LRRK2-mediated inflammation and Parkinson’s disease

Parkinson’s disease (PD) is the most common movement disorder worldwide. Approximately 10% of all PD is inherited through families, with the most prevalent autosomal-dominantly inherited mutations for PD found in leucine-rich repeat kinase 2 (LRRK2). The current thesis aimed to determine how one of the most common pathogenic LRRK2 mutations, G2019S, affects TLR-mediated inflammatory signalling. Unique biospecimens from PD patients and asymptomatic people with and without the LRRK2 G2019S mutation were used to determine the effect of the LRRK2 mutation and if serum cytokines have utility in the prediction of PD progression. State-of-the-art induced pluripotent stem (IPS) cell models were used to determine the effect of the LRRK2 mutation on TLR-mediated inflammation in differentiation monocytes, and CRIPSR/Cas9 genome editing technology were used to generate fluorescent LRRK2 cell lines to monitor LRRK2 trafficking in inflammatory conditions. It was first concluded that levels of inflammatory cytokines were largely similar between PD patients with and without the LRRK2 G2019S mutation. The exception was PDGF, which increased in LRRK2 G2019S patients compared to idiopathic PD. Intriguingly, inflammatory cytokines were stable in individuals over a 1-year time frame and may have utility for aiding prediction of clinical PD progression using machine learning models. It was observed that the presence of the G2019S potentiates TLR-mediated inflammation, but this was not prevented with LRRK2 inhibitors and potentiated inflammation via LRRK2 G2019S is independent of its kinase activity. In conclusion, this thesis provides new results on the effects of LRRK2 G2019S mutation on inflammation and provides insight into the application of machine learning models in the prediction of clinical disease progression using peripheral cytokines