miRNA-mRNA regulatory network and potential pathogens | Japan Aviation Association

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Back to Journal »Asthma and Allergy Journal» Volume 14

Comprehensive analysis reveals the miRNA-mRNA regulatory network and potential pathogens in the airway epithelium of asthma

Authors: Zhang Jie, Wang Zhong, Zhang De, Pan Ya, Liu Xin, Qiao Xin, Cui Wei, Dong Li

Published on October 30, 2021, the 2021 volume: 14 pages, pp. 1307-1321

DOI https://doi.org/10.2147/JAA.S331090

Single anonymous peer review

Editor who approved for publication: Dr. Amrita Dosanjh

Zhang Jintao,1,* Wang Zihan,1,* Zhang Dong,1 Pan Yun,1 Liu Xiaofei,1 Qiao Xinrui,1 Cui Wenjing,1 Liang Dong1,2 1 Department of Respiratory Medicine, Shandong Qianfoshan Hospital, Shandong Qilu Medical College, People's Republic of China University of Jinan; 2 Department of Respiratory Diseases, Shandong Qianfoshan Hospital, Shandong University, Shandong First Affiliated Hospital of Shandong First Medical University, Shandong Institute of Respiratory Diseases, Jinan* These authors contributed equally to this article. Corresponding author: Liang Dong Respiratory Department, Shandong Qianfoshan Hospital, Shandong University, The First Affiliated Hospital of Shandong First Medical University, Shandong Institute of Respiratory Diseases, Jinan, China Email [email protected] Background: In the progression of asthma, complex molecular networks, including microRNA (miRNA) in the airway epithelium The regulation of transcription is still largely undetermined. The abnormal expression of miRNAs in the airway epithelium of asthma is the latest and rapidly developing field for the development of targets for the diagnosis and treatment of asthma. Materials and methods: Analysis was performed to compare airway epithelial miRNA and gene expression between asthma patients and healthy subjects from three data sets (two for miRNA expression profiles and one for gene expression profiles). The interaction network between differential expression (DE)-miRNA and mRNA was further identified for functional analysis. In order to verify the participation and function of all identified miRNAs in asthma, we constructed two asthma cell models. The most promising causal miRNA candidate miR-1246 was examined in an in vitro system to explore its target and role in the pathophysiology of asthma. Results: Through comprehensive analysis, we obtained 6 miRNAs in airway epithelial cells related to asthma, including 31 validated target genes. Next, we confirmed through in vitro functional experiments that these miRNAs are all related to the progression of asthma. They may be involved in eosinophilic inflammation (miR-92b-3p, miR-1246, miR-197-3p and miR-124-5p) or remodeling (miR-197-3p, miR-193a-5p, miR-1246 and miR -92b-3p). In addition, some other unscreened valuable miRNAs (miR-21-5p and miR-19b-3p) were also examined and identified, some of which were detected in the blood related to disease states. In addition, we found that miR-1246 can directly target POSTN and affect the epithelial-mesenchymal transition and fibrosis of airway epithelial cells. Conclusion: We constructed a preliminary epithelial regulatory network for asthma based on 6 identified miRNAs and their valuable target genes. Candidate factors in the miRNA-mRNA network of airway epithelial organisms may provide further information about the pathogenesis of asthma. What is striking is that among all the miRNAs screened, miR-1246, which can interact with POSTN, may have a multifunctional role in the course of asthma and is a promising drug for asthma treatment and molecular subtyping. Keywords: asthma, miRNA, regulatory network, biomarker, molecular subtype

Bronchial asthma is a highly prevalent chronic airway disease, affecting nearly 300 million people worldwide. 1 As air pollution intensifies, the global incidence of asthma is on the rise. 2 Although the step-by-step treatment of asthma has been standardized, some patients still have symptoms or only show some symptoms. 3 Worse, due to the limitations of the diagnosis method, many patients do not receive timely diagnosis and appropriate treatment, resulting in lung function And irreversible damage to the structure. 4 In the era of precision medicine, it has clarified the urgent need to understand the molecular mechanism of asthma pathogenesis and determine the appropriate biomarkers to accurately classify asthma. 5

The airway epithelium is the first barrier to lung pathogens and allergic irritants. 6 Based on single-cell RNA sequencing, many new identifying cell populations with unique molecular characteristics have been discovered in the airway epithelium. 6 Importantly, the airway epithelium is also considered an active participant in the immune response and may play a key role in the development and progression of asthma. 7,8 However, insufficient understanding of the molecular mechanisms of airway epithelium during pathological processes hinders effective treatment strategies. 9

MicroRNA (miRNA) is defined as a non-coding RNA with a length of 18-26 bp. As an essential regulator, miRNA is significantly involved in the development of a variety of pathological events and human diseases. 10 Asthma is a complex heterogeneous disease, affected by many factors, including miRNA. 11 These miRNAs may be involved in the occurrence and progression of diseases. Asthma can be used as a potential biomarker to help diagnose asthma and better phenotype. 11

Here, we constructed a preliminary asthma miRNA-mRNA regulatory network in the airway epithelium by using bioinformatics analysis. Three microarray data sets (GSE25230, GSE142237, and GSE43696) were studied in bronchial epithelial biopsy samples of patients with and without asthma, including miRNA and mRNA expression profile data sets. Three online tools (TargetScan, miRWalk, and TarBase) are used to screen target genes that have identified miRNAs. Researched receiver operating characteristics (ROC), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network to further explore the identified miRNAs and their The function of the target gene. In addition, two major in vitro models of asthma induced by interleukin (IL)-13 or transforming growth factor (TGF)-β1 were used to further characterize all the miRNAs screened and some other miRNAs of interest. miR-1246 regulates the expression of POSTN and remodeling-related genes, and may serve as a new biomarker and target for asthma diagnosis and combination therapy. We also measured the expression of these miRNAs in patients' blood samples to further determine their clinical value in asthma. This study may help clarify the pathophysiological process of asthma and determine the diagnostic biomarkers of asthma.

The data expression profile of asthma was searched in the Gene Expression Comprehensive (GEO) database, and two independent miRNA data sets and one mRNA data set GSE25230, GSE142237 and GSE43696 for the study of human asthma airway epithelium were respectively selected. Differential miRNA and mRNA were screened in airway epithelial samples obtained from biopsies of patients with and without asthma.

Studying the target genes of miRNA is essential to determine the regulatory mechanism and function of miRNA. Here, we used three online tools to identify six potential miRNAs and their target genes: Targetscan, miRWalk, and TarBase. Screen miRNA targets based on overlapping results from three websites. Then, the regulatory network of miRNA-mRNA pairs (based on expression fold change> 2.5 and false discovery rate (FDR) <0.05) was extracted and visualized using Cytoscape software (http://cytoscape.org/). To determine the active regulatory network in the miRNA-mRNA pair, we also downloaded expression profile data from GSE43696 (analysis of isolated fresh bronchial epithelial cells obtained from intrabronchial biopsies of 20 non-asthmatic patients and 88 asthmatic patients).

The ROC curve is drawn using the pROC package of R software (version 3.6.2).

GO annotation and KEGG pathway enrichment analysis are performed using annotation, visualization and integrated discovery database (DAVID) (selected enrichment significance assessment is p <0.05), which reveals biological processes (BPs), cell components (CCs) ), the function (MF) and pathway of the molecule related to the selected miRNA.

In order to gain insight into the interaction between the identified miRNAs and their target genes, we constructed a PPI network and used the STRING tool to analyze it to reveal the molecular mechanism of asthma. The target gene in the PPI network is used as a node, the line between the two nodes indicates the related interaction, and the strength of the interaction is indicated by the color of the line. Use Cytoscape software to visualize the corresponding interactions.

BEAS-2B human bronchial epithelial cells were purchased from Shanghai Fuheng Biological Co., Ltd., and cultured in Dulbecco's modified Eagle medium containing 10% fetal bovine serum (Gibco, USA) and an appropriate amount of antibiotics. The cells are kept at 37°C in a humidified incubator containing 5% CO2. In order to construct asthma-related cell models (airway remodeling/epithelial-mesenchymal transition (EMT) or eosinophil inflammation), we use human IL-13 protein or human TGF-β1 protein (Abbkine Scientific Co., Ltd., China) Different concentrations in a specified time. According to the manufacturer's instructions, use Interfering® reagent (Polyplus-Transfection SA) for BEAS-2B cell transfection. We obtained 293 T cells from the American Type Culture Collection (Manassas, Virginia, USA) and cultured them in DMEM containing 10% FBS. The negative control, miR-1246 mimic and inhibitor were synthesized and purchased from Shanghai Gene Pharmaceutical Co., Ltd.

To further explore the value of the selected miRNAs in asthma, we recruited 10 healthy controls and 10 asthma patients. The diagnosis of asthma is based on the Global Asthma Initiative (GINA) (updated in 2021). The sample collection was approved by Shandong Qianfoshan Hospital affiliated to Shandong University; all patients provided written informed consent. The characteristics of the subjects are shown in Table S1. A 500 μL blood sample was used for RNA extraction and miRNA level expression detection. The research was conducted in accordance with the Declaration of Helsinki.

According to the manufacturer's instructions, use RNAex Pro reagent (China Accurate Biotechnology Co., Ltd.) to extract total RNA from plasma samples of patients or BEAS-2B cells. After quantification by spectrophotometry, a reverse transcription kit (TOYOBO Co., Ltd., Japan) was used to synthesize cDNA with 2 μg RNA. Reverse transcription-quantitative PCR (RT-qPCR) was performed using SYBR Green PCR Master Mix (Accurate Biotechnology Co., Ltd., China). See Table 1 for miRNA primers. Table 1 qRT-PCR primers

Use RIPA to extract total cell protein, and then perform Western blotting for quantification. For western blotting experiments, the total protein was separated using sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride membrane. After a brief wash, the membrane was blocked with 1% Tween 20 in Tris buffered saline containing 3% bovine serum albumin (BSA) for 1 hour. Then, the membrane was incubated with primary antibodies including periostin (1:1000, ab152099, Abcam, USA) and GAPDH (1:1000, BA2913, Boster Biological Technology, China) at 4°C overnight. The next day, the membrane was taken with tweezers, washed with TBST 3 times, and incubated with horseradish peroxidase (HRP) coupled goat anti-rabbit IgG (HL) (1:3000, GB23303, Servicebio, China) at room temperature for 1 hour. After washing again with TBST, the film was chemiluminescent using an enhanced chemiluminescent substrate.

BEAS-2B cells were spread on the cell climbing section of a 24-well plate. According to the specified processing method, the cell climbing section was washed with phosphate buffered saline (PBS), fixed with 4% paraformaldehyde, and then incubated with 0.5% triton X-100 for permeabilization. Then, the cells were blocked with 1% BSA for 1 hour at room temperature. Anti-periostin primary antibody (1:200, ab152099, Abcam, USA), anti-fibronectin antibody (1:200, JF0582, Huabio, China) and anti-COL1A1 antibody (1:200, BA0325, Boster, China) for Incubate the cell climbing section overnight at 4°C. Then remove the primary antibody and add fresh PBS. After washing 3 times with PBS, the climbing sections were incubated with the secondary antibody goat anti-rabbit IgG (1:500, Abbkine, Hubei, China) and protected from light for 1 hour. The nuclei were counterstained with 4ʹ,6-diamidino-2-phenylindole (DAPI) (1:500) for 5 minutes. After the final washing step (3×10 minutes) in PBS, the images were captured under a vertical fluorescence microscope (Leica, Berlin, Germany).

The wild-type 3'-UTR fragment and the miR-1246 "seed" mutant 3'-UTR of POSTN were synthesized, and then inserted into the pmirGLO luciferase reporter plasmid, named POSTN-WT and POSTN-MUT. 293 T cells were co-transfected with Lipofectamine 2000 with recombinant luciferase reporter plasmid (WT or MUT plasmid) and miRNA (NC or miR-1246 mimic). After 48 hours, the relative luciferase activity detection kit (Beyotime, Shanghai, China) was evaluated by a dual luciferase reporter.

Draw ROC curve based on miRNA expression in the sample to analyze the sensitivity, specificity and cut-off value of miRNA, and calculate AUC to evaluate the diagnostic performance of the selected miRNA as a biomarker. All results are expressed as mean±standard deviation; Student's t-test was used to analyze the difference between the two groups. A two-tailed p-value of <0.05 was considered statistically significant.

A |FC|> 1.5 and p value <0.05 are considered as the criteria for screening DE-miRNA. Significantly different miRNA expression patterns were observed in the airway epithelium of normal subjects and asthmatic patients (Figure 1A and B). Among them, 83 DE-miRNAs were found in the GSE25230 profile, including 37 down-regulated and 46 up-regulated miRNAs (Figure 1A), while 209 DE-miRNAs, including 118 up-regulated and 91 down-regulated miRNAs, were found in GSE142237 Found in the configuration file (Figure 1B). Figure 1 Identification of differential expression (DE)-miRNA. (A and B) Volcano plots of differentially expressed miRNAs in the two datasets. According to the analysis of GSE25230 and GSE, many miRNAs are differentially expressed. The red dots represent up-regulated miRNAs, and the green dots represent down-regulated miRNAs. |FC|> 1.5 and p value <0.05 were set as four series of screening criteria (C) Venn diagram. The crossover points represent common DE-miRNAs. The results show that there are 5 crossover miRNAs, including miR-92b-3p, miR-203, miR-124-5p, miR-193a-5p, miR-197-3p, and miR- 1246 (D) Presents the miRNAs that have been identified related to asthma. The red circles represent up-regulated miRNAs, and the green circles represent down-regulated miRNAs. (EJ) Use the pROC package to perform ROC curve analysis on the selected miRNAs. miR-92b-3p (AUC=0.764, accuracy=0.692), miR-203 (AUC=0.727, accuracy=0.731), miR-124-5p (AUC=0.661, accuracy=0.654), miR-193a- The ROC curve of miR-193 is 5p (AUC = 0.764, accuracy = 0.731), miR-197-3p (AUC = 0.794, accuracy = 0.731) and miR-1246 (AUC = 0.836, accuracy = 0.808). Abbreviations: AUC, area under the receiver operating characteristic curve; CI, confidence interval.

Figure 1 Identification of differential expression (DE)-miRNA. (A and B) Volcano plots of differentially expressed miRNAs in the two datasets. According to the analysis of GSE25230 and GSE, many miRNAs are differentially expressed. The red dots represent up-regulated miRNAs, and the green dots represent down-regulated miRNAs. |FC|> 1.5 and p value <0.05 were set as four series of screening criteria (C) Venn diagram. The crossover points represent common DE-miRNAs. The results show that there are 5 crossover miRNAs, including miR-92b-3p, miR-203, miR-124-5p, miR-193a-5p, miR-197-3p, and miR- 1246 (D) Presents the miRNAs that have been identified related to asthma. The red circles represent up-regulated miRNAs, and the green circles represent down-regulated miRNAs. (EJ) Use the pROC package to perform ROC curve analysis on the selected miRNAs. miR-92b-3p (AUC=0.764, accuracy=0.692), miR-203 (AUC=0.727, accuracy=0.731), miR-124-5p (AUC=0.661, accuracy=0.654), miR-193a- The ROC curve of miR-193 is 5p (AUC = 0.764, accuracy = 0.731), miR-197-3p (AUC = 0.794, accuracy = 0.731) and miR-1246 (AUC = 0.836, accuracy = 0.808).

Abbreviations: AUC, area under the receiver operating characteristic curve; CI, confidence interval.

Candidate DE-miRNAs were generated from two intersecting data sets using Venn diagrams (Figure 1C). Table 2 shows the detailed information of cross-DE-miRNAs, including four down-regulated (miR-203a-3p, miR197-3p, miR-92b-3p and miR-193a-5p) and two up-regulated miRNAs (miR-124 -5p and miR-1246), speculated that they are involved in the occurrence and development of asthma (Figure 1D). Table 2 miRNAs identified from the two miRNA data sets

Table 2 miRNAs identified from the two miRNA data sets

In order to study the efficacy of DE-miRNA as a potential biomarker for asthma, we used R software (version 3.6.2) to perform ROC curve analysis. The area under the curve (AUC) represents the potential of DE-miRNA as a diagnostic marker for these six miRNAs (Figure 1E-J) as follows: miR-92b-3p (AUC = 0.764), miR-203 (AUC = 0.727), miR -124-5p (AUC = 0.661), miR-193a-5p (AUC = 0.764), miR-197-3p (AUC = 0.794) and miR-1246 (AUC = 0.836). Overall, the results indicate that these miRNAs, especially miR-1246, have excellent diagnostic efficiency in distinguishing asthma patients from healthy subjects.

Next, use TargetScan, miRWalk and TarBase to predict the target genes of the selected miRNAs. As shown in Figure 2A, the network of miRNA-mRNA interactions is visualized in Cytoscape. Figure 2 (A) Construction of miRNA-mRNA regulatory network in asthmatic epithelium. The interaction network of miRNA and its predicted target genes. The purple dots represent miRNA, and the green dots represent target mRNA. (B) The PPI network of the target gene constructed with Cytoscape software. The orange dots represent the top 10 key Hub genes. (C and D) DE-mRNAs in the airway epithelium of normal controls and asthmatic patients are presented as volcano maps, and heat maps are created to show the different expression patterns of hierarchical clustering analysis. The red dots represent up-regulated mRNA, and the green dots represent down-regulated mRNA. |FC|> 1.5 and ap value <0.05 are set as the screening criteria (E) Venn diagram shows the intersection of DE-mRNA and the identified miRNA target gene. The intersection area represents the result of the intersection. There are 31 genes that intersect in the two groups. Among them, 12 genes are up-regulated (red) and 19 genes are down-regulated (light green) in asthma airway epithelium. (F) The miRNA-mRNA regulatory network during asthma is based on 6 Screened miRNA and 31 mRNA progress. Up-regulated genes are marked in red; down-regulated genes are marked in light green.

Figure 2 (A) Construction of miRNA-mRNA regulatory network in asthmatic epithelium. The interaction network of miRNA and its predicted target genes. The purple dots represent miRNA, and the green dots represent target mRNA. (B) The PPI network of the target gene constructed with Cytoscape software. The orange dots represent the key Hub genes of the Top 10. (C and D) Present DE-mRNA in the airway epithelium of normal controls and asthma patients as volcano maps, and create heat maps to show the different expression patterns of hierarchical clustering analysis. The red dots represent up-regulated mRNA, and the green dots represent down-regulated mRNA. |FC|> 1.5 and ap value <0.05 are set as the screening criteria (E) Venn diagram shows the intersection of DE-mRNA and the identified miRNA target gene. The intersection area represents the result of the intersection. There are 31 genes that intersect in the two groups. Among them, 12 genes are up-regulated (red) and 19 genes are down-regulated (light green) in asthma airway epithelium. (F) The miRNA-mRNA regulatory network during asthma is based on 6 Screened miRNA and 31 mRNA progress. Up-regulated genes are marked in red; down-regulated genes are marked in light green.

The GO annotation and KEGG pathway enrichment analysis of all 1373 target genes were performed by the DAVID online tool. GO biological process (BP) analysis results show that the target genes of DE-miRNA are mainly enriched in transcriptional DNA templated, transcriptional regulatory DNA templated, RNA polymerase II promoter positive regulation, transcription positive regulation and RNA polymerase II promoter DNA template and negative regulation. In addition, the nucleus, cytoplasm, and plasma membrane account for most of the cell component (CC) category. In terms of molecular function, target genes are mainly rich in protein binding, ATP binding and DNA binding (Figure S1A).

KEGG pathway analysis showed that the target genes of DE-miRNA were significantly enriched in the cAMP signaling pathway, focal adhesion and cGMP-PKG signaling pathway (Figure S1B).

The PPI network of the first 100 linked target genes of DE-miRNA was analyzed by STRING database and visualized by Cytoscape software, with 100 nodes and 603 edges, as shown in Figure 2B. The top 10 genes are central genes with a high degree of ranking, which are significantly and closely related to the miRNA marked in orange, and are associated with the larger circle in Figure 2B.

After normalizing the microarray data (using the preprocessing core package in the R software), DE-mRNA was identified in GSE43696, including 124 up-regulated genes and 143 down-regulated genes (Figure 2C). DE-mRNA is further visualized in the heat map (Figure 2D). All samples used to detect differences in miRNA and mRNA expression were from airway biopsies. The intersection of the target genes of DE-miRNAs and DE-mRNAs is shown in the Venn diagram, which indicates that 31 genes are common among the predicted genes of DE-miRNAs and real DE-mRNAs (Figure 2E).

The relationship between DE-miRNAs and DE-mRNAs is shown in Figure 2F. These 6 DE-miRNAs may affect the pathogenesis of asthma by changing the expression of these 31 differentially expressed genes in the airways of asthma patients.

In order to further explore the functions of these selected miRNAs and several other miRNAs of interest (miR-22-5p, miR-19b-3p, miR-24-5p, miR-99a-5p and miR-106b-5p), We constructed two commonly used asthma cell models (airway remodeling/EMT cell model induced by TGF-β1 and eosinophilic inflammatory cell model induced by IL-13), and checked the expression levels of these selected miRNAs . We also verified the expression of the internal reference gene U6, which did not change after treatment with the experimental concentration of TGF-β1/IL-13, and confirmed that it is a stable reference gene (Figure S2). The results of the eosinophilic inflammatory cell model showed that the miRNA levels of miR-92b-3p, miR-1246, miR-197-3p, and miR-124-5p were significantly high in the IL-13 stimulation group, while the number of other miRNAs was Constant (Figure 3A). These findings indicate that these four miRNAs may play a role in eosinophil inflammation, which is a symptom of asthma. Figure 3 miRNA levels identified in asthma-related cell models. (A) IL-13-induced levels of identified miRNAs at different concentrations in BEAS-2B cells. (BD) Quantitative reverse transcription PCR (qRT-PCR) and morphological and immunofluorescence analysis are usually used to confirm the successful construction of airway remodeling/epithelial-mesenchymal transition (EMT) cell models induced by TGF-β1. (E) Different concentrations of miRNA levels in BEAS-2B cells induced by TGF-β1. Data is expressed as the mean ± SEM from 3 independent experiments. *P <0.05; ** P <0.01.

Figure 3 miRNA levels identified in asthma-related cell models. (A) IL-13-induced levels of identified miRNAs at different concentrations in BEAS-2B cells. (BD) Quantitative reverse transcription PCR (qRT-PCR) and morphological and immunofluorescence analysis are usually used to confirm the successful construction of airway remodeling/epithelial-mesenchymal transition (EMT) cell models induced by TGF-β1. (E) Different concentrations of miRNA levels in BEAS-2B cells induced by TGF-β1. Data is expressed as the mean ± SEM from 3 independent experiments. *P <0.05; ** P <0.01.

Subsequently, in order to evaluate the role of these miRNAs in airway remodeling (another key feature of asthma), we used the human recombinant protein TGF-β1 to construct an airway remodeling/EMT cell model. After stimulation with TGF-β1, we observed that the morphology of BEAS-2B cells gradually changed from normal to long-spindle-forming fibroblast-like cells as the stimulation time increased (Figure 3B). RT-PCR and immunofluorescence show the expression of remodeling related genes (MMP-9, collagen, α-SMA and fibronectin) and EMT related genes (E-cadherin, N-cadherin and vimentin) Significantly changed. Figure 3C and D). Taken together, this shows that the model we built is successful. At the same time, we found that the expression of miR-197-3p, miR-193a-5p, miR-1246, miR-21-5p and miR-92b-3p changed significantly during this process (Figure 3E). A similar finding occurred after stimulating BEAS-2B cells with a high concentration of TGF-β1 (20 ng/mL), which demonstrated the high reproducibility of the results (Figure 3E).

Given that IL-13 and TGF-β are two key regulators of asthma, these results indicate that these differentially expressed miRNAs play a key role in regulating the development of asthma. In order to further obtain valuable miRNAs from these we screened, we also collected blood samples collected from 10 asthma patients and 10 healthy subjects (Tables S1 and S2). The miRNA detection workflow including the setup and process is shown in Figure 4A. By detecting the levels of selected miRNAs in the blood, we found significant differences in the levels of miR-92b-3p, miR-1246, miR-203a-3p, miR-21-5p, and miR-19b-3p between the groups. Supports the role of these miRNAs in the development and progression of asthma (Figure 4). Figure 4 DE-miRNA in blood samples of asthma patients and healthy controls. (A) Flow chart of quantification of selected miRNAs in blood (B) hsa-miR-19b-3p (C) hsa-miR-21-5p (D) hsa-miR-22-5p (E) hsa-miR -92b -3p (F) hsa-miR-99a-5p (G) hsa-miR-106b-5p (H) hsa-miR-124-5p (I) hsa-miR-193a-5p. (J) hsa-miR-197-3p (K) hsa-miR-203a-3p (L) hsa-miR-1246. Data is expressed as the mean ± SEM from 3 independent experiments. *P <0.05; ** P <0.01.

Figure 4 DE-miRNA in blood samples of asthma patients and healthy controls. (A) Flow chart of quantification of selected miRNAs in blood (B) hsa-miR-19b-3p (C) hsa-miR-21-5p (D) hsa-miR-22-5p (E) hsa-miR -92b -3p (F) hsa-miR-99a-5p (G) hsa-miR-106b-5p (H) hsa-miR-124-5p (I) hsa-miR-193a-5p. (J) hsa-miR-197-3p (K) hsa-miR-203a-3p (L) hsa-miR-1246. Data is expressed as the mean ± SEM from 3 independent experiments. *P <0.05; ** P <0.01.

Interestingly, combining all the above results, we found that the expression of miRNA-1246 in the blood samples of asthma patients is more prominent, which is consistent with the bioinformatics analysis results of the GSE25230 and GSE142237 data sets. These results aroused our interest in exploring the role of miR-1246 in the pathogenesis of asthma. Initially, the transfection efficiency of miRNA-FAM (stable negative control combined with FAM), miR-1246 mimics, and inhibitors was tested by qRT-PCR and immunofluorescence. As shown in Figure 5A-C, BEAS-2B cells grew well after transfection and the transfection efficiency was high. Figure 5 POSTN is the direct target of miR-1246. (AC) Use qRT-PCR and immunofluorescence analysis to evaluate transfection efficiency. (DH) Use TargetScan to predict the interaction of five DE genes (POSTN, THSD7A, SIAH3, DNAJC12 and CADM2) with relatively high predicted target scores with miR-1246, and verify the use of miR by qRT-PCR and dual luciferase -1246 Reporter gene detection of mimics/inhibitors. Data is expressed as the mean ± SEM from 3 independent experiments. *P <0.05; ** P <0.01.

Figure 5 POSTN is the direct target of miR-1246. (AC) Use qRT-PCR and immunofluorescence analysis to evaluate transfection efficiency. (DH) Use TargetScan to predict the interaction of five DE genes (POSTN, THSD7A, SIAH3, DNAJC12 and CADM2) with relatively high predicted target scores with miR-1246, and verify the use of miR by qRT-PCR and dual luciferase -1246 Reporter gene detection of mimics/inhibitors. Data is expressed as the mean ± SEM from 3 independent experiments. *P <0.05; ** P <0.01.

Using bioinformatics analysis, we predict that miR-1246 can bind to POSTN, THSD7A, SIAH3, DNAJC12 and CADM2, these five miR-1246 target genes with relatively high potential from the above screening studies (Figure 5). However, in our subsequent experiments, the levels of THSD7A, SIAH3, CADM2, and DNAJC12 in BEAS-2B cells transfected with miR-1246 mimics or inhibitors were mixed, indicating that miR-1246 is associated with one of these four genes. The interaction can be complex and involve multiple mechanisms.

We also found that when miR-1246 mimics were transfected, the expression of POSTN (a key immune biomarker for asthma) decreased and began to increase when miR-1246 inhibitors were transfected (Figure 5H). In addition, the dual luciferase reporter gene test found that after co-transfection with miR-1246 mimics, the luciferase activity of the WT 3'-UTR reporter gene was significantly reduced, while the luciferase activity of the mutant reporter gene was not affected (P <0.01). Therefore, we believe that POSTN is one of the genes targeted by miR-1246.

According to previous studies, IL-13 is considered to be an important factor in the induction of periosteal protein. Therefore, in order to further confirm our findings, we determined the optimal IL-13 stimulation duration to induce periostin production (Figure 6A and B). Strikingly, the effect of miR-1246 on POSTN became more obvious after IL-13 was stimulated for an appropriate time (about 12 hours) (Figure 6C-E). Immunofluorescence staining also confirmed the Western blot and RT-qPCR data, which all indicate that POSTN is the target of miR-1246 (Figure 6C-E). Due to the adverse effects of anti-IL-13 therapy (lebrikizumab), the clinical application of periosteal protein as a systemic biomarker of asthmatic airway eosinophilia has been greatly restricted. In this study, our data may make up for this deficiency. Figure 6 The effect of miR-1246 on POSTN and remodeling. (A) Western blot and qRT-PCR were performed to determine the full-time response curve of periostin. The maximum response was observed with 100 ng/mL IL-13 at 12 hours. (CE) The expression of POSTN in BEAS-2B cells transfected with miR-1246 mimic, miR-1246 inhibitor, or miR-control was measured by Western blot, RT-PCR, and immunofluorescence analysis. (FM) RT-PCR is used to examine the effects of miR-1246 on airway remodeling and EMT using miR-1246 mimics (FI) and inhibitors (JM). Similar results were obtained in three independent experiments. *P <0.05; ** P <0.01.

Figure 6 The effect of miR-1246 on POSTN and remodeling. (A) Western blot and qRT-PCR were performed to determine the full-time response curve of periostin. The maximum response was observed with 100 ng/mL IL-13 at 12 hours. (CE) The expression of POSTN in BEAS-2B cells transfected with miR-1246 mimic, miR-1246 inhibitor, or miR-control was measured by Western blot, RT-PCR, and immunofluorescence analysis. (FM) RT-PCR is used to examine the effects of miR-1246 on airway remodeling and EMT using miR-1246 mimics (FI) and inhibitors (JM). Similar results were obtained in three independent experiments. *P <0.05; ** P <0.01.

We also used quantitative PCR to analyze the effects of miR-1246-regulated gene expression in remodeling and EMT, and also found significant effects (Figure 6F-M). In summary, all results indicate that miR-1246 may play a complex role in the progression of asthma and may become a potential target for the treatment and diagnosis of asthma.

According to the latest GINA, asthma exhibits different symptoms and different expiratory flow restrictions, and is defined as a heterogeneous clinical syndrome, rather than a single disease entity. 12 As mentioned above, the lack of effective methods to identify and classify asthma severely limits the diagnosis of asthma. Implement individualized precision treatment. To date, there is no single diagnostic biomarker for asthma. Airway epithelium provides the first-line host defense against invading pathogens, which can be obtained directly from bronchoscopy and in vitro culture. 13 There is ample evidence in the literature that airway epithelium is an important controller of bronchial inflammation, remodeling, and hyperresponsiveness in the clinic. 14 Therefore, strategies targeting airway epithelial cells may prevent the pathological progression of asthma and reduce its impact on patients. Burden. 14 However, the underlying mechanism of asthmatic airway epithelial cell dysfunction remains largely unknown. 15

The discovery of miRNA in 199316 gave a more comprehensive understanding of the pathophysiology of asthma. At the same time, it also provides a basic basis for the precise diagnosis of asthma and the choice of molecular-based alternative classification strategies. 17 There is increasing evidence that different amounts of miRNA (including miR-19, miR-21, miR 22, miR141, miR221, and miR-455) can affect many aspects of the pathogenesis of asthma, such as aggravating airway remodeling , Regulate mucus production and affect T helper cell/cytokine imbalance. 18,19 In recent studies, miRNAs (miR-92b, miR-210, and miR-34a) are altered in extracellular vesicles secreted by airway epithelial cells and are associated with the development of asthma. 20 However, as mentioned above, even if more and more identified miRNAs have been found to maintain airway homeostasis, that is, miRNA expression characteristics in the airway epithelium during the progression of asthma, it has not yet been determined.

In this study, we constructed a miRNA-based regulatory network in airway epithelial samples from asthma patients. The DE-miRNAs in the GSE25230 and GSE142237 datasets were screened in samples from asthma patients and normal biopsy samples, and we finally selected six collective DE-miRNAs as "reasonable" DE-miRNAs. Based on ROC curve analysis, we further calculated AUC and quantified the diagnostic availability of these six miRNAs. Three online prediction tools, TargetScan (version 7.2), miRWalk (version 2.0) and TarBase (version 7.0), were used to construct the miRNAs-mRNAs interaction network of these miRNAs. In addition, GO and KEGG enrichment analysis were performed on mRNA in the miRNA network. In order to further identify the functional target genes in the progression of asthma, another measurement data set GSE43696 was used to analyze the differences in gene expression profiles between the airway epithelium of asthma patients and healthy controls. Through combinatorial analysis, a more value-based miRNA-mRNA network was constructed, including 6 selected miRNAs and 31 targeted genes. This integrated mRNA-miRNA network and subsequent in vitro functional verification studies provide some evidence for the causal relationship between regulatory miRNA and asthma-related gene expression.

In recent years, periosteal protein has become a promising and feasible asthma biomarker, especially in eosinophilic asthma. 21 It is also widely recognized to be involved in abnormal airway epithelial dysfunction in asthma, including promoting the expression of mucin and remodeling-related genes. 22,23 Although some studies are based on small sample sizes, they have shown a correlation between serum periosteal protein and reduced lung function (airway remodeling) and airway hyperresponsiveness (AHR). 24-26 Despite these advantages, several key shortcomings limit the usefulness of the prognosis. Periosteum protein. Currently, in certain specific situations (for example, after IL-13 targeted therapy), the correlation between the level of periosteal protein and the number of eosinophils has become insignificant. 27 In addition, in a prospective, real-world study, researchers recruited and examined 465 eligible patients with severe asthma. The relationship between the risk of asthma exacerbation and the level of serum periostin. Unfortunately, in this one-year longitudinal study, the researchers did not find that serum periostin has any prognostic value in detecting the rate of asthma attacks. 28 These findings, together with some other data, have greatly hindered the clinical application of periostin. 22,29–31 Our research found that during the progression of asthma, miR-1246 not only changes dynamically, but also controls the expression of periosteal protein at the transcriptional and protein levels. As IL-13 stimulates the expression of periosteal protein under appropriate conditions such as time and concentration, these effects become more pronounced. In view of the interaction between miR-1246 and periostin in patients with asthma, the measurement of combining these two factors may reduce the potential influence of confounding factors to a certain extent and serve as a potential strategy for evaluating patients. We are currently planning research to explore this situation and further verify the hypothesis in follow-up experiments.

We have to admit that this study has several limitations. Although we minimized the possibility of potential sources of bias, we did not initially analyze the data from the array. Therefore, the existence of confounding factors such as batch effects or biological differences is inevitable, and the results of this study should be interpreted with caution. In addition, miR-1246 does not exist in mice, which limits the study of its role in in vivo models, and further studies on the function of miR-1246 in asthma are needed.

Based on 6 selected DE-miRNAs and 31 targeted genes, we constructed a preliminary complete miRNA-mRNA regulation map in asthmatic airway epithelium. We further explored their functions in two cell models and analyzed the diagnostic potential of these selected miRNAs in blood samples. Our experiments provide further support for constructing the structure of the airway epithelial microenvironment of asthma in order to seek precise treatment goals. In addition, it was confirmed that miRNA-1246, which is up-regulated by stimulating two pathogenic factors in asthma, can also regulate the expression of POSTN. In view of the important regulatory role of periosteal protein in the progression of asthma, targeting miR-1246 may be a promising target for asthma treatment. In addition, compared with a single marker, the combined detection of periosteal protein and miR-1246 may be a valuable prognostic and diagnostic marker. However, miR-1246 does not exist in mice, which limits the in-depth study of its role in asthma.

AUC, area under the curve; BP, biological process; CC, honeycomb component; DAVID, database for annotation, visualization and integrated discovery; DE, differential expression; EMT, epithelial-mesenchymal transition; FDR, false discovery rate; GEO, gene Expression synthesis; GO, gene ontology; IL, interleukin; KEGG, Kyoto Encyclopedia of Genes and Genomes; MFs, molecular functions; miRNA, microRNA; PPI, protein-protein interaction; ROC, receiver operating characteristics; RT -qPCR, reverse transcription quantitative PCR; TGF, transforming growth factor.

The data used and analyzed in this study can be obtained from the corresponding author upon reasonable request.

This study was approved by the Ethics Committee of Qianfoshan Hospital Affiliated to Shandong University (ethics review number: 2021-S923). All patients gave informed consent to participate and publish data.

A published written informed consent form was obtained from all participants.

All authors have made significant contributions to the work of the report, whether in terms of concept, research design, execution, data acquisition, analysis and interpretation, or in all these areas; participating in drafting, revising or critically reviewing articles; final approval requirements Published version; agreed on the journal to which the article was submitted; and agreed to be responsible for all aspects of the work.

This work was funded by the National Natural Science Foundation of China (81770029). Funders have no role in research design, data collection, data analysis, or manuscript preparation.

The authors declare that they have no conflicts of interest in this work.

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