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Deployment of Label-Free Quantitative Olfactory Proteomics to Detect Cerebrospinal Fluid Biomarker Candidates in Synucleinopathies

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Cerebrospinal Fluid (CSF) Proteomics

Abstract

Nowadays, diagnosis of neurodegenerative disorders is mainly based on neuroimaging and clinical symptoms, although postmortem neuropathological confirmation remains the gold standard diagnostic technique. Therefore, cerebrospinal fluid (CSF) proteome is considered a valuable molecular repository for diagnosing and targeting the neurodegenerative process. It is well known that olfactory dysfunction is among the earliest features of synucleinopathies such as Parkinson’s disease (PD). Consequently, we consider that the application of tissue proteomics in primary olfactory structures is an ideal approach to explore early pathophysiological changes, detecting olfactory proteins that might be tested in CSF as potential biomarkers. Data mining of mass spectrometry-generated datasets has revealed that 30% of the olfactory bulb (OB) proteome is also localized in CSF. In this chapter, we describe a method that utilizes label-free quantitative proteomics and computational analysis to characterize human OB proteomes and potential cerebrospinal fluid (CSF) biomarkers associated with neurodegenerative syndromes. For that, we applied peptide fractionation methods, followed by tandem mass spectrometry (nanoLC-MS/MS), in silico analysis, and semi-quantitative orthogonal techniques in OB derived from PD subjects. After obtaining the differential OB proteome across Lewy-type alpha-synucleinopathy (LTS) stages and further validating the method, this workflow was applied to probe changes in NEGR1 (neuronal growth regulator 1) and GNPDA2 (glucosamine-6-phosphate deaminase 2) protein levels in CSF derived from parkinsonian subjects with respect to controls, observing an inverse correlation between both proteins and α-synuclein, the principal component analysis of Lewy pathology.

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References

  1. Attems J, Walker L, Jellinger KA (2014) Olfactory bulb involvement in neurodegenerative diseases. Acta Neuropathol 127(4):459–475. https://doi.org/10.1007/s00401-014-1261-7

    Article  CAS  PubMed  Google Scholar 

  2. Doty RL (2012) Olfactory dysfunction in Parkinson disease. Nat Rev Neurol 8(6):329–339. https://doi.org/10.1038/nrneurol.2012.80. nrneurol.2012.80 [pii]

    Article  CAS  PubMed  Google Scholar 

  3. Baba T, Kikuchi A, Hirayama K, Nishio Y, Hosokai Y, Kanno S, Hasegawa T, Sugeno N, Konno M, Suzuki K, Takahashi S, Fukuda H, Aoki M, Itoyama Y, Mori E, Takeda A (2012) Severe olfactory dysfunction is a prodromal symptom of dementia associated with Parkinson’s disease: a 3 year longitudinal study. Brain 135(Pt 1):161–169. https://doi.org/10.1093/brain/awr321. awr321 [pii]

    Article  PubMed  Google Scholar 

  4. Beach TG, White CL III, Hladik CL, Sabbagh MN, Connor DJ, Shill HA, Sue LI, Sasse J, Bachalakuri J, Henry-Watson J, Akiyama H, Adler CH (2009) Olfactory bulb alpha-synucleinopathy has high specificity and sensitivity for Lewy body disorders. Acta Neuropathol 117(2):169–174. https://doi.org/10.1007/s00401-008-0450-7

    Article  CAS  PubMed  Google Scholar 

  5. Doty RL (2008) The olfactory vector hypothesis of neurodegenerative disease: is it viable? Ann Neurol 63(1):7–15. https://doi.org/10.1002/ana.21327

    Article  PubMed  Google Scholar 

  6. Doty RL (2012) Olfaction in Parkinson’s disease and related disorders. Neurobiol Dis 46(3):527–552. https://doi.org/10.1016/j.nbd.2011.10.026. S0969-9961(11)00358-5 [pii]

    Article  PubMed  Google Scholar 

  7. Brodoehl S, Klingner C, Volk GF, Bitter T, Witte OW, Redecker C (2012) Decreased olfactory bulb volume in idiopathic Parkinson’s disease detected by 3.0-tesla magnetic resonance imaging. Mov Disord 27(8):1019–1025. https://doi.org/10.1002/mds.25087

    Article  PubMed  Google Scholar 

  8. Li J, Gu CZ, Su JB, Zhu LH, Zhou Y, Huang HY, Liu CF (2016) Changes in olfactory bulb volume in Parkinson’s disease: a systematic review and meta-analysis. PLoS One 11(2):e0149286. https://doi.org/10.1371/journal.pone.0149286. PONE-D-15-43501 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Saito Y, Shioya A, Sano T, Sumikura H, Murata M, Murayama S (2016) Lewy body pathology involves the olfactory cells in Parkinson’s disease and related disorders. Mov Disord 31(1):135–138. https://doi.org/10.1002/mds.26463

    Article  CAS  PubMed  Google Scholar 

  10. Ubeda-Banon I, Saiz-Sanchez D, de la Rosa-Prieto C, Argandona-Palacios L, Garcia-Munozguren S, Martinez-Marcos A (2010) alpha-Synucleinopathy in the human olfactory system in Parkinson’s disease: involvement of calcium-binding protein- and substance P-positive cells. Acta Neuropathol 119(6):723–735. https://doi.org/10.1007/s00401-010-0687-9

    Article  CAS  PubMed  Google Scholar 

  11. Ubeda-Banon I, Saiz-Sanchez D, de la Rosa-Prieto C, Martinez-Marcos A (2012) alpha-Synuclein in the olfactory system of a mouse model of Parkinson’s disease: correlation with olfactory projections. Brain Struct Funct 217(2):447–458. https://doi.org/10.1007/s00429-011-0347-4

    Article  CAS  PubMed  Google Scholar 

  12. Ubeda-Banon I, Saiz-Sanchez D, de la Rosa-Prieto C, Mohedano-Moriano A, Fradejas N, Calvo S, Argandona-Palacios L, Garcia-Munozguren S, Martinez-Marcos A (2010) Staging of alpha-synuclein in the olfactory bulb in a model of Parkinson’s disease: cell types involved. Mov Disord 25(11):1701–1707. https://doi.org/10.1002/mds.23197

    Article  PubMed  Google Scholar 

  13. Klingelhoefer L, Reichmann H (2015) Pathogenesis of Parkinson disease--the gut-brain axis and environmental factors. Nat Rev Neurol 11(11):625–636. https://doi.org/10.1038/nrneurol.2015.197. nrneurol.2015.197 [pii],

    Article  CAS  PubMed  Google Scholar 

  14. Rey NL, Steiner JA, Maroof N, Luk KC, Madaj Z, Trojanowski JQ, Lee VM, Brundin P (2016) Widespread transneuronal propagation of alpha-synucleinopathy triggered in olfactory bulb mimics prodromal Parkinson’s disease. J Exp Med 213(9):1759–1778. https://doi.org/10.1084/jem.20160368. jem.20160368 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fernandez-Irigoyen J, Corrales FJ, Santamaria E (2012) Proteomic atlas of the human olfactory bulb. J Proteomics 75(13):4005–4016. https://doi.org/10.1016/j.jprot.2012.05.011. S1874-3919(12)00299-0 [pii]

    Article  CAS  PubMed  Google Scholar 

  16. Lachen-Montes M, Gonzalez-Morales A, de Morentin XM, Perez-Valderrama E, Ausin K, Zelaya MV, Serna A, Aso E, Ferrer I, Fernandez-Irigoyen J, Santamaria E (2016) An early dysregulation of FAK and MEK/ERK signaling pathways precedes the beta-amyloid deposition in the olfactory bulb of APP/PS1 mouse model of Alzheimer’s disease. J Proteomics 148:149–158. https://doi.org/10.1016/j.jprot.2016.07.032. S1874-3919(16)30345-1 [pii]

    Article  CAS  PubMed  Google Scholar 

  17. Lachen-Montes M, Gonzalez-Morales A, Zelaya MV, Perez-Valderrama E, Ausin K, Ferrer I, Fernandez-Irigoyen J, Santamaria E (2017) Olfactory bulb neuroproteomics reveals a chronological perturbation of survival routes and a disruption of prohibitin complex during Alzheimer’s disease progression. Sci Rep 7(1):9115. https://doi.org/10.1038/s41598-017-09481-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zelaya MV, Perez-Valderrama E, de Morentin XM, Tunon T, Ferrer I, Luquin MR, Fernandez-Irigoyen J, Santamaria E (2015) Olfactory bulb proteome dynamics during the progression of sporadic Alzheimer’s disease: identification of common and distinct olfactory targets across Alzheimer-related co-pathologies. Oncotarget 6(37):39437–39456. https://doi.org/10.18632/oncotarget.6254. 6254 [pii]

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lachen-Montes M, Fernandez-Irigoyen J, Santamaria E (2016) Deconstructing the molecular architecture of olfactory areas using proteomics. Proteomics Clin Appl 10:1178. https://doi.org/10.1002/prca.201500147

    Article  CAS  PubMed  Google Scholar 

  20. Lachen-Montes M, Zelaya MV, Segura V, Fernandez-Irigoyen J, Santamaria E (2017) Progressive modulation of the human olfactory bulb transcriptome during Alzheimer s disease evolution: novel insights into the olfactory signaling across proteinopathies. Oncotarget 8(41):69663–69679. https://doi.org/10.18632/oncotarget.18193. 18193 [pii]

    Article  PubMed  PubMed Central  Google Scholar 

  21. Palomino-Alonso M, Lachen-Montes M, Gonzalez-Morales A, Ausin K, Perez-Mediavilla A, Fernandez-Irigoyen J, Santamaria E (2017) Network-driven proteogenomics unveils an aging-related imbalance in the olfactory IkappaBalpha-NFkappaB p65 complex functionality in Tg2576 Alzheimer’s disease mouse model. Int J Mol Sci 18(11):2260. https://doi.org/10.3390/ijms18112260. ijms18112260 [pii]

    Article  CAS  PubMed Central  Google Scholar 

  22. Lachen-Montes M, Gonzalez-Morales A, Iloro I, Elortza F, Ferrer I, Gveric D, Fernandez-Irigoyen J, Santamaria E (2018) Unveiling the olfactory proteostatic disarrangement in Parkinson’s disease by proteome-wide profiling. Neurobiol Aging 73:123–134. https://doi.org/10.1016/j.neurobiolaging.2018.09.018. S0197-4580(18)30343-9 [pii]

    Article  CAS  PubMed  Google Scholar 

  23. Nagayama S, Homma R, Imamura F (2014) Neuronal organization of olfactory bulb circuits. Front Neural Circ 8:98. https://doi.org/10.3389/fncir.2014.00098

    Article  Google Scholar 

  24. Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537(7620):347–355. https://doi.org/10.1038/nature19949. nature19949 [pii]

    Article  CAS  PubMed  Google Scholar 

  25. Huhmer AF, Biringer RG, Amato H, Fonteh AN, Harrington MG (2006) Protein analysis in human cerebrospinal fluid: physiological aspects, current progress and future challenges. Dis Markers 22(1-2):3–26

    Article  PubMed  Google Scholar 

  26. Parnetti L, Chiasserini D, Bellomo G, Giannandrea D, De Carlo C, Qureshi MM, Ardah MT, Varghese S, Bonanni L, Borroni B, Tambasco N, Eusebi P, Rossi A, Onofrj M, Padovani A, Calabresi P, El-Agnaf O (2011) Cerebrospinal fluid Tau/alpha-synuclein ratio in Parkinson’s disease and degenerative dementias. Mov Disord 26(8):1428–1435. https://doi.org/10.1002/mds.23670

    Article  PubMed  Google Scholar 

  27. Parnetti L, Paciotti S, Eusebi P, Dardis A, Zampieri S, Chiasserini D, Tasegian A, Tambasco N, Bembi B, Calabresi P, Beccari T (2017) Cerebrospinal fluid beta-glucocerebrosidase activity is reduced in Parkinson’s disease patients. Mov Disord 32(10):1423–1431. https://doi.org/10.1002/mds.27136

    Article  CAS  PubMed  Google Scholar 

  28. Parnetti L, Castrioto A, Chiasserini D, Persichetti E, Tambasco N, El-Agnaf O, Calabresi P (2013) Cerebrospinal fluid biomarkers in Parkinson disease. Nat Rev Neurol 9(3):131–140. https://doi.org/10.1038/nrneurol.2013.10. nrneurol.2013.10 [pii]

    Article  CAS  PubMed  Google Scholar 

  29. Herbert MK, Aerts MB, Beenes M, Norgren N, Esselink RA, Bloem BR, Kuiperij HB, Verbeek MM (2015) CSF Neurofilament light chain but not FLT3 ligand discriminates parkinsonian disorders. Front Neurol 6:91. https://doi.org/10.3389/fneur.2015.00091

    Article  PubMed  PubMed Central  Google Scholar 

  30. Farotti L, Paciotti S, Tasegian A, Eusebi P, Parnetti L (2017) Discovery, validation and optimization of cerebrospinal fluid biomarkers for use in Parkinson’s disease. Expert Rev Mol Diagn 17(8):771–780. https://doi.org/10.1080/14737159.2017.1341312

    Article  CAS  PubMed  Google Scholar 

  31. Pan S, Zhu D, Quinn JF, Peskind ER, Montine TJ, Lin B, Goodlett DR, Taylor G, Eng J, Zhang J (2007) A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry. Proteomics 7(3):469–473. https://doi.org/10.1002/pmic.200600756

    Article  CAS  PubMed  Google Scholar 

  32. Schutzer SE, Liu T, Natelson BH, Angel TE, Schepmoes AA, Purvine SO, Hixson KK, Lipton MS, Camp DG, Coyle PK, Smith RD, Bergquist J (2010) Establishing the proteome of normal human cerebrospinal fluid. PLoS One 5(6):e10980. https://doi.org/10.1371/journal.pone.0010980

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mouton-Barbosa E, Roux-Dalvai F, Bouyssie D, Berger F, Schmidt E, Righetti PG, Guerrier L, Boschetti E, Burlet-Schiltz O, Monsarrat B (2010) Gonzalez de Peredo A In-depth exploration of cerebrospinal fluid by combining peptide ligand library treatment and label-free protein quantification. Mol Cell Proteomics 9(5):1006–1021. https://doi.org/10.1074/mcp.M900513-MCP200. M900513-MCP200 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Guldbrandsen A, Vethe H, Farag Y, Oveland E, Garberg H, Berle M, Myhr KM, Opsahl JA, Barsnes H, Berven FS (2014) In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR). Mol Cell Proteomics 13(11):3152–3163. https://doi.org/10.1074/mcp.M114.038554. M114.038554 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Macron C, Lane L, Nunez Galindo A, Dayon L (2018) Deep dive on the proteome of human cerebrospinal fluid: a valuable data resource for biomarker discovery and missing protein identification. J Proteome Res 17:4113. https://doi.org/10.1021/acs.jproteome.8b00300

    Article  CAS  PubMed  Google Scholar 

  36. Macron C, Lane L, Nunez Galindo A, Dayon L (2018) Identification of missing proteins in normal human cerebrospinal fluid. J Proteome Res 17:4315. https://doi.org/10.1021/acs.jproteome.8b00194

    Article  CAS  PubMed  Google Scholar 

  37. Fernandez-Irigoyen J, Labarga A, Zabaleta A, de Morentin XM, Perez-Valderrama E, Zelaya MV, Santamaria E (2015) Toward defining the anatomo-proteomic puzzle of the human brain: an integrative analysis. Proteomics Clin Appl 9(9-10):796–807. https://doi.org/10.1002/prca.201400127

    Article  CAS  PubMed  Google Scholar 

  38. Alafuzoff I, Ince PG, Arzberger T, Al-Sarraj S, Bell J, Bodi I, Bogdanovic N, Bugiani O, Ferrer I, Gelpi E, Gentleman S, Giaccone G, Ironside JW, Kavantzas N, King A, Korkolopoulou P, Kovacs GG, Meyronet D, Monoranu C, Parchi P, Parkkinen L, Patsouris E, Roggendorf W, Rozemuller A, Stadelmann-Nessler C, Streichenberger N, Thal DR, Kretzschmar H (2009) Staging/typing of Lewy body related alpha-synuclein pathology: a study of the BrainNet Europe Consortium. Acta Neuropathol 117(6):635–652. https://doi.org/10.1007/s00401-009-0523-2

    Article  CAS  PubMed  Google Scholar 

  39. Andrews GL, Simons BL, Young JB, Hawkridge AM, Muddiman DC (2011) Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). Anal Chem 83(13):5442–5446. https://doi.org/10.1021/ac200812d

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Shilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, Hunter CL, Nuwaysir LM, Schaeffer DA (2007) The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics 6(9):1638–1655. https://doi.org/10.1074/mcp.T600050-MCP200. T600050-MCP200 [pii]

    Article  CAS  PubMed  Google Scholar 

  41. Tang WH, Shilov IV, Seymour SL (2008) Nonlinear fitting method for determining local false discovery rates from decoy database searches. J Proteome Res 7(9):3661–3667. https://doi.org/10.1021/pr070492f

    Article  CAS  PubMed  Google Scholar 

  42. Guldbrandsen A, Farag Y, Kroksveen AC, Oveland E, Lereim RR, Opsahl JA, Myhr KM, Berven FS, Barsnes H (2017) CSF-PR 2.0: an interactive literature guide to quantitative cerebrospinal fluid mass spectrometry data from neurodegenerative disorders. Mol Cell Proteomics 16(2):300–309. https://doi.org/10.1074/mcp.O116.064477. O116.064477 [pii]

    Article  CAS  PubMed  Google Scholar 

  43. Moritz CP (2017) Tubulin or not tubulin: heading toward total protein staining as loading control in western blots. Proteomics 17(20). https://doi.org/10.1002/pmic.201600189

    Article  Google Scholar 

  44. Banerjee S, Liao L, Russo R, Nakamura T, McKercher SR, Okamoto S, Haun F, Nikzad R, Zaidi R, Holland E, Eroyshkin A, Yates JR III, Lipton SA (2012) Isobaric tagging-based quantification by mass spectrometry of differentially regulated proteins in synaptosomes of HIV/gp120 transgenic mice: implications for HIV-associated neurodegeneration. Exp Neurol 236(2):298–306. https://doi.org/10.1016/j.expneurol.2012.04.013. S0014-4886(12)00185-9 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ping L, Duong DM, Yin L, Gearing M, Lah JJ, Levey AI, Seyfried NT (2018) Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease. Sci Data 5:180036. https://doi.org/10.1038/sdata.2018.36. sdata201836 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Shao S, Guo T, Koh CC, Gillessen S, Joerger M, Jochum W, Aebersold R (2015) Minimal sample requirement for highly multiplexed protein quantification in cell lines and tissues by PCT-SWATH mass spectrometry. Proteomics 15(21):3711–3721. https://doi.org/10.1002/pmic.201500161

    Article  CAS  PubMed  Google Scholar 

  47. Chang RY, Etheridge N, Nouwens AS, Dodd PR (2015) SWATH analysis of the synaptic proteome in Alzheimer’s disease. Neurochem Int 87:1–12. https://doi.org/10.1016/j.neuint.2015.04.004. S0197-0186(15)00065-0 [pii]

    Article  CAS  PubMed  Google Scholar 

  48. Navarro P, Vazquez J (2009) A refined method to calculate false discovery rates for peptide identification using decoy databases. J Proteome Res 8(4):1792–1796. https://doi.org/10.1021/pr800362h

    Article  CAS  PubMed  Google Scholar 

  49. Nesvizhskii AI (2010) A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteomics 73(11):2092–2123. https://doi.org/10.1016/j.jprot.2010.08.009. S1874-3919(10)00249-6 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lehnert S, Jesse S, Rist W, Steinacker P, Soininen H, Herukka SK, Tumani H, Lenter M, Oeckl P, Ferger B, Hengerer B, Otto M (2012) iTRAQ and multiple reaction monitoring as proteomic tools for biomarker search in cerebrospinal fluid of patients with Parkinson’s disease dementia. Exp Neurol 234(2):499–505. https://doi.org/10.1016/j.expneurol.2012.01.024. S0014-4886(12)00044-1 [pii]

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We are very grateful to the patients who generously donated the brain tissue or fluid samples for research purposes. We thank the collaboration of Parkinson’s UK Brain Bank funded by Parkinson’s UK, a charity registered in England and Wales (258197) and in Scotland (SC037554), the Neurological Tissue Bank of the Biobank from the Hospital Clinic-Institut d’Investigacions Biomédiques August Pi i Sunyer (IDIBAPS, Barcelona), the Neurological Tissue Bank of HUB-ICO-IDIBELL (Barcelona, Spain), the Neurological Tissue Bank of Navarrabiomed (Pamplona, Spain) for providing us the OB specimens, and CSF samples as well as the associated clinical and neuropathological data. This work was funded by grants from the Spanish Ministry of Economy and Competitiveness (MINECO) (Ref. SAF2014-59340-R), Department of Economic Development from Government of Navarra (Ref. PC023-PC024, PC025, PC081-82, and PI059, and PC107-108) and Obra Social la Caixa to ES. AGM was supported by PEJ-2014-A-61949 (MINECO) and a predoctoral fellowship from the Public University of Navarra (UPNA). MLM is supported by a predoctoral fellowship from the Public University of Navarra (UPNA). The Proteomics Unit of Navarrabiomed is a member of Proteored, PRB3-ISCIII, and is supported by grant PT17/0019, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF. The Clinical Neuroproteomics Laboratory of Navarrabiomed is member of the Spanish Network of Olfaction (ROE). This project is part of the HUPO Brain Proteome Project and is lined up with the Spanish Initiative on the Human Proteome Project (SpHPP).

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Lachén-Montes, M., González-Morales, A., Fernández-Irigoyen, J., Santamaría, E. (2019). Deployment of Label-Free Quantitative Olfactory Proteomics to Detect Cerebrospinal Fluid Biomarker Candidates in Synucleinopathies. In: Santamaría, E., Fernández-Irigoyen, J. (eds) Cerebrospinal Fluid (CSF) Proteomics. Methods in Molecular Biology, vol 2044. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9706-0_17

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