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Japanese encephalitis virus (JEV) is the etiological agent of Japanese encephalitis (JE). The most commonly used vaccine used to prevent JE is the live-attenuated strain SA14-14-2, which was generated by serial passage of the wild-type (WT) JEV strain SA14. Two other vaccine candidates, SA14-5-3 and SA14-2-8 were derived from SA14. Both were shown to be attenuated but lacked sufficient immunogenicity to be considered effective vaccines. To better contrast the SA14-14-2 vaccine with its less-immunogenic counterparts, genetic diversity, ribavirin sensitivity, mouse virulence and mouse immunogenicity of the three vaccines were investigated. Next generation sequencing demonstrated that SA14-14-2 was significantly more diverse than both SA14-5-3 and SA14-2-8, and was slightly less diverse than WT SA14. Notably, WT SA14 had unpredictable levels of diversity across its genome whereas SA14-14-2 is highly diverse, but genetic diversity is not random, rather the virus only tolerates variability at certain residues. Using Ribavirin sensitivity in vitro, it was found that SA14-14-2 has a lower fidelity replication complex compared to SA14-5-3 and SA14-2-8. Mouse virulence studies showed that SA14-2-8 was the most virulent of the three vaccine strains while SA14-14-2 had the most favorable combination of safety (virulence) and immunogenicity for all vaccines tested. SA14-14-2 contains genetic diversity and sensitivity to the antiviral Ribavirin similar to WT parent SA14, and this genetic diversity likely explains the (1) differences in genomic sequences reported for SA14-14-2 and (2) the encoding of major attenuation determinants by the viral E protein.
Pubmed ID: 34475404
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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