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p53 pulses lead to distinct patterns of gene expression albeit similar DNA-binding dynamics

Nature Structural & Molecular Biology volume 24, pages 840–847 (2017)Cite this article

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Abstract

The dynamics of transcription factors play important roles in a variety of biological systems. However, the mechanisms by which these dynamics are decoded into different transcriptional responses are not well understood. Here we focus on the dynamics of the tumor-suppressor protein p53, which exhibits a series of pulses in response to DNA damage. We performed time course RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) measurements to determine how p53 oscillations are linked with gene expression genome wide. We discovered multiple distinct patterns of gene expression in response to p53 pulses. Surprisingly, p53-binding dynamics were uniform across all genomic loci, even for genes that exhibited distinct mRNA dynamics. Using a mathematical model, supported by additional experimental measurements in response to sustained p53 input, we determined that p53 binds to and activates transcription of its target genes uniformly, whereas post-transcriptional mechanisms are responsible for the differences in gene expression dynamics.

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Figure 1: Time course mRNA-seq reveals distinct clusters of gene expression dynamics in response to γ-irradiation.
Figure 2: Time course p53 ChIP-seq experiment shows pulsatile dynamics genome wide.
Figure 3: p53 target genes with distinct expression dynamics show similar dynamics of p53 binding.
Figure 4: Mathematical model links the input–output relationship between p53 protein dynamics and the dynamics of its target genes genome wide.
Figure 5: Mathematical model can predict gene expression for sustained p53 input dynamics.

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Acknowledgements

We thank S. Boswell for help with RNA-seq experiments and X. Zhang (Broad Institute) for help with p53 ChIP experiments, M. Springer for helpful discussions and advice on modeling, and members of the Lahav lab for comments and discussion. This research was supported by Novartis Institutes for Biomedical Research and NIH grant GM083303. A.H. was supported by Boehringer Ingelheim Fonds, PhD fellowship. J.S.-O. was supported by NIH grant CA207727. J.E.P. was supported by NIH grant GM102372. M.L.B. was supported by NIH grant HG003985.

Author information

Authors and Affiliations

  1. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Antonina Hafner, Jacob Stewart-Ornstein & Galit Lahav

  2. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Jeremy E Purvis

  3. Developmental and Molecular Pathways, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA

    William C Forrester

  4. Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Martha L Bulyk

  5. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Martha L Bulyk

Authors
  1. Antonina Hafner
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  4. William C Forrester
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  5. Martha L Bulyk
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  6. Galit Lahav
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Contributions

A.H., J.E.P., W.C.F. and G.L. conceived experiments. A.H., M.L.B. and G.L. conceived analyses. A.H. performed experiments and A.H. and J.S.-O. performed analyses. A.H. and G.L. wrote the paper.

Corresponding author

Correspondence to Galit Lahav.

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Integrated supplementary information

Supplementary Figure 1 Characterization of gene expression clusters shows p53 dependence and enrichment of known p53 target genes among induced genes. (related to Figure 1)

A. Quantiles (median in black, shaded area represents the 25% and 75% quantiles) of RNA-Seq data by cluster for MCF7 p53 wild-type cell line (blue) and MCF7 p53-shRNA cell line (gray). B. Mapping of the genes in each cluster to known target genes, obtained from Riley et al. 200830. P-values were calculated using the Binomial statistic, * = p-value<0.01. C. Top five enriched GO Biological Process categories in each cluster (FDR<0.05). GO Enrichment analysis was done using Enrichr software39,40. Both p-value and FDR are shown.

Supplementary Figure 2 Properties of p53 ChIP peaks that map to the induced clusters (related to Figure 3).

Distribution of maximal reads of p53 peaks per cluster. T-test was done to evaluate significance; ns stands for not significant (pval >0.2 for all comparisons). Black lines indicate the median, and the boxes and whiskers extend to the 25%-75% and the 5%-95% quantiles, respectively.

Supplementary Figure 3 Evaluation of model fit and parameters. (Related to Figures 4 and 5).

A. Distribution of R2 values for the model fit. Comparison between induced genes with p53 ChIP peak and induced genes without a p53 ChIP peak. B. Correlation of model derived kp values with maximal p53 ChIP peak signal for each gene. The signal from the closest peak was used for genes with multiple p53 ChIP peaks. C. Correlation of model derived kd values with published mRNA half-life data in MCF7 cells34. D. Schematic of gene expression dynamical features that were computed for the model stimulation. E-H. Simulation of the model (equation 1) for the shown range of kp and kd values. Heat maps of the gene expression properties derived for each kp and kd combination. I. Distribution of R2 values for the model fit for sustained data. J. Model simulation with p53 sustained input and only varying the kd parameter values.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 and Supplementary Table 1. (PDF 501 kb)

Life Sciences Reporting Summary (PDF 204 kb)

Supplementary Data Set 1

229 differentially expressed genes (Figure 1). (XLSX 67 kb)

Mean FPKM, log2(fold change) across 2 independent experiments as well as cluster assignments are provided.

Supplementary Data Set 2

p53 and H3K27ac ChIP peaks (Figure 2). (XLSX 4738 kb)

Genomic positions, normalized read counts per time-point for the ChIP and corresponding Input samples are provided. Closest gene assignments, with distance to TSS (bp) are provided for p53 ChIP peaks. Distance (bp) to the closest p53 ChIP peak is given for all H3K27ac peaks (used for Figure 2H).

Supplementary Data Set 3

p53 bound and differentially induced genes (Figure 3B) and results of model fit (Figure 4). (XLSX 11 kb)

Cluster number assignments and the kp and kd parameters resulting from the model fit (Figure 4) are provided.

Supplementary Data Set 4

Gene expression under the sustained p53 condition (Figure 5). (XLSX 16 kb)

Cluster assignments, mean FPKM values across 2 independent replicates and the resulting R2 values from the model prediction are provided.

Supplementary Data Set 5

Un-cropped western blot images for Figures 1A, 2A, 5A. (PDF 785 kb)

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Hafner, A., Stewart-Ornstein, J., Purvis, J. et al. p53 pulses lead to distinct patterns of gene expression albeit similar DNA-binding dynamics. Nat Struct Mol Biol 24, 840–847 (2017). https://doi.org/10.1038/nsmb.3452

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