Elevated mRNA Expression and Low Methylation of SNCA in Japanese Alzheimer’s Disease Subjects

Abstract

Despite the continuing debate about the amyloid hypothesis in Alzheimer’s disease (AD), the precise pathogenesis is still unclear. Mixed pathology is common and multiple different protein aggregates are seen in human postmortem brains. Aggregates consisting of the alpha-synuclein protein encoded by the Synuclein Alpha gene (SCNA) are common in both dementia with Lewy bodies and AD. We examined SNCA mRNA expression and methylation rates of the CpG island at intron 1 of SNCA in peripheral leukocytes in 50 AD and age- and sex-matched control subjects to verify whether alpha-synuclein pathology affects the AD pathogenesis. SNCA mRNA expression in AD subjects was significantly higher than that in control subjects (1.62±0.73 versus 0.98±0.50, p < 0.001). We found significant differences between AD and control subjects at seven CpG sites (average rate; 8.8±2.7 versus 9.5±2.5, respectively: p = 0.027). The methylation rates tended to be lower in AD subjects at all CpG sites. We conclude that mRNA expression and methylation of SNCA intron 1 are altered in AD, which may be caused by Lewy body pathology in AD.

Keywords

Alpha-synuclein Alzheimer’s disease Lewy bodies methylation

INTRODUCTION

Alzheimer’s disease (AD) is the most common disease exhibiting dementia with progressive deterioration in cognition, function, behavior, and mood. In 2015, 46.8 million patients had AD, and the number is expected to increase to 131.5 million by 2050 [1].

Pathologically, the presence of amyloid plaques that contain amyloid-β (Aβ), a peptide fragment of the amyloid-β protein precursor, is partially responsible for causing AD [2]. Some reports indicate that extracellular deposits of Aβ play a key role in the pathogenesis of AD [3 –5]. In accordance with the amyloid hypothesis, several genes such as amyloid precursor protein (APP), presenilin 1 (PSEN1), and PSEN2 have been well studied [6, 7].

On the other hand, about 15–20% of AD patients frequently have cortical and subcortical Lewy body (LB) pathology [8, 9]. AD subjects with LB have higher frequencies of psychotic symptoms than those without LB [10, 11]. The insoluble alpha-synuclein protein encoded by the Synuclein Alpha gene (SNCA) is the major component of LBs [12]. LBs are one of the main types of pathology in Parkinson’s disease (PD), Parkinson’s disease with dementia, and dementia with Lewy bodies (DLB) [13, 14]. The single nucleotide polymorphism (SNP) of rs356219 was reported to correlate with sporadic PD subjects in meta-analysis [15]. The alpha-synuclein level in cerebrospinal fluid (CSF) is significantly elevated in AD subjects compared with both DLB and control subjects [16].

Epigenetic changes including DNA methylation are involved in neuropsychiatric conditions [17, 18]. SNCA has two CpG islands. One is located in exon 1, which is a non-coding exon [19]. The second CpG island is located in intron 1, which clearly influences expression of SNCA mRNA [20, 21]. Although no definitive consensus has been reached, some studies have addressed DNA methylation at SNCA intron 1 in leukocytes of patients with PD. For example, two studies reported no difference between PD and healthy controls regarding DNA methylation of SNCA intron 1 in leukocytes [22, 23], whereas three other studies indicated hypomethylation of the same sites in PD subjects relative to controls [24 –26]. Notably, the methylation levels in this region in leukocytes from AD patients have not been examined.

Here, we examined SNCA mRNA expression and methylation rates of SNCA intron 1 in leukocytes from AD patients and age- and sex-matched healthy controls.

MATERIAL AND METHODS

AD subjects and healthy controls

Demographic data for each group of participants are shown in Table 1. We enrolled 50 AD patients (15 males and 35 females, mean age±standard deviation (SD) = 76.2±5.8 years) who were outpatients in Ehime University Hospital and Zaidan Niihama Hospital, Ehime, Japan. AD subjects were diagnosed with probable AD dementia according to the Aging/Alzheimer’s Association criteria [27] and were living with at least one caregiver. All AD subjects had no cerebral vascular lesions as determined with head computed tomography or magnetic resonance imaging. AD subjects were evaluated with the Mini-Mental State Examination and Alzheimer’s Disease Assessment Scale to assess their cognitive functions [28, 29], the Montgomery-Åsberg Depression Rating Scale to assess their depression symptoms [30], Clinical Dementia Rating measured by family caregivers [31], and the Neuropsychiatric Inventory to assess their psychological symptoms [32]. Control subjects were 50 elderly participants (15 males and 35 females, mean age±SD = 79.7±4.41 years) without cognitive impairment, psychiatric signs, or a past history of mental disorders and who were diagnosed as mentally and cognitively normal by at least two certified psychiatrists based on clinical interviews. All participants were unrelated and of Japanese origin and provided written informed consent forms approved by the institutional ethics committees of Ehime University Hospital and Zaidan Niihama Hospital.

Blood sample collection and processing of complementary DNA (cDNA) and genomic DNA (gDNA)

Total RNA samples were obtained from whole peripheral blood samples, which were collected in PaxGene Blood RNA Systems tubes (BD, Tokyo, Japan) according to the manufacturer’s protocol. The RNA concentration and purity were measured with the NanoDrop-1000 (Thermo Fisher Scientific, Yokohama, Japan) system, and acceptable 260/280 ratios were in the range of 1.8–2.0. For each 40μl reaction, 1.0μg RNA was used as a template to synthesize cDNAs with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). The gDNA samples were obtained from whole peripheral blood samples collected in potassium EDTA tubes, and gDNA was extracted using the QIAamp DNA Blood Mini Kit (Qiagen, Tokyo, Japan) according to the manufacturer’s protocol.

PCR procedure

For the mRNA expression analysis, real-time quantitative reverse transcription-PCR (RT-PCR) was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems). The specific TaqMan probes were Hs01103383_m1 for SNCA and Hs99999905_m1 for GAPDH (Applied Biosystems). GAPDH was suitable for normalization of quantitative RT-PCR and was used as the reference gene in the study [33 –35]. The final volume of each reaction was 10μl, and each contained TaqMan Universal Master Mix (Applied Biosystems). Expression levels were examined in duplicate. The ΔΔCt method and StepOne software (Applied Biosystems) were used to determine relative expression levels.

Genotyping

Genotyping of SNPs (rs429358 and rs7412) in apolipoprotein E (APOE) and rs356219 in SNCA was conducted using the TaqMan 5^′-exonuclease allelic discrimination assay (Assay ID: rs429538; C___3084793_20, rs7412; C__904973_10, rs356219; C_1020193_10, respectively, Applied Biosystems) using the StepOnePlus real-time PCR system (Applied Biosystems). Genotyping call rates were 96.0% (rs429358), 96.0% (rs7412), and 98.0% (rs356219). No deviation from the Hardy-Weinberg equilibrium in each examined single nucleotide polymorphism was detected in the patients (p > 0.05). The APOE isotype-related genotypes are combinations of the APOE ɛ2, ɛ3, and ɛ4 alleles derived from the two genotypes of rs429358 (T334C) and rs7412 (C472T): ɛ2, 334T/472T; ɛ3, 334T/472C; and ɛ4, 334C/472C. The ɛ4 genotype is a risk factor for AD [36].

Bisulfite conversion and pyrosequencing

The SNCA intron 1 region that was subjected to methylation analysis is depicted in Fig. 1. Based on a previous study, one CpG-rich region in this intron 1 region affects SNCA mRNA expression levels; therefore, we analyzed the same region, which includes 10 CpG sites [23, 26] (Fig. 1).The gDNA extracted from leukocytes (1000 ng/sample) was converted with bisulfate using the EpiTect Plus DNA Bisulfite Kit (Qiagen, Valencia, CA, USA). Converted gDNA was then used as template for PCR amplification with a forward primer(5^′-GGAAGTGTAAGGAGGTTAAGT-3^′) andreverse primer (5^′-[Biotin]- TCCACCCCCCCCCTCAACTAT-3^′). The final volume of each PCR reaction was 20.0μl, and each reaction contained 1.6μl gDNAs, 0.2μM forward and reverse primers, AmpliTaq gold (Applied Biosystems), 10×PCR buffer with 15 mM MgCl₂, and 2 mM dNTP. Cycling conditions were as follows: denaturation for 10 min at 94°C; followed by 45 cycles, each comprising 94°C for 30 s, 56°C for 30 s, and 72°C for 1 min; and a final extension of 10 min at 72°C. The PCR products were sequenced with PyroMark Q24 Advanced (Qiagen) and the sequencing primer, 5^′-GTTAATAAGTGTTG-3^′. Methylation rates at each CpG sites were quantified in duplicate using PyroMark Q24 Advanced Software (Qiagen).

Statistical analysis

We performed statistical analyses with SPSS 22.0 software (IBM Japan, Tokyo, Japan). Sex and APOE genotype were analyzed with Fisher’s exact test. We conducted a test of normality with the Shapiro-Wilk test. Comparisons of SNCA mRNA expression and each CpG methylation rate between AD subjects and controls were conducted with the Mann-Whitney U test. Comparisons of SNCA mRNA expression with sex, APOE genotypes, and acetylcholine esterase inhibitor medications (AChEIs) were conducted with the Mann-Whitney U test. Comparison of SNCA mRNA expression with rs356219 and comparison of methylation rates and rs356219 were conducted with Kruskal-Wallis test. Correlations of SNCA mRNA expression with age, duration of illness, and methylation rates at each CpG site were conducted with the Spearman rank correlation. Discriminant analysis was performed using SNCA mRNA expression and methylation rates of 10 CpG sites to reveal the diagnostic ability. Statistical significance was defined at the 95% level (p = 0.05).

RESULTS

Participant characteristics

Demographic data and APOE genotypes of participants are shown in Table 1. AD and control subjects did not differ in sex or age. However, a significant difference in APOE genotype between AD and control subjects was noted (Fisher’s exact test; p = 0.001). Clinical characteristics and results of psychological tests in AD subjects are shown in Table 2.

SNCA mRNA expression levels

SNCA mRNA expression levels were not correlated with gender (AD subjects, p = 0.924; control subjects, p = 0.931), age at blood sampling (AD, p = 0.578; control, p = 0.997), APOE genotype (AD, p = 0.346; control, p = 0.549), or duration of illness in AD subjects (p = 0.577). The SNCA mRNA expression level in AD subjects was significantly higher than that in controls (1.62±0.73 versus 0.98±0.50, p < 0.001, Fig. 2). The SNCA mRNA expression level in AD subjects treated with AChEIs (n = 16) was not significantly different from those not treated with AChEIs (n = 34) (p = 0.298). The SNCA mRNA expression levels were not correlated with rs356219 in control (p = 0.898) and AD subjects (p = 0.305).

Methylation status

The sequence and position of the 10 CpG sites in SNCA intron 1 are depicted in Fig. 1. The methylation rates at each CpG site are shown in Fig. 3. Methylation rates at almost all CpG sites were correlated with each other in the AD and control groups (Fig. 1). Methylation rates were significantly lower in the AD group than in the control group at CpG2 (average±SD = 8.8±3.2 versus 9.4±2.8, p = 0.037), CpG3 (11.3±3.8 versus 12.3±3.2, p = 0.032), CpG6 (6.9±2.2 versus 7.7±2.4, p = 0.014), CpG7 (6.8±2.4 versus 7.5±2.4, p = 0.035), CpG8 (10.9±3.2 versus 12.4±3.2, p = 0.005), CpG9 (12.9±3.2 versus 14.1±2.8, p = 0.016), and CpG10 (7.7±2.4 versus 8.6±2.4, p = 0.011), respectively; the average rate was also lower (8.8±2.7 (AD) versus 9.5±2.5 (control), p = 0.027). The methylation rates tended to be lower in AD patients at all other CpG sites. Methylation rates at each CpG site and total methylation average of all sites were not correlated with SNCA mRNA expression. We did not find significant association between all CpG methylation rates and rs356219 about both AD and control subjects.

Discriminant analysis

Discriminant analysis was performed with the variables included in the model for comparisons with the control groups (Wilks lambda = 0.599, p < 0.001).

A D-score was calculated for each sample in this study of AD patients as follows:

D-score = 0.612×expression –0.388×CpG 1 + 0.729×CpG 2 –0.247×CpG 3 + 0.277×CpG 4 + 0.299×CpG 5 –0.321×CpG 6 + 0.192×CpG 7 –0.403×CpG 8 –0.015×CpG 9 + 0.014×CpG 10 + 0.672

Discriminant analysis demonstrated sensitivity and specificity of 70.0% and 85.1%, respectively. Receiver operating characteristic curve analyses were performed (Fig. 4, area under the curve = 0.874, confidence interval = 0.806–0.942, p < 0.001).

DISCUSSION

We showed elevated SNCA mRNA expression and low methylation rates on intron 1 of SNCA in AD subjects compared to control subjects. SNCA mRNA expression was not associated with sex, age, duration of illness, the ɛ4 genotype of APOE, rs356219 in SNCA, or AChEI medication use. In addition, there were no association between all CpG methylation rates and rs356219 about both AD and control subjects.

To our knowledge, this is the first report to examine SNCA mRNA expression in peripheral leukocytes of AD subjects. Interestingly, SNCA mRNA expression in AD subjects was significantly elevated. On the other hand, in an analysis of the superior temporal cortex, SNCA mRNA expression was increased in DLB patients but not changed in AD subjects compared with control subjects [37]. Increased levels of alpha-synuclein immunoreactivity in brain regions were shown in early-stage AD [38, 39]. In CSF studies, reports have shown increases [40 –42] or no change [43] in CSF alpha-synuclein levels. However, neuropathologically-confirmed AD subjects show a clear increase in alpha-synuclein levels in CSF [44], and alpha-synuclein is up to 10,000-fold higher in whole blood than in CSF [45]. Neurons in the brain and spinal cord are the principal source of alpha-synuclein in CSF [46]. To our knowledge, there is no report about correlation between SNCA mRNA expression of peripheral blood and alpha-synuclein levels in the CSF and the brain tissues. Although our results may reflect alpha-synuclein levels in the CSF and/or the brain tissues, we should confirm those relationships in future study.

Interestingly, CpGs 2, 3, 6, 7, 8, 9, and 10 as well as the average methylation rate were lower in AD patients than healthy controls. This is also the first study showing hypomethylation of any CpG island located in intron 1 of SNCA in AD. This result may be consistent with results from previous studies showing hypomethylation of the same site in peripheral blood of PD patients [20, 23] and both peripheral blood and the brain of PD patients [25], who have the same synucleinopathy as DLB [47]. Generally, the methylation rate is associated with gene expression [48, 49]. Although methylation rates at all CpG sites tended to be lower and mRNA expression was higher in AD subjects compared to control subjects, SNCA mRNA expression was not significantly correlated with methylation rates in this study. Not only SNCA but also other gene’s intron 1 methylation were reported to be associated with the gene expression [50 –52]. Intragenic DNA methylation seems to be actively involved in multiple gene regulation processes [53]. It is reported that hypomethylation of these CpG sites were associated with increased expression of SNCA in vitro study [20] and in postmortem brain study [19]. It is also reported in genome-wide gene methylation analyses that DNA methylation pattern in leukocytes was significantly correlated with that in four brain regions of AD and concluded that leukocyte DNA methylation alterations related to both aging and AD could be exploited for identification of AD biomarkers [56]. In addition, GATA at these CpG sites [51] and ZSCAN21 [55] at upstream of these CpG site were reported to bind at intron 1. From these, we concluded that this region was one of the methylation regulatory elements of SNCA. However, there was no correlation between SNCA mRNA expression and methylation rates. The correlation might not be detected due to too small sample size included in this study.

Alpha-synuclein encoded by SNCA has been well studied in synucleinopathies such as PD and DLB [44, 57]. Recently, multiple different pathological protein aggregates are frequently seen in human postmortem brains and hence mixed pathology is common in AD and DLB [58]. DNA methylation analysis of AD, DLB and PD postmortem brains showed that neurodegenerative diseases shared similar aberrant CpG methylation shift and might have similar pathogenic mechanisms [59]. Thus, inclusion of AD subjects who may have had LB pathology may have affected our results. On the other hand, several reports have indicated the co-aggregation of Aβ and alpha-synuclein in AD [60 –62]. Alpha-synuclein leads to inhibition of Aβ deposition and reduced plaque formation in mice [63]. Elevated SNCA mRNA expression in this study may be a consequence of a defense reaction to Aβ deposition in AD pathogenesis. Interestingly, discriminant analysis using 10 CpG sites and expression levels in AD subjects demonstrated sensitivity and specificity of 70.0% and 85.1%. We may be able to obtain higher sensitivity and specificity with additional markers that are not yet identified.

Our study has several limitations. First, the sample size was relatively small. Second, whether methylation rates of SNCA in leukocytes are equivalent to those in the brain has not been thoroughly examined. However, several studies have shown that the methylation rate in leukocytes is correlated with that in the brain [64, 65]. Third, we conducted this study with clinically diagnosed AD subjects. Discriminating DLB from AD is difficult because of overlapping symptoms between the two conditions [66]. Thus, DLB patients may have been included in our study and may have affected the results. Although we excluded the patients who represented even a single one of the core diagnostic features of DLB, their diagnoses were not confirmed pathologically with autopsy. Thus, we could not exclude the possibility of future development of DLB. In a longitudinal study with the same subjects, it is necessary to confirm the pathological diagnosis using postmortem brain tissue. In addition, we have to evaluate the effects of LB pathology on our results using samples of DLB and PD because it is possible that AD with LB pathology affect our results. In future study, our results should be confirmed in autopsy material. Lastly, there is no information about education of control subjects.

In conclusion, we revealed elevated mRNA expression and decreased methylation rates of SNCA in peripheral leukocytes of AD subjects, which may be caused by LB pathology in AD. Although the discriminant analysis using 10 CpG sites and the expression levels demonstrated sensitivity and specificity of 70.0% and 85.1%, there are substantial overlapping in SNCA mRNA expression and methylation levels between AD and control subjects. Further research with a large number of samples is needed to confirm the performance of the test across various mental illnesses to examine any additional diagnostic implications of the test.

Footnotes

ACKNOWLEDGMENTS

We wish to thank Zaidan Niihama Hospital for collecting blood samples and Ms. Chiemi Onishi for her technical assistance. This work was partially supported by a Health and Labor Science Research Grant from the Japanese Ministry of Health, Labour and Welfare and a Grant-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology, JSPS KAKENHI Grant Number 15K09808. We wish to thank the sponsor about the samples collection, data analysis, and interpretation of data.

Authors’ disclosures available online ().

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