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Lipid-nanoparticle-mediated base editing of the trabecular meshwork rescues glaucoma in vivo
Balasankara Reddy Kaipa, Linya Li, Prakadeeswari Gopalakrishnan, Samuel Du, Jiin Felgner, Krzysztof Palczewski, Philip Felgner, Gulab S. Zode
Balasankara Reddy Kaipa, Linya Li, Prakadeeswari Gopalakrishnan, Samuel Du, Jiin Felgner, Krzysztof Palczewski, Philip Felgner, Gulab S. Zode
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Research Article Genetics Ophthalmology

Lipid-nanoparticle-mediated base editing of the trabecular meshwork rescues glaucoma in vivo

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Abstract

Mutations in MYOC, the most common genetic cause of glaucoma, cause misfolded myocilin to accumulate in the endoplasmic reticulum (ER), leading to trabecular meshwork (TM) dysfunction, elevated intraocular pressure, and progressive vision loss. While gene editing offers curative potential, current delivery methods rely on viral vectors, which are limited by inflammation, off-target effects, and poor translatability. Here, we report a nonviral lipid nanoparticle (LNP) platform that enables selective in vivo delivery of mRNA encoding an adenine base editor and single guide RNA (LNP-ABE) to TM cells. A direct comparison of LNP-mCherry with lentiviral GFP revealed that LNPs outperform viral vectors, achieving markedly higher efficiency and greater selectivity for the TM without inducing ocular inflammation. In a Cre-inducible Tg.CreMYOCY437H glaucoma mouse model, LNP-Cre mRNA selectively induced mutant MYOC expression in the TM, faithfully recapitulating key disease features. A single administration of LNP-ABE achieved efficient on-target editing of mutant MYOC, reducing mutant myocilin protein by approximately 46%, decreasing aggregates, alleviating ER stress, and fully rescuing the glaucomatous phenotype in Tg.CreMYOCY437H mice. Importantly, no off-target editing or ocular toxicity was detected. These findings establish LNP-based mRNA delivery as a safe, efficient, and clinically translatable approach for TM-targeted genome editing with broad therapeutic potential in glaucoma.

Authors

Balasankara Reddy Kaipa, Linya Li, Prakadeeswari Gopalakrishnan, Samuel Du, Jiin Felgner, Krzysztof Palczewski, Philip Felgner, Gulab S. Zode

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