Gene regulation and signaling pathways in immune response to respiratory sensitizers: a database analysis

TL;DR

This study analyzes gene expression data to identify immune response pathways involved in respiratory sensitization and suggests using multiple databases for better biomarker identification.

Contribution

The study demonstrates the importance of using multiple databases to uncover gene regulation patterns in respiratory sensitization.

Findings

43 upregulated genes were identified using GO, and 52 using KEGG, with only 18 overlapping.

26 downregulated genes were found via GO and 40 via KEGG, with only 9 overlapping.

The results suggest combining multiple databases improves biomarker identification for sensitizers.

Abstract

Humans are regularly exposed to environmental substances through inhaled air. Some chemicals or particles are respiratory sensitizers that can cause adverse respiratory health effects by triggering amplified immune responses. Understanding the process of respiratory sensitization and identifying potential sensitizers have been challenging due to the complexity of the underlying mechanisms.

This study leverages the transcriptomics from a previous in vitro 3D human lung model to investigate the pathways of chemical respiratory sensitization. Differentially expressed genes between two known and two nonsensitizers are cross-referenced against databases on biological processes and disease pathways.

The GO results revealed 43 upregulated genes, and the KEGG revealed 52. However, only 18 upregulated genes were common between GO and KEGG. The GO results revealed 26 downregulated genes, and the KEGG revealed 40. However, only 9 of those downregulated genes were common.

These findings support using multiple databases in perturbed gene analyses. The results from this study and data available in the scientific literature contribute toward building a biomarker profile for identifying respiratory sensitizers.

Figures