KAS-seq profiling captures transcription dynamics during oocyte maturation

Abstract

In fully grown oocytes, the genome is considered to be globally transcriptionally silenced. However, this conclusion is primarily derived from the results obtained through immunofluorescence staining or inferred from the highly condensed state of chromosomes, lacking more direct evidence. Here, by using a kethoxal-assisted single-stranded DNA sequencing (KAS-seq) approach, we investigated the landscape of single-strand DNA (ssDNA) throughout the genome and provided a readout of the activity and dynamics of transcription during oocyte meiotic maturation. In non-surrounded nucleolus (NSN) oocytes, we observed a robust KAS-seq signal, indicating the high transcriptional activity. In surrounded nucleolus (SN) oocytes, the presence of ssDNA still persists although the KAS-seq signal was relatively weak, suggesting the presence of transcription. Accompanying with the meiotic resumption, the transcriptional activity gradually decreased, and global repression was detected in matured oocytes. Moreover, we preformed the integrative genomics analysis to dissect the transcriptional dynamics during mouse oocyte maturation. In sum, the present study delineates the detailed transcriptional activity during mammalian oocyte maturation.

Keywords: KAS-seq; Meiosis; Oocytes; Transcription; ssDNA.

Fig. 1

KAS-seq probes regions of single-stranded DNA. N3-kethoxal (indicated by the purple box) selectively engages with single-stranded guanine residues within the genomic DNA (resolved by DNA-binding proteins), subsequently amenable to biotinylation (highlighted in orange) and subsequent enrichment for sequencing

Fig. 2

KAS-seq profile of mouse oocyte maturation. A. Schematic overview of the workflow for KAS-seq profiling of mouse oocytes isolated at GV (SN and NSN), GVBD, and MII stages. B. A snapshot from UCSC browser views showing KAS-seq peaks in GV (SN and NSN), GVBD and MII oocytes. C. Number of ssDNA peaks detected by KAS-seq in GV (SN and NSN), GVBD and MII oocytes. D. The distribution of KAS-seq peaks across the genome was analyzed. KAS-seq signifies the percentage of overlap between KAS-seq peaks and various genomic features, while Random represents the percentage of overlap between randomly generated regions, matching the number and length of actual peaks, with various genomic features. E. KAS-seq signal distribution at gene-coding regions in GV (SN and NSN), GVBD and MII oocytes

Fig. 3

Integrative genomics analysis of transcriptional features in SN and NSN Oocytes. A, B. Genome-wide Pearson correlation heatmap depicting the association between KAS-seq, ATAC-seq and Pol II Stacc-seq. C-E. Heatmap showing KAS-seq, Pol II Stacc-seq and ATAC-seq signal distribution at gene-coding regions, respectively. Regions 2 kb upstream of TSS and 2 kb downstream of TTS were shown

Fig. 4

Integration analysis of KAS-seq and transcriptome in SN and NSN oocytes. A. Volcano plots comparing differentially expressed genes between SN and NSN oocytes (p.adjust < 0.05, Fold chang > 2). B. Bar charts showing the unique peaks identified in SN and NSN oocytes. C. Genes were grouped according to different expression levels based on transcriptome. D, E. KAS-seq reads density at gene-coding regions of genes with different expression levels (defined by transcriptome) in SN and NSN oocytes. F-I. Genome browser snapshot of KAS-seq peaks in SN and NSN oocytes near Padi6, Gdf9, Bmp15 and Zp2, respectively. J. Bar charts showing the relative expression levels of Padi6, Gdf9, Bmp15 and Zp2 quantified by RT-qPCR in SN and NSN oocytes. # p > 0.05

Fig. 5

The dynamics of KAS-seq signals in SN and NSN oocytes. A. Heatmaps showing the dynamics of KAS-seq signals in SN and NSN oocytes. The three clusters are displayed in the left panel, and the representative genes and biological processes of each cluster are shown in right panel. B. Alluvial plots showing the global dynamics and the number of KAS-seq signals of cluster 1(open to open), cluster 2 (open to close) and cluster 3 (close to open) in NSN and SN oocytes. The TSS ± 2 Kb genomic regions were clustered by using deepTools (plotHeatmap –kmeans 4). C. Transcription factor motifs identified from promoter KAS-seq signals in both SN and NSN oocytes