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Zika virus (ZIKV) outbreak caused one of the most significant medical emergencies in the Americas due to associated microcephaly in newborns. To evaluate the impact of ZIKV infection on neuronal cells over time, we retrieved gene expression data from several ZIKV-infected samples obtained at different time point post-infection (pi). Differential gene expression analysis was applied at each time point, with more differentially expressed genes (DEG) identified at 72h pi. There were 5 DEGs (PLA2G2F, TMEM71, PKD1L2, UBD, and TNFAIP3 genes) across all timepoints, which clearly distinguished between infected and healthy samples. The highest expression levels of all five genes were identified at 72h pi. Taken together, our results indicate that ZIKV infection greatly impacts human neural cells at early times of infection, with peak perturbation observed at 72h pi. Our analysis revealed that all five DEGs, in samples of ZIKV-infected human neural stem cells, remained highly upregulated across the timepoints evaluated. Moreover, despite the pronounced inflammatory host response observed throughout infection, the impact of ZIKV is variable over time. Finally, the five DEGs identified herein play prominent roles in infection, and could serve to guide future investigations into virus-host interaction, as well as constitute targets for therapeutic drug development. [ABSTRACT FROM AUTHOR]

Background: Leishmaniasis is caused by intracellular Leishmania parasites that induce a T-cell mediated response associated with recognition of CD4+ and CD8+ T cell Line 1Lineepitopes. Identification of CD8+ antigenic determinants is crucial for vaccine and therapy development. Herein, we developed an open-source software dedicated to search and compile data obtained from currently available on line prediction algorithms. Methodology/Principal Findings: We developed a two-phase algorithm and implemented in an open source software called EPIBOT, that consolidates the results obtained with single prediction algorithms, generating a final output in which epitopes are ranked. EPIBOT was initially trained using a set of 831 known epitopes from 397 proteins from IEDB. We then screened 63 Leishmania braziliensis vaccine candidates with the EPIBOT trained tool to search for CD8+ T cell epitopes. A proof-of-concept experiment was conducted with the top eight CD8+ epitopes, elected by EPIBOT. To do this, the elected peptides were synthesized and validated for their in vivo cytotoxicity. Among the tested epitopes, three were able to induce lysis of pulsed-target cells. Conclusion: Our results show that EPIBOT can successfully search across existing prediction tools, generating a compiled list of candidate CD8+ epitopes. This software is fast and a simple search engine that can be customized to search over different MHC alleles or HLA haplotypes. [ABSTRACT FROM AUTHOR]