DOI: 10.5061/dryad.pc866t1x9
title: Gene expression analysis of alveolar macrophages tolerized to ozone

description: In this study, we examined the effect of repeated exposure to the air
 pollutant ozone (O3) on the transcriptome of alveolar macrophages (AMs).
 Using flow cytometry, we isolated purified AMs from the lungs of female
 C57BL6/NJ mice exposed to filtered air or 0.8 ppm O3 for four days and
 then collected 2 days later, with six mice per group. RNA was isolated
 from AMs and then subjected to RNA-seq analysis. We performed differential
 gene expression analysis followed by gene ontology enrichment analysis of
 differentially expressed genes.
description: Alveolar macrophages were isolated from mouse whole lung tissue
 using flow cytometry. Total RNA was extracted from two batches of
 flow-sorted AMs using QIAGEN RNAeasy kits per the manufacturer’s
 instructions. RNA integrity was analyzed using an Agilent Bioanalyzer. RIN
 values ranged from 8.9-9.7, indicating intact RNA. PolyA+ RNA libraries
 were prepared with the Roche Kapa mRNA stranded library preparation kit as
 per the manufacturer's instructions. Paired-end sequencing (50
 cycles) was performed on an Illumina NovaSeq SP to a depth of &gt;55M
 read pairs per sample by the UNC High-Throughput Sequencing
 Facility.  Raw reads were trimmed and filtered of
 adapter contamination using cutadapt (Martin, 2011), and further filtered
 such that at least 90% of bases had a quality score of at least 20 using
 fastx_toolkit v0.0.14. Reads were then aligned to the reference mouse
 genome (mm10) (Supplementary Table 3) and GENCODE vM25 transcript
 annotations using STAR v2.7.7a (Dobin <em>et al.</em>, 2013),
 and transcript abundance was estimated using salmon v1.5.2 (Patro
 <em>et al.</em>, 2017). Differential expression between
 ozone-exposed vs. filtered air groups was then detected using DESeq2
 v1.26.0 (Love <em>et al.</em>, 2014) in R v3.6.0, using a
 design that corrected for flow cytometry batch dates. These batch effects
 were also removed from the VST-normalized expression values using limma
 v3.42.2 (Ritchie <em>et al.</em>, 2015). All log2 fold-changes
 reported were shrunken using ashr 2.2-47 (Stephens, 2017). Gene ontology
 enrichments were then assessed using gprofiler2 v0.2.1 (Raudvere
 <em>et al.</em>, 2019). Dobin, A.
 <em>et al.</em> (2013) STAR: ultrafast universal RNA-seq
 aligner. <em>Bioinformatics</em>,
 <strong>29</strong>, 15–21. Love, M.I.
 <em>et al.</em> (2014) Moderated estimation of fold change and
 dispersion for RNA-seq data with DESeq2. <em>Genome
 Biol.</em>, <strong>15</strong>, 550.
 Martin, M. (2011) Cutadapt removes adapter sequences from
 high-throughput sequencing reads. <em>EMBnet.journal</em>,
 <strong>17</strong>, 10. Patro, R.
 <em>et al.</em> (2017) Salmon provides fast and bias-aware
 quantification of transcript expression. <em>Nat.
 Methods</em>, <strong>14</strong>, 417–419.
 Raudvere, U. <em>et al.</em> (2019) g:Profiler: a web
 server for functional enrichment analysis and conversions of gene lists
 (2019 update). <em>Nucleic Acids Res.</em>,
 <strong>47</strong>, W191–W198. Ritchie,
 M.E. <em>et al.</em> (2015) Limma powers differential
 expression analyses for RNA-sequencing and microarray studies.
 <em>Nucleic Acids Res.</em>, <strong>43</strong>,
 e47. Stephens, M. (2017) False discovery rates: a new
 deal. <em>Biostatistics</em>, <strong>18</strong>,
 275–294.
description: # Gene expression analysis of alveolar macrophages tolerized to ozone
 [https://doi.org/10.5061/dryad.pc866t1x9](https://doi.org/10.5061/dryad.pc866t1x9) The datasets here correspond to supplementary tables 2-5 of a manuscript submission to the journal Toxicological Sciences and include RNA-seq alignments, differential gene expression analysis, and pathway analysis of differentially expressed genes. ## Description of the data and file structure * Supplementary Table 2 (TableS2) contains RNA-seq alignment results and contains four columns corresponding to the sample name, the number of total reads, the number of uniquely aligned reads, and the percent of mapped reads. * Supplementary Table 3 (Table S3) contains complete results of differential expression analysis and contains six columns corresponding to gene name, expression level (baseMean, normalized counts), fold change (log2FoldChange, in log2 units), standard error of the fold change (lfcSE), p-value, and adjusted p-value. * Supplementary Tables 4 (Table S4) and 5 (Table S5) contain pathway enrichment analysis of down and up-regulated genes, respectively, generated using gProfiler. These two tables each have 12 columns (of gProfiler output) with the following information (taken from the gProfiler website): * p-value: Hypergeometric p-value after correction for multiple testing. * term_size: The number of genes that are annotated to the term. * query_size: The size of the gene list used in enrichment analysis (i.e., number of DEGs) * intersection_size: The number of DEGs that intersect the given pathway queried * precision: The proportion of genes in the input list that are annotated to the function * recall: The proportion of functionally annotated genes that the query recovers * term_id: The pathway identifier * source: The pathway source (e.g., GO, KEGG, etc.) * term_name: The name of the pathway * effective_domain_size: The total number of genes "in the universe " which is used as one of the four parameters for the hypergeometric probability function of statistical significance * source_order: The numeric order for the term within its datasource * intersection: The list of differentially expressed genes that intersect with the given pathway The gene expression data have also been deposited in NCBI GEO under accession GSE248291. ## Version changes **9 June 2025** renamed the filenames because there was a change to the file number/order in the accompanying manuscript ## Code/Software The results presented in Supplementary Table 2 were generated as follows: Raw reads were trimmed and filtered of adapter contamination using cutadapt (Martin, 2011), and further filtered such that at least 90% of bases had a quality score of at least 20 using fastx_toolkit v0.0.14. Reads were then aligned to the reference mouse genome (mm10) and GENCODE vM25 transcript annotations using STAR v2.7.7a (Dobin *et al.*, 2013). The results presented in Supplementary Table 3 were generated using detected using DESeq2 v1.26.0 (Love *et al.*, 2014) in R v3.6.0, using a design that corrected for flow cytometry batch dates. These batch effects were also removed from the VST-normalized expression values using limma v3.42.2 (Ritchie *et al.*, 2015). All log2 fold-changes reported were shrunken using ashr 2.2-47 (Stephens, 2017). The results presented in Supplementary Tables 4 and 5 were generated using gprofiler2 v0.2.1 (Raudvere *et al.*, 2019), which can be run on a web-based server.