The Thai Journal of Veterinary Medicine


Concurrent gene expression profiling meta-analysis of in vitro and in vivo matured oocytes among mammals can provide crucial knowledge to assist reproductive technologies. Due to the lack of methodology to prepare oocyte datasets for such analysis, we illustrated the procedures to merge in vitro and in vivo matured oocyte expression profiling datasets of rhesus monkey (Macaca mulatta) and mouse (Mus musculus). Datasets acquired from both species were pooled together based on types of their orthologous genes. To determine the feasibility of constructed pooled data, top orthologous genes differentially expressed between in vitro and in vivo oocytes were identified by Linear models and empirical Bayes methods with 500 generated learning datasets (FDR<0.01). Several clustering algorithms were then applied for oocyte sample clustering using the acquired differentially expressed genes. Gene enrichment analysis to determine biological processes associated with the differentially expressed genes was performed using DAVID Bioinformatics Resources 6.7. The results revealed successful construction of pooled oocyte expression profiles of monkey and mouse, and the pooled datasets used for subsequent analyses consisted of 10,214 one-to-one orthologous genes. With total selected 100 differentially expressed genes, oocyte clustering results revealed the correct clustering of in vivo and in vitro oocyte samples. Interestingly, enrichment analysis revealed association of several differentially expressed genes with maturation and developmental process of oocytes. Of note, the acquired results strongly suggested the feasibility of the prepared data, and its preparation’s methodology. Hopefully, this approach would be beneficial for cross-species gene expression profiling analyses of several mammalian oocytes in the future.

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