# Integrative multi-omics analysis reveals cellular and molecular insights into gestational diabetes mellitus

## TL;DR

This study uses multi-omics data to uncover immune and metabolic changes in gestational diabetes mellitus, identifying key genes that could help in early diagnosis and treatment.

## Contribution

The study integrates multiple omics layers to reveal cell-type-specific alterations and prioritizes novel candidate genes with potential causal links to GDM.

## Key findings

- Single-cell profiling revealed immune compartment remodeling in GDM, particularly in monocytes and T-cell subsets.
- MR analysis identified 15 genes, including CTSB, LMNA, and SLC7A5, with putative causal links to GDM.
- qPCR confirmed differential expression of CTSB, LMNA, and SLC7A5 in GDM placentas, linking them to immune and metabolic pathways.

## Abstract

Gestational diabetes mellitus (GDM) is a frequent pregnancy complication that increases short- and long-term risks for both mother and child. However, its underlying molecular mechanisms remain poorly understood. This study aims to unravel the molecular basis of GDM and explore potential therapeutic targets.

We integrated genomic, transcriptomic, and single-cell RNA sequencing datasets to delineate cell-type-specific alterations in GDM. Candidate genes were prioritized using Mendelian randomization (MR), followed by quantitative PCR (qPCR) validation in placental samples. Pathway and immune-network analyses were performed to contextualize biological function.

Single-cell profiling showed marked remodeling of immune compartments in GDM, with prominent changes in monocytes and T-cell subsets. Two-sample MR prioritized 15 genes with putative causal links to GDM, including BNIP3L, COMT, CTSB, LMNA, and SLC7A5. qPCR further demonstrated significant differential expression of CTSB, LMNA, and SLC7A5 between GDM and control placentas (human or mouse). Pathway enrichment implicated CTSB in immune regulation and metabolic processes, whereas LMNA and SLC7A5 mapped to insulin resistance and glucose/amino-acid transport pathways. Immune-network analysis revealed significant correlations between these genes and immune mediators, supporting immune dysregulation as a contributor to GDM pathogenesis.

This study provides a comprehensive analysis of the immune-metabolic landscape of GDM. Key genes identified in this study may serve as potential biomarkers and therapeutic targets for early diagnosis and personalized treatment of GDM. Further studies are warranted to elucidate the underlying mechanisms and develop targeted therapies for this disease.