miRNA-based signatures in cerebrospinal fluid as potential diagnostic tools for early stage Parkinson’s disease

// Marcia Cristina T. dos Santos 1 , Miguel Arturo Barreto-Sanz 2 , Bruna Renata S. Correia 3 , Rosie Bell 4 , Catherine Widnall 5 , Luis Tosar Perez 6 , Caroline Berteau 5 , Claudia Schulte 7 , Dieter Scheller 8 , Daniela Berg 7, 9 , Walter Maetzler 7, 9 , Pedro A. F. Galante 3 and Andre Nogueira da Costa 1 1 Experimental Medicine and Diagnostics, Global Exploratory Development, UCB Biopharma SPRL, Braine-l'Alleud, Belgium 2 SimplicityBio SA, Monthey, Switzerland 3 Hospital Sirio Libanes, Sao Paulo, Brazil 4 Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK 5 Leeds Institute of Biomedical and Clinical Sciences, University of Leeds, Leeds, UK 6 Bioanalytical Sciences, Non Clinical Development, UCB Biopharma SPRL, Belgium 7 Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen and German Center for Neurodegenerative Diseases, Tuebingen, Germany 8 Consultancy Neuropharm, Neukirchener, Neuss, Germany 9 Department of Neurology, Christian-Albrechts-University Kiel, Kiel, Germany Correspondence to: Andre Nogueira da Costa, email: andre.dacosta@ucb.com Keywords: exosomal miRNA; early stage PD diagnosis; CSF; machine learning Received: January 06, 2018      Accepted: February 25, 2018      Published: April 03, 2018 ABSTRACT Parkinson’s Disease is the second most common neurodegenerative disorder, affecting 1–2% of the elderly population. Its diagnosis is still based on the identification of motor symptoms when a considerable number of dopaminergic neurons are already lost. The development of translatable biomarkers for accurate diagnosis at the earliest stages of PD is of extreme interest. Several microRNAs have been associated with PD pathophysiology. Consequently, microRNAs are emerging as potential biomarkers, especially due to their presence in Cerebrospinal Fluid and peripheral circulation. This study employed small RNA sequencing, protein binding ligand assays and machine learning in a cross-sectional cohort comprising 40 early stage PD patients and 40 well-matched controls. We identified a panel comprising 5 microRNAs (Let-7f-5p, miR-27a-3p, miR-125a-5p, miR-151a-3p and miR-423-5p), with 90% sensitivity, 80% specificity and 82% area under the curve (AUC) for the differentiation of the cohorts. Moreover, we combined miRNA profiles with hallmark-proteins of PD and identified a panel (miR-10b-5p, miR-22-3p, miR-151a-3p and α-synuclein) reaching 97% sensitivity, 90% specificity and 96% AUC. We performed a gene ontology analysis for the genes targeted by the microRNAs present in each panel and showed the likely association of the models with pathways involved in PD pathogenesis.