GSE234741: Multiparity increases the risk of diabetes by impairing proliferative capacity of beta cells II

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Info Studies were incorporated in a barcode, matrix, feature format. This format can be found on the 10x Genomics website for processing of single cell studies to obtain the gene expression matrices. For each study, the metadata incorporated in GEO were manually curated into profiles and the samples were separated based on applicable groups and conditions. The expression data from the cells for the samples within the same profile and condition were aggregated into an expression matrix with the cell barcodes having the sample name appended to it to ensure unique cell names.

Description (from GEO)

Submission Date: Jun 12, 2023

Summary: Pregnancy poses significant metabolic challenges for women, yet the impact of repeated deliveries on overall glucose metabolism in females remains inadequately explored. In this study, we investigate alterations in female glucose metabolism following multiple deliveries using a comprehensive phenotyping mouse model that closely resembles humans. To establish a multiparty mouse model, a 9-week-old female mouse underwent three consecutive pregnancies. Three weeks post-pregnancy, multiparous mice exhibited improved glucose tolerance and enhanced glucose-stimulated insulin secretion (GSIS), without changes in insulin sensitivity compared to virgin mice. Histological analysis revealed an increased beta cell mass in multiparous mice at this stage of life. Intriguingly, 16 weeks after the last delivery, multiparous mice demonstrated impaired glucose tolerance, accompanied by compromised GSIS and increased basal insulin secretion both in vivo and ex vivo. These mice also developed insulin resistance at 16 weeks post-delivery, with no discernible difference in beta cell mass. To assess whether multiparous islets were impaired in their compensatory ability to meet increased insulin demand, multiparous and virgin islets (3 weeks after the last delivery) were transplanted to opposite sides of the kidney capsule in a single recipient mouse. Upon S-961 injection, multiparous mice were reduced in Ki-67 expressing cells compared with virgin mice. Bulk RNA sequencing (RNA-seq) analysis of S-961-injected multiparous islet genes revealed downregulation of cell cycle-related genes (Cenpi and Cenpf, Cdk1, Cdk2, and Cdkn2d). Single-cell RNA-seq analysis identified multiparous-specific beta cell clusters that displayed upregulation of stress-related genes (Ddit3, Fkbp11, Sdf2l1, Nupr1, Atf5, Atf3, Hspa1a, Hspa1b, and Dnajb1). Furthermore, multiparous mice beta cells exhibited shortened telomere lengths and increased expression of Cdkn2a, indicating an accelerated aging phenotype. In humans, women with higher parities (parity > 3 times) exhibited greater impairment in glucose tolerance and increased obesity compared to women with lower parities (parity < 3 times) at 2 months postpartum. Women with higher parity who experienced weight gain after delivery also demonstrated a greater decline in beta cell function (disposition index). Overall, our findings indicate that multiparty increases the risk of diabetes by impairing beta cells ability to compensate for the increased insulin demand.

GEO Accession ID: GSE234741

PMID: No Pubmed ID

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Info Preprocessing and downstream analysis were computed using the scanpy Python library and the steps of processing followed the Seurat vignette. Cells and genes with no expression or very low expression were removed from the dataset based on a predefined threshold. The data was then normalized across the expression within the cells and log normalized. The top 2000 highly variable genes were extracted to be used for downstream analysis. For each of these aggregated data matrices, the clusters were computed using the leiden algorithm. Scanpy was then used to compute the PCA, t-SNE, and UMAPs. The points in the plots are labelled by their corresponding cell type labels. The cell type labels were computed using the wilcoxon method as the differential gene expression method. The top 250 genes were then used for enrichment analysis against the CellMarker library in order to determine the most appropriate cell type label with the lowest p-value.
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