Introduction

The NLRP3 inflammasome induced by Helicobacter pylori (H. pylori) infection serves as a crucial platform for the maturation and secretion of IL-1β and IL-18, leading to pyroptosis, which thus exacerbates H. pylori-associated gastritis and accelerates its progression to gastric cancer. Inhibiting NLRP3 expression can attenuate the intensity of the inflammatory response and impede the progression of H. pylori-induced gastric mucosal lesions. However, the post-transcriptional regulatory mechanisms governing NLRP3 in H. pylori infection remain unclear. Therefore, elucidating the molecular mechanisms underlying post-transcriptional regulation of NLRP3 could offer potential intervention strategies for H. pylori-related gastric mucosal diseases.

Aims & Methods

In the co-culture experiment of human gastric epithelial cells or mouse mononuclear macrophage and H. pylori in vitro, knockdown or overexpression of AUF1 was performed, and the effect of AUF1 on the activation of NLRP3 inflammasome was detected by Western blot assay and ELISA. Scanning or transmission electron microscopy, immunofluorescence (IF) assay, lactate dehydrogenase (LDH) release assays were used to detect the effect of AUF1 on pyroptosis. Transcriptome analysis was used to explore the changes of inflammatory cytokine genes in gastric epithelial cells with H. pylori by AUF1 knockdown. RNA immunoprecipitation and RNA-pulldown assay were used to detect the interaction of AUF1 and NLRP3 mRNA. Immunohistochemical staining was used to detect the expression of AUF1, NLRP3, Caspase1 and GSDMD in gastric mucosa of H. pylori-infected patients. Exosomes from gastric juice of H. pylori-related gastric mucosal diseases were collected by ultracentrifugation and AUF1 expression was detected.

Results

The expression of AUF1 in gastric mucosa and gastric juice exosomes of H. pylori-infected patients with chronic superficial gastritis was significantly up-regulated, and gradually increased with the aggravation of inflammation. Transcriptome analysis of H. pylori infected gastric epithelial cells showed that inflammatory pathways were significantly inhibited and NLRP3 was the most significantly altered inflammatory gene after AUF1 knockdown. After H. pylori infection, AUF1 knockdown can inhibit NLRP3 expression, NLRP3 inflammasome activation and the secretion of IL-1β and IL18 both in gastric epithelial cells and macrophages. On the contrary, overexpression of AUF1 promoted NLRP3 expression and inflammasome activation, while NLRP3 inhibitor MCC950 can counteract the activating effect of AUF1 overexpression on NLRP3 inflammasome. Also, we confirmed that AUF1 promoted pyroptosis in H. pylori-infected gastric epithelial cells, and the treatment of MCC950 could relieve pyroptosis. Mechanically, we found that AUF1 can bind to the 3'UTR region of NLRP3 mRNA, thereby enhancing the stability of NLRP3 mRNA and upregulating the expression of NLRP3. In gastric mucosa tissues of H. pylori-infected patients, AUF1 expression was positively correlated with NLRP3, Caspase1 and GSDMD expression. In addition, H. pylori CagA promotes the interaction between AUF1 and exportin CRM1, thus promoting the cytoplasmic redistribution of AUF1.

Conclusion

H. pylori infection promotes the cytoplasmic redistribution of AUF1 through CRM1 and AUF1 promotes NLRP3 inflammasomes activation by upregulating the expression of NLRP3. Our findings identify AUF1 as a novel positive regulator of NLRP3 inflammasomes and suggest that dysregulation of AUF1 expression leads to an increased risk of H. pylori-gastritis.

References

1. Ng GZ, Menheniott TR, Every AL, et al. The MUC1 mucin protects against Helicobacter pylori pathogenesis in mice by regulation of the NLRP3 inflammasome. Gut. 2016;65(7):1087-1099.
2. Li S, Liang X, Ma L, et al. MiR-22 sustains NLRP3 expression and attenuates H. pylori-induced gastric carcinogenesis. Oncogene. 2018;37(7):884-896.
3. Yu Q, Shi H, Ding Z, Wang Z, Yao H, Lin R. The E3 ubiquitin ligase TRIM31 attenuates NLRP3 inflammasome activation in Helicobacter pylori-associated gastritis by regulating ROS and autophagy. Cell Commun Signal. 2023;21(1):1

Introduction

Recent evidence suggests a possible role of artificial intelligence in procedural quality measurement for endoscopic submucosal dissection by artificial intelligence¹. Precise frame by frame differentiation of operational phases may allow for a deeper understanding of the procedure and open a wide range of possibilities from automated documentation to concise training support.

Aims & Methods

In this study, we aimed to develop an algorithm for frame by frame procedural phase recognition during ESD. To this end, a dataset (27 ESDs, 2071546 single frames) was annotated for the operational supra-phases diagnostics, marking, needle injection, dissection and bleeding. Furthermore, the subphases scope manipulation, injection and application of electrical current were annotated where appropriate. A training-subset (17 ESDs, 898440 single frames) was used to train a video swin transformer with uniform frame sampling to detect the classes. The trained algorithm was evaluated internally (6 ESDs, 769523 single frames) and on a separate dataset (4 ESDs, 403583 single frames).

Results

Overall F1 Scores for internal validation and validation on the separate dataset were 83% and 90%, respectively. True positive rates for diagnostics, marking, needle-injection, dissection and bleeding were 100%, 100%, 96%, 97% and 93%, respectively. Overall F1 score over all supra-phases was 94%, overall accuracy was 93%. True positive rates for scope manipulation, injection and application of electrical current were 92%, 98% and 91%, respectively. Overall F1 score for subphases was 91%, overall accuracy was 97%.

Conclusion

The trained algorithm classified full length ESD videos on a frame by frame basis for concise surgical phases with high accuracy. Future research will focus on the development of intraprocedural quality measures, as well as automated reporting.

References

1. Cao J, Yip HC, Chen Y, et al. Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study. Nat Commun 2023;14:6676.

Disclosure

All authors declare no conflict of interest concerning this study.