Tissue-Resident Memory T Cells in Allergy.

Tissue-resident memory T (TRM) cells constitute a distinct subset within the memory T cell population, serving as the vanguard against invading pathogens and antigens in peripheral non-lymphoid tissues, including the respiratory tract, intestines, and skin. Notably, TRM cells adapt to the specific microenvironment of each tissue, predominantly maintaining a sessile state with distinctive phenotypic and functional attributes. Their role is to ensure continuous immunological surveillance and protection. Recent findings have highlighted the pivotal contribution of TRM cells to the modulation of adaptive immune responses in allergic disorders such as allergic rhinitis, asthma, and dermatitis. A comprehensive understanding of the involvement of TRM cells in allergic diseases bears profound implications for allergy prevention and treatment. This review comprehensively explores the phenotypic characteristics, developmental mechanisms, and functional roles of TRM cells, focusing on their intricate relationship with allergic diseases.

Comprehensive analysis of mitophagy-related genes in diagnosis and heterogeneous endothelial cells in chronic rhinosinusitis: based on bulk and single-cell RNA sequencing data.

Background: Chronic rhinosinusitis (CRS) is a complex inflammatory disorder affecting the nasal and paranasal sinuses. Mitophagy, the process of selective mitochondrial degradation via autophagy, is crucial for maintaining cellular balance. However, the role of mitophagy in CRS is not well-studied. This research aims to examine the role of mitophagy-related genes (MRGs) in CRS, with a particular focus on the heterogeneity of endothelial cells (ECs). Methods: We employed both bulk and single-cell RNA sequencing data to investigate the role of MRGs in CRS. We compiled a combined database of 92 CRS samples and 35 healthy control samples from the Gene Expression Omnibus (GEO) database and we explored the differential expression of MRGs between them. A logistic regression model was built based on seven key genes identified through Random Forests and Support Vector Machines - Recursive Feature Elimination (SVM-RFE). Consensus cluster analysis was used to categorize CRS patients based on MRG expression patterns and weighted gene co-expression network analysis (WGCNA) was performed to find modules of highly correlated genes of the different clusters. Single-cell RNA sequencing data was utilized to analyze MRGs and EC heterogeneity in CRS. Results: Seven hub genes-SQSTM1, SRC, UBA52, MFN2, UBC, RPS27A, and ATG12-showed differential expression between two groups. A diagnostic model based on hub genes showed excellent prognostic accuracy. A strong positive correlation was found between the seven hub MRGs and resting dendritic cells, while a significant negative correlation was observed with mast cells and CD8+ T cells. CRS could be divided into two subclusters based on MRG expression patterns. WGCNA analysis identified modules of highly correlated genes of these two different subclusters. At the single-cell level, two types of venous ECs with different MRG scores were identified, suggesting their varying roles in CRS pathogenesis, especially in the non-eosinophilic CRS subtype. Conclusion: Our comprehensive study of CRS reveals the significant role of MRGs and underscores the heterogeneity of ECs. We highlighted the importance of Migration Inhibitory Factor (MIF) and TGFb pathways in mediating the effects of mitophagy, particularly the MIF. Overall, our findings enhance the understanding of mitophagy in CRS, providing a foundation for future research and potential therapeutic developments.

Identification of key genes and the pathophysiology associated with allergen-specific immunotherapy for allergic rhinitis.

BACKGROUND: Allergen-specific immunotherapy (AIT) is a causative treatment in allergic rhinitis (AR), comprising long-term allergen administration and over three years of treatment. This study is carried out for revealing the mechanisms and key genes of AIT in AR. METHODS: The present study utilized online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE37157 and GSE29521 to analyze the hub genes changes related to AIT in AR. Based on limma package, differential expression analysis for the two groups (samples of allergic patients prior to AIT and samples of allergic patients undergoing AIT) was performed to obtain differentially expressed genes (DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were conducted using DAVID database. A Protein-Protein Interaction network (PPI) was built and a significant network module was acquired by using Cytoscape software (Cytoscape, 3.7.2). Utilizing the miRWalk database, we identified potential gene biomarkers, constructed interaction networks of target genes and microRNAs (miRNAs) using Cytoscape software, and explore the cell type-specific expression patterns of these genes in peripheral blood using publicly available single-cell RNA sequencing data (GSE200107). Finally, we are using PCR to detect changes in the hub genes that are screened using the above method in peripheral blood before and after AIT treatment. RESULTS: GSE37157 and GSE29521 included 28 and 13 samples, respectively. A total of 119 significantly co-upregulated DEGs and 33 co-downregulated DEGs were obtained from two datasets. The GO and KEGG analyses demonstrated that protein transport, positive regulation of apoptotic process, Natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, TNF signaling pathway, B cell receptor signaling pathway and Apoptosis may be potential candidate therapeutic targets for AIT of AR. From the PPI network, 20 hub genes were obtained. Among them, the PPI sub-networks of CASP3, FOXO3, PIK3R1, PIK3R3, ATF4, and POLD3 screened out from our study have been identified as reliable predictors of AIT in AR, especially the PIK3R1. CONCLUSION: Our analysis has identified novel gene signatures, thereby contributing to a more comprehensive understanding of the molecular mechanisms underlying AIT in the treatment of AR.

A Noncanonical Hedgehog Signaling Exerts a Tumor-Promoting Effect on Pancreatic Cancer Cells Via Induction of Osteopontin Expression.

Objective: Sonic Hedgehog (Shh)-Gli1 signaling and osteopontin (OPN) play vital roles in pancreatic cancer. However, the precise mechanisms of both signals have not been fully clarified, and whether there is a correlation between them in pancreatic ductal adenocarcinoma (PDAC) is unknown. This study aims to confirm the effect of OPN on human PDAC and assess whether Hh signaling affects pancreatic cancer cells through upregulation of OPN. Materials and Methods: OPN expression in human PDAC tissues and cell lines was investigated. Proliferation, apoptosis, migration, and invasion of OPN-knockdown BxPC-3 cells were observed. We analyzed the correlation between Shh or Gli1 and OPN expression in human PDAC. Hh signaling inhibitors and shRNA against Gli1 were used to confirm if OPN expression in BxPC-3 cells was regulated by Hh canonical or noncanonical pathway. We also evaluated the proliferation, apoptosis, migration, and invasion of Gli1-knockdown BxPC-3 cells. Results: OPN is highly expressed in human PDAC tissues and cell lines. The proliferation, migration, and invasion of BxPC-3 cell lines were decreased, whereas apoptosis was increased when OPN was knocked down. Correlation analysis showed that Gli1, but not Shh, was associated with OPN expression in human PDAC, and Gli1 regulated OPN production in BxPC-3 cells through a noncanonical pathway because Gli but not Smo inhibitor reduced OPN expression. Similar to above, the proliferation, migration, and invasion of BxPC-3 cells were decreased, whereas the apoptosis was increased when Gli1 was knocked down. Supplement of exogenous OPN protein could partially reverse the effect of both OPN knockdown and Gli1 knockdown on the bio-behavior of BxPC-3 cells. Conclusion: Hh signaling promotes proliferation, migration, and invasion but inhibits apoptosis of pancreatic cancer cells through upregulation of OPN in a noncanonical pathway.

The Relationship between Insecure Attachment to Depression: Mediating Role of Sleep and Cognitive Reappraisal.

Previously, we have shown that neuromodulators are important factors in stress-induced emotional disorders, such as depression, for example, serotonin is the major substance for depression. Many psychological studies have proved that depression is due to insecure attachment. In addition, sleep is a major symptom of depression. Furthermore, serotonin is the substrate for both sleep and depression. To explore the role of sleep in the relationships between insecure attachment and depression, we investigated 755 college students with Close Relationship Inventory, Emotion Regulation Questionnaire, Self-rated Depression Scale, and Pittsburgh Sleep Quality Index. The results showed that (1) insecure attachment positively predicted poor sleep quality; (2) sleep quality partially affected depression, possibly due the same stress neuromodulators such as norepinephrine and cortisol; and (3) cognitive reappraisal moderated the mediating path leading from attachment anxiety to poor sleep quality. These findings highlight the moderating role of cognitive reappraisal in the effects of attachment anxiety on sleep quality and finally on depression. In conclusion, sleep quality links attachment anxiety and emotional disorders.

Relationship Between Stressful Life Events and Sleep Quality: Rumination as a Mediator and Resilience as a Moderator.

Purpose: The aim of this study was to investigate the relationship between stressful life events and sleep quality and to probe the role of rumination and resilience in the relationship. Method: The Adolescent Self-Rating Life Events Checklist, Ruminative Responses Scale, Connor-Davidson Resilience Scale, and Pittsburgh Sleep Quality Index were used among 1,065 college students. Statistical Product and Service Solutions (SPSS) 20.0 and the SPSS macro Process, which were specifically developed for assessing complex models including both mediators and moderators, were used to analyze the data. Results: High scores of stressful life events predicted worse sleep quality. Rumination partially mediated the relations between stressful life events and sleep quality. Resilience moderated the direct and indirect paths leading from stressful life events to sleep quality. Conclusions: The results demonstrate that stressful life events can directly affect the sleep quality of college students and indirectly through rumination. Additionally, increasing psychological resilience could decrease both the direct effect and the indirect effect of stressful life events affecting sleep quality. The results of this study may contribute to a better understanding of the effects, as well as the paths and conditions, of stressful life events on sleep quality in college students. Moreover, these findings can provide constructive suggestions for improving college students' sleep quality.