bioRxivImmunologyPreprint
Nodamuravirus protein B2 boosts self-amplifying mRNA efficacy by overcoming innate immune barriers.
Raul Y. Sanchez-DavidiD, Hoang Duy Le, Josephine NemegeeriD, Amanda Costa GonçalvesiD, Andres MeritsiD, Pierre V. MaillardiD
Abstract
Self-amplifying RNA (sa-RNA) technology is a promising strategy for vaccine design, as its intracellular replication boosts transgene expression and provides self-adjuvanticity. However, sa-RNA efficiency is limited by innate immune responses triggered by the presence of intracellular double-stranded RNA (dsRNA). In vertebrates, differentiated cells mainly use type I interferon (IFN) system for protection against viruses, while stem cells rely on IFN-independent mechanisms such as antiviral RNA interference (RNAi). Here, we found that the efficiency of sa-RNAs based on chikungunya virus (CHIKV) or Venezuelan equine encephalitis virus (VEEV) genomes is enhanced when co-expressed in cis with the Nodamura virus (NoV) B2 protein, a viral suppressor of RNAi. In stem cells, NoV B2 prevents Dicer-mediated processing of dsRNA, while in somatic cells, it blocks the translation shutdown caused by protein kinase R (PKR), a key effector of the IFN system. Notably, NoV B2 does not interfere with IFN induction and signalling, preserving sa-RNAs self-adjuvant properties. Mechanistically, NoV B2 sequesters replication-derived dsRNA at the cell periphery, offering a novel strategy to boost sa-RNA efficiency without compromising its immune stimulatory properties. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/661928v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@4412aforg.highwire.dtl.DTLVardef@126d089org.highwire.dtl.DTLVardef@144dd38org.highwire.dtl.DTLVardef@179bac_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: metagenomics
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: host-pathogen & immune response
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 2
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