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.

RNA sequencing of whole blood reveals early alterations in immune cells and gene expression in Parkinson’s disease

Abstract

Changes in the blood-based RNA transcriptome have the potential to inform biomarkers of Parkinson’s disease (PD) progression. Here we sequenced a discovery set of whole-blood RNA species in 4,871 longitudinally collected samples from 1,570 clinically phenotyped individuals from the Parkinson’s Progression Marker Initiative (PPMI) cohort. Samples were sequenced to an average of 100 million read pairs to create a high-quality transcriptome. Participants with PD in the PPMI had significantly altered RNA expression (>2,000 differentially expressed genes), including an early and persistent increase in neutrophil gene expression, with a concomitant decrease in lymphocyte cell counts. This was validated in a cohort from the Parkinson’s Disease Biomarkers Program (PDBP) consisting of 1,599 participants and by alterations in immune cell subtypes. This publicly available transcriptomic dataset, coupled with available detailed clinical data, provides new insights into PD biological processes impacting whole blood and new paths for developing diagnostic and prognostic PD biomarkers.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Samples and analysis overview and QC.
Fig. 2: Variance analysis by covariates.
Fig. 3: Differential expression in patients with PD versus controls.
Fig. 4: Cell count percentage in the PPMI cohort and genes enriched in neutrophils and lymphocytes in the PPMI and PDBP cohorts.
Fig. 5: Cell count percentage and genes enriched in neutrophils and in different types of PD represented in the PPMI cohort.
Fig. 6: Neutrophil gene expression increases over time in prodromal participants.

Data availability

Raw sequencing data (FASTQ files), alignment files (BAM files), TPM data and counts for each sample are available at the LONI IDA. (https://fairsharing.org/, IDA; LONI IDA, https://doi.org/10.25504/FAIRsharing.r4ph5f). Data are also available through the AMP PD (https://amp-pd.org/). These are the requirements for downloading from the AMP PD: (1) personal and institutional or company details; (2) description of intended data use, for example, proposed analyses; (3) institutional signature on the AMP PD Data Use Agreement (for researchers requesting access to individual-level, ’omics data). Additional data, including but not limited to study arm, motor assessments, DaTscan and MRI imaging, genetic testing results, whole-exome and genome sequencing data, patient history and standardized techniques and protocols for data collection are also available through the IDA. To access complete data, researchers need to fill out a data-use agreement. Data are available in a public (institutional, general or participant-specific) repository that does not issue datasets with DOIs (non-mandated deposition).

Code availability

All code used for data analysis is available at GitHub: https://github.com/tgen/ppmi-qc-wt-paper and https://github.com/tgen/ppmi-rnaseq-wb-paper.

References

  1. Dorsey, E. R., Sherer, T., Okun, M. S. & Bloem, B. R. The emerging evidence of the Parkinson pandemic. J. Parkinsons Dis. 8, S3–S8 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Yang, W. et al. Current and projected future economic burden of Parkinson’s disease in the U.S. NPJ Parkinsons Dis. 6, 15 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Latourelle, J. C. et al. Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson’s disease: a longitudinal cohort study and validation. Lancet Neurol. 16, 908–916 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Nalls, M. A. et al. Baseline genetic associations in the Parkinson’s Progression Markers Initiative (PPMI). Mov. Disord. 31, 79–85 (2016).

    Article  PubMed  CAS  Google Scholar 

  6. Nalls, M. A. et al. Diagnosis of Parkinson’s disease on the basis of clinical and genetic classification: a population-based modelling study. Lancet Neurol. 14, 1002–1009 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Parkinson Progression Marker Initiative. The Parkinson Progression Marker Initiative (PPMI). Prog. Neurobiol. 95, 629–635 (2011).

    Article  Google Scholar 

  8. Simuni, T. et al. Longitudinal change of clinical and biological measures in early Parkinson’s disease: Parkinson’s Progression Markers Initiative cohort. Mov. Disord. 33, 771–782 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Caspell-Garcia, C. et al. Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease. PLoS ONE 12, e0175674 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Kang, J. H. et al. Association of cerebrospinal fluid β-amyloid 1-42, T-tau, P-tau181, and α-synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA Neurol. 70, 1277–1287 (2013).

    PubMed  PubMed Central  Google Scholar 

  11. Simuni, T. et al. Clinical and dopamine transporter imaging characteristics of non-manifest LRRK2 and GBA mutation carriers in the Parkinson’s Progression Markers Initiative (PPMI): a cross-sectional study. Lancet Neurol. 19, 71–80 (2020).

    Article  PubMed  CAS  Google Scholar 

  12. Polymeropoulos, M. H. et al. Mutation in the α-synuclein gene identified in families with Parkinson’s disease. Science 276, 2045–2047 (1997).

    Article  PubMed  CAS  Google Scholar 

  13. Hernandez, D. G., Reed, X. & Singleton, A. B. Genetics in Parkinson disease: Mendelian versus non-Mendelian inheritance. J. Neurochem. 139, 59–74 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Bandres-Ciga, S., Diez-Fairen, M., Kim, J. J. & Singleton, A. B. Genetics of Parkinson’s disease: an introspection of its journey towards precision medicine. Neurobiol. Dis. 137, 104782 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Funayama, M. et al. An LRRK2 mutation as a cause for the parkinsonism in the original PARK8 family. Ann. Neurol. 57, 918–921 (2005).

    Article  PubMed  CAS  Google Scholar 

  16. Paisan-Ruiz, C. et al. Cloning of the gene containing mutations that cause PARK8-linked Parkinson’s disease. Neuron 44, 595–600 (2004).

    Article  PubMed  CAS  Google Scholar 

  17. Zimprich, A. et al. Mutations in LRRK2 cause autosomal-dominant parkinsonism with pleomorphic pathology. Neuron 44, 601–607 (2004).

    Article  PubMed  CAS  Google Scholar 

  18. Sardi, S. P., Cedarbaum, J. M. & Brundin, P. Targeted therapies for Parkinson’s disease: from genetics to the clinic. Mov. Disord. 33, 684–696 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hirsch, E. C. & Hunot, S. Neuroinflammation in Parkinson’s disease: a target for neuroprotection? Lancet Neurol. 8, 382–397 (2009).

    Article  PubMed  CAS  Google Scholar 

  20. McGeer, P. L., Itagaki, S., Boyes, B. E. & McGeer, E. G. Reactive microglia are positive for HLA-DR in the substantia nigra of Parkinson’s and Alzheimer’s disease brains. Neurology 38, 1285–1291 (1988).

    Article  PubMed  CAS  Google Scholar 

  21. Damier, P., Hirsch, E. C., Zhang, P., Agid, Y. & Javoy-Agid, F. Glutathione peroxidase, glial cells and Parkinson’s disease. Neuroscience 52, 1–6 (1993).

    Article  PubMed  CAS  Google Scholar 

  22. Brochard, V. et al. Infiltration of CD4+ lymphocytes into the brain contributes to neurodegeneration in a mouse model of Parkinson disease. J. Clin. Invest. 119, 182–192 (2009).

    PubMed  CAS  Google Scholar 

  23. Garretti, F., Agalliu, D., Lindestam Arlehamn, C. S., Sette, A. & Sulzer, D. Autoimmunity in Parkinson’s disease: the role of α-synuclein-specific T cells. Front. Immunol. 10, 303 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Akil, E. et al. The increase of carcinoembryonic antigen (CEA), high-sensitivity C-reactive protein, and neutrophil/lymphocyte ratio in Parkinson’s disease. Neurol. Sci. 36, 423–428 (2015).

    Article  PubMed  Google Scholar 

  25. Jin, H., Gu, H. Y., Mao, C. J., Chen, J. & Liu, C. F. Association of inflammatory factors and aging in Parkinson’s disease. Neurosci. Lett. 736, 135259 (2020).

    Article  PubMed  CAS  Google Scholar 

  26. Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. 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 

  28. Borrageiro, G., Haylett, W., Seedat, S., Kuivaniemi, H. & Bardien, S. A review of genome-wide transcriptomics studies in Parkinson’s disease. Eur. J. Neurosci. 47, 1–16 (2018).

    Article  PubMed  Google Scholar 

  29. 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  PubMed  PubMed Central  CAS  Google Scholar 

  30. Uhlen, M. et al. A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science 366, eaax9198 (2019).

    Article  PubMed  CAS  Google Scholar 

  31. Scherzer, C. R. et al. Molecular markers of early Parkinson’s disease based on gene expression in blood. Proc. Natl Acad. Sci. USA 104, 955–960 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Soreq, L. et al. Long non-coding RNA and alternative splicing modulations in Parkinson’s leukocytes identified by RNA sequencing. PLoS Comput. Biol. 10, e1003517 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. 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 

  34. Parnetti, L. et al. CSF and blood biomarkers for Parkinson’s disease. Lancet Neurol. 18, 573–586 (2019).

    Article  PubMed  CAS  Google Scholar 

  35. Wang, Y. & Wang, Z. An integrated network analysis of mRNA and gene expression profiles in Parkinson’s disease. Med. Sci. Monit. 26, e920846 (2020).

    PubMed  PubMed Central  CAS  Google Scholar 

  36. Amor, S. et al. Inflammation in neurodegenerative diseases—an update. Immunology 142, 151–166 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Prinz, M. & Priller, J. The role of peripheral immune cells in the CNS in steady state and disease. Nat. Neurosci. 20, 136–144 (2017).

    Article  PubMed  CAS  Google Scholar 

  38. Haghshomar, M. et al. White matter changes correlates of peripheral neuroinflammation in patients with Parkinson’s disease. Neuroscience 403, 70–78 (2019).

    Article  PubMed  CAS  Google Scholar 

  39. Gao, A. Identification of blood-based biomarkers for early stage Parkinson’s disease. Preprint at medRxiv https://doi.org/10.1101/2020.10.22.20217893 (2020).

  40. Courtney, E., Kornfeld, S., Janitz, K. & Janitz, M. Transcriptome profiling in neurodegenerative disease. J. Neurosci. Methods 193, 189–202 (2010).

    Article  PubMed  CAS  Google Scholar 

  41. Grunblatt, E. et al. Gene expression profiling of parkinsonian substantia nigra pars compacta; alterations in ubiquitin–proteasome, heat shock protein, iron and oxidative stress regulated proteins, cell adhesion/cellular matrix and vesicle trafficking genes. J. Neural Transm. 111, 1543–1573 (2004).

    Article  PubMed  CAS  Google Scholar 

  42. 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 

  43. Kedmi, M., Bar-Shira, A., Gurevich, T., Giladi, N. & Orr-Urtreger, A. Decreased expression of B cell related genes in leukocytes of women with Parkinson’s disease. Mol. Neurodegener. 6, 66 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Locascio, J. J. et al. Association between α-synuclein blood transcripts and early, neuroimaging-supported Parkinson’s disease. Brain 138, 2659–2671 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Mollenhauer, B. et al. α-synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study. Lancet Neurol. 10, 230–240 (2011).

    Article  PubMed  CAS  Google Scholar 

  46. Jellinger, K. A. Synuclein deposition and non-motor symptoms in Parkinson disease. J. Neurol. Sci. 310, 107–111 (2011).

    Article  PubMed  CAS  Google Scholar 

  47. Cai, C. et al. Is human blood a good surrogate for brain tissue in transcriptional studies? BMC Genomics 11, 589 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Sullivan, P. F., Fan, C. & Perou, C. M. Evaluating the comparability of gene expression in blood and brain. Am. J. Med. Genet. B Neuropsychiatr. Genet. 141B, 261–268 (2006).

    Article  PubMed  Google Scholar 

  49. Kern, F. 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).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  51. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  52. Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

    Article  PubMed  CAS  Google Scholar 

  53. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Law, C. W. et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res 5, 1408 (2016).

    Article  CAS  Google Scholar 

  59. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  PubMed  CAS  Google Scholar 

  61. Prufer, K. et al. FUNC: a package for detecting significant associations between gene sets and ontological annotations. BMC Bioinformatics 8, 41 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Manjang, K., Tripathi, S., Yli-Harja, O., Dehmer, M. & Emmert-Streib, F. Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Sci. Rep. 10, 16672 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was supported and funded by the MJFF under grant numbers 12749 (K.V.K.-J.), 12749.01 (K.V.K.-J., M.R.C.) and 14696 (K.V.K.-J., A.K., D.W.C.). Many MJFF staff assisted in harmonizing and transferring data. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Institute on Aging. Data used in the preparation of this article were obtained from the PPMI database (https://www.ppmi-info.org/data). For up-to-date information on the study, visit https://www.ppmi-info.org/. We also acknowledge industry partners of the PPMI: AbbVie, Allergan, Amathus Therapeutics, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol Myers Squibb, Celgene, Denali, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lilly, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily and Voyager Therapeutics. We also thank the AMP PD for allowing us access to PDBP data. The PDBP consortium is supported by the National Institute of Neurological Disorders and Stroke at the National Institutes of Health. A full list of PDBP investigators can be found at https://pdbp.ninds.nih.gov/policy. PDBP investigators have not participated in reviewing the data analysis or content of the text. Data used in the preparation of this article were obtained from the AMP PD Knowledge Platform. For up-to-date information on the study, visit https://www.amp-pd.org. The AMP PD, a public–private partnership, is managed by the FNIH and funded by Celgene, GlaxoSmithKline, the MJFF, the National Institute of Neurological Disorders and Stroke, Pfizer, Sanofi and Verily. We thank all people with PD and families for participating in the study and donating their samples and time. We would like to thank the group at IU for RNA isolation, QC metrics and safe shipment of samples. We thank the group at the HAIB for running sequencing and sample tracking.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

D.W.C., E.H., I.V., E.A., J.R.G., T.G.W., B.C., A.R., S.H., M.F., A.K., M.R.C. and K.V.K.-J. each contributed to the overall study design. D.W.C., E.H., I.V., E.A., S.K., F.K., T. Fehlman, A.K., M.R.C. and K.V.K.-J. each contributed to primary data analysis, including statistical evaluation. D.W.C., E.H., I.V., E.A., K.L.C. and A.W.T. contributed to data organization and data storage. T. Foroud, S.L. and M.R. each contributed to nucleic acid isolation and sequencing. D.W.C., E.H., T.G.W., M.R.C. and K.V.K.-J. each substantially contributed to writing the manuscript. All authors contributed to data interpretation and review and editing of the manuscript.

Corresponding author

Correspondence to Kendall Van Keuren-Jensen.

Ethics declarations

Competing interests

B.C., S.H., A.R. and M.F. are employees of the MJFF. E.H., D.W.C., I.V., E.A., K.L.C., A.W.T. and K.V.K.-J. are funded by the MJFF. The other authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks the anonymous reviewers 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.

Supplementary information

Supplementary Information

PPMI author list and Supplementary Methods, Figs. 1–10 and Table 2.

Reporting Summary

Supplementary Tables

Supplementary Tables 1 and 3–11.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Craig, D.W., Hutchins, E., Violich, I. et al. RNA sequencing of whole blood reveals early alterations in immune cells and gene expression in Parkinson’s disease. Nat Aging 1, 734–747 (2021). https://doi.org/10.1038/s43587-021-00088-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-021-00088-6

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing