Proteomic and mass spectral data mining to search for biomarkers of pathogenic neisseria

Neisseria meningitidis causes over 1 million cases of meningitis and septicaemia annually. Currently available vaccines show only poor immunogenicity in children and do not induce immunological memory. The main focus of this study was on exploring the organism by mass spectrometry and 2-D GE to attempt to identify potential biomarkers and get a better understanding of the biology of N. meningitidis. A novel diagnostic approach using SELDI TOP MS was developed and a number of different approaches to mass spectral data mining were investigated. Several species specific, and some serogroup specific, putative biomarker for TV! meningitidis were identified. The ability of ANNs to interrogate SELDI TOP MS data and identify delineating bacterial biomarkers was evaluated and an ANN model was developed for N. meningitidis displaying 100% sensitivity and 99% specificity. A novel technique for sequence typing, based on mass spectrometry, was developed showing full correlation with the established MLST method. Partial 2-D GE reference maps for serogroup A, B and C were created showing between 63 and 85 annotated protein species. The majority of identified protein species were involved in common metabolic pathways. However, several hypothetical proteins were detected. The technologies applied show great potential for future investigations into the subpopulations of this pathogen. Overall, the results demonstrated that future approaches to study the biology of this pathogen should be polyphasic, combining genomic and proteomic analyses to provide a more holistic overview of strains. Such an approach will likely enable new stable targets to be identified from which a
strategic therapeutic approach may be devised.

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