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Particulate matter-induced gene expression patterns in human-derived cells based on 11 public gene expression datasets

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Abstract

Background

Exposure to particulate matter (PM) and house dust mite (HDM) can change the expression patterns of inflammation-, oxidative stress-, and cell death-related genes. We investigated the changes in gene expression patterns owing to PM exposure.

Objective

This study examined the changes in gene expression patterns following PM exposure.

Methods

We searched for differentially expressed genes (DEGs) following PM exposure using five cell line-based RNA-seq or microarray datasets and six human-derived datasets. The enrichment terms of the DEGs were assessed.

Results

DEG analysis yielded two gene sets. Thus, enrichment analysis was performed for each gene set, and the enrichment terms related to respiratory diseases were presented. The intersection of six human-derived datasets and two gene sets was obtained, and the expression patterns following PM exposure were observed.

Conclusions

Two gene sets were obtained for cells treated with PM and their expression patterns were presented following verification in human-derived cells. Our findings suggest that exposure to PM2.5 and HDM may reveal changes in genes that are associated with diseases, such as allergies, highlighting the importance of mitigating PM2.5 and HDM exposure for disease prevention.

Highlights

Two gene sets with differences in expression were obtained after the treatment of cells with PM.

Enrichment terms for each gene set were extracted.

DEGs in patient-derived cells after PM treatment were compared with those in cell lines.

When PBMC-derived cells were treated with PM, a relatively high overlap of DEGs was found.

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Funding

This work was supported by the Korea Environmental Industry & Technology Institute (KEITI), funded by the Korea Ministry of Environment (MOE) (1485019161, RS-2022-KE-002339).

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Correspondence to Dae Jin Song or Jeong-An Gim.

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Roh, S., Hwang, J., Park, JH. et al. Particulate matter-induced gene expression patterns in human-derived cells based on 11 public gene expression datasets. Genes Genom (2024). https://doi.org/10.1007/s13258-024-01512-y

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