p53 pulse modulation differentially regulates target gene promoters to regulate cell fate decisions

Abstract

The p53 tumor suppressor regulates distinct responses to cellular stresses. Although different stresses generate different p53 dynamics, the mechanisms by which cells decode p53 dynamics to differentially regulate target genes are not well understood. Here, we determined in individual cells how canonical p53 target gene promoters vary in responsiveness to features of p53 dynamics. Employing a chemical perturbation approach, we independently modulated p53 pulse amplitude, duration, or frequency, and we then monitored p53 levels and target promoter activation in individual cells. We identified distinct signal processing features—thresholding in response to amplitude modulation, a refractory period in response to duration modulation, and dynamic filtering in response to frequency modulation. We then showed that the signal processing features not only affect p53 target promoter activation, they also affect p53 regulation and downstream cellular functions. Our study shows how different promoters can differentially decode features of p53 dynamics to generate distinct responses, providing insight into how perturbing p53 dynamics can be used to generate distinct cell fates.

Stochastic transcription in the p53‐mediated response to DNA damage is modulated by burst frequency

“…It will be interesting to investigate in future studies to which extent these mechanisms contribute to regulating gene‐specific stochastic transcription of p53 target genes in the response to DNA damage. Interestingly, previous studies have suggested that expression patterns of p53 targets are mainly determined by RNA and protein stability (Porter et al , ; Hafner et al , ; Hanson et al , ), while changes in p53 dynamics are filtered at target gene promoters by distinct activation thresholds (Harton et al , ). Based on our model of single‐cell TSS activity, we suggest that direct transcriptional regulation of stochastic bursting provides an important contribution as well.…”

Cell-cycle-gated feedback control mediates desensitization to interferon stimulation

“…In mammalian systems, it has been shown that the nuclear factor κB (NFκB) pathway can process the pulsatile stimulation of tumor necrosis factor-α (TNF-α) to determine the timing and specificity of downstream gene expression ( Ashall et al, 2009 ; Tay et al, 2010 ; Nelson et al, 2004 ). Similarly, the p53 tumor suppressor differentially regulates target genes and cell fates by processing temporal patterns of DNA damage cues ( Harton et al, 2019 ; Purvis et al, 2012 ; Batchelor et al, 2011 ). Intriguingly, many of these studies observed that individual cells exhibit widely different behaviors even to the same stimuli, and, as a result, population-based measurements may obscure the actual response dynamics of individual cells, leading to inaccurate interpretation of the data.…”

Mapping the dynamic transfer functions of epigenome editing

“…We therefore asked if it could inform the development and architecture of mass action models of eukaryotic transcription, and if this model could subsequently provide additional insights into the filtering property of the system. We tested several previous models (Benzinger and Khammash, 2018;Chen et al, 2020;Hansen and O'Shea, 2013;Harton et al, 2019) along with some modifications and found that a four state model (probability of each state is represented by Punbound, Pbound, Pinactive, and Pactive) best fit the experimental data with R 2 =0.835 ( Figure 3, Figure S3). Compared to a similar prior model that differed in the number of Hill functions incorporated into the rate constants (Hansen and O'Shea, 2013), this model exhibited less intense fluorescence oscillations over time, producing a 'smoother' response ( Figure S3B).…”