Dynamic Changes of DNA Methylation and Transcriptome Expression in Porcine Ovaries during Aging.
Xiaoyu XiQin ZouYingying WeiYan ChenXue WangDaojun LvPeilin LiAnxiang WenLi ZhuGuoqing TangJideng MaMingzhou LiXuewei LiYanzhi JiangPublished in: BioMed research international (2019)
The biological function of human ovaries declines along with aging. To identify the underlying molecular changes during ovarian aging, pigs were used as model animals. Genome-wide DNA methylation and transcriptome-wide RNA expression analyses were performed via high-throughput sequencing of ovaries from young pigs (180 days, puberty stage of first ovulation) and old pigs (eight years, reproductive exhaustion stage). The results identified 422 different methylation regions between old and young pigs; furthermore, a total of 2,243 mRNAs, 95 microRNAs, 248 long noncoding RNAs (lncRNAs), and 116 circular RNAs (circRNAs) were differentially expressed during both developmental stages. Gene ontology analysis showed that these genes related to different methylation and expression are involved in the ovarian aging cycle. Specifically, these are involved in cell apoptosis, death effector domain binding, embryonic development, reproduction and fertilization process, ovarian cumulus expansion, and the ovulation cycle. Multigroup cooperative control relationships were also assessed, and competing endogenous RNA (ceRNA) networks were constructed in the ovarian aging cycle. These data will help to clarify ovary age-associated potential molecular changes in DNA methylation and transcriptional patterns over time.
Keyphrases
- genome wide
- dna methylation
- gene expression
- poor prognosis
- copy number
- binding protein
- long non coding rna
- polycystic ovary syndrome
- cell proliferation
- high throughput sequencing
- type diabetes
- immune response
- skeletal muscle
- regulatory t cells
- machine learning
- single cell
- wastewater treatment
- deep learning
- dendritic cells
- induced pluripotent stem cells
- genome wide analysis
- rna seq
- data analysis
- risk assessment
- nucleic acid
- network analysis