bioRxivDevelopmental BiologyPreprint
Conservation and divergence of poly(A) tail regulation during the mammalian oocyte-to-embryo transition
Yusheng LiuiD, Jun‐Xue JiniD, Yiwei ZhangiD, Leyun WangiD, Chuanxin ZhangiD, Zhenzhen HouiD, Wei LiiD, Zhonghua Liu, Falong LuiD, Jiaqiang WangiD
Abstract
Poly(A) tail length and non-A residues are vital for oocyte-to-embryo transition (OET) in mice and humans1-5. However, the role of poly(A) tail length and non-A residues during OET in other commonly used mammalian animal models for human diseases remains unexplored. In addition, the degree of conservation in maternal mRNA poly(A) tail dynamics during OET across different mammal species is unknown. Here, we conduct a comparative analysis of the poly(A) tails during OET across four species: mice, rats, pigs, and humans. Dynamics during OET found to be conserved across all four species include: maternal mRNA deadenylation during oocyte maturation and re-polyadenylation after fertilization; a fall-rise trend in poly(A) tail length distribution; a rise-fall trend in the ratio of poly(A) tails with non-A residues; higher abundance of non-A residues in poly(A) tails of maternal mRNA than in zygotic genome activation (ZGA) mRNA; maternal mRNA with U residues degrades faster than those without U residues at the stage when ZGA takes place. While in mice and rats maternal mRNA deadenylation is impaired in parthenogenetic embryos and ZGA inhibition leads to blocked maternal mRNA deadenylation in mice and humans. In contrast, the length of consecutive U residues and the duration time of U residues in poly(A) tail diverges across the four species. Together, these findings reveal that the poly(A) tail mediated maternal mRNA post-transcriptional regulation is highly conserved in mammals with unique divergences in the length and life-span of U residues, providing new insights for the further understanding of OET across different mammals.
- organism: mouse
- organism: rat
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 11
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