Comprehensive characterization and translational implications of the GalnsR384C mouse model of Mucopolysaccharidosis IVA.
Mucopolysaccharidosis IVA (MPS IVA) is a lysosomal storage disorder caused by a deficiency of N-acetylgalactosamine-6-sulfate sulfatase (GALNS), leading to progressive accumulation of keratan sulfate (KS) and chondroitin-6-sulfate (C6S) and resulting in systemic skeletal dysplasia. Severe, early-onset disease is frequently associated with destabilizing structural missense variants, including p.R386C. To model a loss-of-function missense variant associated with severe MPS IVA, we generated a GalnsR384C knock-in mouse, the murine ortholog of the most common human variant, p.R386C. Biochemical, histological, and skeletal phenotypes were evaluated across multiple tissues, and bone microarchitecture was assessed using microcomputed tomography (micro-CT). Genomic and biochemical assays were performed to assess allelic integrity, and principal component analysis (PCA) was used to integrate biochemical and structural parameters. GalnsR384C mice exhibited significantly reduced GALNS activity and elevated KS levels across various tissues. Histological examination revealed considerable vacuolization in cartilage and cardiac valves, while micro-CT illustrated altered bone microarchitecture consistent with disrupted endochondral ossification. During allele validation, a secondary missense variant (p.R384Y) was identified and characterized as a comparative model that led to defective GALNS activity, substrate accumulation, and analogous skeletal and cardiovascular pathology. PCA demonstrated clear differentiation between WT and mutant groups, with considerable multivariate overlap observed between GalnsR384C and GalnsR384Y mice. In conclusion, GalnsR384C and GalnsR384Y mice recapitulate key biochemical, skeletal, and histopathological features of MPS IVA and provide well-characterized murine models of severe GALNS loss-of-function resulting from clinically relevant missense variants. Rigorous genomic validation underscores the importance of careful allele-level characterization during genome-editing-based model generation.