Funded Studies
Study Rationale: Lysosomes are structures that help clear cells of molecular debris, and lysosomal dysfunction has been linked to neurodegeneration in Parkinson’s disease (PD). In this study, we will use powerful computational methods to identify and validate biomarkers of lysosomal dysfunction from databases that include information collected from patient blood samples. The identified biomarkers will enable detection of lysosomal dysfunction in PD with potential for use also in other lysosomal disorders.
Hypothesis: We hypothesize that we can develop a test for detecting biomarkers of lysosomal dysfunction from blood samples of people with PD, an approach that could allow clinicians to monitor disease progression and facilitate early detection.
Study Design: Using tools for predicting protein function, we will analyze existing databases to generate a database of lysosomal proteins. We will measure the proteins’ post-translational modifications, particularly those that are abundant in lysosomes. Features that are frequently altered in PD samples will be used in signatures that will then be validated in an independent cohort. We will also explore the power of convolutional neural networks for pattern recognition from this data.
Impact on Diagnosis/Treatment of Parkinson’s disease: If successful, this study will produce a sensitive proteomic screen for detecting lysosome dysfunction in a blood sample. Such a tool could bolster the success of clinical trials and assist clinicians in treatment planning.
Next Steps for Development: The next steps would involve partnering with interested drug companies for the purpose of clinical trials.