PRL/PRLR Can Promote Insulin Resistance by Activating the JAK2/STAT5 Signaling Pathway

Author:

Wang Pei-yu1,Jin Cong-cong1,Liu Chang1,Zhao Zhou-jian2,Yang Hai-yan1ORCID

Affiliation:

1. Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China

2. Zhuji Yaojiang Town Central Health Center, Shaoxing, Zhejiang 311822, China

Abstract

Objective. Although prolactin (PRL) is known to affect food intake, weight gain, and insulin resistance, its effects on lipid metabolism and underlying mechanisms remain underinvestigated. This study aimed to investigate the effects of PRL and its receptor (PRLR) on fat metabolism in regulating the JAK2/STAT5 signaling pathway. Methods. SW872 adipocytes were incubated with oleic acid to establish an insulin resistance (IR) model. Western blot was used to detect the expression of PRLR, JAK2, p-JAK2, STAT5, and p-STAT5. Triglyceride (TG) mass was detected by chemical colorimetry methods. Results. Fat droplets in the high-dose and medium-dose PRL groups were significantly higher than in the IR model group. TG mass in the cells was increased significantly compared with the model group. Compared with the control group, the expression of PRLR, p-JAK2, and p-STAT5 were significantly decreased in the IR model group when PRL was intervened for 24 h and 48 h. The expression of PRLR, p-JAK2, and p-STAT5 in the high-dose PRL intervention group increased significantly compared with the model group. The PRLR overexpressing group had significantly increased TG content and PRLR, and JAK2, p-JAK2, STAT5, and p-STAT5 levels compared with the IR model. Conclusion. PRL and PRLR are related to fat metabolism, and the PRL/PRLR signaling pathway can promote insulin resistance by activating the JAK2/STAT5 signaling pathway and increasing the deposition of TGs.

Funder

Project of Zhejiang Province Health Commission

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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