Identification of critical genes associated with the development of asthma by co-expression modules construction

Obesity Enhances Non-Th2 Airway Inflammation in a Murine Model of Allergic Asthma

Obese patients with asthma present with aggravated symptoms that are also harder to treat. Here, we used a mouse model of allergic asthma sensitised and challenged to house dust mite (HDM) extracts to determine whether high-fat-diet consumption would exacerbate the key features of allergic airway inflammation. C57BL/6 mice were intranasally sensitised and challenged with HDM extracts over a duration of 3 weeks. The impact of high-fat-diet (HFD) vs. normal diet (ND) chow was studied on HDM-induced lung inflammation and inflammatory cell infiltration as well as cytokine production. HFD-fed mice had greater inflammatory cell infiltration around airways and blood vessels, and an overall more severe degree of inflammation than in the ND-fed mice (semiquantitative blinded evaluation). Quantitative assessment of HDM-associated Th2 responses (numbers of lung CD4+ T cells, eosinophils, serum levels of allergen-specific IgE as well as the expression of Th2 cytokines (Il5 and Il13)) did not show significant changes between the HFD and ND groups. Interestingly, the HFD group exhibited a more pronounced neutrophilic infiltration within their lung tissues and an increase in non-Th2 cytokines (Il17, Tnfa, Tgf-b, Il-1b). These findings provide additional evidence that obesity triggered by a high-fat-diet regimen may exacerbate asthma by involving non-Th2 and neutrophilic pathways.

CDC167 exhibits potential as a biomarker for airway inflammation in asthma

Current asthma treatments have been discovered to decrease the risk of disease progression. Herein, we aimed to characterize novel potential therapeutic targets for asthma. Differentially expressed genes (DEGs) for GSE64913 and GSE137268 datasets were characterized. Weighted correlation network analysis (WGCNA) was used to identify trait-related module genes within the GSE67472 dataset. The intersection of the module genes of interest, as well as the DEGs, comprised the key module genes that underwent additional candidate gene screening using machine learning. In addition, a bioinformatics-based approach was used to analyze the relative expression levels, diagnostic values, and reverently enriched pathways of the screened candidate genes. Furthermore, the candidate genes were silenced in asthmatic mice, and the inflammation and lung injury in the mice were validated. A total of 1710 DEGs were characterized in GSE64913 and GSE137268 for asthma patients. WGCNA identified 2367 asthma module genes, of which 285 overlapped with 1710 DEGs. Four candidate genes, CDC167, POSTN, SEC14L1, and SERPINB2, were validated using the intersection genes of three machine learning algorithms, including Least Absolute Shrinkage and Selection Operator, Random Forest, and Support Vector Machine. All the candidate genes were significantly upregulated in asthma patients and demonstrated diagnostic utility for asthma. Furthermore, silencing CDC167 reduced the levels of inflammatory cytokines significantly and alleviated lung injury in ovalbumin (OVA)-induced asthmatic mice. Our study demonstrated that CDC167 exhibits potential as diagnostic markers and therapeutic targets for asthma patients.

Decreased TLR7 expression was associated with airway eosinophilic inflammation and lung function in asthma: evidence from machine learning approaches and experimental validation

BACKGROUND

Asthma is a global public health concern. The underlying pathogenetic mechanisms of asthma were poorly understood. This study aims to explore potential biomarkers associated with asthma and analyze the pathological role of immune cell infiltration in the disease.

METHODS

The gene expression profiles of induced sputum were obtained from Gene Expression Omnibus datasets (GSE76262 and GSE137268) and were combined for analysis. Toll-like receptor 7 (TLR7) was identified as the core gene by the intersection of two different machine learning algorithms, namely, least absolute shrinkage and selector operation (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE), and the top 10 core networks based on Cytohubba. CIBERSORT algorithm was used to analyze the difference of immune cell infiltration between asthma and healthy control groups. Finally, the expression level of TLR7 was validated in induced sputum samples of patients with asthma.

RESULTS

A total of 320 differential expression genes between the asthma and healthy control groups were screened, including 184 upregulated genes and 136 downregulated genes. TLR7 was identified as the core gene after combining the results of LASSO regression, SVM-RFE algorithm, and top 10 hub genes. Significant differences were observed in the distribution of 13 out of 22 infiltrating immune cells in asthma. TLR7 was found to be closely related to the level of several infiltrating immune cells. TLR7 mRNA levels were downregulated in asthmatic patients compared with healthy controls (p = 0.0049). The area under the curve of TLR7 for the diagnosis of asthma was 0.7674 (95% CI 0.631-0.904, p = 0.006). Moreover, TLR7 mRNA levels were negatively correlated with exhaled nitric oxide fraction (r = - 0.3268, p = 0.0347) and the percentage of peripheral blood eosinophils (%) (r = - 0.3472, p = 0.041), and positively correlated with forced expiratory volume in the first second (FEV1) (% predicted) (r = 0.3960, p = 0.0071) and FEV1/forced vital capacity (r = 0.3213, p = 0.0314) in asthmatic patients.

CONCLUSIONS

Decreased TLR7 in the induced sputum of eosinophilic asthmatic patients was involved in immune cell infiltration and airway inflammation, which may serve as a new biomarker for the diagnosis of eosinophilic asthma.

Proline is increased in allergic asthma and promotes airway remodeling

Proline and its synthesis enzyme pyrroline-5-carboxylate reductase 1 (PYCR1) are implicated in epithelial-mesenchymal transition (EMT), yet how proline and PYCR1 function in allergic asthmatic airway remodeling via EMT has not yet been addressed to our knowledge. In the present study, increased levels of plasma proline and PYCR1 were observed in patients with asthma. Similarly, proline and PYCR1 in lung tissues were high in a murine allergic asthma model induced by house dust mites (HDMs). Pycr1 knockout decreased proline in lung tissues, with reduced airway remodeling and EMT. Mechanistically, loss of Pycr1 restrained HDM-induced EMT by modulating mitochondrial fission, metabolic reprogramming, and the AKT/mTORC1 and WNT3a/β-catenin signaling pathways in airway epithelial cells. Therapeutic inhibition of PYCR1 in wild-type mice disrupted HDM-induced airway inflammation and remodeling. Deprivation of exogenous proline relieved HDM-induced airway remodeling to some extent. Collectively, this study illuminates that proline and PYCR1 involved with airway remodeling in allergic asthma could be viable targets for asthma treatment.

Gene expression profiles and bioinformatics analysis in lung samples from ovalbumin-induced asthmatic mice

BACKGROUND

Asthma is characterized by chronic inflammation and airway remodeling. However, limited study is conducted on the gene expression profiles of ovalbumin (OVA) induced asthma in mice. Here, we explored the gene expression profiles in lung tissues from mice with OVA-induced asthma using microarray and bioinformatics analysis.

METHODS

For establishment of OVA-induced asthma model, mice first received intraperitoneal sensitization with OVA on day 0, 7 and 14, followed by atomizing inhalation of OVA 3 times a week for 8 weeks. The lung tissues were collected and subjected to microarray analysis, bioinformatics analysis and expression validation.

RESULTS

Microarray data of lung tissues suggested that 3754 lncRNAs and 2976 mRNAs were differentially expressed in lung tissues between control and asthmatic mice, including 1647 up-regulated and 2106 down-regulated lncRNAs, and 1201 up-regulated and 1766 down-regulated mRNAs. GO analysis displayed that the up-regulated genes were enriched in inflammatory response, leukocyte migration involved in inflammatory response, and Notch signaling pathway. KEGG pathway analysis indicated that the enriched pathway terms of the up-regulated gene included Toll-like receptor signaling pathway and Th17 cell differentiation signaling pathway. Additionally, based on the previously published literatures on asthma and inflammation, we screened out down-regulated genes, such as Smg7, Sumo2, and Stat5a, and up-regulated genes, such as Myl9, Fos and Tlr4. According to the mRNA-lncRNA co-expression network, we selected lncRNAs associated with above genes, including the down-regulated lncRNAs of NONMMUT032848, NONMMUT008873, NONMMUT009478, and NONMMUT006807, and the up-regulated lncRNAs of NONMMUT052633, NONMMUT05340 and NONMMUT042325. The expression changes of the above genes were validated in lung tissues by real-time quantitaive PCR and Western blot.

CONCLUSIONS

Overall, we performed gene microarray on lung samples from OVA-induced asthmatic mice and summarized core mRNAs and their related lncRNAs. This study may provide evidence for further research on the therapeutic targets of asthma.

Construction of Severe Eosinophilic Asthma Related Competing Endogenous RNA Network by Weighted Gene Co-Expression Network Analysis

Background: Currently, disease control in patients with severe eosinophilic asthma is not optimistic. Competing endogenous RNA (ceRNA) networks have been found to play a key role in asthma in recent years. However, it is unclear whether ceRNA networks play an important part in severe eosinophilic asthma. Methods: Firstly, gene expression profiles related to severe eosinophilic asthma were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, the key modules were identified by the weighted gene co-expression network analysis (WGCNA). Thirdly, genes in modules highly associated with severe eosinophilic asthma were selected for further construction of the ceRNA network. Fourthly, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on hub genes. Finally, the results of this study were validated on the GSE143303, GSE137268, and GSE147878 datasets. Results: 22 severe eosinophilic asthmatics and 13 healthy controls were extracted for WGCNA. We found that the genes in the black module (r = -0.75, p < 0.05) and yellow module (r = 0.65, p < 0.05) were highly associated with severe eosinophilic asthma. EP300 was discovered to serve the key connecting function in the ceRNA network. Surprisingly, lncRNAs seem to eliminate the role of EP300 in the black module and we discovered that CCT8 and miRNA-mRNA formed a circRNA-miRNA-mRNA network in the yellow module. We found that EP300 and FOXO3 in the black module were regulated by steroid hormones in the enrichment analysis, which were related to the medication used by the patient. Through validation of other datasets, we found that the hub genes in the yellow module were the key genes in the treatment of severe eosinophilic asthma. In particular, RPL17 and HNRNPK might specifically regulate severe eosinophilic asthma. Conclusion: RPL17 and HNRNPK might particularly regulate severe eosinophilic asthma. Our results could be useful to provide potential immunotherapy targets and prognostic markers for severe eosinophilic asthma.

Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis

Background

Asthma is a chronic lung disease characterized by reversible inflammation of the airways. The imbalance of Th1/Th2 cells plays a significant role in the mechanisms of asthma. The aim of this study was to identify asthma-related key genes and functionally enriched pathways in a Th2-high group by using weighted gene coexpression network analysis (WGCNA).

Methods

The gene expression profiles of GSE4302, which included 42 asthma patients and 28 controls, were selected from the Gene Expression Omnibus (GEO). A gene network was constructed, and genes were classified into different modules using WGCNA. Gene ontology (GO) was performed to further explore the potential function of the genes in the most related module. In addition, the expression profile and diagnostic capacity (ROC curve) of hub genes of interest were verified by dataset GSE67472.

Results

In dataset GSE4302, subjects with asthma were divided into Th2-high and Th2-low groups according to the expression of the SERPINB2, POSTN and CLCA1 genes. A weighted gene coexpression network was constructed, and genes were classified into 7 modules. Among them, the red module was most closely associated with Th2-high asthma, which contained 60 genes. These genes were significantly enriched in different biological processes and molecular functions. A total of 8 hub genes (TPSB2, CPA3, ITLN1, CST1, SERPINB10, CEACAM5, CHD26 and P2RY14) were identified, and the expression levels of these genes (except TPSB2) were confirmed in dataset GSE67472. ROC curve analysis validated that the expression of these 8 genes exhibited excellent diagnostic efficiency for Th2-high asthma and Th2-low asthma.

Conclusions

The study provides a novel perspective on Th2-high asthma by WGCNA, and the hub genes and potential pathways involved may be beneficial for the diagnosis and management of Th2-high asthma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01241-9.

ANO7: Insights into topology, function, and potential applications as a biomarker and immunotherapy target

Anoctamin 7 (ANO7) is a member of the transmembrane protein TMEM16 family. It has a conservative topology similar to other members in this family, such as the typical eight-transmembrane domain, but it also has unique features. Although the ion channel role of ANO7 has been well accepted, evolutionary analyses and relevant studies suggest that ANO7 may be a multi-facet protein in function. Studies have shown that ANO7 may also function as a scramblase. ANO7 is highly expressed in prostate cancer as well as normal prostate tissues. A considerable amount of evidence has confirmed that ANO7 is associated with human physiology and pathology, particularly with the development of prostate cancer, which makes ANO7 a good candidate as a diagnostic and prognostic biomarker. In addition, ANO7 may be a potential target for prostate cancer immunotherapy. Antibody-based or T cell-mediated immunotherapies against prostate cancer by targeting ANO7 have been highly anticipated. ANO7 may also correlate with several other types of cancers or diseases, where further studies are warranted.

Identification of biomarkers and pathogenesis in severe asthma by coexpression network analysis

BACKGROUND

Severe asthma is a heterogeneous inflammatory disease. The increase in precise immunotherapy for severe asthmatics requires a greater understanding of molecular mechanisms and biomarkers. In this study, we aimed to identify the underlying mechanisms and hub genes that determine asthma severity.

METHODS

Differentially expressed genes (DEGs) were identified based on bronchial epithelial brushings from mild and severe asthmatics. Then, weighted gene coexpression network analysis (WGCNA) was used to identify gene networks and the module most significantly associated with asthma severity. Furthermore, hub gene screening and functional enrichment analysis were performed. Replication with another dataset was conducted to validate the hub genes.

RESULTS

DEGs from 14 mild and 11 severe asthmatics were subjected to WGCNA. Six modules associated with asthma severity were identified. Three modules were positively correlated (P < 0.001) with asthma severity and contained genes that were upregulated in severe asthmatics. Functional enrichment analysis showed that genes in the most significant module were mainly enriched in neutrophil activation and degranulation, and cytokine receptor interaction. Hub genes included CXCR1, CXCR2, CCR1, CCR7, TLR2, FPR1, FCGR3B, FCGR2A, ITGAM, and PLEK; CXCR1, CXCR2, and TLR2 were significantly related to asthma severity in the validation dataset. The combination of ten hub genes exhibited a moderate ability to distinguish between severe and mild-moderate asthmatics.

CONCLUSION

Our results identified biomarkers and characterized potential pathogenesis of severe asthma, providing insight into treatment targets and prognostic markers.

IL-1 signaling pathway molecules as key markers in childhood asthma

OBJECTIVE

The prevalence of childhood asthma has been increasing in recent years. This study aims to investigate the involvement of the key molecules of IL-1 (interleukin-1) signaling pathways in pediatric patients with asthma.

METHODS

Differentially expressed genes (DEGs) associated with IL-1 signaling pathways were identified with RNA-seq from peripheral blood samples collected from asthmatic or healthy children and were further verified in clinical peripheral blood samples. Cellular models and asthmatic mice were subsequently developed to validate the identified asthmatic markers.

RESULTS

Among the DEGs identified by RNA-seq, eight signal transducers associated with the IL-1 signaling network, namely IL-1RN, IL-1β, IL-1RAP, IRAK3, IL-1R1, MYD88, IRAK2, and PELI1, were found to be substantially upregulated in children with asthma. Interestingly, a significant serially increased expression of four genes (IL-1RN, IL-1RAP, IRAK3, and PELI1) was observed in healthy subjects, patients with chronic persistent asthma and patients with acute exacerbation asthma. In particular, these four genes were continuously overexpressed in recurrent patients. A significant induction of the above four genes was then observed in house dust mite (HDM)-stimulated peripheral blood mononuclear cells (PBMCs) and ovalbumin (OVA)-induced asthmatic mice. In addition, a time-dependent induction of IL-1RAP and PELI1 was also detected in HDM-treated THP-1 cells, an acute monocytic leukemia cell line.

CONCLUSIONS

These results demonstrate that IL-1RN, IL-1RAP, IRAK3, and PELI1, which are signal transducers of the IL-1 signaling pathway, could serve as biomarkers for the pathogenesis of childhood asthma and for potential therapeutic targets of asthma.