Staphylococcus aureus (S. aureus) is the etiological agent of food-borne diseases, skin infections in humans and mastitis in bovines. S. aureus is also known to exist as a commensal on skin, nose and other mucosal surfaces of the host. This symbiotic association is a result of immune dampening or tolerance induced in the host by this pathogen. We proposed the variation in protein expression by commensal and pathogenic strain as an important factor behind the difference in pathogenicity. The identification of differentially expressed proteins was carried out using a quantitative mass spectrometry (MS)-based proteomic approach, known as stable isotope labeling of amino acids in cell culture (SILAC). Four commensal and pathogenic strains each were grown in the SILAC minimal media (RPMI 1640), containing light (12C) and heavy (13C) form of lysine, respectively, until early stationary growth phase. Various protein fractions, including cell wall, membrane and secreted, were extracted from the bacterial cultures and mixed in a 1:1 ratio. The relative abundance of proteins present in light and heavy labeled samples was determined using MS analysis. From a total of 151 differentially expressed proteins, 58 were found to be upregulated in the pathogenic strains. These proteins are involved in a variety of cellular functions, including immune modulation, iron-binding, cellular transport, redox reactions, and metabolic enzymes. The differentially expressed proteins can serve as putative candidates to improve current approach towards development of a vaccine against S. aureus. / Master of Science

It is evident that the study of proteins is crucial for a deeper understanding of how drug treatments affect the body. However, differential protein expression analysis, which can be described as the method of finding which proteins are affected by a treatment, faces some major challenges. First of all, because proteomics data typically comprise several thousand different proteins for just a small number of biological tissues, there are both problems concerning multiple comparisons and low statistical power. Secondly, proteomics data are prone to suffer high rates of missing values, which could bias the results. One approach to handle these issues, which is gaining popularity, is to apply Bayesian hierarchical modeling in order to pool information from the complete dataset of all proteins when making inferences for each protein individually. Yet, in practice, there seems to be essentially only one Bayesian hierarchical model that currently is being employed, which uses a conjugate prior for the error variances but has no prior for the coefficients or the missing values. Given this, the aim of the thesis is to investigate how the model can be improved by adding priors for the coefficients and the missing values. The results show that by adding a hierarchical prior for the coefficients prediction accuracy may be increased. Furthermore, the results show that by adding a prior for the missing values differently expressed proteins can be detected that otherwise would have been overlooked.