Current Pediatric Research

Abstract

Background: The pathogenic mechanisms of children asthma and the relationship between children asthma and children wheeze are complex and not fully understood. The purpose of this study was to identify the pathways and hub genes along with common Differentially Expressed Genes (DEGs) between children asthma and children wheeze, and to explore the specific insights for the clinical asthma and wheeze therapies in children.

Methods: The GSE123750 dataset was downloaded from the gene expression omnibus database. Differentially Expressed Genes (DEGs) between the children asthma and children wheeze groups were identified using the “Limma” package in the R-language. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, along with Gene Set Enrichment Analysis (GSEA), were performed to explore potential signaling pathways associated with the DEGs. Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to identify gene network modules associated with children asthma using microarray data. Functional enrichment analysis was performed on the co-expression genes within specific highlighted modules.

Results: A total of 334 DEGs were identified in children with asthma compared to those with wheezing. Furthermore, five KEGG pathways, namely ribosome, oocyte meiosis, p53 signaling pathway, B-cell receptor signaling pathway, and cellular senescence, exhibited significant enrichment. The Protein-Protein Interaction (PPI) network analysis in cytoscape highlighted four hub genes: TOP2A, CDK1, CENPA, and KIF11. GSEA results indicated a positive correlation between asthma and down-regulation of early-stage progenitor T-lymphocyte genes, up-regulation of plasma cell genes in bone marrow and blood, deadenylation-dependent mRNA decay, and down-regulation of genes in monocytes. The WGCNA analysis identified the pink module as being highly associated with asthma. Genes within this key module were primarily associated with organ or tissue-specific immune response, mucosal immune response, and the ribosome signaling pathway according to GO and KEGG pathway enrichment analyses. Furthermore, a Protein-Protein Interaction (PPI) network analysis using STRING and Cytoscape revealed one hub gene, IMPA1, among the key genes.

Conclusions: In conclusion, our study not only advances the current understanding of childhood asthma but also provides valuable insights for identifying potential biomarkers to improve early diagnosis of asthma in preschool children with wheezing. These findings can assist clinicians in making informed decisions regarding early treatment interventions.

Keywords

Asthma, Wheeze, Bioinformatics analysis, Differentially expressed genes, WGCNA

Abbreviations

GEO: Gene Expression Omnibus; DEGs: Differentially Expressed Genes; WGCNA: Weighted Gene Co- Expression Network Analysis; PPI: Protein–Protein Interaction Network; TOM: Topological Overlap Matrix; GS: Gene Significance; MEs: Module Eigengenes; MM: Module Membership; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: Gene Set Enrichment Analysis.

Introduction

Asthma is a chronic heterogeneous, inflammatory airway disease that is characterized by airway hyper responsiveness, reversible airflow limitation, mucus overproduction and airway wall remodeling [1-3]. Among children, asthma is the most common chronic disease [4], with a rising prevalence worldwide [5]. In the United States, one out of every six children diagnosed with asthma still seeks treatment at an emergency department, and one out of every 20 children with asthma requires hospitalization [6].

Wheezing is not a disorder but rather a symptom characterized by a persistent whistling sound that occurs during breathing. This sound typically indicates a narrowing or obstruction in certain areas of the respiratory airways. Nearly 50% of young children experience wheezing by the time they reach 6 years of age. Preschool-aged children contribute to a disproportionately large share of healthcare and economic resources due to wheeze and asthma. However, the exact mechanisms underlying these episodes and their connection to the development of asthma in later stages of life remain largely unknown. Identifying which preschoolers with wheezing will eventually develop asthma poses a significant challenge [7-9].

In recent years, with the development of microarray technology [10,11], non-invasive biomarkers can be the key to personalized medicine in wheeze and asthma whereby patients receive specific treatments due to their identifiable molecular signatures. Diverse biomarkers have been used in the diagnosis of children asthma [12,13]. However, none of these biomarkers has sufficient specificity and/or sensitivity in the clinical practice [14]. Furthermore, most previous studies on the airway micro biota have relied on bronchoscopic brushing or sputum samples. Published prediction rules for identifying preschool children with asthma at school age lack a gold standard for diagnosis [15]. Consequently, reliable biomarkers that can be used in routine medical settings are currently lacking. Therefore, there is an urgent need for new biomarkers.

The aim of this study is to discuss a practical approach for determining which preschool-aged children with wheeze or asthma should receive treatment and to identify some new biomarkers for childhood asthma. We investigated specific genes and enrichment pathways associated with wheeze and asthma. Subsequently, we constructed gene co-expression networks and identified highly specific hub genes for wheeze and asthma. These hub genes have the potential to serve as therapeutic targets and biomarkers for children with wheeze or asthma.

Materials and Methods

Retrieving data

The gene expression omnibus database is a public genome database (http://www.ncbi.nlm.nih.gov/geo). One microarray dataset (GSE123750) of comparing children asthma and the wheeze was extracted from the database. The gene expression data of GSE123750 was downloading from the platform Affymetrix HT HG-U133+PM array plate. The dataset was generated based on the samples of induced plasma from 104 children with asthma and 112 children with wheeze.

Data processing and identification of DEGs

First, the microarray data were preprocessed using the RMA (robust multi-array average). Then the affy package (https:// bioconductor.org/biocLite.R) in the R software bio-conductor was used to read the data. The adjusted P-value (adjust P) and log Fold Change (log FC) were used to select significant DEGs. The cut-off criteria of |logFC| ≥ 0.5 and adjusted Pvalue< 0.05 were considered statistically significant. Additionally, DEGs were performed by Limma package in R software (version 4.1.2). Finally, we visualized the DEGs. Volcano plot and heat map were plotted by ggplot2 and complex heat map package.

GO Functional enrichment analysis

Functional enrichment of DEGs was analyzed in 3 categories of the Gene Ontology (GO) domains: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). GO functional enrichment analysis and KEGG pathways of the DEGs was performed using the cluster profiler package in R.

PPI network and module analysis

STRING database (http://www.string-db.org/) was utilized to construct the PPI network of the DEGs-coded proteins. The avg local clustering coefficient was 0.423. To screen for the hub genes, cytoNCA plugin from cytoscape (version 3.9.1) software was used to visualize the most significant PPI network based on the betweenness. We identified top 4 hub genes, including TOP2A, CDK1, CENPA, KIF11.

Gene set enrichment analysis

GSEA is a computational method for calculating whether a set of genes defined by a priori show statistical significance between two disease type and biological processes. It was used to explore the differential gene sets between asthma and wheeze in dataset GSE123750. Compared with the GO and KEGG pathway enrichment analysis was only used to detect DEGs, GSEA was used to detect all genes in the dataset and judge its influence on phenotype. The annotated gene sets downloaded from the Molecular Signature Database (MSigDB), were considered as the reference gene sets. The number of permutations was set to 1,000, and the permutation type was set as “phenotype”. Pvalues< 0.05 and FDR < 0.25 were significant in statistics.

Gene Set Enrichment Analysis (GSEA) plots of the most enriched gene sets in the asthma and wheeze group. The top 4 most (a-d) enriched pathways in the asthma group; (a): Genes down-regulated at early stages of progenitor T lymphocyte; (b): Up-regulated of memory IgG IgA B cells vs. plasma cells; (c): Genes down-regulated in monocytes; (d): Deadenylationdependent mRNA decay. The top 4 (e-h) most enriched pathways in the wheeze group; (e): Hepatic graft vs. host disease; (f): FGFR3 signaling in chondrocyte proliferation and terminal differentiation; (g): Genes up-regulated of effective memory CD4 T cells; (h): Glycosaminoglycan degradation.

WGCNA

Weighted Correlation Network Analysis (WGCNA) is a powerful algorithm to identify significant clusters and module Eigen gene. WGCNA is widely used to find the co-expressed gene modules with high biological significance and explore the relationship between gene networks and diseases. Therefore, we used the WGCNA to obtain the asthma and wheeze associated modules. More than 10000 genes were obtained by sequencing in the GEO dataset, and most of these genes did not have different expression between samples, we selected top 5,000 Median Absolute Deviation (MAD) genes to construct the representation matrix and the appropriate power parameter was decided by pickSoftThreshold function.

The “WGCNA” package in R.4.1.2 software were used to performed the WGCNA analysis. Before analysis, the hierarchical clustering analysis was performed using the Hclust function in R language to exclude the outlier samples. Then the appropriate soft powers β (ranged from 1 to 20) was selected using the function of “pickSoftThreshold” in the WGCNA package according to the standard of scale-free network. Next, the soft power value β and gene correlations matrix among all gene pairs calculated by Pearson analysis were used to build adjacency matrix. Then the Topological Overlap Matrix (TOM) and the corresponding dissimilarity (1−TOM) were transformed from the adjacency matrix.

A hierarchical clustering dendrogram was further built and similar gene expressions were divided into different modules. Finally, the expression profiles of each module were summarized by the Module Eigengene (ME) and the correlation between the ME and a clinical feature was calculated. Therefore, the modules with high correlation coefficient with clinical features were focused and the genes in these modules were selected for subsequent analyses. In this study, the soft threshold β was 7 in the WGCNA analysis. (The other parameters were the following: network type = “unsigned”, min module size=30, merge cut height=0.25 and deep split=2).

Results

Identification of DEGs

334 DEGs were obtained from the GSE123750. It included 83 up-regulation genes and 251 down-regulation genes. The gene expression profile of DEGs containing 2 sets of sample data is shown in Figure 1.

Figure 1: Identification of differentially expressed genes in the merged dataset. (A): Volcano plot of the genes, the blue dots represent the down-regulated genes, and tangerine dots represent the up-regulated genes, while the gray dots showed genes with no significance. (B): A heat map plot of the differentially expressed genes.

Pathway analysis

In order to better understand the biological function of DEGs, we conducted GO function and KEGG enrichment analysis. GO results showed that DEGs significantly enriched in cyclindependent protein serine/threonine kinase regulator activity, protein kinase regulator activity, kinase regulator activity of MF, condensed nuclear chromosome kinetochore, cytosolic ribosome, immunoglobulin complex of CC, B cell activation, nuclear chromosome segregation, humoral immune response of BP. Moreover, KEGG analysis showed that the DEGs were enriched in p53 signaling pathway, Ribosome, Oocyte meiosis Figure 2.

Figure 2: Gene ontology enrichment and KEGG enrichment analysis. (): BP; (): CC; (): MF; (): KEGG.

Analysis of the PPI network

STRING was used to construct DEGs PPI networks with confidence scores ≥ 0.4 and visualized using cytoscape software (version 3.9.1); we constructed a PPI network complex with 36 nodes and 302 edges. The top 4 genes, including TOP2A, CDK1, CENPA, and KIF11, were taken to as hub genes based on the node degree score generated via cytoscape Figure 3.

Figure 3: Four hub genes selected from PPI network. Hub genes screened by between centrality according to cytoNCA plugin.

Screening differential gene sets with GSEA

To further elucidate the different pathway involved in wheeze and asthma, GSEA was performed between the two groups in GSE123750. Pathways related to genes down-regulated at early stages of progenitor T lymphocyte ，up-regulated of memory IgG IgA B cells vs. plasma cells, genes downregulated in monocytes, and deadenylation-dependent mRNA decay were most enriched in asthma. Hepatic graft vs. host disease, FGFR3 signaling in chondrocyte proliferation and terminal differentiation, genes up-regulated of effective memory CD4 T cells, and glycosaminoglycan degradation were most significantly enriched in wheeze group Figure 4.

Figure 4: Gene Set Enrichment Analysis (GSEA) plots of the most enriched gene sets in the asthma and wheeze group. (A): The top 4 (a-d) most enriched pathways in the asthma group; (B): Up-regulated of memory IgG IgA B cells vs. plasma cells; (C): Genes down-regulated in monocytes; (D): Deadenylationdependent mRNA decay. The top 4 (e-h) most enriched pathways in the wheeze group; (E): Hepatic graft vs. host disease; (F): FGFR3 signaling in chondrocyte proliferation and terminal differentiation; (G): Genes up-regulated of effective memory CD4 T cells; (H): Glycosaminoglycan degradation.

Enrichment analyses of module genes identified by weighted gene co-expression network analysis

WGCNA was performed to get a deeper insight into the association between the key modules and asthma. Sample dendrogram is shown in Figure 5A. The scale independence and mean connectivity are shown in Figure 5B by setting the soft threshold to 7. Afterwards, a topological overlap matrix was generated; a series of modules were subsequently detected by hierarchical clustering, and we narrowed down the number of modules using dynamic tree cut algorithm, whereby modules with high similarity were merged. We present the cluster dendrogram in Figure 5C. And find that 12 modules were identified. The network heat map of DEGs is shown in Figure 5D. Finally, the module trait relationships are shown in Figure 5E. The pink module was significantly correlated with asthma and selected for further analysis.

Figure 5: Identifcation of hub modules using the WGCNA analysis. (A): Sample dendrogram and trait heat map; (B): Analyses of network topology for various soft-thresholding powers and the scale-free topology were set as 0.9 roughly; (C): The dendrogram of the co-expression network was clustered based on the dissimilarity; (D): Network heat map of DEGs; (E): The module-trait heatmap showing the correlation between Module Eigengenes (ME) and traits.

The GO enrichment analysis of pink module genes were significantly enriched in biological processes related to organ or tissue specific immune response and mucosal immune response. The KEGG pathway enrichment analysis indicated that the pink module genes were significantly enriched ribosome pathways Figure 6A. The key genes in the pink module were analyzed with PPI network. There were 13 interactions among 10 key genes in the pink module. One hub genes were found in PPI networks using cytohubba Figure 6B.

Figure 6: The gene functional enrichment analysis of pink module. (A): GO and KEGG enrichment in the pink module;(B): Protein–protein network of the hub genes in the pink module.

Discussion

The pathogenesis of asthma is multifactorial, with environmental and genetic factors interacting to produce some pathological features. In current investigation, 334 DEGs in total were identified, consisting of 83 up-regulated genes and 251 down-regulated genes. The results of GO functional classification indicated that the DEGs were mainly enriched in cyclin-dependent protein serine/threonine kinase regulator activity, protein kinase regulator activity, kinase regulator activity of MF, condensed nuclear chromosome kinetochore, cytosolic ribosome, immunoglobulin complex of CC, B cell activation, nuclear chromosome segregation, humoral immune response of BP. KEGG analysis showed that the DEGs were enriched in p53 signaling pathway, ribosome, oocyte meiosis.

In the PPI network of DEGs, 4 (TOP2A, CDK1, CENPA, and KIF11) out of 334 DEGs. These 4 genes were all up regulated in pediatric patients with asthma. TOP2A encodes for the enzyme topoisomerase II alpha, which plays a crucial role in DNA replication and repair. Previous studies have shown that TOP2A undergoes regulation throughout the cell cycle, experiencing a notable surge beginning from the midpoint of the S phase and persisting through mitosis. This regulation encompasses three essential processes: the activation of TOP2A through transcriptional mechanisms, the stabilization of TOP2A mRNA, and the activation of the deubiquitylase enzyme USP [15]. These coordinated processes ensure the proper functioning and presence of TOP2A during the various stages of the cell cycle [16]. Studies have shown that TOP2A is associated with susceptibility in asthma patients [17,18].

CDK1, also known as Cyclin-Dependent Kinase 1, is a protein that plays a critical role in regulating the cell cycle. The regulation of the "CDK1/APC/C-Cdh1" signaling pathway is associated with the effective inhibition of Th17 differentiation and the exhibition of an anti-neutrophil effect in asthma [19]. Meanwhile, Cdk1 phosphorylates numerous proteins involved in regulating epigenetics, including writers and erasers of major histone marks. Consistent with these discoveries, inhibiting Cdk1 has an impact on the histone modification status of ES cells. Elevated levels of Cdk1 in ES cells phosphorylate and partially deactivate Dot1l, the H3K79 methyl transferase responsible for adding activating marks to gene bodies. Reduction of Cdk1 activity during ES cell differentiation relieves the repression of Dot1l, thereby enabling the coordinated expression of differentiation genes.

These analyses suggest that Cdk1 plays a crucial role in maintaining the epigenetic characteristics of ES cells [20]. The GSEA data suggested that genes down-regulated at early stages of progenitor T lymphocyte, up-regulated of memory IgG IgA B cells plasma cells, genes down-regulated in monocytes, and deadenylation-dependent mRNA decay enriched in asthma patients. Several studies have implied that TH2 cells are contribute to the development of chronic inflammation commonly observed in asthma and other allergic disorders [21-23]. Moreover, children diagnosed with atopic diseases exhibit increased numbers of Th2, memory B-cells in comparison to children without any atopic conditions [24].

It has been reported that monocytes play a significant role in the asthma severity or subsequent recurrent wheezing [25]. IgG memory B cells are a special type of B cell that can survive in the body for a long time and remember previously infected pathogens. IgG memory B cells can differentiate into plasma cells to produce antibodies so that they can respond quickly when infected again. The generation of memory B and T cells, as well as long-lived plasma cells, is a crucial feature of the adaptive immune system. These specialized B cells play a vital role in providing protective immunity against recurring infectious agents [26]. In the pathogenesis of atopic diseases, the role of IgG Memory B cells as precursors for the production of pathogenic IgE plasma cells is of significant importance [27,28]. Hepatic graft vs. host disease, FGFR3 signaling in chondrocyte proliferation and terminal differentiation, genes up-regulated of effective memory CD4⁺ T cells, and Glycosaminoglycan degradation were mainly enriched in wheeze group.

The subsequent WGCNA revealed one module that closely associated with asthma, namely, pink module that was positively associated with asthma. Function enrichment analyses showed that, in the category of CC, pink module genes were most significantly enriched in “tertiary granule”, “specific granule”, and “specific granule lumen”. Tertiary granule is a type of secretory granule that contains cathepsin and gelatinase and is readily exocytose upon cell activation; found primarily in mature neutrophil cells [29]. Airway neutrophils have been identified as a contributing factor to the severity of asthma and the occurrence of acute exacerbations. Recent research has shown that neutrophilic airway inflammation is associated with severe asthma in cases where there is non-type 2 inflammation.

This finding highlights the importance of considering non-type 2 inflammations in the diagnosis and treatment of severe asthma, as it may have a significant impact on the effectiveness of current therapies [30]. Elevated concentrations of extracellular DNA in sputum serve as a marker for a specific subset of asthma patients who have more severe symptoms. There was previous evidence indicating that the source of this extracellular DNA is neutrophils, supported by substantial positive correlations between concentrations of eDNA, sputum neutrophil percentages, and MPO concentrations [31].

With regard to MF terms, pink module genes were predominantly involved in “carbohydrate binding”, “structural constituent of ribosome”, and “lipopolysaccharide binding”. The relationship between ribosome and asthma may be related to Ribosomal Proteins (RPs). RPs is structural components of ribosome that participate in ribosome biogenesis and protein synthesis. Some studies suggest that abnormal expression or mutation of RPs may cause some human diseases [32]. Defects in the process of ribosome formation, translation, and the roles of specific Ribosomal Proteins (RPs), including mutations in RPs, have been associated with a diverse range of human congenital disorders collectively known as ribosomopathies [33].

These conditions are characterized by tissue-specific phenotypic abnormalities and an increased risk of developing cancer later in life [34]. Recent findings of somatic mutations in RPs across multiple types of tumors further highlight the links between ribosomal defects and cancer, emphasizing the potential significance of investigating the role of ribosomes in cancer development and progression [35]. Some previous studies have suggested that asthma and allergies can induce chronic and systemic immune system activation, which could potentially increase the risk of developing lung cancer [36]. However, the specific mechanisms are still not well understood. The relationship between lipopolysaccharide binding and asthma. There is no definitive answer to this topic, but some studies suggest that Lipopolysaccharide Binding Protein (LBP) may play an important role in regulating the inflammatory response of asthma [37].

Some studies also found that lipopolysaccharide exposure can alleviate the symptoms of asthma in mice by regulating the Th1/Th2 and Treg/Th17 balance [38]. These results indicate that lipopolysaccharide binding has a complex interaction with the occurrence and development of asthma and further research is needed to elucidate it. As for the BP category, pink module genes were mainly clustered in “organ or tissue specific immune response”, “mucosal immune response”, and “innate immune response in mucosa”. The results of pink module were further confirming the roles of immune responses in the pathogenesis of asthma. Allergic asthma in childhood has been extensively studied, revealing the presence of eosinophilic inflammation [39]. However, research has also shown that neutrophilic asthma displays a T-helper 17-shifted phenotype [40].

The susceptibility of newborns to respiratory infections may be linked to impaired neonatal neutrophil function regarding adherence, chemotaxis, and migration. Additionally, a deficiency of Polymorphonuclear Leukocytes (PMNs) during development may contribute to the development of childhood asthma. It is worth noting that a bias towards Th17-cell development may occur in human neonates, which may help regulate the initial colonization by microbes. Furthermore, it is important to highlight that childhood asthma phenotypes cannot be directly translated into adult phenotypes, which may include eosinophilic and neutrophilic asthma, among others. It is essential to prioritize the enhanced characterization of diverse childhood asthma phenotypes and their underlying immune mechanisms to devise more effective treatment strategies that are tailored to individual patients [41].

The understanding that asthma involves more than just allergic inflammation has led to a greater focus on the role of innate immunity in this disease. Recent advancements in this area have identified new targets for preventing and treating asthma. These include identifying pathways for innate stimulation by environmental or endogenous Pathogen-Associated Molecular Patterns (PAMPs) and Danger-Associated Molecular Patterns (DAMPs) that affect the activation and movement of DCs, identifying innate sources of cytokines, and discovering new subsets of T cells and lymphoid cells [42]. Previous studies have suggested that a weakened innate immune response may contribute to an increased susceptibility to asthma [43]. Furthermore, the NLRP3 inflammasome is proposed to play a key role in innate immunity by being involved in airway inflammation in asthma or exacerbations induced by viral infections.

This macromolecular signaling complex controls proteolytic activation of the highly inflammatory cytokines interleukin 18 and interleukin-1β and might regulate inflammatory processes, especially in the neutrophilic asthma phenotype. Increased interleukin-1β concentrations in serum, sputum, and Bronchoalveolar Lavage (BAL) of allergic asthmatics provide evidence for NLRP3 activation [44]. Therefore, finding new biomarkers is essential. IMPA1 was identified as a critical gene for asthma after further protein-protein interaction network research. IMPA1 (Inositol Monophosphatase 1) is a proteincoding gene that encodes an enzyme responsible for the provision of inositol required for synthesis of phosphatidylinositol and polyphosphoinositides.

It has been implicated as the pharmacological target for lithium action in the brain [45]. Diseases associated with IMPA1 include intellectual developmental disorder. Autosomal recessive 59 and autosomal recessive non-syndromic intellectual disability [46]. Among its related pathways are superpathway of D-myo-inositol (1,4,5) trisphosphate metabolism and Inositol phosphate metabolism [47]. Gene Ontology (GO) annotations related to this gene include protein homodimerization activity and magnesium ion binding [48]. A related study shows that the IMPA1 mutation specifically affects neuronal progenitor cell survival and neuronal differentiation [49].

Not only that, IMPA1 deficiency causes inositol decline, which elicits AMP-Activated Protein Kinase (AMPK) activation and mitochondrial fission without affecting ATP level. Mitochondrial fusion and fission are important for maintaining mitochondrial functions. Inositol is a metabolite that restricts AMPK-dependent mitochondrial fission. Inositol accumulation prevents AMPK-dependent mitochondrial fission. The AMP/ inositol ratio is critical for AMPK activation. AMPK activation requires inositol decline to release AMPKγ for AMP binding [50].

Conclusion

In summary, our integrated bioinformatics study presented five hub genes TOP2A, CDK1, CENPA, KIF11 and IMPA1. These core genes establish relationship with children wheeze and asthma. These findings provide insights about the mechanisms underlying asthma progression, which is of great significance to the treatment of children asthma. Our study reveals the value of IMPA1 in children asthma, where the role of IMPA1 was previously unknown. Hence, AMPK is an inositol sensor that restricts mitochondrial fission when inactivated by inositol.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Availability of data and materials Microarray dataset (GSE123750) for this study is openly available in Gene Expression Omnibus database at https:// www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse123750.

Competing interests

The authors declare that they have no competing interests.

Funding

No sources of funding exist for this research. All costs were covered by the researchers.

Authors’ contributions

YF designed the study, analyzed the data, and wrote the manuscript. JM contributed to data interpretation. YL and QL reviewed and revised the manuscript. YL and QL also supervised the study. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to extend their appreciation to all the participants.

References

1. Holgate ST, Wenzel S, Postma DS, et al. Asthma. Nat Rev Dis Primers 2015; 1(1): 15025.

   [Crossref][Google Scholar][Indexed]

2. Fuchs O, Bahmer T, Rabe KF, et al. Asthma transition from childhood into adulthood. Lancet Respir Med 2017; 5(3): 224-34.

   [Crossref][Google Scholar][Indexed]

3. Martinez FD, Vercelli D. Asthma. Lancet 2013; 382(9901): 1360-72.

   [Crossref][Indexed]

4. Forno E, Weiner DJ, Mullen J, et al. Obesity and airway dysanapsis in children with and without asthma. Am J Respir Crit Care Med 2017; 195(3): 314-23.

   [Crossref][Google Scholar][Indexed]

5. Asher MI, Montefort S, Bjorksten B, et al. Worldwide time trends in the prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and eczema in childhood: ISAAC phases one and three repeat multicountry cross-sectional surveys. Lancet 2006; 368(9537): 733-43.

   [Crossref][Google Scholar][Indexed]

6. Lancet T. Asthma in US children. Lancet 2018; 391(10121): 632.

   [Crossref][Indexed]

7. Martinez FD, Wright AL, Taussig LM, et al. Asthma and wheezing in the first six years of life. The group health medical associates. N Engl J Med 1995; 332(3): 133-8.

   [Crossref][Google Scholar][Indexed]

8. Bacharier LB, Guilbert TW, Jartti T, et al. Which wheezing preschoolers should be treated for asthma? J Allergy Clin Immunol Pract 2021; 9(7): 2611-8.

   [Crossref][Google Scholar][Indexed]

9. Ducharme FM, Tse SM, Chauhan B. Diagnosis, management, and prognosis of preschool wheeze. Lancet 2014; 383(9928): 1593-604.

   [Crossref][Google Scholar][Indexed]

10. Chen X, Xie D, Zhao Q, et al. MicroRNAs and complex diseases: From experimental results to computational models. Brief Bioinform 2019; 20(2): 515-39.

    [Crossref][Google Scholar][Indexed]

11. Chen X, Sun LG, Zhao Y. NCMCMDA: miRNA-disease association prediction through neighborhood constraint matrix completion. Brief Bioinform 2021; 22(1): 485-96.

    [Crossref][Google Scholar][Indexed]

12. El-Husseini ZW, Gosens R, Dekker F, et al. The genetics of asthma and the promise of genomics-guided drug target discovery. Lancet Respir Med 2020; 8(10): 1045-56.

    [Crossref][Google Scholar][Indexed]

13. Pividori M, Schoettler N, Nicolae DL, et al. Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: Genome-wide and transcriptome-wide studies. Lancet Respir Med 2019; 7(6): 509-22.

    [Crossref][Google Scholar][Indexed]

14. Custovic A, Siddiqui S, Saglani S. Considering biomarkers in asthma disease severity. J Allergy Clin Immunol 2022; 149(2): 480-7.

    [Crossref][Google Scholar][indexed]

15. mcdowell km. A clinical index to define risk of asthma in young children with recurrent wheezing. Clinical Pediatrics 2016; 40(7): 417-8.

    [Crossref][Google Scholar][Indexed]

16. Pommier Y, Nussenzweig A, Takeda S, et al. Human topoisomerases and their roles in genome stability and organization. Nat Rev Mol Cell Biol 2022; 23(6): 407-27.

    [Crossref][Google Scholar][Indexed]

17. Bisgaard H, Bonnelykke K, Sleiman PM, et al. Chromosome 17q21 gene variants are associated with asthma and exacerbations but not atopy in early childhood. Am J Respir Crit Care Med 2009; 179(3): 179-85.

    [Crossref][Google Scholar][Indexed]

18. Marinho S, Custovic A, Marsden P, et al. 17q12-21 variants are associated with asthma and interact with active smoking in an adult population from the United Kingdom. Ann Allergy Asthma Immunol 2012; 108(6): 402-11.

    [Crossref][Google Scholar][Indexed]

19. Yang L, Zheng Y, Miao YM, et al. Bergenin, a PPARgamma agonist, inhibits Th17 differentiation and subsequent neutrophilic asthma by preventing GLS1-dependent glutaminolysis. Acta Pharmacol Sin 2022; 43(4): 963-76.

    [Crossref][Google Scholar][Indexed]

20. Michowski W, Chick JM, Chu C, et al. Cdk1 controls global epigenetic landscape in embryonic stem cells. Molecular Cell 2020; 78(3): 459-76.e13.

    [Crossref][Google Scholar][Indexed]

21. Anthony RM, Rutitzky LI, Urban JF, et al. Protective immune mechanisms in helminth infection. Nat Rev Immunol 2007; 7(12): 975-87.

    [Crossref][Google Scholar][Indexed]

22. Saenz SA, Siracusa MC, Perrigoue JG, et al. IL25 elicits a multipotent progenitor cell population that promotes T(H)2 cytokine responses. Nature 2010; 464(7293): 1362-6.

    [Crossref][Google Scholar][Indexed]

23. Umetsu DT, McIntire JJ, Akbari O, et al. Asthma: An epidemic of dysregulated immunity. Nat Immunol 2002; 3(8): 715-20.

    [Crossref][Google Scholar][Indexed]

24. Looman KIM, van Meel ER, Grosserichter-Wagener C, et al. Associations of Th2, Th17, Treg cells, and IgA(+) memory B cells with atopic disease in children: The generation R study. Allergy 2020; 75(1): 178-87.

    [Crossref][Google Scholar][Indexed]

25. Kimpen JL. Respiratory syncytial virus and asthma. The role of monocytes. Am J Respir Crit Care Med 2001; 163(3 Pt 2): S7-9.

    [Crossref][Google Scholar][Indexed]

26. Seifert M, Küppers RJL. Human memory B cells. 2016; 30(12): 2283-92.

    [Crossref][Google Scholar][Indexed]

27. Aranda CJ, Gonzalez‐Kozlova E, Saunders SP, et al. IgG memory B cells expressing IL4R and FCER2 are associated with atopic diseases. 2023; 78(3): 752-66.

    [Crossref][Google Scholar][Indexed]

28. Gould HJ, James LK. IgG(+) memory B cells: Friends or foes in allergic disease? J Allergy Clin Immunol 2020; 146(1): 77-9.

    [Crossref][Google Scholar][Indexed]

29. Faurschou M, Borregaard N. Neutrophil granules and secretory vesicles in inflammation. Microbes Infect 2003; 5(14): 1317-27.

    [Crossref][Google Scholar][Indexed]

30. Ray A, Kolls JK. Neutrophilic inflammation in asthma and association with disease severity. Trends Immunol 2017; 38(12): 942-54.

    [Crossref][Google Scholar][Indexed]

31. Lachowicz-Scroggins ME, Dunican EM, Charbit AR, et al. Extracellular DNA, neutrophil extracellular traps, and inflammasome activation in severe asthma. Am J Respir Crit Care Med 2019; 199(9): 1076-85.

    [Crossref][Google Scholar][Indexed]

32. de la Cruz J, Karbstein K, Woolford JL. Functions of ribosomal proteins in assembly of eukaryotic ribosomes in vivo. Annu Rev Biochem 2015; 84: 93-129.

    [Crossref][Google Scholar][Indexed]

33. Mills EW, Green R. Ribosomopathies: There's strength in numbers. Science 2017; 358(6363): 1-10.

    [Crossref][Google Scholar][Indexed]

34. Aspesi A, Ellis SR. Rare ribosomopathies: insights into mechanisms of cancer. Nat Rev Cancer 2019; 19(4): 228-38.

    [Crossref][Google Scholar][Indexed]

35. Kang J, Brajanovski N, Chan KT, et al. Ribosomal proteins and human diseases: Molecular mechanisms and targeted therapy. Signal Transduct Target Ther 2021; 6(1): 323.

    [Crossref][Google Scholar][Indexed]

36. Cui Y, Hill AW. Atopy and specific cancer sites: A review of epidemiological studies. Clin Rev Allergy Immunol 2016; 51(3): 338-52.

    [Crossref][Google Scholar][Indexed]

37. Meng L, Song Z, Liu A, et al. Effects of Lipopolysaccharide-Binding Protein (LBP) Single Nucleotide Polymorphism (SNP) in infections, inflammatory diseases, metabolic disorders and cancers. Front Immunol 2021; 12: 681810.

    [Crossref][Google Scholar][Indexed]

38. ding f, fu z, liu b. Lipopolysaccharide exposure alleviates asthma in mice by regulating th1/th2 and treg/th17 balance. Med Sci Monit 2018; 24: 3220-9.

    [Crossref][Google Scholar][Indexed]

39. Komlosi ZI, van de Veen W, Kovacs N, et al. Cellular and molecular mechanisms of allergic asthma. Mol Aspects Med 2022; 85: 100995.

    [Crossref][Google Scholar][Indexed]

40. Herrick CA, Bottomly K. To respond or not to respond: T cells in allergic asthma. Nature Rev Immun 2003; 3(5): 405-12.

    [Crossref][Google Scholar][Indexed]

41. Raedler D, Schaub B. Immune mechanisms and development of childhood asthma. Lancet Respir Med 2014; 2(8): 647-56.

    [Crossref][Google Scholar][Indexed]

42. Holgate ST. Innate and adaptive immune responses in asthma. Nat Med 2012; 18(5): 673-83.

    [Crossref][Google Scholar][Indexed]

43. Stein MM, Hrusch CL, Gozdz J, et al. Innate immunity and asthma risk in amish and hutterite farm children. N Engl J Med 2016; 375(5): 411-21.

    [Crossref][Google Scholar][Indexed]

44. Kim RY, Pinkerton JW, Essilfie AT, et al. Role for NLRP3 inflammasome-mediated, IL-1beta-dependent responses in severe, steroid-resistant asthma. Am J Respir Crit Care Med 2017; 196(3): 283-97.

    [Crossref][Google Scholar][Indexed]

45. Resnick AC, Saiardi A. Inositol polyphosphate multikinase: Metabolic architect of nuclear inositides. Front Biosci 2008; 13: 856-66.

    [Crossref][Google Scholar][Indexed]

46. Figueiredo T, Melo US, Pessoa AL, et al. A homozygous loss-of-function mutation in inositol monophosphatase 1 (IMPA1) causes severe intellectual disability. Mol Psychiatry 2016; 21(8): 1125-9.

    [Crossref][Google Scholar][Indexed]

47. Berridge MJ, Bootman MD, Roderick HL. Calcium signalling: Dynamics, homeostasis and remodelling. Nat Rev Mol Cell Biol 2003; 4(7): 517-29.

    [Crossref][Google Scholar][Indexed]

48. Andreassi C, Zimmermann C, Mitter R, et al. An NGF-responsive element targets myo-inositol monophosphatase-1 mRNA to sympathetic neuron axons. Nat Neurosci 2010; 13(3): 291-301.

    [Crossref][Google Scholar][Indexed]

49. Figueiredo T, Mendes APD, Moreira DP, et al. Inositol monophosphatase 1 (IMPA1) mutation in intellectual disability patients impairs neurogenesis but not gliogenesis. Mol Psychiatry 2021; 26(7): 3558-71.

    [Crossref][Google Scholar][Indexed]

50. Hsu CC, Zhang X, Wang G, et al. Inositol serves as a natural inhibitor of mitochondrial fission by directly targeting AMPK. Mol Cell 2021; 81(18): 3803-19.

    [Crossref][Google Scholar][Indexed]