bioRxivImmunology
Unleashing the Immune Arsenal: Development of Broad-spectrum Multiepitope Bluetongue Vaccine Targeting Conserved T Cell Epitopes of Structural Proteins
H. B. Kolla, A. Kumar, M. Dutt, R. H. Nanjunadappa, K. P. Singh, P. P. C. Mertens, D. Kelvin, C. S. Umeshappa
Abstract
Bluetongue (BT) is a severe arboviral disease affecting sheep, cows, and other wild ruminants, caused by the Bluetongue virus (BTV). The virus has evolved into over 32 serotypes, rendering existing vaccines less effective. While the structural proteins of this virus represent promising targets for vaccine development, they unfortunately exhibit high amino acid polymorphism and are laden with numerous inhibitory epitopes. Structural proteins such as VP1 and VP7 are highly conserved and may contain epitopes capable of triggering cross-reactive cell-mediated immunity (CMI). In this study, we identified highly conserved MHC-I and -II-restricted T cell epitopes within VP1, VP5, and VP7 BTV proteins and developed an effective in silico-immuno-informatics-based broad-spectrum BT multiepitope vaccine for bovine and laboratory mouse systems. The conserved epitopes utilized in the vaccines are highly antigenic, non-allergenic, non-toxic, and capable of inducing IFN-{gamma} (only CD4+ T cell epitopes). Both mouse and bovine vaccines were tethered with Toll-like receptor (TLR)-4-agonist adjuvants, beta-defensin 2, and the 50s ribosomal unit to stimulate innate immunity for CMI development. Protein-protein docking analysis revealed strong binding affinities, while extensive 100-nanosecond molecular dynamics simulations indicated stable complexes between the vaccine structures and TLR4. Vaccination simulation studies demonstrated their ability to trigger proinflammatory responses. Therefore, these novel vaccine designs necessitate further exploration through wet lab experiments to evaluate their immunogenicity, safety, and effectiveness for practical deployment in livestock.
- organism: mouse
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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