Submitted to: Proteomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 30, 2007
Publication Date: October 20, 2007
Citation: Cooper, B., Padliya, N., Garrett, W.M., Campbell, K., Tabb, D. 2007. Tandem Mass Spectrometry for the Detection of Plant Pathogenic Fungi and the Effects of Database Composition on Protein Inferences. Proteomics. 7:3932-3942. Interpretive Summary: The accidental or deliberate release of a plant pathogen could result in a major devastation to both the economy and food supply of a nation. With international travel on the rise and changes to global weather patterns, the possibility of exotic pathogens entering the country is greater now than ever before. Rapid pathogen identification is one plausible defense against the introduction of new diseases. We show that mass spectrometry, with its ability to discriminate protein and peptides masses, can be used to identify proteins in different fungal pathogens and that the detection of pathogen-specific proteins is sufficient for identifying and indicating the presence of the pathogen. However, successful identification was dependent upon the amount of known genetic sequence information available for these pathogens since this information was required for the interpretation of the mass spectrometry results. Thus, we conclude that it will be easier to use mass spectrometry for detecting pathogens whose genomes are well-characterized as opposed to those whose are not. These conclusions are expected to enable scientists at universities, government agencies and companies to comprehend the limitations of mass spectrometry for the detection of pathogens and will lead to new methods for the detection of pathogens for which little genetic sequence information exists.
Technical Abstract: Mass spectrometry has shown potential for identifying and detecting plant pathogens. Unlike antibody-based assays like ELISA, mass spectrometry does not require the use of pathogen-specific reagents for the detection of pathogen-specific proteins and peptides. However, the mass spectrometry approach we have used for detection does require a protein sequence database for the interpretation of the peptide tandem mass spectra that are generated. To evaluate the limitations of database composition for protein and pathogen identification, we examined proteins from pure cultures of Ustilago maydis, Phytophthora sojae, Fusarium graminearum, and Rhizoctonia solani. Far more proteins from U. maydis, a pathogen with a sequenced genome, were identified than from R. solani, a fungus with limited amounts of sequence information. When the database did not contain sequences for a specific pathogen, or contained sequences to related pathogens, then matches were made to protein sequences from other or related organisms, giving an illusion that the other organisms were identified. We conclude that the availability of protein sequences in a database is a limiting factor when using software to interpret pathogen-specific peptide tandem mass spectra. Paradoxically, the addition of more sequences may result in more cross-species protein matches and misidentification of pathogens. Thus, alternative means for mass spectral data interpretation are needed for organism identification.