Reduced NGF Level Promotes Epithelial–Mesenchymal Transition in Human Lens Epithelial Cells Exposed to High Dexamethasone Concentrations

ABSTRACT

Purpose: To investigate the protective effects of nerve growth factor (NGF) against steroid-induced cataract formation in dexamethasone (Dex)-treated human lens epithelial B-3 (HLE-B3) cells and the possible molecular mechanisms underlying this protection.

Materials and Methods: HLE-B3 cells were treated with Dex, and cell viability was assessed using the Cell Counting Kit-8 (CCK-8) assay. The levels of expression of NGF, fibronectin, α-smooth muscle actin (α-SMA), and E-cadherin mRNAs were measured by real-time quantitative polymerase chain reaction (qPCR), and the levels of NGF, fibronectin, α-SMA, E-cadherin, tropomyosin receptor kinase A (TrkA), and Akt proteins were measured by Western blot analysis. Gene expression profiles of growth factors in Dex-treated HLE-B3 cells were determined by PCR arrays. In addition, anterior capsule tissue was obtained during cataract surgery, and the specimens were also examined expressions of NGF.

Results: NGF was expressed in HLE-B3 cells and also in lens epithelial cells of anterior lens capsules. Dex treatment of HLE-B3 cells increased their expression of epithelial–mesenchymal transition (EMT) markers and migration activity, while markedly downregulating the expression of NGF. NGF treatment significantly reduced the expression of α-SMA and fibronectin, as well as cell proliferation. The decreased phosphorylation of p38 MAPK and Akt induced by Dex treatment was significantly reversed by treatment with NGF.

Conclusion: NGF/TrkA may repress EMT by targeting the p38 MAPK and pAkt pathways in Dex-treated HLE-B3 cells. NGF may be a novel therapeutic target for patients with steroid-induced cataract.

KEYWORDS:

• Steroid
• cataract
• nerve growth factor
• dexamethasone
• epithelial-mesenchymal transition

Declaration of Interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

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Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning [NRF-2015R1C1A1A02037702], by Biochemical Research Institute funds [GNUHBRIF-2014-0003] from the Gyeongsang National University Hospital, and by the ICT R&D program of MSIT/IITP [2018-0-00242, Development of AI ophthalmologic diagnosis and smart treatment platform based on big data].