Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of familial Parkinson's disease. We found LRRK2 to be degraded in lysosomes by chaperone-mediated autophagy (CMA), whereas the most common pathogenic mutant form of LRRK2, G2019S, was poorly degraded by this pathway. In contrast to the behavior of typical CMA substrates, lysosomal binding of both wild-type and several pathogenic mutant LRRK2 proteins was enhanced in the presence of other CMA substrates, which interfered with the organization of the CMA translocation complex, resulting in defective CMA. Cells responded to such LRRK2-mediated CMA compromise by increasing levels of the CMA lysosomal receptor, as seen in neuronal cultures and brains of LRRK2 transgenic mice, induced pluripotent stem cell-derived dopaminergic neurons and brains of Parkinson's disease patients with LRRK2 mutations. This newly described LRRK2 self-perpetuating inhibitory effect on CMA could underlie toxicity in Parkinson's disease by compromising the degradation of α-synuclein, another Parkinson's disease-related protein degraded by this pathway.

A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.

Mutations in PTEN-induced putative kinase 1 (PINK1) are a cause of autosomal recessive familial Parkinson's disease (PD). Efforts in deducing the PINK1 signaling pathway have been hindered by controversy around its subcellular and submitochondrial localization and the authenticity of its reported substrates. We show here that this mitochondrial protein exhibits a topology in which the kinase domain faces the cytoplasm and the N-terminal tail is inside the mitochondria. Although deletion of the transmembrane domain disrupts this topology, common PD-linked PINK1 mutations do not. These results are critical in rectifying the location and orientation of PINK1 in mitochondria, and they should help decipher its normal physiological function and potential pathogenic role in PD.