Table_1_Hypermethylation of the Gene Coding for PGC-1α in Peripheral Blood Leukocytes of Patients With Parkinson’s Disease.pdf
Decreased expression of peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) is implicated in the pathophysiology of Parkinson’s disease (PD). However, our understanding of the mechanism regulating the PGC-1α expression is still limited. We sought to determine whether the epigenetic modification of PPARGC1A (the gene encoding PGC-1α) could account for its diminished expression. We performed a study of PPARGC1A risk-SNP genotypes, methylation level, and the expression in blood from 171 subjects. The mean DNA methylation level of PPARGC1A intron 1 in patients with PD was higher than that in the controls (7.18 ± 1.74 vs. 6.36 ± 1.28, P = 0.007). A detailed comparison of the DNA methylation level at each CpG site showed that CpG_1, CpG_13.14, CpG_17.18, and CpG_20 were significantly hypermethylated in patients with PD. There was a significant negative correlation between PPARGC1A methylation and expression level (R = −0.404, P < 0.001). We found no correlations between the PPARGC1A methylation level and the clinical features, while the CpG_13.14 site methylation level was positively correlated with H&Y stage (R = 0.246, P = 0.020) and was increased in people carrying the rs2970848 AA genotype compared with that in carriers of the AG/GG genotype (7.27 ± 1.86 vs. 6.65 ± 1.92, P = 0.032). Our results support a link between PPARGC1A methylation, gene expression, and variability, which indicated that a novel epigenetic regulatory mechanism controlling PPARGC1A expression influences PD pathogenesis.
History
References
- https://doi.org//10.3233/JPD-181549
- https://doi.org//10.1007/s12017-011-8163-9
- https://doi.org//10.1002/ana.24294
- https://doi.org//10.1016/j.pneurobio.2011.03.005
- https://doi.org//10.1093/eep/dvx009
- https://doi.org//10.1186/s13041-017-0285-z
- https://doi.org//10.1038/nn.3607
- https://doi.org//10.1093/hmg/ddq561
- https://doi.org//10.1136/jnnp.55.3.181
- https://doi.org//10.3233/JPD-160914
- https://doi.org//10.1038/nrg3230
- https://doi.org//10.1523/JNEUROSCI.6119-09.2010
- https://doi.org//10.1016/j.pneurobio.2015.05.002
- https://doi.org//10.1186/s40035-019-0162-z
- https://doi.org//10.1038/ng.946
- https://doi.org//10.1007/s00018-012-1043-0
- https://doi.org//10.1038/nature08514
- https://doi.org//10.4161/epi.25865
- https://doi.org//10.3389/fnmol.2017.00225
- https://doi.org//10.1002/mds.26073
- https://doi.org//10.1002/mds.24907
- https://doi.org//10.1016/j.cell.2011.02.010
- https://doi.org//10.1371/journal.pone.0134087
- https://doi.org//10.1016/j.neuron.2013.10.023
- https://doi.org//10.1016/j.parkreldis.2013.12.002
- https://doi.org//10.1002/mds.23429
- https://doi.org//10.1016/j.jns.2016.05.037
- https://doi.org//10.1016/j.bbi.2017.03.003
- https://doi.org//10.1016/j.parkreldis.2018.02.037
- https://doi.org//10.3389/fnins.2017.00275
Usage metrics
Read the peer-reviewed publication
Categories
- Radiology and Organ Imaging
- Decision Making
- Clinical Nursing: Tertiary (Rehabilitative)
- Image Processing
- Autonomic Nervous System
- Cellular Nervous System
- Biological Engineering
- Sensory Systems
- Central Nervous System
- Neuroscience
- Endocrinology
- Artificial Intelligence and Image Processing
- Signal Processing
- Rehabilitation Engineering
- Biomedical Engineering not elsewhere classified
- Stem Cells
- Neurogenetics
- Developmental Biology