Glucotoxicity Activation of IL6 and IL11 and Subsequent Induction of Fibrosis May Be Involved in the Pathogenesis of Islet Dysfunction

Abstract

Background: Islet dysfunction is the main pathological process of type 2 diabetes mellitus (T2DM). Fibrosis causes islet dysfunction, but the current mechanism is still unclear. Here, bioinformatics analysis identified gene clusters closely related to T2DM and differentially expressed genes related to fibrosis, and animal models verified the roles of these genes.Methods: Human islet transcriptomic datasets were obtained from the Gene Expression Omnibus (GEO), and weighted gene coexpression network analysis (WGCNA) was applied to screen the key gene modules related to T2DM and analyze the correlations between the modules and clinical characteristics. Enrichment analysis was performed to identify the functions and pathways of the key module genes. WGCNA, protein-protein interaction (PPI) analysis and receiver operating characteristic (ROC) curve analysis were used to screen the hub genes. The hub genes were verified in another GEO dataset, the islets of high-fat diet (HFD)-fed Sprague-Dawley rats were observed by H&E and Masson’s trichrome staining, the fibrotic proteins were verified by immunofluorescence, and the hub genes were tested by immunohistochemistry.Results: The top 5,000 genes were selected according to the median absolute deviation, and 18 modules were analyzed. The yellow module was highly associated with T2DM, and its positive correlation with glycated hemoglobin (HbA1c) was significantly stronger than that with body mass index (BMI). Enrichment analysis revealed that extracellular matrix organization, the collagen-containing extracellular matrix and cytokine−cytokine receptor interaction might influence T2DM progression. The top three hub genes, interleukin 6 (IL6), IL11 and prostaglandin-endoperoxide synthase 2 (PTGS2), showed upregulated expression in T2DM. In the validation dataset, IL6, IL11, and PTGS2 levels were upregulated in T2DM, and IL6 and PTGS2 expression was positively correlated with HbA1c and BMI; however, IL11 was positively correlated only with HbA1c. In HFD-fed Sprague-Dawley rats, the positive of IL6 and IL11 in islets was stronger, but PTGS2 expression was not significantly altered. The extent of fibrosis, irregular cellular arrangement and positive actin alpha 2 (ACTA2) staining in islets was significantly greater in HFD-fed rats than in normal diet-fed rats.Conclusion: Glucotoxicity is a major factor leading to increased IL6 and IL11 expression, and IL6-and IL11-induced fibrosis might be involved in islet dysfunction.

Genome-wide association study of blood lipid levels in Southern Han Chinese adults with prediabetes

“…The NECTIN2 cell adhesion molecule has been shown to participate in lipid metabolism and serves as a potential biomarker for disease progression in carotid artery stenosis (31). Additionally, the cytokines IL6 and IL11 are upregulated under glucotoxic conditions, and IL6/IL11-mediated islet fibrosis may contribute to dysfunction in T2DM (32). Furthermore, the BPI-like proteins LBP and BPI are involved in lipid homeostasis and have been implicated in cardiovascular pathogenesis (19).…”

Elucidating shared biomarkers and pathways in kidney stones and diabetes: insights into novel therapeutic targets and the role of resveratrol

“…In advanced T2DM patients, the abnormal buildup of ECM, known as fibrosis, is frequently observed and may contribute to organ malfunction [41]. Research involving bioinformatics analysis and animal models has revealed that high glucose levels could trigger the upregulation of IL11, leading to inflammation and fibrosis in islets, thus contributing to the development of islet dysfunction in T2DM [42]. Similarly, renal fibrosis is a common occurrence in various kidney diseases, which can lead to mechanical and electrical dysfunction and the progression of kidney dysfunction [40].…”

Bioinformatics and Experimental Analyses Reveal Immune-Related LncRNA–mRNA Pair AC011483.1-CCR7 as a Biomarker and Therapeutic Target for Ischemic Cardiomyopathy

“…The co-expression network was constructed using the “WGCNA” package (version 1.71) in R software (version 4.1.2) with the top 5000 genes with the highest median absolute deviation [ 50 , 51 ]. We constructed the adjacency matrix with an appropriate soft threshold (β = 6) when R 2 reached 0.85 after calculating the Pearson correlation coefficient between any two genes in the matrix.…”