B cells Can Modulate the CD8 Memory T Cell after DNA Vaccination Against Experimental Tuberculosis

Abstract Background Although B cells are important as antigen presenting cells (APC) during the immune response, their role in DNA vaccination models is unknown. Methods In this study in vitro and in vivo experiments were performed to evaluate the ability of B cells to protect mice against Mycobacterium tuberculosis challenge. Results In vitro and in vivo studies showed that B cells efficiently present antigens after naked plasmid pcDNA3 encoding M. leprae 65-kDa heat shock protein (pcDNA3-Hsp65) internalization and protect B knock-out (BKO) mice against Mycobacterium tuberculosis infection. pcDNA3-Hsp65-transfected B cells adoptively transferred into BKO mice rescued the memory phenotypes and reduced the number of CFU compared to wild-type mice. Conclusions These data not only suggest that B cells play an important role in the induction of CD8 T cells but also that they improve bacterial clearance in DNA vaccine model.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.