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A toolbox to delineate the genetic architecture of Parkinson’s disease

Author: Guochang Lyu


You may have known, as a common neurodegenerative disorder, Parkinson's disease (PD) is mainly affecting the population over 65 years of age and unfortunately, the prevalence of PD is likely to increase substantially over the next few decades. Among all the known pathological features, the selective loss of midbrain dopaminergic (mDA) neurons can be spotted in most patient samples despite the diversity of clinical symptoms, serving as one crucial clue to investigate the mechanisms that underlie the initiation and progression of PD.


In fact, the PD research community has been investigating how different risk factors contribute to vulnerabilities of mDA neurons for many decades; all the time, genetic variants, including mutations, are believed to play a critical role in this process. So far, we have accumulated hundreds of candidates with different potentials and also found that many of them are sharing the same molecular and cellular pathway in PD development. This inter-connected genetic architecture of PD suggests promising applications of transferring discoveries from one mutant to another, which could boost the identification of therapeutic targets and biomarkers.


The advent of induced pluripotent stem cell (iPSC) technologies has enabled us to derive mDA neurons from patients carrying genetic risk variants, facilitating us to outline features of PD within a petri dish. In ASCTN training, we have developed a novel protocol to differentiate neurons highly resemble their counterparts in real brains; moreover, as you have heard from Clelia, we intend to bring other types of cells to mDA neurons in the Brain-on-chip models to mimic local cellular interactions. Friend or foe? It will be intriguing to see what insights this combination will bring to us, and how far we will characterize other cell components in the PD microenvironment which cannot be recapitulated in any other unpatterned systems. Remarkably, even cultured in the same condition, cells can still behave in different ways and it is possible that only a small fraction and/or specific stages of them are responsible for the PD development. In this light, we will apply the cutting-edge single-cell technologies to resolve molecular differences that were too tiny to capture before. Supposedly, it will be much more possible to identify gene candidates that are implicated in PD even though they are expressed at relatively lower levels and restricted to certain groups of cells.


Sharp tools make good work; through the combination of iPSC and single-cell technologies, we aim to investigate the molecular phenotype of distinct genetic forms of PD. Hopefully, discoveries from this pipeline will serve as the basis for precision medicine that maximizes the identification of individuals at risk and the efficacy of specific therapeutic regimens in the future.



References and links:

- Aguet, F., Brown, A., Castel, S., Davis, J., He, Y., Jo, B., Mohammadi, P., Park, Y., Parsana, P., Segrè, A., et al. (2017). Genetic effects on gene expression across human tissues. Nature 550, 204–213.

- Blauwendraat, C., Nalls, M.A., and Singleton, A.B. (2019). The genetic architecture of Parkinson’s disease. Lancet Neurology 19, 170–178.

- Lang, C., Campbell, K.R., Ryan, B.J., Carling, P., Attar, M., Vowles, J., Perestenko, O.V., Bowden, R., Baig, F., Kasten, M., et al. (2019). Single-Cell Sequencing of iPSC-Dopamine Neurons Reconstructs Disease Progression and Identifies HDAC4 as a Regulator of Parkinson Cell Phenotypes. Cell Stem Cell 24, 93-106.e6.

- Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J.Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019). Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337.

- Nalls, M.A., Blauwendraat, C., Vallerga, C.L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D.A., Noyce, A.J., Xue, A., et al. (2019). Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurology 18, 1091–1102.

- Tysnes, O.-B., and Storstein, A. (2017). Epidemiology of Parkinson’s disease. J Neural Transm Vienna Austria 1996 124, 901–905.

- Schapira, A.H.V., Chaudhuri, K.R., and Jenner, P. (2017). Non-motor features of Parkinson disease. Nat Rev Neurosci 18, 435–450.

- https://scienceofparkinsons.com/parkinsons-disease/

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