Dani Flinkman defended his PhD entitled “Proteomics analysis of Parkinson’s disease mechanism and biomarkers” on May 17th. The opponent was Daniel Otzen from Aarhus University.

Summary of the dissertation

Parkinson’s disease (PD) is the second most common neurodegenerative disease without a cure and a single known cause. The disease is characterized by the loss of the dopaminergic neurons in the mid brain, and causes progressive motor symptoms as well non motor symptoms such as depression and defects in smelling ability. Although there is no single cause to the disease, up to ~15% of sporadic PD patients have monogenic mutations associated with PD. Roughly 3% of individuals suffering from PD have a mutation in leucine-rich repeat kinase 2, causing increased kinase activity and the most common LRRK2 mutation is glycine 2019 to serine (G2019S). PD patients with LRRK2 have almost indistinguishable symptoms to PD cases and LRRK2 activation is present in sporadic PD. Proteins rarely function alone, and to study complex diseases it becomes important to understand how proteins function together to create a phenotype. Mass-spectrometry based proteomics is the modern field of science suitable for studying protein networks.

The work presented in the thesis presented a novel function for LRRK2 in suppressing protein synthesis, and data analysis tool for studying kinase function from proteomics data. We found that LRRK2 phosphorylates ribosomal sub-fraction of the rodent brain and in rodent model of PD many important protein synthesis arrest checkpoints are turned on. We also found that LRRK2 kinase activity in neurons and PD patient fibroblasts in both sporadic and G2019S form of PD functions to suppress protein synthesis. To gain understanding what are the proteins that are affected by LRRK2 mediated protein synthesis suppression, we performed unbiased newly synthesized proteome screen to identify, and targeted proteomic experiment to validate the changes in sporadic and LRRK2 G2019S patient fibroblasts. We observed that in both sporadic and G2019S PD proteins related to proteostasis regulation are downregulated. The findings shed light on possible key players disturbed in PD and potential novel biomarkers of PD. Finally to understand complex kinase signaling we developed an automated pipeline to study proteomic phosphorylation data: PhosPiR. PhosPiR performs data quality control, statistical analysis, functional enrichment analysis and kinase pathway and activation analysis from the complex phosphorylation data. This simplifies complex data analysis and allows a non-bioinformatician to analyse the phosphorylation data coming from proteomics experiments