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Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression

Abstract

Noncoding RNAs have diagnostic and prognostic importance in Parkinson’s disease (PD). We studied circulating small noncoding RNAs (sncRNAs) in two large-scale longitudinal PD cohorts (Parkinson’s Progression Markers Initiative (PPMI) and Luxembourg Parkinson’s Study (NCER-PD)) and modeled their impact on the transcriptome. Sequencing of sncRNAs in 5,450 blood samples of 1,614 individuals in PPMI yielded 323 billion reads, most of which mapped to microRNAs but covered also other RNA classes such as piwi-interacting RNAs, ribosomal RNAs and small nucleolar RNAs. Dysregulated microRNAs associated with disease and disease progression occur in two distinct waves in the third and seventh decade of life. Originating predominantly from immune cells, they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. Profiling 1,553 samples from 1,024 individuals in the NCER-PD cohort validated biomarkers and main findings by an independent technology. Finally, network analysis of sncRNA and transcriptome sequencing from PPMI identified regulatory modules emerging in patients with progressing PD.

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Fig. 1: Study overview and sample characteristics.
Fig. 2: Association between miRNA expression and PD.
Fig. 3: Progressive dysregulation of miRNAs within PD and predicted cell-type origins.
Fig. 4: Age-related changes of miRNA expression and the association with molecular hallmarks and progression of PD.
Fig. 5: Progression marker analysis and downstream modeling using anticorrelated mRNAs.

Data availability

Individual-level PPMI RNA-seq data supporting the findings of this work are deposited for distribution in accordance with approved ethics and data-oversight requirements. Access to the full complement of standardized protocols and de-identified human participant data associated with this study is available to researchers through the study data repository record at https://ppmi-info.org. The authors declare that all other data supporting the findings of this study are freely available upon request from the corresponding author.

Code availability

The computer code written for the primary analysis is available upon request from the corresponding author.

References

  1. Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544 (2016).

    Article  Google Scholar 

  2. Deweerdt, S. Parkinson’s disease: 4 big questions. Nature 538, S17 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Jankovic, J. Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79, 368–376 (2008).

    Article  CAS  PubMed  Google Scholar 

  5. Hamza, T. H. et al. Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson’s disease. Nat. Genet. 42, 781–785 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Klemann, C. J. H. M. et al. Integrated molecular landscape of Parkinson’s disease. NPJ Parkinsons Dis. 3, 14 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Scherzer, C. R. et al. Molecular markers of early Parkinson’s disease based on gene expression in blood. Proc. Natl Acad. Sci. USA 104, 955 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat. Commun. 10, 994 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Wang, Q. et al. The landscape of multiscale transcriptomic networks and key regulators in Parkinson’s disease. Nat. Commun. 10, 5234 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Calligaris, R. et al. Blood transcriptomics of drug-naive sporadic Parkinson’s disease patients. BMC Genomics 16, 876 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Chen-Plotkin, A. S. Blood transcriptomics for Parkinson disease? Nat. Rev. Neurol. 14, 5–6 (2018).

    Article  PubMed  Google Scholar 

  12. Wang, C., Chen, L., Yang, Y., Zhang, M. & Wong, G. Identification of potential blood biomarkers for Parkinson’s disease by gene expression and DNA methylation data integration analysis. Clin. Epigenetics 11, 24 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hossein-nezhad, A. et al. Transcriptomic profiling of extracellular RNAs present in cerebrospinal fluid identifies differentially expressed transcripts in Parkinson’s disease. J. Parkinsons Dis. 6, 109–117 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Marz, M., Ferracin, M. & Klein, C. MicroRNAs as biomarker of Parkinson disease? Neurology 84, 636 (2015).

    Article  PubMed  Google Scholar 

  15. Leggio, L. et al. microRNAs in Parkinson’s disease: from pathogenesis to novel diagnostic and therapeutic approaches. Int. J. Mol. Sci. https://doi.org/10.3390/ijms18122698 (2017).

  16. Starhof, C. et al. The biomarker potential of cell-free microRNA from cerebrospinal fluid in Parkinsonian syndromes. Mov. Disord. 34, 246–254 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Keller, A. et al. Toward the blood-borne miRNome of human diseases. Nat. Methods 8, 841–843 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Leidinger, P. et al. A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biol. 14, R78 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Keller, A. et al. Validating Alzheimer’s disease micro RNAs using next-generation sequencing. Alzheimers Dement. 12, 565–576 (2016).

    Article  PubMed  Google Scholar 

  20. Ludwig, N. et al. Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs. Genomics Proteomics Bioinformatics 17, 430–440 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Fehlmann, T. et al. Evaluating the use of circulating microRNA profiles for lung cancer detection in symptomatic patients. JAMA Oncol. https://doi.org/10.1001/jamaoncol.2020.0001 (2020).

  22. Hipp, G. et al. The Luxembourg Parkinson’s Study: a comprehensive approach for stratification and early diagnosis. Front. Aging Neurosci. https://doi.org/10.3389/fnagi.2018.00326 (2018).

  23. Valentine, M. N. Z. et al. Multi-year whole-blood transcriptome data for the study of onset and progression of Parkinson’s disease. Sci. Data 6, 20 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lawton, M. et al. Blood biomarkers with Parkinson’s disease clusters and prognosis: the Oxford discovery cohort. Mov. Disord. 35, 279–287 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Marek, K. et al. The Parkinson’s Progression Markers Initiative (PPMI) – establishing a PD biomarker cohort. Ann. Clin. Translat. Neurol. 5, 1460–1477 (2018).

    Article  CAS  Google Scholar 

  26. Ludwig, N. et al. Bias in recent miRBase annotations potentially associated with RNA quality issues. Sci. Rep. https://doi.org/10.1038/s41598-017-05070-0 (2017).

  27. Ludwig, N. et al. Small ncRNA-seq results of human tissues: variations depending on sample integrity. Clin. Chem. 64, 1074–1084 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Fehlmann, T. et al. Web-based NGS data analysis using miRMaster: a large-scale meta-analysis of human miRNAs. Nucleic Acids Res. 45, 8731–8744 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Amand, J., Fehlmann, T., Backes, C. & Keller, A. DynaVenn: web-based computation of the most significant overlap between ordered sets. BMC Bioinformatics 20, 743 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kern, F. et al. miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems. Nucleic Acids Res. 48, W521–W528 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Antony, P. M. A., Diederich, N. J., Krüger, R. & Balling, R. The hallmarks of Parkinson’s disease. FEBS J. 280, 5981–5993 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Huang, Z. et al. HMDD v3.0: a database for experimentally supported human microRNA–disease associations. Nucleic Acids Res. 47, D1013–D1017 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Ding, H. et al. Identification of a panel of five serum miRNAs as a biomarker for Parkinson’s disease. Parkinsonism Relat. Disord. 22, 68–73 (2016).

    Article  PubMed  Google Scholar 

  34. Liu, X. et al. miRNAs and target genes in the blood as biomarkers for the early diagnosis of Parkinson’s disease. BMC Syst. Biol. 13, 10 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Martins, M. et al. Convergence of miRNA expression profiling, α-synuclein interaction and GWAS in Parkinson’s disease. PLoS ONE 6, e25443 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Caggiu, E. et al. Differential expression of miRNA 155 and miRNA 146a in Parkinson’s disease patients. eNeurologicalSci 13, 1–4 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Chi, J. et al. Integrated analysis and identification of novel biomarkers in Parkinson’s disease. Front. Aging Neurosci. 10, 178 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Ravanidis, S. et al. Validation of differentially expressed brain-enriched microRNAs in the plasma of PD patients. Ann. Clin. Translat. Neurol. 7, 1594–1607 (2020).

    Article  CAS  Google Scholar 

  39. Botta-Orfila, T. et al. Identification of blood serum micro-RNAs associated with idiopathic and LRRK2 Parkinson’s disease. J. Neurosci. Res. 92, 1071–1077 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Bai, X. et al. Downregulation of blood serum microRNA 29 family in patients with Parkinson’s disease. Sci. Rep. 7, 5411 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Cao, X.-Y. et al. MicroRNA biomarkers of Parkinson’s disease in serum exosome-like microvesicles. Neurosci. Lett. 644, 94–99 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Barbagallo, C. et al. Specific signatures of serum miRNAs as potential biomarkers to discriminate clinically similar neurodegenerative and vascular-related diseases. Cell. Mol. Neurobiol. https://doi.org/10.1007/s10571-019-00751-y (2019).

  43. Burgos, K. et al. Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer’s and Parkinson’s diseases correlate with disease status and features of pathology. PLoS ONE 9, e94839 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Paschon, V. et al. Interplay between exosomes, microRNAs and Toll-Like receptors in brain disorders. Mol. Neurobiol. 53, 2016–2028 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Schlachetzki, J. C. M. et al. A monocyte gene expression signature in the early clinical course of Parkinson’s disease. Sci. Rep. 8, 10757 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Nissen, S. K. et al. Alterations in blood monocyte functions in Parkinson’s disease. Mov. Disord. 34, 1711–1721 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Ravanidis, S. et al. Circulating brain-enriched microRNAs for detection and discrimination of idiopathic and genetic Parkinson’s disease. Mov. Disord. 35, 457–467 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Billingsley, K. J. et al. Mitochondria function associated genes contribute to Parkinson’s disease risk and later age at onset. NPJ Parkinsons Dis. 5, 8 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Shamir, R. et al. Analysis of blood-based gene expression in idiopathic Parkinson disease. Neurology 89, 1676 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Backes, C. et al. MiRCarta: a central repository for collecting miRNA candidates. Nucleic Acids Res. 46, D160–D167 (2018).

    Article  CAS  PubMed  Google Scholar 

  51. Goh, Y. S., Chao, X. Y., Dheen, T. S., Tan, E.-K. & Tay, S. S. Role of microRNAs in Parkinson’s disease. Int. J. Mol. Sci. https://doi.org/10.3390/ijms20225649 (2019).

  52. Keller, A. et al. miRNAs can be generally associated with human pathologies as exemplified for miR-144*. BMC Med. 12, 224 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Fehlmann, T. et al. Common diseases alter the physiological age-related blood microRNA profile. Nat. Commun. 11, 5958 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Blauwendraat, C. et al. NeuroChip, an updated version of the NeuroX genotyping platform to rapidly screen for variants associated with neurological diseases. Neurobiol. Aging 57, 247.e9–247.e13 (2017).

    Article  CAS  Google Scholar 

  55. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. J. Open Source Softw. 3, 861 (2018).

    Article  Google Scholar 

  56. Juzenas, S. et al. A comprehensive, cell specific microRNA catalogue of human peripheral blood. Nucleic Acids Res. 45, 9290–9301 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

PPMI, a public-private partnership, is funded by the Michael J. Fox Foundation (MJFF) for Parkinson’s Research and funding partners, including Abbvie, Allergan, Amathus Therapeutics, Avid, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Denali, GE Healthcare, Genetech, GlaxoSmithKline, Handl Therapeutics, Insitro, Janssen Neuroscience, Lilly, Lundbeck, Merck, MSD, Pfizer, Piramal, Prevail, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily, Voyager and Golub Capital. We highly appreciate the encouragement and support of K. Nikolich in setting up and performing the study. We thank S. Levy and N. Prasad at the HudsonAlpha Institute for Biotechnology for adapting the small RNA sequencing protocol to the instruments and platforms used and performing the sequencing experiments. The microarray experiments were performed as fee-for-service by Hummingbird Diagnostics. We acknowledge the support of HbDx. We give special thanks to all participating patients in the study. Additionally, we are very grateful for all received funding and private donations that enabled us to carry out the project. Furthermore, we acknowledge the joint effort of the NCER-PD consortium members generally contributing to the Luxembourg Parkinson’s Study. The study is funded by the MJFF for Parkinson’s Research under reference 14446 and by the Schaller-Nikolich Foundation.

Author information

Authors and Affiliations

Authors

Contributions

F.K. led statistical analysis of data and contributed to writing the manuscript; T.F. performed primary analysis of data and supported statistical analysis; I.V. and E.A. contributed to primary analysis of data and matching to clinical variables; E.H. contributed to general analysis of sequencing data; M.K. and C.B. assisted general analysis of microarray data; N.L.G. supported statistical analyses and visualization of aggregated data. P.G. supported statistical analysis with a focus on disease progression; K.L.P. contributed to clinical interpretation of data; B.C. contributed to study setup and interpretation of data; R.B., L.G. and R.K. contributed to the setup of NCER-PD and interpretation of microarray data; D.G. and B.M. contributed to development of standard operating procedures, participant recruitment and clinical interpretation of data; E.M. contributed to miRNA-gene interaction network analysis and manuscript writing. T.W.C. contributed to interpreting and discussing data as well as writing the manuscript. D.W.C. and K.V.K.-J. worked on mRNA expression data and blood cell-type analyses and contributed to study setup; A.K. contributed to study setup, supported statistical analyses and contributed to manuscript writing.

Corresponding author

Correspondence to Andreas Keller.

Ethics declarations

Competing interests

The authors declare the following competing interests: E.H. received funding from the MJFF; M.K. is employed by Hummingbird Diagnostics; K.L.P. is a member of the executive steering committee of PPMI and received funding from the MJFF; B.C. is an Associate Director in MJFF’s Research Programs division and employed by the MJFF; B.M. is a member of the executive steering committee of PPMI and principal investigator of the Systemic Synuclein Sampling Study of the MJFF; T.W.C. is a founder and scientific advisor of Alkahest; D.W.C. and K.V.K.-J. received funding from MJFF and are principal investigators of RNA bioinformatics at PPMI; A.K. received funding from the MJFF.

Additional information

Peer review information Nature Aging thanks Alice Chen-Plotkin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Detailed sncRNA-seq read statistics.

a–j, Histogram plot for the number/percentage of reads (x-axis) and the number of samples (y-axis) on top, and boxplot for the distribution of reads per sequencing plate (x-axis) on the bottom. Colored boxes span the first to the third quartile with the line inside the box representing the median value. The whiskers show the minimum and maximum values or values up to 1.5 times the interquartile range below or above the first or third quartile if outliers are present (shown as separate, colored dots). Each panel shows a different subset of reads containing all samples fully analyzed with miRMaster (n = 4,624). a, Shown are the total read counts. b, Shown are the percentage of valid reads. c, Shown are the percentage of reads aligned to the human genome. d, Shown are the percentage of reads mapping to miRBase human miRNA entries. e, Shown are the percentage of reads mapping to piRNA entries from piRBase. f, Shown are the percentage of reads mapping to ribosomal RNA. g, Shown are the percentage of reads mapping to small-nucleolar RNAs. h, Shown are the percentage of reads mapping to tRNA entries from GtRNAdb. i, Shown are the percentage of reads mapping to coding exons. j, Shown are the percentage of reads mapping to small cajal body-specific RNAs.

Extended Data Fig. 2 Quality control, batch variable analysis, and dimension reduction.

a, Histogram of RNA integrity numbers (RINs) for all valid sequencing samples. The dashed grey line indicates the distribution mean located at 7.8. b, Clustered pairwise correlation matrix of (pooled) technical controls and sequencing replicates based on the sncRNA counts. c, Clustered pairwise correlation matrix of all valid samples based on the known miRNA counts and with major annotation variables depicted on the left. d, The number of principal components resulting from PCA of the miRNA to sample expression matrix versus the cumulative percentage of variance explained (continuous blue line), the fraction of non-zero coefficients (dashed blue line), and the standard error (dashed orange line). e, Distribution of miRNA coefficient loadings (n = 897) onto the first two principal components. The smoothed trendline (orange) is enclosed by light grey bands showing the standard error. Center of measurements were computed by LOESS fitting. Standard errors correspond to the 95% confidence intervals. f, Stacked coefficient barplots for the miRNAs showing the largest sum of absolute coefficient contributions along the top 10 principal components as indicated by the color legend. g, PVCA based on sncRNA counts using the major annotation variables and combinations of such for all valid samples. h, UMAP embeddings for all valid samples using the sncRNA counts and colored in order by PPMI project phase, sequencing plate, study participant, age binning, genetic status, and Hoehn and Yahr staging. i, PVCA based on miRNA counts using the major annotation variables and combinations of such. j, UMAP embeddings for all valid samples using the miRNA counts and colored in order by gender, biogroup, PPMI project phase, genetic status, age binning, and clinical visits.

Extended Data Fig. 3 Complementary biogroup and cohort comparisons of miRNA expression.

a–j, Volcano plots with absolute effect size (cohen’s d) of expressed miRNAs for the secondary group comparisons considered in the PPMI study. MiRNAs that exhibit both a considerable effect size and fold-change are colored in blue or green when being down- or upregulated, respectively. Orange points depict miRNAs with a considerable effect size but a small fold-change. k, Normalized and log2-scaled expression of miRNAs showing a progressive depletion in PD (cf. Figure 3q) grid-wrapped by clinical visit and disease status / subcohort.

Extended Data Fig. 4 miRNA expression marker validation using 1,440 microarray samples of the independent NCER-PD cohort.

a, Scatter-plot of miRNA effect size (n = 416, black circles) between total PD and controls obtained from sncRNA-seq (PPMI) and microarray (NCER-PD). The smoothed trendline (orange) is enclosed by light grey bands showing the standard error. Center of measurements were computed by LOESS fitting. Standard errors correspond to the 95% confidence intervals. One-dimensional histograms are shown on the right and top for sncRNA-seq results and microarray results, respectively. b, Similar to a but restricted to the effect sizes between gPD and unaffected genetic carriers from the sequencing study. c, Venn-diagram for the most significant overlap between the miRNAs detected with sequencing or microarray as computed by DynaVenn. Input miRNAs were sorted by decreasing AUC for iPD vs. healthy controls in case of the sequencing data and iPD vs. controls in case of the microarray data. d, The most significant overlap obtained (adj. p = 3.39 × 10−18) at n = 222 shown in c as a function of the position in the DynaVenn input list. P values were computed using a two-sided hypergeometric test and subsequently corrected using the BH-FDR procedure at an α-cutoff of 0.05. e, Two-dimensional representation of the entire P value search space investigated by DynaVenn for all possible overlaps of miRNAs from the sequencing and microarray studies. P values were computed as described in d. f, Results of a miEAA 2.0 over-representation/enrichment analysis using the miRNAs determined for the most significant overlap displayed in c. The x-axis shows the BH-FDR adjusted P value for each category on the y-axis. Integers on the right display the number of hits observed per category or biological pathway. The color shading corresponds to the number of hits per category. P values were computed using a two-sided hypergeometric test and subsequently corrected using the BH-FDR procedure at an α-cutoff of 0.05.

Supplementary information

Supplementary Information

Detailed acknowledgements of the NCER-PD consortium.

Reporting Summary

Supplementary Table 1

Full sample and patient annotations for the PPMI cohort.

Supplementary Table 2

Results of primary (main cohorts) differential expression analysis comparisons of miRBase v22 miRNAs for the PPMI cohort. The table includes the statistics shown in the main results of the manuscript and raw as well as adjusted P values for the Wilcoxon rank-sum and Student’s t-test.

Supplementary Table 3

Results of primary differential expression analysis comparisons of new miRNA candidates for the PPMI cohort.

Supplementary Table 4

Results of secondary (other cohorts) differential expression analysis comparisons of miRBase v22 miRNAs for the PPMI cohort.

Supplementary Table 5

Results of secondary differential expression analysis comparisons of new miRNA candidates for the PPMI cohort.

Supplementary Table 6

Results of primary differential expression analysis comparisons of known sncRNAs for the PPMI cohort.

Supplementary Table 7

Results of secondary differential expression analysis comparisons of known sncRNAs for the PPMI cohort.

Supplementary Table 8

ANOVA results for primary sample annotation variables using miRBase miRNA counts from the PPMI cohort.

Supplementary Table 9

Full sample and patient annotations for the NCER-PD cohort.

Supplementary Table 10

Results of primary and secondary differential expression analysis comparisons of miRBase v21 miRNAs for the NCER-PD cohort.

Supplementary Table 11

Comparison of miRNA effect size and directionality of dysregulation (idiopathic PD versus healthy controls) between PPMI and NCER-PD. Results on concordance and disconcordance of miRNAs reported in the main text in contrast to NCER-PD are provided in a separate sheet.

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Kern, F., Fehlmann, T., Violich, I. et al. Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression. Nat Aging 1, 309–322 (2021). https://doi.org/10.1038/s43587-021-00042-6

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