Ghrelin regulates the endoplasmic reticulum stress signalling pathway in gestational diabetes mellitus.

OBJECTIVE: We aimed to investigate the effects and mechanisms of the ghrelin-regulated endoplasmic reticulum stress (ERS) signalling pathway in gestational diabetes mellitus (GDM). METHODS: Pregnant female C57BL/6 mice were randomly divided into a normal group, GDM group (high-fat diet + STZ), GDM + ghrelin group (acyl ghrelin), and GDM + ghrelin + ghrelin inhibitor group ([D-lys3]-GHRP-6). We measured body weight, the intake of water and food, glucose, cholesterol, triglyceride and fasting insulin levels in each group. HE staining was used to observe the morphological changes in the pancreas. The TUNEL method was used to detect the apoptosis rate of islet cells. qPCR and Western boltting were performed to detect the relative expression levels of PERK, ATF6, IREIα, GRP78, CHOP and caspase-12, which are related to the ERS signalling pathway in the pancreas. Then, NIT-1 cells were cultured to verify whether ghrelin regulates ERS under high-glucose or tunicamycin conditions. RESULTS: Compared with the GDM group, the GDM + ghrelin group showed improved physical conditions and significantly decreased the fasting blood glucose, glucose tolerance, cholesterol, triglyceride and fasting insulin levels. Damaged islet areas were inhibited by ghrelin in the GDM group. The GDM + ghrelin group showed reduced ß-cell apoptosis compared to the GDM and GDM + ghrelin + ghrelin inhibitor groups. ERS-associated factors (PERK, ATF6, IREIα, GRP78, CHOP and caspase-12) mRNA and protein levels were obviously lower in the GDM + ghrelin group than in the GDM group, while expression levels were restored in the inhibitor group. Ghrelin treatment improved the high-glucose or tunicamycin-induced apoptosis, increased insulin levels and upregulation of GRP78, CHOP and caspase-12 in NIT-1 cells. CONCLUSION: Ghrelin suppressed ERS signalling and apoptosis in GDM mice and in NIT-1 cells. This study established a link between ghrelin and GDM, and the targeting of ERS with ghrelin represents a promising therapeutic strategy for GDM.

Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis.

Background: The tumor microenvironment (TME) of lung adenocarcinoma (LUAD) comprise various cell types that communicate with each other through ligand-receptor interactions. This study focused on the identification of cell types in LUAD by single-cell RNA sequencing (scRNA-seq) data and screening of intercellular communication-related genes. Methods: The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo) provided the RNA-seq data of LUAD patients in the GSE149655, GSE31210, and GSE72094 datasets. Quality control of the scRNA-seq data in GSE149655 was performed by the Seurat package (http://seurat.r-forge.r-project.org) for identifying highly variable genes for principal component analysis (PCA) and cell clustering. The CellPhoneDB (http://www.cellphonedb.org) was used for filtering intercellular communication-related ligand-receptor pairs. According to ligand and receptor expressions, LUAD samples were clustered using ConsensusClusterPlus (https://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus). Additionally, the identification of prognosis-related ligand and receptor genes was conducted along with the development of a risk prediction model by the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results: This study identified twelve cell types in 8170 cells of LUAD tissues along with 219 ligand and receptor genes. LUAD was classified into three different molecular subtypes, among which cluster 3 (C3) had the longest overall survival (OS) time and cluster (C1) had the shortest OS time. In comparison with the other two molecular subtypes, it was observed that C1 had a higher rate of somatic mutations and lower levels of infiltrating immune cells and immune scores. Ten genes were screened from the total ligand and receptor genes to construct a risk model, which showed a strong prediction power in the prognosis of patients with LUAD. Conclusion: The results of this study revealed cell types specific to LUAD, which were classified into different molecular subtypes according to intercellular communication-related genes. A novel prognostic risk model was developed in this study, providing new insights into prognostic assessment models for LUAD.