Gene Expression Signatures in AML-12 Hepatocyte Cells upon Dengue virus Infection and Acetaminophen Treatment.

Dengue is an acute viral disease caused by Dengue virus (DENV) and is considered to be the most common arbovirus worldwide. The clinical characteristics of dengue may vary from asymptomatic to severe complications and severe organ impairment, particularly affecting the liver. Dengue treatment is palliative with acetaminophen (APAP), usually known as Paracetamol, being the most used drug aiming to relieve the mild symptoms of dengue. APAP is a safe and effective drug but, like dengue, can trigger the development of liver disorders. Given this scenario, it is necessary to investigate the effects of combining these two factors on hepatocyte homeostasis. Therefore, this study aimed to evaluate the molecular changes in hepatocytes resulting from the association between DENV infection and treatment with sub-toxic APAP concentrations. Using an in vitro experimental model of DENV-2 infected hepatocytes (AML-12 cells) treated with APAP, we evaluated the influence of the virus and drug association on the transcriptome of these hepatocytes by RNA sequencing (RNAseq). The virus-drug association was able to induce changes in the gene expression profile of AML-12 cells and here we highlight and explore these changes and its putative influence on biological processes for cellular homeostasis.

Increased Levels of Txa2 Induced by Dengue Virus Infection in IgM Positive Individuals Is Related to the Mild Symptoms of Dengue.

The inflammatory process plays a major role in the prognosis of dengue. In this context, the eicosanoids may have considerable influence on the regulation of the Dengue virus-induced inflammatory process. To quantify the molecules involved in the cyclooxygenase and lipoxygenase pathways during Dengue virus infection, plasma levels of thromboxane A2, prostaglandin E2 and leukotriene B4; mRNA levels of thromboxane A2 synthase, prostaglandin E2 synthase, leukotriene A4 hydrolase, cyclooxygenase-2 and 5-lipoxygenase; and the levels of lipid bodies in peripheral blood leukocytes collected from IgM-positive and IgM-negative volunteers with mild dengue, and non-infected volunteers, were evaluated. Dengue virus infection increases the levels of thromboxane A2 in IgM-positive individuals as well as the amount of lipid bodies in monocytes in IgM-negative individuals. We suggest that increased levels of thromboxane A2 in IgM-positive individuals plays a protective role against the development of severe symptoms of dengue, such as vascular leakage.

An assessment on epitope prediction methods for protozoa genomes.

BACKGROUND: Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. RESULTS: A database-driven epitope prediction method was developed with built-in functions that were capable of: a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and WoLF PSORT is used. CONCLUSIONS: Our study indicates that the combination of B cells epitope predictors is the best tool for predicting epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the three algorithms predictions were combined. The developed pipeline is available upon request to authors.