Metabolic dysfunction-associated steatotic liver disease/steatohepatitis (MASLD/MASH) has become the most common chronic liver disease worldwide. Due to increasing obesity and metabolic problems, more than 30% of people globally have varying degrees of fatty liver disease. Current approved drugs mainly include resmetirom and semaglutide, but these are limited in number, expensive, and have restricted effects, far from meeting the needs of so many patients. Therefore, there is an urgent need to identify new pathogenic mechanisms and molecular targets that can simultaneously improve hepatic steatosis, inflammation, and fibrosis.
On February 18, a collaborative study by Associate Professor Qi Wei’s team at the School of Life Science and Technology (SLST), ShanghaiTech University, and Professor Song Bao-Liang’s team at Wuhan University was published in Nature under the title “RYK is a GPNMB receptor that drives MASH.”
The study found that hepatocytes produce a protein called GPNMB. After proteolytic cleavage, GPNMB releases a portion called the secreted GPNMB ectodomain (G-ECD) into the blood. G-ECD binds to a protein called RYK on the surface of hepatocytes. Upon binding, RYK initiates signaling that causes the liver to take up more fat and produce more fat itself, leading to increased hepatic steatosis, worsened inflammation, and aggravated fibrosis, ultimately resulting in MASH. The team first confirmed that RYK is the protein to which G-ECD binds and exerts its effects. They also tested several methods to block this pathway: including G-ECD vaccination in mice, neutralizing antibody against G-ECD, gene knockdown to reduce GPNMB expression, and liver-specific siRNA to silence GPNMB. These approaches effectively prevented or reversed MASH in mouse models.
This study identified a novel ligand–receptor pathway and clearly explained how the GPNMB–RYK axis drives lipid metabolic dysregulation and MASH progression, providing a new, translatable target for future MASH treatment.
Postdoctoral researcher Xi Yue from Wuhan University is the first author. Prof. Song Bao-Liang and Prof. Qi Wei are the co-corresponding authors.