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Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations

ABSTRACT: Transcription factors (TFs) can relay signals through temporal alterations in their levels. The cell stress responsive TF p53 exhibits different dynamics depending on the type of stress, which influence the magnitude and dynamics of expression of its target genes and subsequent cellular outcomes. The mechanisms decoding p53 dynamics into levels and dynamics of RNA and protein of its targets remain unclear. We systematically quantified p53 target mRNA and protein levels over time under two p53 dynamical regimes – oscillatory and sustained – using RNA-seq and quantitative mass spectrometry. We categorized the mRNA dynamical patterns arising from oscillatory or sustained p53 expression, as well as the corresponding protein dynamics. We found that in both cases oscillatory dynamics allowed for the greatest variety of dynamical patterns of the downstream species. Target proteins from a wide range of functional categories were induced under both p53 dynamic conditions, with induction levels being overall higher under sustained dynamics. Mathematical modeling combined with analysis of empirical data revealed three mechanisms of decoding p53 dynamics: adjustment of mRNA and protein degradation rates, adjustment of activation thresholds for expression of mRNA and corresponding protein levels, and usage of coherent and incoherent feed-forward loop motifs in generating exclusive responses to p53 oscillatory or sustained dynamics, respectively. Our results highlight the diversity of mechanisms that decode complex TF dynamics into dynamical patterns of mRNA and proteins.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Breast, Cell Culture

DISEASE(S): Breast Cancer

SUBMITTER: Marian Kalocsay

LAB HEAD: Galit Lahav

PROVIDER: PXD027030 | Pride | 2022-02-11

REPOSITORIES: Pride

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Project description:Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARF This model is described in the article: Explaining oscillations and variability in the p53-Mdm2 system. Proctor CJ, Gray DA. BMC Syst Biol 2008; 2: 75 Abstract: BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer. This model is hosted on BioModels Database and identified by: BIOMD0000000188. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM This model is described in the article: Explaining oscillations and variability in the p53-Mdm2 system. Proctor CJ, Gray DA. BMC Syst Biol 2008; 2: 75 Abstract: BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer. This model is hosted on BioModels Database and identified by: BIOMD0000000188. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:The t(11;22)(p13;q12) translocation is pathognomonic for the highly aggressive desmoplastic small round cell tumour (DSRCT). The translocation fuses exon 7 of the EWS gene to exon 8 of the WT1 gene (EWS/WT1). Two splice variants of EWS/WT1 exist, arising from the presence (+KTS) or absence (-KTS) of three amino acids: lysine, threonine and serine between zinc fingers 3 and 4. To investigate the oncogenic properties of both isoforms of EWS/WT1, we over-expressed EWS/WT1 in untransformed murine embryonic fibroblasts (MEFs). We demonstrate that neither isoform of EWS/WT1 is sufficient to transform wild type MEFs, however the oncogenic potential of both isoforms is unmasked by the loss of p53. Expression of EWS/WT1 in MEFs lacking at least one allele of p53 resulted in enhanced cell proliferation, clonogenic survival and anchorage-independent growth. In addition EWS/WT1 expression in wild type MEFs attenuated several p53-dependent responses, including cell cycle arrest after irradiation and daunorubicin induced apoptosis. We show that DSRCT commonly have copy number amplification of MDM2 and MDMX, suggesting loss of p53 function in the tumours. Expression of either isoform of EWS/WT1 in MEFs induced characteristic mRNA expression profiles, including up-regulation of canonical Wnt pathway signaling. This was validated in cell lines and in a series of DSCRT, which confirmed canonical Wnt pathway activation in the tumours. We show for the first time that both isoforms of EWS/WT1 have oncogenic potential and that in addition to co-operating with loss of p53 function can also further attenuate p53-mediated responses. In addition we provide the first link between EWS/WT1 and Wnt pathway signaling. These data provide novel insights into the function of the EWS/WT1 fusion protein which characterizes DSCRT. Four independently generated pools of wild type MEFs were infected with tetracycline repressible EWS/WT1+KTS, EWS/WT1-KTS or GFP, selected with hygromycin and protein expression confirmed. Four GFP, four KTS+ and four KTS- samples were hybridized to the Ilumina BeadChip.

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Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations

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