number of additional bacterial genera. Moreover, the mass spectrometry approach presented allows the integration of data from different biological levels such as the genome and the proteome.