Chronic, non-healing wounds are a severe diabetic complication. The underlying mechanisms are not fully understood, and the role of ATF7 in this context has not been well characterized. In our study, we utilized db/db diabetic mice and AAV-mediated keratinocyte-specific Atf7 overexpression in vivo. HaCaT keratinocyte/THP-1 macrophage cocultures under high glucose were used in vitro. Our results showed that ATF7 was upregulated in diabetic wounds. Keratinocyte-specific Atf7 overexpression accelerated diabetic wound closure, enhanced re-epithelialization, granulation tissue formation, and keratinocyte proliferation, while suppressing macrophage M1 polarization and inflammation. Multiomics screening identified NOTCH1 as a key ATF7 target. ATF7 transcriptionally repressed NOTCH1 by recruiting Suv39h1, increasing H3K9me3 at the NOTCH1 promoter. This reduced NOTCH1 protein and its active intracellular domain (N1ICD) within keratinocyte-derived exosomes. ATF7-overexpressing keratinocyte exosomes carried less N1ICD, leading to decreased N1ICD transfer to macrophages and subsequent inhibition of M1 polarization. Notably, local injection of exosomes from ATF7-overexpressing keratinocytes accelerated wound healing in db/db mice. In summary, ATF7 promotes diabetic wound healing by repressing NOTCH1 transcription via H3K9me3, thereby reducing exosomal N1ICD secretion from keratinocytes and inhibiting macrophage M1 polarization. This identifies the ATF7/NOTCH1/exosome axis as a therapeutic target.
Pengcheng Xu, Yuan Xue, Linlin Feng, Jingwen Kuang, Xiaochen Hu, Huiyi Tang, Biao Cheng, Limin Wei
Usage data is cumulative from August 2026 through August 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 143 | 0 |
| 26 | 0 | |
| Figure | 91 | 0 |
| Supplemental data | 32 | 0 |
| Citation downloads | 23 | 0 |
| Totals | 315 | 0 |
| Total Views | 315 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.