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Transcriptomics analysis of human iPSC-derived dopaminergic neurons reveals a novel model for sporadic Parkinson’s disease

Molecular Psychiatry volume 27, pages 4355–4367 (2022)Cite this article

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Abstract

Parkinson’s disease (PD) is a progressive, neurodegenerative disease affecting over 1% of the population beyond 65 years of age. Although some PD cases are inheritable, the majority of PD cases occur in a sporadic manner. Risk factors comprise next to heredity, age, and gender also exposure to neurotoxins from for instance pesticides and herbicides. As PD is characterized by a loss of dopaminergic neurons in the substantia nigra, it is nearly impossible to access and extract these cells from patients for investigating disease mechanisms. The emergence of induced pluripotent stem (iPSC) technology allows differentiating and growing human dopaminergic neurons, which can be used for in vitro disease modeling. Here, we differentiated human iPSCs into dopaminergic neurons, and subsequently exposed the cells to increasing concentrations of the neurotoxin MPP+. Temporal transcriptomics analysis revealed a strong time- and dose-dependent response with genes over-represented across pathways involved in PD etiology such as “Parkinson’s Disease”, “Dopaminergic signaling” and “calcium signaling”. Moreover, we validated this disease model by showing robust overlap with a meta-analysis of transcriptomics data from substantia nigra from post-mortem PD patients. The overlap included genes linked to e.g. mitochondrial dysfunction, neuron differentiation, apoptosis and inflammation. Our data shows, that MPP+-induced, human iPSC-derived dopaminergic neurons present molecular perturbations as observed in the etiology of PD. Therefore we propose iPSC-derived dopaminergic neurons as a foundation for a novel sporadic PD model to study the pathomolecular mechanisms of PD, but also to screen for novel anti-PD drugs and to develop and test new treatment strategies.

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Fig. 1: Sigma0028 iPSC were differentiated to dopaminergic neurons over a period of 54 days.
Fig. 2: Significantly deregulated transcripts.
Fig. 3: Volcano plots of deregulated transcripts from iPSCs at 100, 200 and 400 µM MPP+ exposure at 24 and 72 h.
Fig. 4: KEGG pathways after 24 and 72 h for 100, 200 and 400 µM MPP+ exposure and PD related genes retrieved from DisGenNet.
Fig. 5: KEGG ‘Parkinson’s Disease’ pathway and deregulated transcripts for iPSCs exposed to 400 µM MPP+ at 72 h.
Fig. 6: Overlap of altered genes derived from the iPSC model upon MPP+ exposure and the meta-analysis of human brain-derived transcriptomics data.
Fig. 7: The Upset plot shows the overlap/intersection among DE genes across previous cellular PD models carrying LRRK2-G2019S variants, and Rotenone treated models carrying isogenic SNCA-A53T mutations and a wild type (WT), with the 400 µM MPP+ induced model at 72 h (0.1, **p value < = 0.05).

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Data availability

Data that support the findings of this study have been deposited in Gene Expression Omnibus with the accession code GSE196190.

References

  1. Gonera EG, Van’t Hof M, Berger HJC, Van Weel C, Horstink MWIM. Symptoms and duration of the prodromal phase in Parkinson’s disease. Mov Disord. 1997;12:871–6.

    Article  CAS  PubMed  Google Scholar 

  2. Ray Dorsey E, Elbaz A, Nichols E, Abd-Allah F, Abdelalim A, Adsuar JC, et al. Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018;17:939–53.

    Article  Google Scholar 

  3. Fearnley JM, Lees AJ. Ageing and parkinson’s disease: substantia nigra regional selectivity. Brain. 1991;114:2283–301.

    Article  PubMed  Google Scholar 

  4. Dauer W, Przedborski S. Parkinson’s disease: mechanisms and models. Neuron. 2003;39:889–909.

    Article  CAS  PubMed  Google Scholar 

  5. Moore TJ, Glenmullen J, Mattison DR. Reports of pathological gambling, hypersexuality, and compulsive shopping associated with dopamine receptor agonist drugs. JAMA Intern Med. 2014;174:1930–3.

    Article  PubMed  Google Scholar 

  6. Bastide MF, Meissner WG, Picconi B, Fasano S, Fernagut PO, Feyder M, et al. Pathophysiology of L-dopa-induced motor and non-motor complications in Parkinson’s disease. Prog Neurobiol. 2015;132:96–168.

    Article  CAS  PubMed  Google Scholar 

  7. Lashuel HA, Overk CR, Oueslati A, Masliah E. The many faces of α-synuclein: from structure and toxicity to therapeutic target. Nat Rev Neurosci. 2013;14:38–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Minakaki G, Menges S, Kittel A, Emmanouilidou E, Schaeffner I, Barkovits K, et al. Autophagy inhibition promotes SNCA/alpha-synuclein release and transfer via extracellular vesicles with a hybrid autophagosome-exosome-like phenotype. Autophagy. 2018;14:98–119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gao F, Yang J, Wang D, Li C, Fu Y, Wang H, et al. Mitophagy in Parkinson’s disease: pathogenic and therapeutic implications. Front Neurol. 2017;8:527.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bose A, Beal MF. Mitochondrial dysfunction in Parkinson’s disease. J Neurochemistry. 2016;139:216–31.Suppl 1.

    Article  CAS  Google Scholar 

  11. Kaur K, Gill JS, Bansal PK, Deshmukh R. Neuroinflammation - A major cause for striatal dopaminergic degeneration in Parkinson’s disease. J Neurological Sci. 2017;381:308–14.

    Article  CAS  Google Scholar 

  12. Berry C, La Vecchia C, Nicotera P. Paraquat and parkinson’s disease. Cell Death Differ. 2010;17:1115–25.

    Article  CAS  PubMed  Google Scholar 

  13. Curtin K, Fleckenstein AE, Robison RJ, Crookston MJ, Smith KR, Hanson GR. Methamphetamine/amphetamine abuse and risk of Parkinson’s disease in Utah: A population-based assessment. Drug Alcohol Depend 2015;146:30–38.

    Article  CAS  PubMed  Google Scholar 

  14. Bohler S, Krauskopf J, Espín-Pérez A, Gebel S, Palli D, Rantakokko P, et al. Genes associated with Parkinson’s disease respond to increasing polychlorinated biphenyl levels in the blood of healthy females. Environ Pollut 2019;250:107–17.

    Article  CAS  PubMed  Google Scholar 

  15. Ke M, Chong CM, Su H. Using induced pluripotent stem cells for modeling Parkinson’s disease. World J Stem Cells. 2019;11:634–49.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126:663–76.

    Article  CAS  PubMed  Google Scholar 

  17. Kopin IJ. MPTP: An industrial chemical and contaminant of illicit narcotics stimulates a new era in research on Parkinson’s disease. Environ Health Perspect. 1987;75:45–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Glaab E, Schneider R. Comparative pathway and network analysis of brain transcriptome changes during adult aging and in Parkinson’s disease. Neurobiol Dis 2015;74:1–13.

    Article  CAS  PubMed  Google Scholar 

  19. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017;45:D833–D839.

    Article  PubMed  Google Scholar 

  20. Russell AC, Šimurina M, Garcia MT, Novokmet M, Wang Y, Rudan I, et al. The N-glycosylation of immunoglobulin G as a novel biomarker of Parkinson’s disease. Glycobiology. 2017;27:501–10.

    Article  CAS  PubMed  Google Scholar 

  21. Sandor C, Robertson P, Lang C, Heger A, Booth H, Vowles J, et al. Transcriptomic profiling of purified patient-derived dopamine neurons identifies convergent perturbations and therapeutics for Parkinson’s disease. Hum Mol Genet. 2017;26:552–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Fernandes HJR, Patikas N, Foskolou S, Field SF, Park JE, Byrne ML, et al. Single-cell transcriptomics of parkinson’s disease human in vitro models reveals dopamine neuron-specific stress responses. Cell Rep. 2020;33:108263.

    Article  CAS  PubMed  Google Scholar 

  23. Sacchetti P, Carpentier R, Ségard P, Olivé-Cren C, Lefebvre P. Multiple signaling pathways regulate the transcriptional activity of the orphan nuclear receptor NURR1. Nucleic Acids Res. 2006;34:5515–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Takahashi M, Suzuki M, Fukuoka M, Fujikake N, Watanabe S, Murata M, et al. Normalization of overexpressed α-synuclein causing Parkinson’s disease by a moderate gene silencing with RNA interference. Mol Ther - Nucleic Acids. 2015;4:e241.

    Article  PubMed  Google Scholar 

  25. Smirnova L, Harris G, Delp J, Valadares M, Pamies D, Hogberg HT, et al. A LUHMES 3D dopaminergic neuronal model for neurotoxicity testing allowing long-term exposure and cellular resilience analysis. Arch Toxicol. 2016;90:2725–43.

    Article  CAS  PubMed  Google Scholar 

  26. Pamies D, Wiersma D, Katt ME, Zhao L, Burtscher J, Harris G, et al. Human IPSC 3D brain model as a tool to study chemical-induced dopaminergic neuronal toxicity. Neurobiol Dis. 2022;169:105719.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kriks S, Shim JW, Piao J, Ganat YM, Wakeman DR, Xie Z, et al. Dopamine neurons derived from human ES cells efficiently engraft in animal models of Parkinson’s disease. Nature. 2011;480:547–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    Article  CAS  PubMed  Google Scholar 

  30. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 2011;12:323.

    Article  CAS  Google Scholar 

  31. Radley AH, Schwab RM, Tan Y, Kim J, Lo EKW, Cahan P. Assessment of engineered cells using CellNet and RNA-seq. Nat Protoc 2017;12:1089–102.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Soneson C, Love MI, Robinson, MD. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences [version 2; referees: 2 approved]. F1000Research. 2016;4:1521.

    Article  Google Scholar 

  33. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple. Test J R Stat Soc Ser B. 1995;57:289–300.

    Google Scholar 

  35. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9.

    Article  CAS  PubMed  Google Scholar 

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Author notes

  1. These authors contributed equally: Julian Krauskopf, Kristel Eggermont.

Authors and Affiliations

  1. Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands

    Julian Krauskopf, Sacha Bohler, Duncan Hauser, Florian Caiment, Theo M. de Kok & Jos C. Kleinjans

  2. Stem Cell Institute, Department of Development and Regeneration, Katholieke Universiteit Leuven, Leuven, Belgium

    Kristel Eggermont, Rodrigo Furtado Madeiro Da Costa & Catherine Verfaillie

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  1. Julian Krauskopf
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Contributions

JCK and CV designed the experiments. KE, RFMC, SB, DH, and FC performed and organized the experimental work. JK analyzed the data. Advice and supervision were from JCK, CV, TMK and FC. JK wrote the manuscript and all authors reviewed and approved the final version of this article.

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Correspondence to Julian Krauskopf.

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Krauskopf, J., Eggermont, K., Madeiro Da Costa, R.F. et al. Transcriptomics analysis of human iPSC-derived dopaminergic neurons reveals a novel model for sporadic Parkinson’s disease. Mol Psychiatry 27, 4355–4367 (2022). https://doi.org/10.1038/s41380-022-01663-y

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