ArticleBMC medical genomics2016
Differential gene network analysis for the identification of asthma-associated therapeutic targets in allergen-specific T-helper memory responses.
Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 57 citations in OpenAlex.
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- LPS binding protein and activation signatures are upregulated during asthma exacerbations in children.Respiratory research · 2023Article
- Airway and parenchyma transcriptomics in a house dust mite model of experimental asthma.Respiratory research · 2023Article
- Significance of RNA N6-Methyladenosine Regulators in the Diagnosis and Subtype Classification of Childhood Asthma Using the Gene Expression Omnibus Database.Frontiers in genetics · 2021Article
- Comparative efficacy of glucocorticoid receptor agonists on Th2 cell function and attenuation by progesterone.BMC immunology · 2020Article
- MicroRNAs in Asthma and Respiratory Infections: Identifying Common Pathways.Allergy, asthma & immunology research · 2020Review
- Airway Epithelial Cell Immunity Is Delayed During Rhinovirus Infection in Asthma and COPD.Frontiers in immunology · 2020Article
- QuantSeq. 3' Sequencing combined with Salmon provides a fast, reliable approach for high throughput RNA expression analysis.Scientific reports · 2019Article
- Purification, Characterization of Two Polysaccharides fromInternational journal of molecular sciences · 2019Article
- Retinoic Acid Receptor Alpha Represses a Th9 Transcriptional and Epigenomic Program to Reduce Allergic Pathology.Immunity · 2019Article
- Airway Microbiome in Different Inflammatory Phenotypes of Asthma: A Cross-Sectional Study in Northeast China.International journal of medical sciences · 2019Article
- Nasal DNA methylation is associated with childhood asthma.Epigenomics · 2018Article
- Differential gene regulatory networks in development and disease.Cellular and molecular life sciences : CMLS · 2018Review
- Integrative transcriptomic analysis reveals key drivers of acute peanut allergic reactions.Nature communications · 2017Article
- Baicalein, wogonin, andAnatomy & cell biology · 2017Article
- Gene Expression Analysis Reveals Novel Shared Gene Signatures and Candidate Molecular Mechanisms between Pemphigus and Systemic Lupus Erythematosus in CD4Frontiers in immunology · 2017Article
- Critical Role of Plasmacytoid Dendritic Cells in Regulating Gene Expression and Innate Immune Responses to Human Rhinovirus-16.Frontiers in immunology · 2017Article
- International consensus (ICON) on: clinical consequences of mite hypersensitivity, a global problem.The World Allergy Organization journal · 2017Article
- Respiratory viral infections and host responses; insights from genomics.Respiratory research · 2016Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAsthma is strongly associated with allergic sensitization, but the mechanisms that determine why only a subset of atopics develop asthma are not well understood. The aim of this study was to test the hypothesis that variations in allergen-driven CD4 T cell responses are associated with susceptibility to expression of asthma symptoms.
methodsThe study population consisted of house dust mite (HDM) sensitized atopics with current asthma (n = 22), HDM-sensitized atopics without current asthma (n = 26), and HDM-nonsensitized controls (n = 24). Peripheral blood mononuclear cells from these groups were cultured in the presence or absence of HDM extract for 24 h. CD4 T cells were then isolated by immunomagnetic separation, and gene expression patterns were profiled on microarrays.
resultsDifferential network analysis of HDM-induced CD4 T cell responses in sensitized atopics with or without asthma unveiled a cohort of asthma-associated genes that escaped detection by more conventional data analysis techniques. These asthma-associated genes were enriched for targets of STAT6 signaling, and they were nested within a larger coexpression module comprising 406 genes. Upstream regulator analysis suggested that this module was driven primarily by IL-2, IL-4, and TNF signaling; reconstruction of the wiring diagram of the module revealed a series of hub genes involved in inflammation (IL-1B, NFkB, STAT1, STAT3), apoptosis (BCL2, MYC), and regulatory T cells (IL-2Ra, FoxP3). Finally, we identified several negative regulators of asthmatic CD4 T cell responses to allergens (e.g. IL-10, type I interferons, microRNAs, drugs, metabolites), and these represent logical candidates for therapeutic intervention.
conclusionDifferential network analysis of allergen-induced CD4 T cell responses can unmask covert disease-associated genes and pin point novel therapeutic targets.
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