Parkinsons Disease-related Circulating microRNA Biomarkers——a Validation Study

David Petillo; Stephen Orey; Aik Choon Tan; Lars Forsgren; Sok Kean Khoo; David Petillo; Stephen Orey; Aik Choon Tan; Lars Forsgren; Sok Kean Khoo

doi:10.3934/medsci.2015.1.7

AIMS Medical Science

2015, Volume 2, Issue 1: 7-14. doi: 10.3934/medsci.2015.1.7

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Parkinsons Disease-related Circulating microRNA Biomarkers——a Validation Study

1.
Molecular Diagnostics Program, College of Health Professions, Ferris State University, Grand Rapids, MI 49503, USA;
2.
Department of Cell and Molecular Biology, Grand Valley State University, Grand Rapids, MI 49503, USA;
3.
School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
4.
Department of Pharmacology and Clinical Neuroscience, Umeå University, SE-901 87 Umeå, Sweden

Received: 06 February 2014 Accepted: 04 February 2015 Published: 06 February 2015

Parkinson's disease (PD) is the second most common neurodegenerative disease. One of the major challenges in studying this progressive neurological disorder is to identify and develop biomarkers for early detection. Recently, several blood-based microRNA (miRNA) biomarkers for PD have been reported. However, follow-up studies with new, independent cohorts have been rare. Previously, we identified a panel of four circulating miRNA biomarkers for PD (miR-1826, miR-450b-3p, miR-505, and miR-626) with biomarker performance of 91% sensitivity and 100% specificity. However, the expression of miR-450b-3p could not be detected in a new, independent validation set. In our current study, we improved the detection power by including a non-biased pre-amplification step in quantitative real-time PCR (qRT-PCR) and reevaluated the biomarker performance. We found the panel of four PD-related miRNAs achieved the predictive power of 83% sensitivity and 75% specificity in our validation set. This is the first biomarker validation study of PD which showed reproducibility and robustness of plasma-based circulating miRNAs as molecular biomarkers and qRT-PCR as potential diagnostic assay.
- Parkinson's disease,
- circulating microRNAs,
- plasma,
- biomarkers,
- validation,
- quantitative real-time PCR
Citation: David Petillo, Stephen Orey, Aik Choon Tan, Lars Forsgren, Sok Kean Khoo. Parkinsons Disease-related Circulating microRNA Biomarkers——a Validation Study[J]. AIMS Medical Science, 2015, 2(1): 7-14. doi: 10.3934/medsci.2015.1.7

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Abstract

Parkinson's disease (PD) is the second most common neurodegenerative disease. One of the major challenges in studying this progressive neurological disorder is to identify and develop biomarkers for early detection. Recently, several blood-based microRNA (miRNA) biomarkers for PD have been reported. However, follow-up studies with new, independent cohorts have been rare. Previously, we identified a panel of four circulating miRNA biomarkers for PD (miR-1826, miR-450b-3p, miR-505, and miR-626) with biomarker performance of 91% sensitivity and 100% specificity. However, the expression of miR-450b-3p could not be detected in a new, independent validation set. In our current study, we improved the detection power by including a non-biased pre-amplification step in quantitative real-time PCR (qRT-PCR) and reevaluated the biomarker performance. We found the panel of four PD-related miRNAs achieved the predictive power of 83% sensitivity and 75% specificity in our validation set. This is the first biomarker validation study of PD which showed reproducibility and robustness of plasma-based circulating miRNAs as molecular biomarkers and qRT-PCR as potential diagnostic assay.

Table 1. Characteristics of study subjects for PD biomarker validation set.

	PD	Healthy Controls	PSP	MSA
PD = Parkinson’s disease; PSP = progressive supranuclear palsy; MSA = multiple system atrophy; M = Male; F = Female
Sample size Age Hoehn & Yahr	16 M 14 F 69.4 ± 9.0 2.2 ± 0.7	3 M 4 F 72.3± 3.4	2 M 3 F 75.2 ± 8.8 4 ± 1.2	2 M 2 F 72.5 ± 17.8 4.5 ± 1.0

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Table 2. Average ΔΔC_T in log2 fold change for miRNA-1826, miR-450b-3_p, miR-505, and miR-626.

	CTR	MSA	PSP
PD = Parkinson’s disease; CTR = healthy controls; MSA = multiple system atrophy; PSP =progressive supranuclear palsy
PD miR-1826 miR-450b-3_p miR-505 miR-626	2.046 −9.355 −1.260 0.326	1.055 −2.857 1.316 5.125	−1.613 −1.920 −1.067 3.918
MSA miR-1826 miR-450b-3_p miR-505 miR-626	1.939 −3.274 −1.659 −15.698		−1.702 1.488 −1.405 −1.308
PSP	miR-1826 miR-450b-3 miR-505 miR-626	3.300 −4.874 −1.181 −12.000

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Figure 1. miRNA expression levels of circulating miR-1826 in healthy controls (CTR), Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) patients. * Statistically significance between groups (P < 0.05).

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Figure 2.miRNA expression levels of circulating miR-450b-3_p in healthy controls (CTR), Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) patients. * Statistically significance between groups (P < 0.05).

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Figure 3.miRNA expression levels of circulating miR-505 in healthy controls (CTR), Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) patients.

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Figure 4.miRNA expression levels of circulating miR-626 in healthy controls (CTR), Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) patients.

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References

[1]	Margis R, Rieder CR (2011) Identification of blood microRNAs associated to Parkinsonis disease. J biotechnol 152: 96-101. doi: 10.1016/j.jbiotec.2011.01.023
[2]	Martins M, Rosa A, Guedes LC, et al. (2011) Convergence of miRNA expression profiling, alpha-synuclein interacton and GWAS in Parkinson's disease. PloS one 6: e25443. doi: 10.1371/journal.pone.0025443
[3]	Khoo SK, Petillo D, Kang UJ, et al. (2012) Plasma-based circulating microRNA biomarkers for Parkinson's disease. J Parkinson Dis 2: 321-331.
[4]	Cardo LF, Coto E, de Mena L, et al. (2013) Profile of microRNAs in the plasma of Parkinson's disease patients and healthy controls. J Neurol 260: 1420-1422. doi: 10.1007/s00415-013-6900-8
[5]	Soreq L, Salomonis N, Bronstein M, et al. (2013) Small RNA sequencing-microarray analyses in Parkinson leukocytes reveal deep brain stimulation-induced splicing changes that classify brain region transcriptomes. Front Mol Neurosci 6: 10.
[6]	Vallelunga A, Ragusa M, Di Mauro S, et al. (2014) Identification of circulating microRNAs for the differential diagnosis of Parkinson's disease and Multiple System Atrophy. Front Cell Neurosci8: 156.
[7]	Botta-Orfila T, Morato X, Compta Y, et al. (2014) Identification of blood serum micro-RNAs associated with idiopathic and LRRK2 Parkinson's disease. J Neurosci Res 92: 1071-1077. doi: 10.1002/jnr.23377
[8]	Burgos K, Malenica I, Metpally R, et al. (2014) Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer's and Parkinson's diseases correlate with disease status and features of pathology. PLoS One 9: e94839. doi: 10.1371/journal.pone.0094839
[9]	Khoo SK, Neuman LA, Forsgren L, et al. (2013) Could miRNA expression changes be a reliable clinical biomarker for Parkinson's disease? Neurodegener Dis Manag 3: 455-465. doi: 10.2217/nmt.13.53
[10]	Kroh EM, Parkin RK, Mitchell PS, et al. (2010) Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods 50: 298-301. doi: 10.1016/j.ymeth.2010.01.032
[11]	Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25: 402-408. doi: 10.1006/meth.2001.1262
[12]	Warnock DG, Peck CC (2010) A roadmap for biomarker qualification. Nat Biotechnol 28:444-445. doi: 10.1038/nbt0510-444
[13]	Tan AC, Naiman DQ, Xu L, et al. (2005) Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21: 3896-3904. doi: 10.1093/bioinformatics/bti631
[14]	Paulovich AG, Whiteaker JR, Hoofnagle AN, et al. (2008) The interface between biomarker discovery and clinical validation: The tar pit of the protein biomarker pipeline. Proteomics Clin Appl 2: 1386-1402. doi: 10.1002/prca.200780174

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