Identification of a possible proteomic biomarker in Parkinson’s disease: discovery and replication in blood, brain and cerebrospinal fluid

Abstract Biomarkers to aid diagnosis and delineate the progression of Parkinson’s disease are vital for targeting treatment in the early phases of the disease. Here, we aim to discover a multi-protein panel representative of Parkinson’s and make mechanistic inferences from protein expression profiles within the broader objective of finding novel biomarkers. We used aptamer-based technology (SomaLogic®) to measure proteins in 1599 serum samples, 85 cerebrospinal fluid samples and 37 brain tissue samples collected from two observational longitudinal cohorts (the Oxford Parkinson’s Disease Centre and Tracking Parkinson’s) and the Parkinson’s Disease Brain Bank, respectively. Random forest machine learning was performed to discover new proteins related to disease status and generate multi-protein expression signatures with potential novel biomarkers. Differential regulation analysis and pathway analysis were performed to identify functional and mechanistic disease associations. The most consistent diagnostic classifier signature was tested across modalities [cerebrospinal fluid (area under curve) = 0.74, P = 0.0009; brain area under curve = 0.75, P = 0.006; serum area under curve = 0.66, P = 0.0002]. Focusing on serum samples and using only those with severe disease compared with controls increased the area under curve to 0.72 (P = 1.0 × 10−4). In the validation data set, we showed that the same classifiers were significantly related to disease status (P < 0.001). Differential expression analysis and weighted gene correlation network analysis highlighted key proteins and pathways with known relationships to Parkinson’s. Proteins from the complement and coagulation cascades suggest a disease relationship to immune response. The combined analytical approaches in a relatively large number of samples, across tissue types, with replication and validation, provide mechanistic insights into the disease as well as nominate a protein signature classifier that deserves further biomarker evaluation.

[1]  Houeto Jean-Luc [Parkinson's disease]. , 2022, La Revue du praticien.

[2]  T. Friede,et al.  Influence of individual, illness and environmental factors on place of death among people with neurodegenerative diseases: a retrospective, observational, comparative cohort study , 2021, BMJ supportive & palliative care.

[3]  B. Mollenhauer,et al.  Systematic Assessment of 10 Biomarker Candidates Focusing on α‐Synuclein‐Related Disorders , 2021, Movement disorders : official journal of the Movement Disorder Society.

[4]  W. Poewe,et al.  Application of the Updated Movement Disorder Society Criteria for Prodromal Parkinson's Disease to a Population‐Based 10‐Year Study , 2021, Movement disorders : official journal of the Movement Disorder Society.

[5]  K. Blennow,et al.  The validation status of blood biomarkers of amyloid and phospho-tau assessed with the 5-phase development framework for AD biomarkers , 2021, European Journal of Nuclear Medicine and Molecular Imaging.

[6]  K. Blennow,et al.  Moving fluid biomarkers for Alzheimer’s disease from research tools to routine clinical diagnostics , 2021, Molecular neurodegeneration.

[7]  A. Tenner,et al.  The good, the bad, and the opportunities of the complement system in neurodegenerative disease , 2020, Journal of neuroinflammation.

[8]  A. Singleton,et al.  Validation of Serum Neurofilament Light Chain as a Biomarker of Parkinson’s Disease Progression , 2020, Movement disorders : official journal of the Movement Disorder Society.

[9]  K. Blennow,et al.  An update on fluid biomarkers for neurodegenerative diseases: recent success and challenges ahead , 2019, Current Opinion in Neurobiology.

[10]  Y. Ben-Shlomo,et al.  Blood biomarkers with Parkinson's disease clusters and prognosis: The oxford discovery cohort , 2019, Movement disorders : official journal of the Movement Disorder Society.

[11]  J. Trojanowski,et al.  Characterization of Parkinson’s disease using blood-based biomarkers: A multicohort proteomic analysis , 2019, PLoS medicine.

[12]  K. Blennow,et al.  Discovery and validation of plasma proteomic biomarkers relating to brain amyloid burden by SOMAscan assay , 2019, Alzheimer's & Dementia.

[13]  Paolo Eusebi,et al.  CSF and blood biomarkers for Parkinson's disease , 2019, The Lancet Neurology.

[14]  B. Morgan,et al.  Complement in the pathogenesis of Alzheimer’s disease , 2017, Seminars in Immunopathology.

[15]  Madhav Thambisetty,et al.  A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer's Disease. , 2017, Cell systems.

[16]  R. Barker,et al.  Tracking Parkinson’s: Study Design and Baseline Patient Data , 2015, Journal of Parkinson's disease.

[17]  H. Kiyonari,et al.  The Parkinson’s Disease-Associated Protein Kinase LRRK2 Modulates Notch Signaling through the Endosomal Pathway , 2015, PLoS genetics.

[18]  N. Jetté,et al.  The prevalence of Parkinson's disease: A systematic review and meta‐analysis , 2014, Movement disorders : official journal of the Movement Disorder Society.

[19]  Robert E. Burke,et al.  Axon degeneration in Parkinson's disease , 2013, Experimental Neurology.

[20]  T. Montine,et al.  Plasma apolipoprotein A1 as a biomarker for Parkinson disease , 2013, Annals of neurology.

[21]  Guangchuang Yu,et al.  clusterProfiler: an R package for comparing biological themes among gene clusters. , 2012, Omics : a journal of integrative biology.

[22]  William T. Hu,et al.  Plasma epidermal growth factor levels predict cognitive decline in Parkinson disease , 2011, Annals of neurology.

[23]  Tracy R. Keeney,et al.  Aptamer-based multiplexed proteomic technology for biomarker discovery , 2010, PloS one.

[24]  S. Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[25]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[26]  Cornelius J Werner,et al.  Proteome analysis of human substantia nigra in Parkinson's disease , 2008, Proteome Science.

[27]  Yoav Ben-Shlomo,et al.  The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. , 2002, Brain : a journal of neurology.

[28]  Y. Ben-Shlomo,et al.  The influence of age and gender on motor and non-motor features of early Parkinson's disease: initial findings from the Oxford Parkinson Disease Center (OPDC) discovery cohort. , 2014, Parkinsonism & Related Disorders.

[29]  D. Selkoe Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[30]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.