Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2017.7995 mmr-17-01-0852 Articles Serum microRNA expression profiling in patients with multiple system atrophy KumeKodai 1 2 IwamaHisakazu 3 DeguchiKazushi 1 4 IkedaKazuyo 4 TakataTadayuki 4 KokudoYohei 5 KamadaMasaki 5 FujikawaKeiko 4 HiroseKayo 4 MasugataHisashi 2 TougeTetsuo 6 MasakiTsutomu 4 Department of Neurology, Kagawa University Hospital, Kita-gun, Kagawa 761-0793, Japan Department of Integrated Medicine, Faculty of Medicine, Kagawa University, Kita-gun, Kagawa 761-0793, Japan Life Science Research Center, Faculty of Medicine, Kagawa University, Kita-gun, Kagawa 761-0793, Japan Department of Gastroenterology and Neurology, Faculty of Medicine, Kagawa University, Kita-gun, Kagawa 761-0793, Japan Department of Intractable Neurological Research, Faculty of Medicine, Kagawa University, Kita-gun, Kagawa 761-0793, Japan Department of Health Sciences, Faculty of Medicine, Kagawa University, Kita-gun, Kagawa 761-0793, Japan Correspondence to: Professor Tsutomu Masaki, Department of Gastroenterology and Neurology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa 761-0793, Japan, E-mail: tmasaki@med.kagawa-u.ac.jp 012018 07112017 17 1 852 860 15052016 26042017 Copyright: © Kume et al. 2018 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Multiple system atrophy (MSA) is a sporadic neurodegenerative disease that is pathologically characterized by α-synuclein positive glial cytoplasmic inclusions in oligodendrocytes. The clinical diagnosis of MSA is often challenging as there are no established biomarkers and diagnoses are now based on clinical findings alone. At present, the etiology and pathogenesis of MSA are unclear. It has been reported that dysregulation of microRNA (miRNA/miR) serves an important role in neurodegenerative disorders including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. The miRNA profile of patients with MSA remains to be established. The present study investigated the serum miRNA expression level of 10 patients with MSA, using microarray chips including 668 miRNAs. It was identified that 50 miRNAs were significantly upregulated and 17 miRNAs were significantly downregulated in the serum of the patients with MSA. The most upregulated miRNA was miR-16, which may induce the accumulation of α-synuclein. The target genes of some miRNAs upregulated in MSA (including miR-17, 20a, 24, 25, 30d and 451) were associated with autophagy-associated molecules. The present study concluded that the expression pattern of miRNAs may be a clinical biomarker for MSA and targeting these miRNAs may provide a novel treatment for MSA.

 multiple system atrophy microRNA microRNA-16 α-synuclein autophagy Introduction

Multiple system atrophy (MSA) is a sporadic neurodegenerative disorder characterized by a combination of various degrees of parkinsonism, cerebellar ataxia and autonomic dysfunction (1). At present, there are no established biomarkers of MSA and so the clinical diagnosis of MSA is dependent on the assessment of clinical symptoms, thus misdiagnoses are frequent (2). The characteristic pathological hallmark of MSA is the accumulation of α-synuclein positive glial cytoplasmic inclusions in oligodendrocytes (3). The pathogenesis of MSA remains unclear, although it has been reported that α-synuclein accumulation serves a key role in neurodegeneration (3,4).

microRNA (miRNA) are short RNA molecules that function as post-transcriptional regulators that bind to complementary sequences on target mRNA transcripts, typically resulting in translational repression or target degradation and gene silencing (5). Previous studies have demonstrated that the dysregulation of miRNA serves an important role in a number of neurological disorders, including Alzheimer's disease, Parkinson's disease (PD) (6) and amyotrophic lateral sclerosis (7). However, the miRNA profile of patients with MSA remains to be fully investigated.

Increased serum levels of insulin like growth factor (IGF)-1 have been reported in patients with MSA (8,9). Since IGF-1 signaling has been revealed to be regulated by several miRNAs (10,11), it may be hypothesized that miRNAs and IGF-1 contribute to the pathogenesis of MSA.

In the present study, the expression of serum miRNAs and IGF-1 was investigated in patients with MSA, in order to identify potential biomarkers and clarify the pathogenesis of MSA.

 Materials and methods <sec> <title>Patients and samples

A total of 10 patients with MSA (7 males and 3 females; mean age, 64±6.9 years; range, 49–75 years) and 6 age- and sex-matched healthy controls (caregivers of the MSA patients without MSA; 4 males and 2 females; mean age, 64±2.9; range, 58–66 years) were recruited for the present study between January and December 2013, from the Department of Neurology, Kagawa University Hospital. All of the patients with MSA were diagnosed according to the 2008 consensus statement on the diagnosis of MSA: Definite, 1 patient; probable, 4 patients; possible, 5 patients (12). Five of the 10 patients had MSA of the parkinsonian form (MSA-P), and the other 5 patients had MSA of the cerebellar form (MSA-C). The mean duration of the illness was 5.5±2.8 years (Table I). Blood samples were harvested upon recruitment and processed for serum isolation within 2 h following withdrawal. Whole blood was centrifuged at 1,500 × g for 15 min at 4°C. Each serum sample was divided into aliquots and stored at −80°C until analysis. Informed consent was obtained from all of the participants and the present study was approved by the Ethics Committee of Kagawa University (Kagawa, Japan).

 RNA isolation

Total RNA was extracted from the serum samples using an miRNeasy Mini kit (Qiagen, Inc., Valencia, CA, USA) according to the manufacturer's instructions. All RNA samples used in the present study exhibited A260/280 ratios between 2.0 and 2.1. The integrity of RNA was determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA). The quality of total RNA was determined using the RNA Nano 6000 chips from the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA) according to the manufacturer's protocol, and all RNA samples used for the microarray analyses had RNA Integrity Number values >8.2. Briefly, total RNA from all serum samples was heated at 70°C for 2 min and incubated on ice for 5 min. Subsequently, samples (1 µl) were loaded into each lane on the RNA Nano 6000 chips, and the bands of 18S (arrow) and 28S ribosomal RNA (arrowhead) in the gel were detected using the Agilent 2100 Bioanalyzer (Fig. 1A). These RNA samples were stored at −80°C.

 miRNA arrays

Total RNA was labeled with Hy3 dye using an miRCURY LNA microRNA Array Hi-Power Labeling kit (Exiqon A/S, Vedbæk, Denmark). Total RNA (2 µg) was incubated with a spike of 30 min at 37°C and then at 95°C for 5 min. Hy3 dye and Hi-Power Labeling enzyme were then added to each sample. The enzyme was then heat-inactivated at 16°C for 1 h and at 65°C for 15 min, protected from light. The samples were loaded onto the arrays by capillary force using 3D-Gene miRNA oligo chips (version 17; Toray Industries, Inc., Tokyo, Japan). The chips enabled the examination of the expression of 679 miRNAs printed in duplicate spots.

The arrays were incubated at 32°C for 16 h, then briefly washed in a 30°C wash buffer solution [0.5X saline-sodium citrate (SSC), 0.1% SDS], rinsed in wash buffer solution (0.2X SSC, 0.1% SDS) and then washed again in another buffer solution (0.05X SSC), according to the manufacturer's instructions (Toray Industries, Inc.). The arrays were centrifuged for 1 min at 600 × g at room temperature for drying, followed by immediate scanning using a 3D-Gene 3000 miRNA microarray scanner (Toray Industries, Inc.). It was calculated that the relative expression level of each miRNA by comparing the average signal intensities of the valid spots with their mean value throughout the microarray experiments, following normalization to their adjusted median values.

 Quantification of miRNA

Isolation of RNA was performed using a miRNeasy serum/plasma kit adding spike in control cel-miR-39 (Qiagen, Inc.), according to the manufacturer's protocol. cDNA was individually synthesized for each target miRNA using miRNA Reverse Transcription kit (Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. The detection of miRNA expression was performed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), using TaqMan miRNA Assays and TaqMan Universal Master MixII (Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. Thermocycling conditions were as follows: Initial denaturation at 95°C for 10 min, followed by 40 cycles at 95°C for 15 sec and at 60°C for 60 sec. The relative expression levels of miRNA were calculated using the comparative Cq method (13) and normalized to cel-miR-39 expression. Experiments were performed in triplicate.

 Heatmap

A heatmap was created using R software version 3.2.3 (https://www.R-project.org) in which the expression levels of miRNAs from each of the 10 MSA patients and 6 healthy controls were represented using unsupervised hierarchical clustering Brunner-Munzel analysis. The heatmap was color-coded according to the log2-transformed expression levels. The center level of the color code is set as the median value over all of the values used in the heatmap. White color represented mean values, red indicated an increase and blue represented a decrease in expression.

 Measurement of IGF-1

Serum IGF-1 levels were measured by Shikoku Chuken (Ayagawa, Japan) using an immunoradiometric assay.

 Statistical analysis

Data are expressed as the mean ± standard deviation. The statistical significance of the differences between groups was assessed using unpaired Student's t-test. P<0.05 was considered to indicate a statistically significant difference. Statistical analysis was performed using SPSS software version 23.0 (IBM Corp., Armonk, NY, USA).

 Results <sec> <title>Identification of differentially expressed plasma miRNA in the patients with MSA and healthy controls

The miRNA expression levels in serum obtained from the patients with MSA and healthy controls were compared. The custom microarray platform identified 50 miRNAs that were upregulated and 17 miRNAs that were downregulated in the serum of the patients with MSA (Table II). As shown in Fig. 1B, the representative upregulated miRNAs were: miR-16 (spot no. 1), miR-223 (spot no. 2), miR-25 (spot no. 3), let-7c (spot no. 4), miR-17 (spot no. 5), let-7d (spot no. 6), let-7i (spot no. 7), let-7b (spot no. 8), miR-24 (spot no. 9), let-7a (spot no. 10) and miR-20a (spot no. 11). An unsupervised hierarchical clustering analysis using a Brunner-Munzel test revealed that the patients with MSA clustered separately from the healthy control group (Fig. 2).

 Identification of differentially expressed plasma miRNA in the MSA-P and MSA-C patients

The miRNA expression levels in serum obtained from the MSA-P and MSA-C patients were then compared. This analysis identified that 22 miRNAs were upregulated and 17 miRNAs were downregulated in the serum of the patients with MSA-P (Table III). An unsupervised hierarchical clustering analysis using a Brunner-Munzel test demonstrated that the MSA-P patients clustered separately from the MSA-C patients (Fig. 3).

 Quantification of miR-16 and miR-223

The expression levels of miR-16 and miR-223 were determined using RT-qPCR to validate the miRNA array data. The mean ΔCq ± standard deviation of miR-16 was 3.6±1.1 and 3.2±1.4 for the control and MSA groups, respectively (Fig. 4A). Similarly, for miR-223, the value was 2.3±1.1 and 2.0±1.4 for the control and MSA groups (Fig. 4B). There were no significant differences between the miRNA levels of the patients with MSA and the healthy controls.

 Quantification of IGF-1

Mean serum IGF-1 levels were revealed to be 68.1±34.2 ng/ml in control group and 105±42.1 ng/ml in the MSA group (Fig. 4C). However, no statistically significant difference in IGF-1 levels was detected between patients with MSA and healthy controls.

 Discussion

In the present study, 50 upregulated miRNAs and 17 downregulated miRNAs were identified in serum from patients with MSA, using a microarray platform. The hierarchical clustering analysis identified marked differences between the miRNA profiles of the MSA and the control groups.

There are few reports on miRNA profiling in patients with MSA (14,15). It has been demonstrated that miR-24, miR-223 and miR-324-3p are upregulated in the serum of patients with MSA or PD (14), and a greater upregulation of miR-24, miR-34b and miR-148b was observed in MSA when compared with PD. In studies investigating miRNA in the MSA brain, it was observed that miR-96 was upregulated in the frontal cortex (16) and miR-202 was upregulated in the cerebellum (15).

The results of the present study also identified that miR-24 and miR-223 were upregulated in MSA serum, as described previously (14). These results indicate that the methods used by the present study to determine the profile of miRNA are valid. However, the upregulation of miR-96 and miR-202 described in these previous studies in MSA brain tissue was not observed in the present study. These differing results may be due to the differences between the brain tissue and serum samples used in each study.

A previous study demonstrated that the expression of several miRNAs was altered in a mouse model of pre-motor stage MSA (17) however, in the present study, this altered expression of miRNA was not observed. These differing results may be due to differences between mouse brain tissue and human serum.

miR-16 was the most elevated miRNA in the present study. The function of miR-16 has been primarily investigated in the field of oncology, and has been demonstrated to act as a tumor suppressor, an oncomiR (an miRNA associated with cancer), a modulator of the immune response and a negative regulator of angiogenesis (18). It has also been reported that miR-16 promotes α-synuclein aggregation by downregulating heat shock protein 70 (HSP70) in a neuroblastoma cell line (19). A previous study of brain tissue with MSA identified that the dysfunction of HSP70 may contribute to neuronal cell death (20). Therefore, the elevation of miR-16 in the serum of patients with MSA may cause an accumulation of α-synuclein via the downregulation of HSP70, which may be associated with the pathogenesis of MSA.

Previous studies have demonstrated that miR-24 (21), and the other upregulated miRNAs identified in the present study [miR-17 (22), miR-20a (23,24), miR-25 (25), miR-30d (26,27) and miR-451 (28)], are associated with autophagy. These miRNAs serve a role in inhibiting autophagy, downregulating the expression of target genes (Table IV). In brain tissue with MSA, an impairment of autophagy has been observed (29). These miRNAs may decrease the level of autophagy-associated protein and induce the impairment of autophagy in patients with MSA.

In agreement with a previous study, miR-223 was also upregulated in the present study (14). It has been demonstrated that upregulation of miR-223 is associated with the pathophysiology of infection, inflammation and cancer (30). An in vitro study indicated that miR-223 downregulated the IGF-1 receptor and inhibited cell proliferation (10). In addition, the serum IGF-1 level increased in patients with MSA and is associated with disease progression (9). Therefore, an upregulation of miR-223 expression may contribute to the inhibition of IGF-1 signaling and thus induce cell death. Elevated IGF-1 levels in patients with MSA may be a compensatory response to upregulated miR-223. The present study assessed serum IGF-1 levels by the immune radio metric assay and serum miR-223 levels were measured by RT-qPCR. However, there were no significant differences observed between the IGF-1 and miR-223 levels in patients with MSA and the healthy controls in the present study; these results may be due to the small sample size.

The present study also observed that members of the let-7 family were upregulated in the serum of patients with MSA. The human let-7 family, which contains 13 members, is widely recognized as a class of miRNAs that have a tumor-suppressing effect (31). A previous study demonstrated that extracellular let-7b induced neurodegeneration through the neuronal toll-like receptor 7 and that the levels of cerebrospinal fluid let-7b expression in patients with Alzheimer's disease were higher than those observed in the healthy control subjects (32). Similarly, let-7 family members, including let-7b, may induce neurodegeneration in patients with MSA.

Using a microarray platform, the present study identified 22 upregulated miRNAs and 17 downregulated miRNAs in the serum of patients with MSA-P. The hierarchical clustering analysis observed marked differences in the miRNA profiles between the MSA and control groups. These alterations in the expression of miRNA may explain the differences between the pathophysiology of patients with MSA-P and MSA-C.

The present study had a number of limitations. The sample size was small and no validation of the level of miRNA expression was performed. In future investigations, a greater number of patients with MSA should be examined and individual miRNA expression levels should be determined by RT-qPCR. In addition, the present study analyzed serum miRNA, not cerebrospinal fluid (CSF) miRNA, the latter of which may be more appropriate for investigating neurodegenerative disorders such as MSA. However, analyses of miRNA expression in the serum of individuals with MSA may be more suitable, as obtaining serum samples is easier than collecting CSF samples.

In conclusion, the present study identified dysregulated miRNAs in the serum of patients with MSA. These miRNAs may serve as effective biomarkers for MSA and contribute to the pathogenesis of MSA, which may involve the accumulation of α-synuclein and the suppression of autophagy.

 References StefanovaNBückePDuerrSWenningGKMultiple system atrophy: An updateLancet Neurol811721178200910.1016/S1474-4422(09)70288-119909915 JoutsaJGardbergMRöyttäMKaasinenVDiagnostic accuracy of parkinsonism syndromes by general neurologistsParkinsonism Relat Disord20840844201410.1016/j.parkreldis.2014.04.01924816002 JellingerKANeuropathology of multiple system atrophy: New thoughts about pathogenesisMov Disord2917201741201410.1002/mds.2605225297524 StefanovaNKaufmannWAHumpelCPoeweWWenningGKSystemic proteasome inhibition triggers neurodegeneration in a transgenic mouse model expressing human α-synuclein under oligodendrocyte promoter: Implications for multiple system atrophyActa Neuropathol1245165201210.1007/s00401-012-0977-5224919593377902 BartelDPMicroRNAs: Target recognition and regulatory functionsCell136215233200910.1016/j.cell.2009.01.002191673263794896 SaltaEDe StrooperBNon-coding RNAs with essential roles in neurodegenerative disordersLancet Neurol11189200201210.1016/S1474-4422(11)70286-122265214 CloutierFMarreroAO'ConnellCMorinPJrMicroRNAs as potential circulating biomarkers for amyotrophic lateral sclerosisJ Mol Neurosci56102112201510.1007/s12031-014-0471-825433762 PellecchiaMTPivonelloRLongoKManfrediMTessitoreAAmboniMPivonelloCRoccoMCozzolinoAColaoABaronePMultiple system atrophy is associated with changes in peripheral insulin-like growth factor systemMov Disord2526212626201010.1002/mds.2332020683839 NumaoASuzukiKMiyamotoMMiyamotoTHirataKClinical correlates of serum insulin-like growth factor-1 in patients with Parkinson's disease, multiple system atrophy and progressive supranuclear palsyParkinsonism Relat Disord20212216201410.1016/j.parkreldis.2013.11.00524280021 JiaCYLiHHZhuXCDongYWFuDZhaoQLWuWWuXZMiR-223 suppresses cell proliferation by targeting IGF-1RPLoS One6e27008201110.1371/journal.pone.0027008220732383206888 ZhuSPengWLiXWengJZhangXGuoJHuangDRongQChenSmiR-1827 inhibits osteogenic differentiation by targeting IGF1 in MSMSCsSci Rep746136201710.1038/srep46136283872485384002 GilmanSWenningGKLowPABrooksDJMathiasCJTrojanowskiJQWoodNWColosimoCDürrAFowlerCJSecond consensus statement on the diagnosis of multiple system atrophyNeurology71670676200810.1212/01.wnl.0000324625.00404.15187255922676993 LivakKJSchmittgenTDAnalysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) methodMethods25402408200110.1006/meth.2001.126211846609 VallelungaARagusaMDi MauroSIannittiTPilleriMBiundoRWeisLDi PietroCDe IuliisANicolettiAIdentification of circulating microRNAs for the differential diagnosis of Parkinson's disease and multiple system atrophyFront Cell Neurosci8156201410.3389/fncel.2014.00156249591194051126 LeeSTChuKJungKHBanJJImWSJoHYParkJHLimJYShinJWMoonJAltered expression of miR-202 in cerebellum of multiple-system atrophyMol Neurobiol51180186201510.1007/s12035-014-8788-424981430 UbhiKRockensteinEKraghCInglisCSpencerBMichaelSManteMAdameAGalaskoDMasliahEWidespread microRNA dysregulation in multiple system atrophy-disease-related alteration in miR-96Eur J Neurosci3910261041201410.1111/ejn.1244424304186 SchaffererSKhuranaRRefoloVVeneziaSSturmEPiattiPHechenbergerCHacklHKesslerRWilliMChanges in the miRNA-mRNA regulatory network precede motor symptoms in a mouse model of multiple system atrophy: Clinical implicationsPLoS One11e0150705201610.1371/journal.pone.0150705269628584786272 HuangELiuRChuYmiRNA-15a/16: As tumor suppressors and moreFuture Oncol1123512363201510.2217/fon.15.10126260813 ZhangZChengYmiR-16-1 promotes the aberrant α-synuclein accumulation in Parkinson disease via targeting heat shock protein 70ScientificWorldJournal20149383482014250541894094852 KawamotoYAkiguchiIShirakashiYHonjoYTomimotoHTakahashiRBudkaHAccumulation of Hsc70 and Hsp70 in glial cytoplasmic inclusions in patients with multiple system atrophyBrain Res1136219227200710.1016/j.brainres.2006.12.04917240362 PanBChenYSongHXuYWangRChenLMir-24-3p downregulation contributes to VP16-DDP resistance in small-cell lung cancer by targeting ATG4AOncotarget6317331201510.18632/oncotarget.278725426560 CominciniSAllavenaGPalumboSMoriniMDurandoFAngelettiFPirtoliLMiraccoCmicroRNA-17 regulates the expression of ATG7 and modulates the autophagy process, improving the sensitivity to temozolomide and low-dose ionizing radiation treatments in human glioblastoma cellsCancer Biol Ther14574586201310.4161/cbt.24597237926423742487 SunKTChenMYTuMGWangIKChangSSLiCYMicroRNA-20a regulates autophagy related protein-ATG16L1 in hypoxia-induced osteoclast differentiationBone73145153201510.1016/j.bone.2014.11.02625485521 WuHWangFHuSYinCLiXZhaoSWangJYanXMiR-20a and miR-106b negatively regulate autophagy induced by leucine deprivation via suppression of ULK1 expression in C2C12 myoblastsCell Signal2421792186201210.1016/j.cellsig.2012.07.00122781751 WangZWangNLiuPChenQSituHXieTZhangJPengCLinYChenJMicroRNA-25 regulates chemoresistance-associated autophagy in breast cancer cells, a process modulated by the natural autophagy inducer isoliquiritigeninOncotarget570137026201410.18632/oncotarget.2192250262964196180 YangXZhongXTanyiJLShenJXuCGaoPZhengTMDeMicheleAZhangLmir-30d regulates multiple genes in the autophagy pathway and impairs autophagy process in human cancer cellsBiochem Biophys Res Commun431617622201310.1016/j.bbrc.2012.12.08323274497 ZhangYYangWQZhuHQianYYZhouLRenYJRenXCZhangLLiuXPLiuCGRegulation of autophagy by miR-30d impacts sensitivity of anaplastic thyroid carcinoma to cisplatinBiochem Pharmacol87562570201410.1016/j.bcp.2013.12.00424345332 SongLSuMWangSZouYWangXWangYCuiHZhaoPHuiRWangJMiR-451 is decreased in hypertrophic cardiomyopathy and regulates autophagy by targeting TSC1J Cell Mol Med1822662274201410.1111/jcmm.12380252099004224559 TanjiKOdagiriSMaruyamaAMoriFKakitaATakahashiHWakabayashiKAlteration of autophagosomal proteins in the brain of multiple system atrophyNeurobiol Dis49190198201310.1016/j.nbd.2012.08.01722959883 HaneklausMGerlicMO'NeillLAMastersSLmiR-223: Infection, inflammation and cancerJ Intern Med274215226201310.1111/joim.1209923772809 BoyerinasBParkSMHauAMurmannAEPeterMEThe role of let-7 in cell differentiation and cancerEndocr Relat Cancer17F19F36201010.1677/ERC-09-018419779035 LehmannSMKrügerCParkBDerkowKRosenbergerKBaumgartJTrimbuchTEomGHinzMKaulDAn unconventional role for miRNA: Let-7 activates Toll-like receptor 7 and causes neurodegenerationNat Neurosci15827835201210.1038/nn.311322610069

Quality of total RNA and miRNA expression in the serum of a typical patient with MSA. (A) The bands of 18S (arrow) and 28S (arrowhead) ribosomal RNA in the gel were detected using a 2100 Bioanalyzer. (B) Representative miRNA expression in control and MSA cases, using miRNA chip analysis. Spot numbers 1 to 11 are presented: 1, miR-16; 2, miR-223; 3, miR-25; 4, let-7c; 5, miR-17; 6, let-7d; 7, let-7i; 8, let-7b; 9, miR-24; 10, let-7a; 11, miR-20a. MSA, multiple system atrophy; miRNA/miR, microRNA.

Hierarchical clustering of miRNAs from the healthy controls and patients with MSA. Samples are arranged in columns and miRNAs in rows. The miRNA clustering tree is presented on the left, and the sample clustering tree is present at the top of each heat map. Heat maps depict the relative expression intensity for each miRNA in which the base-2 logarithm of the intensity is median-centered for each row. The color coding is indicated as a horizontal bar. MSA, multiple system atrophy; miRNA, microRNA.

Hierarchical clustering of miRNAs from the MSA-P and MSA-C patients. Samples are arranged in columns and miRNAs in rows. The miRNA clustering tree is shown on the left, and the sample clustering tree is shown at the top of each heat map. Heat maps depict the relative expression intensity for each miRNA in which the base-2 logarithm of the intensity is median-centered for each row. The color coding is indicated as a horizontal bar. MSA, multiple system atrophy; MSA-P, parkinsonian MSA; MSA-C, cerebellar MSA; miRNA, microRNA.

Expression levels of miR-16 and miR-223, and IGF-1 concentration in the serum of healthy controls and patients with MSA. Expression levels of (a) mir-16 and (b) mir-223 were compared between the controls and patients with MSA. (c) Serum IGF-1 concentration was compared between the controls and patients with MSA. Statistical significance was evaluated using unpaired Student's t-test. n.s, not significant; miR, microRNA; IGF-1, insulin-like growth factor-1; MSA, multiple system atrophy.

Profiles of patients with MSA-P and -C.

Patient number Age (years) Sex Subtype Diagnostic criteria Disease duration (years)
  1 59 M P Possible 3
  2 64 M C Probable 5
  3 69 M C Possible 3
  4 64 M C Probable 7
  5 63 M P Definite 8
  6 75 F P Probable 9
  7 62 F C Probable 10
  8 66 M P Probable 3
  9 69 M C Possible 5
10 49 F P Possible 2

Diagnostic criteria, all of the patients with MSA were diagnosed according to the 2008 consensus statement on the diagnosis of MSA (8). MSA, multiple system atrophy; P, parkinsonian form of MSA; C, cerebellar form of MSA; M, male; F, female.

Statistical results and chromosomal locations of miRNAs in the 10 patients with MSA and 6 healthy controls.

miRNA Fold change (MSA/control) P-value Chromosomal localization
Upregulated
  hsa-miR-16 4.42 0.009 13q14.2
  hsa-miR-451 4.21 0.017 17q11.2
  hsa-miR-103a 3.43 0.006 5q34
  hsa-miR-223 3.28 0.007 Xq12
  hsa-miR-486-5p 2.70 0.023 8p11.21
  hsa-miR-107 2.45 0.014 10q23.31
  hsa-miR-25 2.42 0.029 7q22.1
  hsa-miR-3135b 2.31 0.035 6
  hsa-miR-15b 2.19 0.009 3q25.33
  hsa-miR-185 2.14 0.010 22q11.21
  hsa-miR-939 2.09 0.048 8q24.3
  hsa-miR-92a 2.08 0.036 13q31.3
  hsa-miR-4298 2.07 0.006 11
  hsa-miR-92b 2.07 0.019 1q22
  hsa-let-7c 2.02 0.006 21q21.1
  hsa-miR-17 1.97 0.007 13q31.3
  hsa-miR-4693-3p 1.95 0.005 11
  hsa-miR-130a 1.95 0.010 5q34
  hsa-let-7d 1.91 0.012 9q22.32
  hsa-let-7i 1.91 0.003 12q14.1
  hsa-miR-484 1.90 0.004 16p13.11
  hsa-miR-4791 1.89 0.019 3
  hsa-miR-522 1.88 0.003 19q13.42
  hsa-miR-26a 1.88 0.033 3p22.2
  hsa-let-7b 1.86 0.024 22q13.31
  hsa-miR-3605-3p 1.86 0.009 1
  hsa-miR-30d 1.83 0.006 8q24.22
  hsa-miR-4434 1.80 0.016 2
  hsa-miR-4281 1.80 0.007 5
  hsa-miR-106a 1.76 0.015 Xq26.2
  hsa-miR-3667-3p 1.74 0.008 22
  hsa-miR-99a 1.74 0.040 21q21.1
  hsa-miR-24 1.74 0.017 9q22.32
  hsa-miR-221 1.73 0.020 Xp11.3
  hsa-miR-31 1.73 0.024 9p21.3
  hsa-miR-1285 1.73 0.007 7q21-q22
  hsa-miR-218-2 1.70 0.041 5q34
  hsa-let-7a 1.70 0.025 9q22.32
  hsa-miR-27a 1.68 0.019 19p13.13
  hsa-miR-20a 1.68 0.015 13q31.3
  hsa-miR-518a-3p 1.67 0.043 19q13.42
  hsa-miR-19b 1.67 0.028 13q31.3
  hsa-miR-10b 1.67 0.028 2q31.1
  hsa-miR-377 1.66 0.015 14q32.31
  hsa-miR-4698 1.65 0.033 12
  hsa-miR-186 1.65 0.042 1p31.1
  hsa-miR-126 1.64 0.032 9q34.3
  hsa-miR-1303 1.61 0.032 5
  hsa-miR-500b 1.61 0.029 Xp11.23
  hsa-miR-3622a-5p 1.61 0.002 8
  hsa-miR-3139 0.50 0.037 4
Downregulated
  hsa-miR-4325 0.46 0.0003 20
  hsa-miR-380 0.50 0.0008 14q32.31
  hsa-miR-3912 0.51 0.0019 5
  hsa-miR-4661-3p 0.54 0.0032 8
  hsa-miR-4795-3p 0.55 0.0038 3
  hsa-miR-4458 0.55 0.041 5p15.31
  hsa-miR-3155 0.55 0.0082 10
  hsa-miR-590-3p 0.55 0.045 7q11.23
  hsa-miR-147b 0.56 0.0034 15q21.1
  hsa-miR-4439 0.56 0.046 2
  hsa-miR-378i 0.56 0.016 22
  hsa-miR-3939 0.57 0.0058 6
  hsa-miR-4495 0.58 0.026 12
  hsa-miR-526b 0.58 0.036 19q13.42
  hsa-miR-548z 0.58 0.041 12
  hsa-miR-3183 0.59 0.0082 17

miRNA/miR, microRNA; MSA, multiple system atrophy; hsa, human (Homo sapiens).

Statistical results and chromosomal locations of miRNAs in the patients with MSA-P and -C.

miRNA Fold change (MSA-P/-C) P-value Chromosomal localization
Upregulated
  hsa-miR-4790-3p 3 0.0016 3p26.1
  hsa-miR-3919 2.31 0.015 3q25.32
  hsa-miR-4436a 2 0.025 2p11.2
  hsa-miR-3202 1.97 0.027 Xq28
  hsa-miR-4450 1.95 0.0014 4q21.1
  hsa-miR-4771 1.9 0.032 2p11.2
  hsa-miR-331-3p 1.89 0.021 12q22
  hsa-miR-3064-3p 1.87 0.033 17q23.3
  hsa-miR-4751 1.87 0.019 19q13.33
  hsa-miR-617 1.82 0.03 12q21.31
  hsa-miR-4470 1.8 0.014 8q12.3
  hsa-miR-4755-3p 1.76 0.045 20q11.22
  hsa-miR-379 1.75 0.048 14q32.31
  hsa-miR-3714 1.7 0.001 3p24.3
  hsa-miR-4506 1.69 0.0032 14q32.12
  hsa-miR-454 1.68 0.035 17q22
  hsa-miR-135a 1.66 0.038 3p21.2
  hsa-miR-652 1.61 0.007 Xq23
  hsa-miR-4790-3p 3 0.0016 3p26.1
  hsa-miR-3919 2.31 0.015 3q25.32
  hsa-miR-4436a 2 0.025 2p11.2
  hsa-miR-3202 1.97 0.027 Xq28
Downregulated
  hsa-miR-518f 0.37 0.041 19q13.42
  hsa-miR-4703-5p 0.41 0.043 13q14.3
  hsa-miR-3189-3p 0.44 0.008 19p13.11
  hsa-miR-655 0.46 0.017 14q32.31
  hsa-miR-3168 0.5 0.015 13q14.11
  hsa-miR-105 0.51 0.014 Xq28
  hsa-miR-1294 0.53 0.011 5q33.2
  hsa-miR-3686 0.53 0.019 8q24.21
  hsa-miR-4330 0.53 0.012 Xq28
  hsa-miR-508-5p 0.53 0.014 Xq27.3
  hsa-miR-3591-3p 0.53 0.027 18q21.31
  hsa-miR-298 0.54 0.042 20q13.32
  hsa-miR-3682-5p 0.54 0.032 2p16.2
  hsa-miR-3115 0.55 0.046 1p36.12
  hsa-miR-3192 0.55 0.035 20p11.23
  hsa-miR-3975 0.55 0.037 18q12.2
  hsa-miR-518f 0.37 0.041 19q13.42

miRNA/miR, microRNA; MSA, multiple system atrophy; P, parkinsonian form of MSA; C, cerebellar form of MSA; hsa, human (Homo sapiens).

miRNAs and target genes associated with autophagy.

Author, year miRNA Target gene (Refs.)
Kawamoto et al, 2007 miR-24 ATG4 (20)
Pan et al, 2015 miR-17 ATG7 (21)
Comincini et al, 2013; Sun et al, 2015; Wang et al, 2015 miR-20a ULK1, LC3-II, ATG16L1 (22,23,25)
Wu et al, 2012 miR-25 ULK1 (24)
Wang et al, 2015; Yang et al, 2013 miR-30d Beclin-1, BNIP3L, ATG2, ATG5, ATG12 (25,26)
Zhang et al, 2014 miR-451 TSC1 (27)

miRNA/miR, microRNA; ATG, autophagy-related; ULK, Unc-51 like autophagy activating kinase; LC3, microtubule-associated protein 1A/1B-light chain 3; BNIP3L, BCL2 interacting protein 3 like; TSC1, tuberous sclerosis 1.