MicroRNAs in Cerebrospinal Fluid as Potential Biomarkers for Parkinson’s Disease and Multiple System AtrophyReport as inadecuate




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Molecular Neurobiology

pp 1–10

First Online: 14 November 2016Received: 18 July 2016Accepted: 24 October 2016

Abstract

Parkinson’s disease PD and multiple system atrophy MSA are both part of the spectrum of neurodegenerative movement disorders and α-synucleinopathies with overlap of symptoms especially at early stages of the disease but with distinct disease progression and responses to dopaminergic treatment. Therefore, having biomarkers that specifically classify patients, which could discriminate PD from MSA, would be very useful. MicroRNAs miRNAs regulate protein translation and are observed in biological fluids, including cerebrospinal fluid CSF, and may therefore have potential as biomarkers of disease. The aim of our study was to determine if miRNAs in CSF could be used as biomarkers for either PD or MSA. Using quantitative PCR qPCR, we evaluated expression levels of 10 miRNAs in CSF patient samples from PD n = 28, MSA n = 17, and non-neurological controls n = 28. We identified two miRNAs miR-24 and miR-205 that distinguished PD from controls and four miRNAs that differentiated MSA from controls miR-19a, miR-19b, miR-24, and miR-34c. Combinations of miRNAs accurately discriminated either PD area under the curve AUC = 0.96 or MSA AUC = 0.86 from controls. In MSA, we also observed that miR-24 and miR-148b correlated with cerebellar ataxia symptoms, suggesting that these miRNAs are involved in cerebellar degeneration in MSA. Our findings support the potential of miRNA panels as biomarkers for movement disorders and may provide more insights into the pathological mechanisms related to these disorders.

KeywordsParkinson’s disease Multiple system atrophy Biomarkers microRNA Cerebrospinal fluid AbbreviationsAUCArea under the curve

CSFCerebrospinal fluid

CtCycle threshold

GMGeometric mean

miRNAmicroRNA

MSAMultiple system atrophy

PDParkinson’s disease

qPCRQuantitative PCR

RELRelative expression

ROCReceiver operator characteristics

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Author: Tainá M. Marques - H. Bea Kuiperij - Ilona B. Bruinsma - Anouke van Rumund - Marjolein B. Aerts - Rianne A. J. Essel

Source: https://link.springer.com/



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