Theme 2: Cellular and genetic models

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Theme 2 Lead: Richard Wade-Martins

In Theme 2 we will build on our knowledge of the genetics underlying Parkinson's to understand the molecular mechanisms of disease using human neuronal models. The genetic variations predisposing to PD are numerous, and there is growing evidence that many of the known variants act in common biological pathways and processes. We will use unique approaches developed by members of the OPDC to identify such pathways and the convergent action by which genetic variants may cause disease, both individually and cumulatively.

A major strength in the OPDC program lies in our ability to generate hypotheses from bioinformatics pathway analysis to predict potential therapeutic targets which can be tested in patient neurons. The OPDC has generated over 100 induced pluripotent stem cell (iPSC) lines from a range of subjects, including monogenic and idiopathic PD, and healthy controls. We have established a differentiation platform to generate functional and highly physiological midbrain dopaminergic neurons from iPSC-lines. This work provides a neurophysiologically-defined model of previously inaccessible vulnerable SNc dopaminergic neurons to bridge the gap between clinical PD and animal models.

• Building on our exome resequencing, SNP genotyping and pathway-based analyses, in 2015-2020 we will use genetic modelling of extreme clinical phenotypes in OPDC and PRoBaND cohorts to increase power to detect variants predisposing to PD occurrence and subtype.

• Deep phenotyping of our rich resource of Parkinson's patient iPSC DA neurons generated by OPDC and our partner StemBANCC will provide rationale for target discovery assay design.

• Phenotypes previously revealed in GBA, LRRK2 and sporadic Parkinson's will be used in highthroughput phenotype-driven screens to identify novel targets and repositionable drugs.

3 iPSC images
1. Neuronal sphere undergoing differentiation (Hugo Fernandes). 2. In vitro neural rosettes which resemble the neural tube (Hugo Fernandes). 3. Control iPS-derived DAn (Federico Zambon)