Neurol Sci DOI 10.1007/s10072-017-2945-2

ORIGINAL ARTICLE

SNCA 3′UTR genetic variants in patients with Parkinson’s disease and REM sleep behavior disorder M. Toffoli 1 & E. Dreussi 2 & E. Cecchin 2 & M. Valente 1,3 & N. Sanvilli 1 & M. Montico 2 & S. Gagno 2 & M. Garziera 2 & M. Polano 2 & M. Savarese 4 & G. Calandra-Buonaura 5 & F. Placidi 6 & M. Terzaghi 7 & G. Toffoli 2 & G. L. Gigli 1,3

Received: 6 November 2016 / Accepted: 1 April 2017 # Springer-Verlag Italia 2017

Abstract REM sleep behavior disorder (RBD) is an early marker of Parkinson’s disease (PD); however, it is still unclear which patients with RBD will eventually develop PD. Single nucleotide polymorphisms (SNPs) in the 3′untranslated region (3′UTR) of alpha-synuclein (SNCA) have been associated with PD, but at present, no data is available about RBD. The 3′UTR hosts regulatory regions involved in gene expression control, such as microRNA binding sites. The aim of this study was to determine RBD specific genetic features associated to an increased risk of progression to PD, by sequencing of the SNCA-3′UTR in patients with Bidiopathic^ RBD (iRBD) and in patients with PD. We recruited 113 consecutive patients with a diagnosis of iRBD (56 patients) or PD (with or without RBD, M. Toffoli and E. Dreussi contributed equally to the study. * M. Toffoli [email protected]

1

2

Neurology Unit, Department of Experimental and Clinical Medical Sciences, University of Udine Medical School, Udine, Italy Experimental and Clinical Pharmacology, Centro Di Riferimento Oncologico-National Cancer Institute, Aviano, Italy

3

Department of Neurosciences, BS. Maria della Misericordia^ University Hospital, Udine, Italy

4

Center of Sleep Disorders, Department of Basic Medical Sciences, Neurosciences and Sense Organs, University BAldo Moro^, Bari, Italy

5

IRCCS Institute of Neurological Sciences, Bologna and Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy

6

Neurophysiopathology Unit, Sleep Medicine Centre, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy

7

Unit of Sleep Medicine and Epilepsy, C. Mondino National Neurological Institute, Pavia, Italy

57 patients). Sequencing of SNCA-3′UTR was performed on genomic DNA extracted from peripheral blood samples. Bioinformatic analyses were carried out to predict the potential effect of the identified genetic variants on microRNA binding. We found three SNCA-3′UTR SNPs (rs356165, rs3857053, rs1045722) to be more frequent in PD patients than in iRBD patients (p = 0.014, 0.008, and 0.008, respectively). Four new or previously reported but not annotated specific genetic variants (KP876057, KP876056, NM_000345.3:c*860T>A, NM_000345.3:c*2320A>T) have been observed in the RBD population. The in silico approach highlighted that these variants could affect microRNA-mediated gene expression control. Our data show specific SNPs in the SNCA-3′UTR that may bear a risk for RBD to be associated with PD. Moreover, new genetic variants were identified in patients with iRBD. Keywords Parkinson’s disease . REM sleep behavior disorder . RBD . Alpha-synuclein . SNCA . Genetic variants

Introduction REM sleep behavior disorder (RBD) is a sleep disorder characterized by abnormal motor behavior associated with dream mentation and loss of atonia during REM sleep [1, 2]. Scientific attention over RBD has increased due to its connection with other neurologic disorders, especially with Parkinson’s disease (PD). PD is a neurodegenerative disease affecting more than 1% of population aged over 60 [3] and characterized by loss of dopaminergic neurons in various regions of the brain, notably in the substantia nigra pars compacta, and by the presence of Lewy bodies, in which main compound is alpha-synuclein (α-syn), encoded by the gene SNCA. Destruction of dopaminergic neurons precedes the onset of motor symptoms by many years, and thus, biological

Neurol Sci

markers are needed to identify this condition in early stages. RBD is one of the most promising PD early markers, as people diagnosed with RBD have an estimated risk of developing a parkinsonian disorder of approximately 50% at 10 years [4]. Recent studies have demonstrated that genetic variants in the 3′UTR of SNCA are independently associated with the development of PD [5–8]. This region is of particular interest due to its role in translational control, thus affecting protein expression levels. Specifically, different regulating sequences are present, such as those bound by microRNAs (miRNAs). miRNAs are brief non-coding RNAs that undergo a complex maturation inside the cell and play a pivotal role in physiological and pathological conditions. After binding the 3′UTR, they can induce mRNA degradation or inhibit translation [9, 10]. Genetic variants in 3′UTR can thus affect miRNA activity, leading to an alteration of the translational process and, consequently, have an impact on protein levels. The aim of this multicenter study was the characterization of the 3′UTR of SNCA in patients with Bidiopathic^ RBD (iRBD) and in patients with PD (with or without RBD), in order to define specific genetic features that could be associated with a progression from RBD to PD and to identify specific genetic variants associated to RBD.

Methods Patients From May 2012 to December 2013, we recruited 113 consecutive patients: 56 with iRBD and 57 with PD (31 with associated RBD and 26 without RBD). Patients were recruited from the neurology and sleep disorders unit of five independent centers: BAzienda Ospedaliero-Universitaria Santa Maria della Misericordia^ (Udine), BUniversity Aldo Moro^ (Bari), BBellaria Hospital^ (Bologna), BIRCCS Fondazione Istituto Neurologico Nazionale C. Mondino^ (Pavia), and BPoliclinico Tor Vergata^ (Roma). Each patient underwent a physical and neurological examination and a polysomnography. Demographics and clinical features of the two groups are reported in Table 1. No significant differences in age and sex were observed. The diagnosis of PD was made by an expert neurologist in accordance with the UK PD Society Brain Bank criteria [11] and was based on the presence of bradykinesia and at least one of the following symptoms: muscular rigidity, 4–6 Hz resting tremor, or postural instability. The diagnosis of RBD was made by an expert in sleep disorders according to the criteria of the International Classification of Sleep Disorders second edition [1] and required absence of atonia during REM sleep recorded during a nocturnal polysomnography and history or videopolysomnographic evidence of abnormal behavior during REM sleep. Patients with other sleep disorders, with any

cause of secondary parkinsonism, with a family history clearly suggestive of a familiar form of PD or scoring less than 24 on mini-mental state examination were excluded. Patients with RBD and without any motor sign of PD but with a history of non-motor signs that could point toward a neurodegenerative disorder were included in the iRBD group if they tested negative to the DAT-SCAN (DAT-SCAN was not part of the routine assessment). Patients were not tested for other known PD causing mutations. They were divided into two groups according to whether they had a diagnosis of iRBD or of PD (with or without RBD). All procedures were approved by the ethical committee of each participating institution and were in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. Molecular analysis A 3 ml whole blood sample was obtained from all patients. Genomic DNA was extracted using the automated extractor BioRobot EZ1, in association with the BEZ1 DNA Blood Kit 350 μl^ (Qiagen SPA, Milano, Italy), and stored at 4 °C until analyzed. Sequencing of the 3′UTR of SNCA (chr4 90645250–90647778, RefSeq NM_000345.3) was performed using Sanger direct sequencing technology. Two amplification reactions were performed to sequence this region. Samples were run on an ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems) and analyzed with Gene Scan software (Applied Biosystems). The sequencing results were read with the Chromas program (http://technelysium.com.au/). The obtained sequences were aligned with the reference sequence (NM_000345.3) according to Clustalw program (www.ebi.ac.uk/Tools/msa/clustalw2/). Nomenclature for new single nucleotide variants (SNVs) was assigned in accordance with HGVS guidelines (www.hgvs.org/) using GenBank NCBI reference sequence NM_000345.3. Primers and PCR thermal profiles are available upon request. With the first PCR product, we sequenced the region spanning from NM_000345.3:c.*89 to NM_000345.3:c.*1092; with the second PCR product, we analyzed the region spanning from NM_000345.3:c.*1638 to NM_000345.3:c.*2529. Each PCR product was then analyzed with three sequencing reactions. Statistical analysis Continuous data are presented as mean and standard deviation, categorical data as absolute frequencies and percentage. Differences in age at recruitment between groups were accessed with a two-tailed Student’s t test. Differences in gender distribution between the two groups were assessed with Fisher exact test. In order to analyze the independent

Neurol Sci Table 1

Demographics and clinical features of the population studied RBD

All PD

No. of patients

56

57

113

Males Age at recruitment, mean (SD)

45 (80.4%) 69.9 (8.1)

38 (66.7%) 71.6 (5.7)

83 (73.5%) 70.7 (7.0)

Age at onset of RBD symptoms, mean (SD) Age at diagnosis of PD, mean (SD)

61.4 (10.8) N/A

N/A 67.9 (7.0)

Bradykinesia

4/56 (7.14%)

57/57 (100%)

61/113 (53.98%)

Muscular rigidity Resting tremor

3/56 (5.36%) 3/56 (5.36%)

52/57 (91.23%) 37/57 (64.91%)

55/113 (48.67%) 40/113 (35.40%)

Postural instability Hyposmiab Constipationb Orthostatic hypotensionb

3/56 (5.36%) 14/56 (25%) 9/56 (16.07%) 10/56 (17.86%)

31/57 (54.39%) 21/57 (36.84) 38/57 (66.67%) 15/57 (26.32%)

34/113 (30.09%) 35/113 (30.97%) 47/113 (41.59%) 25/113 (22.12%)

a

Tot

p value

PD without RBDa

RBD + PDa

26

31

0.136 0.193

16 (61.5%) 70.3 (6.5)

22 (71.0%) 72.6 (4.8)

N/A 67.0 (6.7)

64.4 (10.8) 68.7 (7.3)

Subgroups of the Ball PD^ groupb Non-motor symptoms are obtained from anamnestic data, not measured or tested

relation between RBD diagnosis and the SNPs and to take into consideration age and sex, logistic regression analysis was performed. Results are reported as adjusted OR and 95% confidence interval (95% CI). For each SNP, the additive, dominant, and recessive models were tested. A p value of less than 0.05 was considered as statistically significant. The analyses were carried out using Stata 13.1 (StataCorp, Texas).

Bioinformatic analysis The potential effect on miRNA binding of the newly discovered and not annotated SNVs (NM_000345.3:c.*1067C>T, NM_000345.3:c.*2138C>T, NM_000345.3:c.*860T>A, and NM_000345.3:c.*2320A>T) was predicted with miRDB (mirdb.org/miRDB/) and miRanda (www.microrna.org). We selected miRNAs predicted by miRDB according to the score assigned by the program (cutoff = 70) and to the binding site position (±30 nucleotides surrounding the genetic variant). The analysis with miRanda was performed referring to the NM_000345 transcript. We selected only miRNAs binding a region in the proximity of the analyzed genetic variant (±30 nucleotides). To optimize the predictions, we evaluated the expression of miRNAs in the central nervous system according to miRIAD (http://www.bioinfo.mochsl.org.br/ miriad). SNPs with a reference sequence number in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/) and without an experimentally validated function were analyzed with SNPinfo (snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm) and microSNiper (epicenter.ie-freiburg.mpg.de/services/ microsniper/). Only miRNAs binding a region in the proximity of the analyzed genetic variant (±30 nucleotides) and expressed in the central nervous system according to miRIAD were considered reliable.

Results Molecular analyses For the first PCR product, 334 out of the 339 sequencing products were analyzed (334/339 = 98.52%). For the second PCR product, 316 out of 339 sequencing products were analyzed (316/339 = 93.21%). We detected five previously reported SNPs (rs356165, rs17016074, rs186189862, rs3857053, and rs1045722), two new genetic variants KP876057 (NM_000345.3:c.*1067C>T) and KP876056 (NM_000345.3:c.*2138C>T), two previously reported but not annotated SNVs (NM_000345.3:c.*860T>A and NM_000345.3:c.*2320A>T), and one previously reported but not annotated deletion (c.*575_579del ATTTT deletion).

Comparison of SNCA SNPs in RBD and PD Genotype frequencies of five previously reported SNPs (rs356165, rs17016074, rs186189862, rs3857053, and rs1045722) are reported in Table 2. rs3857053 and rs1045722 were four bases far from each other, and the genotyping data show that they are in complete linkage disequilibrium (R2 = 1, D′ = 1). No Hardy-Weinberg disequilibrium was observed (p > 0.05). Statistical analysis highlighted a significantly higher frequency of the minor allele of rs356165, rs3857053, and rs1045722 in PD patients as compared with iRBD patients (p = 0.014, 0.008, and 0.008, respectively—Table 3, Figs. 1 and 2). When comparing the iRBD group and the PD + RBD subgroup, these trends were conserved, but there was no significance (data not showed). No differences were observed when comparing only the two subgroups of patients with PD + RBD and PD without RBD (data not showed). No

Neurol Sci Table 2

Genotype frequencies RBD

All PD

Tot

PD without RBDa

RBD + PDa

No. of patients TT

55 31 (56.4%)

57 19 (33.4%)

112 50 (44.6%)

26 8 (30.8%)

31 11 (35.5%)

TC CC

17 (30.9%) 7 (12.7%)

30 (52.6%) 8 (14.0%)

47 (42.0%) 15 (13.4%)

15 (57.7%) 3 (11.5%)

15 (48.4%) 5 (16.1%)

*T

79 (71.8%)

68 (59.6%)

147 (65.6%)

31 (59.6%)

37 (59.7%)

31 (28.2%)

46 (40.4%)

77 (34.4%)

21 (40.4%)

25 (40.3%)

No. of patients CC

55 50 (90.9%)

57 54 (94.7%)

112 104 (92.9%)

26 25 (96.2%)

31 29 (93.6%)

TC

5 (9.1%)

3 (5.3%)

8 (7.1%)

1 (3.9%)

2 (6.5%)

TT *C

0 105 (95.5%)

0 111 (97.4%)

0 216 (96.4%)

0 51 (98.1%)

0 60 (96.8%)

5 (4.5%)

3 (2.6%)

8 (3.6%)

1 (1.9%)

2 (3.2%)

50 50 (100%)

56 55 (98.2%)

106 105 (99.1%)

25 25 (100%)

31 30 (97.8%)

0 0

1 (1.8%) 0

1 (0.9%) 0

0 0

1 (3.2%) 0

100 (100%) 0

111 (99.1%) 1 (0.9%)

211 (99.5%) 1 (0.5%)

50 (100%) 0

61 (98.4%) 1 (1.6%)

50 44 (88.0%) 6 (12.0%) 0

55 36 (65.5%) 19 (34.5%) 0

105 80 (76.2%) 25 (23.8%) 0

25 17 (68.0%) 8 (32.0%) 0

30 19 (63.3%) 11 (36.7%) 0

94 (94.0%) 6 (6.0%)

91 (82.7%) 19 (17.3%)

185 (88.1%) 25 (11.9%)

42 (84.0%) 8 (16.0%)

49 (81.7%) 11 (18.3%)

50 44 (88.0%)

55 36 (65.5%)

105 80 (76.2%)

25 17 (68.0%)

30 19 (63.3%)

6 (12.0%) 0 94 (94.0%) 6 (6.0%)

19 (34.5%) 0 91 (82.7%) 19 (17.3%)

25 (23.8%) 0 185 (88.1%) 25 (11.9%)

8 (32.0%) 0 42 (84.0%) 8 (16.0%)

11 (36.7%) 0 49 (81.7%) 11 (18.3%)

rs356165

*C rs17016074

*T rs186189862 No. of patients GG AG AA *G *A rs3857053 No. of GG GA AA

patients

*G *A rs1045722 No. of patients TT AT AA *T *A a

These columns show data for the two subgroups of PD patients

Table 3 Logistic regression: effect of genotype on status (iRBD or PD), adjusted for age at recruitment, and sex

iRBD (n = 55)

rs356165 (CC+TC vs TT) rs3857053 (AA+GA vs GG) and rs1045722 (AA+TA vs TT)a a

All PD (n = 57) Adj OR (95% CI)

p value

Power analysis

1

0.4 (0.2–0.8)

0.014

57%

1

0.2 (0.1–0.7)

0.008

81%

These two SNPs are presented together because they are in total linkage disequilibrium

Neurol Sci

Fig. 1 rs356165 and patients’ pathological status (PD or iRBD)

significant differences between RBD and PD patients were observed for rs17016074 and rs186189862. Not annotated and new SNVs BNot annotated variants^ are genetic variants already reported in the 1000 genome database (http://www.1000genomes.org/) but without a reference sequence. Two new variants were identified in this study, submitted to NCBI GenBank and recorded as KP876057 (NM_000345.3:c.*1067C>T) and KP876056 (NM_000345.3:c.*2138C>T). KP876057 was detected in heterozygosity in one male patient with RBD whose symptoms began at age 58. KP876056 was observed in heterozygosity in a male patient with RBD, with an age at onset of symptoms of 66. Both patients presented hyposmia; none of them had family history of PD or RBD. Two previously reported but not annotated SNVs (NM_000345.3:c.*860T>A and NM_000345.3:c.*2320A>T) and one previously reported but not annotated deletion (c.*575_579del ATTTT deletion) [12] were observed in six

Fig. 2 rs3857053 and rs1045722 and patients’ pathological status (PD or iRBD)

patients. Four patients carried in heterozygous form NM_000345.3:c.*860T>A and NM_000345.3:c.*2320A>T. The first was a male with RBD, who reported onset of symptoms at 66. He complained of hyposmia, orthostatic hypotension, and constipation and had family history for dementia. The second was a male patient with PD, who was 55 at symptoms onset and had no evidence of non-motor symptoms. The third patient was a female with RBD who complained of orthostatic hypotension; her symptoms began when she was 62. The fourth patient was a male with RBD and PD, with no complaint of non-motor symptoms; his RBD symptoms started at 58 years, but he could not remember when he got a diagnosis of PD. None of them reported any family history for PD or RBD. One female patient with RBD was found to have solely NM_000345.3:c*2320A>T; she was 63 at onset of symptoms, and she did not report any non-motor symptoms or family history for PD or RBD. Finally, the patient with c.*575_579delATTTT was a woman with PD diagnosed when she was 71 years old. She complained of constipation. No family history of PD or RBD was reported.

Bioinformatic analysis The bioinformatic analysis was performed to predict the potential impact of the detected genetic variants on miRNAs binding on 3′UTR. Two different approaches were designed to investigate the role of the detected variants. Regarding the new SNVs, according to miRanda prediction, hsa-miR-361-5p binding may be potentially affected by the NM_000345.3:c.*1067C>T SNV, whereas no prediction came out from miRDB. Several miRNAs expressed in the brain could bind the region containing NM_000345.3:c.*2138C>T SNV. In particular, hsamiR-113a and hsa-miR-133b miRNAs were indicated by both miRDB and miRanda. Interesting suggestions arose also for the still not annotated variants: NM_000345.3:c.*860T>A SNV seems to impact hsa-miR-539 and hsa-miR-6760-5p binding. The other not annotated SNV, NM_000345.3:c.*2320A>T, potentially affects the activity of various miRNAs. Specifically, a high score was predicted by miRDB for hsa-miR-30c-2-3p and hsa-miR-30c-2-5p miRNAs, and their binding sites resulted in the very proximity of this variant. Thus, we could state that these miRNAs are quite plausible to be affected by NM_000345.3:c.*2320A>T. A bioinformatic study was performed for rs17016074, rs3857053, rs186189862, and rs1045722 because, at the best of our knowledge, no functional data are available. This analysis pointed out hsa-miR-323-3p and hsa-miR-377 as miRNAs potentially affected by rs17016074, whereas rs3857053 seems to impact the binding of hsa-miR-888-5p. We were not able to predict any function for neither rs186189862 nor rs1045722. The complete list of predicted miRNAs is reported in Table 4.

Neurol Sci Table 4 The potential effects on miRNA binding of the identified genetic variants SNV

Predicted miRNAs

NM_000345.3:c.*1067C>T

hsa-miR-361-5p

NM_000345.3:c.*2138C>T

hsa-miR-30c-2-3p hsa-miR-30c-1-3p hsa-miR-30c-1* hsa-miR-133a hsa-miR-133b hsa-miR-340

NM_000345.3:c.*860T>A

hsa-miR-6760-5p hsa-miR-539

NM_000345.3:c.*2320A>T

hsa-miR-335-3p hsa-miR-30c-2-3p hsa-miR-30c-1-3p hsa-miR-222-3p hsa-miR-221-3p hsa-miR-708-5p hsa-miR-28-5p hsa-miR-133b hsa-miR-133a-3p hsa-miR-887-5p hsa-miR-485-5p hsa-miR-17-3p hsa-miR-106b

rs17016074

hsa-miR-93 hsa-miR-323-3p hsa-miR-377

rs3857053 rs186189862 rs1045722

hsa-miR-888-5p NP NP

In bold, miRNAs predicted by two different algorithms

Discussion RBD is a sleep disorder strongly associated with other common severe neurodegenerative diseases, like PD. Its clinical relevance lies in its capability to predict the forthcoming of such diseases in a very early stage, when it would be possible to use neuroprotective therapies effectively. Unfortunately at the current time, we are unable to determine which patients with RBD will develop other more severe diseases. Moreover, much is still unknown about its pathophysiology, and to our knowledge, there are no studies on the genetics of human RBD. In this study, we compared the sequence of the 3′UTR of SNCA in patients with iRBD and in patients with PD (with or without RBD). We addressed SNCA as it is an important causative locus for sporadic PD [5–8, 13, 14], and we focused on its 3′ UTR region as it is thought to have post-transcriptional regulation of SNCA expression [15]. We observed a higher prevalence of RBD among PD patients (54.4%) compared with other

studies [16–18]. This may be due to chance alone (fluctuations are possible with low sample sizes) or may reflect the higher capability of polysomnography to detect RBD. It is also possible that there was a selection bias due to the characteristics of the centers involved in the study as all are specialized in sleep disorders. In our analysis, we found a higher prevalence of the variant C allele of rs356165 in PD patients compared with patients with iRBD with a dominant-like effect (TT vs TC+ CC—Table 3, Fig. 1). In previous studies, rs356165 has been associated with sporadic PD [12, 19–21] and is thought to have a role in PD pathophysiology by influencing the expression of the isoform 112 of SNCA, which lacks exon 5 and is more prone to aggregation. By studying the frontal cortex of patients with PD, a higher level of this isoform of SNCA was found in subjects homozygous for rs356165 risk C allele as compared with homozygous wild-type patients [22]. Data from literature for rs356165 report a frequency of 61.3/38.7% for T/C alleles [23]. Despite the long time between RBD symptoms onset and recruitment to the study (8.7 ± 8.0 years), it is possible that some of the patients in the iRBD group will develop PD later in life; by this assumption, one would expect a frequency of rs356165 C allele in the iRBD group to lie in between that of PD patients and data from the general population, whereas our results show a lower prevalence of the C allele in the iRBD group than that reported in the general population (Table 3). Taken together, this data suggest that the variant C allele of rs356165 may confer a higher risk of developing PD to patients with RBD. In that regard, it is interesting that the same trend of allele distribution was observed when comparing the iRBD group and the PD + RBD subgroup, although it did not reach statistical significance (Table 2, statistical analysis not shown). Two detected SNPs (rs3857053 and rs1045722) are located very close to each other and according to our data are in total linkage disequilibrium (Table 2). We found a significantly higher frequency of the variant alleles of these SNPs (AA+GA vs GG and AA+TA vs TT) in patients with PD than in patients with iRBD. Reported frequencies for rs3857053 and rs1045722 in a European population (92.8/0.07 for G/A and T/A, respectively) [23] are very similar to those we found in the iRBD group, while in the PD group, the minor allele of these two SNPs showed a higher frequency (Table 2, Fig. 2). In the literature, there is no data of a possible association between rs3857053 and rs1045722 and PD, so it is difficult to draw conclusions, and additional studies are needed to investigate a possible role they may have in the pathogenesis of PD and RBD. Sequence analysis allowed us to identify two new SNVs in SNCA-3′UTR (NM_000345.3:c.*1067C>T and NM_000345.3:c.*2138C>T) in patients with iRBD and two previously reported but not annotated SNV (NM_000345.3:c.*860T>A and NM_000345.3:c.*2320A>T) mainly in iRBD patients (Table 4). As a final remark, with the

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performed bioinformatic analysis, we identified some miRNAs expressed in the brain and in the cerebellum whose binding could be affected by the presence of the characterized genetic variants, such as hsa-miR-340, hsa-miR-30c, and hsa-miR-133. To the best of our knowledge, none of the predicted miRNAs have been already associated with SNCA regulation. A large case-control study by Schmitt analyzed the 3′UTR of SNCA [24], performing the functional analysis of the detected genetic variants. They highlighted the limit of performing only bioinformatic analysis since they did not validate the prediction in vitro. The lack of in vivo and in vitro validation of our bioinformatic results is a major limitation to our study. To try to overcome it, two different prediction softwares were applied, and we considered also the expression sites of miRNAs using miRIAD. This gave additional value to our work since often, prediction programs do not take into account where miRNAs are expressed. Thus, in line with the study by Schmitt, our analysis could propt new attention on this DNA region. In future studies, the role of the detected SNPs should be analyzed in vivo and in vitro to validate new miRNA-related mechanisms that regulate α-syn levels inside the central nervous system. Another limitation of this study is the small sample size, reflecting the difficulty in the recruitment of patients and application of the elaborate inclusion criteria to larger numbers of suitable subjects of a rare disorder like RBD. Therefore, independent confirmation in a different cohort is necessary. Moreover, the cross-sectional design of the study bears an intrinsic inability to define with certainty patients with iRBD, due to the fact that they could develop PD in subsequent years. Nonetheless, as mentioned above, the mean difference in years between age at RBD symptoms onset and age at enrollment for our iRBD group is quite long (8.4 years) when compared with already published data, that report an average delay of 5 years between RBD symptoms onset and neurodegenerative disorders onset [25–27].

Conclusions We highlighted three SNPs with different frequencies in iRBD and PD patients. One of them (rs356165) is already known to be a risk factor for the development of PD. In addition, we identified new rare and not annotated SNVs in patients with iRBD that may affect miRNA binding. The potential role of rare variants is rising to the attention of the scientific community. Rare variants are hypothesized to account for a large part of the observed inter-individual variability and could greatly improve the interpretation of rare diseases [28]. These results look promising and might contribute to develop interest for wider studies. Acknowledgements For their suggestions and support, the authors thank Prof. Giancarlo Logroscino and Dr. Valentina Cardinali from the

Department of Basic Medical Science of the Univerity BAldo Moro^ of Bari; Dr. Raffaele Manni from the Neurological Institute BC. Mondino^; Dr. Federica Provini from the Department of Biomedical and Neuromotor Sciences of BBellaria Hospital,^ University of Bologna; and Dr. Francesca Izza from the Neurophysiopathology Unit, Sleep Medicine Centre for the University of Rome Tor Vergata. Compliance with ethical standards All procedures were approved by the ethical committee of each participating institution and were in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. Conflict of interest The authors declare that they have no conflict of interest. Funding This work was supported by the BAssociazione Italiana per la Ricerca sul Cancro^ (AIRC; Special Program Molecular Clinical Oncology, 5 × 1000 [No. 12214]).

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