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Preeclampsia is a unique multiple system disorder during human pregnancy, which affects ≈5% to 8% of pregnancies. Its risks and complications have become the major causes of maternal and fetal morbidity and mortality. Although abnormal placentation to which DNA methylation dysregulation is always linked is speculated to be one of the reasons causing preeclampsia, the underlying mechanisms still remain elusive to date. Here we revealed that aberrant DNA methyltransferase 3A (DNMT3A) plays a critical role in preeclampsia. Our results show that the expression and localization of DNMT3A are dysregulated in preeclamptic placenta. Moreover, knockdown of DNMT3A obviously inhibits trophoblast cell migration and invasion. Mechanistically, IGFBP5 (insulin-like growth factor-binding protein 5), known as a suppressor, is upregulated by decreased DNMT3A because of promoter hypomethylation. Importantly, IGFBP5 downregulation can rescue the defects caused by DNMT3A knockdown, thereby, consolidating the significance of IGFBP5 in the downstream of DNMT3A in trophoblast. Furthermore, we detected low promoter methylation and high protein expression of IGFBP5 in the clinical samples of preeclamptic placenta. Collectively, our study suggests that dysregulation of DNMT3A and IGFBP5 is relevant to preeclampsia. Thus, we propose that DNMT3A and IGFBP5 can serve as potential markers and targets for the clinical diagnosis and therapy of preeclampsia. [ABSTRACT FROM AUTHOR]

Background: Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong association between metabolic disorders and sPTB. To determine the metabolic alterations in sPTB patients, we used various bioinformatics methods to analyze the abnormal changes in metabolic pathways in the preterm placenta via existing datasets., Methods: In this study, we integrated two datasets (GSE203507 and GSE174415) from the NCBI GEO database for the following analysis. We utilized the "Deseq2" R package and WGCNA for differentially expressed genes (DEGs) analysis; the identified DEGs were subsequently compared with metabolism-related genes. To identify the altered metabolism-related pathways and hub genes in sPTB patients, we performed multiple functional enrichment analysis and applied three machine learning algorithms, LASSO, SVM-RFE, and RF, with the hub genes that were verified by immunohistochemistry. Additionally, we conducted single-sample gene set enrichment analysis to assess immune infiltration in the placenta., Results: We identified 228 sPTB-related DEGs that were enriched in pathways such as arachidonic acid and glutathione metabolism. A total of 3 metabolism-related hub genes, namely, ANPEP, CKMT1B, and PLA2G4A, were identified and validated in external datasets and experiments. A nomogram model was developed and evaluated with 3 hub genes; the model could reliably distinguish sPTB patients and term labor patients with an area under the curve (AUC) > 0.75 for both the training and validation sets. Immune infiltration analysis revealed immune dysregulation in sPTB patients., Conclusion: Three potential hub genes that influence the occurrence of sPTB through shadow participation in placental metabolism were identified; these results provide a new perspective for the development and targeting of treatments for sPTB., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lv, Xie, Wu, Dong, Jia and Ying.)