HECT, UBA and WWE domain containing 1 represses cholesterol efflux during CD4+ T cell activation in Sjögren’s syndrome

Introduction: Sjögren’s syndrome (SS) is a chronic autoimmune disorder characterized by exocrine gland dysfunction, leading to loss of salivary function. Histological analysis of salivary glands from SS patients reveals a high infiltration of immune cells, particularly activated CD4+ T cells. Thus, interventions targeting abnormal activation of CD4+ T cells may provide promising therapeutic strategies for SS. Here, we demonstrate that Hect, uba, and wwe domain containing 1 (HUWE1), a member of the eukaryotic Hect E3 ubiquitin ligase family, plays a critical role in CD4+ T-cell activation and SS pathophysiology.Methods: In the context of HUWE1 inhibition, we investigated the impact of the HUWE1 inhibitor BI8626 and sh-Huwe1 on CD4+ T cells in mice, focusing on the assessment of activation levels, proliferation capacity, and cholesterol abundance. Furthermore, we examined the therapeutic potential of BI8626 in NOD/ShiLtj mice and evaluated its efficacy as a treatment strategy.Results: Inhibition of HUWE1 reduces ABCA1 ubiquitination and promotes cholesterol efflux, decreasing intracellular cholesterol and reducing the expression of phosphorylated ZAP-70, CD25, and other activation markers, culminating in the suppressed proliferation of CD4+ T cells. Moreover, pharmacological inhibition of HUWE1 significantly reduces CD4+ T-cell infiltration in the submandibular glands and improves salivary flow rate in NOD/ShiLtj mice.Conclusion: These findings suggest that HUWE1 may regulate CD4+ T-cell activation and SS development by modulating ABCA1-mediated cholesterol efflux and presents a promising target for SS treatment

An evaluation of bone depth at different three-dimensional paths in infrazygomatic crest region for miniscrew insertion: A cone beam computed tomography study

Objective: To investigate the difference and distribution of bone depth at different three-dimensional simulated paths to help optimize the insertion path for miniscrew placement in the infrazygomatic crest. Methods: Cone beam computed tomography scans of 80 adults (38 males and 42 females; mean age, 27.0 years) were assessed. For each subject, bone depth of 81 simulated insertion paths at different insertion points and three-dimensional angulations was measured in 160 infrazygomatic crests; the differences were evaluated using the adjusted Friedman test. The bone deficiency ratio for each path was calculated. Distributions of measurements were analyzed and reported as specially designed colormaps. Results: Bone depth increased, and bone deficiency ratio reduced mesially to distally (P < 0.001), apically to coronally (P < 0.01), and at a greater gingival and distal inclination (P < 0.05). The maximum bone depth (10.72 mm) was observed 13 mm above the maxillary occlusal plane in the mesiobuccal root of the maxillary second molar. The minimum bone depth (3.4 mm) was observed 17 mm above the maxillary occlusal plane in the distobuccal root of the maxillary first molar. No bone deficiency was detected at the paths of 13 mm above the maxillary occlusal plane at a gingival inclination of 70° and distal inclination of 30° in the mesiobuccal root of the maxillary second molar. The highest bone deficiency ratio is present 17 mm above the maxillary occlusal plane at a gingival inclination of 60° and a distal inclination of 0° in the distobuccal root of the maxillary first molar (89/160). Conclusion: Insertion paths located at 13 mm above the maxillary occlusal plane in the mesiobuccal root of the maxillary second molar were optimal. A gingival inclination of 70° and a distal inclination of 30° could be beneficial. The distobuccal root of the maxillary first molar region or above the 17 mm insertion plane may not be recommended

Identification and assessment of differentially expressed necroptosis long non-coding RNAs associated with periodontitis in human

Abstract Background Periodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis. Material and methods Transcriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman’s correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR. Results A total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results. Conclusions The 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis