Optimization of CNGA3 Gene Therapy for Achromatopsia Using Stochastic Modeling and Sensitivity Analysis

Overview

Achromatopsia is an inherited retinal degeneration disorder caused by a mutation in the CNGA3 (Cyclic Nucleotide Gated Channel Subunit Alpha 3) gene, affecting every 1 in 30,000 people. Mutations in the CNGA3 cause myopia, nystagmus, and loss of photopic v ision. Achromatopsia gene therapy delivers a functional copy of the CNGA3 gene to the affected cone cells using adeno-associated viruses (AAVs). Stochastic models can be used to analyze the randomness in achromatopsia gene therapy, ensuring the ability to configure efficacy by modeling the sensitivities of the delivery efficiency and immune rejection. The stochastic model was developed to assess the variability of the CNGA3 gene therapy in photopic and scotopic vision. Pycharm, a Python programming software was used to study the stochastic and sensitivity models from the data. Based on the variability f rom the stochastic models, a sensitivity analysis was used to evaluate the impact of delivery efficiency and immune rejection probabilities on the sensitivity of scotopic and photopic vision. From the stochastic modeling, it was concluded that photopic vision has higher variability, which revealed that poor delivery efficiency was caused by the immune rejection of the delivery vectors rather than the biological component. Enhancing the efficiency of CNGA3 gene therapy can be achieved by utilizing immunosuppressive delivery vectors to mitigate vector -related immune responses. Stochastic modeling is valuable in pinpointing critical variables responsible for CNGA3 therapy’s su ccess and can help more than 200,000 individuals worldwide benefit from therapeutic CNGA3 gene therapy, enlisting them for absolute freedom. Assessing immune infiltration and finding potential prognostic factors in acral melanoma on the cell-specific transcriptional level Angela Wang Westtown School, West Chester, PA Acral melanoma (AM) is an aggressive melanoma subtype with high morbidity and mortality, disproportionately affecting individuals of Asian, African, and Hispanic descent. Despite its clinical significance, AM remains understudied due to its rarity and unde rrepresentation in large -scale studies. Understanding the tumor microenvironment (TME) is crucial for identifying prognostic factors and developing targeted therapies. This study uses both bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA- seq) to assess AM progression. I hypothesize that AM’s immune profile is distinct throughout developmental stages, and that novel prognostic markers identified are rel ated to immunosuppressive phenotypes of lymphocytes. RNA-seq data across four stages of AM progression were analyzed using GO and KEGG enrichment analyses, revealing that early-stage tumorigenesis is associated with cell proliferation and immune activation, while later stages involve extracellular matrix rem odeling and apoptotic pathways. TIMER analysis indicated a higher abundance of Bcells, CD4+ Tcells, and NK cells in early-stage AM, whereas CD8+ Tcells and M2 macrophages dominated later stages. ScRNA- seq data were clustered and annotated to identify im mune cell subtypes involved in AM progression. Survival analysis using the TCGA dataset identified potential immunoprognostic markers. FANK1 was linked to regulatory Tcells, suggesting a role in immunosuppression, while TP53INP1 was associated with B -cell differentiation, and USP3 showed correlations with Th17 cells and NK cells. Further experimental validation can be performed with these prognostic markers in AM progression. Puerto Rico