RESUMO
Non-Hodgkin lymphoma comprises a variety of neoplasms, many of which arise from germinal center (GC)-experienced B cells. microRNA-28 (miR-28) is a GC-specific miRNA whose expression is lost in numerous mature B-cell neoplasms. Here we show that miR-28 regulates the GC reaction in primary B cells by impairing class switch recombination and memory B and plasma cell differentiation. Deep quantitative proteomics combined with transcriptome analysis identified miR-28 targets involved in cell-cycle and B-cell receptor signaling. Accordingly, we found that miR-28 expression diminished proliferation in primary and lymphoma cells in vitro. Importantly, miR-28 reexpression in human Burkitt (BL) and diffuse large B-cell lymphoma (DLBCL) xenografts blocked tumor growth, both when delivered in viral vectors or as synthetic, clinically amenable, molecules. Further, the antitumoral effect of miR-28 is conserved in a primary murine in vivo model of BL. Thus, miR-28 replacement is uncovered as a novel therapeutic strategy for DLBCL and BL treatment.
Assuntos
Linfócitos B/imunologia , Linfoma de Burkitt/terapia , Regulação Neoplásica da Expressão Gênica , Centro Germinativo/imunologia , Linfoma Difuso de Grandes Células B/terapia , MicroRNAs/genética , Animais , Linfócitos B/patologia , Linfoma de Burkitt/genética , Linfoma de Burkitt/imunologia , Linfoma de Burkitt/patologia , Diferenciação Celular , Proliferação de Células , Vetores Genéticos/química , Vetores Genéticos/metabolismo , Centro Germinativo/patologia , Humanos , Switching de Imunoglobulina , Memória Imunológica , Lentivirus/genética , Lentivirus/metabolismo , Linfoma Difuso de Grandes Células B/genética , Linfoma Difuso de Grandes Células B/imunologia , Linfoma Difuso de Grandes Células B/patologia , Camundongos , Camundongos Endogâmicos NOD , Camundongos SCID , MicroRNAs/imunologia , Plasmócitos/imunologia , Plasmócitos/patologia , Proteômica , Transcriptoma , Transfecção , Ensaios Antitumorais Modelo de XenoenxertoRESUMO
MOTIVATION: Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects. RESULTS: We propose to identify DIGE DE proteins using a two-stage linear mixed model. The proposal consists of splitting the overall model for the measured intensity into two interconnected models. First, we fit a normalization model that accounts for the general experimental effects, such as gel and CyDye effects as well as for the features of the associated random term distributions. Second, we fit a model that uses the residuals from the first step to account for differences between treatments in protein-by-protein basis. The modeling strategy was evaluated using data from a melanoma cell study. We found that a heteroskedastic model in the first stage, which also account for CyDye and gel effects, best normalized the data, while allowing for an efficient estimation of the treatment effects. The Cy2 reference channel was used as a covariate in the normalization model to avoid skewness of the residual distribution. Its inclusion improved the detection of DE proteins in the second stage.