Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson's disease progression.
Journal
Nature aging
ISSN: 2662-8465
Titre abrégé: Nat Aging
Pays: United States
ID NLM: 101773306
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
08
10
2020
accepted:
08
02
2021
medline:
1
3
2021
pubmed:
1
3
2021
entrez:
28
4
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37118411
doi: 10.1038/s43587-021-00042-6
pii: 10.1038/s43587-021-00042-6
doi:
Substances chimiques
RNA, Small Untranslated
0
MicroRNAs
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
309-322Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc. part of Springer Nature.
Références
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).
doi: 10.1016/S0140-6736(16)31012-1
Deweerdt, S. Parkinson’s disease: 4 big questions. Nature 538, S17 (2016).
pubmed: 27783579
doi: 10.1038/538S17a
Kalia, L. V. & Lang, A. E. Parkinson’s disease. Lancet 386, 896–912 (2015).
pubmed: 25904081
doi: 10.1016/S0140-6736(14)61393-3
Jankovic, J. Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79, 368–376 (2008).
pubmed: 18344392
doi: 10.1136/jnnp.2007.131045
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).
pubmed: 20711177
pmcid: 2930111
doi: 10.1038/ng.642
Klemann, C. J. H. M. et al. Integrated molecular landscape of Parkinson’s disease. NPJ Parkinsons Dis. 3, 14 (2017).
pubmed: 28649614
pmcid: 5460267
doi: 10.1038/s41531-017-0015-3
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).
pubmed: 17215369
doi: 10.1073/pnas.0610204104
pmcid: 1766335
Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat. Commun. 10, 994 (2019).
pubmed: 30824768
pmcid: 6397174
doi: 10.1038/s41467-019-08912-9
Wang, Q. et al. The landscape of multiscale transcriptomic networks and key regulators in Parkinson’s disease. Nat. Commun. 10, 5234 (2019).
pubmed: 31748532
pmcid: 6868244
doi: 10.1038/s41467-019-13144-y
Calligaris, R. et al. Blood transcriptomics of drug-naive sporadic Parkinson’s disease patients. BMC Genomics 16, 876 (2015).
pubmed: 26510930
pmcid: 4625854
doi: 10.1186/s12864-015-2058-3
Chen-Plotkin, A. S. Blood transcriptomics for Parkinson disease? Nat. Rev. Neurol. 14, 5–6 (2018).
pubmed: 29192261
doi: 10.1038/nrneurol.2017.166
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).
pubmed: 30744671
pmcid: 6371578
doi: 10.1186/s13148-019-0621-5
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).
pubmed: 26889637
pmcid: 4927907
doi: 10.3233/JPD-150737
Marz, M., Ferracin, M. & Klein, C. MicroRNAs as biomarker of Parkinson disease? Neurology 84, 636 (2015).
pubmed: 25596504
doi: 10.1212/WNL.0000000000001275
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).
Starhof, C. et al. The biomarker potential of cell-free microRNA from cerebrospinal fluid in Parkinsonian syndromes. Mov. Disord. 34, 246–254 (2019).
pubmed: 30557454
doi: 10.1002/mds.27542
Keller, A. et al. Toward the blood-borne miRNome of human diseases. Nat. Methods 8, 841–843 (2011).
pubmed: 21892151
doi: 10.1038/nmeth.1682
Leidinger, P. et al. A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biol. 14, R78 (2013).
pubmed: 23895045
pmcid: 4053778
doi: 10.1186/gb-2013-14-7-r78
Keller, A. et al. Validating Alzheimer’s disease micro RNAs using next-generation sequencing. Alzheimers Dement. 12, 565–576 (2016).
pubmed: 26806387
doi: 10.1016/j.jalz.2015.12.012
Ludwig, N. et al. Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs. Genomics Proteomics Bioinformatics 17, 430–440 (2019).
pubmed: 31809862
pmcid: 6943763
doi: 10.1016/j.gpb.2019.09.004
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).
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).
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).
pubmed: 30952910
pmcid: 6472336
doi: 10.1038/s41597-019-0022-9
Lawton, M. et al. Blood biomarkers with Parkinson’s disease clusters and prognosis: the Oxford discovery cohort. Mov. Disord. 35, 279–287 (2020).
pubmed: 31693246
doi: 10.1002/mds.27888
Marek, K. et al. The Parkinson’s Progression Markers Initiative (PPMI) – establishing a PD biomarker cohort. Ann. Clin. Translat. Neurol. 5, 1460–1477 (2018).
doi: 10.1002/acn3.644
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).
Ludwig, N. et al. Small ncRNA-seq results of human tissues: variations depending on sample integrity. Clin. Chem. 64, 1074–1084 (2018).
pubmed: 29691221
doi: 10.1373/clinchem.2017.285767
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).
pubmed: 28911107
pmcid: 5587802
doi: 10.1093/nar/gkx595
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).
pubmed: 31888436
pmcid: 6937821
doi: 10.1186/s12859-019-3320-5
Kern, F. et al. miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems. Nucleic Acids Res. 48, W521–W528 (2020).
pubmed: 32374865
pmcid: 7319446
doi: 10.1093/nar/gkaa309
Antony, P. M. A., Diederich, N. J., Krüger, R. & Balling, R. The hallmarks of Parkinson’s disease. FEBS J. 280, 5981–5993 (2013).
pubmed: 23663200
doi: 10.1111/febs.12335
Huang, Z. et al. HMDD v3.0: a database for experimentally supported human microRNA–disease associations. Nucleic Acids Res. 47, D1013–D1017 (2019).
pubmed: 30364956
doi: 10.1093/nar/gky1010
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).
pubmed: 26631952
doi: 10.1016/j.parkreldis.2015.11.014
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).
pubmed: 30665415
pmcid: 6341689
doi: 10.1186/s12918-019-0680-4
Martins, M. et al. Convergence of miRNA expression profiling, α-synuclein interaction and GWAS in Parkinson’s disease. PLoS ONE 6, e25443 (2011).
pubmed: 22003392
pmcid: 3189215
doi: 10.1371/journal.pone.0025443
Caggiu, E. et al. Differential expression of miRNA 155 and miRNA 146a in Parkinson’s disease patients. eNeurologicalSci 13, 1–4 (2018).
pubmed: 30255159
pmcid: 6149197
doi: 10.1016/j.ensci.2018.09.002
Chi, J. et al. Integrated analysis and identification of novel biomarkers in Parkinson’s disease. Front. Aging Neurosci. 10, 178 (2018).
pubmed: 29967579
pmcid: 6016006
doi: 10.3389/fnagi.2018.00178
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).
doi: 10.1002/acn3.51146
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).
pubmed: 24648008
doi: 10.1002/jnr.23377
Bai, X. et al. Downregulation of blood serum microRNA 29 family in patients with Parkinson’s disease. Sci. Rep. 7, 5411 (2017).
pubmed: 28710399
pmcid: 5511199
doi: 10.1038/s41598-017-03887-3
Cao, X.-Y. et al. MicroRNA biomarkers of Parkinson’s disease in serum exosome-like microvesicles. Neurosci. Lett. 644, 94–99 (2017).
pubmed: 28223160
doi: 10.1016/j.neulet.2017.02.045
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).
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).
pubmed: 24797360
pmcid: 4010405
doi: 10.1371/journal.pone.0094839
Paschon, V. et al. Interplay between exosomes, microRNAs and Toll-Like receptors in brain disorders. Mol. Neurobiol. 53, 2016–2028 (2016).
pubmed: 25862375
doi: 10.1007/s12035-015-9142-1
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).
pubmed: 30018301
pmcid: 6050266
doi: 10.1038/s41598-018-28986-7
Nissen, S. K. et al. Alterations in blood monocyte functions in Parkinson’s disease. Mov. Disord. 34, 1711–1721 (2019).
pubmed: 31449711
doi: 10.1002/mds.27815
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).
pubmed: 31799764
doi: 10.1002/mds.27928
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).
pubmed: 31123700
pmcid: 6531455
doi: 10.1038/s41531-019-0080-x
Shamir, R. et al. Analysis of blood-based gene expression in idiopathic Parkinson disease. Neurology 89, 1676 (2017).
pubmed: 28916538
pmcid: 5644465
doi: 10.1212/WNL.0000000000004516
Backes, C. et al. MiRCarta: a central repository for collecting miRNA candidates. Nucleic Acids Res. 46, D160–D167 (2018).
pubmed: 29036653
doi: 10.1093/nar/gkx851
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).
Keller, A. et al. miRNAs can be generally associated with human pathologies as exemplified for miR-144*. BMC Med. 12, 224 (2014).
pubmed: 25465851
pmcid: 4268797
doi: 10.1186/s12916-014-0224-0
Fehlmann, T. et al. Common diseases alter the physiological age-related blood microRNA profile. Nat. Commun. 11, 5958 (2020).
pubmed: 33235214
pmcid: 7686493
doi: 10.1038/s41467-020-19665-1
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).
doi: 10.1016/j.neurobiolaging.2017.05.009
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. J. Open Source Softw. 3, 861 (2018).
doi: 10.21105/joss.00861
Juzenas, S. et al. A comprehensive, cell specific microRNA catalogue of human peripheral blood. Nucleic Acids Res. 45, 9290–9301 (2017).
pubmed: 28934507
pmcid: 5766192
doi: 10.1093/nar/gkx706