Influence of DAT1 Promotor Methylation on Sports Performance
by
, , , , , and
Anna Grzywacz
1,*
,
Krzysztof Chmielowiec
2,
Agnieszka Boroń
3
,
Monika Michałowska-Sawczyn
4,
Jolanta Chmielowiec
2,
Grzegorz Trybek
5
,
Bożena Mroczek
6,
Katarzyna Leźnicka
4,
Paweł Cieszczyk
4 and
Jolanta Masiak
7 1
Independent Laboratory of Health Promotion of the Pomeranian Medical University in Szczecin, 11 Chlapowskiego St., 70-204 Szczecin, Poland
2
Department of Hygiene and Epidemiology, Collegium Medicum, University of Zielona Góra, 28 Zyty St., 65-046 Zielona Góra, Poland
3
Department of Clinical and Molecular Biochemistry, Pomeranian Medical University, 72 Powstańców Wielkopolskich St., 70-111 Szczecin, Poland
4
Faculty of Physical Education, Gdansk University of Physical Education and Sport, 1 K.Górskiego St., 80-336 Gdansk, Poland
5
Department of Oral Surgery, Pomeranian Medical University in Szczecin, 72 Powstańców Wlkp. St., 70-111 Szczecin, Poland
6
Department of Human Sciences in Medicine, Pomeranian Medical University in Szczecin, 11 Chlapowskiego St., 70-204 Szczecin, Poland
7
II Department of Psychiatry and Psychiatric Rehabilitation, Medical University of Lublin, 1 Głuska St., 20-059 Lublin, Poland
*
Author to whom correspondence should be addressed.
Genes 2021, 12(9), 1425; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091425
Submission received: 12 July 2021
/
Revised: 11 September 2021
/
Accepted: 13 September 2021
/
Published: 16 September 2021
(This article belongs to the Special Issue Genetics and Genomics in Sport)
Abstract
:In the mammalian genome, DNA methylation is an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factors (TFs) to DNA. As there are still many questions concerning the role of methylation in creating personality, we concentrated on searching for such associations. The research group was 100 sports male subjects (mean age = 22.88, SD = 6.35), whereas the control group included 239 healthy male volunteers matched for age (mean age = 21.69, SD = 3.39), both of European origin. The methods used in our research were as follows: DNA isolation, methylation-specific PCR, sequencing chromatophores, all conducted according to the manufacturer’s procedure. To evaluate personality traits, the NEO Five-Factor Personality Inventory (NEO-FFI) and STAI Inventory were used. We observed the existence of a statistically significant correlation for all the aspects of personality covered and CpG islands’ methylation. Nonetheless, we think that the tested group and the number of tested promotor islands in the DAT1 gene are still too small to make explicit conclusions, so it needs further profound analysis.
1. Introduction
1.1. DNA Methylation
In the mammalian genome, DNA methylation is an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factors (TFs) to DNA. One distinct feature of transcription factors is that they have DNA-binding domains that give them the ability to bind to specific sequences of DNA called enhancer or promoter sequences. Some transcription factors bind to a DNA promoter sequence near the transcription start site and help form the transcription initiation complex. Other transcription factors bind to regulatory sequences, such as enhancer sequences, and can either stimulate or repress transcription of the related gene. These regulatory sequences can be thousands of base pairs upstream or downstream from the gene being transcribed. Regulation of transcription is the most common form of gene control [1]. DNA methylation in the mammalian genome predominantly occurs on cytosine in the context of the 5′-CpG-3′ dinucleotides; this is the only type of epigenetic modification to change the DNA molecule directly. Stretches of GC-rich sequences in the genome called CpG islands (CGIs) that are associated with open transcriptionally competent chromatin structures were discovered in gene promoters [2]. Promoters play an essential role in understanding the transcriptional mechanisms of genes. CpG islands located within promoter regions appear to create a more conducive chromatin state that favors transcription or allows gene expression silencing through intensive CpG methylation [3]. The relevance of GC content and CpG dinucleotide concentration to the regulation of gene activity points to its physiological significance. The DNA methylation pattern established during the development and differentiation is preserved with high fidelity during cell division by DNA methyltransferases (DNMTs). DNMTs are highly expressed in developing tissues, but their activity declines during differentiation in all tissues except the brain, wherein they are expressed throughout the lifetime of the brain [4,5]. Dynamic DNMT activity in the brain is essential for synaptic plasticity and memory formation [6,7]. The environment is a potent genetic modifier, influencing gene expression via epigenetic mechanisms. Ubiquitous epigenetic mechanisms represent an essential element of normal development and maturation. Aberrant epigenetic processes can cause maladaptive changes (gene dysregulations and dysfunctions) and consequently lead to disease. In the brain, in contrast to somatic tissues, epigenetic processes remain active throughout the lifespan: they are ultimately involved in maintaining brain functions, enabling adaptive plasticity and the ability to accommodate varying environmental challenges [8]. The fidelity of epigenetic processes is critical for the human brain since its development creates enormously complex biological patterning. Consequently, it is most susceptible to aberrant activity of epigenetic modifiers: epigenetic dysregulation is implicated in the pathogenesis of a variety of brain-related diseases, including mental retardation and complex psychiatric disorders [9].
Physical exercises have a beneficial influence on both, brain and body, and particularly on skeletal muscles. They increase the effectiveness of muscles metabolism, improve the biological functions of mitochondria, adjust the transformation of muscle fiber types, and increase muscle strength. Currently, conducted research shows that epigenetic regulation is one of the important factors during these processes. The epigenetic environment within skeletal muscles modified with physical exercises precisely adjusts the delicate balance between gene expression and silencing under the control of contemporary constant transcriptional or post-transcriptional mechanisms [10,11].
1.2. DAT1
The human dopamine transporter gene (DAT1 or SLC6A3) has been associated with various brain-related diseases and behavioral traits and, as such, has been investigated intensely in experimental- and clinical settings.
Dopamine (DA) neurotransmission underlies core brain functions, including locomotion, behavior, cognition, and motivation; consequently, disruption in dopamine signaling gives rise to various neuropsychiatric disorders and conditions [12]. A key player in the regulation of DA signaling is the dopamine transporter (DAT); it modulates the dynamics and the levels of DA in the synaptic cleft by recycling extracellular DA back into the presynaptic terminal. Alterations in the DAT availability in the brain directly affect the concentration of synaptic DA and the kinetics of its reuptake [13].
As our knowledge about methylation in particular promotor regions among athletes is still in its infancy, in hereto research, we presented its influence and correlation in connection with personality traits, simultaneously asking if the differences between individual personalities influence methylation of chosen promoter groups in the DAT1 gene. We are aware that the causal relationship between personality traits and methylation processes can go both ways; hence we want to analyze the issue more precisely. Nonetheless, it needs more numerous groups.
2. Materials and Methods
2.1. Samples
The research group was 100 sports male subjects (mean age = 22.88, SD = 6.35), whereas the control group included 239 healthy male volunteers matched for age (mean age = 21.69, SD = 3.39). Both groups were composed of individuals of European origin from the same region of Poland. The research was based on 100 Polish healthy (no prior history of substance dependency or psychosis) male combat athletes (MMA, n = 23; judo, n = 40; boxing, n = 5; karate, n = 15; kickboxing, n = 15; wrestling, n = 2). Several methods were applied to prepare the samples, including targeting national teams and providing information to national coaching personnel and athletes attending training camps. All athletes and controls were European to reduce the possibility of genetic admixture and to overcome any potential problems due to population stratification.
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by KOMISJA BIOETYCZNA przy Okręgowej Izbie Lekarskiej w Szczecinie, ul. Marii Skłodowskiej-Curie 11, 71-332 (protocol nr 13/KB/VI/2016, 08.12.2016).
2.2. Methylation Status Assessment
Methylation of 33 promotor islands of the DAT1 gene for the research and control group was accomplished and published previously by Michałowska-Sawczyn and coauthors [14].
DNA isolation kit (A&A Biotechnology, Gdynia, Poland) was used for DNA extraction from peripheral blood leukocytes. Extracted DNA was stored at −20 °C. Bisulfite modification of 250 ng DNA was accomplished with the usage of the EZ DNA Methylation Kit (Zymo Research, Orange, CA, USA), following the manufacturer’s instructions. Methylation-specific PCR assay was carried out in a Mastercycler ep gradient S (Eppendorf, Germany).
Primer oligonucleotides were obtained from Genomed.pl (Warsaw, Poland). Primer sequences were designed using MethPrimer (http://www.urogene.orgbin/methprimer/.cgi, accessed on 11 January 2020). The status of the DAT1 promoter (ENSG00000142319) was assessed by PCR using primers specific to a fragment of the gene, i.e., DATF: 5′-GGTTTTTGTTTTTTTTATTGTTGAG-3′; DATR: 5′-AAATCCCCTAAACCTAATCCC-3′. The PCR conditions in order to amplify the 447-bp fragment covering 33 CpG sites in DAT1 gene promoter were as follows: initial denaturation (94 °C/5 min), followed by 35 cycles (94 °C/61 °C/72 °C, 25 s each step) with final elongation at 72 °C for 5 min. The concentration of magnesium chloride ions was 2.5 mM. After amplification assay, the PCR products were subjected to sequencing as previously described [14]. Briefly, samples were verified by sequencing using the Bigdye v3.1 kit (Applied Biosystems, Darmstadt, Germany) and separation by ethanol extraction using the ABI Prism 3130XL (Applied Biosystems, Darmstadt, Germany) in a 36 cm capillary in a POP7 polymer, using the reverse primer in accordance with the manufacturer’s protocol.
Sequencing chromatograms were analyzed using 4peaks software (Mek and Tosj, Amsterdam, The Netherlands). Methylation of cytosine was considered positive, when the G/A + G ratio reached at least 20% of a total signal.
2.3. Assessment of the Ability to Bind Transcription Factors
For the analysis of transcription binding sites for the DAT1 promoter region, we used PROMO software (http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB = TF_8.3, accessed on 12 July 2021). In PROMO, for the identification of potential binding sites in sequences, weight matrices are constructed from known binding sites extracted from version 8.3 of the TRANSFAC database (http://genexplain.com/transfac/#section0, (11.01.2020), accessed on 12 July 2021). The ability of transcription factors to bind individual regions was assessed with different similarity rates, i.e., 100%, 95%, or 85%. As promotor sites showed to be PAX5 transcription factor binding sites (positions 3, 22, and 33), and their hypomethylation showed to be important for personality traits, we searched for CpG status in relation with DAT1.
2.4. Psychometric Tests
Sports subjects and control subjects, both male, were examined by the NEO Five-Factor Personality Inventory (NEO-FFI) scales. The NEO Five-Factor Personality Inventory (NEO Five-Factor Inventory, NEO-FFI) includes 6 dimensions for each of the five traits–Extraversion (Positive Emotion, Warmth, Gregariousness, Activity, Excitement Seeking, Assertiveness), Agreeableness (Tender-mindedness, Trust, Altruism, Straightforwardness, Compliance, Modesty), Openness to experience (Fantasy, Feelings, Aesthetics, Actions, Values, Ideas), Conscientiousness (Deliberation, Competence, Dutifulness, Order, Achievement striving, Self-discipline), Neuroticism (Anxiety, Vulnerability to stress, Hostility, Self-consciousness, Impulsiveness, Depression) [15].
The results of NEO-FFI and STAI inventories were given as sten scores. The conversion of the raw score into the sten scale was performed according to Polish norms for adults; it was assumed that: sten 1–2—very low scores, 3–4—low scores, 5–6—average scores, 7–8—high scores, 9–10—very high scores.
2.5. Statistical Analysis
The relationship between DAT1 promotor methylation status, sports sand control subjects, and NEO Five-Factor Inventory (NEO-FFI) was analyzed by a multivariate analysis of Factor effects ANOVA (NEO-FFI× methylation status × sports sand control subjects × (methylation status × sports sand control subjects)). Not all assumptions required for the ANOVA analysis have been met. The assumption about the normal distribution was not fulfilled for all dependent variables, but the variance was the same (Levene’s test p > 0.05). Because the number of subjects in groups was also large, it was therefore decided to use multivariate analysis 2 × 3 factorial ANOVA. The NEO Five-Factor Inventory (Neuroticism, Extraversion, Openness, Agreeability Conscientiousness) was measured and compared using the U Mann-Whitney test. Methylation status data was analyzed using a chi-square test with a p < 0.05 being considered statistically significant. The whole process of calculation was performed with the usage of STATISTICA 13 (Tibco Software Inc, Palo Alto, CA, USA) for Windows (Microsoft Corporation, Redmond, WA, USA).
3. Results
The methylation status of the CpG PAX5 4 DAT1 promotor sites (3, 13, 22, 33) in sports and control subjects is shown in Table 1.
The means and standard deviations for all NEO Five-Factor Inventory and interaction for sports subjects and control subjects are presented in Table 2.
When comparing the controls and the study group subjects, for the second one, we observed significantly higher scores (Table 2) on the NEO Five-Factor Inventory scale of Conscientiousness (M 7.23 vs. M 5.83, p < 0.0001).
3.1. PAX 5 CpG Position: Sites 3
The results of 2 × 3 factorial ANOVA of the NEO Five-Factor Personality Inventory (NEO-FFI) sten scales and DAT1 promotor methylation status of the CpG PAX5 sites 3 and controls and the study group subjects are summarized in Table 3. When comparing groups, we found a significant result in the interactions (sports subjects vs. controls) for the NEO FFI Openness scale and DAT1 promotor methylation status of the CpG PAX5 sites 3 (Figure 1, F2,332 = 4.52, p = 0.034), accounting for 1.3% of the variance, respectively. The results of the post hoc test are included in Table 4.
3.2. PAX 5 CpG Position: Sites 13
The results of 2 × 3 factorial ANOVA of the NEO Five-Factor Personality Inventory (NEO-FFI) sten scales and DAT1 promotor methylation status of the CpG PAX5 sites 13, controls, and the study group subjects are summarized in Table 3. We found interactions a significant result when comparing groups (sports subjects vs. controls) for NEO FFI Neuroticism and DAT1 promotor methylation status of the CpG PAX5 sites 13 (Figure 2, F2,332 = 7.89, p = 0.005), accounting for 2.3% of the variance, respectively. The results of the post hoc test are included in Table 4.
3.3. PAX 5 CpG Position: Sites 22
The results of 2 × 3 factorial ANOVA of the NEO Five-Factor Personality Inventory (NEO-FFI) sten scales and DAT1 promotor methylation status of the CpG PAX5 sites 13, controls, and the study group subjects are summarized in Table 3.
3.4. Neuroticism Scale
We found interactions a significant result when comparing groups (sports subjects vs. controls) for NEO FFI Neuroticism, and DAT1 promotor methylation status of the CpG PAX5 sites 22 (Figure 3, F2,332 = 7.55, p = 0.006), accounting for 2.2% of the variance, respectively. The results of the post hoc test are included in Table 4.
3.5. Extraversion Scale
We found a significant result when comparing for NEO-FFI Extraversion scale for DAT1 promotor methylation status of the CpG PAX5 sites 22 (F2,332 = 4.52, p = 0.0342), accounting for 1.3% of the variance, respectively. We found interactions a significant result when comparing groups (sports subjects vs. controls) for NEO FFI Extraversion and DAT1 promotor methylation status of the CpG PAX5 sites 22 (Figure 4, F2,332 = 14.27, p = 0.0001), accounting for 4.1% of the variance, respectively. The results of the post hoc test are included in Table 4.
3.6. Openness Scale
We found a significant result when comparing for NEO-FFI Openness scale for DAT1 promotor methylation status of the CpG PAX5 sites 22 (F2,332 = 16.39, p = 0.0001), accounting for 4.7% of the variance, respectively. We found interactions a significant result when comparing groups (sports subjects vs. controls) for NEO FFI Openness and DAT1 promotor methylation status of the CpG PAX5 sites 22 (Figure 5, F2,332 = 15.24, p = 0.0001), accounting for 4.4% of the variance, respectively. The results of the post hoc test are included in Table 4.
3.7. Agreeability Scale
We found a significant result when comparing for NEO-FFI Agreeability scale for DAT1 promotor methylation status of the CpG PAX5 sites 22 (F2,332 = 12.68, p = 0.0004), accounting for 3.7% of the variance, respectively. We found interactions a significant result when comparing groups (sports subjects vs. controls) for NEO FFI Agreeability and DAT1 promotor methylation status of the CpG PAX5 sites 22 (Figure 6, F2,332 = 5.78, p = 0.0167), accounting for 1.7% of the variance, respectively. The results of the post hoc test are included in Table 4.
3.8. PAX 5 CpG Position: Sites 33
We did not find significant results for the 2 × 3 factorial ANOVA of the NEO Five-Factor Personality Inventory (NEO-FFI) sten scales, the DAT1 methylation status of the CpG PAX5 sites 33 and controls and the study group subjects.
4. Discussion
DAT1 and Methylation
Experimental and clinical evidence indicates that sequence variations upstream of the transcription start site (TSS) affect DAT1 transcriptional regulation [16]. The DAT1 core promoter lacks “TATA” and “CAT” boxes (these DNA sequences provide docking sites for basal transcriptional complex) [17]. Transcriptional initiation of neuronal genes often is facilitated via binding of core transcriptional machinery to the CCAAT element [18]. Shumay et al. [19] detected several CCAAT consensuses upstream and downstream of the DAT1 TSS, suggesting that the DAT1 might be one of these. Human genes that have CCAAT-promoters display several common characteristics; in general, they are less precise in terms of TSS than the genes with TATA-promoters, and they mostly overlap with CpG islands. Transcription initiation from the CCAAT box involves NF-Y, an element with histone-like features, and a particular subset of transcription factors [20].
The complex structure of human gene promoters with a range of alternative transcription start sites (TSSs) [21] supports differential temporal- and spatial- patterns of gene expression and provides an additional level of gene regulation by modulating translational efficiency [22]. About one-fifth of human genes have alternative promoters; this phenomenon is most frequent in brain-related genes [23].
The interaction of the cis-regulatory elements of a gene with transcription factors (TF) largely determined transcription events; therefore, an assessment of the putative TF binding sites in the regulatory region of the gene under analysis yields important information on this gene’s regulation. In the nervous system, TFs define the basic framework; their availability varies across the brain regions and cell types, thus contributing to phenotypic diversity [24]. As we mentioned, the nature of the DAT1 promoter (CCAAT promoter) implies its sensitivity to selective TFs, because NF-Y synergistically interacts with a subset of TFs [20]. Shumay et al. [19] inspection of the DAT1 5′-flanking sequence (−2 kb, JASPAR database) revealed that it contains binding sites for Sp1, GATA−1, CREB, and c-Myc cis-acting regulatory elements –all those TFs interact with NF-Y [20].
The human DAT1 gene is remarkably GC dense and has multiple CpG islands: in contrast to most vertebrate genes that have only a promoter-overlapping CpG Island. On average, inactive promoters of the human genes cytosine and guanine account for 57% of the nucleotides [25], but GCs represent 79% of the DAT1 promoter sequence.
In vivo, the DNA molecule forms a complex with proteins that allow its packaging into chromatin. Nucleosomes are the structural units of chromatin represented by histone octamers around which the DNA coils. The close interaction of the DNA molecule with a nucleosome core results in condensed chromatin that is inaccessible to the transcription machinery; hence, the transcriptional activation of a gene requires the local transition of compact chromatin domains into decondensed loops. Nucleosome remodeling and covalent modifications of histones provide the basis for epigenetic gene regulation that occurs via the modulation of the accessibility of the genomic loci to transcriptional machinery [26]. CG-rich motifs in DNA sequences inherently disfavor nucleosomes and are referred to as “nucleosome exclusion sequences” (NX) [27]. Shumay et al. [19] found that both the DAT1 and the 5-HTT (SLC6A4) genes have high NX-scoring sequences near the TSS. The predicted nucleosome positioning in the DAT1 and the 5-HTT loci notably differ: the entire DAT1 locus comprises of numerous nucleosome-dysfavouring sequences, while in the 5-HTT, NX-peaks are sparse. It was suggested that intragenic regions with high NX Scores might function as transcriptional enhancers.
The most notable characteristic of the human DAT1 is its high sensitivity to epigenetic regulation: in contrast to the relative enrichment in GC nucleotides in the promoter-proximal region as occurs in most human genes, the entire DAT1 locus has GC-bias sequence composition (0.55) and comprises multiple CpG sites comprising 27 bona fide CGIs (CpG islands). [19].
Previous studies found that DAT1 methylation derived from blood correlated with symptoms of hyperactivity and impulsivity in children and adolescents with ADHD [28] and with impulsivity (and basal ganglia DAT availability) in monkeys [29]. Hence, in our research, we combined methylation with personality traits. In the presented research, we noticed that in DAT1 PAX5, CpG island 3 observed a statistically significant interaction between the occurrence of methylation in martial arts athletes and lower results in sten scale of NEO FFI Openness in comparison with the control group (Figure 1, 4.20 vs. 4.87, p = 0.0026, Table 4). In DAT1 PAX5, CpG island 13 observed a statistically significant interaction between methylation in martial arts athletes and increased results in sten scale NEO FFI Neuroticism compared to the control group (Figure 2, 5.36 vs. 3.36, p = 0.0069, Table 4). The part of the research shows a distinct occurrence of methylation in these subgroups of athletes. However, we observed additionally that in particular subgroups based on the personality traits tests, methylation does not occur. Still, in DAT1 PAX5, CpG island 22 noticed statistically significant interaction between the shortage of methylation in the martial arts athletes and lower results in sten scale NEO FFI Neuroticism in comparison with the control group (Figure 3, 2.40 vs. 5.25, p = 0.0098, Table 4). In DAT1 PAX5, CpG island 22 noticed statistically significant interaction between the shortage of methylation in the martial arts athletes and increased results in sten scale NEO FFI Openness compared to the control group (Figure 5, 7.80 vs. 4.92, p = 0.0004, Table 4). However, reverse interaction was observed in the case of methylation; subjects from the martial arts athletes group obtained lower results in sten scale NEO FFI Openness compared to the control group (Figure 5, 4.26 vs. 4.85, p = 0.0033, Table 4).
In DAT1 PAX5, CpG island 22 noticed statistically significant interaction between the shortage of methylation in the martial arts athletes and increased results in sten scale NEO FFI Agreeability compared to the control group (Figure 6, 8.40 vs. 6.25, p = 0.0494, Table 4). However, reverse interaction was observed in the case of methylation; subjects from the martial arts athletes group obtained lower results in sten scale NEO FFI Agreeability compared to the control group (Figure 6, 5.06 vs. 5.60, p = 0.0322, Table 4).
Epigenetic sensitivity of the DAT1 gene increased during the process of evolution. The genetic drift of the DAT1 sequence oriented on the accumulation of GC nucleotides may reflect its strengthening epigenetic potential, important in the regulatory processes resulting from more and more complex functions of the human brain [19].
We also want to emphasize that the discussed PAX5 (BSAP) functions as both a transcriptional activator and repressor during midbrain patterning, B-cell development, and lymphomagenesis [30].
In spite of the same genetic make-up of all cells of an organism, each tissue, and even each cell, has its own methylation pattern that determines its identity and functions as a result of dynamic interactions with other cells and the environment. Environmental stimuli through the mediation of several elements/factors such as neurotransmitters, hormones, and transcription factors modulate promoter methylation patterns and corresponding expression levels of various genes. Since successive bindings of transcription factors to a gene’s regulatory regions are associated with a decrease in DNA methylation level and an increase in the capability of gene expression for a prolonged period of time in the future, DNA methylation is considered as a mechanism for cell memory. The pattern of DNA methylation is generally maintained throughout cell division; therefore, any DNA methylation changes could either be global in the entire genome affecting all progenies of the affected cell or local, affecting only specific genes in specific cells [31].
Although the effects of gene methylation on gene expression are complex, gene methylation is generally seen as a ‘silencing’ epigenetic mark. That is, various studies have found that methylation of CpG islands in the promoter area has an inhibitory effect on transcription initiation, resulting in reduced gene expression [32,33]. The observed association between the methylation of DAT1 and its expression might reflect the high concentration of CpG islands in the gene, which makes DAT expression particularly susceptible to modulation through epigenetic mechanisms, specifically DNA methylation [19]. Because DNA methylation is a dynamic measure, it might, however, better reflect the expression of proteins sensitive to modification by environmental exposures. The expression of DAT1 is dynamic and sensitive to circadian rhythms [34], age [35], addictive drugs including tobacco [36,37,38], medication exposures [39], among others. Although multiple factors regulate DAT expression, methylation of DAT1 is of particular interest as it changes dynamically in response to various environmental influences [19].
5. Conclusions
Although the effects of gene methylation on gene expression are complex, gene methylation is generally seen as a ‘silencing’ epigenetic mark. When analyzing genetic conditioning or associations in sport, it is also important to take into consideration the factors connected with the functioning of the human brain. Individual personality traits can differ in the area of methylation factors’ sensitivity. However, the tested group and the number of tested promotor islands in the DAT1 gene are still too small to make explicit conclusions, so it still needs further analysis.
Author Contributions
Conceptualization, A.G., K.C. and P.C.; methodology, B.M., K.L., G.T.; software, K.C.; validation, K.C., J.C. and J.M.; formal analysis, P.C.; investigation, A.G., P.C., J.M.; resources, K.C.; data curation, K.C.; writing—original draft preparation, A.G., A.B., M.M.-S., K.C.; writing—review and editing, A.G.; visualization, P.C.; supervision, P.C.; project administration, A.G.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.
Funding
The study was supported by the National Science Centre of Poland (No. UMO-2016/21/B/NZ7/01068).
Institutional Review Board Statement
The study was conducted according to the guidelines of the Bioethical Committee at the District Medical Chamber in szczecin (permit number: 13/KB/VI/2016).
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Moore, L.D.; Le, T.; Fan, G. DNA Methylation and Its Basic Function. Neuropsychopharmacology 2012, 38, 23–38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, G.-J.; Chang, L.; Volkow, N.D.; Telang, F.; Logan, J.; Ernst, T.; Fowler, J.S. Decreased brain dopaminergic transporters in HIV-associated dementia patients. Brain 2004, 127, 2452–2458. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Deaton, A.; Bird, A. CpG islands and the regulation of transcription. Genes Dev. 2011, 25, 1010–1022. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Levenson, J.M.; Roth, T.L.; Lubin, F.D.; Miller, C.; Huang, I.-C.; Desai, P.; Malone, L.M.; Sweatt, J.D. Evidence That DNA (Cytosine-5) Methyltransferase Regulates Synaptic Plasticity in the Hippocampus. J. Biol. Chem. 2006, 281, 15763–15773. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nelson, E.D.; Kavalali, E.T.; Monteggia, L.M. Activity-Dependent Suppression of Miniature Neurotransmission through the Regulation of DNA Methylation. J. Neurosci. 2008, 28, 395–406. [Google Scholar] [CrossRef] [Green Version]
- Frazer, K.A.; Ballinger, D.G.; Cox, D.R.; Hinds, D.A.; Stuve, L.L.; Gibbs, R.A.; Belmont, J.W.; Boudreau, A.; Hardenbol, P.; Leal, S.M.; et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 2007, 449, 851–861. [Google Scholar] [PubMed]
- Miller, C.; Campbell, S.L.; Sweatt, J.D. DNA methylation and histone acetylation work in concert to regulate memory formation and synaptic plasticity. Neurobiol. Learn. Mem. 2008, 89, 599–603. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- McGowan, P.O.; Meaney, M.J.; Szyf, M. Diet and the epigenetic (re)programming of phenotypic differences in behavior. Brain Res. 2008, 1237, 12–24. [Google Scholar] [CrossRef] [Green Version]
- Cirulli, F.; Francia, N.; Berry, A.; Aloe, L.; Alleva, E.; Suomi, S.J. Early life stress as a risk factor for mental health: Role of neuro-trophins from rodents to non-human primates. Neurosci. Biobehav. Rev. 2009, 33, 573–585. [Google Scholar] [CrossRef] [Green Version]
- Barrès, R.; Zierath, J.R. The role of diet and exercise in the transgenerational epigenetic landscape of T2DM. Nat. Rev. Endocrinol. 2016, 12, 441–451. [Google Scholar] [CrossRef] [PubMed]
- McGee, S.L.; Walder, K.R. Exercise and the Skeletal Muscle Epigenome. Cold Spring Harb. Perspect. Med. 2017, 7, a029876. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mehler-Wex, C.; Riederer, P.; Gerlach, M. Dopaminergic dysbalance in distinct basal ganglia neurocircuits: Implications for the pathophysiology of Parkinson’s disease, schizophrenia and attention deficit hyperactivity disorder. Neurotox. Res. 2006, 10, 167–179. [Google Scholar] [CrossRef] [PubMed]
- Greenwood, T.A.; Schork, N.J.; Eskin, E.; Kelsoe, J.R. Identification of additional variants within the human dopamine transporter gene provides further evidence for an association with bipolar disorder in two independent samples. Mol. Psychiatry 2006, 11, 125–133. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Michałowska-Sawczyn, M.; Grzywacz, A.; Masiak, J.; Chmielowiec, K.; Chmielowiec, J.; Chycki, J.; Maculewicz, E.; Cięszczyk, P. Associations Between Physical Effort and DNA Methylation in the Promotor Region of the Dopamine Transporter Gene (DAT1). J. Hum. Kinet. 2021, 77, 125–133. [Google Scholar] [CrossRef] [PubMed]
- Costa, P.; McCrae, R.R. The Revised NEO Personality Inventory (NEO-PI-R); Sage Pub-lications Inc.: Thousand Oaks, CA, USA, 2008; Volume 2, pp. 179–198. [Google Scholar]
- Drgon, T.; Lin, Z.; Wang, G.-J.; Fowler, J.; Pablo, J.; Mash, D.C.; Volkow, N.; Uhl, G.R. Common Human 5′ Dopamine Transporter (SLC6A3) Haplotypes Yield Varying Expression Levels In Vivo. Cell. Mol. Neurobiol. 2006, 26, 873–887. [Google Scholar] [CrossRef] [PubMed]
- Martinat, C.; Bacci, J.-J.; Leete, T.; Kim, J.; Vanti, W.B.; Newman, A.H.; Cha, J.H.; Gether, U.; Wang, H.; Abeliovich, A. Cooperative transcription activation by Nurr1 and Pitx3 induces embryonic stem cell maturation to the midbrain dopamine neuron phenotype. Proc. Natl. Acad. Sci. USA 2006, 103, 2874–2879. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Alberini, C.M. Transcription Factors in Long-Term Memory and Synaptic Plasticity. Physiol. Rev. 2009, 89, 121–145. [Google Scholar] [CrossRef] [PubMed]
- Shumay, E.; Fowler, J.S.; Volkow, N.D. Genomic Features of the Human Dopamine Transporter Gene and Its Potential Epi-genetic States: Implications for Phenotypic Diversity. PLoS ONE 2010, 5, e11067. [Google Scholar] [CrossRef] [Green Version]
- Dolfini, D.; Mantovani, R.; Zambelli, F.; Pavesi, G. A perspective of promoter architecture from the CCAAT box. Cell Cycle 2009, 8, 4127–4137. [Google Scholar] [CrossRef] [Green Version]
- Finotti, A.; Treves, S.; Zorzato, F.; Gambari, R.; Feriotto, G. Upstream stimulatory factors are involved in the P1 promoter directed transcription of the A beta H-J-J locus. BMC Mol. Biol. 2008, 9, 110. [Google Scholar] [CrossRef] [Green Version]
- Carninci, P.; Sandelin, A.; Lenhard, B.; Katayama, S.; Shimokawa, K.; Ponjavic, J.; Semple, C.; Taylor, M.; Engström, P.; Frith, M.; et al. Genome-wide analysis of mammalian promoter architecture and evolution. Nat. Genet. 2006, 38, 626–635. [Google Scholar] [CrossRef]
- Schulz, R.; McCole, R.B.; Woodfine, K.; Wood, A.J.; Chahal, M.; Monk, D.; Moore, G.E.; Oakey, R.J. Transcript- and tissue-specific imprinting of a tumour suppressor gene. Hum. Mol. Genet. 2008, 18, 118–127. [Google Scholar] [CrossRef] [Green Version]
- Ma, Q. Transcriptional regulation of neuronal phenotype in mammals. J. Physiol. 2006, 575, 379–387. [Google Scholar] [CrossRef] [PubMed]
- Cooper, S.J.; Trinklein, N.D.; Anton, E.D.; Nguyen, L.; Myers, R.M. Comprehensive analysis of transcriptional promoter structure and function in 1% of the human genome. Genome Res. 2005, 16, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zelko, I.N.; Mueller, M.R.; Folz, R.J. Transcription Factors Sp1 and Sp3 Regulate Expression of Human Extracellular Superoxide Dismutase in Lung Fibroblasts. Am. J. Respir. Cell Mol. Biol. 2008, 39, 243–251. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Radwan, A.; Younis, A.; Luykx, P.; Khuri, S. Prediction and analysis of nucleosome exclusion regions in the human genome. BMC Genom. 2008, 9, 186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ding, K.; Yang, J.; Reynolds, G.P.; Chen, B.; Shao, J.; Liu, R.; Qian, Q.; Liu, H.; Yang, R.; Wen, J.; et al. DAT1 methylation is associated with methylphenidate response on oppositional and hyperactive-impulsive symptoms in children and ado-lescents with ADHD. World J. Biol. Psychiatry Off. J. World Fed. Soc. Biol. Psychiatry 2017, 18, 291–299. [Google Scholar] [CrossRef] [PubMed]
- Rajala, A.Z.; Zaitoun, I.; Henriques, J.B.; Converse, A.K.; Murali, D.; Epstein, M.L.; Populin, L.C. Dopamine transporter gene susceptibility to methylation is associated with impulsivity in nonhuman primates. J. Neurophysiol. 2014, 112, 2138–2146. [Google Scholar] [CrossRef] [Green Version]
- Eberhard, D.; Jiménez, G.; Heavey, B.; Busslinger, M. Transcriptional repression by Pax5 (BSAP) through interaction with corepressors of the Groucho family. EMBO J. 2000, 19, 2292–2303. [Google Scholar] [CrossRef] [Green Version]
- Abdolmaleky, H.M.; Smith, C.L.; Zhou, J.-R.; Thiagalingam, S. Epigenetic Alterations of the Dopaminergic System in Major Psychiatric Disorders. Methods Mol. Biol. 2008, 448, 187–212. [Google Scholar] [CrossRef]
- Brenet, F.; Moh, M.; Funk, P.; Feierstein, E.; Viale, A.J.; Socci, N.D.; Scandura, J. DNA Methylation of the First Exon Is Tightly Linked to Transcriptional Silencing. PLoS ONE 2011, 6, e14524. [Google Scholar] [CrossRef] [PubMed]
- Jones, P.A. Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 2012, 13, 484–492. [Google Scholar] [CrossRef]
- Ferris, M.J.; Espana, R.A.; Locke, J.L.; Konstantopoulos, J.K.; Rose, J.H.; Chen, R.; Jones, S.R. Dopamine transporters govern diurnal variation in extracellular dopamine tone. Proc. Natl. Acad. Sci. USA 2014, 111, E2751–E2759. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Volkow, N.D.; Ding, Y.S.; Fowler, J.S.; Wang, G.J.; Logan, J.; Gatley, S.J.; Hitzemann, R.; Smith, G.; Fields, S.D.; Gur, R. Dopamine transporters decrease with age. J. Nucl. Med. Off. Publ. Soc. Nucl. Med. 1996, 37, 554–559. [Google Scholar]
- Newberg, A.; Lerman, C.; Wintering, N.; Ploessl, K.; Mozley, P.D. Dopamine transporter binding in smokers and non-smokers. Clin. Nucl. Med. 2007, 32, 452–455. [Google Scholar] [CrossRef] [PubMed]
- Volkow, N.D.; Wang, G.-J.; Smith, L.; Fowler, J.S.; Telang, F.; Logan, J.; Tomasi, D. Recovery of dopamine transporters with methamphetamine detoxification is not linked to changes in dopamine release. NeuroImage 2015, 121, 20–28. [Google Scholar] [CrossRef] [PubMed]
- Ashok, A.H.; Mizuno, Y.; Volkow, N.D.; Howes, O.D. Association of Stimulant Use with Dopaminergic Alterations in Users of Cocaine, Amphetamine, or Methamphetamine: A Systematic Review and Meta-analysis. JAMA Psychiatry 2017, 74, 511–519. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, G.-J.; Volkow, N.D.; Wigal, T.; Kollins, S.H.; Newcorn, J.H.; Telang, F.; Logan, J.; Jayne, M.; Wong, C.T.; Han, H.; et al. Long-Term Stimulant Treatment Affects Brain Dopamine Transporter Level in Patients with Attention Deficit Hyperactive Disorder. PLoS ONE 2013, 8, e63023. [Google Scholar] [CrossRef] [Green Version]
Figure 1.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 3 and NEO FFI Openness scale. Methylation status: yes—1, no—0. M—mean, M ± SE—mean ± standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 1.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 3 and NEO FFI Openness scale. Methylation status: yes—1, no—0. M—mean, M ± SE—mean ± standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 2.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 13 and NEO FFI Neuroticism scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 2.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 13 and NEO FFI Neuroticism scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 3.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Neuroticism scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 3.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Neuroticism scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 4.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Extraversion scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 4.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Extraversion scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 5.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Openness scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 5.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Openness scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 6.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Agreeability scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Figure 6.
Interaction between sports subjects/control and DAT1 PAX5 CpG sites 22 and NEO FFI Agreeability scale. Methylation status: yes—1, no—0. M ± SE—mean ±standard error, M ± 1.96*SE—mean ± s1.96*standard error.
Table 1.
Methylation status of PAX5 4 DAT1 CpG sites (3, 13,22,33) in sports subjects and control group. A group of 100 sports and 239 control individuals were studied to compare methylation status in indicated CpG sites. Chi-square test χ2(p), Chi-square; OR, odds ratio; CI, Confidence Interval; (−95%, +95%) [14].
Table 1.
Methylation status of PAX5 4 DAT1 CpG sites (3, 13,22,33) in sports subjects and control group. A group of 100 sports and 239 control individuals were studied to compare methylation status in indicated CpG sites. Chi-square test χ2(p), Chi-square; OR, odds ratio; CI, Confidence Interval; (−95%, +95%) [14].
| CpG Site | Studied Group | Methylation Status (%) | χ2(p) | OR | 95% CI (−95%, +95%) |
|---|---|---|---|---|---|
| 3 * | sports subjects N (100) | 78% | 20.471 (0.00001) | 4.838 | (2.326; 10.065) |
| control N (239) | 94% | ||||
| 13 * | sports subjects N (100) | 28% | 37.290 (0.00001) | 0.126 | (0.059; 0.265) |
| control N (239) | 5% | ||||
| 22 | sports subjects N (100) | 95% | 0.001 (0.974) | 0.982 | (0.337; 2.865) |
| control N (239) | 95% | ||||
| 33 | sports subjects N (100) | 66% | 9.291 (0.0023) | 2.247 | (1.326; 3.810) |
| control N (239) | 81% |
* Significant between-group differences.
Table 2.
NEO Five-Factor Inventory results (sten scale) and between healthy control and sports subjects.
Table 2.
NEO Five-Factor Inventory results (sten scale) and between healthy control and sports subjects.
| STAI/NEO Five Factor Inventory/ | Sports Subjects (N = 100) | Control (N = 236) | Z | p Value |
|---|---|---|---|---|
| Neuroticism/scale | 4.76 ± 2.28 | 4.66 ± 1.99 | 0.145 | 0.884 |
| Extraversion/scale | 6.27 ± 1.89 | 6.41 ± 1.97 | −0.692 | 0.488 |
| Openness/scale | 4.44 ± 1.63 | 4.83 ± 1.70 | −1.531 | 0.126 |
| Agreeability/scale | 5.23 ± 2.13 | 5.65 ± 2.07 | −1.428 | 0.153 |
| Conscientiousness/scale | 7.23 ± 1.86 | 5.83 ± 2.13 | 5.498 | 0.0000 * |
p-statistical significance U Mana’s test, N—number of subjects, M ± SD—Mean ± Standard Deviation. *—Significant between-group differences.
Table 3.
Differences in methylation DAT1 PAX5 CpG sites 3, 13, 22, 33 and NEO Five-Factor Inventory between healthy control subjects and sports subjects.
Table 3.
Differences in methylation DAT1 PAX5 CpG sites 3, 13, 22, 33 and NEO Five-Factor Inventory between healthy control subjects and sports subjects.
| STAI/NEO Five-Factor Inventory/ | ANOVA | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Sports (N = 100) M ± SD | Control (N = 236) M ± SD | Methylation Status No (N = 17) M ± SD | Methylation Status Yes (N = 319) M ± SD | Full Model F p Value R2 | Factor | F (p Value) | ɳ2 | Power (alfa = 0.05) | |
| PAX 5 CpG Position: sites 3 | |||||||||
| Openness/scale | 4.44 ± 1.63 | 4.83 ± 1.70 | 5.02 ± 1.60 | 4.70 ± 1.69 | F3,332 = 3.913 p = 0.0091 * R2 = 0.034 | intercept | F1,332 = 929.65 (p < 0.0001) | 0.737 | 1.000 |
| sports/control | F1,332 = 0.0001 (p = 0.990) | 0.00001 | 0.050 | ||||||
| CpG sites 3 | F2,332 = 1.71 (p = 0.192) | 0.005 | 0.256 | ||||||
| addicts/control x CpG sites 3 | F2,332 = 4.52 (p = 0.034) * | 0.013 | 0.563 | ||||||
| PAX 5 CpG Position: sites 13 | |||||||||
| Neuroticism/scale | 4.76 ± 2.28 | 4.66 ± 1.99 | 4.69 ± 2.04 | 4.79 ± 2.34 | F3,332 = 2.687 p = 0.0465 * R2 = 0.024 | intercept | F1,332 = 526.07 (p < 0.0001) * | 0.613 | 1.000 |
| sports/control | F1,332 = 5.17 (p = 0.024) * | 0.015 | 0.621 | ||||||
| CpG sites 13 | F2,332 = 0.48 (p = 0.488) | 0.001 | 0.107 | ||||||
| addicts/control x CpG sites 13 | F2,332 = 7.89 (p = 0.005) * | 0.023 | 0.800 | ||||||
| PAX 5 CpG Position: sites 22 | |||||||||
| Neuroticism/scale | 4.76 ± 2.28 | 4.66 ± 1.99 | 4.41 ± 1.80 | 4.71 ± 2.08 | F5,332 = 2.674 p = 0.0473 * R2 = 0.024 | intercept | F1,332 = 233.11 (p < 0.0001) * | 0.412 | 1.000 |
| sports/control | F1,332 = 5.38 (p = 0.0210) * | 0.016 | 0.638 | ||||||
| CpG sites 22 | F2,332 = 2.78 (p = 0.0962) | 0.008 | 0.383 | ||||||
| addicts/control x CpG sites 22 | F2,332 = 7.55 (p = 0.006) * | 0.022 | 0.782 | ||||||
| Extraversion/scale | 6.27 ± 1.89 | 6.41 ± 1.97 | 6.65 ± 2.42 | 6.35 ± 1.92 | F5,332 = 5.001 p = 0.0021 * R2 = 0.0432 | intercept | F1,332 = 685.04 (p < 0.0001) * | 0.673 | 1.000 |
| sports/control | F1,389 = 9.89 (p = 0.0018) * | 0.029 | 0.880 | ||||||
| CpG sites 22 | F2,389 = 4.52 (p = 0.0342) * | 0.013 | 0.563 | ||||||
| addicts/control x CpG sites 22 | F2,389 = 14.27 (p = 0.0001) * | 0.041 | 0.965 | ||||||
| Openness/scale | 4.44 ± 1.63 | 4.83 ± 1.70 | 5.76 ± 1.86 | 4.68 ± 1.66 | F5,332 = 9.003 p = 0.00001 * R2 = 0.075 | intercept | F1,332 = 602.61 (p < 0.0001) * | 0.644 | 1.000 |
| sports/control | F1,332 = 6.65 (p = 0.0103) * | 0.020 | 0.729 | ||||||
| CpG sites 22 | F2,332 = 16.39 (p = 0.0001) * | 0.047 | 0.981 | ||||||
| addicts/control x CpG sites 22 | F2,332 = 15.24 (p = 0.0001) * | 0.044 | 0.973 | ||||||
| Agreeability/scale | 5.23 ± 2.13 | 5.65 ± 2.07 | 6.88 ± 1.99 | 5.44 ± 2.07 | F5,332 = 5.500 p = 0.0011 * R2 = 0.047 | intercept | F1,332 = 512.12 (p < 0.0001) * | 0.607 | 1.000 |
| sports/control | F1,332 = 2.07 (p = 0.1509) * | 0.006 | 0.300 | ||||||
| CpG sites 22 | F2,332 = 12.68 (p = 0.0004) * | 0.037 | 0.944 | ||||||
| addicts/control x CpG sites 22 | F2,332 = 5.78 (p = 0.0167) * | 0.017 | 0.669 | ||||||
M—mean, SD—standard deviation. *—Statistically significant between-group differences.
Table 4.
Post hoc analysis of interactions between sports subjects/control and DAT1 PAX5 CpG sites 3, 13, 22, 33 and NEO FFI scale.
Table 4.
Post hoc analysis of interactions between sports subjects/control and DAT1 PAX5 CpG sites 3, 13, 22, 33 and NEO FFI scale.
| DAT1 PAX5 CpG Sites 3 Openness Scale | ||||
|---|---|---|---|---|
| {1} M = 5.27 | {2} M = 4.20 | {3} M = 4.62 | {4} M = 4.87 | |
| Sport; methylation status no {1} | 0.0082 * | 0.2596 | 0.2796 | |
| Sport; methylation status yes {2} | 0.4111 | 0.0026* | ||
| Control; methylation status no {3} | 0.5922 | |||
| Control; methylation status yes {4} | ||||
| DAT1 PAX5 CpG sites 13 Neuroticism scale | ||||
| {1} M = 4.53 | {2} M = 5.36 | {3} M = 4.74 | {4} M = 3.36 | |
| Sport; methylation status no {1} | 0.07151 | 0.4520 | 0.0817 | |
| Sport; methylation status yes {2} | 0.1344 | 0.0069 * | ||
| Control; methylation status no {3} | 0.0314 * | |||
| Control; methylation status yes {4} | ||||
| DAT1 PAX5 CpG sites 22 Neuroticism scale | ||||
| {1} M = 2.40 | {2} M = 4.88 | {3} M = 5.25 | {4} M = 4.64 | |
| Sport; methylation status no {1} | 0.0090 * | 0.0098 * | 0.0166 * | |
| Sport; methylation status yes {2} | 0.5625 | 0.3393 | ||
| Control; methylation status no {3} | 0.3206 | |||
| Control; methylation status yes {4} | ||||
| DAT1 PAX5 CpG sites 22 Extraversion scale | ||||
| {1} M = 9.20 | {2} M = 6.12 | {3} M = 5.58 | {4} M = 6.45 | |
| Sport; methylation status no {1} | 0.0005 * | 0.0004 * | 0.0016 * | |
| Sport; methylation status yes {2} | 0.3642 | 0.1590 | ||
| Control; methylation status no {3} | 0.1288 | |||
| Control; methylation status yes {4} | ||||
| DAT1 PAX5 CpG sites 22 Openness scale | ||||
| {1} M = 7.80 | {2} M = 4.26 | {3} M = 4.92 | {4} M = 4.85 | |
| Sport; methylation status no {1} | 0.0000 * | 0.0010 * | 0.0001 * | |
| Sport; methylation status yes {2} | 0.1911 | 0.0033 * | ||
| Control; methylation status no {3} | 0.8946 | |||
| Control; methylation status yes {4} | ||||
| DAT1 PAX5CpG sites 22 Agreeability scale | ||||
| {1} M = 8.40 | {2} M = 5.06 | {3} M = 6.25 | {4} M = 5.60 | |
| Sport; methylation status no {1} | 0.0004 | 0.0494 * | 0.0027 * | |
| Sport; methylation status yes {2} | 0.0594 | 0.0322 * | ||
| Control; methylation status no {3} | 0.2869 | |||
| Control; methylation status yes {4} | ||||
*—significant statistical differences, M—mean.
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Grzywacz, A.; Chmielowiec, K.; Boroń, A.; Michałowska-Sawczyn, M.; Chmielowiec, J.; Trybek, G.; Mroczek, B.; Leźnicka, K.; Cieszczyk, P.; Masiak, J. Influence of DAT1 Promotor Methylation on Sports Performance. Genes 2021, 12, 1425. https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091425
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Grzywacz A, Chmielowiec K, Boroń A, Michałowska-Sawczyn M, Chmielowiec J, Trybek G, Mroczek B, Leźnicka K, Cieszczyk P, Masiak J. Influence of DAT1 Promotor Methylation on Sports Performance. Genes. 2021; 12(9):1425. https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091425
Chicago/Turabian StyleGrzywacz, Anna, Krzysztof Chmielowiec, Agnieszka Boroń, Monika Michałowska-Sawczyn, Jolanta Chmielowiec, Grzegorz Trybek, Bożena Mroczek, Katarzyna Leźnicka, Paweł Cieszczyk, and Jolanta Masiak. 2021. "Influence of DAT1 Promotor Methylation on Sports Performance" Genes 12, no. 9: 1425. https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091425
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