Stochastic transcription in the p53-mediated response to DNA damage is modulated by burst frequency (2019)
Figure 3

Figure 3 - SmFISH based analysis at the first and second p53 pulse after IR reveals gene specific stochastic expression patterns.

A Schematic illustration of the life-cycle of an mRNA and the rate constants that influence RNA abundance due to stochastic bursting according to previously published models of promoter activity. While burst frequency (bf) describes the switching of a promoter between a transcriptionally active and inactive state with the rate constants kon and koff, the burst size (bs) describes the number RNAs transcribed in an active period. Additionally, degradation (δ) further influences RNA levels by reducing the cytoplasmic RNA pool.

B Illustration of promoter activity according to the random telegraph model. An increase in RNA levels per cell can be due to a higher burst frequency (more active promoter periods, a higher rate of transcription initiation), or an increase in burst size (a higher rate of RNA transcription in an active period). Additionally, also mixtures of both scenarios are possible.

C We used smFISH data to calculated promoter activity based on previously published models. An overview of the calculations characterizing stochastic gene expression is shown. XRNA: number of quantified RNAs/cell, n: number of genomic loci, f: fraction of active promoters (proxy for burst frequency bf), µ: transcription rate per cell [RNA/h] (proxy for burst size bs), δRNA: RNA degradation rate per cell [1/h], M: Polymerase occupancy [RNAs/h], v: RNAP2 speed (estimated as 3 kb/min), l: gene length, TSS: active TSS at the moment of measurement. Further details can be found in Methods section.

D Quantification of stochastic gene expression for the indicated p53 target genes before (basal, grey) and 3 h (red), 6 h (blue) and 9 h (orange) after DNA damage (10 Gy IR). The fraction (f) of active promoters (proxy for burst frequency) increases, while the transcription rate (µ) (proxy for burst size) at active TSS remains similar upon DNA damage for all time points. Left panel: The percentage of cells with active TSS is shown as stacked bar graphs. We subdivided the population in cells with strong TSS activity (> 75% of TSS active, solid colors) and those with partial TSS activity (at least one, but less than 75% of TSS active, shaded colors). The mean fraction of active promoters (ratio of all active TSS to the total number of genomic loci analyzed) is indicated above each bar. Right panel: Distributions of calculated transcription rates µ [RNAs/h] at active TSS are presented for each time point as probability density estimates (PDF, see Data Visualization section). The number of TSS analyzed is indicated in each plot (compare Fig EV2C).

E Mean degradation rates of indicated RNAs in transcriptionally active cells before (basal, grey) and 3 h (red), 6 h (blue) and 9 h (orange) after DNA damage (10 Gy IR) as calculated from smFISH data. RNA stability is not changing in the measured time frame upon DNA damage. The plot displays the average RNA degradation rate per cell [1/h] over time after DNA damage, calculated from model (C) in actively transcribing cells for each gene.

F Based on promoter activity, we allocated target gene promoters along three archetypical expression patterns illustrated by a schematic triangle.

G Amount of p53 bound to indicated target gene promoters before (basal, grey) and 3 h (red), 6 h (blue) and 9 h (orange) after DNA damage (10 Gy IR) as measured by ChIP. The amount of bound p53 was calculated as percentage of input and normalized to the time-point of the first p53 peak at 3 h. Individual data points (mean values of triplicate quantification in qRT-PCR measurements) from 3-4 biological repeats are shown as dots, mean values are displayed as black horizontal lines. Dashed lines serve as guide to the eyes. We could not detect p53 binding above IgG controls at the published p53 response element in the PPM1D promoter (indicated by n.d.)