On March 24, 2026, Prof. Jinlin Song, Prof. Tao Chen, and Dr. Liangjing Xin from the College of Stomatology of our university, together with Prof. Chao Huang from the College of Pharmacy at Chongqing University, published their latest research article entitled “ESCRT-Mimetic Nanodegrader Targets STING for Anti-Inflammatory Therapy in Advanced Science.” The study focused on STING, a key regulator of innate immunity, and revealed that although endogenous degradation mechanisms are activated during inflammation, their clearance efficiency is insufficient, resulting in persistent accumulation of STING signaling and amplification of inflammatory responses. Based on these findings, the team proposed a novel ESCRT-mimetic nanodegradation strategy, providing a new therapeutic perspective for inflammatory diseases.
Under physiological conditions, activated STING is normally degraded through the ESCRT-mediated lysosomal pathway, thereby terminating inflammatory signaling. However, the study found that under persistent inflammatory stimulation, although cells upregulate ESCRT-related proteins such as TSG101 and VPS4b, the endogenous clearance capacity remains insufficient to effectively eliminate continuously activated STING, leading to sustained inflammatory signaling. Moreover, inhibition of ESCRT or lysosomal function further aggravated STING accumulation and enhanced inflammatory signaling pathways including TBK1 and IL-1β, suggesting that “insufficient clearance” may be a key driver of chronic inflammation.
Based on this concept, the researchers developed a strategy distinct from conventional STING inhibition by artificially reconstructing the degradation pathway of STING for its precise elimination. The team designed an autophagy-targeting chimera molecule termed STING-ATTEC, which simultaneously binds STING and the autophagy protein LC3, functioning as a “molecular bridge” to actively direct STING into the autophagy–lysosome system for degradation, thereby bypassing the impaired ESCRT pathway. Mechanistic studies demonstrated that STING-ATTEC significantly reduced STING protein levels without affecting mRNA expression, and its degradation effect was markedly attenuated by lysosomal inhibition or LC3 knockdown, confirming its dependence on the autophagy pathway. The study also incorporated the deep-learning tool DeepPROTACs to optimize molecular structures and identified a triazole linker with the highest degradation efficiency. Furthermore, the researchers constructed a folic acid-modified cationic lipid nanoparticle system (FA-LNP⁺@STING-ATTEC) to achieve macrophage-targeted delivery while simultaneously enhancing autophagy activity, thus producing a synergistic “delivery plus degradation enhancement” effect. In multiple disease models, including sepsis, periodontitis, and inflammatory wound healing, this strategy exhibited remarkable anti-inflammatory and tissue repair effects.
Overall, this study not only developed a novel STING-targeted nanodegrader, but also proposed a new paradigm for inflammatory disease intervention. The persistence of inflammation may arise not only from excessive activation, but also from insufficient clearance of pathogenic proteins. Compared with simply suppressing inflammatory signaling, reinforcing or reconstructing the cellular clearance system to actively remove pathogenic molecules may represent an important direction for next-generation anti-inflammatory therapies.
Prof. Jinlin Song, Prof. Tao Chen, Dr. Liangjing Xin, and Prof.r Chao Huang served as co-corresponding authors of the study. PhD candidate Fuyuan Zhou from the college of Stomatology of our university and Dr. Qiming Zhai from the Stomatological Hospital of Chongqing Medical University were co-first authors. This work was supported by the National Key Research and Development Program of China, the National Natural Science Foundation of China Young Scientists Fund (Category B), and the National Natural Science Foundation of China General Program.
Original article: https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202523601
(Translated by AI)
