Impaired primordial follicle assembly in offspring ovaries from zearalenone-exposed mothers involves reduced mitochondrial activity and altered epigenetics in oocytes.
Yan-Qin FengJun-Jie WangMing-Hao LiYu TianAi-Hong ZhaoLan LiMassimo De FeliciWei ShenPublished in: Cellular and molecular life sciences : CMLS (2022)
Previous works have shown that zearalenone (ZEA), as an estrogenic pollutant, has adverse effects on mammalian folliculogenesis. In the present study, we found that prolonged exposure of female mice to ZEA around the end of pregnancy caused severe impairment of primordial follicle formation in the ovaries of newborn mice and altered the expression of many genes in oocytes as revealed by single-cell RNA sequencing (scRNA-seq). These changes were associated with morphological and molecular alterations of mitochondria, increased autophagic markers in oocytes, and epigenetic changes in the ovaries of newborn mice from ZEA-exposed mothers. The latter increased expression of HDAC2 deacetylases was leading to decreased levels of H3K9ac and H4K12ac. Most of these modifications were relieved when the expression of Hdac2 in newborn ovaries was reduced by RNA interference during in vitro culture in the presence of ZEA. Such changes were also alleviated in offspring ovaries from mothers treated with both ZEA and the coenzyme Q10 (CoQ10), which is known to be able to restore mitochondrial activities. We concluded that impaired mitochondrial activities in oocytes caused by ZEA are at the origin of metabolic alterations that modify the expression of genes controlling autophagy and primordial follicle assembly through changes in epigenetic histones.
Keyphrases
- poor prognosis
- single cell
- oxidative stress
- cell death
- dna methylation
- genome wide
- gene expression
- high fat diet induced
- binding protein
- rna seq
- long non coding rna
- emergency department
- type diabetes
- signaling pathway
- metabolic syndrome
- high throughput
- insulin resistance
- histone deacetylase
- single molecule
- nucleic acid
- bioinformatics analysis
- estrogen receptor