AI-driven therapeutic antisense oligonucleotide for processing-deficient progeroid laminopathies

Progeroid laminopathies (PLs), including Hutchinson-Gilford progeria syndrome (HGPS), are rare premature aging disorders in which cardiovascular complications drive early mortality. Antisense oligonucleotides (ASOs) represent a promising therapeutic strategy, yet optimal design principles and their impact on cardiovascular pathology remain insufficiently defined.

We developed an AI-driven pipeline to design ASOs targeting the 3'UTR of LMNA transcripts to suppress processing-deficient pathogenic lamin A isoforms. Lead candidates were evaluated in patient-derived induced pluripotent stem cell-derived cardiomyocytes (PL-iCMs), 3D cardiac organoids, and LMNA transgenic mouse models. Efficacy, toxicity, and systemic transcriptional responses were assessed using molecular, histological, and serum biochemical analyses.

The optimized ASO, LM2556, selectively reduced progerin and farnesylated pre-lamin A expression while preserving lamin C. LM2556 mitigated cellular senescence and improved structural and functional phenotypes in PL-iCMs and cardiac organoids. In vivo, LM2556 decreased pathogenic lamin A isoforms across multiple tissues without evidence of hepatotoxicity or nephrotoxicity. Long-term administration ameliorated progeroid features, improved cardiovascular pathology, extended median lifespan by 82.86%, and enhanced overall healthspan in LMNA transgenic mice.

These findings establish an AI-based framework for therapeutic ASO design and provide proof-of-concept evidence that targeting the 3'UTR of LMNA transcripts can effectively suppress pathogenic lamin A isoforms and ameliorate systemic progeroid phenotypes.

This work was funded by the National Natural Science Foundation of China (82450116 and 82502251).