Integration of TGF-β-induced Smad signaling in the insulin-induced transcriptional response in endothelial cells.
Erine H BudiSteven HoffmanShaojian GaoYing E ZhangRik DerynckPublished in: Scientific reports (2019)
Insulin signaling governs many processes including glucose homeostasis and metabolism, and is therapeutically used to treat hyperglycemia in diabetes. We demonstrated that insulin-induced Akt activation enhances the sensitivity to TGF-β by directing an increase in cell surface TGF-β receptors from a pool of intracellular TGF-β receptors. Consequently, increased autocrine TGF-β signaling in response to insulin participates in insulin-induced angiogenic responses of endothelial cells. With TGF-β signaling controlling many cell responses, including differentiation and extracellular matrix deposition, and pathologically promoting fibrosis and cancer cell dissemination, we addressed to which extent autocrine TGF-β signaling participates in insulin-induced gene responses of human endothelial cells. Transcriptome analyses of the insulin response, in the absence or presence of a TGF-β receptor kinase inhibitor, revealed substantial positive and negative contributions of autocrine TGF-β signaling in insulin-responsive gene responses. Furthermore, insulin-induced responses of many genes depended on or resulted from autocrine TGF-β signaling. Our analyses also highlight extensive contributions of autocrine TGF-β signaling to basal gene expression in the absence of insulin, and identified many novel TGF-β-responsive genes. This data resource may aid in the appreciation of the roles of autocrine TGF-β signaling in normal physiological responses to insulin, and implications of therapeutic insulin usage.
Keyphrases
- type diabetes
- transforming growth factor
- high glucose
- endothelial cells
- glycemic control
- gene expression
- diabetic rats
- epithelial mesenchymal transition
- genome wide
- extracellular matrix
- cardiovascular disease
- oxidative stress
- bone marrow
- cell surface
- cell proliferation
- genome wide identification
- deep learning
- cell therapy
- liver fibrosis
- data analysis