Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients.

Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.

Identification of hub genes and potential biomarkers of neutrophilic asthma: evidence from a bioinformatics analysis.

OBJECTIVE: Asthma is a chronic airway inflammatory disease caused by multiple genetic and environmental factors. This study mainly sought to provide potential therapeutic targets and biomarkers for neutrophilic asthma (NA). METHODS: Three gene expression profiling datasets were obtained from the Genome Expression Omnibus (GEO) database. GSE45111 and GSE41863 were used to identify hub genes and potential biomarkers, and GSE137268 was used for data verification. We verified the repeatability of intragroup data and identified differentially expressed genes (DEGs). Then, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs, and a protein-protein interaction (PPI) network was constructed to identify the hub genes. Finally, receiver operating characteristic (ROC) analysis was used to verify the ability of the hub genes to differentiate between NA and eosinophilic asthma (EA). RESULTS: In this study, we identified 411 DEGs by comprehensive analysis of NA/EA patients and NA/healthy controls (HCs). Ten hub genes (CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, IL6R, JAK3, CD48, and GNG2) were identified from the PPI network. Finally, based on the ROC analysis, 7 genes showed good diagnostic value for discriminating NA from EA-CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, and IL6R (AUC > 0.7). CONCLUSION: We identified 7 hub genes that can distinguish NA from EA. The IL-8-mediated signaling may be the primary pathway to determine the NA phenotype in asthma. CXCR1/2 and S100A12 may be the primary genes determining the NA phenotype. CXCR1/2 and S100A12 might be biomarkers and new therapeutic targets for NA.Supplemental data for this article is available online at at.

MicroRNA-15b-5p inhibits tumor necrosis factor alpha-induced proliferation, migration, and extracellular matrix production of airway smooth muscle cells via targeting yes-associated protein 1.

The excessive proliferation and the deposition of extracellular matrix (ECM) of airway smooth muscle (ASM) cells facilitates airway remodeling in asthma. This study explores how microRNA-15b-5p (miR-15b-5p) functions in modulating the proliferation, migration, inflammatory response, and ECM deposition of ASM cells. MiR-15b-5p and yes-associated protein 1 (YAP1) mRNA expression levels in tumor necrosis factor alpha (TNF-α)-induced ASM cells were, respectively, examined by real-time quantitative polymerase-chain reaction. Besides, the proliferative ability and migrative potential of ASM cells were examined by cell counting kit-8 assay, 5-bromo-2 '-deoxyuridine assay, and transwell assays, respectively. Interleukin-6 and interleukin-8 levels in ASM cells were detected by enzyme-linked immunosorbent assay. YAP1, collagen I, and collagen III expressions in ASM cells were detected by Western blot. With dual-luciferase reporter gene assay, the relations between miR-15b-5p and YAP1 3'UTR in ASM cells was examined. MiR-15b-5p expression level was reduced in ASM cells treated with TNF-α. MiR-15b-5p repressed TNF-α-initiated growth and migration of ASM cells and also suppressed IL-6 and IL-8 secretion, and inhibited collagen I and collagen III expressions in ASM cells. Furthermore, it was validated that YAP1 was a downstream target of miR-15b-5p in ASM cells. Notably, YAP1 overexpression attenuated the inhibitory effects of miR-15b-5p up-regulation on the proliferation, migration, and inflammatory response, as well as ECM deposition of TNF-α-induced ASM cells. In conclusion, miR-15b-5p/YAP1 axis modulates the growth, migration, inflammatory response, and ECM deposition of ASM cells, thus participating in the pathogenesis of asthma.

Abnormal immunity of non-survivors with COVID-19: predictors for mortality.

BACKGROUND: The number of coronavirus disease 2019 (COVID-19) cases has rapidly increased all over the world. Specific information about immunity in non-survivors with COVID-19 is scarce. This study aimed to analyse the clinical characteristics and abnormal immunity of the confirmed COVID-19 non-survivors. METHODS: In this single-centered, retrospective, observational study, we enrolled 125 patients with COVID-19 who were died between January 13 and March 4, 2020 in Renmin Hospital of Wuhan University. A total of 414 randomly recruited patients with confirmed COVID-19 who were discharged from the same hospital during the same period served as control. The demographic, clinical characteristics and laboratory findings at admission, and treatment used in these patients were collected. The immunity-related risk factors associated with in-hospital death were tested by logistic regression models and Receiver Operating Characteristic (ROC) curve. RESULTS: Non-survivors (70 years, IQR: 61.5-80) were significantly older than survivors (54 years, IQR: 37-65) (P <  0.001). 56.8% of non-survivors was male. Nearly half of the patients (44.9%) had chronic medical illness. In non-survivors, hypertension (49.6%) was the most common comorbidity, followed by diabetes (20.0%) and coronary heart disease (16.0%). The common signs and symptoms at admission of non-survivors were fever (88%), followed by cough (64.8%), dyspnea (62.4%), fatigue (62.4%) and chest tightness (58.4%). Compared with survivors, non-survivors had higher white blood cell (WBC) count (7.85 vs 5.07 × 109/L), more elevated neutrophil count (6.41 vs 3.08 × 109/L), smaller lymphocyte count (0.69 vs 1.20 × 109/L) and lower platelet count (172 vs 211 × 109/L), raised concentrations of procalcitonin (0.21 vs 0.06 ng/mL) and CRP (70.5 vs 7.2 mg/L) (P < 0.001). This was accompanied with significantly decreased levels of CD3+ T cells (277 vs 814 cells/µl), CD4+ T cells (172 vs 473 cells/µl), CD8+ T cells (84 vs 262.5 cells/µl, P < 0.001), CD19+ T cells (88 vs 141 cells/µl) and CD16+ 56+ T cells (79 vs 128.5 cells/µl) (P < 0.001). The concentrations of immunoglobulins (Ig) G (13.30 vs 11.95 g/L), IgA (2.54 vs 2.21 g/L), and IgE (71.30 vs 42.25 IU/ml) were increased, whereas the levels of complement proteins (C)3 (0.89 vs 0.99 g/L) and C4 (0.22 vs 0.24 g/L) were decreased in non-survivors when compared with survivors (all P < 0.05). The non-survivors presented lower levels of oximetry saturation (90 vs 97%) at rest and lactate (2.40 vs 1.90 mmol/L) (P < 0.001). Old age, comorbidity of malignant tumor, neutrophilia, lymphocytopenia, low CD4+ T cells, decreased C3, and low oximetry saturation were the risk factors of death in patients with confirmed COVID-19. The frequency of CD4+ T cells positively correlated with the numbers of lymphocytes (r = 0.787) and the level of oximetry saturation (r = 0.295), Whereas CD4+ T cells were negatively correlated with age (r =-0.323) and the numbers of neutrophils (r = - 0.244) (all P < 0.001). CONCLUSIONS: Abnormal cellular immunity and humoral immunity were key features of non-survivors with COVID-19. Neutrophilia, lymphocytopenia, low CD4+ T cells, and decreased C3 were immunity-related risk factors predicting mortality of patients with COVID-19.

Clinical features in 52 patients with COVID-19 who have increased leukocyte count: a retrospective analysis.

Recent reports have showed that a proportion of patients with Coronavirus Disease 2019 (COVID-19) presented elevated leukocyte count. Clinical data about these patients is scarce. We aimed to evaluate the clinical findings of patients with COVID-19 who have increased leukocyte at admission. We retrospectively collected the clinical data on the 52 patients who have increased leukocyte count at admission from the 619 patients with confirmed COVID-19 who had pneumonia with abnormal features on chest CT scan in Renmin Hospital of Wuhan University in Wuhan, China, from February 3 to March 3, 2020. The mean age of the 52 patients with increased leukocyte count was 64.7 (SD 11.4) years, 32 (61.5%) were men and 47 (90.4%) had fever. Compared with the patients with non-increased leukocyte count, the patients with increased leukocyte count were significantly older (P < 0.01), were more likely to have underlying chronic diseases (P < 0.01), more likely to develop critically illness (P < 0.01), more likely to admit to an ICU (P < 0.01), more likely to receive mechanical ventilation (P < 0.01), had higher rate of death (P < 0.01) and the blood levels of neutrophil count and the serum concentrations of CRP and IL-6 were significantly increased, (P < 0.01). The older patients with COVID-19 who had underlying chronic disorders are more likely to develop leukocytosis. These patients are more likely to develop critical illness, with a high admission to an ICU and a high mortality rate.