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Data from: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation

Cite this dataset

Smith, Ley et al. (2024). Data from: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation [Dataset]. Dryad. https://doi.org/10.5061/dryad.b8gtht7mq

Abstract

Macrophages play a key role in ozone-induced lung injury by regulating both the initiation and resolution of inflammation. These distinct activities are mediated by pro-inflammatory and anti-inflammatory/pro-resolution macrophages which sequentially accumulate in injured tissues. Macrophage activation is dependent, in part, on intracellular metabolism. Herein, we used RNA-sequencing (seq) to identify signaling pathways regulating macrophage immunometabolic activity following exposure of mice to ozone (0.8 ppm, 3 hr) or air control. Analysis of lung macrophages using an Agilent Seahorse showed that inhalation of ozone increased macrophage glycolytic activity and oxidative phosphorylation at 24 and 72 hr post exposure. An increase in the percentage of macrophages in the S phase of the cell cycle was observed 24 hr post ozone. RNA-seq revealed significant enrichment of pathways involved in innate immune signaling and cytokine production among differentially expressed genes at both 24 and 72 hr after ozone, while pathways involved in cell cycle regulation were upregulated at 24 hr and intracellular metabolism at 72 hr. An interaction network analysis identified tumor suppressor 53 (TP53), E2F family of transcription factors (E2Fs), Cyclin Dependent Kinase Inhibitor 1A (CDKN1a/p21), and Cyclin D1 (CCND1) as upstream regulators of cell cycle pathways at 24 hr and TP53, nuclear receptor subfamily 4 group a member 1 (NR4A1/Nur77), and estrogen receptor alpha (ESR1/ERα) as central upstream regulators of mitochondrial respiration pathways at 72 hr. These results highlight the complex interaction between cell cycle, intracellular metabolism, and macrophage activation which may be important in the initiation and resolution of inflammation following ozone exposure.

README: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation

https://doi.org/10.5061/dryad.b8gtht7mq

We analyzed gene expression profiles in bronchoalveolar lavage cells (>95% macrophages) isolated from adult female mice exposed to ozone using bulk RNA-sequencing. Mice were sampled 24 and 72 hr after exposure. Specific analysis details are available in the associated manuscript.

Description of the data and file structure

Counts data were analyzed using DESeq2 which resulted in multiple results files:

  • File "Supplementary File 1_24 hr DEGs" contains differential expression data generated from DESeq2 comparing counts at 24 hr to counts in air controls.
  • File "Supplementary File 2_72 hr DEGs" contains differential expression data generated from DESeq2 comparing counts at 72 hr to counts in air controls.
  • File "Supplementary File 3_72 hr_vs_24 hr DEGs" contains differential expression data generated from DESeq2 comparing counts at 72 hr to counts at 24 hr post exposure.

In each of the aforementioned files, Column "baseMean" represents the mean of normalized counts for all samples, log2FoldChange represents the Log base 2 of the fold change, lfcSE represents the standard error of the log base 2 of the fold change, pvalue represents the Wald test p-value: condition treated vs untreated, and padj represents the BH adjusted p-values as described at https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#more-information-on-results-columns.

The files above were uploaded to Ingenuity Pathway Analysis software to identify significant enrichment of upstream regulators and canonical pathways among the differentially expressed genes in the datasets. Specific Analysis details are available in the associated manuscript.

  • File "Dryad - 24 hr vs Air canonical pathways" contains results of the Canonical Pathways analysis of DEGs in the 24 hr vs Air dataset.
  • File "Dryad - 24 hr vs Air upstream analysis" contains results of the Upstream Regulators analysis of DEGs in the 24 hr vs Air dataset.
  • File "Dryad - 72 hr vs Air canonical pathways" contains results of the Canonical Pathways analysis of DEGs in the 72 hr vs Air dataset.
  • File "Dryad - 72 hr vs Air upstream analysis" contains results of the Upstream Regulators analysis of DEGs in the 72 hr vs Air dataset.
  • File "Dryad - 72 vs 24 hr canonical pathways" contains results of the Canonical Pathways analysis of DEGs in the 72 hr vs 24 hr dataset.
  • File "Dryad - 72 vs 24 hr upstream analysis" contains results of the Upstream Regulators analysis of DEGs in the 72 hr vs 24 hr dataset.

In these files, significance of enrichment is provided as either p-value or the -log base 10 of the p-value. The z-score predicts whether a pathway or upstream regulator is activated or inhibited based on the direction of regulation of genes comprising the pathway in the dataset (doi:10.1093/bioinformatics/btt703). #NUM! or a blank cell indicates that a z-score was not able to be determined. For more detailed information on interpreting other columns in the files exported from IPA, the reader is referred to the IPA knowledgebase at https://qiagen.my.salesforce-sites.com/KnowledgeBase/KnowledgeNavigatorPage?categoryName=IPA

Sharing/Access information

Links to other publicly accessible locations of the data:

Methods

Total RNA was extracted as described above from 3 mice/treatment group. In a pilot study, we found that 3 mice were sufficient to identify a significant difference in Ptgs2 gene expression by qPCR at α = 0.05 and power = 80%. RNA integrity numbers (RINs) were confirmed to be ≥ 8.8 using a 2100 Bioanalyzer Instrument (Agilent, Santa Clara, CA). cDNA libraries were prepared using mouse TruSeq® Stranded Total RNA Library Prep kit (illumina, San Diego, CA) and quantified using a KAPA Library Quantification kit (Roche, Pleasanton, CA). cDNA libraries were sequenced (75 bp single-ended, ~35-44M reads per sample) on an Illumina NextSeq instrument. Raw reads in FastQ files were trimmed using Trimmomatic-0.39 (Bolger et al. 2014) and quality control of trimmed files performed using FastQC. Salmon was used to align reads in mapping-based mode with selective alignment against a decoy-aware transcriptome generated from mouse transcriptome GENCODE Release M23 (GRCm38.p6). Estimated counts per transcript were generated using the gcBias flag and normalized to transcript length to correct for potential changes in gene length across samples from differential isoform usage (Love et al. 2016; Patro et al. 2017). Transcript level quantitation data were aggregated to the gene-level using tximport (Soneson et al. 2015). Differential gene expression analysis was performed with air exposed mice as controls using DESeq2 with corrections for differences in library size (Love et al. 2014) in R version 4.0.3. Significantly enriched canonical pathways and upstream regulators were identified with Ingenuity IPA Version 65367011 (QIAGEN Inc, https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/) using a right-tailed Fisher’s Exact Test (Krämer et al. 2014). A less stringent criteria (fold change > 1.3 and experimental false discovery rate [padj] < 0.05) was used to augment the number of genes included in the pathway analysis (Bennett et al. 2024). Data were deposited NCBI’s Gene Expression Omnibus (Edgar et al. 2002) and are accessible through GEO Series accession number GSE237594 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237594).

Funding

National Institute of Environmental Health Sciences, Award: ES030984

National Institute of Environmental Health Sciences, Award: ES032473

National Institute of Environmental Health Sciences, Award: ES004738

National Institute of Environmental Health Sciences, Award: ES029254

National Heart Lung and Blood Institute, Award: HL086621

National Institute of Environmental Health Sciences, Award: ES007148

National Institute of Environmental Health Sciences, Award: ES005022

National Institute of Environmental Health Sciences, Award: ES033698