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Related Concept Videos

Asthma: Pathogenesis and Management01:20

Asthma: Pathogenesis and Management

Asthma is a chronic pulmonary condition involving inflammation of the airways, hyper-reactivity, and reversible obstruction of the airways. This condition can significantly impact a person's quality of life, making breathing difficult and leading to distressing symptoms.
Asthma is classified as allergic and non-allergic. Allergens such as dust mites, pollen, and pet dander trigger allergic asthma, while factors like cold air, intense emotions, or exercise can induce non-allergic asthma.
Asthma-II: Pathophysiology and Classification01:26

Asthma-II: Pathophysiology and Classification

Asthma is a prevalent chronic respiratory condition marked by inflammation and hyperresponsiveness of the airways. Its pathophysiology involves complex interactions among inflammatory pathways, immune responses, and neural mechanisms.
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
Asthma-IV: Diagnostic and Management01:30

Asthma-IV: Diagnostic and Management

The diagnosis and management of asthma are comprehensive, encompassing clinical assessments, lung function tests, and pharmacological interventions. Here's an overview:
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:

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Related Experiment Video

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The Utility of Resolving Asthma Molecular Signatures Using Tissue-Specific Transcriptome Data.

Debajyoti Ghosh1, Lili Ding2, Jonathan A Bernstein1

  • 1Immunology and Allergy, Department of Internal Medicine, University of Cincinnati, OH.

G3 (Bethesda, Md.)
|September 9, 2020
PubMed
Summary

This multi-tissue transcriptomic analysis of asthma reveals distinct tissue-specific and shared pathways, identifying potential drug candidates and highlighting the importance of diverse sample types for future research.

Keywords:
Asthma transcriptomeConnectivity MapGWAS Catalogilincsmachine learningpathways/networkstissue-specific analysis

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Area of Science:

  • Genomics
  • Immunology
  • Computational Biology

Background:

  • Multi-tissue transcriptomic analysis for asthma is lacking, hindering the identification of tissue-specific disease pathways and drug targets.
  • Previous studies often analyze single tissue types, potentially missing crucial inter-tissue interactions and disease mechanisms.

Purpose of the Study:

  • To conduct an integrative, multi-tissue transcriptomic analysis of asthma.
  • To identify tissue-specific and shared asthma pathways and differentially expressed genes (DEGs).
  • To connect DEGs to Genome-Wide Association Studies (GWAS) data, identify surrogate tissues, and discover potential drug candidates.

Main Methods:

  • Retrieved and analyzed transcriptome data from 609 asthma cases and 196 controls across multiple tissues (airway epithelium, bronchial, nasal, macrophages, fibroblasts, lymphocytes, whole blood, sputum).
  • Identified tissue-specific and shared DEGs to map disease-relevant pathways.
  • Utilized connectivity map analysis for drug candidate identification and Support Vector Machine for discriminatory accuracy assessment.

Main Results:

  • Gene expression similarity was higher at the pathway level than the gene level across tissues.
  • Identified key pathways like IL-1b and ERK signaling as significant across multiple tissues, while others like Insulin-like growth factor and TGF-beta signaling were tissue-specific.
  • Discovered potential drug candidates (entinostat, BMS-345541) and genetic perturbagens (KLF6, BCL10, INFB1, BAMBI) for asthma treatment.
  • Macrophages and epithelial cells showed the highest and lowest discriminatory accuracy for DEGs, respectively.

Conclusions:

  • Multi-tissue transcriptomic analysis provides a comprehensive understanding of asthma, revealing unique and shared pathway contributions.
  • The study highlights the differential relevance of DEGs, perturbagens, and disease connections across various tissue types.
  • Emphasizes the need for future studies to collect multi-tissue, multi-demographic data with better annotations for advancing asthma research and treatment.