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• W4285490901 abstract "Abstract Dengue virus (DENV) infection is a global health problem with no specific therapy or vaccine currently available to combat it. Here we performed a comprehensive analysis of publicly available transcriptome data of patients with natural DENV infection (NDI) and DENV vaccine trials (DVT1, DVT2, and DVT3), identifying common transcriptional signatures according to the time of infection in NDI and DVTs, and disease severity in NDI. We identified 237 common differentially expressed genes (DEGs) between NDI and DVT1. Of those, 20 were commonly affected by dengue vaccination during DVT2 and DVT3. Machine learn analysis ranked the 10 NDI's most critical predictors for disease severity, e.g., IFIT5, ISG15, and HERC5, which play an essential role in the anti-viral immune response. Hence, this work provides new insight into the NDI and vaccine-induced overlapping immune response and suggests novel targets for developing anti-dengue-specific therapies and monitoring the effectiveness of vaccination." @default.
• W4285490901 created "2022-07-15" @default.
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• W4285490901 date "2022-07-15" @default.
• W4285490901 modified "2023-10-18" @default.
• W4285490901 title "Integrative systems vaccinology identification of shared gene signatures between natural dengue infection and vaccination trials" @default.
• W4285490901 doi "https://doi.org/10.21203/rs.3.rs-1829181/v1" @default.
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