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Updated: Nov 4, 2025
Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform
Published on: January 30, 2017
Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic
Maria B Rabaglino1, Alan O'Doherty2, Jan Bojsen-Møller Secher3
1Quantitative Genetics, Bioinformatics and Computational Biology Group, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
Abstract:
Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact with the maternal endometrium. The main objective was to apply a multi-omics data integration approach to identify both temporally differentially expressed and differentially methylated genes (DEG and DMG), between IVP and MOET embryos, that could impact TE function. To start, four and five published transcriptomic and epigenomic datasets, respectively, were processed for data integration. Second, DEG from day 7 to days 13 and 16 and DMG from day 7 to day 17 were determined in the TE from IVP vs. MOET embryos. Third, genes that were both DE and DM were subjected to hierarchical clustering and functional enrichment analysis. Finally, findings were validated through a machine learning approach with two additional datasets from day 15 embryos. There were 1535 DEG and 6360 DMG, with 490 overlapped genes, whose expression profiles at days 13 and 16 resulted in three main clusters. Cluster 1 (188) and Cluster 2 (191) genes were down-regulated at day 13 or day 16, respectively, while Cluster 3 genes (111) were up-regulated at both days, in IVP embryos compared to MOET embryos. The top enriched terms were the KEGG pathway "focal adhesion" in Cluster 1 (FDR = 0.003), and the cellular component: "extracellular exosome" in Cluster 2 (FDR<0.0001), also enriched in Cluster 1 (FDR = 0.04). According to the machine learning approach, genes in Cluster 1 showed a similar expression pattern between IVP and less developed (short) MOET conceptuses; and between MOET and DKK1-treated (advanced) IVP conceptuses. In conclusion, these results suggest that early conceptuses derived from IVP embryos exhibit epigenomic and transcriptomic changes that later affect its elongation and focal adhesion, impairing post-transfer survival.
