Epigenetic Modifications of the <i>α</i>-Synuclein Gene and Relative Protein Content Are Affected by Ageing and Physical Exercise in Blood from Healthy Subjects

Daniele, Simona; Costa, Barbara; Pietrobono, Deborah; Giacomelli, Chiara; Iofrida, Caterina; Trincavelli, Maria Letizia; Fusi, Jonathan; Franzoni, Ferdinando; Martini, Claudia

doi:https://doi.org/10.1155/2018/3740345

Oxidative Medicine and Cellular Longevity

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Volume 2018 | Article ID 3740345 | https://doi.org/10.1155/2018/3740345

Epigenetic Modifications of the α-Synuclein Gene and Relative Protein Content Are Affected by Ageing and Physical Exercise in Blood from Healthy Subjects

Simona Daniele,¹Barbara Costa,¹Deborah Pietrobono,¹Chiara Giacomelli,¹Caterina Iofrida,¹Maria Letizia Trincavelli,¹Jonathan Fusi,²Ferdinando Franzoni,²and Claudia Martini¹

Academic Editor: Zsolt Radák

Received22 Dec 2017

Accepted15 Mar 2018

Published15 Apr 2018

Abstract

Epigenetic regulation may contribute to the beneficial effects of physical activity against age-related neurodegeneration. For example, epigenetic alterations of the gene encoding for α-synuclein (SNCA) have been widely explored in both brain and peripheral tissues of Parkinson’s disease samples. However, no data are currently available about the effects of physical exercise on SNCA epigenetic regulation in ageing healthy subjects. The present paper explored whether, in healthy individuals, age and physical activity are related to blood intron1-SNCA (SNCA_I1) methylation, as well as further parameters linked to such epigenetic modification (total, oligomeric α-synuclein and DNA methyltransferase concentrations in the blood). Here, the SNCA_I1 methylation status increased with ageing, and consistent with this result, low α-synuclein levels were found in the blood. The direct relationship between SNCA_I1 methylation and α-synuclein levels was observed in samples characterized by blood α-synuclein concentrations of 76.3 ng/mg protein or lower (confidence interval (CI) = 95%). In this selected population, higher physical activity reduced the total and oligomeric α-synuclein levels. Taken together, our data shed light on ageing- and physical exercise-induced changes on the SNCA methylation status and protein levels of α-synuclein.

1. Introduction

Ageing is characterized by common cellular features, such as increased oxidative stress, reduction in protein synthesis, mitochondrial dysfunction, stem cell depletion, and telomere attrition [1]. Moreover, the ageing process is strictly linked to epigenetic control of the genome through DNA and histone modifications [1, 2], such that DNA methylation has been suggested as an ageing “clock” [3, 4].

In the central nervous system, DNA methylation [5] regulates memory formation [6] and its age-related disruption, which precede cognitive decline [7, 8]. The epigenome is extremely dynamic, with changes in response not only to ageing or development, but it is also affected by exogenous factors, such as nutrient availability and physical exercise [9–11]. Indeed, several studies indicate that epigenetic regulation may contribute to the widely known beneficial effects of regular exercise, although the exact mechanisms remain to be determined at molecular levels [12]. For example, regular exercise has been suggested to affect the methylation status of global and CpG-rich specific genes in the muscle cells, the different peripheral tissues, and the brain. Moreover, the gene for neurotrophic factor BDNF (brain-derived growth factor) is known to be remodelled epigenetically in the hippocampus in response to physical exercise [13]. Such studies are consistent with the hypothesis that epigenetics is central to coordinating the transcriptional response to the environment, as well as being involved in neuronal development, maintenance, and degeneration processes [14–16]. Based on this, it is not surprising that physical exercise has been suggested as a potential preventive countermeasure against many chronic and degenerative diseases [11, 17–19], including age-related neurodegeneration [20]. Among these, evidence was reported for Parkinson’s disease (PD) [21–28], which is characterized by an accumulation of misfolded α-synuclein (α-syn) in Lewy bodies, causing synaptic dysfunction and neuronal loss [29–31]. The exercise-mediated neuroprotective effects against PD are related to a reduction in cerebral inflammation and protein misfolding, as well as changes in gene expression profiles [32, 33]. In this respect, in addition to α-syn misfolding, epigenetic regulation of its encoding gene (SNCA) has been widely explored, particularly focusing on the methylation status of the intron1 CpG island [34–37]. Indeed, this intron1 region was demonstrated to control gene expression by the differential methylation of its CpG island, as well as by the recruitment of different transcription factors [34, 35, 38]. Thus, a decrease of intron1 methylation of SNCA has been hypothesized to increase α-syn expression in brain tissues and to lead to PD pathogenesis factors [34, 35]. Moreover, the pathological significance of DNA methylation was also highlighted in peripheral tissues. For example, leukocytes from PD subjects showed hypomethylation of intron1-SNCA compared to controls. [36, 39, 40]. By contrast, other studies reported no difference between PD and healthy subjects [41, 42].

In the light of the emerging role of physical activity in the management of neurodegenerative diseases, gaining further insight into epigenetic mechanisms regulating gene transcription during exercise will help further improve lifestyle interventions. Therefore, the aim of our study was to investigate the potential impact of regular training on α-syn expression and specific DNA methylation in healthy subjects. In particular, sedentary and exercise subjects were enrolled to determine the α-syn methylation status of intron1 CpG islands. The methylation degrees were correlated with the amount of total and oligomeric α-syn content in red blood cells (RBCs), selected as a valid cellular model, because they accumulate α-syn and are particularly susceptible to oxidative stress [43–45].

2. Methods

2.1. Study Population

Thirty-two endurance athletes (ATHL, mean age 41.4 ± 13.7 years) and 52 healthy sedentary controls (SED, mean age 45.9 ± 14.3 years) were selected for the study (Table 1). Only subjects free of cardiovascular diseases were included. Major inclusion criteria were reported previously [46]. All subjects were nonsmoking and had no regular medication or supplementation of vitamins or trace elements. Moreover, subjects with a family history of cardiovascular disease, hypertension, and other cardiovascular risk factors were excluded [46, 47].

Athletes were recruited from the outpatient clinic of the Sports Medicine Unit of the Department of Clinical and Experimental Medicine of the University of Pisa. Sedentary subjects were not performing any regular physical training.

The level of intensity was evaluated by the use of the 15-point Borg RPE scale [46, 48, 49] for each participant. The study population was further divided into the following subgroups: (1) sedentary (SED, ) and athlete (ATHL, ) subgroups, when the parameter for subdivision was the degree of physical activity; (2) “young” () and “older” () subgroups, when the parameter for subdivision was age.

This study was approved by the Ethics Committee of the Great North West Area of Tuscany (271/2014 to F. F.) and was performed in accordance with the Declaration of Helsinki. All subjects gave informed and written consent to participate in the study [46].

2.2. RBC Collection

Whole blood was collected into EDTA tubes. RBCs were separated from plasma by centrifugation at 200 ×g at 4°C for 10 min [46]. The resulting pellet was washed three times with PBS and frozen at −20°C until use. For athletes, the time period between the last exercise session and blood sampling was at least 48 h. The various parameters estimated included (i) CpG site methylation within SNCA intron1 (SNCA_I1); (ii) concentration of the protein encoded by SNCA (α-syn), considering total levels and the contribution of its oligomeric form; and (iii) levels of the DNA methyltransferase enzymes of maintenance (Dnmt1) and ex novo (Dnmt3a).

2.3. SNCA Intron1 Relative DNA Methylation Analysis

Genomic DNA was extracted from the whole blood of healthy individuals () using the QIAamp DNA Blood Kit (catalogue number: 51104; QIAGEN, CA, USA) and quantified using a NanoDrop Lite Spectrophotometer (Thermo Fisher Scientific Inc., USA). The SNCA intron1 region previously associated with low levels of CpG island methylation in PD patients [35–37, 40, 50] was considered in the present study. Intron1 methylation levels were assessed by methylation-sensitive restriction enzyme (MRSE) digestion of genomic DNA followed by quantitative real-time polymerase chain reaction (PCR) according to Pihlstrøm and coworkers [36]. Briefly, for each DNA sample (20 ng), duplicates of a “test reaction” (with the MRSEs AccII and HpaII) and a “reference reaction” (without MSREs) were incubated at 37°C for 2 h. Then, samples were amplified using forward and reverse primers (FOR-5ATTAGGCTGCTTCTCCGGGATC-3, REV-5GTTCTCAGCCTCCACCCTAG-3). A melt curve was performed at the end of the experiment to confirm amplification specificity. The proportion of genomic DNA methylated at cut sites was calculated with the equation percent methylation = 100 × 2^{−(CT[test reaction] − CT[reference reaction])}.

2.4. Levels of DNA Methyltransferase (Dnmt)

The concentrations of Dnmt1 and Dnmt3a were determined in the blood by specific immunoenzymatic assays, following the manufacturer’s instructions (Biomatik Corporation, Ontario, Canada, http://www.biomatik.com). Briefly, standards or samples were added to the appropriate precoated microtitre plate wells with a biotin-conjugated antibody. Then, samples were incubated with avidin-HRP followed by the addition of the TMB substrate solution (3,3,5,5-tetramethylbenzidine). After stopping the reaction, the absorbance was read at 450 nm (http://www.biomatik.com).

2.5. Detection of Total α-Synuclein

Total α-syn was measured in RBCs as previously described [46, 51]. In brief, precoated wells (α-syn full-length antibody, sc-10717, Santa Cruz Biotechnology) were treated with bovine serum albumin (BSA). RBCs (0.150 mg/100 μl) were captured on wells for 2 h at 25°C, and aliquots of recombinant α-syn were analysed in parallel to obtain a standard curve. After extensive washing, the samples were treated with a mouse monoclonal antibody to α-syn (sc-12767, Santa Cruz Biotechnology) and subsequently with an antimouse HRP antibody [46]. The samples were washed three times with PBS-T (phosphate-buffered saline containing 0.01% Tween 20) before the addition of the enzyme substrate TMB (Thermo Scientific). Absorbance values were read at 450 nm.

2.6. Detection of Oligomeric α-Syn

Oligomeric α-syn levels in RBCs were assessed using an immunoenzymatic assay, as previously described [46, 51–53] using an α-syn biotinylated antibody [53]. The wells were coated with the mouse monoclonal α-syn 211 antibody (sc-12767, Santa Cruz Biotechnology) and incubated with RBCs (0.04 mg/100 μl) for 2 h. Streptavidin-horseradish peroxidase conjugate antibody (1 : 1000, GE Healthcare) was used for antigen detection of the biotinylated antibody. After three washes with PBS-T, TMB was added in each sample, as reported above.

2.7. Statistical Analysis

Data are expressed as the mean value ± SD. A normal distribution for age was found for the subjects included in this study. Differences between groups (i.e., young versus older, ATHL versus SED) were evaluated by one-way ANOVA followed by a Kruskal-Wallis post hoc test. values were adjusted with Sidak’s multiple comparison test. Such analyses were confirmed by a two-way ANOVA test. Correlation between variables was determined by simple linear regression analysis, whereas covariate analysis was performed by partial correlation matrix. All statistical procedures were performed using the StatView programme (Abacus Concepts Inc., SAS Institute, Cary, NC) [46, 54].

3. Results

3.1. Research Plan and Measured Parameters

The present study explored whether, in healthy individuals, physical activity or age is related to different parameters evaluated at the peripheral level, including the CpG site methylation within SNCA intron 1 (SNCA_I1); the concentration of the protein encoded by SNCA (α-syn), considering total levels and the contribution of its oligomeric form; and the levels of the DNA methyltransferase enzymes of maintenance (Dnmt1) and ex novo (Dnmt3a).

Healthy subjects (, sedentary and athletes) were enrolled in the study. The calculation of mean values and correlation analyses of the parameters related to SNCA epigenetic modification were initially performed in the entire population. Then, statistical analyses of such parameters (in terms of mean value comparisons and correlation analyses) were conducted in samples derived from the stratification of the same initial entire population in the following subgroups: (1) sedentary (SED, ) and athlete (ATHL, ) subgroups, when the parameter for subdivision was the degree of physical activity; (2) “young” () and “older” () subgroups, when the parameter for subdivision was age.

3.2. Descriptive Statistics

A descriptive table reporting age, body mass index (BMI), heart rate, and physical activity level (15-level Borg’s scale) of the entire population and of each group is shown (Table 1). Young and older groups presented a mean age of 34.6 ± 8.6 and 58.8 ± 7.2, respectively, and did not present differences in sex and BMI (). ATHL and SED did not present differences in age () and BMI (). As expected, the level of physical exercise was significantly higher in the ATHL group than in the SED group ().

3.3. Entire Population Stratified in Subgroups: Mean Values and Comparison of the Measured Parameters

3.3.1. SNCA_I1 Relative DNA Methylation

The levels of SNCA CpG island relative methylation were measured in DNA samples extracted from the blood of all subjects. Specifically, a CpG island region within SNCA_I1, previously demonstrated as a transcriptionally active region [35], was considered. The analysed CpG islands were from 89,836,281 to 89,836,520 of chromosome 4 (NC_000004.12). Herein, the percentage of SNCA_I1 relative DNA methylation of the total population was 4.37 ± 3.07 (Table 2).

3.3.2. Dnmt1 and Dnmt3a Expression Levels

Dnmt1 is the maintenance methylation enzyme, which preserves the methylation patterns established early in development [55]. By contrast, Dnmt3a has been shown to methylate hemimethylated and unmethylated DNA with equal efficiencies in vitro [56]. Both Dnmts are expressed not only in the adult brain [57] but also in the hematopoietic cells [58] and constitute candidate targets to study the variation of DNA methylation with ageing and physical exercise.

In the entire population, the Dnmt1 and Dnmt3a expression level was 490 ± 372 and 158 ± 119 pg/mg protein, respectively (Table 2). ELISA assays did not show significant differences in Dnmt1 protein levels in the different subgroups (older versus young subjects: ; ATHL versus SED: ; Figure 1(b)). Similar results were obtained for Dnmt3a protein concentration (older versus young subjects: ; ATHL versus SED: P = 0.7012; Figure 1(c)). Overall, these data suggest that the observed differences in the SNCA methylation status between young and older subjects are not related to changes in Dnmt1-3a levels.

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Figure 1

(a) SNCA_I1 relative DNA methylation levels were evaluated by MRSE digestion of genomic DNA extracted from the blood cells of healthy subjects and followed by quantitative real-time PCR. The results were expressed as the percentage of methylation in young, older, ATHL, and SED subgroups. (b) Dnmt1 and (c) Dnmt3a levels were determined in the blood of young, older, ATHL, and SED subgroups. Differences between groups (i.e., young versus older and ATHL versus SED) were evaluated by one-way ANOVA followed by a Kruskal-Wallis post hoc test. values were adjusted with Sidak’s multiple comparison test. between the indicated subgroups.

3.3.3. α-Syn Concentrations

Total and oligomeric concentrations of the gene product, α-syn, were determined in RBCs isolated from the entire population (Table 2). RBCs were chosen among blood cells because they contain approximately 98% of the total amount of circulating α-syn [43]. Total α-syn and oligomeric α-syn mean values were 62.5 ± 52.3 and 11.0 ± 5.4 ng/mg protein, respectively (Table 2).

As depicted in Figure 2(a), older subjects displayed significant lower α-syn concentrations compared to young subjects (), suggesting that α-syn may decrease with age in RBCs. By contrast, ATHL and SED presented comparable concentrations of the protein (Figure 2(a)), suggesting that physical exercise poorly modulated the RBC pool of α-syn.

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(b)

Due to the high standard deviation of total α-syn values (see Table 2), the frequency distribution of this parameter was analysed. The distributions of values led us to select a population presenting RBC α-syn concentrations of 76.3 ng/mg protein or lower (confidence interval (CI) = 95%), on which some correlation analyses were performed (see below).

As depicted in Figure 2(b), oligomeric α-syn in RBCs did not significantly differ between young and older subjects (), as well as between ATHL and SED ().

3.4. Correlation of the Measured Parameters with Age

SNCA_I1 methylation at the CpG site presented a positive correlation with age for blood sampling in the total population (Figure 3(a)), consistent with the data in Figure 1(a). The negative correlation between SNCA methylation and age was particularly stronger in subjects presenting RBC α-syn concentrations of 76 ng/mg protein or lower (Figure 3(b)). Interestingly, such a correlation was found in the SED subgroup (Figure 3(c)) but not in the ATHL (). These data suggest that ATHL may present additional factors other than age that contribute to the level of intron1-SNCA methylation.

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Figure 3

Correlation analyses between SNCA_I1 relative DNA methylation levels, Dnmt1, total α-syn, and age. Correlation analysis between SNCA_I1 relative DNA methylation and age (a) in the total population, (b) in the “95% CI α-syn population” (i.e., subjects characterized by RBC α-syn concentration of 76 ng/mg protein or lower), and (c) in the SED group. (d) Correlation analysis between Dnmt1 levels and age in the ATHL group. (e) Correlation analysis between total α-syn levels in RBCs and age in the ATHL group. The correlation between variables was determined by simple linear regression analysis. P and R² were reported in the respective panels.

For Dnmt levels, a weak inverse correlation between Dnmt1 expression and age was observed in the ATHL subgroup (Figure 3(d)). No other significant relationship with age was found either for Dnmt1 (total population: ; young: ; older: ; and SED: ) or Dnmt3a (total population: ; young: ; older: ; ATHL: ; and SED: ) concentrations.

Consistent with the data obtained for Dnmt1, total α-syn concentrations in RBCs showed an inverse correlation with age of blood sampling in the ATHL subgroup only (Figure 3(e); young: ; older: ; and SED: ).

Finally, no statistical significance was observed between age for blood sampling and oligomeric α-syn levels (total population: ; young: ; older: ; ATHL: ; and SED: ).

3.5. Correlation of the Measured Parameters with the Level of Physical Activity

The two-way ANOVA analysis suggests significant differences in the SNCA_I1 methylation rate between ATHL and SED groups (). Nevertheless, the level of physical activity did not show any significant correlation with SNCA_I1 methylation rate in any of the analysed groups (young: ; older: ; ATHL: ; and SED: ). Similarly, the level of physical exercise did not correlate either with Dnmt1 (total population: ; young: ; older: ; ATHL: ; and SED: ) or Dnmt3a (total population: ; female: ; male: ; young: ; older: ; ATHL: ; and SED: ) expression. Interestingly, total α-syn concentrations in RBCs inversely correlated with the rate of physical activity in the older subjects (Figure 4(a)). In contrast, the oligomeric form of α-syn showed an inverse correlation with the physical activity score in the SED subgroup only (Figure 4(b)). Moreover, the two-way ANOVA analysis evidenced significant differences in oligomeric α-syn content between the ATHL and SED groups ().

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Figure 4

Correlation between the levels of total and oligomeric α-syn in RBCs and the level of physical activity. (a) Correlation analysis between total α-syn levels and physical activity in the older group. (b) Correlation analysis between oligomeric α-syn levels and physical activity in the SED group. Correlation analysis between (c) total α-syn, (d) oligomeric α-syn levels and (e) Dnmt3a and physical activity in the “95% CI α-syn population.” Correlation between variables was determined by simple linear regression analysis. P and R² were reported in the respective panels.

When the population presenting RBC α-syn concentrations ≤ 76 ng/mg protein was selected, the negative correlations with level of physical exercise were evidenced not only for total α-syn (Figure 4(c)) but also for oligomeric α-syn (Figure 4(d)) and Dnmt3a levels (Figure 4(e)).

3.6. Correlation of SNCA_I1 Relative DNA Methylation with Dnmt Levels

SNCA_I1 methylation levels correlated directly with blood Dnmt1 expression in ATHL (Figure 5(a)). No significant correlations were found in the other subgroups for Dnmt1 (young: ; older: ; and SED: ).

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(b)

The expression of Dnmt3a did not correlate with the SNCA methylation rate in any of the analysed groups (young: ; older: ; ATHL: ; and SED: ), thus confirming that blood Dnmt3a poorly modulates intron1-SNCA methylation.

3.7. Correlation of SNCA_I1 Relative DNA Methylation with α-syn Concentrations

Interestingly, in subjects presenting RBC α-syn concentrations ≤ 76 ng/mg protein, a significant correlation between intron1-SNCA methylation rates and total α-syn levels was evidenced (Figure 5(b)). These data suggest a causal relationship between such an epigenetic modification and production of the protein encoded by SNCA_I1 up to this range of α-syn.

By contrast, the SNCA_I1 methylation degree was not related to the RBC content of α-syn (total population: ; young: ; older: ; ATHL: ; and SED: ). Similar results were obtained for the oligomeric form of α-syn (total population: ; young: ; older: ; ATHL: ; and SED: ). These data indicate the lack of a direct relationship between the expression of these enzymes and the levels of SNCA protein product.

3.8. Correlation of Dnmt Levels with α-Syn Concentrations

Furthermore, the relationship between Dnmt levels and α-syn concentrations was observed. Surprisingly, a strong direct correlation between Dnmt1 expression and total α-syn levels in RBCs was found in the total population (Figure 6(a)). Such a direct association remained significant in all the subgroups (Figures 6(b)–6(e)).

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Similarly, the Dnmt3a content in the blood directly correlated with the total α-syn levels in RBCs in the total population (Figure 7(a)), as well as in all the subgroups (Figures 7(b)–7(e)).

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The abovementioned positive correlations between Dnmt1/Dnmt3a levels and total α-syn concentrations were maintained for its oligomeric form, in all the analysed subgroups (Dnmt1 in Figures 8(a)–8(e)); Dnmt3a in Figures 9(a)–9(e).

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4. Discussion

Here, the influence of age and physical activity on the epigenetic modification of the α-syn gene (SNCA) was explored. The major findings of this paper are as follows: (i) DNA methylation of intron1-SNCA was directly correlated with age; (ii) total α-syn concentrations in RBCs were lower in the older subjects; (iii) Dnmt levels were directly correlated with both total and oligomeric α-syn; (iv) in the 95-percentile population, the RBC levels of total α-syn levels were inversely related to the methylation status of intron1 SNCA; and (v) in the same selected population, the physical activity level was inversely related to the total and oligomeric α-syn levels in RBCs, as well as to Dnmt3a concentrations. Taken together, our data shed light on ageing- and physical exercise-induced changes on the methylation status and protein levels of α-syn, which accumulates as a misfolded oligomer in PD.

Epigenetic mechanisms and particularly DNA methylation were demonstrated to regulate brain ageing and age-related neurodegenerations. In particular, epigenetic regulation of the α-syn-encoding gene (SNCA) has been greatly explored focusing on the methylation status of intron1 CpG islands [35–37, 39–42, 50, 59]. Nevertheless, controversial findings have been reported in both brain and peripheral tissues of patients affected by PD [34, 36, 39–42].

If ageing remains the main contributing factor to PD pathogenesis, beneficial effects have been described for moderate physical activity [12, 21, 32], although the underlying regulatory mechanisms are not completely understood [60]. Alterations in gene expression as a consequence of physical training are frequently described. The mechanisms of the described alteration in gene expression mediated by physical activity could depend on epigenetic changes in global or gene-specific DNA methylation levels [12]. In this respect, a genome-wide analysis of DNA methylation recently highlighted the importance of epigenetic mechanisms in muscle adaptation to regular exercise [57, 60].

In the present study, healthy subjects (sedentary and athletes) were enrolled to investigate the influence of age and physical exercise on the methylation status of SNCA. This parameter was related to the blood content of total and oligomeric forms of α-syn protein, as well as to the blood concentrations of DNA methyltransferases. In particular, RBCs were chosen as a peripheral model because they accumulate misfolded proteins and are particularly susceptible to oxidative stress [47–49].

First, SNCA methylation levels were shown to be significantly higher in the older subgroup than in the young subgroup and to directly correlate with age. Similarly, DNA analysis from brain tissues recently revealed a slight increase of intron1-SNCA DNA methylation levels in presumably healthy individuals during ageing [61].

Next, α-syn protein accumulation was measured in RBCs. Older subjects presented lower levels of total but not oligomeric α-syn. Similar data on the protein levels were reported in RBCs, plasma, or platelets of healthy subjects or PD patients [62–66]. Moreover, a significant inverse correlation with age in the selected population presenting RBC α-syn concentrations up to 76.3 ng/mg protein (confidence interval = 95%) was observed. In the latter population, intron1-SNCA DNA methylation levels were inversely related to total α-syn concentrations. These data suggest that hypermethylation of intron1-SNCA can lead to a lower concentration of total α-syn in the blood, at least for a population presenting up to 76.3 ng/mg protein. In fact, the current hypothesis is that the minor levels presented by an elderly population or PD patients are the result of protein sequestration into oligomeric forms [62–66]. Further investigations are needed to clarify the effective influence of SNCA methylation on α-syn accumulation in the peripheral cells.

The link between SNCA methylation, α-syn concentrations, and Dnmts was then examined. Indeed, the decrease in global genomic methylation (hypomethylation) that occurs with ageing has been often proposed as a consequence of decreased Dnmt expression [9, 67–69]. However, contradictory evidence for both methylcytosine levels and Dnmt expression was shown during hippocampal ageing [7, 70–76]. Here, no significant differences in the expression of Dnmt1 or Dnmt3a were found between the analysed subgroups. By contrast, a reduction of nuclear Dnmt1 levels was demonstrated in brain samples from PD patients and α-syn transgenic mice, suggesting a mechanism by which α-syn may exclude the enzyme from the nucleus, resulting in further hypomethylation of α-syn CpG islands [59].

Surprisingly, both Dnmt1 and Dnmt3a levels directly correlated with the concentrations of total and oligomeric α-syn. These data suggest that Dnmt concentrations are not strictly related to SNCA methylation status in this group of healthy subjects. It is hypothesized that such Dnmt is highly involved in the methylation of genes different from SNCA.

For the role of physical exercise, sedentary and athletes were found to present similar percentages of SNCA methylation, α-syn concentrations, and Dnmt levels. Although it is well recognized that physical activity can control gene expression through epigenetic alterations [11], effective modulation of the methylation status of neurodegeneration-related genes remains to be largely investigated, particularly in peripheral tissues. Recently, exercise was shown to increase the global DNA methylation profile in the rat hypothalamus [77] or to induce DNA hypomethylation of the promoter IV-BDNF [18], as well as “at specific CpG site located within a VegfA promoter” [11, 78]. The latter findings were related to a significant reduction of Dnmt3b mRNA in the hippocampus of exercised rats, suggesting that genome-wide DNA hypomethylation can occur in the brain.

Surprisingly, when the population presenting up to 76.3 ng/mg protein of α-syn was selected, the level of physical exercise inversely correlated not only with total α-syn levels in RBCs but also with the oligomeric form and with Dnmt levels. These findings suggest that within the limits of α-syn accumulation, regular activity can modulate DNA methylation enzymes and reduce protein accumulation. In this respect, it should be mentioned that additional mechanisms on the regulation of α-syn expression cannot be excluded and will be investigated in future studies.

5. Conclusions

In conclusion, we show that DNA methylation of intron1-SNCA was modified with ageing but not with physical exercise. Within a moderate range of α-syn, RBC levels of the protein were inversely related to the methylation status of intron1-SNCA and the physical activity level.

Such findings shed light on the effects of physical exercise on ageing-associated alterations of epigenetic processes in peripheral cells.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Simona Daniele and Barbara Costa equally contributed to this work.

References

C. López-Otín, M. A. Blasco, L. Partridge, M. Serrano, and G. Kroemer, “The hallmarks of aging,” Cell, vol. 153, no. 6, pp. 1194–1217, 2013.
View at: Publisher Site | Google Scholar
N. Hadad, D. R. Masser, S. Logan et al., “Absence of genomic hypomethylation or regulation of cytosine-modifying enzymes with aging in male and female mice,” Epigenetics & Chromatin, vol. 9, no. 1, p. 30, 2016.
View at: Publisher Site | Google Scholar
S. Bocklandt, W. Lin, M. E. Sehl et al., “Epigenetic predictor of age,” PLoS ONE, vol. 6, no. 6, article e14821, 2011.
View at: Publisher Site | Google Scholar
S. Horvath, “DNA methylation age of human tissues and cell types,” Genome Biology, vol. 14, no. 10, p. R115, 2013.
View at: Publisher Site | Google Scholar
C. A. Miller and J. D. Sweatt, “Covalent modification of DNA regulates memory formation,” Neuron, vol. 53, no. 6, pp. 857–869, 2007.
View at: Publisher Site | Google Scholar
R. Lister and E. A. Mukamel, “Turning over DNA methylation in the mind,” Frontiers in Neuroscience, vol. 9, 2015.
View at: Publisher Site | Google Scholar
A. M. M. Oliveira, T. J. Hemstedt, and H. Bading, “Rescue of aging-associated decline in Dnmt3a2 expression restores cognitive abilities,” Nature Neuroscience, vol. 15, no. 8, pp. 1111–1113, 2012.
View at: Publisher Site | Google Scholar
M. R. Penner, T. L. Roth, C. A. Barnes, and J. D. Sweatt, “An epigenetic hypothesis of aging-related cognitive dysfunction,” Frontiers in Aging Neuroscience, vol. 2, no. 9, 2010.
View at: Publisher Site | Google Scholar
S. M. Meeran, A. Ahmed, and T. O. Tollefsbol, “Epigenetic targets of bioactive dietary components for cancer prevention and therapy,” Clinical Epigenetics, vol. 1, no. 3-4, pp. 101–116, 2010.
View at: Publisher Site | Google Scholar
F. Sanchis-Gomar, J. L. Garcia-Gimenez, C. Perez-Quilis, M. C. Gomez-Cabrera, F. V. Pallardo, and G. Lippi, “Physical exercise as an epigenetic modulator: eustress, the “positive stress” as an effector of gene expression,” Journal of Strength and Conditioning Research, vol. 26, no. 12, pp. 3469–3472, 2012.
View at: Publisher Site | Google Scholar
E. Grazioli, I. Dimauro, N. Mercatelli et al., “Physical activity in the prevention of human diseases: role of epigenetic modifications,” BMC Genomics, vol. 18, Supplement 8, p. 802, 2017.
View at: Publisher Site | Google Scholar
T. S. King-Himmelreich, S. Schramm, M. C. Wolters et al., “The impact of endurance exercise on global and AMPK gene-specific DNA methylation,” Biochemical and Biophysical Research Communications, vol. 474, no. 2, pp. 284–290, 2016.
View at: Publisher Site | Google Scholar
R. Barrès, J. Yan, B. Egan et al., “Acute exercise remodels promoter methylation in human skeletal muscle,” Cell Metabolism, vol. 15, no. 3, pp. 405–411, 2012.
View at: Publisher Site | Google Scholar
F. Gomez-Pinilla, Y. Zhuang, J. Feng, Z. Ying, and G. Fan, “Exercise impacts brain-derived neurotrophic factor plasticity by engaging mechanisms of epigenetic regulation,” European Journal of Neuroscience, vol. 33, no. 3, pp. 383–390, 2011.
View at: Publisher Site | Google Scholar
T. Arendt, “Synaptic degeneration in Alzheimer’s disease,” Acta Neuropathologica, vol. 118, no. 1, pp. 167–179, 2009.
View at: Publisher Site | Google Scholar
N. Tsankova, W. Renthal, A. Kumar, and E. J. Nestler, “Epigenetic regulation in psychiatric disorders,” Nature Reviews Neuroscience, vol. 8, no. 5, pp. 355–367, 2007.
View at: Publisher Site | Google Scholar
V. E. A. Russo, R. A. Martienssen, and A. D. Riggs, Epigenetic Mechanisms of Gene Regulation, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 1996.
C. B. Santos-Rebouças and M. M. G. Pimentel, “Implication of abnormal epigenetic patterns for human diseases,” European Journal of Human Genetics, vol. 15, no. 1, pp. 10–17, 2007.
View at: Publisher Site | Google Scholar
S. B. Baylin and P. A. Jones, “A decade of exploring the cancer epigenome biological and translational implications,” Nature Reviews Cancer, vol. 11, no. 10, pp. 726–734, 2011.
View at: Publisher Site | Google Scholar
R. Lardenoije, A. Iatrou, G. Kenis et al., “The epigenetics of aging and neurodegeneration,” Progress in Neurobiology, vol. 131, pp. 21–64, 2015.
View at: Publisher Site | Google Scholar
M. Al-Jarrah, H. Obaidat, Z. Bataineh, L. Walton, and A. Al-Khateeb, “Endurance exercise training protects against the upregulation of nitric oxide in the striatum of MPTP/probenecid mouse model of Parkinson’s disease,” NeuroRehabilitation, vol. 32, no. 1, pp. 141–147, 2013.
View at: Publisher Site | Google Scholar
G. M. Petzinger, J. P. Walsh, G. Akopian et al., “Effects of treadmill exercise on dopaminergic transmission in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-lesioned mouse model of basal ganglia injury,” Journal of Neuroscience, vol. 27, no. 20, pp. 5291–5300, 2007.
View at: Publisher Site | Google Scholar
A. L. Ridgel, J. L. Vitek, and J. L. Alberts, “Forced, not voluntary, exercise improves motor function in Parkinson’s disease patients,” Neurorehabilitation and Neural Repair, vol. 23, no. 6, pp. 600–608, 2009.
View at: Publisher Site | Google Scholar
J. Y. Cho, D. Y. Hwang, T. S. Kang et al., “Use of NSE/PS2m-transgenic mice in the study of the protective effect of exercise on Alzheimer’s disease,” Journal of Sports Sciences, vol. 21, no. 11, pp. 943–951, 2003.
View at: Publisher Site | Google Scholar
C. W. Cotman and N. C. Berchtold, “Exercise: a behavioral intervention to enhance brain health and plasticity,” Trends in Neurosciences, vol. 25, no. 6, pp. 295–301, 2002.
View at: Publisher Site | Google Scholar
L. Fratiglioni, S. Paillard-Borg, and B. Winblad, “An active and socially integrated lifestyle in late life might protect against dementia,” The Lancet Neurology, vol. 3, no. 6, pp. 343–353, 2004.
View at: Publisher Site | Google Scholar
H.-S. Um, E.-B. Kang, J.-H. Koo et al., “Treadmill exercise represses neuronal cell death in an aged transgenic mouse model of Alzheimer’s disease,” Neuroscience Research, vol. 69, no. 2, pp. 161–173, 2011.
View at: Publisher Site | Google Scholar
H. S. Um, E. B. Kang, Y. H. Leem et al., “Exercise training acts as a therapeutic strategy for reduction of the pathogenic phenotypes for Alzheimer’s disease in an NSE/APPsw-transgenic model,” International Journal of Molecular Medicine, vol. 22, no. 4, pp. 529–539, 2008.
View at: Google Scholar
C. Giacomelli, S. Daniele, and C. Martini, “Potential biomarkers and novel pharmacological targets in protein aggregation-related neurodegenerative diseases,” Biochemical Pharmacology, vol. 131, pp. 1–15, 2017.
View at: Publisher Site | Google Scholar
C. Hetz and S. Saxena, “ER stress and the unfolded protein response in neurodegeneration,” Nature Reviews Neurology, vol. 13, no. 8, pp. 477–491, 2017.
View at: Publisher Site | Google Scholar
J. H. T. Power, O. L. Barnes, and F. Chegini, “Lewy bodies and the mechanisms of neuronal cell death in Parkinson’s disease and dementia with Lewy bodies,” Brain Pathology, vol. 27, no. 1, pp. 3–12, 2017.
View at: Publisher Site | Google Scholar
Y. Jang, J. H. Koo, I. Kwon et al., “Neuroprotective effects of endurance exercise against neuroinflammation in MPTP-induced Parkinson’s disease mice,” Brain Research, vol. 1655, pp. 186–193, 2017.
View at: Publisher Site | Google Scholar
L. J. Spielman, J. P. Little, and A. Klegeris, “Physical activity and exercise attenuate neuroinflammation in neurological diseases,” Brain Research Bulletin, vol. 125, pp. 19–29, 2016.
View at: Publisher Site | Google Scholar
S. Guhathakurta, B. A. Evangelista, S. Ghosh, S. Basu, and Y.-S. Kim, “Hypomethylation of intron1 of α-synuclein gene does not correlate with Parkinson’s disease,” Molecular Brain, vol. 10, no. 1, p. 6, 2017.
View at: Publisher Site | Google Scholar
A. Jowaed, I. Schmitt, O. Kaut, and U. Wullner, “Methylation regulates alpha-synuclein expression and is decreased in Parkinson’s disease patients’ brains,” Journal of Neuroscience, vol. 30, no. 18, pp. 6355–6359, 2010.
View at: Publisher Site | Google Scholar
L. Pihlstrøm, V. Berge, A. Rengmark, and M. Toft, “Parkinson’s disease correlates with promoter methylation in the α-synuclein gene,” Movement Disorders, vol. 30, no. 4, pp. 577–580, 2015.
View at: Publisher Site | Google Scholar
L. de Boni, S. Tierling, S. Roeber, J. Walter, A. Giese, and H. A. Kretzschmar, “Next-generation sequencing reveals regional differences of the α-synuclein methylation state independent of Lewy body disease,” Neuromolecular Medicine, vol. 13, no. 4, pp. 310–320, 2011.
View at: Publisher Site | Google Scholar
C. R. Scherzer, J. A. Grass, Z. Liao et al., “GATA transcription factors directly regulate the Parkinson’s disease-linked gene α-synuclein,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 31, pp. 10907–10912, 2008.
View at: Publisher Site | Google Scholar
Y.-y. Tan, L. Wu, Z.-b. Zhao et al., “Methylation of α-synuclein and leucine-rich repeat kinase 2 in leukocyte DNA of Parkinson’s disease patients,” Parkinsonism & Related Disorders, vol. 20, no. 3, pp. 308–313, 2014.
View at: Publisher Site | Google Scholar
S.-x. Ai, Q. Xu, Y.-c. Hu et al., “Hypomethylation of SNCA in blood of patients with sporadic Parkinson’s disease,” Journal of the Neurological Sciences, vol. 337, no. 1-2, pp. 123–128, 2014.
View at: Publisher Site | Google Scholar
J. Richter, S. Appenzeller, O. Ammerpohl et al., “No evidence for differential methylation of α-synuclein in leukocyte DNA of Parkinson’s disease patients,” Movement Disorders, vol. 27, no. 4, pp. 590-591, 2012.
View at: Publisher Site | Google Scholar
Y. Song, H. Ding, J. Yang et al., “Pyrosequencing analysis of SNCA methylation levels in leukocytes from Parkinson’s disease patients,” Neuroscience Letters, vol. 569, pp. 85–88, 2014.
View at: Publisher Site | Google Scholar
R. Barbour, K. Kling, J. P. Anderson et al., “Red blood cells are the major source of alpha-synuclein in blood,” Neurodegenerative Diseases, vol. 5, no. 2, pp. 55–59, 2008.
View at: Publisher Site | Google Scholar
K. B. Pandey and S. I. Rizvi, “Markers of oxidative stress in erythrocytes and plasma during aging in humans,” Oxidative Medicine and Cellular Longevity, vol. 3, no. 1, 12 pages, 2010.
View at: Publisher Site | Google Scholar
K. B. Pandey and S. I. Rizvi, “Biomarkers of oxidative stress in red blood cells,” Biomedical Papers, vol. 155, no. 2, pp. 131–136, 2011.
View at: Publisher Site | Google Scholar
S. Daniele, D. Pietrobono, J. Fusi et al., “α-Synuclein aggregates with β-amyloid or tau in human red blood cells: correlation with antioxidant capability and physical exercise in human healthy subjects,” Molecular Neurobiology, vol. 55, no. 3, pp. 2653–2675, 2018.
View at: Publisher Site | Google Scholar
M. H. Whaley, P. H. Brubaker, R. M. Otto, and L. E. Armstrong, Medicine ACoS Guidelines for Exercise Testing and Prescription, Lippincott Williams & Wilkins, Philadelphia, 7th edn edition, 2006.
G. A. V. Borg, “Psychophysical bases of perceived exertion,” Medicine & Science in Sports & Exercise, vol. 14, no. 5, pp. 377–381, 1982.
View at: Publisher Site | Google Scholar
P. Hamer and B. Slocombe, “The psychophysical and heart rate relationship between treadmill and deep-water running,” Australian Journal of Physiotherapy, vol. 43, no. 4, pp. 265–271, 1997.
View at: Publisher Site | Google Scholar
L. Matsumoto, H. Takuma, A. Tamaoka et al., “CpG demethylation enhances alpha-synuclein expression and affects the pathogenesis of Parkinson’s disease,” PLoS One, vol. 5, no. 11, article e15522, 2010.
View at: Publisher Site | Google Scholar
P. G. Foulds, J. D. Mitchell, A. Parker et al., “Phosphorylated α-synuclein can be detected in blood plasma and is potentially a useful biomarker for Parkinson’s disease,” The FASEB Journal, vol. 25, no. 12, pp. 4127–4137, 2011.
View at: Publisher Site | Google Scholar
O. M. A. El-Agnaf, S. A. Salem, K. E. Paleologou et al., “Detection of oligomeric forms of α-synuclein protein in human plasma as a potential biomarker for Parkinson’s disease,” The FASEB Journal, vol. 20, no. 3, pp. 419–425, 2006.
View at: Publisher Site | Google Scholar
P. Pesini, V. Pérez-Grijalba, I. Monleón et al., “Reliable measurements of the β-amyloid pool in blood could help in the early diagnosis of AD,” International Journal of Alzheimer’s Disease, vol. 2012, Article ID 604141, 10 pages, 2012.
View at: Publisher Site | Google Scholar
F. Franzoni, L. Ghiadoni, F. Galetta et al., “Physical activity, plasma antioxidant capacity, and endothelium-dependent vasodilation in young and older men,” American Journal of Hypertension, vol. 18, no. 4, pp. 510–516, 2005.
View at: Publisher Site | Google Scholar
T. H. Bestor, “Activation of mammalian DNA methyltransferase by cleavage of a Zn binding regulatory domain,” The EMBO Journal, vol. 11, no. 7, pp. 2611–7, 1992.
View at: Google Scholar
M. Okano, S. Xie, and E. Li, “Cloning and characterization of a family of novel mammalian DNA (cytosine-5) methyltransferases,” Nature Genetics, vol. 19, no. 3, pp. 219-220, 1998.
View at: Publisher Site | Google Scholar
M. D. Nitert, T. Dayeh, P. Volkov et al., “Impact of an exercise intervention on DNA methylation in skeletal muscle from first-degree relatives of patients with type 2 diabetes,” Diabetes, vol. 61, no. 12, pp. 3322–3332, 2012.
View at: Publisher Site | Google Scholar
S.-i. Mizuno, T. Chijiwa, T. Okamura et al., “Expression of DNA methyltransferases DNMT1, 3A, and 3B in normal hematopoiesis and in acute and chronic myelogenous leukemia,” Blood, vol. 97, no. 5, pp. 1172–1179, 2001.
View at: Publisher Site | Google Scholar
P. Desplats, B. Spencer, E. Coffee et al., “Alpha-synuclein sequesters Dnmt1 from the nucleus: a novel mechanism for epigenetic alterations in Lewy body diseases,” Journal of Biological Chemistry, vol. 286, no. 11, pp. 9031–9037, 2011.
View at: Publisher Site | Google Scholar
T. Kanzleiter, M. Jähnert, G. Schulze et al., “Exercise training alters DNA methylation patterns in genes related to muscle growth and differentiation in mice,” American Journal of Physiology-Endocrinology and Metabolism, vol. 308, no. 10, pp. E912–E920, 2015.
View at: Publisher Site | Google Scholar
L. de Boni, L. Riedel, I. Schmitt et al., “DNA methylation levels of α-synuclein intron 1 in the aging brain,” Neurobiology of Aging, vol. 36, no. 12, pp. 3334.e7–3334.e11, 2015.
View at: Publisher Site | Google Scholar
J. G. Mohanty, E. Nagababu, and J. M. Rifkind, “Red blood cell oxidative stress impairs oxygen delivery and induces red blood cell aging,” Frontiers in Physiology, vol. 5, 2014.
View at: Publisher Site | Google Scholar
X. Wang, S. Yu, F. Li, and T. Feng, “Detection of α-synuclein oligomers in red blood cells as a potential biomarker of Parkinson’s disease,” Neuroscience Letters, vol. 599, pp. 115–119, 2015.
View at: Publisher Site | Google Scholar
N. K. U. Koehler, E. Stransky, M. Meyer et al., “Alpha-synuclein levels in blood plasma decline with healthy aging,” PLoS One, vol. 10, no. 4, article e0123444, 2015.
View at: Publisher Site | Google Scholar
H. Q. Zhao, F. F. Li, Z. Wang, X. M. Wang, and T. Feng, “A comparative study of the amount of α-synuclein in ischemic stroke and Parkinson’s disease,” Neurological Sciences, vol. 37, no. 5, pp. 749–754, 2016.
View at: Publisher Site | Google Scholar
S. Daniele, D. Pietrobono, J. Fusi et al., “α-Synuclein aggregated with tau and β-amyloid in human platelets from healthy subjects: correlation with physical exercise,” Frontiers in Aging Neuroscience, vol. 10, p. 17, 2018.
View at: Publisher Site | Google Scholar
M. Zampieri, F. Ciccarone, R. Calabrese, C. Franceschi, A. Burkle, and P. Caiafa, “Reconfiguration of DNA methylation in aging,” Mechanisms of Ageing and Development, vol. 151, pp. 60–70, 2015.
View at: Publisher Site | Google Scholar
H. M. Chow and K. Herrup, “Genomic integrity and the ageing brain,” Nature Reviews Neuroscience, vol. 16, no. 11, pp. 672–684, 2015.
View at: Publisher Site | Google Scholar
H. A. Irier and P. Jin, “Dynamics of DNA methylation in aging and Alzheimer’s disease,” DNA and Cell Biology, vol. 31, no. S1, pp. S-42–S-48, 2012.
View at: Publisher Site | Google Scholar
L. Chouliaras, D. L. A. van den Hove, G. Kenis et al., “Prevention of age-related changes in hippocampal levels of 5-methylcytidine by caloric restriction,” Neurobiology of Aging, vol. 33, no. 8, pp. 1672–1681, 2012.
View at: Publisher Site | Google Scholar
H. Chen, S. Dzitoyeva, and H. Manev, “Effect of aging on 5-hydroxymethylcytosine in the mouse hippocampus,” Restorative Neurology and Neuroscience, vol. 30, no. 3, pp. 237–245, 2012.
View at: Publisher Site | Google Scholar
L. Liu, T. van Groen, I. Kadish et al., “Insufficient DNA methylation affects healthy aging and promotes age-related health problems,” Clinical Epigenetics, vol. 2, no. 2, pp. 349–360, 2011.
View at: Publisher Site | Google Scholar
Y. Mei, C. Jiang, Y. Wan et al., “Aging-associated formaldehyde-induced norepinephrine deficiency contributes to age-related memory decline,” Aging Cell, vol. 14, no. 4, pp. 659–668, 2015.
View at: Publisher Site | Google Scholar
Z. Tong, C. Han, M. Qiang et al., “Age-related formaldehyde interferes with DNA methyltransferase function, causing memory loss in Alzheimer’s disease,” Neurobiology of Aging, vol. 36, no. 1, pp. 100–110, 2015.
View at: Publisher Site | Google Scholar
P. Singh and M. K. Thakur, “Reduced recognition memory is correlated with decrease in DNA methyltransferase1 and increase in histone deacetylase2 protein expression in old male mice,” Biogerontology, vol. 15, no. 4, pp. 339–346, 2014.
View at: Publisher Site | Google Scholar
V. R. Elsner, G. A. Lovatel, F. Moysés et al., “Exercise induces age-dependent changes on epigenetic parameters in rat hippocampus: a preliminary study,” Experimental Gerontology, vol. 48, no. 2, pp. 136–139, 2013.
View at: Publisher Site | Google Scholar
R. K. Kashimoto, L. V. Toffoli, M. H. F. Manfredo et al., “Physical exercise affects the epigenetic programming of rat brain and modulates the adaptive response evoked by repeated restraint stress,” Behavioural Brain Research, vol. 296, pp. 286–289, 2016.
View at: Publisher Site | Google Scholar
C. A. E. Sølvsten, F. de Paoli, J. H. Christensen, and A. L. Nielsen, “Voluntary physical exercise induces expression and epigenetic remodeling of VegfA in the rat hippocampus,” Molecular Neurobiology, vol. 55, no. 1, pp. 567–582, 2018.
View at: Publisher Site | Google Scholar

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Copyright © 2018 Simona Daniele et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Oxidative Medicine and Cellular Longevity

Exercise in the Prevention and Management of Oxidative Stress-Linked Diseases

Epigenetic Modifications of the α-Synuclein Gene and Relative Protein Content Are Affected by Ageing and Physical Exercise in Blood from Healthy Subjects

Abstract

1. Introduction

2. Methods

2.1. Study Population

2.2. RBC Collection

2.3. SNCA Intron1 Relative DNA Methylation Analysis

2.4. Levels of DNA Methyltransferase (Dnmt)

2.5. Detection of Total α-Synuclein

2.6. Detection of Oligomeric α-Syn

2.7. Statistical Analysis

3. Results

3.1. Research Plan and Measured Parameters

3.2. Descriptive Statistics

3.3. Entire Population Stratified in Subgroups: Mean Values and Comparison of the Measured Parameters

3.3.1. SNCAI1 Relative DNA Methylation

3.3.2. Dnmt1 and Dnmt3a Expression Levels

3.3.3. α-Syn Concentrations

3.4. Correlation of the Measured Parameters with Age

3.5. Correlation of the Measured Parameters with the Level of Physical Activity

3.6. Correlation of SNCAI1 Relative DNA Methylation with Dnmt Levels

3.7. Correlation of SNCAI1 Relative DNA Methylation with α-syn Concentrations

3.8. Correlation of Dnmt Levels with α-Syn Concentrations

4. Discussion

5. Conclusions

Conflicts of Interest

Authors’ Contributions

References

Copyright

3.3.1. SNCA_I1 Relative DNA Methylation

3.6. Correlation of SNCA_I1 Relative DNA Methylation with Dnmt Levels

3.7. Correlation of SNCA_I1 Relative DNA Methylation with α-syn Concentrations