Allergy, Asthma & Immunology Research最新文献

We aimed to assess the validity and reliability of the Korean versions of the Food Allergy Quality of Life Questionnaire-Child Form (K-FAQLQ-CF) and the Food Allergy Quality of Life Questionnaire-Teenager Form (K-FAQLQ-TF). Patients aged 8-17 years with food allergy (FA) were enrolled and completed the Korean versions of the questionnaires, including the K-FAQLQ-CF, the Food Allergy Independent Measure-Child Form (K-FAIM-CF), and the Pediatric Quality of Life Inventory™ (K-PedsQL™ 4.0) for children and the K-FAQLQ-TF, the Food Allergy Independent Measure-Teenager Form (K-FAIM-TF), and the K-PedsQL™ 4.0 for adolescents. We enrolled 56 children and 23 adolescents in this study. The K-FAQLQ-CF showed a good internal consistency (Cronbach's α coefficient = 0.969) and an excellent test-retest reliability (intraclass correlation coefficient = 0.914, P = 0.011). There was a moderate correlation between the K-FAQLQ-CF and K-FAIM-CF scores (β = 0.736, P < 0.001), indicating construct validity. The K-FAQLQ-CF score was weakly associated with the K-PedsQL™ 4.0 score (β = -0.289, P = 0.031), verifying convergent and discriminant validities. The K-FAQLQ-TF also showed a good internal consistency (Cronbach's α coefficient = 0.966) and test-retest reliability (intraclass correlation coefficient = 0.974, P = 0.005). Construct validity was also established by a moderate correlation with the K-FAIM-TF (β = 0.699, P < 0.001). Our results suggest that the K-FAQLQ-CF and K-FAQLQ-TF are valid and reliable tools to evaluate the quality of life of children and adolescents with FA in Korea.

Purpose: Airway remodeling is a critical feature of asthma. Secreted protein acidic and rich in cysteine (SPARC), which plays a cardinal role in regulating cell-matrix interactions, has been implicated in various fibrotic diseases. However, the effect of SPARC in asthma remains unknown.

Methods: We studied the expression of SPARC in human bronchial epithelial cells and serum of asthmatics as well as in the lung tissues of chronic asthma mice. The role of SPARC was examined by using a Lentivirus-mediated SPARC knockdown method in the ovalbumin (OVA)-induced asthma mice. The biological processes regulated by SPARC were identified using RNA sequencing. The function of SPARC in the remodeling process induced by transforming growth factor β1 (TGFβ1) was conducted by using SPARC small interfering RNA (siRNA) or recombinant human SPARC protein in 16HBE cells.

Results: We observed that SPARC was up-regulated in human bronchial epithelia of asthmatics and the asthmatic mice. The levels of serum SPARC in asthmatics were also elevated and negatively correlated with the forced expiratory volume in one second (FEV1) to forced vital capacity ratio (FVC) (r = -0.485, P < 0.01) and FEV1 (%predicted) (r = -0.425, P = 0.001). In the chronic asthmatic mice, Lentivirus-mediated SPARC knockdown significantly decreased airway remodeling and airway hyper-responsiveness. According to gene set enrichment analysis, negatively enriched pathways found in the OVA + short hairpin-SPARC group included ECM organization and collagen formation. In the lung function studies, knockdown of SPARC by siRNA reduced the expression of remodeling-associated biomarkers, cell migration, and contraction by blocking the TGFβ1/Smad2 pathway. Addition of human recombinant SPARC protein promoted the TGFβ1-induced remodeling process, cell migration, and contraction in 16HBE cells via the TGFβ1/Smad2 pathway.

Conclusions: Our studies provided evidence for the involvement of SPARC in the airway remodeling of asthma via the TGFβ1/Smad2 pathway.

Purpose: Pollen forecasting systems can provide information for coping with respiratory allergies. They estimate daily pollen production, dispersal, deposition, and removal based on daily weather conditions to predict daily pollen concentrations and provide allergy warnings. As of 2023, the Korea Meteorological Administration (KMA) provides 2-day forecast of allergenic pollens. However, unlike these models, long-term analysis of annual observations of tree pollen reveal annual variations. Therefore, in this study, we aimed to develop annual prediction models for allergenic tree pollens based on long-term multi-site pollen and meteorological data.

Methods: Daily pollen concentrations were observed using Hirst-type volumetric spore traps at nine sites in Korea from 1998 to 2021, and daily weather data from the closest KMA stations were utilized. Models were developed to predict the seasonal pollen integral of seven tree species based on monthly mean temperature, wind speed, and total precipitation using three variable selection methods: 1) the t-test based key variable screening followed by linear regression with stepwise procedure (TM), 2) direct linear regression with stepwise procedure from the full variable model (FM), and 3) LASSO regression from the full variable model (LM).

Results: Data obtained during 1998-2017 and 2018=2021 were utilized for model development and validation, respectively. The root mean squared error, mean absolute error, mean error, and coefficient of determination (R²) revealed that the TM models were best suited for actual forecasting, even though R² in the TM model was lower than those of the FM and LM models.

Conclusions: The annual variation model in this study can be integrated with the daily pollen forecast model by controlling the annual pollen potential, and the accuracy of the daily forecast can be improved accordingly.

Allergen immunotherapy is regarded as the only disease-modifying treatment option for various allergic conditions, including allergic rhinitis and asthma. Among the routes of administration of allergens, sublingual immunotherapy (SLIT) has gained clinical interest recently, and the prescription of SLIT is increasing among patients with allergies. After 30 years of SLIT use, numerous pieces of evidence supporting its efficacy, safety, and mechanism allows SLIT to be considered as an alternative option to subcutaneous immunotherapy. Based on the progressive development of SLIT, the current guideline from the Korean Academy of Asthma, Allergy, and Clinical Immunology aims to provide an expert opinion by allergy, pediatrics, and otorhinolaryngology specialists with an extensive literature review. This guideline addresses the use of SLIT, including 1) mechanisms of action, 2) appropriate patient selection for SLIT, 3) the currently available SLIT products in Korea, and 4) updated information on its efficacy and safety. This guideline will facilitate a better understanding of practical considerations for SLIT.

Purpose: Eosinophilic asthma (EA) and non-asthmatic eosinophilic bronchitis (EB) share similar eosinophilic airway inflammation. Unlike EA, EB did not present airway hyperresponsiveness or airflow obstruction. We aimed to compare the mechanism underlying the different manifestations between EA and EB via sputum transcriptomics analysis.

Methods: Induced-sputum cells from newly physician-diagnosed EA, EB patients, and healthy controls (HCs) were collected for RNA sequencing.

Results: Bulk RNA sequencing was performed using sputum cells from patients with EA (n = 18), EB (n = 15) and HCs (n = 28). Principal component analysis revealed similar gene expression patterns in EA and EB. The most differentially expressed genes in EB compared with HC were also shared by EA, including IL4, IL5 IL13, CLC, CPA3, and DNASE1L3. However, gene set enrichment analysis showed that the signatures regulating macrophage activation were enriched in EA compared to EB. Sputum cells were profiled using single-cell RNA sequencing. FABP4+ macrophages, SPP1+ macrophages, FCN1+ macrophages, dendritic cells, T cells, B cells, mast cells, and epithelial cells were identified based on gene expression profiling. Analysis of cell-cell communication revealed that interactions between FCN1+ macrophages and other cells were higher in EA than in EB. A wealth of transforming growth factor beta (TGF-β) and vascular endothelial growth factor (VEGF) interactions between FCN1+ macrophages and other cells have been shown in EA. The gene expression levels of EREG, TGFBI, and VEGFA in FCN1+ macrophages of EA were significantly higher than those of EB. Furthermore, signatures associated with the response to TGF-β, cellular response to VEGF stimulus and developmental cell growth were enriched in FCN1+ macrophages of EA compared to those of EB.

Conclusions: FCN1+ macrophage activation associated with airway remodeling processes was upregulated in EA compared to that in EB, which may contribute to airway hyperresponsiveness and airflow obstruction.

Purpose: The methacholine challenge test (MCT) has high sensitivity but relatively low specificity for asthma diagnosis. This study aimed to develop and validate machine learning (ML) models to improve the diagnostic performance of MCT for asthma.

Methods: Data from 1,501 patients with asthma symptoms who underwent MCT between 2015 and 2020 were analyzed. The patients were grouped as either the training (80%, n = 1,265) and test sets (20%, n = 236) depending on the time of referral. The conventional model (provocative concentration that causes a 20% decrease in forced expiratory volume in one second [FEV₁]; PC₂₀ ≤ 16 mg/mL) was compared with the prediction models derived from five ML methods: logistic regression, support vector machine, random forest, extreme gradient boosting, and artificial neural network. The area under the receiver operator characteristic curves (AUROC) and area under the precision-recall curves (AUPRC) of each model were compared. The prediction models were further analyzed using different input combinations of FEV₁, forced vital capacity (FVC), and forced expiratory flow at 25%-75% of forced vital capacity (FEF_25%-75%) values obtained during MCT.

Results: In total, 545 patients (36.3%) were diagnosed with asthma. The AUROC of the conventional model was 0.856 (95% confidence interval [CI], 0.852-0.861), and the AUPRC was 0.759 (95% CI, 0.751-0.766). All the five ML prediction models had higher AUROC and AUPRC values than those of the conventional model, and random forest showed both highest AUROC (0.950; 95% CI, 0.948-0.952) and AUROC (0.909; 95% CI, 0.905-0.914) when FEV₁, FVC, and FEF_25%-75% were included as inputs.

Conclusions: Artificial intelligence-based models showed excellent performance in asthma prediction compared to using PC₂₀ ≤ 16 mg/mL. The novel technology could be used to enhance the clinical diagnosis of asthma.

Purpose: Suppression of tumorigenicity 2 (ST2) has been proposed as the receptor contributing to neutrophilic inflammation in patients with type 2-low asthma. However, the exact role of ST2 in neutrophil activation remains poorly understood.

Methods: A total of 105 asthmatic patients (classified into 3 groups according to control status: the controlled asthma [CA], partly-controlled asthma [PA], and uncontrolled asthma [UA] groups), and 104 healthy controls were enrolled to compare serum levels of soluble ST2 (sST2) and interleukin (IL)-33. Moreover, the functions of ST2 in neutrophils and macrophages (Mϕ) were evaluated ex vivo and in vivo.

Results: Serum sST2 levels were significantly higher in the UA group than in the CA or PA groups (P < 0.05 for all) with a negative correlation between serum sST2 and forced expiratory volume in 1 second % (r = -0.203, P = 0.038). Significantly higher expression of ST2 receptors on peripheral neutrophils was noted in the UA group than in the PA or CA groups. IL-33 exerted its effects on the production of reactive oxygen species, the formation of extracellular traps from neutrophils, and Mϕ polarization/activation. In neutrophilic asthmatic mice, treatment with anti-ST2 antibody significantly suppressed proinflammatory cytokines (tumor necrosis factor-alpha and IL-17A) as well as the numbers of immune cells (neutrophils, Mϕ, and group 3 innate lymphoid cells) in the lungs.

Conclusions: These results suggest that IL-33 induces the activation of neutrophils and Mϕ via ST2 receptors, leading to neutrophilic airway inflammation and poor control status of asthma. ST2 could be a therapeutic target for neutrophilic airway inflammation in patients with UA.

Purpose: The roles and mechanisms of long noncoding RNAs (lncRNAs) in T helper 2 (Th2) differentiation from allergic asthma are poorly understood. We aimed to explore a novel lncRNA, LincR-protein phosphatase 2 regulatory subunit B' gamma (PPP2R5C), in Th2 differentiation in a mouse model of asthma.

Methods: LincR-PPP2R5C from RNA-seq data of CD4⁺ T cells of asthma-like mice were validated and confirmed by quantitative reverse transcription polymerase chain reaction, northern blotting, nuclear and cytoplasmic separation, and fluorescence in situ hybridization (FISH). Lentiviruses encoding LincR-PPP2R5C or shRNA were used to overexpress or silence LincR-PPP2R5C in CD4⁺ T cells. The interactions between LincR-PPP2R5C and PPP2R5C were explored with western blotting, chromatin isolation by RNA purification assay, and fluorescence resonance energy transfer. An ovalbumin-induced acute asthma model in knockout (KO) mice (LincR-PPP2R5C KO, CD4 conditional LincR-PPP2R5C KO) was established to explore the roles of LincR-PPP2R5C in Th2 differentiation.

Results: LncR-PPP2R5C was significantly higher in CD4⁺ T cells from asthmatic mice ex vivo and Th2 cells in vitro. The lentivirus encoding LincR-PPP2R5C suppressed Th1 differentiation; in contrast, the short hairpin RNA (shRNA) lentivirus decreased LincR-PPP2R5C and Th2 differentiation. Mechanistically, LincR-PPP2R5C deficiency suppressed the phosphatase activity of the protein phosphatase 2A (PP2A) holocomplex, resulting in a decline in Th2 differentiation. The formation of an RNA-DNA triplex between LincR-PPP2R5C and the PPP2R5C promoter enhanced PPP2R5C expression and activated PP2A. LincR-PPP2R5C KO and CD4 conditional KO decreased Th2 differentiation, airway hyperresponsiveness and inflammatory responses.

Conclusions: LincR-PPP2R5C regulated PPP2R5C expression and PP2A activity by forming an RNA-DNA triplex with the PPP2R5C promoter, leading to Th2 polarization in a mouse model of acute asthma. Our data presented the first definitive evidence of lncRNAs in the regulation of Th2 cells in asthma.