bioRxivPhysiology
Transcriptomic responses to hypoxia in endometrial and decidual stromal cells
K. T. Rytkonen, T. Saloniemi, M. Mahmoudian, X. Ma, A. Perheentupa, L. L. Elo, M. Poutanen, G. P. Wagner
Abstract
Human reproductive success depends on a properly decidualized uterine endometrium that allows implantation and the formation of the placenta. At the core of the decidualization process are endometrial stromal fibroblasts (ESF) that differentiate to decidual stromal cells (DSC). As variations in oxygen levels are functionally relevant in endometrium both upon menstruation and during placentation, we assessed the transcriptomic responses to hypoxia in ESF and DSC. In both cell types hypoxia upregulated genes in classical hypoxia pathways such as glycolysis and the epithelial mesenchymal transition. In DSC hypoxia restored an ESF like transcriptional state for a subset of transcription factors that are known targets of the progesterone receptor, suggesting that hypoxia partially interferes with progesterone signaling. In both cell types hypoxia modified transcription of several inflammatory transcription factors that are known regulators of decidualization, including decreased transcription of STATs and increased transcription of CEBPs. We observed that hypoxia upregulated genes had a significant overlap with genes previously detected to be upregulated in endometriotic stromal cells. Promoter analysis of the genes in this overlap suggested the hypoxia upregulated Jun/Fos and CEBP transcription factors as potential drivers of endometriosis-associated transcription. Using immunohistochemistry we observed increased expression of JUND and CEBPD in endometriosis lesions compared to healthy endometria. Overall the findings suggest that hypoxic stress establishes distinct transcriptional states in ESF and DSC, and that hypoxia influences the expression of genes that contribute to the core gene regulation of endometriotic stromal cells.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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