Maintenance of the genome is essential for cell survival, and impairment of the DNA damage response is associated with multiple pathologies including cancer and neurological abnormalities. DNA-PKcs is a DNA repair protein and a core component of the classical nonhomologous end-joining pathway, but it also has roles in modulating gene expression and thus, the overall cellular response to DNA damage. Using cells producing either wild-type (WT) or kinase-inactive (KR) DNA-PKcs, we assessed global alterations in gene expression in the absence or presence of DNA damage. We evaluated differential gene expression in untreated cells and observed differences in genes associated with cellular adhesion, cell cycle regulation, and inflammation-related pathways. Following exposure to etoposide, we compared how KR versus WT cells responded transcriptionally to DNA damage. Downregulated genes were mostly involved in protein, sugar, and nucleic acid biosynthesis pathways in both genotypes, but enriched biological pathways were divergent, again with KR cells manifesting a more robust inflammatory response compared to WT cells. To determine what major transcriptional regulators are controlling the differences in gene expression noted, we used pathway analysis and found that many master regulators of histone modifications, proinflammatory pathways, cell cycle regulation, Wnt/β-catenin signaling, and cellular development and differentiation were impacted by DNA-PKcs status. Finally, we have used qPCR to validate selected genes among the differentially regulated pathways to validate RNA sequence data. Overall, our results indicate that DNA-PKcs, in a kinase-dependent fashion, decreases proinflammatory signaling following genotoxic insult. As multiple DNA-PK kinase inhibitors are in clinical trials as cancer therapeutics utilized in combination with DNA-damaging agents, understanding the transcriptional response when DNA-PKcs cannot phosphorylate downstream targets will inform the overall patient response to combined treatment.

The dataset contains ensemble ID, gene name if applicable, comparisons between genotypes with and without drug (etoposide) treatment. Within each comparison are log fold change, average expression, p value, and adjusted p value. 

Cells culture: V3-derived Chinese Hamster Ovary (CHO) cell lines were kindly provided by Dr. Katherine Meek, complemented with either human wild-type (WT), null (Null) or kinase inactivate DNA-PKcs, by K3753R mutation (KR), as previously reported (21). All cell lines were cultured in alpha-MEM (LifeTechnologies, Waltham, MA) supplemented with 10% FBS (MilliporeSigma, St. Louis, MO), 1% penicillin/streptomycin (LifeTechnologies), 200 µg/ml G418 (LifeTechnologies) and 10 µg/ml puromycin (Santa Cruz Biotechnology, Santa Cruz, CA) at 37ºC with 5% CO₂and 100% humidity. All standard laboratory chemicals were purchased from MilliporeSigma unless otherwise indicated. Cells were cultured on 100 mm dishes overnight and next day, after washing, trizol reagent (MilliporeSigma) was applied to cells and the cell lysate was collected. Total RNA isolation was according to the manufacturer’s instructions. Total RNA was dissolved in nuclease-free water followed by spectrophotometric analysis of the RNA quantity; RNA was subjected to electrophoresis on an agarose gel to permit evaluation of both RNA quality and assure no DNA contamination was observed.

Differentially expressed gene analysis: The low-quality raw reads (fastq format) were filtered based on Q30 and GC content, then the Illumina adaptors were trimmed of reads for a minimum read length of 36 bases using Trimmomatic v0.34 (Bolger, Lohse, and Usadel 2014). The index of the Chinese hamster reference genome (CHOK1GS_HDv1) was built using HISAT2 v2.1.0 (Kim, Langmead, and Salzberg 2015). Hisat2 v2.2.1 tool was utilized to align the reads to the genome sequences in FASTA format and output aligned reads in binary alignment map (BAM) format were translated into the transcriptomes of each sample using stringtie v2.0 tool which uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus (Pertea et al. 2015). The stringtie outputs (GTF files) were merged to create a single master transcriptome GTF with exact same naming and numbering scheme across all transcripts. The feature counts tool implemented under subread v2.0 was utilized to quantify transcripts assembled by stringtie mapped to each gene  (Liao, Smyth, and Shi 2014). Eventually, the differentially expressed (DE) gene profiles were statistically analyzed through edgeR and limma R libraries (Ritchie et al. 2015; Robinson, McCarthy, and Smyth 2010).