bioRxivBioengineeringPreprint
Engineered mRNA nanostructures expand the design space of mRNA therapeutics through programmable protein expression and immune stimulation
Mark Knappenberger, Skylar J.W. HenryiD, Archana KiklaiD, Matthew Sample, Olivia Roy, Erik PoppletoniD, Nicholas StephanopoulosiD, Michael P. GustafsoniD, Karen S. AndersoniD, Petr ŠulciD
Abstract
Messenger RNA (mRNA) therapeutics have transformed vaccination and protein replacement strategies, yet efforts to improve their performance have focused largely on sequence engineering, nucleotide modification, and delivery vehicles. Here we show that mRNA function can be controlled by rational design of higher-order RNA architectures. We develop self-assembling mRNA origami (mRNA-OG), a class of unimolecular RNA nanostructures that encode protein-coding sequences within higher-order, programmable, and compact nucleic acid architectures. Using computational design and experimental validation, we demonstrate that mRNA-OG folds into well-defined nanostructures while remaining translationally competent in mammalian cells. Although folded mRNA-OG recruits ribosomes comparably to unfolded constructs, it produces lower protein output, indicating that RNA architecture can directly influence translational efficiency. The compact geometry of mRNA-OG also enhances encapsulation by cationic lipid delivery systems, suggesting a structural route to improved cargo packaging. Beyond its effects on translation, mRNA architecture modulates innate immune recognition. In primary human dendritic cells, folded and unfolded mRNA-OG elicit distinct cytokine programs and differential activation of stress-response pathways, including a modest induction of the integrated stress response that is not fully explained by canonical PKR signaling. Our results establish programmable structure as a new design parameter for mRNA therapeutics that can affect its functionality, delivery properties, and immune sensing.
- organism: human
- assay: multiome
- research focus: drug response / drug discovery
- research focus: protein structure prediction
- research focus: protein design
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 0
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