Mining of combined human placental gene expression data across pregnancy, applied to PPAR signaling pathway

To date, we have only an incomplete understanding of how gene expression in the human placenta changes at the genome-wide scale from very early in gestation to term. Our aim was to investigate the dynamic changes in gene expression throughout placentation.

In our study, gene expression profiles were collected of human placentas from 4 to 40 gestational weeks of age. Simple linear regression and weighted correlation network analysis were applied to identify genes of interest. Analyses of gene enrichment, including gene ontology and pathways from the Kyoto Encyclopedia of Genes and Genomes, were performed using clusterProfiler. Finally, dynamic changes in the expression of individual genes were represented using line graphs of scaled and adjusted gene expression.

Our results highlighted a total of 5173 genes that are involved in different periods of placentation. Downstream annotation of these genes revealed the biological processes and pathways involved, from which we chose to further investigate the PPAR signaling pathway. We were able to detect changes over time in many genes involved in lipid storage/metabolism, including members of the FABP family and LPL. These patterns were corroborated by lipid staining of placental sections, which revealed a significant decrease in lipid droplet content in placentas from early in the first trimester to term.

Our study provides detailed information on the dynamics of biological processes and pathways across human placentation. These findings give us new clues for deciphering the normal functions of placentation and the ways in which the mis-regulation of these pathways may be linked to pregnancy-related diseases. As an example, our results show that the PPAR signaling pathway mediates a constant decrease in placental lipid content over the course of pregnancy.