CLN1 disease is a fatal pediatric neurodegenerative disorder caused by autosomal recessive loss-of function mutations in the PPT1 gene. PPT1 encodes lysosomal enzyme palmitoyl protein thioesterase-1 (PPT1). PPT1 deficiency causes build-up of storage material in many cell populations, but for unknown reasons leads to particular devastation in the CNS. Previous therapeutic approaches in Ppt1-/- mice used AAV-mediated gene therapy to the brain and spinal cord to replace the missing enzyme. While this strategy significantly improved survival, Ppt1-/- mice still died prematurely. New preliminary data suggest CLN1 disease also causes profound pathological changes in the enteric nervous system (ENS) and bowel motility abnormalities. Gut dysfunction is also common in children with CLN1 disease. In this project we sought to characterize and measure ex vivo neurogenic motility patterns in the colon and distal small bowel of Ppt1-/- mice at an advanced disease stage using an organ bath apparatus.
The findings from this study are novel in three ways. Firstly, our findings represent the first reported motility defect in an animal model of CLN1 disease. Second, our findings suggest that disease progression in the enteric nervous system is sex-dependent. This finding is unique because sex has not been reported to influence brain and spinal cord symptom onset, therefore our data suggest that disease progression outside the CNS is different than that within. Lastly, our findings demonstrate region specific motility defects. This compelling finding has yet to be fully explored, but points to neuron subtype sensitivity in the ENS. Next, we plan to increase the number of mice evaluated. Later we hope to evaluate the ability of ENS-directed gene therapy to return normal contractile patterns.
Through this research experience, I solidified my independent research skills and gained new experiences in data analysis. The most difficult portion of this project was understanding the correct statistical tests to be completed and interpreting results correctly. The coursework I’d completed in data analysis strategies and methods and statistics gave me the opportunity to intersect and apply my classroom knowledge to a real-world dataset. Because of this opportunity, I feel well-prepared for post-graduate research.