The RETORNA team aims to understand, diagnose, and treat retinal diseases through a multidisciplinary approach combining molecular biology, stem cell technology, gene editing, computational modeling, and health economics. Key efforts focus on retinitis pigmentosa (RP), age-related macular degeneration (AMD) or diabetic retinopathy (DR), among other disorders, using in silico and in vitro models, retinal organoids, and animal models.
The consortium explores the roles of microRNAs, long non-coding RNAs, and extracellular vesicles in retinal disease mechanisms, while developing novel RNA-based therapies, including aptamers and circular RNAs targeting pathways like TrkB, PHD2, and AT1R-AngII. CRISPR and prime editing technologies are employed to correct mutations in inherited retinal disorders. Advanced computational tools, including AI-driven aptamer design and deep learning simulations, support therapeutic development. A comprehensive economic analysis underscores the relevance and potential impact of RNA therapies.
In the first periodic report, the consortium created diabetic retinopathy models using iPS cells, confirmed by analyzing the structure and key molecular markers. High glucose exposure led to inflammation, oxidative stress, and cell loss, mimicking patient conditions. The TrkB-aptamer was cloned into the ToRNAdo system for circular RNA expression, achieving high expression in hTERT-RPE1 cells and activating the Akt-pathway.
Using CRISPR/Cas9, homozygous PHD2 knockouts were generated in CRB1 patient-derived hiPSC lines and differentiated into retinal organoids. CRISPR-based tools were developed to correct the Prph2 c.584G>T mutation causing Central Areolar Choroidal Dystrophy (CACD). The effectiveness of CRISPR/Cas9 and prime editing was evaluated in mouse models and human cells. Retinal morphology and physiology analyses in mouse models identified neuropathological events similar to human CACD. mRNA and miRNA profiling revealed key deregulated genes in retinal degeneration. miRNA and lncRNA expression profiles were analyzed in retinitis pigmentosa models, expanding to other retinal models.
An oligonucleotide RNA ligand library was constructed, and an AI-driven algorithm was specifically developed to enable aptamer selection. To support this process, several custom codes and computational algorithms were developed for this aim. Consequently, promising RNA aptamers targeting the AT1R-AngII system were successfully designed and identified. In parallel, a deep learning based model was developed and integrated in physics-based molecular simulations to enhance efficiency and scalability.
Tissue samples from rd10 mice were collected for apoptosis studies and extracellular vesicle extraction, followed by miRNOME analysis. iPS cell lines from RP patients were used to generate organoids for comparative studies. The impact of plasma-derived extracellular vesicles on angiogenesis and epithelial/endothelial dysfunction was investigated. A protocol for isolating RPE cells from porcine eyes was developed, enhancing the understanding of vascular and epithelial barrier function.
These studies are complemented by a systematic literature review examining the economic costs of retinal diseases treatable with RNA therapies. Together, these efforts contribute to a deeper understanding of retinal pathologies and support the development of innovative RNA-based treatments.
