BackgroundWe 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 FindingsThe 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/SignificanceSome 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.

Cancer immunotherapy is the most promising trend in oncology, focusing on helping or activating the patient's immune system to identify and fight against cancer. In the last decade, interest in metabolic reprogramming of tumor-associated macrophages from M2-like phenotype (promoting tumor progression) to M1-like phenotypes (suppressing tumor growth) as a therapeutic strategy against cancer has increased considerably. Iron metabolism has been standing out as a target for the reprogramming of tumor-associated macrophages to M1-like phenotype with therapeutic purposes against cancer. Due to the importance of the iron levels in macrophage polarization states, iron oxide nanoparticles can be used to change the activation state of tumor-associated macrophages for a tumor suppressor phenotype and as an anti-tumor strategy.

BackgroundSymptomatic 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 FindingsmRNA 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 ∼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-α 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 ∼96%.Conclusions/SignificanceHere, 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-α, 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.

The dynamics of dengue virus (DENV) circulation depends on serotype, genotype and lineage replacement and turnover. In São José do Rio Preto, Brazil, we observed that the L6 lineage of DENV-1 (genotype V) remained the dominant circulating lineage even after the introduction of the L1 lineage. We investigated viral fitness and immunogenicity of the L1 and L6 lineages and which factors interfered with the dynamics of DENV epidemics. The results showed a more efficient replicative fitness of L1 over L6 in mosquitoes and in human and non-human primate cell lines. Infections by the L6 lineage were associated with reduced antigenicity, weak B and T cell stimulation and weak host immune system interactions, which were associated with higher viremia. Our data, therefore, demonstrate that reduced viral immunogenicity and consequent greater viremia determined the increased epidemiological fitness of DENV-1 L6 lineage in São José do Rio Preto.

Currently, several assays can confirm acute dengue infection at the point-of-care. However, none of these assays can predict the severity of the disease symptoms. A prognosis test that predicts the likelihood of a dengue patient to develop a severe form of the disease could permit more efficient patient triage and treatment. We hypothesise that mRNA expression of apoptosis and innate immune response-related genes will be differentially regulated during the early stages of dengue and might predict the clinical outcome. Aiming to identify biomarkers for dengue prognosis, we extracted mRNA from the peripheral blood mononuclear cells of mild and severe dengue patients during the febrile stage of the disease to measure the expression levels of selected genes by quantitative polymerase chain reaction. The selected candidate biomarkers were previously identified by our group as differentially expressed in microarray studies. We verified that the mRNA coding for CFD, MAGED1, PSMB9, PRDX4 and FCGR3B were differentially expressed between patients who developed clinical symptoms associated with the mild type of dengue and patients who showed clinical symptoms associated with severe dengue. We suggest that this gene expression panel could putatively serve as biomarkers for the clinical prognosis of dengue haemorrhagic fever.