"We aim to understand the earliest molecular events underlying Parkinson’s disease, how and why patients vary in their disease presentations and progression, and to deliver the cellular assays and drug targets for future therapeutics." Caleb Webber
UK DRI Director of Data Science & Group Leader
Prof Caleb Webber is combining state-of-the-art stem cell models with bioinformatics techniques to boost our understanding of the biological mechanisms underlying Parkinson’s disease. He is also aiming to identify new risk genes and investigate how these impact on the function of neurons. The team are also using advanced computational methods to investigate how genetic risk factors can influence a varying symptoms presented by patients. They hope to pinpoint key biological pathways that could be targeted with new drugs to prevent or treat Parkinson’s disease– and potentially other diseases with overlapping molecular causes.
In 2022, Prof Webber took on the additional role of UK DRI Director of Data Science. In this role, he is responsible for developing and implementing an Institute-wide strategy to harness the power of data resources and tools in our mission to find new treatments and technologies for dementia.
1. At a glance
State-of-the-art modelling of Parkinson’s disease
Approximately 145,000 people in the UK are living with Parkinson’s disease. Its symptoms vary widely from person to person, but often include tremors, stiffness and slow movement. These appear when the brain can’t make enough of a chemical called dopamine and gradually get worse over time as more and more dopamine-producing neurons die.
Although the precise causes of Parkinson’s aren’t fully understood, a complex combination of genetic, lifestyle and environmental factors are involved. Using a relatively new approach called genome-wide association studies, scientists have so far identified a number of genetic variations that can influence risk but don’t guarantee that a person will develop the condition.
Prof Caleb Webber is combining state-of-the-art stem cell models with bioinformatics techniques to boost our understanding of the biological mechanisms underlying Parkinson’s disease. He is aiming to identify new risk genes and investigate how these impact on the function of neurons. The team are also using advanced computational methods to investigate how genetic risk factors can influence varying symptoms presented by patients. They hope to pinpoint key biological pathways that could be targeted with new drugs to prevent or treat Parkinson’s disease– and potentially other diseases with overlapping molecular causes.
2. Scientific goals
This UK DRI programme from Prof Caleb Webber is focused on Parkinson’s disease (PD) and split into three key work streams:
(1) Pseudotemporal modelling of disease mechanisms.
Revealing the causal chain of events behind disease-associated variation can yield key insights and offer multiple opportunities to therapeutically intervene. Prof Caleb Webber and his team are applying state-of-the-art single cell pseudotemporal approaches to stem cell models carrying PD-implicated mutations in the genes, SNCA and GBA. The long-term goal is to identify the causal networks and pathways that lead from genetic or environmental insult through to face-valid cellular phenotypes. Their immediate objectives for this stream are to create an iPSC reporter line (LMX1a) that will enable the sorting of neurons without prior fixing, removing heterogeneity from downstream experiments without limiting their study.
(2) Identifying new disease risk genes.
The team is analysing PD genome wide association (GWA) risk loci to identify genes that underlie the risk in these regions. They have made progress in the informatic prioritisation of these genes and will begin rapid modulation of these genes once the LMX1a-reporter line is in place. The long-term objectives of identifying risk mechanisms will involve generating cell-type specific chromatin accessibility maps in iPSC models and nigral brain tissue, and then combining this with fine mapping and CRISPR to identify the risk-modulating genetic variants and their target genes.
(3) Understanding patient-phenotypic heterogeneity.
The team’s new multiple phenotype mixed model approach has identified three axes of variation which have now been re-identified (replicated) in all three deeply-phenotyped PD cohorts in the world. The first two axes alone account for 75% of all clinical observations, and the quantitative traits these axes provide have identified an overlap between the genetic influences of coronary artery disease and Axis 1, and schizophrenia and Axis 2.
Prof Webber’s current research goal is to extend the approach to the longitudinal data, which is already looking very promising. This work is delivering the underlying nature of PD patient phenotypic variation, providing new routes into the influencing biology, and new opportunities to intervene, most rapidly by enabling import of knowledge through shared risk. Furthermore, identifying how PD patients vary is enabling them to devise how deeply-phenotyped cohorts are best phenotyped.
3. Team members
Dr Viola Volpato (Postdoctoral Researcher)
Dr Emeka Uzochukwu (Postdoctoral Researcher)
Dr Jimena Monzon Sandoval (Staff Scientist)
Dr Michal Rokicki (Postdoctoral Researcher)
Agata Zaremba (PhD Student)
Brier Rigby Dames (PhD Student)
Within UK DRI:
- Dr Cynthia Sandor, UK DRI at Cardiff
- Prof Julie Williams, UK DRI at Cardiff
- Prof Valentina Escott-Price, UK DRI at Cardiff
- Prof Phil Taylor, UK DRI at Cardiff
Beyond UK DRI:
- Prof Meng Li, Cardiff University
- The Oxford Parkinson’s Disease Centre (Webber; Genomics Informatics Lead)
IMI IM2PACT Blood-Brain Barrier Transport Programme (Webber; Genomics Lead)
- ARUK Oxford Drug Discovery Institute
Parkinson’s disease, Alzheimer’s disease, iPSC models, bioinformatics, genomics, functional genomics, transcriptomics, single cell analyses
Bioinformatics and network approaches, single cell transcriptomics, GWAS, iPSC modelling
7. Key publications
Single-Cell Sequencing of iPSC-Dopamine Neurons Reconstructs Disease Progression and Identifies HDAC4 as a Regulator of Parkinson Cell Phenotypes. Lang C, Campbell KR, Ryan BJ, Carling P, Attar M, Vowles J, Perestenko OV, Bowden R, Baig F, Kasten M, Hu MT, Cowley SA, Webber C*, Wade-Martins R*. Cell Stem Cell. 2019 Jan 3;24(1):93-106.e6. doi: 10.1016/j.stem.2018.10.023. Epub 2018 Nov 29. PMID: 30503143
Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study. Volpato V, Smith J, Sandor C, Ried JS, Baud A, Handel A, Newey SE, Wessely F, Attar M, Whiteley E, Chintawar S, Verheyen A, Barta T, Lako M, Armstrong L, Muschet C, Artati A, Cusulin C, Christensen K, Patsch C, Sharma E, Nicod J, Brownjohn P, Stubbs V, Heywood WE, Gissen P, De Filippis R, Janssen K, Reinhardt P, Adamski J, Royaux I, Peeters PJ, Terstappen GC, Graf M, Livesey FJ, Akerman CJ, Mills K, Bowden R, Nicholson G, Webber C, Cader MZ, Lakics V. Stem Cell Reports. 2018 Oct 9;11(4):897-911. doi:10.1016/j.stemcr.2018.08.013. Epub 2018 Sep 20. PMID: 30245212
Transcriptomic profiling of purified patient-derived dopamine neurons identifies convergent perturbations and therapeutics for Parkinson's disease. Sandor C, Robertson P, Lang C, Heger A, Booth H, Vowles J, Witty L, Bowden R, Hu M, Cowley SA, Wade-Martins R, Webber C. Hum Mol Genet. 2017 Feb 1;26(3):552-566. doi: 10.1093/hmg/ddw412. PMID: 28096185