ArticleMolecular therapy : the journal of the American Society of Gene Therapy2026
Transgene sequence codon optimization and composition determines replication competence of self-amplifying RNA.
Article in Molecular therapy : the journal of the American Society of Gene Therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Self-amplifying RNA (saRNA) is an emerging RNA therapeutic modality that can facilitate higher magnitude and more durable protein expression at substantially lower doses than nonreplicating mRNA. Unlike conventional messenger RNA (mRNA), alphavirus-derived saRNA must support a replicase-driven RNA amplification step in addition to translation, raising the possibility that transgene coding sequences impose sequence-level constraints on replication. Here, saRNA replication was found to be dependent on the codon composition of the transgene; multiple therapeutic transgenes were replication defective despite an intact Venezuelan Equine Encephalitis Virus (VEEV)-derived saRNA backbone. Replication defects were rescued by synonymous codon re-optimization of the same transgenes, indicating that nucleotide-level features of the coding sequence, rather than the encoded protein, govern replication competence. Comparative compositional analyses identified a distinct signature associated with productive replication, characterized by elevated GC (>53%) and GC3 (>63%) content, higher codon adaptation to human (>0.75), and reduced UpA (<43/kb) and UpU (<41/kb) dinucleotide density. Moreover, deliberate compositional perturbation of an otherwise replication-competent transgene shifted these features and abolished replication, supporting a causal and combinatorial role for sequence composition in defining saRNA replication outcome. These findings define an underappreciated constraint in saRNA therapeutics and motivate saRNA-specific payload design frameworks that incorporate alphavirus-associated compositional biases during transgene sequence optimization.
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