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

Orientador: Marco Aurélio Zezzi Arruda / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-25T14:38:07Z (GMT). No. of bitstreams: 1 Maciel_BrunaCarolineMiranda_M.pdf: 2287580 bytes, checksum: 69534e40c093aed8660eb365cf0fa8f6 (MD5) Previous issue date: 2014 / Resumo: Tendo em vista que a prática da transgenia reflete uma realidade mundial no setor agrícola, o principal objetivo desse trabalho de Dissertação é avaliar algumas espécies de proteínas diferenciais entre folhas de Arabidopsis thaliana não transgênica (NT) e transgênica (T), ambas irrigadas com uma solução de Selênio (Se), frente a uma condição controle. De maneira a averiguar as diferenças entre as folhas NT e T, usando como parâmetros o efeito da transgenia e do estresse oxidativo induzido por Se, um estudo proteômico comparativo foi desenvolvido por meio da metodologia 2-D DIGE, uma variante da técnica de separação por 2-D PAGE, que oferece vantagens, tais como na detectabilidade das proteínas de baixa abundância e precisão para análise quantitativa entre as intensidades diferenciais dos spots proteicos, resultando, com efeito, um mapa proteômico mais representativo destas folhas. Quatro grupos de plantio foram combinados para análise comparativa, de tal forma que NT x T, NT x Se-NT, Se-NT x Se-T e T x Se-T. Embora não tenha sido detectadas espécies de proteínas diferencias no grupo T x Se-T, para os outros, 68 proteínas diferenciais foram detectadas, usando um fator de regulação 1,5 baseado no teste de t para p < 0,05. Dentre este total, 27 proteínas diferenciais foram precisamente identificadas por ESI-QTOF-MS/MS. Estas proteínas estão classificadas quanto às funções metabólicas, energéticas, de transdução de sinal, doenças/defensa vegetal, e algumas delas estão envolvidas nas vias da Glicólise, Fotossistema I e II, e combate à ERO (espécies reativas de oxigênio). Adicionalmente, um imageamento por ablação a laser foi feito para avaliar a distribuição de Se e Enxofre (S) em folhas dos grupos diferentes, corroborando com alguns resultados obtidos, principalmente àquelas proteínas envolvidas nas vias da Glicólise. A partir destes resultados é possível concluir que o vetor inserido também confere à planta a resistência ao estresse oxidativo, no qual o Selênio adicionado foi o efeito mais influente comparado com o efeito da transgenia / Abstract: In view of the techniques related to transgenesis show the global reality in the agricultural sector, the main goal of this Thesis work is to evaluate some differential protein species in non-transgenic (NT) and transgenic (T) Arabidopsis thaliana leaves, both they irrigated with Selenium (Se) solution in front of control condition. In order to estimate the differences between NT and T leaves, using as parameters the transgenesis effect and the oxidative stress induced by Se, a comparative proteomic study was performed using 2-D DIGE methodology, a variant technique of separation by 2-D PAGE that offers ideal advantages, such as detectability of low abundance proteins and accuracy to quantitative analysis among differential intensities of protein spots, resulting in a more representative proteomic map these leaves. Four plant groups were combined to comparative analysis, so that NT x T, NT x Se-NT, Se-NT x Se-T e T x Se-T. Although there differential protein species from T x Se-T group were not detected, for the others, 68 differential protein species were detected, using regulation factor of 1.5, which was based in t test to p < 0.05. Among them, 27 differential proteins were accurately identified by ESI-QTOF-MS/MS. These proteins are classified regarding functions as metabolism, energy, signal transduction, diseases/vegetal defense and some of these were involved in the Glycolysis pathway, Photosystem I and II, and combat the ROS (reactive oxygen species). Additionally an imaging by laser ablation was done to evaluate the Se and Sulfur (S) distribution in leaves of different groups corroborating with some obtained results, mainly those proteins involved in the Glycolysis pathway. From these results, it is possible to conclude that insert vector confers resistance to the T plant regarding oxidative stress where the added Se was the effect more influential than compared with transgenesis effect / Mestrado / Quimica Analitica / Mestra em Química

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.

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