DOI: 10.5061/dryad.b8gtht7mq
title: Data from: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation

description: 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.
description: 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 <em>Ptgs2</em> 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 <em>tximport </em>(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 &gt; 1.3 and experimental false
 discovery rate [padj] &lt; 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).
description: # Transcriptional profiling of lung macrophages following ozone exposure
 in mice identifies signaling pathways regulating immunometabolic
 activation
 [https://doi.org/10.5061/dryad.b8gtht7mq](https://doi.org/10.5061/dryad.b8gtht7mq) We analyzed gene expression profiles in bronchoalveolar lavage cells (&gt;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.](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](https://qiagen.my.salesforce-sites.com/KnowledgeBase/KnowledgeNavigatorPage?categoryName=IPA).  ## Sharing/Access information Links to other publicly accessible locations of the data: * [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237594](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237594)