Exploring the role of long noncoding RNAs in immune regulation and pediatric asthma pathogenesis

Abstract

Pediatric asthma, a growing chronic condition, faces diagnostic and treatment challenges in the post-COVID era. This study uses bioinformatics to explore lncRNAs’ roles in pediatric asthma, aiming to improve diagnosis and treatment. To identify differentially expressed lncRNAs, the study will utilize a 2-pronged approach: obtaining gene expression datasets from the gene expression omnibus and recruiting patients for gene sequencing. The intersection of these 2 datasets will be analyzed to pinpoint lncRNAs that exhibit differential expression. The CIBERSORT approach was employed to estimate the composition of immune-infiltrating cells within the microenvironment of each patient in the gene expression omnibus dataset. Ultimately, real-time quantitative PCR was employed to confirm the variation in lncRNA in peripheral blood. In pediatric asthma patients, MIR22HG is significantly underexpressed compared to healthy subjects. Concurrently, there is a notable increase in naive CD4 T cells in the microenvironment during asthma, along with a negative correlation between MIR22HG and naive CD4 T cells.. In summary, MIR22HG might influence the onset and progression of pediatric asthma, which could serve as a diagnostic marker for pediatric asthma and offer valuable insights for asthma diagnosis.

1. Introduction

Pediatric asthma is a prevalent chronic respiratory condition that is becoming increasingly common worldwide, posing significant challenges to treatment and substantially impacting children’s quality of life.^[1,2] The diagnosis of pediatric asthma primarily relies on clinical presentation and patient history.^[1] However, the emergence of the novel coronavirus has led to an increase in respiratory symptoms that closely resemble those of asthma in children.^[3] Furthermore, children are often referred to as “black boxes” due to their difficulty in accurately describing their symptoms and sensations, which poses significant challenges for healthcare professionals in making a diagnosis.^[4] In light of these factors, there is an urgent need to continue exploring diagnostic markers and molecular pathogenesis of pediatric asthma. This will provide new insights and strategies for early detection, diagnosis, and treatment of pediatric asthma, ultimately improving patient outcomes.

Epigenetic mechanisms, including the role of long noncoding RNAs (lncRNAs), are crucial in the pathogenesis of allergic asthma but remain poorly understood.^[5,6] As the fields of immunotherapy and microenvironment studies advance, the importance of lncRNAs is becoming increasingly recognized.^[7] Furthermore, several comprehensive reviews have underscored the promise of RNAs as both diagnostic biomarkers and therapeutic targets.^[8–14] Therefore, it is essential to explore the functions and classifications of new lncRNAs to gain a comprehensive understanding of the biological processes involved in asthma.

MIR22HG is a lncRNA known for its regulatory roles in gene expression and cellular functions such as differentiation, proliferation, and apoptosis.^[15–18] Although its broader biological roles have been recognized, the specific functions of MIR22HG in respiratory diseases, including asthma, are still being explored.

The current study aims to fill this gap by identifying lncRNAs that are differentially expressed in pediatric asthma patients compared to healthy controls. By integrating bioinformatics analysis of publicly available gene expression datasets from the gene expression omnibus with direct gene sequencing of patient samples, this research seeks to uncover specific lncRNAs that could be implicated in asthma pathology. Additionally, the study will employ the CIBERSORT method to analyze immune cell infiltration in the lung tissues of asthmatic children, providing a comprehensive view of the inflammatory processes at play.

Understanding these molecular interactions and their impact on the immune system could lead to significant improvements in the diagnostic precision and therapeutic strategies for pediatric asthma, ultimately enhancing patient care and management. This research is particularly timely and critical given the overlapping respiratory symptoms seen in COVID-19, which can complicate the diagnosis of asthma in children. By identifying specific lncRNA signatures associated with asthma, this study aims to contribute valuable tools for distinguishing asthma from other respiratory ailments in the pediatric population.

2. Materials and methods

2.1. Patients recruitment

This study recruited a total of 36 patients with pediatric asthma and 36 healthy individuals as controls from the Jiaxing Maternity and Children Health Care Hospital. The inclusion criteria for asthma patients are asthma patients who visited the pediatric department from January 2021 to January 2023 and have no other underlying diseases. Normal controls are healthy children with pediatric examinations. The research received approval from the hospital’s ethical review board and was carried out in compliance with the ethical guidelines of the Declaration of Helsinki. All individuals involved in the study were thoroughly educated on the research objectives and methods, and they gave their written informed consent. Participants under the age of 16 have obtained informed consent from their parents or legal guardians.

2.2. Data analysis

Three individuals were randomly selected from both the disease cohort and the control group for this study. The data analysis for this study involved several steps. First, paired-end reads were harvested from the Illumina HiSeq sequencer and underwent quality filtering. The resulting high-quality trimmed reads were then aligned to the reference genome (UCSC hg19) using TopHat2 software, guided by the Ensembl GFF gene annotation file. After 3’ adapter trimming with cutadapt software (v1.9), gene-level counts were obtained using cuffdiff software (part of cufflinks) to generate expression profiles for both lncRNA and mRNA (Supplemental Table 1, Supplemental Digital Content, https://links.lww.com/MD/P751).

2.3. Collection of data and identification of differentially expressed genes

The datasets used for analysis were obtained from the gene expression omnibus database, consisting of 15 healthy nonatopic and 37 nonsmoking subjects with allergic asthma (12 subjects were treated with inhaled corticosteroids and 25 subjects were treated only with inhaled bronchodilators) (accession number: GSE75011). Upon acquiring gene-level counts for lncRNA expression profiles, differentially expressed genes were determined using the edgeR package in R software, applying a threshold of |logFC| >0.5 and FDR below 0.05. We chose a |logFC| threshold of 0.5 – rather than the more stringent 1.0 – to increase sensitivity for lncRNAs, which often exhibit moderate expression changes but may still be biologically meaningful in asthma. The same selection criteria were applied to our sequencing data. To evaluate the predictive efficacy of this algorithm, we utilized the receiver operating characteristic curve and calculated the area under curve.

2.4. Reverse transcription PCR

A total of 33 pairs were selected from both the disease cohort and the control cohort for the study. To confirm the gene expression analysis findings, RNA was isolated from the samples using Takara isolation kits, following the manufacturer’s guidelines. Subsequently, first-strand cDNA synthesis was conducted using Takara. PCR was performed utilizing the Takara system, with GAPDH serving as an internal reference. The relative expression of target genes was determined using the 2^−ΔΔCt method, and a P value of <.05 was deemed significant. To guarantee the precision and dependability of the outcomes, all PCR reactions were executed in triplicate.

2.5. Immune cell infiltration analysis

To obtain data on immune cell infiltration, the CIBERSORT analysis was conducted on 22 types of immune cells within the matrix of GSE75011. Bar charts were generated to visualize the infiltration rates of these 22 immune cell types. Violin plots were used to display the differences in immune cell distribution between asthma patients and controls. Finally, the correlation between MIR22HG and immune cells was analyzed.

3. Results

3.1. MIR22HG is downregulated in pediatric asthma

The GSE75011 dataset screened 9 differentially expressed lncRNAs (Supplemental Table 2, Supplemental Digital Content, https://links.lww.com/MD/P751). At the same time, 567 differentially expressed lncRNAs were screened by sequencing data. The screening results were further visualized using heatmaps (Fig. 1A and B) (Supplemental Table 3, Supplemental Digital Content, https://links.lww.com/MD/P751). MIR22HG was found to appear in 2 datasets at the same time, with area under curve of 0.702 and 1 (Fig. 1C–E). In the results of PCR, MIR22HG was also significantly low in pediatric asthma (P = 4.3 × ⁻¹¹) (Fig. 1F).

Figure 1.

Differential expression and diagnostic performance of candidate lncRNAs. (A) Heatmap of the differentially expressed lncRNAs in the GSE75011 cohort. Upregulated lncRNAs are shown in red and downregulated in green. (B) Heatmap of the top 20 differentially expressed lncRNAs in our sequencing cohort from Jiaxing Maternity and Children Health Care Hospital (n = 3 pediatric asthma vs n = 3 healthy controls). Data are processed as in (A), with identical color coding and clustering parameters. (C) Venn diagram showing the overlap of DEGs identified in GSE75011 and our sequencing cohort, highlighting shared lncRNAs between the 2 datasets. (D) ROC curve for MIR22HG in the GSE75011 cohort, with AUC = 0.702 (95% CI 0.546–0.847), calculated using the pROC package. (E) ROC curve for MIR22HG in our sequencing cohort, with AUC = 1.000 (95% CI 1.000–1.000). (F) qRT-PCR validation of MIR22HG expression in an independent cohort (n = 36 pediatric asthma vs n = 36 healthy controls). Statistical significance was assessed by unpaired t-test (P = 4.3 × 10^–11). AUC = area under curve, CI =confidence interval, DEGs = differentially expressed genes, lncRNA = long noncoding RNA, ROC = receiver operating characteristic.

3.2. Results of immune cell infiltration

The proportions of immune cells from GSE75011 data were analyzed using the CIBERSORT algorithm, and the results were shown in bar charts (Fig. 2A). There is a notable increase in naïve CD4⁺T cells in the microenvironment during asthma (Fig. 2B), Spearman correlation analysis further demonstrated that MIR22HG expression was positively correlated with the proportion of naïve CD4⁺T cells (R = 0.385, P = .014; Fig. 2C), suggesting a potential role for MIR22HG in modulating naïve CD4⁺T cell mediated immune responses in asthma.

Figure 2.

Immune infiltration analysis and its correlation with MIR22HG expression. (A) Relative proportions of 22 immune cell subsets estimated by CIBERSORT in peripheral blood samples from GSE75011. Each bar represents 1 sample; colored segments correspond to different cell types, ordered by sample group. (B) Violin plots comparing the distribution of key immune cell proportions between asthma (red) and control (blue) groups. The width of each violin reflects the density of samples at each proportion value. Statistical significance was determined by the Wilcoxon rank-sum test (*P < .05, **P < .01). Asthma patients show a significant increase in naïve CD4⁺ T cells and a decrease in CD4⁺ memory resting T cells compared with controls. (C) Spearman correlation heatmap depicting relationships between MIR22HG expression and immune cell fractions in asthma. Each cell displays the correlation coefficient (r); the color scale ranges from blue (negative correlation) to red (positive correlation).

4. Discussion

This study elucidated the role of MIR22HG in the immune dysregulation observed in pediatric asthma, particularly noting its significant underexpression and negative correlation with elevated naive CD4 T cells. These findings suggest a potential role of MIR22HG in modulating the differentiation and activity of T cells, which are central to the pathophysiological mechanisms of asthma.^[19,20] By associating decreased MIR22HG levels with an increase in pro-inflammatory naive CD4 T cells, our results highlight a crucial epigenetic element that may contribute to the disease’s immune response abnormalities.

The results of this study align with the growing body of literature emphasizing the role of lncRNAs in immune regulation and asthma.^[21,22] However, contrasting findings exist regarding the specific pathways and the extent of influence lncRNAs have on asthma pathogenesis. This underscores the complexity of asthma as a multifactorial disease and highlights the need for further comprehensive studies to elucidate the specific roles of lncRNAs.

The significance of this study lies in its potential to advance diagnostic strategies and therapeutic interventions for pediatric asthma. Identifying MIR22HG as a biomarker for asthma could lead to more precise diagnosis and personalized treatment approaches, distinguishing asthma from other respiratory conditions with overlapping symptoms, such as viral-induced wheeze and COVID-19. This is particularly critical in the current global health context, where accurate diagnosis is essential for effective disease management.

This single-center, cross-sectional study with a modest sample size may lack generalizability and preclude causal inference. Although CIBERSORT offers an initial immune-cell estimate from transcriptomic data, its predefined gene signatures may misestimate true cell proportions. Subsequent functional assays will elucidate MIR22HG’s role in immune regulation by assessing its impact on naïve CD4⁺ T cell differentiation.

5. Conclusions

In conclusion, this study contributes to the understanding of lncRNAs in pediatric asthma by highlighting the role of MIR22HG in modulating immune responses. The findings support the hypothesis that MIR22HG could serve as a potential diagnostic marker and therapeutic target in asthma management. Future research should expand on these findings with larger, multi-center studies to explore the causal relationships and potential clinical applications of MIR22HG in pediatric asthma.

Author contributions

Conceptualization: Keng Ling.

Data curation: Siyi Zhang, Liqin Jin.

Formal analysis: Siyi Zhang, Liqin Jin.

Writing – original draft: JianGuo Wang.

Writing – review & editing: JianGuo Wang.