JNK activation dynamics drive distinct gene expression patterns over time mediated by mRNA stability

c-Jun N-terminal kinase (JNK) plays a major role in the regulation of cell death. Numerous studies have highlighted how the dynamics of this kinase dictate whether cells survive in response to cellular stress or induce cell death mechanisms. However, it is less clear how these dynamics potentially contribute to downstream gene expression patterns through regulated transcription factors like c-Jun. To investigate this question, we used a treatment strategy with the JNK agonist anisomycin to drive specific dynamics; sustained, transient, or pulsed activation, and assessed the impact on downstream gene expression patterns. We observed that multiple gene expression patterns emerged depending on the dynamics of JNK activation. Ordinary Differential Equation (ODE) models suggest that a subset of these clusters are mediated by mRNA stability and supported by measured mRNA decay rates. Specific gene clusters also show enrichment in specific cellular pathways, including cell death and inflammatory signaling, suggesting these dynamics contribute to differential regulation of these pathways. These findings highlight another contribution of JNK dynamics to the regulation of cellular responses to stress stimuli. To induce JNK activation, cells were treated with 50 ng/ml of anisomycin. For sustained activation of JNK, cells received a single treatment with anisomycin. To induce transient activation cells were treated with anisomycin for 1 hour followed by three washes with DMEM/F12 growth media and then placed into fresh media. Pulsed activation of JNK was induced by introducing a second anisomycin treatment at 4 hours followed by an additional washout 1 hour later as performed for transient conditions. JNK inhibitor conditions also received 10 uM tanzisertib to inhibit JNK activation. Total mRNA was isolated using the Qiagen RNeasy Mini Kit (Qiagen, 74106) at the indicated time points (0, 2, 4, 6, or 8-hours post-treatment). RNA was provided to the SDSU Genomics Sequencing Core Facility for quality assessment, library preparation, and sequencing. Libraries were prepared using the Illumina TrueSeq mRNA and 45 samples prepared for NExtSeq500 (1x75bp) High output reads. Sequencing was performed using 3 runs to get sufficient reads for all samples and performed by the SDSU Genomics Sequencing Core Facility. All bioinformatic analyses were done using CLC Genomics Workbench (vs 24.0). Trimmed reads were mapped to the human genome vs hg38 as reference using a minimum length fraction of 0.8 and a minimum similarity fraction of 0.8. Differential expression was calculated on a gene basis using an absolute fold change cut-off of 2 and a FDR adjusted p-value of 0.05.