The field relating to the study of cellular proteins is known as proteomics. This dissertation describes the use of proteomic techniques to study the sulfate-reducing bacterium Desulfovibria vulgaris Hildenborough. This organism is of interest because it has been shown to have the ability to reduce heavy metals including uranium and chromium, and is therefore a target organism for bioremediation of contaminated waste sites. The ability to harness the bioremediation capabilities of organisms relies on our understanding of their responses toward the various bio-geochemical perturbation conditions in their environment. Growth and stress conditions for D. vulgaris were carefully designed to ensure the survival of the majority of the population. Proteins were obtained from total cellular lysates and digested into peptides. Two dimensional liquid chromatography, first by offline strong cation exchange followed by reverse phase was used to separate peptides prior to detection by tandem MS, ICAT and iTRAQ labeling techniques were used to provide differential proteomic analysis. Stress response in D. vulgaris was investigated under environmentally relevant conditions, including salt stress, nitrate stress, oxygen stress, growth in biofilm, and syntrophic growth with Methanococcus maripaludis. In each case, proteomic analysis has provided valuable insights about the physiology of D. vulgaris.

Overall, 1100 proteins have been identified in our analyses, based on observation of two or more unique, high confidence peptides. While individual experiments can add incrementally to the knowledge about the organism, multiple sets of data can be mined for much more information than any single data set can provide. The investigation of post-translational modifications revealed interesting modifications in many proteins in the sulfate reducing pathway. D. vulgaris is an organism in which approximately 42% of its genes are annotated to be hypothetical proteins, meaning that there is no information about their function, or even whether those DNA sequences actually encode for protein. 277 hypothetical proteins have been identified as part of this analysis. Finally, these datasets may be used in the re-annotation of the genome sequence of D. vulgaris by providing additional information about pathways where multiple proteins are annotated to complete the same function.