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Progranulin alleviates podocyte injury via regulating CAMKK/AMPK-mediated autophagy under diabetic conditions.

Di ZhouMeng ZhouZiying WangYi FuMeng JiaXiaojie WangMin LiuYan ZhangYu SunYabin ZhouYi LuWei TangFan Yi
Published in: Journal of molecular medicine (Berlin, Germany) (2019)
Podocyte injury is considered a major contributor to the development of diabetic nephropathy (DN). Therefore, identification of potential therapeutic targets for preventing podocyte injury has clinical importance. Recent studies have indicated that autophagy is a key homeostatic mechanism to maintaining podocyte integrity and function. This study was to elucidate the role of progranulin (PGRN), a secreted glycoprotein, in the modulation of podocyte autophagic process and podocyte injury under a diabetic condition. PGRN was downregulated in the kidney from diabetic mice and podocytes under a high-glucose (HG) condition. PGRN deficiency exacerbated the renal dysfunction and glomerular structural alterations. In vitro, treatment with recombinant human PGRN (rPGRN) attenuated HG-induced podocyte injury accompanied by enhanced autophagy. Inhibition of autophagy disturbed the protective effects of PGRN in HG-induced podocytotoxicity. Furthermore, PGRN induced autophagy via the PGRN-CAMKK-AMPK pathway. Collectively, our data identified the protective role of PGRN in podocyte injury via restoring autophagy and activating the CAMKK-AMPK pathway, which may pave the road to new therapeutic modalities for the treatment of diabetic nephropathy. KEY MESSAGES: • PGRN level is reduced in kidney of diabetic mice and high-glucose-treated podocytes. • PGRN deficiency exacerbates renal injury in diabetic mice. • PGRN protects against high-glucose-induced podocyte injury. • PGRN restores high-glucose-inhibited autophagy in podocytes. • CAMKK-AMPK pathway is required for the protective role of PGRN in podocyte injury.
Keyphrases
  • high glucose
  • endothelial cells
  • diabetic nephropathy
  • cell death
  • signaling pathway
  • endoplasmic reticulum stress
  • oxidative stress
  • type diabetes
  • machine learning
  • electronic health record
  • data analysis