Parkinson’s disease (PD) is the fastest growing neurodegenerative disorder worldwide, but its underlying biology is complex and heterogeneous. While targeted treatments are beginning to emerge, there remains an urgent need to stratify patients into biological subgroups to ensure that disease-modifying therapies can be delivered to the right people at the right time. Biomarkers that define these subgroups are therefore central to the future of precision medicine in PD.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene account for approximately 1–4% of PD cases globally, making them one of the most common known genetic contributors. Importantly, LRRK2 biology is likely relevant to a much broader patient population. Evidence from genetics and cellular models shows that the pathway can also be activated independently of direct LRRK2 mutations, for example via the VPS35 D620N mutation in the retromer complex (PMID: 29743203). This highlights the need for reliable biomarkers of LRRK2 pathway activity that could guide treatment not only in mutation carriers, but also in idiopathic PD patients who share this biology.

This PhD studentship is embedded within the translational pillar of the Michael J. Fox Foundation–funded LRRK2 Investigative Therapeutic Exchange (LITE) initiative - a landmark international collaboration designed to accelerate the development of LRRK2-targeted therapies and biomarkers. The LITE study offers an unprecedented cohort of deeply phenotyped LRRK2 mutation carriers and related loci, with exceptional biosample depth that includes clinical and imaging phenotyping, genetic data, and mass spectrometry–based profiling of urine and plasma. Critically, LITE also incorporates innovative “tagless” lysosomal immunoprecipitations (LysoIP), enabling immunoenrichment of lysosomes for multimodal mass spectrometry profiling of proteins, lipids, and metabolites (PMID: 39724071). This combination of clinical depth and molecular resolution provides a unique opportunity to uncover robust biological signatures of LRRK2 dysfunction.

The biological rationale for this approach is strong. Proximal in its pathway, LRRK2 hyperphosphorylates Rab substrates (PMID: 36007011, PMID: 34125248, PMID: 26824392) disrupting vesicular trafficking. Further downstream, it perturbs lysosomal function, leading to increased release of negatively charged phospholipids - likely derived from the inner membranes of endolysosomal exosomes - into urine (PMID: 37015928). These measurable changes in human biosamples provide tangible entry points for biomarker discovery.

In this project, emerging AI technologies such as unsupervised learning and computational and experimental approaches will be combined to interrogate the LITE datasets, integrating genetics, large scale mass-spectrometry datasets and clinical phenotyping to identify wet biomarker signatures of LRRK2 driven biology. Identified biomarker signatures in LRRK2 mutation carriers will then be extended to idiopathic PD, with the aim of stratifying patients who might benefit from LRRK2-targeted therapies.

Computational discoveries will be validated experimentally in the laboratory using patient-derived samples and model systems. Approaches will include CRISPR–Cas9 genome editing, knock-in mouse models, immunoblotting, cell-based assays, and ultra-sensitive mass spectrometry, linking computational predictions to biological mechanisms.

The studentship offers comprehensive, cross-disciplinary training at the interface of AI, computational biology, and experimental neuroscience. Supervision will be provided by Dr Esther Sammler (translational clinician scientist), Professor Dario Alessi (kinase signaling and cell biology), and Dr Hajk Drost (computational biology) within the collaborative environment of the MRC Protein Phosphorylation and Ubiquitylation Unit (MRC PPU) at the University of Dundee.

Applicants should have a strong academic background in biomedical sciences, bioinformatics, computational biology, or a related field, and a genuine interest in neurodegeneration. Curiosity about how AI can accelerate biomarker discovery and therapeutic development in Parkinson’s disease will be essential. For informal enquiries, please contact Dr Esther Sammler (e.m.sammler@dundee.ac.uk).

https://www.michaeljfox.org/grant/lrrk2-investigative-therapeutics-exchange-lite