Allergy Asthma Immunol Res.  2020 May;12(3):399-411. 10.4168/aair.2020.12.3.399.

Understanding the Molecular Mechanisms of Asthma through Transcriptomics

• Park HW
• Weiss ST

• KMID: 2471225
• DOI: http://doi.org/10.4168/aair.2020.12.3.399

Abstract

The transcriptome represents the complete set of RNA transcripts that are produced by the genome under a specific circumstance or in a specific cell. High-throughput methods, including microarray and bulk RNA sequencing, as well as recent advances in biostatistics based on machine learning approaches provides a quick and effective way of identifying novel genes and pathways related to asthma, which is a heterogeneous disease with diverse pathophysiological mechanisms. In this manuscript, we briefly review how to analyze transcriptome data and then provide a summary of recent transcriptome studies focusing on asthma pathogenesis and asthma drug responses. Studies reviewed here are classified into 2 classes based on the tissues utilized: blood and airway cells.

Keyword

Asthma; genetics; transcriptome; etiology

MeSH Terms

Asthma*
Biostatistics
Genetics
Genome
Machine Learning
RNA
Sequence Analysis, RNA
Transcriptome
RNA

Figure

• 

  Figure The outline of asthma transcriptomic study.Gene expression is cell- or tissue-specific. Various cells can be used transcriptomics studies focusing on asthma pathogenesis and asthma drug responses; blood cells, cells from induced sputum, bronchial epithelial cells, airway smooth muscle cell, and nasal epithelial cells. A direct and intuitive method of transcriptome analysis is to evaluate differentially expressed genes between sample groups. A gene set enrichment is the way to assign additional meaning to a list or grouping of differentially expressed genes. Using a gene set, rather than an individual gene, we can deepen our understanding of underlying biological pathways and processes considering gene-gene interactions. Gene co-expression patterns can be extracted from transcriptome data as meaningful biological information and can be used for construction of edges in networks. Gene regulatory networks attempt to look beyond gene co-expression and to identify the influencing patterns of transcription factors on gene expression in a mechanistic fashion.

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