Dataset Information

Transcriptome dataset of omental and subcutaneous adipose tissues from gestational diabetes patients.

ABSTRACT: Gestational diabetes (GD) is one of the most prevalent metabolic diseases in pregnant women worldwide. GD is a risk factor for adverse pregnancy outcomes, including macrosomia and preeclampsia. Given the multifactorial etiology and the complexity of its pathogenesis, GD requires advanced omics analyses to expand our understanding of the disease. Next generation RNA sequencing (RNA-seq) was used to evaluate the transcriptomic profile of subcutaneous and omental adipose tissues (AT) collected from patients with gestational diabetes and matched controls. Samples were harvested during cesarean delivery. Results show differences based on anatomical location and provide whole-transcriptome data for further exploration of gene expression patterns unique to GD patients.

SUBMITTER: Salcedo-Tacuma D

PROVIDER: S-EPMC9205947 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcriptome dataset of omental and subcutaneous adipose tissues from gestational diabetes patients.

Salcedo-Tacuma David D Bonilla Leonardo L Montes Maria Cristina Geney MCG Gonzalez Jorge Ernesto Niño JEN Gutierrez Sandra Milena Sanchez SMS Chirivi Miguel M Contreras G Andres GA

Scientific data 20220617 1

Gestational diabetes (GD) is one of the most prevalent metabolic diseases in pregnant women worldwide. GD is a risk factor for adverse pregnancy outcomes, including macrosomia and preeclampsia. Given the multifactorial etiology and the complexity of its pathogenesis, GD requires advanced omics analyses to expand our understanding of the disease. Next generation RNA sequencing (RNA-seq) was used to evaluate the transcriptomic profile of subcutaneous and omental adipose tissues (AT) collected from ...[more]

PMID: 35715414

Similar Datasets

Project description:Increasing evidence has shown that insulin resistance in omental visceral adipose tissue (OVAT) is a characteristic of gestational diabetes mellitus (GDM). The present study aimed to identify differentially expressed genes (DEGs) and their associated functions and pathways involved in the pathogenesis of GDM by comparing the expression profiles of OVATs obtained from pregnant Chinese women with and without GDM during caesarian section. A total of 935 DEGs were identified, including 450 downregulated and 485 upregulated genes. In the gene ontology category cellular components, the DEGs were predominantly associated with functions of the extracellular region, while receptor binding was predominant in the molecular function category and biological process terms included antigen processing and presentation, extracellular matrix organization, positive regulation of cell-substrate adhesion, response to nutrients and response to dietary excess. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed and a functional interaction network was constructed. Functions of downregulated genes included antigen processing and presentation as well as cell adhesion molecules, while those of upregulated genes included transforming growth factor (TGF)-β-signaling, focal adhesion, phosphoinositide-3 kinase-Akt-signaling, P53 signaling, extracellular matrix-receptor interaction and regulation of actin cytoskeleton pathway. The five main pathways associated with GDM were antigen processing and presentation, cell adhesion molecules, Type 1 diabetes mellitus, natural killer cell-mediated cytotoxicity and TGF-β signaling. These pathways were included in the KEGG pathway categories of 'signaling molecules and interaction', 'immune system' and 'inflammatory response', suggesting that these processes are involved in GDM. The results of the present study enhanced the present understanding of the mechanisms associated with insulin resistance in OVATs of GDM.

Dataset Information

Transcriptome dataset of omental and subcutaneous adipose tissues from gestational diabetes patients.

Publications

Transcriptome dataset of omental and subcutaneous adipose tissues from gestational diabetes patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets