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

Epithelial and immune transcriptomic characteristics and possible regulatory mechanisms in asthma exacerbation: insights from integrated studies

Background

Asthma exacerbation significantly contribute to disease mortality and result in heightened health care expenditures. This study was aimed at gaining important new insights into the heterogeneity of epithelial and immune cells and elucidating key regulatory genes involved in the pathogenesis of asthma exacerbation.

Methods

Functional enrichment, pseudotime, metabolism and cell-cell communication analyses of epithelial cells and immune cells in single-cell RNA sequencing (scRNA-seq) dataset were applied. Immune infiltration analysis was performed in bulk RNA sequencing (bulk RNA-seq) dataset. Key regulatory genes were obtained by taking the intersection of the differentially expressed genes (DEGs) between control and asthma group in epithelial cells, immune cells and bulk RNA-seq data. Asthma animal and in vitro cell line models were established to verify the key regulatory genes expression by employing quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results

ScRNA-seq analysis identified 7 epithelial subpopulations and 14 distinct immune cell types based on gene expression profiles. Further analysis demonstrated that these cells manifested high heterogeneity at the levels of functional variations, dynamics, communication patterns and metabolic changes. Notably, TMPRSS11A, TUBA1A, SCEL, ICAM4, TMPRSS11B, IGFBP2, CLC, NFAM1 and F13A1 were identified as key regulatory genes of asthma. The results of the qRT-PCR demonstrated that the 9 key regulatory genes were involved in asthma.

Conclusions

We systematically explored epithelial and immune characteristics in asthma exacerbation and identified 9 key regulatory genes underlying asthma occurrence and progression, which may be valuable for providing new insights into the cellular and molecular mechanisms driving asthma exacerbations.

Genetic biomarker prediction based on gender disparity in asthma throughout machine learning

Background

Asthma is a chronic respiratory condition affecting populations worldwide, with prevalence ranging from 1-18% across different nations. Gender differences in asthma prevalence have attracted much attention.

Purpose

The aim of this study was to investigate biomarkers of gender differences in asthma prevalence based on machine learning.

Method

The data came from the gene expression omnibus database (GSE69683, GSE76262, and GSE41863), which involved in a number of 575 individuals, including 240 males and 335 females. Theses samples were divided into male group and female group, respectively. Grid search and cross-validation were employed to adjust model parameters for support vector machine, random forest, decision tree and logistic regression model. Accuracy, precision, recall, and F1 score were used to evaluate the performance of the models during the training process. After model optimization, four machine learning models were utilized to predict biomarkers of sex differences in asthma. In order to validate the accuracy of our results, we performed Wilcoxon tests on the genes expression.

Result

In datasets GSE76262 and GSE69683, support vector machine, random forest, logistic regression, and decision tree all achieve 100% accuracy, precision, recall, and F1 score. Our findings reveal that XIST serves as a common biomarker among the three samples, comprising a total of 575 individuals, with higher expression levels in females compared to males (p < 0.01).

Conclusion

XIST serves as a genetic biomarker for gender differences in the prevalence of asthma.

Insight into the role of mitochondrion-related gene anchor signature in mitochondrial dysfunction of neutrophilic asthma

OBJECTIVE

At present, targeting molecular-pharmacological therapy is still difficult in neutrophilic asthma. The investigation aims to identify and validate mitochondrion-related gene signatures for diagnosis and specific targeting therapeutics in neutrophilic asthma.

METHODS

Bronchial biopsy samples of neutrophilic asthma and healthy people were identified from the GSE143303 dataset and then matched with human mitochondrial gene data to obtain mitochondria-related differential genes (MitoDEGs). Signature mitochondria-related diagnostic markers were jointly screened by support vector machine (SVM) analysis, least absolute shrinkage, and selection operator (LASSO) regression. The expression of marker MitoDEGs was evaluated by validation datasets GSE147878 and GSE43696. The diagnostic value was evaluated by receiver operating characteristic (ROC) curve analysis. Meanwhile, the infiltrating immune cells were analyzed by the CIBERSORT. Finally, oxidative stress level and mitochondrial functional morphology for asthmatic mice and BEAS-2B cells were evaluated. The expression of signature MitoDEGs was verified by qPCR.

RESULTS

67 MitoDEGs were identified. Five signature MitoDEGs (SOD2, MTHFD2, PPTC7, NME6, and SLC25A18) were further screened out. The area under the curve (AUC) of signature MitoDEGs presented a good diagnostic performance (more than 0.9). There were significant differences in the expression of signature MitoDEGs between neutrophilic asthma and non-neutrophilic asthma. In addition, the basic features of mitochondrial dysfunction were demonstrated by in vitro and in vivo experiments. The expression of signature MitoDEGs in the neutrophilic asthma mice presented a significant difference from the control group.

CONCLUSIONS

These MitoDEGs signatures in neutrophilic asthma may hold potential as anchor diagnostic and therapeutic targets in neutrophilic asthma.

Single-cell profiling of bronchoalveolar cells reveals a Th17 signature in neutrophilic severe equine asthma

Severe equine asthma (SEA) is a complex respiratory condition characterized by chronic airway inflammation. It shares many clinical and pathological features with human neutrophilic asthma, making it a valuable model for studying this condition. However, the immune mechanisms driving SEA have remained elusive. Although SEA has been primarily associated with a Th2 response, there have also been reports of Th1, Th17, or mixed-mediated responses. To uncover the elusive immune mechanisms driving SEA, we performed single-cell mRNA sequencing (scRNA-seq) on cryopreserved bronchoalveolar cells from 11 Warmblood horses, 5 controls and 6 with SEA. We identified six major cell types, including B cells, T cells, monocytes-macrophages, dendritic cells, neutrophils, and mast cells. All cell types exhibited significant heterogeneity, with previously identified and novel cell subtypes. Notably, we observed monocyte-lymphocyte complexes and detected a robust Th17 signature in SEA, with CXCL13 upregulation in intermediate monocytes. Asthmatic horses exhibited expansion of the B-cell population, Th17 polarization of the T-cell populations, and dysregulation of genes associated with T-cell function. Neutrophils demonstrated enhanced migratory capacity and heightened aptitude for neutrophil extracellular trap formation. These findings provide compelling evidence for a predominant Th17 immune response in neutrophilic SEA, driven by dysregulation of monocyte and T-cell genes. The dysregulated genes identified through scRNA-seq have potential as biomarkers and therapeutic targets for SEA and provide insights into human neutrophilic asthma.

Bioinformatics analysis of ceRNA network of autophagy-related genes in pediatric asthma

The molecular underpinnings of pediatric asthma present avenues for targeted therapies. A deeper exploration into the significance of differentially expressed autophagy-related genes (DE-ARGs) and their interactions with the long noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA network may offer insights into the pathogenesis of pediatric asthma. DE-ARGs were retrieved from the Gene Expression Omnibus and the Human Autophagy Database. These DE-ARGs were subjected to comprehensive analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, Gene Set Enrichment Analysis, and protein-protein interaction networks. The identified DE-ARGs were further verified for core gene expression. The miRDB and ENCORI databases were used for inverse miRNA predictions. Furthermore, miRNA-lncRNA interactions were predicted using LncBase and ENCORI platforms. Following the exclusion of lncRNAs exclusively localized in the nucleus and extracellular space, a competitive endogenous RNA (ceRNA) network was established and subsequently subjected to detailed analysis. The mRNA expression patterns in the ceRNA network were validated using quantitative real-time PCR. In total, 31 DE-ARGs were obtained, of which 29 were up-regulated and 2 were down-regulated. Notably, the autophagy, regulation of apoptotic signaling pathways, interferon-α/β signaling, interferon γ signaling, autophagy-animal, and apoptosis pathways were predominantly enriched in pediatric asthma. Five hub genes (VEGFA, CFLAR, RELA, FAS, and ATF6) were further analyzed using the Gene Expression Omnibus dataset to verify their expression patterns and diagnostic efficacy. Four hub genes (VEGFA, CFLAR, RELA, and FAS) were obtained. Finally, a ceRNA network of 4 mRNAs (VEGFA, CFLAR, RELA, and FAS), 3 miRNAs (hsa-miR-320b, hsa-miR-22-3p, and hsa-miR-625-5p), and 35 lncRNAs was constructed by integrating data from literature review and analyzing the predicted miRNAs and lncRNAs. Moreover, the quantitative real-time PCR data revealed a pronounced upregulation of Fas cell surface death receptor. The identification of 4 DE-ARGs, especially Fas cell surface death receptor, has shed light on their potential pivotal role in the pathogenesis of pediatric asthma. The established ceRNA network provides novel insights into the autophagy mechanism in asthma and suggests promising avenues for the development of potential therapeutic strategies.

Identification of key modules and hub genes for eosinophilic asthma by weighted gene co-expression network analysis

Objective: Eosinophilic asthma (EA) is one of the most important asthma phenotypes with distinct features. However, its genetic characteristics are not fully understood. This study aimed to investigate the transcriptome features and to identify hub genes of EA.Methods: Differentially expressed genes (DEGs) analysis, weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were performed to construct gene networks and to identify hub genes. Enrichment analyses were performed to investigate the biological processes, pathways and immune status of EA. The hub genes were validated in another dataset. The diagnostic value of the identified hub genes was assessed by receiver operator characteristic curve (ROC) analysis.Results: Compared with NEA, EA had a different gene expression pattern, in which 81 genes were differentially expressed. WGCNA identified two gene modules significantly associated with EA. Intersections of the DEGs and the genes in the modules associated with EA were mainly enriched in chemotaxis and signal transduction by GO and KEGG enrichment analyses. Single-sample gene set enrichment analysis indicated that EA had different immune infiltration and functions compared with NEA. Seven hub genes of EA were identified and validated, including CCL17, CCL26, CD1C, CXCL11, CXCL10, CCL22 and CCR7, all of which have diagnostic value for distinguishing EA from NEA (All AUC >0.7) .Conclusions: This study demonstrated the distinct gene expression patterns, biological processes and immune status of EA. Hub genes of EA were identified and validated. Our study could provide a framework of co-expression gene modules and potential therapeutic targets for EA.