Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

iPSC modeling of young-onset Parkinson’s disease reveals a molecular signature of disease and novel therapeutic candidates

Nature Medicine volume 26, pages 289–299 (2020)Cite this article

Subjects

Abstract

Young-onset Parkinson’s disease (YOPD), defined by onset at <50 years, accounts for approximately 10% of all Parkinson’s disease cases and, while some cases are associated with known genetic mutations, most are not. Here induced pluripotent stem cells were generated from control individuals and from patients with YOPD with no known mutations. Following differentiation into cultures containing dopamine neurons, induced pluripotent stem cells from patients with YOPD showed increased accumulation of soluble α-synuclein protein and phosphorylated protein kinase Cα, as well as reduced abundance of lysosomal membrane proteins such as LAMP1. Testing activators of lysosomal function showed that specific phorbol esters, such as PEP005, reduced α-synuclein and phosphorylated protein kinase Cα levels while increasing LAMP1 abundance. Interestingly, the reduction in α-synuclein occurred through proteasomal degradation. PEP005 delivery to mouse striatum also decreased α-synuclein production in vivo. Induced pluripotent stem cell-derived dopaminergic cultures reveal a signature in patients with YOPD who have no known Parkinson’s disease-related mutations, suggesting that there might be other genetic contributions to this disorder. This signature was normalized by specific phorbol esters, making them promising therapeutic candidates.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: YOPD-derived iPSCs can be differentiated into mDA neural cultures that accumulate α-synuclein.
Fig. 2: Paired RNA-seq and proteomic analyses from mDA cultures.
Fig. 3: Lysosomal α-synuclein degradation is specifically impaired in YOPD mDA cultures.
Fig. 4: Treatment of YOPD mDA cultures with a PKC agonist reduces intracellular α-synuclein levels.
Fig. 5: YOPD signatures across 10 control individuals and 12 patients with YOPD.
Fig. 6: Confirmation of the effects and mechanism of PEP005.

Similar content being viewed by others

Data availability

All requests for raw and analyzed data and materials are promptly reviewed by the Cedars-Sinai Board of Governor’s Regenerative Medicine Institute to verify whether the request is subject to any intellectual property or confidentiality obligations. Patient-related data not included in the paper may be subject to patient confidentiality. Any data and materials that can be shared will be released via a material transfer agreement. All transcriptomic data from this study are available in the GEO repository under GSE120746. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD011326.

References

  1. Puschmann, A. Monogenic Parkinson’s disease and Parkinsonism: clinical phenotypes and frequencies of known mutations. Parkinsonism Relat. Disord. 19, 407–415 (2013).

    Article  PubMed  Google Scholar 

  2. Kalia, L. V. & Lang, A. E. Parkinson’s disease. Lancet 386, 896–912 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Spillantini, M. G. et al. α-Synuclein in Lewy bodies. Nature 388, 839–840 (1997).

    Article  CAS  PubMed  Google Scholar 

  4. Lashuel, H. A., Overk, C. R., Oueslati, A. & Masliah, E. The many faces of α-synuclein: from structure and toxicity to therapeutic target. Nat. Rev. Neurosci. 14, 38–48 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Singleton, A. B. et al. α-Synuclein locus triplication causes Parkinson’s disease. Science 302, 841–841 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Shi, Y., Inoue, H., Wu, J. C. & Yamanaka, S. Induced pluripotent stem cell technology: a decade of progress. Nat. Rev. Drug Discovery 16, 115–130 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Studer, L., Vera, E. & Cornacchia, D. Programming and reprogramming cellular age in the era of induced pluripotency. Cell Stem Cell 16, 591–600 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kim, K. et al. Epigenetic memory in induced pluripotent stem cells. Nature 467, 285–290 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Woodard, ChrisM. et al. iPSC-derived dopamine neurons reveal differences between monozygotic twins discordant for Parkinson’s disease. Cell Rep. 9, 1173–1182 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Soldner, F. et al. Parkinson’s disease patient-derived induced pluripotent stem cells free of viral reprogramming factors. Cell 136, 964–977 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Byers, B. et al. SNCA triplication Parkinson’s patient’s iPSC-derived DA neurons accumulate α-synuclein and are susceptible to oxidative stress. PLoS One 6, e26159 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Devine, M. J. et al. Parkinson’s disease induced pluripotent stem cells with triplication of the α-synuclein locus. Nat. Commun. 2, 440 (2011).

    Article  PubMed  CAS  Google Scholar 

  13. Oliveira, L. M. et al. Elevated α-synuclein caused by SNCA gene triplication impairs neuronal differentiation and maturation in Parkinson’s patient-derived induced pluripotent stem cells. Cell Death Dis. 6, e1994 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Schondorf, D. C. et al. iPSC-derived neurons from GBA1-associated Parkinson’s disease patients show autophagic defects and impaired calcium homeostasis. Nat. Commun. 5, 4028 (2014).

    Article  PubMed  CAS  Google Scholar 

  15. Sanchez-Danes, A. et al. Disease-specific phenotypes in dopamine neurons from human iPS-based models of genetic and sporadic Parkinson’s disease. EMBO Mol. Med. 4, 380–395 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sanders, L. H. et al. LRRK2 mutations cause mitochondrial DNA damage in iPSC-derived neural cells from Parkinson’s disease patients: reversal by gene correction. Neurobiol. Dis. 62, 381–386 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Schwab, A. J. & Ebert, A. D. Neurite aggregation and calcium dysfunction in iPSC-derived sensory neurons with Parkinson’s disease-related LRRK2 G2019S mutation. Stem Cell Rep. 5, 1039–1052 (2015).

    Article  CAS  Google Scholar 

  18. Schrag, A. & Schott, J. M. Epidemiological, clinical, and genetic characteristics of early-onset Parkinsonism. Lancet Neurol. 5, 355–363 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Rizek, P., Kumar, N. & Jog, M. S. An update on the diagnosis and treatment of Parkinson disease. CMAJ 188, 1157–1165 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Pagano, G., Ferrara, N., Brooks, D. J. & Pavese, N. Age at onset and Parkinson disease phenotype. Neurology 86, 1400–1407 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Alcalay, R. N. et al. Frequency of known mutations in early-onset parkinson disease: implication for genetic counseling: the consortium on risk for early-onset Parkinson disease study. Arch. Neurol. 67, 1116–1122 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ebert, A. D. et al. Induced pluripotent stem cells from a spinal muscular atrophy patient. Nature 457, 277–280 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Nalls, M. A. et al. NeuroX, a fast and efficient genotyping platform for investigation of neurodegenerative diseases. Neurobiol. Aging 36, 1605 e1607–1605 e1612 (2015).

    Article  CAS  Google Scholar 

  24. Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kriks, S. et al. Dopamine neurons derived from human ES cells efficiently engraft in animal models of Parkinson’s disease. Nature 480, 547–551 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kikuchi, T. et al. Human iPS cell-derived dopaminergic neurons function in a primate Parkinson’s disease model. Nature 548, 592–596 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Reyes, S. et al. GIRK2 expression in dopamine neurons of the substantia nigra and ventral tegmental area. J. Comp. Neurol. 520, 2591–2607 (2012).

    Article  CAS  PubMed  Google Scholar 

  28. Bennett, M. C. et al. Degradation of α-synuclein by proteasome. J. Biol. Chem. 274, 33855–33858 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Webb, J. L., Ravikumar, B., Atkins, J., Skepper, J. N. & Rubinsztein, D. C. α-Synuclein is degraded by both autophagy and the proteasome. J. Biol. Chem. 278, 25009–25013 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Mak, S. K., McCormack, A. L., Manning-Bog, A. B., Cuervo, A. M. & Di Monte, D. A. Lysosomal degradation of α-synuclein in vivo. J. Biol. Chem. 285, 13621–13629 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Xilouri, M., Brekk, O. R. & Stefanis, L. α-Synuclein and protein degradation systems: a reciprocal relationship. Mol. Neurobiol. 47, 537–551 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Mazzulli, J. R., Zunke, F., Isacson, O., Studer, L. & Krainc, D. α-Synuclein-induced lysosomal dysfunction occurs through disruptions in protein trafficking in human midbrain synucleinopathy models. Proc. Natl Acad. Sci. USA 113, 1931–1936 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Murphy, K. E. et al. Reduced glucocerebrosidase is associated with increased α-synuclein in sporadic Parkinson’s disease. Brain 137, 834–848 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Burbulla, L. F. et al. Dopamine oxidation mediates mitochondrial and lysosomal dysfunction in Parkinson’s disease. Science 357, 1255–1261 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Decressac, M. et al. TFEB-mediated autophagy rescues midbrain dopamine neurons from α-synuclein toxicity. Proc. Natl Acad. Sci. USA 110, E1817–E1826 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Li, Y. et al. Protein kinase C controls lysosome biogenesis independently of mTORC1. Nat. Cell Biol. 18, 1065–1077 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Sarkar, S. et al. Small molecules enhance autophagy and reduce toxicity in Huntington’s disease models. Nat. Chem. Biol. 3, 331–338 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sarkar, S., Davies, J. E., Huang, Z., Tunnacliffe, A. & Rubinsztein, D. C. Trehalose, a novel mTOR-independent autophagy enhancer, accelerates the clearance of mutant huntingtin and α-synuclein. J. Biol. Chem. 282, 5641–5652 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Li, Y. H. et al. α-Synuclein functions as a negative regulator for expression of tyrosine hydroxylase. Acta Neurol. Belg. 111, 130–135 (2011).

    PubMed  Google Scholar 

  40. Deary, I. J. et al. The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond. BMC Geriatr. 7, 28 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Asati, V., Mahapatra, D. K. & Bharti, S. K. PI3K/Akt/mTOR and Ras/Raf/MEK/ERK signaling pathways inhibitors as anticancer agents: structural and pharmacological perspectives. Eur. J. Med. Chem. 109, 314–341 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Roczniak-Ferguson, A. et al. The transcription factor TFEB links mTORC1 signaling to transcriptional control of lysosome homeostasis. Sci. Signal. 5, ra42 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Schneider, B. L. et al. Over-expression of α-synuclein in human neural progenitors leads to specific changes in fate and differentiation. Hum. Mol. Genet. 16, 651–666 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Chang, D. et al. A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat. Genet. 49, 1511–1516 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wong, Y. C. & Krainc, D. α-Synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies. Nat. Med. 23, 1–13 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Dehay, B. et al. Lysosomal impairment in Parkinson’s disease. Mov. Disord. 28, 725–732 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Imaizumi, Y. et al. Mitochondrial dysfunction associated with increased oxidative stress and α-synuclein accumulation in PARK2 iPSC-derived neurons and postmortem brain tissue. Mol. Brain 5, 35 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Suzuki, S. et al. Efficient induction of dopaminergic neuron differentiation from induced pluripotent stem cells reveals impaired mitophagy in PARK2 neurons. Biochem. Biophys. Res. Commun. 483, 88–93 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Ishikawa, K. I., Yamaguchi, A., Okano, H. & Akamatsu, W. Assessment of mitophagy in iPS cell-derived neurons. Methods Mol. Biol. 1759, 59–67 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Ridge, P. G. & Kauwe, J. S. K. Mitochondria and Alzheimer’s disease: the role of mitochondrial genetic variation. Curr. Genet. Med. Rep. 6, 1–10 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Fujimori, K. et al. Modeling sporadic ALS in iPSC-derived motor neurons identifies a potential therapeutic agent. Nat. Med. 24, 1579–1589 (2018).

    Article  CAS  PubMed  Google Scholar 

  52. Yu, S. et al. Inhibition of tyrosine hydroxylase expression in α-synuclein-transfected dopaminergic neuronal cells. Neurosci. Lett. 367, 34–39 (2004).

    Article  CAS  PubMed  Google Scholar 

  53. Halskau, O. Jr. et al. Three-way interaction between 14-3-3 proteins, the N-terminal region of tyrosine hydroxylase, and negatively charged membranes. J. Biol. Chem. 284, 32758–32769 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ostrerova, N. et al. α-Synuclein shares physical and functional homology with 14-3-3 proteins. J. Neurosci. 19, 5782–5791 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Xu, Y. et al. YWHA/14-3-3 proteins recognize phosphorylated TFEB by a noncanonical mode for controlling TFEB cytoplasmic localization. Autophagy 15, 1017–1030 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hampson, P. et al. Kinetics of ERK1/2 activation determine sensitivity of acute myeloid leukaemia cells to the induction of apoptosis by the novel small molecule ingenol-3-angelate (PEP005). Apoptosis 15, 946–955 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Jiang, G. et al. Synergistic reactivation of latent HIV expression by ingenol-3-angelate, PEP005, targeted NF-kB signaling in combination with JQ1-induced p-TEFb activation. PLoS Pathog. 11, e1005066 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Garg, R. et al. Protein kinase C and cancer: what we know and what we do not. Oncogene 33, 5225–5237 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Scheuer, T. Regulation of sodium channel activity by phosphorylation. Semin. Cell Dev. Biol. 22, 160–165 (2011).

    Article  CAS  PubMed  Google Scholar 

  60. Chesselet, M. F. et al. A progressive mouse model of Parkinson’s disease: the Thy1-aSyn (“Line 61”) mice. Neurotherapeutics 9, 297–314 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Barrett, R. et al. Reliable generation of induced pluripotent stem cells from human lymphoblastoid cell lines. Stem Cell Transl. Med. 3, 1429–1434 (2014).

    Article  CAS  Google Scholar 

  62. Mazzulli, J. R. et al. Gaucher disease glucocerebrosidase and α-synuclein form a bidirectional pathogenic loop in synucleinopathies. Cell 146, 37–52 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Marshall, J. et al. Demonstration of feasibility of in vivo gene therapy for gaucher disease using a chemically induced mouse model. Mol. Ther. 6, 179–189 (2002).

    Article  CAS  PubMed  Google Scholar 

  64. Ho, R. et al. ALS disrupts spinal motor neuron maturation and aging pathways within gene co-expression networks. Nat. Neurosci. 19, 1256–1267 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  66. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinforma. 12, 323 (2011).

    Article  CAS  Google Scholar 

  67. Schubert, O. T. et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat. Protoc. 10, 426–441 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. Teo, G. et al. mapDIA: preprocessing and statistical analysis of quantitative proteomics data from data-independent acquisition mass spectrometry. J. Proteom. 129, 108–120 (2015).

    Article  CAS  Google Scholar 

  69. Parker, S. J., Venkatraman, V. & Van Eyk, J. E. Effect of peptide assay library size and composition in targeted data-independent acquisition-MS analyses. Proteomics 16, 2221–2237 (2016).

    Article  CAS  PubMed  Google Scholar 

  70. Subramanian, A. et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhang, G., Kazanietz, M. G., Blumberg, P. M. & Hurley, J. H. Crystal structure of the cys2 activator-binding domain of protein kinase C in complex with phorbol ester. Cell 81, 917–924 (1995).

    Article  CAS  PubMed  Google Scholar 

  72. Kim, D. E., Chivian, D. & Baker, D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 32, W526–W531 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Morin, A. et al. Collaboration gets the most out of software. eLife 2, e01456 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Holm, L., Kaariainen, S., Wilton, C. & Plewczynski, D. Using Dali for structural comparison of proteins. Curr. Protoc. Bioinformatics Chapter 5, 5 (2006).

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank S. Svendsen for critical input and editing of the manuscript and T. Pierson for consultation on protein degradation and additional editing of the manuscript. We also thank A. Singleton and D. Hernandez for NeuroX analysis and WGS of patient samples, R. Holewinski for assistance with proteomic analysis and sample acquisition, V. Mattis and H. Park for assisting with striatal dissections, D. Torolina for sectioning and staining of mouse brains and G. Lawless for assistance with ELISA assays. We also thank the Cedars-Sinai Proteomics and Metabolomics Core. The majority of this work was supported by the Joseph Drown Foundation (C.N.S.) and the Board of Governors Regenerative Medicine Institute (C.N.S.) and a National Institutes of Health grant, 5UG3NS105703-02 (C.N.S.). Support for the clinical sample collection and patient information came from the Widjaja Family Foundation (M.T.). Support for the proteomics analysis was from the Advanced Clinical Biosystems Institute (J.E.V.E.).

Author information

Author notes

  1. These authors contributed equally: A. H. Laperle, S. Sances, N. Yucer.

Authors and Affiliations

  1. Cedars-Sinai Board of Governors Regenerative Medicine Institute, Los Angeles, CA, USA

    A. H. Laperle, S. Sances, N. Yucer, V. J. Dardov, V. J. Garcia, R. Ho, A. N. Fulton, K. M. Roxas, P. Avalos, D. West, M. G. Banuelos & C. N. Svendsen

  2. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    M. R. Jones, R. Murali & J. E. Van Eyk

  3. Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    M. R. Jones

  4. Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA

    Z. Shu & N. T. Maidment

  5. Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, USA

    R. Murali

  6. Research Division of Immunology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    R. Murali

  7. Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    J. E. Van Eyk

  8. Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    J. E. Van Eyk

  9. Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    M. Tagliati

Authors
  1. A. H. Laperle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Sances
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. Yucer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. J. Dardov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. V. J. Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. R. Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. A. N. Fulton
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. M. R. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. K. M. Roxas
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. P. Avalos
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. D. West
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. M. G. Banuelos
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Z. Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. R. Murali
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. N. T. Maidment
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. J. E. Van Eyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. M. Tagliati
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. C. N. Svendsen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.H.L., S.S., N.Y. and C.N.S. designed experiments. A.H.L., S.S., N.Y., V.J.D., V.J.G. and A.N.F. performed experiments. A.H.L., S.S. and C.N.S. wrote the manuscript. S.S. and R.H. performed transcriptomic analysis. P.A. performed all animal work and K.M.R. processed brain tissue. S.S., A.H.L., D.W. and M.G.B. generated YOPD iPSC lines. R.M. performed in silico modeling. M.R.J. performed WGS analysis. Z.S. and N.T.M. performed dopamine release experiments. J.E.V.E. supervised the proteomic data analysis. M.T. provided patient samples and clinical data, and helped supervise the project. C.N.S. supervised the project.

Corresponding author

Correspondence to C. N. Svendsen.

Ethics declarations

Competing interests

An intellectual protection patent is pending for diagnostic and drug screening for molecular signatures of early-onset sporadic Parkinson’s disease.

Additional information

Peer review information Brett Benedetti and Kate Gao were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Generation of YOPD iPSCs.

(a) SSEA4 (green) and OCT-4 immunostaining in undifferentiated iPSCs from YOPD patients. (b) Normal karyotypes from YOPD patient iPSCs, 20 metaphase spreads were analyzed for each line.

Extended Data Fig. 2 Additional characterization of mDA cultures.

(a) Expression of dopaminergic neuron genes in day 30 mDA cultures from 6 iPSC lines. 3 biological replicates per line were averaged. Data are normalized to average expression in control lines and presented as 3 CTR vs 3 PD lines. No significant differences were detected using multiple t-tests with Holm-Sidak correction for multiple comparisons. (b) HPLC detection of total dopamine in d30 mDA culture lysates. n=9 CTR n=9 PD not significant p=0.29 two-tailed t-test with Welch’s correction. (c) HPLC detection of released dopamine in aCSF following a 15 min incubation at 37oC. n=9 CTR n=9 not significant p=0.14 two-tailed t-test with Welch’s correction. (d) MEA recording of spontaneous activity from 02iCTR mDA neurons at day 21 of differentiation. (e) MEA recordings of control and YOPD mDA neurons at d30 of differentiation. (f) Average sorted spikes per neuron at d30. Points represent an average of 4 independent wells, n=6 CTR n=6 PD colors indicate iPSC lines. Bar graphs represent mean, error bars represent standard deviation (s.d.).

Source data

Extended Data Fig. 3 Whole-cell patch clamp measurements from control and YOPD mDA cultures.

(a) Capacitance is similar between control and YOPD. (b) No difference in resting membrane potential (RMP) is observed. (c) Voltage-gated sodium current density is decreased in YOPD compared to control. No difference is observed in either the (d) inwards rectifying potassium current density, or (e) delayed rectifier potassium current density. * denotes significance p = 0.018 two tailed t-test with Welch’s correction. Bar graphs represent mean, error bars represent standard deviation (s.d.).

Source data

Extended Data Fig. 4 Western blots of soluble and insoluble lysate fractions.

Western blot of D30 mDA cultures under non-denaturing conditions for α-synuclein and loading control of β-actin. Fractionation experiment was conducted once in 5 independent iPSC lines.

Extended Data Fig. 5 Western blots of YOPD markers in undifferentiated iPSCs.

(a) Western blot of p-PKCα and α-Syn in undifferentiated iPSCs and (b) relative quantification of α-Syn levels (n=4 CTR n=5 PD p=0.87 two-tailed t-test with Welch’s correction); no quantification of p-PKCα was possible in the iPSCs as no bands were detected. Bar graphs represent mean, error bars represent standard deviation (s.d.).

Source data

Extended Data Fig. 6 Paired transcriptomic and proteomic analysis.

Pearson correlation plots of (a) transcriptomic and (b) proteomic data. (c) PCA plot of all detected proteins. (d) PCA plot of matching RNA-Seq transcripts. (e) PCA plot of matching proteins. (f) PCA plots of filtered data with 190iPD line omitted. (g) Matched GSEA terms conducted on 190iPD omitted data set n=9 CTR n=6 PD term significance determined by FDR <0.1.

Extended Data Fig. 7 Testing of additional Proteosomal inhibitors.

(a) Western blot of D30 mDA cultures in the presence of indicated proteasomal inhibitors. Quantification of blots from multiple differentiations (n=4 CTR, n=4 PD) with each point representing a band intensity from a separate differentiation. (b) P53 one-way ANOVA with Tukey multiple comparisons test (F 17.53 DF 20 p=0.0005 CTR Lac p=0.0003 CTR Epox p=0.0009 PD Lac p=0.001 PD Epox). (c) α-Syn one-way ANOVA with Tukey multiple comparisons test (F 1.6 DF 20 p=0.0.21). Bar graphs represent mean, error bars represent standard deviation (s.d.).

Source data

Extended Data Fig. 8 Additional characterization of PEP005 treatment.

(a) Immunocytochemistry showing TH and α-synuclein (α-Syn) in 200iPD d30 mDA cultures with and without PEP005 treatment. Images are representative of 2 additional lines tested. (b) Day 30 mDA neurons treated with PEP005 from multiple YOPD and control lines. (c) Quantification of LC3I/II and α-Syn band intensities relative to untreated cells from the same line. (d) Time-course of PEP005 treatment in YOPD and control mDA neurons. (e) Quantification of α-Syn, p-PKCα, LAMP1, and TH band intensities in YOPD and control mDA neurons in timecourse study. qPCR from paired samples (n=3 wells 02iCTR n=3 wells 190iPD) over PEP005 time-course showing (f) SNCA and (g) TH expression. Bar graphs represent mean, error bars represent standard deviation (s.d.).

Source data

Extended Data Fig. 9 mDA differentiation across multiple lines and clones.

(a) Tyrosine Hydroxylase (TH) production by western blot across 8 control and 12 YOPD patients. TH levels were first normalized to β-actin, then to compare across blots the levels were normalized to the average signal (TH/ β-actin) of each gel. (b) Western blots of TH and α-synuclein (α-Syn) levels in day 30 mDA cultures across 3 unique clonal lines from ED044iCTR and from 192iPD. (c) Quantification of band intensities for TH and α-Syn normalized to β-actin. Bands represent independent biological replicates from 3 separate wells differentiated in the same experiment. *indicates p=0.0002 via one-way ANOVA (F 51.42, DF 8) with Tukey multiple comparisons test compared to other clones of the same line. Bar graphs represent mean, error bars represent standard deviation (s.d.).

Source data

Extended Data Fig. 10 Dose-response and in silico analysis of PEP005 and related molecules.

(a) Structures of Phorbol esters similar to PEP005 tested in mDA cultures. (b) Western blots of α-synuclein (α-Syn) and p-PKCα in response to varying PEP005 doses. (c) Western blots of α-Syn and p-PKCα in response to varying Prostratin (PRO) doses in both YOPD and control mDA cultures. Dose ranging studies were repeated twice. (d) Predictive modeling of PEP005 binding sites on PKCα and similar affinity sites on additional proteins. (e) Three dimensional model of PEP005 binding sites on PKCα, PKCδ, and Ras overlaid to show similarity.

Supplementary information

Supplementary Information

Supplemental Tables 1–5 and example flow cytometry gating.

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 1

Unprocessed western blots.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Unprocessed western blots.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

Unprocessed western blots.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 7

Unprocessed western blots.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed western blots.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 9

Unprocessed western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laperle, A.H., Sances, S., Yucer, N. et al. iPSC modeling of young-onset Parkinson’s disease reveals a molecular signature of disease and novel therapeutic candidates. Nat Med 26, 289–299 (2020). https://doi.org/10.1038/s41591-019-0739-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-019-0739-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research