RNAseq analysis of oocyte maturation from the germinal vesicle stage to metaphase II in pig and human.
Feng TangKatja HummitzschRaymond J RodgersPublished in: PloS one (2024)
During maturation oocytes at the germinal vesicle (GV) stage progress to metaphase II (MII). However, during in vitro maturation a proportion often fail to progress. To understand these processes, we employed RNA sequencing to examine the transcriptome profile of these three groups of oocytes from the pig. We compared our findings with similar public oocyte data from humans. The transcriptomes in oocytes that failed to progress was similar to those that did. We found in both species, the most upregulated genes in MII oocytes were associated with chromosome segregation and cell cycle processes, while the most down regulated genes were relevant to ribosomal and mitochondrial pathways. Moreover, those genes involved in chromosome segregation during GV to MII transition were conserved in pig and human. We also compared MII and GV oocyte transcriptomes at the isoform transcript level in both species. Several thousands of genes (including DTNBP1, MAPK1, RAB35, GOLGA7, ATP1A1 and ATP2B1) identified as not different in expression at a gene transcript level were found to have differences in isoform transcript levels. Many of these genes were involved in ATPase-dependent or GTPase-dependent intracellular transport in pig and human, respectively. In conclusion, our study suggests the failure to progress to MII in vitro may not be regulated at the level of the genome and that many genes are differentially regulated at the isoform level, particular those involved ATPase- or GTPase-dependent intracellular transport.
Keyphrases
- genome wide
- genome wide identification
- endothelial cells
- cell cycle
- single cell
- transcription factor
- rna seq
- bioinformatics analysis
- copy number
- genome wide analysis
- dna methylation
- induced pluripotent stem cells
- pluripotent stem cells
- healthcare
- gene expression
- poor prognosis
- electronic health record
- machine learning
- long non coding rna
- big data