bioRxivSynthetic BiologyPreprint
Airway applied IVT mRNA vaccine needs specific sequence design and high standard purification that removes devastating dsRNA contaminant
Jingjing ZhangiD, Chao LiiD, Yuheng LiuiD, Rui Liao, Dian HeiD, Lifeng XuiD, Tingting CheniD, Qin Xiao, Mingxing Luo, Yang CheniD, Yali LiiD, Huaxing Zhu, Joseph RoseneckeriD, Xiaoyan DingiD, Shuchen PeiiD, Shan GuaniD
Abstract
The development of next-generation mucosal mRNA vaccines is promising but extremely challenging. Major efforts have been focused on optimizing delivery systems, whereas it is still unknown whether the intrinsic quality of IVT mRNA significantly impacts the potency of airway inoculated mRNA vaccines. Here, we systematically demonstrate the mucosal mRNA vaccine requires a higher standard of purification and tailor-designed sequence to fulfil its potency compared to the parenteral route inoculated counterpart. We found double strand RNA (dsRNA) contaminants are prone to trigger innate immunoreaction in the airway that activates the mRNA degradation mechanism, thereby diminishing the mRNA expression and subsequent antigen-specific immune responses. To address these challenges, we developed a strategy that combines optimized untranslated regions (UTRs) screened from endogenous genes of pulmonary cells with affinity chromatography-based purification which removes almost all the dsRNA contaminants. The optimized mRNA administered via the airway route not only demonstrates superior protein expression (30-fold increase) and reduces inflammation in the lung, but also promotes robust immunity comprising significantly elevated systemic, cellular, and mucosal immune responses, which is in stark contrast to intramuscular injected counterpart that displays less pronounced benefits. Our findings offer new insight into the development of mucosal mRNA therapeutics from an overlooked but crucial perspective of optimizing mRNA components.
- organism: human
- assay: multiome
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 1
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