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Identification of distinct blood-based biomarkers in early stage of Parkinson’s disease

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Abstract

Parkinson’s disease (PD) is a slowly progressive geriatric disease, which can be one of the leading causes of serious socioeconomic burden in the aging society. Clinical trials suggest that prompt treatment of early-stage Parkinson’s disease (EPD) may slow down the disease progress and have a better response. Therefore, conducting proteomics study to identify biomarkers for the diagnosis and disease-modifying therapies of EPD is vital. We aimed at identifying distinct protein autoantibody biomarkers of EPD by using the database of GSE62283 based on the platform GPL13669 downloaded from Gene Expression Omnibus database. Differentially expressed proteins (DEPs) between the EPD group (n = 103) and the normal control (NC) group (n = 111) were identified by protein-specific t test. Cluster analysis of DEPs was conducted by protein–protein interaction network to detect hub proteins. The hub proteins were then evaluated to determine the distinct biomarkers by principal component analysis, as well as functional and pathway enrichment analysis. Their biological functions were confirmed by gene ontology functional (GO) and Kyoto encyclopedia of genes and genomes pathway enrichment (KEGG). Two biomarkers, mitochondrial ribosome recycling factor (MRRF) and ribosomal protein S18 (RPS18), distinguished the EPD samples from the NC samples, and they were regarded as high-confidence distinct protein autoantibody biomarkers of EPD. The most significant GO function was protein serine/threonine kinase activity (GO: 0004674) and most of DEPs were enriched in ATP binding in molecular function category (GO: 0005524). These results may help in establishing the prompt and accurate diagnosis of EPD and may also contribute to develop mechanism-based treatments.

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Abbreviations

PD:

Parkinson’s disease

EPD:

Early-stage Parkinson’s disease

NC:

Normal control

DEPs:

Differentially expressed proteins

KEGG:

Kyoto encyclopedia of genes and genomes

GO:

Gene ontology

PPI:

Protein–protein interaction

α-syn:

α-Synuclein

PCA:

Principal component analysis

References

  1. Lin X, Cook TJ, Zabetian CP, Leverenz JB, Peskind ER, Hu SC, Cain KC, Pan C, Edgar JS, Goodlett DR, Racette BA, Checkoway H, Montine TJ, Shi M, Zhang J (2012) DJ-1 isoforms in whole blood as potential biomarkers of Parkinson disease. Sci Rep 2:954. https://doi.org/10.1038/srep00954

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhao YJ, Wee HL, Chan YH, Seah SH, Au WL, Lau PN, Pica EC, Li SC, Luo N, Tan LC (2010) Progression of Parkinson’s disease as evaluated by Hoehn and Yahr stage transition times. Mov Disord 25(6):710–716. https://doi.org/10.1002/mds.22875

    Article  PubMed  Google Scholar 

  3. Ma CL, Su L, Xie JJ, Long J-x, Wu P, Gu L (2014) The prevalence and incidence of Parkinson’s disease in China systematic review and meta-analysis. J Neural Transm 121:123–134. https://doi.org/10.1007/s00702-013-1092-z)

    Article  PubMed  Google Scholar 

  4. Dorsey ER, Constantinescu R, Thompson JP, Biglan KM, Holloway RG, Kieburtz K, Marshall FJ, Ravina BM, Schifitto G, Siderowf A, Tanner CM (2007) Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68(5):384–386. https://doi.org/10.1212/01.wnl.0000247740.47667.03

    Article  CAS  Google Scholar 

  5. Rodriguez-Blazquez C, Forjaz MJ, Lizan L, Paz S, Martinez-Martin P (2015) Estimating the direct and indirect costs associated with Parkinson’s disease. Expert Rev Pharmacoecon Outcomes Res 15(6):889–911. https://doi.org/10.1586/14737167.2015.1103184

    Article  PubMed  Google Scholar 

  6. Zhao YJ, Wee HL, Au WL, Seah SH, Luo N, Li SC, Tan LC (2011) Selegiline use is associated with a slower progression in early Parkinson’s disease as evaluated by Hoehn and Yahr stage transition times. Parkinsonism Relat Disord 17(3):194–197. https://doi.org/10.1016/j.parkreldis.2010.11.010

    Article  CAS  PubMed  Google Scholar 

  7. Schrag A, Spottke A, Quinn NP, Dodel R (2009) Comparative responsiveness of Parkinson’s disease scales to change over time. Mov Disord 24(6):813–818. https://doi.org/10.1002/mds.22438

    Article  PubMed  Google Scholar 

  8. Scanlon BK, Katzen HL, Levin BE, Singer C, Papapetropoulos S (2008) A formula for the conversion of UPDRS-III scores to Hoehn and Yahr stage. Parkinsonism Relat Disord 14(4):379–380. https://doi.org/10.1016/j.parkreldis.2007.09.010

    Article  PubMed  Google Scholar 

  9. Hoehn M, Yahr M (2011) Parkinsonism: Onset, progression, and mortality. Neurology 77(9):874–874. https://doi.org/10.1212/01.wnl.0000405146.06300.91

    Article  Google Scholar 

  10. Tsanas A, Little MA, McSharry PE, Scanlon BK, Papapetropoulos S (2012) Statistical analysis and mapping of the unified Parkinson’s disease rating scale to Hoehn and Yahr staging. Parkinsonism Relat Disord 18(5):697–699. https://doi.org/10.1016/j.parkreldis.2012.01.011

    Article  PubMed  Google Scholar 

  11. Reichmann H (2010) Clinical criteria for the diagnosis of Parkinson’s disease. Neurodegener Dis 7(5):284–290. https://doi.org/10.1159/000314478

    Article  PubMed  Google Scholar 

  12. Kalia LV, Lang AE (2015) Parkinson’s disease. Lancet 386(9996):896–912. https://doi.org/10.1016/s0140-6736(14)61393-3

    Article  CAS  PubMed  Google Scholar 

  13. Marconi S, Zwingers T (2014) Comparative efficacy of selegiline versus rasagiline in the treatment of early Parkinson’s disease. Eur Rev Med Pharmacol Sci 18(13):1879–1882

    CAS  PubMed  Google Scholar 

  14. Fahn S (2008) The history of dopamine and levodopa in the treatment of Parkinson’s disease. Mov Disord 23(Suppl 3):S497–S508. https://doi.org/10.1002/mds.22028

    Article  PubMed  Google Scholar 

  15. Group PsS (1996) Impact of deprenyl and tocopherol treatment on Parkinson’s disease in DATATOP patients requiring levodopa. Ann Neurol 39(1):37–45

    Article  Google Scholar 

  16. Adler CH, Beach TG, Hentz JG, Shill HA, Caviness JN, Driver-Dunckley E, Sabbagh MN, Sue LI, Jacobson SA, Belden CM, Dugger BN (2014) Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83(5):406–412. https://doi.org/10.1212/WNL.0000000000000641

    Article  PubMed  PubMed Central  Google Scholar 

  17. Miller DB, O’Callaghan JP (2015) Biomarkers of Parkinson’s disease: present and future. Metabolism 64(3 Suppl 1):S40–S46. https://doi.org/10.1016/j.metabol.2014.10.030

    Article  CAS  PubMed  Google Scholar 

  18. Sulzer D, Cassidy C, Horga G, Kang UJ, Fahn S, Casella L, Pezzoli G, Langley J, Hu XP, Zucca FA, Isaias IU, Zecca L (2018) Neuromelanin detection by magnetic resonance imaging (MRI) and its promise as a biomarker for Parkinson’s disease. NPJ Parkinson's disease 4:11. https://doi.org/10.1038/s41531-018-0047-3

    Article  PubMed  PubMed Central  Google Scholar 

  19. George S, Brundin P (2015) Immunotherapy in Parkinson’s disease: micromanaging alpha-synuclein aggregation. J Parkinsons Dis 5(3):413–424. https://doi.org/10.3233/JPD-150630

    Article  PubMed  PubMed Central  Google Scholar 

  20. DeMarshall CA, Han M, Nagele EP, Sarkar A, Acharya NK, Godsey G, Goldwaser EL, Kosciuk M, Thayasivam U, Belinka B, Nagele RG, Parkinson’s Study Group I (2015) Potential utility of autoantibodies as blood-based biomarkers for early detection and diagnosis of Parkinson’s disease. Immunol Lett 168(1):80–88. https://doi.org/10.1016/j.imlet.2015.09.010

    Article  CAS  PubMed  Google Scholar 

  21. Bougea A, Stefanis L, Paraskevas GP, Emmanouilidou E, Vekrelis K, Kapaki E (2019) Plasma alpha-synuclein levels in patients with Parkinson’s disease: a systematic review and meta-analysis. Neurological Sciences 40(5):929–938. https://doi.org/10.1007/s10072-019-03738-1

    Article  PubMed  Google Scholar 

  22. Alegre-Abarrategui J, Ansorge O, Esiri M, Wade-Martins R (2008) LRRK2 is a component of granular alpha-synuclein pathology in the brainstem of Parkinson’s disease. Neuropathol Appl Neurobiol 34(3):272–283. https://doi.org/10.1111/j.1365-2990.2007.00888.x

    Article  CAS  PubMed  Google Scholar 

  23. Mills RD, Sim CH, Mok SS, Mulhern TD, Culvenor JG, Cheng HC (2008) Biochemical aspects of the neuroprotective mechanism of PTEN-induced kinase-1 (PINK1). J Neurochem 105(1):18–33. https://doi.org/10.1111/j.1471-4159.2008.05249.x

    Article  CAS  PubMed  Google Scholar 

  24. Reetz K, Lencer R, Steinlechner S, Gaser C, Hagenah J, Buchel C, Petersen D, Kock N, Djarmati A, Siebner HR, Klein C, Binkofski F (2008) Limbic and frontal cortical degeneration is associated with psychiatric symptoms in PINK1 mutation carriers. Biol Psychiatry 64(3):241–247. https://doi.org/10.1016/j.biopsych.2007.12.010

    Article  CAS  PubMed  Google Scholar 

  25. van Duijn CM, Dekker MC, Bonifati V, Galjaard RJ, Houwing-Duistermaat JJ, Snijders PJ, Testers L, Breedveld GJ, Horstink M, Sandkuijl LA, van Swieten JC, Oostra BA, Heutink P (2001) Park7, a novel locus for autosomal recessive early-onset parkinsonism, on chromosome 1p36. Am J Human Genet 69(3):629–634. https://doi.org/10.1086/322996

    Article  Google Scholar 

  26. Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8(9):R183. https://doi.org/10.1186/gb-2007-8-9-r183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Consortium GO (2015) Gene ontology consortium: going forward. Nucleic Acids Res 43:1049–1056

    Article  Google Scholar 

  28. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38(Database issue):D355–D360. https://doi.org/10.1093/nar/gkp896

    Article  CAS  PubMed  Google Scholar 

  29. Von Mering CHM, Jaeggi D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31:258–261

    Article  Google Scholar 

  30. Kohl M, Wiese S, Warscheid B (2011) Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol 696(696):291–303. https://doi.org/10.1007/978-1-60761-987-1_18

    Article  CAS  PubMed  Google Scholar 

  31. Soreq L, Bergman H, Israel Z, Soreq H (2013) Deep brain stimulation modulates nonsense-mediated RNA decay in Parkinson’s patients leukocytes. BMC Genomics 14:478. https://doi.org/10.1186/1471-2164-14-478

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Perier C, Bove J, Vila M (2012) Mitochondria and programmed cell death in Parkinson’s disease: apoptosis and beyond. Antioxid Redox Signal 16(9):883–895. https://doi.org/10.1089/ars.2011.4074

    Article  CAS  PubMed  Google Scholar 

  33. Niranjan R (2014) The role of inflammatory and oxidative stress mechanisms in the pathogenesis of Parkinson’s disease: focus on astrocytes. Mol Neurobiol 49(1):28–38. https://doi.org/10.1007/s12035-013-8483-x

    Article  CAS  PubMed  Google Scholar 

  34. Sim CH, Lio DS, Mok SS, Masters CL, Hill AF, Culvenor JG, Cheng HC (2006) C-terminal truncation and Parkinson’s disease-associated mutations down-regulate the protein serine/threonine kinase activity of PTEN-induced kinase-1. Human Mol Genet 15(21):3251–3262. https://doi.org/10.1093/hmg/ddl398

    Article  CAS  Google Scholar 

  35. Inamdar AA, Masurekar P, Hossain M, Richardson JR, Bennett JW (2014) Signaling pathways involved in 1-octen-3-ol-mediated neurotoxicity in Drosophila melanogaster: implication in Parkinson’s disease. Neurotox Res 25(2):183–191. https://doi.org/10.1007/s12640-013-9418-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jha SK, Jha NK, Kar R, Ambasta RK, Kumar P (2015) p38 MAPK and PI3K/AKT signalling cascades in Parkinson’s disease. Int J Mol Cell Med 4(2):67–86

    CAS  PubMed  PubMed Central  Google Scholar 

  37. EMBL-EBI Quick GO (2009) https://wwwe.biacuk/QuickGO/. Accessed 3 Apr 2018

  38. ElAli A, Hermann DM (2011) ATP-binding cassette transporters and their roles in protecting the brain. Neuroscientist 17(4):423–436. https://doi.org/10.1177/1073858410391270

    Article  CAS  PubMed  Google Scholar 

  39. Taymans JM, Nkiliza A, Chartier-Harlin MC (2015) Deregulation of protein translation control, a potential game-changing hypothesis for Parkinson’s disease pathogenesis. Trends Mol Med 21(8):466–472. https://doi.org/10.1016/j.molmed.2015.05.004

    Article  CAS  PubMed  Google Scholar 

  40. Olanow CW, Schapira AH (2013) Therapeutic prospects for Parkinson disease. Ann Neurol 74(3):337–347. https://doi.org/10.1002/ana.24011

    Article  CAS  PubMed  Google Scholar 

  41. AlDakheel A, Kalia LV, Lang AE (2014) Pathogenesis-targeted, disease-modifying therapies in Parkinson disease. Neurotherapeutics 11(1):6–23. https://doi.org/10.1007/s13311-013-0218-1

    Article  CAS  PubMed  Google Scholar 

  42. Lin MT, Beal MF (2006) Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 443(7113):787–795. https://doi.org/10.1038/nature05292

    Article  CAS  PubMed  Google Scholar 

  43. Voshavar C, Shah M, Xu L, Dutta AK (2015) Assessment of protective role of multifunctional dopamine agonist D-512 against oxidative stress produced by depletion of glutathione in PC12 cells: implication in neuroprotective therapy for Parkinson’s disease. Neurotox Res 28(4):302–318. https://doi.org/10.1007/s12640-015-9548-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bhat AH, Dar KB, Anees S, Zargar MA, Masood A, Sofi MA, Ganie SA (2015) Oxidative stress, mitochondrial dysfunction and neurodegenerative diseases; a mechanistic insight. Biomed Pharmacother 74:101–110. https://doi.org/10.1016/j.biopha.2015.07.025

    Article  CAS  PubMed  Google Scholar 

  45. Muftuoglu M, Elibol B, Dalmizrak O, Ercan A, Kulaksiz G, Ogus H, Dalkara T, Ozer N (2004) Mitochondrial complex I and IV activities in leukocytes from patients with parkin mutations. Mov Disord 19(5):544–548. https://doi.org/10.1002/mds.10695

    Article  PubMed  Google Scholar 

  46. Ryan BJ, Hoek S, Fon EA, Wade-Martins R (2015) Mitochondrial dysfunction and mitophagy in Parkinson’s: from familial to sporadic disease. Trends Biochem Sci 40(4):200–210. https://doi.org/10.1016/j.tibs.2015.02.003

    Article  CAS  PubMed  Google Scholar 

  47. Gotz ME, Double K, Gerlach M, Youdim MB, Riederer P (2004) The relevance of iron in the pathogenesis of Parkinson’s disease. Ann N Y Acad Sci 1012:193–208

    Article  Google Scholar 

  48. Jiang D, Shi S, Zhang L, Liu L, Ding B, Zhao B, Yagnik G, Zhou F (2013) Inhibition of the Fe(III)-catalyzed dopamine oxidation by ATP and its relevance to oxidative stress in Parkinson’s disease. ACS Chem Neurosci 4(9):1305–1313. https://doi.org/10.1021/cn400105d

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Santiago JA, Potashkin JA (2015) Blood biomarkers associated with cognitive decline in early stage and drug-naive Parkinson’s disease patients. PLoS One 10(11):e0142582. https://doi.org/10.1371/journal.pone.0142582

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sveinbjornsdottir S (2016) The clinical symptoms of Parkinson’s disease. J Neurochem 139(Suppl 1):318–324. https://doi.org/10.1111/jnc.13691

    Article  CAS  PubMed  Google Scholar 

  51. Bonifati V, Rizzu P, van Baren MJ, Schaap O, Breedveld GJ, Krieger E, Dekker MC, Squitieri F, Ibanez P, Joosse M, van Dongen JW, Vanacore N, van Swieten JC, Brice A, Meco G, van Duijn CM, Oostra BA, Heutink P (2003) Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism. Science (New York, NY) 299(5604):256–259. https://doi.org/10.1126/science.1077209

    Article  CAS  Google Scholar 

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Funding

This work was supported by the National Key R&D Program of China (grant number 2016YFC1306000).

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Author notes

  1. Yingyan Wu and Qian Yao contributed equally to this work.

Authors and Affiliations

  1. School of Public Health, Shanghai Jiao Tong University, 227 South Chongqing Road, Shanghai, 200025, China

    Yingyan Wu, Qian Yao & Qi Cheng

  2. Department of Learning, Informatics, Management and Ethics, Karolinska Institute, 17177, Stockholm, Sweden

    Guo-Xin Jiang

  3. Department of Neurology & Neuroscience Institute, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Gang Wang

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  1. Yingyan Wu
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Correspondence to Qi Cheng.

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The original study [20] is approved by Rowan-Stratford Institutional Review Board.

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Wu, Y., Yao, Q., Jiang, GX. et al. Identification of distinct blood-based biomarkers in early stage of Parkinson’s disease. Neurol Sci 41, 893–901 (2020). https://doi.org/10.1007/s10072-019-04165-y

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