Unlocking the secrets of long non-coding RNAs in asthma

Gysens, Fien; Mestdagh, Pieter; Lavergne, Eric de Bony de; Maes, Tania

doi:10.1136/thoraxjnl-2021-218359

Cited by 21 publications

(20 citation statements)

References 78 publications

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“…When compared to healthy controls, bronchial smooth muscle cells from asthmatic patients express different levels of hsa-miR-376a-3p and hsa-miR-330-5p [ 36 ]. ENSG00000267174 is a long noncoding RNA (lncRNA), and many lncRNAs have been shown to be associated with asthma severity or inflammatory phenotype [ 37 ]. ENSG00000004139 can regulate the cell survival and cytokine release after inflammasome activation [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

Benchmarking omics-based prediction of asthma development in children

Wang

Schaub

et al. 2023

Respir Res

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Background Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. Objective We aimed to investigate the computational methods in disease status prediction using multi-omics data. Method We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. Results Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. Conclusions Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

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Section: Resultsmentioning

confidence: 99%

Benchmarking omics-based prediction of asthma development in children

Wang

Schaub

et al. 2023

Respir Res

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“…12 Importantly, ncRNAs, including long noncoding RNAs (lncRNAs) with sizes larger than 200 nucleotides, microRNAs (miRNAs), and circular RNAs (circRNAs), among others, are being recognized as important molecules regulating coding genes at the transcriptional or posttranscriptional level in individuals with allergic asthma. 13 Multiple studies have emphasized that the effects of miRNAs on the initiation and maintenance of asthma are mainly achieved by regulating the expression of mRNAs at the posttranscriptional level. 14 In contrast, lncRNAs and circRNAs employ a similar mechanism by functioning as an miRNA sponge as a competing endogenous RNA (ceRNA) in individuals with asthma.…”

Section: Discussionmentioning

confidence: 99%

The Specific microRNA Profile and Functional Networks for Children with Allergic Asthma

Zhang¹,

Zhang²,

Feng³

et al. 2022

JAA

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Background: Allergic asthma is the most common type of asthma and often occurs in early life with increasing comorbidities, including atopic dermatitis and allergic rhinitis. MicroRNAs (miRNAs) are involved in the pathogenesis of numerous immune and inflammatory disorders, particularly allergic inflammation. The specific miRNA profiles of children with allergic asthma have not been fully delineated and still require in-depth study.Objective: This study aimed to identify the expression profile of miRNAs and constructed a network of the interactions between differentially expressed miRNAs and target mRNAs to provide novel insights into understanding the pathogenesis of allergic asthma. Materials and Methods: In this study, we performed high-throughput sequencing of peripheral blood mononuclear cells (PBMCs) from children in the acute phase of asthma. Bioinformatics approaches, including miRanda, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, were employed to predict novel therapeutic and diagnostic targets for allergic asthma. Real-time quantitative PCR was conducted to detect the expression of aberrantly expressed miRNAs. Results: One hundred and sixty-one differentially expressed miRNAs were identified in children with allergic asthma, including 140 conserved miRNAs and 21 novel miRNAs. A total of 8929 targeted mRNAs (44,186 transcripts) associated with differentially expressed miRNAs were predicted and significantly enriched in the cGMP-PKG signalling pathway, cholinergic synapse, and salivary secretion. We also found that miRNA-370-3p targeted PKG and MLCP molecules in the cGMP-PKG signalling pathway and was involved in the pathogenesis of allergic asthma. Conclusion:We identified the miRNA profile of PBMCs in children with allergic asthma and also found that miRNA-370-3p targeted PKG and MLCP molecules in the cGMP-PKG signalling pathway, which provides a novel insight into understanding the pathogenesis of allergic asthma and investigating new targets for the treatment of allergic asthma in children.

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“…Long noncoding RNAs (lncRNAs) are defined as RNAs longer than 200 nucleotides that are not translated into functional proteins ( Orom & Shiekhattar, 2013 ). In recent years, it has been increasingly evident that lncRNAs are important regulators of gene expression in diverse cellular processes, thus are essential for maintaining normal physiology, and are often dysregulated in various diseases including asthma ( Melissari & Grote, 2016 ; Rafiee et al, 2018 ; Statello et al, 2021 ; Gysens et al, 2022 ; Chen & Deng, 2022 ). A number of studies have compared the expression of different lncRNAs in blood cells between asthma patients and healthy controls and identified several differentially expressed lncRNAs ( Tsitsiou et al, 2012 ; Booton & Lindsay, 2014 ; Feng, Yang & Yan, 2020 ; Chen & Deng, 2022 ), among which maternally expressed gene 3 (MEG3) is of particular interest.…”

Section: Introductionmentioning

confidence: 99%

Novel genetic variants in long non-coding RNA MEG3 are associated with the risk of asthma

Chiu

Chang

Tsai

et al. 2023

PeerJ

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Background Asthma is the most common chronic inflammatory airway disease worldwide. Asthma is a complex disease whose exact etiologic mechanisms remain elusive; however, it is increasingly evident that genetic factors play essential roles in the development of asthma. The purpose of this study is to identify novel genetic susceptibility loci for asthma in Taiwanese. We selected a well-studied long non-coding RNA (lncRNA), MEG3, which is involved in multiple cellular functions and whose expression has been associated with asthma. We hypothesize that genetic variants in MEG3 may influence the risk of asthma. Methods We genotyped four single nucleotide polymorphisms (SNPs) in MEG3, rs7158663, rs3087918, rs11160608, and rs4081134, in 198 patients with asthma and 453 healthy controls and measured serum MEG3 expression level in a subset of controls. Results The variant AG and AA genotypes of MEG3 rs7158663 were significantly over-represented in the patients compared to the controls (P = 0.0024). In logistic regression analyses, compared with the wild-type GG genotype, the heterozygous variant genotype (AG) was associated with a 1.62-fold [95% confidence interval (CI) [1.18–2.32], P = 0.0093] increased risk and the homozygous variant genotype (AA) conferred a 2.68-fold (95% CI [1.52–4.83], P = 0.003) increased risk of asthma. The allelic test showed the A allele was associated with a 1.63-fold increased risk of asthma (95% CI [1.25–2.07], P = 0.0004). The AG plus AA genotypes were also associated with severe symptoms (P = 0.0148). Furthermore, the AG and AA genotype carriers had lower serum MEG3 expression level than the GG genotype carriers, consistent with the reported downregulation of MEG3 in asthma patients. Conclusion MEG3 SNP rs7158663 is a genetic susceptibility locus for asthma in Taiwanese. Individuals carrying the variant genotypes have lower serum MEG3 level and are at increased risks of asthma and severe symptoms.

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Unlocking the secrets of long non-coding RNAs in asthma

Cited by 21 publications

References 78 publications

Benchmarking omics-based prediction of asthma development in children

Benchmarking omics-based prediction of asthma development in children

The Specific microRNA Profile and Functional Networks for Children with Allergic Asthma

Novel genetic variants in long non-coding RNA MEG3 are associated with the risk of asthma

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