Identification of Protein Vaccine Candidates Using Comprehensive Proteomic Analysis Strategies

Abstract

Presented in this dissertation are proteomic analysis studies focused on identifying proteins to be used as vaccine candidates against Coccidioidomycosis, a potentially fatal human pulmonary disease caused by inhalation of a spore from the soil-dwelling pathogenic fungi Coccidioides posadasii and C. immitis. A method of tandem mass spectrometry data analysis using dual protein sequence search algorithms for increasing the total protein identifications from an analysis is described. This method was utilized in a comprehensive proteomic analysis of cell walls isolated from the dimorphic fungal pathogen C. posadasii. A strategy of tandem mass spectrometry-based protein identification coupled with bioinformatic sequence analysis was used to produce a list of protein vaccine candidates for further testing. A differential proteome analysis using stable isotope protein labeling was undertaken to identify vaccine candidate proteins that are more highly expressed in the spherule, or pathogenic phase, of C. posadasii. The results of these analyses are 9 previously undescribed protein vaccine candidates isolated from spherule cell walls that have sequence indications of extracellular association such as GPI anchors and N-terminal signal sequences and antigen potential based on homology to known antigenic or secreted proteins. An additional 14 proteins identified from spherule cell walls are potential vaccine candidates based on extracellular sequence predictions without any indications of antigenic potential. The stable isotope labeling study has identified 3 more proteins that are preferentially expressed in spherules and exhibit antigenic potential based on extracellular localization or homology to known antigenic proteins. Additionally, there were 5 unknown function proteins identified by stable isotope labeling that are more highly expressed in spherules that may be good vaccine candidates but cannot be identified or localized by sequence analysis.The dual algorithm protein identification method presented here is a new technique to address some common shortcomings associated with a proteomic analysis. The comprehensive proteomic analyses of Coccidioides posadasii presented here have provided new targets for Coccidioidomycosis vaccine development as well as insights into the proteome of this pathogen, such as the sequence comparison of C.posadasii proteins to human proteins, as well as a comprehensive analysis of predicted protein function in the Coccidioides proteome.