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We have previously demonstrated that a DNA vaccine encoding HIV-p55gag in association with the lysosomal associated membrane protein-1 (LAMP-1) elicited a greater Gag-specific immune response, in comparison to a DNA encoding the native gag. In vitro studies have also demonstrated that LAMP/Gag was highly expressed and was present in MHCII containing compartments in transfected cells. In this study, the mechanisms involved in these processes and the relative contributions of the increased expression and altered traffic for the enhanced immune response were addressed. Cells transfected with plasmid DNA constructs containing p55gag attached to truncated sequences of LAMP-1 showed that the increased expression of gag mRNA required p55gag in frame with at least 741 bp of the LAMP-1 luminal domain. LAMP luminal domain also showed to be essential for Gag traffic through lysosomes and, in this case, the whole sequence was required. Further analysis of the trafficking pathway of the intact LAMP/Gag chimera demonstrated that it was secreted, at least in part, associated with exosome-like vesicles. Immunization of mice with LAMP/gag chimeric plasmids demonstrated that high expression level alone can induce a substantial transient antibody response, but targeting of the antigen to the endolysosomal/secretory pathways was required for establishment of cellular and memory response. The intact LAMP/gag construct induced polyfunctional CD4+ T cell response, which presence at the time of immunization was required for CD8+ T cell priming. LAMP-mediated targeting to endolysosomal/secretory pathway is an important new mechanistic element in LAMP-mediated enhanced immunity with applications to the development of novel anti-HIV vaccines and to general vaccinology field.

Epitopes from all available full-length sequences of yellow fever virus (YFV) and dengue fever virus (DENV) restricted by Human Leukocyte Antigen class I (HLA-I) alleles covering 12 HLA-I supertypes were predicted using the NetCTL algorithm. A subset of 179 predicted YFV and 158 predicted DENV epitopes were selected using the EpiSelect algorithm to allow for optimal coverage of viral strains. The selected predicted epitopes were synthesized and approximately 75% were found to bind the predicted restricting HLA molecule with an affinity, K(D), stronger than 500 nM. The immunogenicity of 25 HLA-A*02:01, 28 HLA-A*24:02 and 28 HLA-B*07:02 binding peptides was tested in three HLA-transgenic mice models and led to the identification of 17 HLA-A*02:01, 4 HLA-A*2402 and 4 HLA-B*07:02 immunogenic peptides. The immunogenic peptides bound HLA significantly stronger than the non-immunogenic peptides. All except one of the immunogenic peptides had K(D) below 100 nM and the peptides with K(D) below 5 nM were more likely to be immunogenic. In addition, all the immunogenic peptides that were identified as having a high functional avidity had K(D) below 20 nM. A*02:01 transgenic mice were also inoculated twice with the 17DD YFV vaccine strain. Three of the YFV A*02:01 restricted peptides activated T-cells from the infected mice in vitro. All three peptides that elicited responses had an HLA binding affinity of 2 nM or less. The results indicate the importance of the strength of HLA binding in shaping the immune response.

Background: Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity., Methodology/principal Findings: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of approximately 85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-alpha and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to approximately 96%., Conclusions/significance: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-alpha, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease.

Background: We report the detailed development of biomarkers to predict the clinical outcome under dengue infection. Transcriptional signatures from purified peripheral blood mononuclear cells were derived from whole-genome gene-expression microarray data, validated by quantitative PCR and tested in independent samples., Methodology/principal Findings: The study was performed on patients of a well-characterized dengue cohort from Recife, Brazil. The samples analyzed were collected prospectively from acute febrile dengue patients who evolved with different degrees of disease severity: classic dengue fever or dengue hemorrhagic fever (DHF) samples were compared with similar samples from other non-dengue febrile illnesses. The DHF samples were collected 2-3 days before the presentation of the plasma leakage symptoms. Differentially-expressed genes were selected by univariate statistical tests as well as multivariate classification techniques. The results showed that at early stages of dengue infection, the genes involved in effector mechanisms of innate immune response presented a weaker activation on patients who later developed hemorrhagic fever, whereas the genes involved in apoptosis were expressed in higher levels., Conclusions/significance: Some of the gene expression signatures displayed estimated accuracy rates of more than 95%, indicating that expression profiling with these signatures may provide a useful means of DHF prognosis at early stages of infection.

Background: Dengue virus infection causes a wide spectrum of illness, ranging from sub-clinical to severe disease. Severe dengue is associated with sequential viral infections. A strict definition of primary versus secondary dengue infections requires a combination of several tests performed at different stages of the disease, which is not practical., Methods and Findings: We developed a simple method to classify dengue infections as primary or secondary based on the levels of dengue-specific IgG. A group of 109 dengue infection patients were classified as having primary or secondary dengue infection on the basis of a strict combination of results from assays of antigen-specific IgM and IgG, isolation of virus and detection of the viral genome by PCR tests performed on multiple samples, collected from each patient over a period of 30 days. The dengue-specific IgG levels of all samples from 59 of the patients were analyzed by linear discriminant analysis (LDA), and one- and two-dimensional classifiers were designed. The one-dimensional classifier was estimated by bolstered resubstitution error estimation to have 75.1% sensitivity and 92.5% specificity. The two-dimensional classifier was designed by taking also into consideration the number of days after the onset of symptoms, with an estimated sensitivity and specificity of 91.64% and 92.46%. The performance of the two-dimensional classifier was validated using an independent test set of standard samples from the remaining 50 patients. The classifications of the independent set of samples determined by the two-dimensional classifiers were further validated by comparing with two other dengue classification methods: hemagglutination inhibition (HI) assay and an in-house anti-dengue IgG-capture ELISA method. The decisions made with the two-dimensional classifier were in 100% accordance with the HI assay and 96% with the in-house ELISA., Conclusions: Once acute dengue infection has been determined, a 2-D classifier based on common dengue virus IgG kits can reliably distinguish primary and secondary dengue infections. Software for calculation and validation of the 2-D classifier is made available for download.