Assessment of differentially expressed genes from in vitro matured human oocytes: A bioinformatics approach

Abstract

Objective: One of the techniques that has gained much attention is the in vitro maturation of oocytes for patients who use assisted reproduction techniques. However, its results are still inferior to controlled ovarian stimulation methodologies. Understanding the maturation mechanisms based on analyses can help improve this methodology's results. The work aims to identify the central genes differentially expressed in oocytes after in vitro maturation in the germinal vesicle and metaphase II stages.

Methods: This work is a computational analysis. The entire search will be conducted using the Gene Expression Omnibus (GEO) database. To carry out and obtain the data present in the work, an advanced research search was carried out in the GEO database within the period from January 1, 2013, to January 1, 2023. A total of 27 genomic data were available in the GEO database, of which only two were used.

Results: Two datasets were identified on the Gene Expression Omnibus database platform: registration data GSE158802 and GSE95477. From the analysis, we identified five downregulated and thirty-six upregulated genes; the central genes that correlated with the main gene proteins found were CLTA and PANK1.

Conclusions: There was a differential regulation of gene expression. The most central ones are related to energy capture.

Keywords: biology; computational biology; fertility; gene; oocyte; reproductive techniques.

Figure 1

Methodological Flowchart for Obtaining Gene Expression Data.

Figure 2

Volcano Plot of Differential Gene Expression in the Analyzed Datasets. Caption: It was observed the differential expression between GV and MII groups. In A, the magnitude of change in the GSE158802 dataset. The importance of the transition from the GSE95477 data set can be observed in B. All data was generated from the GEO2R web server. In red, the positively and in green, negatively regulated genes for the cutoff point p<0.05 and [log 2 FC] > 2.0.

Figure 3

Heatmap and Expression of the Top 23 Genes in the Datasets. Caption: The expression of the top 23 genes in each dataset can be observed. The presentation by heat map in the GSE158802 dataset can be followed in A. In B, the expression of genes in the GSE95477 set can be observed. In red, positively regulated genes can be marked; in green, negatively regulated genes. The cutoff point adopted was p<0.05 and [log 2 FC] > 2.0. R Studio Software generated graphics.

Figure 4

MCODE Protein-Protein Interaction Network. Caption: The Metascape library performed a protein-protein interaction enrichment analysis for each selected gene in the databases. Those with STRING physical scores > 0.132 were used. The resulting network contains a subset of proteins that form physical interactions with at least one list member.