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Cerebrospinal Fluid Peptides as Potential Parkinson Disease Biomarkers: A Staged Pipeline for Discovery and Validation*

2015; Elsevier BV; Volume: 14; Issue: 3 Linguagem: Inglês

10.1074/mcp.m114.040576

ISSN

1535-9484

Autores

Min Shi, James Movius, Romel Dator, Patrick Aro, Yanchun Zhao, Catherine Pan, Xiangmin Lin, Theo K. Bammler, Tessandra Stewart, Cyrus P. Zabetian, Elaine R. Peskind, Shu-Ching Hu, Joseph F. Quinn, Douglas Galasko, Jing Zhang,

Tópico(s)

RNA and protein synthesis mechanisms

Resumo

Finding robust biomarkers for Parkinson disease (PD) is currently hampered by inherent technical limitations associated with imaging or antibody-based protein assays. To circumvent the challenges, we adapted a staged pipeline, starting from our previous proteomic profiling followed by high-throughput targeted mass spectrometry (MS), to identify peptides in human cerebrospinal fluid (CSF) for PD diagnosis and disease severity correlation. In this multicenter study consisting of training and validation sets, a total of 178 subjects were randomly selected from a retrospective cohort, matching age and sex between PD patients, healthy controls, and neurological controls with Alzheimer disease (AD). From ∼14,000 unique peptides displaying differences between PD and healthy control in proteomic investigations, 126 peptides were selected based on relevance and observability in CSF using bioinformatic analysis and MS screening, and then quantified by highly accurate and sensitive selected reaction monitoring (SRM) in the CSF of 30 PD patients versus 30 healthy controls (training set), followed by diagnostic (receiver operating characteristics) and disease severity correlation analyses. The most promising candidates were further tested in an independent cohort of 40 PD patients, 38 AD patients, and 40 healthy controls (validation set). A panel of five peptides (derived from SPP1, LRP1, CSF1R, EPHA4, and TIMP1) was identified to provide an area under curve (AUC) of 0.873 (sensitivity = 76.7%, specificity = 80.0%) for PD versus healthy controls in the training set. The performance was essentially confirmed in the validation set (AUC = 0.853, sensitivity = 82.5%, specificity = 82.5%). Additionally, this panel could also differentiate the PD and AD groups (AUC = 0.990, sensitivity = 95.0%, specificity = 97.4%). Furthermore, a combination of two peptides belonging to proteins TIMP1 and APLP1 significantly correlated with disease severity as determined by the Unified Parkinson's Disease Rating Scale motor scores in both the training (r = 0.381, p = 0.038)j and the validation (r = 0.339, p = 0.032) sets. The novel panel of CSF peptides, if validated in independent cohorts, could be used to assist in clinical diagnosis of PD and has the potential to help monitoring or predicting disease progression. Finding robust biomarkers for Parkinson disease (PD) is currently hampered by inherent technical limitations associated with imaging or antibody-based protein assays. To circumvent the challenges, we adapted a staged pipeline, starting from our previous proteomic profiling followed by high-throughput targeted mass spectrometry (MS), to identify peptides in human cerebrospinal fluid (CSF) for PD diagnosis and disease severity correlation. In this multicenter study consisting of training and validation sets, a total of 178 subjects were randomly selected from a retrospective cohort, matching age and sex between PD patients, healthy controls, and neurological controls with Alzheimer disease (AD). From ∼14,000 unique peptides displaying differences between PD and healthy control in proteomic investigations, 126 peptides were selected based on relevance and observability in CSF using bioinformatic analysis and MS screening, and then quantified by highly accurate and sensitive selected reaction monitoring (SRM) in the CSF of 30 PD patients versus 30 healthy controls (training set), followed by diagnostic (receiver operating characteristics) and disease severity correlation analyses. The most promising candidates were further tested in an independent cohort of 40 PD patients, 38 AD patients, and 40 healthy controls (validation set). A panel of five peptides (derived from SPP1, LRP1, CSF1R, EPHA4, and TIMP1) was identified to provide an area under curve (AUC) of 0.873 (sensitivity = 76.7%, specificity = 80.0%) for PD versus healthy controls in the training set. The performance was essentially confirmed in the validation set (AUC = 0.853, sensitivity = 82.5%, specificity = 82.5%). Additionally, this panel could also differentiate the PD and AD groups (AUC = 0.990, sensitivity = 95.0%, specificity = 97.4%). Furthermore, a combination of two peptides belonging to proteins TIMP1 and APLP1 significantly correlated with disease severity as determined by the Unified Parkinson's Disease Rating Scale motor scores in both the training (r = 0.381, p = 0.038)j and the validation (r = 0.339, p = 0.032) sets. The novel panel of CSF peptides, if validated in independent cohorts, could be used to assist in clinical diagnosis of PD and has the potential to help monitoring or predicting disease progression. Parkinson disease (PD) 1The abbreviations used are:PDParkinson diseaseADAlzheimer diseaseAIMSaccurate inclusion mass screeningAPLP1amyloid-like protein 1AUCarea under curveCSFcerebrospinal fluidCSF1RMacrophage colony-stimulating factor 1 receptorConhealthy controlCPceruloplasminEPHA4ephrin type-A receptor 4LRP1prolow-density lipoprotein receptor-related protein 1MMSEMini Mental State ExaminationMSmass spectrometryROCreceiver operating characteristicSCXstrong cation-exchangeSPP1osteopontinSRMselected reaction monitoringTIMP1metalloproteinase inhibitor 1UPDRSUnified Parkinson's Disease Rating Scale. 1The abbreviations used are:PDParkinson diseaseADAlzheimer diseaseAIMSaccurate inclusion mass screeningAPLP1amyloid-like protein 1AUCarea under curveCSFcerebrospinal fluidCSF1RMacrophage colony-stimulating factor 1 receptorConhealthy controlCPceruloplasminEPHA4ephrin type-A receptor 4LRP1prolow-density lipoprotein receptor-related protein 1MMSEMini Mental State ExaminationMSmass spectrometryROCreceiver operating characteristicSCXstrong cation-exchangeSPP1osteopontinSRMselected reaction monitoringTIMP1metalloproteinase inhibitor 1UPDRSUnified Parkinson's Disease Rating Scale., the second most common neurodegenerative disease after Alzheimer disease (AD), afflicts roughly 2% of persons over the age of 65 years (1Lang A.E. Lozano A.M. Parkinson's disease. First of two parts.N. Engl. J. Med. 1998; 339: 1044-1053Crossref PubMed Scopus (1769) Google Scholar, 2Thomas B. Beal M.F. Parkinson's disease.Hum. Mol. Genet. 2007; 2 (16 Spec No): R183-R194Crossref Scopus (674) Google Scholar). Currently, PD diagnosis is mainly based on observation of the cardinal motor indicators of the disease, patient response to drug treatment, and medical history (3Jankovic J. Parkinson's disease: clinical features and diagnosis.J. Neurol. Neurosurg. Psychiatry. 2008; 79: 368-376Crossref PubMed Scopus (3292) Google Scholar, 4Tolosa E. Wenning G. Poewe W. The diagnosis of Parkinson's disease.Lancet Neurol. 2006; 5: 75-86Abstract Full Text Full Text PDF PubMed Scopus (540) Google Scholar). There is an appreciable misdiagnosis rate (4Tolosa E. Wenning G. Poewe W. The diagnosis of Parkinson's disease.Lancet Neurol. 2006; 5: 75-86Abstract Full Text Full Text PDF PubMed Scopus (540) Google Scholar), particularly at early disease stages. Additionally, no objective measure of disease progression or treatment effects has been established. Thus, objective, reliable, and reproducible biomarkers are clearly needed to aid in the diagnosis of PD and tracking or predicting the disease progression. Parkinson disease Alzheimer disease accurate inclusion mass screening amyloid-like protein 1 area under curve cerebrospinal fluid Macrophage colony-stimulating factor 1 receptor healthy control ceruloplasmin ephrin type-A receptor 4 prolow-density lipoprotein receptor-related protein 1 Mini Mental State Examination mass spectrometry receiver operating characteristic strong cation-exchange osteopontin selected reaction monitoring metalloproteinase inhibitor 1 Unified Parkinson's Disease Rating Scale. Parkinson disease Alzheimer disease accurate inclusion mass screening amyloid-like protein 1 area under curve cerebrospinal fluid Macrophage colony-stimulating factor 1 receptor healthy control ceruloplasmin ephrin type-A receptor 4 prolow-density lipoprotein receptor-related protein 1 Mini Mental State Examination mass spectrometry receiver operating characteristic strong cation-exchange osteopontin selected reaction monitoring metalloproteinase inhibitor 1 Unified Parkinson's Disease Rating Scale. The most sensitive tests developed to date are based on imaging modalities, which can detect functional and structural abnormalities even prior to the onset of motor dysfunction (5Schapira A.H. Recent developments in biomarkers in Parkinson disease.Curr. Opin. Neurol. 2013; 26: 395-400Crossref PubMed Scopus (79) Google Scholar, 6O'Keeffe G.C. Michell A.W. Barker R.A. Biomarkers in Huntington's and Parkinson's disease.Ann. N.Y. Acad. Sci. 2009; 1180: 97-110Crossref PubMed Scopus (20) Google Scholar). However, the usefulness of neuroimaging techniques is limited by high cost, limited accessibility, difficulty in reliable differentiation of PD from other atypical parkinsonian disorders and subjection to confounding factors such as medication and compensatory responses (4Tolosa E. Wenning G. Poewe W. The diagnosis of Parkinson's disease.Lancet Neurol. 2006; 5: 75-86Abstract Full Text Full Text PDF PubMed Scopus (540) Google Scholar, 5Schapira A.H. Recent developments in biomarkers in Parkinson disease.Curr. Opin. Neurol. 2013; 26: 395-400Crossref PubMed Scopus (79) Google Scholar, 6O'Keeffe G.C. Michell A.W. Barker R.A. Biomarkers in Huntington's and Parkinson's disease.Ann. N.Y. Acad. Sci. 2009; 1180: 97-110Crossref PubMed Scopus (20) Google Scholar, 7Booij J. Berendse H.W. Monitoring therapeutic effects in Parkinson's disease by serial imaging of the nigrostriatal dopaminergic pathway.J. Neurol. Sci. 2011; 310: 40-43Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar). Biochemical and molecular markers in cerebrospinal fluid (CSF) and other body fluids have also been actively investigated (5Schapira A.H. Recent developments in biomarkers in Parkinson disease.Curr. Opin. Neurol. 2013; 26: 395-400Crossref PubMed Scopus (79) Google Scholar, 8Hong Z. Shi M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Leverenz J.B. Baird G. Montine T.J. Hancock A.M. Hwang H. Pan C. Bradner J. Kang U.J. Jensen P.H. Zhang J. DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease.Brain. 2010; 133: 713-726Crossref PubMed Scopus (503) Google Scholar, 9Mollenhauer B. Locascio J.J. Schulz-Schaeffer W. Sixel-Döring F. Trenkwalder C. Schlossmacher M.G. alpha-Synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study.Lancet Neurol. 2011; 10: 230-240Abstract Full Text Full Text PDF PubMed Scopus (495) Google Scholar, 10Kang J.H. Irwin D.J. Chen-Plotkin A.S. Siderowf A. Caspell C. Coffey C.S. Waligorska T. Taylor P. Pan S. Frasier M. Marek K. Kieburtz K. Jennings D. Simuni T. Tanner C.M. Singleton A. Toga A.W. Chowdhury S. Mollenhauer B. Trojanowski J.Q. Shaw L.M. Association of cerebrospinal fluid beta-amyloid 1–42, t-tau, p-tau181, and alpha-synuclein levels with clinical features of drug-naive patients with early Parkinson disease.JAMA Neurol. 2013; 70: 1277-1287PubMed Google Scholar, 11Shi M. Bradner J. Hancock A.M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Kim H.M. Leverenz J.B. Montine T.J. Ginghina C. Kang U.J. Cain K.C. Wang Y. Aasly J. Goldstein D. Zhang J. Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression.Ann. Neurol. 2011; 69: 570-580Crossref PubMed Scopus (329) Google Scholar, 12Hall S. Ohrfelt A. Constantinescu R. Andreasson U. Surova Y. Bostrom F. Nilsson C. Hakan W. Decraemer H. Nagga K. Minthon L. Londos E. Vanmechelen E. Holmberg B. Zetterberg H. Blennow K. Hansson O. Accuracy of a panel of five cerebrospinal fluid biomarkers in the differential diagnosis of patients with dementia and/or parkinsonian disorders.Arch. Neurol. 2012; 69: 1445-1452Crossref PubMed Scopus (346) Google Scholar). The most extensively studied candidate in CSF is probably α-synuclein, the major protein component of Lewy bodies and Lewy neurites, the pathological hallmarks of PD (2Thomas B. Beal M.F. Parkinson's disease.Hum. Mol. Genet. 2007; 2 (16 Spec No): R183-R194Crossref Scopus (674) Google Scholar). The current consensus is that CSF α-synuclein concentrations are generally lower in patients with PD compared with controls (5Schapira A.H. Recent developments in biomarkers in Parkinson disease.Curr. Opin. Neurol. 2013; 26: 395-400Crossref PubMed Scopus (79) Google Scholar, 8Hong Z. Shi M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Leverenz J.B. Baird G. Montine T.J. Hancock A.M. Hwang H. Pan C. Bradner J. Kang U.J. Jensen P.H. Zhang J. DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease.Brain. 2010; 133: 713-726Crossref PubMed Scopus (503) Google Scholar, 9Mollenhauer B. Locascio J.J. Schulz-Schaeffer W. Sixel-Döring F. Trenkwalder C. Schlossmacher M.G. alpha-Synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study.Lancet Neurol. 2011; 10: 230-240Abstract Full Text Full Text PDF PubMed Scopus (495) Google Scholar, 10Kang J.H. Irwin D.J. Chen-Plotkin A.S. Siderowf A. Caspell C. Coffey C.S. Waligorska T. Taylor P. Pan S. Frasier M. Marek K. Kieburtz K. Jennings D. Simuni T. Tanner C.M. Singleton A. Toga A.W. Chowdhury S. Mollenhauer B. Trojanowski J.Q. Shaw L.M. Association of cerebrospinal fluid beta-amyloid 1–42, t-tau, p-tau181, and alpha-synuclein levels with clinical features of drug-naive patients with early Parkinson disease.JAMA Neurol. 2013; 70: 1277-1287PubMed Google Scholar); the sensitivity and specificity, however, appear to be only moderate, and no correlation with PD severity or progression has been observed (8Hong Z. Shi M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Leverenz J.B. Baird G. Montine T.J. Hancock A.M. Hwang H. Pan C. Bradner J. Kang U.J. Jensen P.H. Zhang J. DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease.Brain. 2010; 133: 713-726Crossref PubMed Scopus (503) Google Scholar, 9Mollenhauer B. Locascio J.J. Schulz-Schaeffer W. Sixel-Döring F. Trenkwalder C. Schlossmacher M.G. alpha-Synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study.Lancet Neurol. 2011; 10: 230-240Abstract Full Text Full Text PDF PubMed Scopus (495) Google Scholar). Notably, all these CSF protein markers are measured using antibody-based assays, which are often associated with relatively high variability, particularly when different detection techniques (different antibodies, sample preparation, calibrators, etc.) are used, leading to discrepant results across laboratories (5Schapira A.H. Recent developments in biomarkers in Parkinson disease.Curr. Opin. Neurol. 2013; 26: 395-400Crossref PubMed Scopus (79) Google Scholar). It should also be stressed that this high variability in immunoassays is not unique to PD, because similar difficulty is encountered in AD and other related disorders (13Mattsson N. Blennow K. Zetterberg H. Inter-laboratory variation in cerebrospinal fluid biomarkers for Alzheimer's disease: united we stand, divided we fall.Clin. Chem. Lab. Med. 2010; 48: 603-607Crossref PubMed Scopus (79) Google Scholar, 14Mattsson N. Andreasson U. Persson S. Carrillo M.C. Collins S. Chalbot S. Cutler N. Dufour-Rainfray D. Fagan A.M. Heegaard N.H. Robin Hsiung G.Y. Hyman B. Iqbal K. Lachno D.R. Lleo A. Lewczuk P. Molinuevo J.L. Parchi P. Regeniter A. Rissman R. Rosenmann H. Sancesario G. Schroder J. Shaw L.M. Teunissen C.E. Trojanowski J.Q. Vanderstichele H. Vandijck M. Verbeek M.M. Zetterberg H. Blennow K. Kaser S.A. CSF biomarker variability in the Alzheimer's Association quality control program.Alzheimers Dement. 2013; 9: 251-261Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar). One strategy to avoid the inherent technical limitations associated with antibodies is to use alternative techniques in which unique peptides are selected and precisely quantified with mass spectrometry (MS) techniques, for example, accurate inclusion mass screening (AIMS) (15Jaffe J.D. Keshishian H. Chang B. Addona T.A. Gillette M.A. Carr S.A. Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification.Mol. Cell. Proteomics. 2008; 7: 1952-1962Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar) and selected reaction monitoring (SRM) (16Whiteaker J.R. Lin C. Kennedy J. Hou L. Trute M. Sokal I. Yan P. Schoenherr R.M. Zhao L. Voytovich U.J. Kelly-Spratt K.S. Krasnoselsky A. Gafken P.R. Hogan J.M. Jones L.A. Wang P. Amon L. Chodosh L.A. Nelson P.S. McIntosh M.W. Kemp C.J. Paulovich A.G. A targeted proteomics-based pipeline for verification of biomarkers in plasma.Nat. Biotechnol. 2011; 29: 625-634Crossref PubMed Scopus (291) Google Scholar, 17Addona T.A. Abbatiello S.E. Schilling B. Skates S.J. Mani D.R. Bunk D.M. Spiegelman C.H. Zimmerman L.J. Ham A.J. Keshishian H. Hall S.C. Allen S. Blackman R.K. Borchers C.H. Buck C. Cardasis H.L. Cusack M.P. Dodder N.G. Gibson B.W. Held J.M. Hiltke T. Jackson A. Johansen E.B. Kinsinger C.R. Li J. Mesri M. Neubert T.A. Niles R.K. Pulsipher T.C. Ransohoff D. Rodriguez H. Rudnick P.A. Smith D. Tabb D.L. Tegeler T.J. Variyath A.M. Vega-Montoto L.J. Wahlander A. Waldemarson S. Wang M. Whiteaker J.R. Zhao L. Anderson N.L. Fisher S.J. Liebler D.C. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.Nat. Biotechnol. 2009; 27: 633-641Crossref PubMed Scopus (862) Google Scholar, 18Selevsek N. Matondo M. Sanchez Carbayo M. Aebersold R. Domon B. Systematic quantification of peptides/proteins in urine using selected reaction monitoring.Proteomics. 2011; 11: 1135-1147Crossref PubMed Scopus (64) Google Scholar). To this end, in the last few years, we and others have utilized proteomic technologies to identify novel proteins and peptides associated with different disease states and stages (5Schapira A.H. Recent developments in biomarkers in Parkinson disease.Curr. Opin. Neurol. 2013; 26: 395-400Crossref PubMed Scopus (79) Google Scholar, 6O'Keeffe G.C. Michell A.W. Barker R.A. Biomarkers in Huntington's and Parkinson's disease.Ann. N.Y. Acad. Sci. 2009; 1180: 97-110Crossref PubMed Scopus (20) Google Scholar, 19Abdi F. Quinn J.F. Jankovic J. McIntosh M. Leverenz J.B. Peskind E. Nixon R. Nutt J. Chung K. Zabetian C. Samii A. Lin M. Hattan S. Pan C. Wang Y. Jin J. Zhu D. Li G.J. Liu Y. Waichunas D. Montine T.J. Zhang J. Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders.J. Alzheimers Dis. 2006; 9: 293-348Crossref PubMed Scopus (344) Google Scholar, 20Jin J. Hulette C. Wang Y. Zhang T. Pan C. Wadhwa R. Zhang J. Proteomic identification of a stress protein, mortalin/mthsp70/GRP75: relevance to Parkinson disease.Mol. Cell. Proteomics. 2006; 5: 1193-1204Abstract Full Text Full Text PDF PubMed Scopus (207) Google Scholar, 21Kitsou E. Pan S. Zhang J. Shi M. Zabeti A. Dickson D.W. Albin R. Gearing M. Kashima D.T. Wang Y. Beyer R.P. Zhou Y. Pan C. Caudle W.M. Zhang J. Identification of proteins in human substantia nigra.Proteomics Clin. Appl. 2008; 2: 776-782Crossref PubMed Scopus (30) Google Scholar, 22Pan S. Shi M. Jin J. Albin R.L. Lieberman A. Gearing M. Lin B. Pan C. Yan X. Kashima D.T. Zhang J. Proteomics identification of proteins in human cortex using multidimensional separations and MALDI tandem mass spectrometer.Mol. Cell. Proteomics. 2007; 6: 1818-1823Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar, 23Shi M. Jin J. Wang Y. Beyer R.P. Kitsou E. Albin R.L. Gearing M. Pan C. Zhang J. Mortalin: a protein associated with progression of Parkinson disease?.J. Neuropathol. Exp. Neurol. 2008; 67: 117-124Crossref PubMed Scopus (73) Google Scholar, 24Shi M. Bradner J. Bammler T.K. Eaton D.L. Zhang J. Ye Z. Wilson A.M. Montine T.J. Pan C. Zhang J. Identification of glutathione S-transferase pi as a protein involved in Parkinson disease progression.Am. J. Pathol. 2009; 175: 54-65Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar, 25Hwang H. Zhang J. Chung K.A. Leverenz J.B. Zabetian C.P. Peskind E.R. Jankovic J. Su Z. Hancock A.M. Pan C. Montine T.J. Pan S. Nutt J. Albin R. Gearing M. Beyer R.P. Shi M. Zhang J. Glycoproteomics in neurodegenerative diseases.Mass Spectrom. Rev. 2010; 29: 79-125Crossref PubMed Scopus (92) Google Scholar). Using brain tissue or CSF, these unbiased proteomic profiling studies have revealed disease-related alterations in hundreds of peptides derived from many proteins (19Abdi F. Quinn J.F. Jankovic J. McIntosh M. Leverenz J.B. Peskind E. Nixon R. Nutt J. Chung K. Zabetian C. Samii A. Lin M. Hattan S. Pan C. Wang Y. Jin J. Zhu D. Li G.J. Liu Y. Waichunas D. Montine T.J. Zhang J. Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders.J. Alzheimers Dis. 2006; 9: 293-348Crossref PubMed Scopus (344) Google Scholar, 20Jin J. Hulette C. Wang Y. Zhang T. Pan C. Wadhwa R. Zhang J. Proteomic identification of a stress protein, mortalin/mthsp70/GRP75: relevance to Parkinson disease.Mol. Cell. Proteomics. 2006; 5: 1193-1204Abstract Full Text Full Text PDF PubMed Scopus (207) Google Scholar, 21Kitsou E. Pan S. Zhang J. Shi M. Zabeti A. Dickson D.W. Albin R. Gearing M. Kashima D.T. Wang Y. Beyer R.P. Zhou Y. Pan C. Caudle W.M. Zhang J. Identification of proteins in human substantia nigra.Proteomics Clin. Appl. 2008; 2: 776-782Crossref PubMed Scopus (30) Google Scholar, 22Pan S. Shi M. Jin J. Albin R.L. Lieberman A. Gearing M. Lin B. Pan C. Yan X. Kashima D.T. Zhang J. Proteomics identification of proteins in human cortex using multidimensional separations and MALDI tandem mass spectrometer.Mol. Cell. Proteomics. 2007; 6: 1818-1823Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar, 23Shi M. Jin J. Wang Y. Beyer R.P. Kitsou E. Albin R.L. Gearing M. Pan C. Zhang J. Mortalin: a protein associated with progression of Parkinson disease?.J. Neuropathol. Exp. Neurol. 2008; 67: 117-124Crossref PubMed Scopus (73) Google Scholar, 24Shi M. Bradner J. Bammler T.K. Eaton D.L. Zhang J. Ye Z. Wilson A.M. Montine T.J. Pan C. Zhang J. Identification of glutathione S-transferase pi as a protein involved in Parkinson disease progression.Am. J. Pathol. 2009; 175: 54-65Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar, 25Hwang H. Zhang J. Chung K.A. Leverenz J.B. Zabetian C.P. Peskind E.R. Jankovic J. Su Z. Hancock A.M. Pan C. Montine T.J. Pan S. Nutt J. Albin R. Gearing M. Beyer R.P. Shi M. Zhang J. Glycoproteomics in neurodegenerative diseases.Mass Spectrom. Rev. 2010; 29: 79-125Crossref PubMed Scopus (92) Google Scholar). However, there are no quantitative assays for the majority of these candidate proteins/peptides, and development of such assays is limited by the lack of antibodies available for many of them. Thus, although a large library of potential peptide biomarkers has been developed, the vast majority never reach the stage of validation and clinical testing, hampered by the difficulty of de novo development of immunoassays, a process that is time consuming, prohibitively expensive to develop and very difficult to multiplex. In this study, we aim to establish a PD biomarker identification and verification pipeline, with the goal of prioritizing candidates and swiftly developing reliable quantitative assays. We focused on identifying peptides by SRM and AIMS, because these targeted proteomic technologies have been proposed as the basis of a viable biomarker pipeline (16Whiteaker J.R. Lin C. Kennedy J. Hou L. Trute M. Sokal I. Yan P. Schoenherr R.M. Zhao L. Voytovich U.J. Kelly-Spratt K.S. Krasnoselsky A. Gafken P.R. Hogan J.M. Jones L.A. Wang P. Amon L. Chodosh L.A. Nelson P.S. McIntosh M.W. Kemp C.J. Paulovich A.G. A targeted proteomics-based pipeline for verification of biomarkers in plasma.Nat. Biotechnol. 2011; 29: 625-634Crossref PubMed Scopus (291) Google Scholar) and have become a powerful tool in biomarker discovery because of their high sensitivity, accuracy and specificity. SRM, in particular, has emerged as an alternative to immunoaffinity-based measurements of defined protein sets with excellent reproducibility across different laboratories and instrument platforms (17Addona T.A. Abbatiello S.E. Schilling B. Skates S.J. Mani D.R. Bunk D.M. Spiegelman C.H. Zimmerman L.J. Ham A.J. Keshishian H. Hall S.C. Allen S. Blackman R.K. Borchers C.H. Buck C. Cardasis H.L. Cusack M.P. Dodder N.G. Gibson B.W. Held J.M. Hiltke T. Jackson A. Johansen E.B. Kinsinger C.R. Li J. Mesri M. Neubert T.A. Niles R.K. Pulsipher T.C. Ransohoff D. Rodriguez H. Rudnick P.A. Smith D. Tabb D.L. Tegeler T.J. Variyath A.M. Vega-Montoto L.J. Wahlander A. Waldemarson S. Wang M. Whiteaker J.R. Zhao L. Anderson N.L. Fisher S.J. Liebler D.C. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.Nat. Biotechnol. 2009; 27: 633-641Crossref PubMed Scopus (862) Google Scholar, 18Selevsek N. Matondo M. Sanchez Carbayo M. Aebersold R. Domon B. Systematic quantification of peptides/proteins in urine using selected reaction monitoring.Proteomics. 2011; 11: 1135-1147Crossref PubMed Scopus (64) Google Scholar). The staged pipeline in the current investigation (Fig. 1) includes: (1) data-dependent and bioinformatic prioritization of thousands of candidate biomarkers identified in our previous profiling studies, (2) de novo development of antibody-free multiplex SRM assays to reliably measure tens to hundreds of peptides simultaneously, and (3) multiplex biomarker verification studies allowing identification and validation of models or panels of candidates in independent sample sets, two of which were used in this study. A total of 178 subjects (70 PD, 38 AD, and 70 healthy controls) were randomly selected from a previously described well-characterized multicenter, retrospective cohort (8Hong Z. Shi M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Leverenz J.B. Baird G. Montine T.J. Hancock A.M. Hwang H. Pan C. Bradner J. Kang U.J. Jensen P.H. Zhang J. DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease.Brain. 2010; 133: 713-726Crossref PubMed Scopus (503) Google Scholar, 11Shi M. Bradner J. Hancock A.M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Kim H.M. Leverenz J.B. Montine T.J. Ginghina C. Kang U.J. Cain K.C. Wang Y. Aasly J. Goldstein D. Zhang J. Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression.Ann. Neurol. 2011; 69: 570-580Crossref PubMed Scopus (329) Google Scholar), with age and sex matched between groups. Participating institutions include Baylor College of Medicine, Oregon Health and Science University, the University of California at San Diego, VA Puget Sound Health Care Systems at Seattle, and the University of Washington (UW). This study was approved by the Institutional Review Boards of all participating sites. All subjects provided informed consent and underwent evaluations consisting of medical history, physical and neurological examinations, laboratory tests, and neuropsychological assessments. The inclusion and exclusion criteria were previously described (8Hong Z. Shi M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Leverenz J.B. Baird G. Montine T.J. Hancock A.M. Hwang H. Pan C. Bradner J. Kang U.J. Jensen P.H. Zhang J. DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease.Brain. 2010; 133: 713-726Crossref PubMed Scopus (503) Google Scholar, 11Shi M. Bradner J. Hancock A.M. Chung K.A. Quinn J.F. Peskind E.R. Galasko D. Jankovic J. Zabetian C.P. Kim H.M. Leverenz J.B. Montine T.J. Ginghina C. Kang U.J. Cain K.C. Wang Y. Aasly J. Goldstein D. Zhang J. Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression.Ann. Neurol. 2011; 69: 570-580Crossref PubMed Scopus (329) Google Scholar) and a brief description is provided in the supplemental Methods. Thirty (30Ellington A.A. Kullo I.J. Bailey K.R. Klee G.G. Antibody-based protein multiplex platforms: technical and operational challenges.Clin. Chem. 2010; 56: 186-193Crossref PubMed Scopus (242) Google Scholar) patients with PD and 30 healthy controls were included as the training set in this study, and 40 patients with PD, 38 patients with AD, and 40 healthy controls were included as the validation set. Demographic information is listed in Table I for all subjects.Table IDemographic data of participating subjectsTraining setValidation setConPDConPDADSubject number3030404038AgeaMean±S.D. AD, Alzheimer disease; Con, healthy control; MMSE, Mini Mental State Examination; PD, Parkinson disease; UPDRS, Unified Parkinson's Disease Rating Scale. (range)65.9 ± 13.6 (42–89)60.3 ± 10.8 (41–80)66.8 ± 8.3 (55–87)64.6 ± 9.7 (45–83)69.0 ± 8.4 (52–82)Sex (male:female)20:1022:825:1525:1520:18UPDRS motoraMean±S.D. AD, Alzheimer disease; Con, healthy control; MMSE, Mini Mental State Examination; PD, Parkinson disease; UPDRS, Unified Parkinson's Disease Rating Scale. (range)19.7 ± 11.8 (1–54)22.7 ± 12.5 (6–50)–MMSEaMean±S.D. AD, Alzheimer disease; Con, healthy control; MMSE, Mini Mental State Examination; PD, Parkinson disease; UPDRS, Unified Parkinson's Disease Rating Scale. (range)29.3 ± 1.2 (26–30)28.2 ± 3.5 (11–30)17.7 ± 5.8 (5–27)Disease duration, yearaMean±S.D. AD, Alzheimer disease; Con, healthy control; MMSE, Mini Mental State Examination; PD, Parkinso

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