Malaria is a complex disease caused by parasites of the genus Plasmodium and is the leading cause of morbidity and mortality worldwide. The most severe form of malaria disease is caused by Plasmodium falciparum. Thus, a combination of different approaches is needed to control malaria. Resistance to first-line drugs and insecticides, on the other hand, makes the need for an effective vaccination more urgent than ever. Because erythrocyte parasites cause the most clinical symptoms, developing a vaccination for this stage of infection might be highly beneficial. In this research, we employed various bioinformatics methods to create an efficient multi-epitope vaccine that induces antibodies against the blood stage of malaria infection. For this purpose, we selected the malaria PfGARP protein as the target here. The B, HTL epitopes, and epitope conservation were predicted. The predicted epitopes (including 5 B and 5 HTL epitopes) were connected using suitable linkers, and the flagellin molecule was used as an adjuvant to improve its immunogenicity. The final construct vaccine with 414 amino acids long was designed. The vaccine's allergenicity, antigenicity, solubility, physicochemical characteristics, 2D and 3D structure modeling, molecular docking, molecular dynamics simulation, in silico cloning, and immunological simulation were tested. In silico immune simulation results showed significantly elevated IgG1 and IgM and T helper cells, INF γ, IL 2, and B-cell populations after the injection of the designed vaccine. These significant computational analyses indicated that our proposed vaccine candidate might activate suitable immune responses against malaria. However, in vitro and in vivo studies are essential for further validation.

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Background Pancreatic adenocarcinoma is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify key genes and the involved mechanisms in pancreatic adenocarcinoma, we used bioinformatics analyzes in our study to introduce potential biomarkers in pancreatic cancer management. Methods In this study, gene expression profiles of pancreatic adenocarcinoma patients and normal adjacent tissues were screened and downloaded from The Cancer Genom Atlas (TCGA) bioinformatics database. Differentially expressed genes (DEGs) were identified between normal and pancreatic cancer gene expression signatures using R software. Then, Enrichment analysis of DEGs [including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis] was performed by an enrichr (interactive and collaborative HTML5 gene list enrichment analysis) web-based tool. The protein-protein interaction (PPI) network was also constructed using STRING (Search Tool for the Retrieval of Interacting Genes) and Cytoscape software to identify the hub genes according to the top 100 DEGs in pancreatic adenocarcinoma. Results In our study, more than 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1-4, H1-5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 with 99.91% probability might distinguish pancreatic adenocarcinoma from normal tissue. GO analysis of these 20 top DEGs showed that they have more enriched in negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic adenocarcinoma and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In protein-protein interaction (PPI) network analysis, we found that different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic adenocarcinoma. Conclusions Our bioinformatics analysis showed that the DEGs and hub genes as key genes identified in this study may serve as new biomarkers in the near future for better management of pancreatic cancer. Although, H1.3 is currently one of the prognostic biomarkers in pancreatic cancer.

Malaria is a complex disease caused by genus Plasmodiumis parasites and is the leading cause of morbidity and mortality worldwide. The most severe form of malaria disease is caused by Plasmodium falciparum. A combination of different approaches is needed to control malaria, and on the other hand, resistance to first-line drugs and insecticides makes the need for an effective vaccine more mandatory than ever. Erythrocyte parasites have the most clinical symptoms, so designing the potential vaccine for this stage of infection could be very helpful. In this research, we used various bioinformatics tools to design an effective antibody-inducing multi-epitope vaccine against the blood-stage of malaria infection. For this purpose, we selected the malaria PfGARP protein as the target here. The predicted B and HTL epitopes and flagellin molecule (as an adjuvant) were connected with suitable linkers and the final construct vaccine was designed. The various properties of this construct, including physicochemical properties, 3D structures, molecular docking, molecular simulations, and in silico cloning were then carried out. Based on preliminary findings, our designed fusion construct could be proposed as a novel potential vaccine candidate against Malaria. However, in vitro and in vivo studies are essential for further validation.