bioRxivDevelopmental BiologyPreprint
Dynamics of mitochondrial mRNA abundance and poly(A) tail during the mammalian oocyte-to-embryo transition
Yusheng LiuiD, Yiwei ZhangiD, Z.H. LiuiD, Falong LuiD, Jiaqiang WangiD
Abstract
Mitochondria are responsible for producing a cells energy and play a central role in many metabolic tasks, as well as signaling transduction and cell death1. Mitochondria dysfunctions cause several human diseases and aging processes2-8. Mammalian oocytes contain far more mitochondria than somatic cells. The nuclear localization of mitochondrial tricarboxylic acid cycle (TCA) cycle enzymes, which normally localize in the mitochondria, is critical for zygotic genome activation (ZGA) and the oocyte-to-embryo transition (OET) in mice9. However, during the mammalian OET, the abundance and post-transcriptional regulation of mitochondrial mRNA (MT-mRNA), particularly the poly(A) tail, has never been studied. Here, we used two independent sequencing methods (PAIso-seq1 and PAIso-seq2) to describe the features of MT-mRNA in mouse cell lines, thirteen mouse tissues and during the OET in mouse, rat, pig, and humans. These features included expression abundance, poly(A) tail length, and non-A residues in poly(A) tails. Unlike nuclear mRNA, we discovered that MT-mRNA has a stable distribution pattern of poly(A) tail length in different cell lines, across tissues, and during mammalian OET. MT-mRNA possesses non-A residues in the poly(A) tail (non-A residues hereafter), which change slightly across tissues and during the OET. We also found that the abundance of MT-mRNA varies substantially across tissues, increases during the OET, and increases along major ZGA in mice, rats, pigs, and humans. These findings provide insights into changes in MT-mRNA abundance and poly(A) tail during the mammalian OET and provide a resource for future studies on the posttranscriptional regulation of mitochondrial transcripts.
- organism: mouse
- organism: rat
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 2
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