Genes & DiseasesCell BiologyPeer-reviewed journal article
The maternal and fetal metabolic and immune landscapes of gestational diabetes mellitus
Abstract
ObjectivesGestational diabetes mellitus (GDM) is the most common pregnancy-related medical complication. It is characterized by the development of hyperglycaemia during pregnancy and is known to lead to higher risk of metabolic disorders and other pathologies in both mothers and offsprings. Some studies probed the impacts of GDM, focusing on specific organs like placenta or adipose tissue, but so far, a systematic overview is lacking. Here, we aimed to curate a comprehensive atlas from currently available transcriptomic data for GDM, to comprehensively unravel how GDM influences the metabolic and immune landscapes in affected pregnancy. MethodsRNA-sequencing (RNA-seq) data for maternal subcutaneous and omental fat, peripheral blood mononuclear cells (PBMCs), and fetal umbilical vein endothelial cells (HUVECs), amniocytes and cord blood mononuclear cells (CBMCs); and single-cell RNA sequencing (scRNA-seq) data for placenta and CBMCs were collated from previous publications. Comparative analyses and gene set enrichment analyses (GSEA) were carried out for the control versus GDM pregnancy. ResultsMaternal metabolic landscapes were consistently shifted by GDM, with reduced oxidative phosphorylation and fatty acid metabolism in maternal adipose tissues and PBMCs. GDM also caused inflammation solely in maternal subcutaneous fat. scRNA-seq analysis of placenta revealed that GDM reduced granulocytes and myelocytes but increased extravillous trophoblast cells. GDM also differentially impacted the metabolic and immune signals in different placental cell subsets. Contrarily, metabolisms in fetal compartments were minimally influenced by GDM. However, they consistently exhibited elevated inflammatory signals. ConclusionGDM differentially reprogrammed the maternal and fetal metabolisms and immunity.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: genetic perturbation / CRISPR
- Citations
- 4
- FWCI
- 3.62
- Field percentile
- top 9%
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