- Research article
- Open Access
Stress-specific response of the p53-Mdm2 feedback loop
© Hunziker et al; licensee BioMed Central Ltd. 2010
Received: 20 October 2009
Accepted: 12 July 2010
Published: 12 July 2010
The p53 signalling pathway has hundreds of inputs and outputs. It can trigger cellular senescence, cell-cycle arrest and apoptosis in response to diverse stress conditions, including DNA damage, hypoxia and nutrient deprivation. Signals from all these inputs are channeled through a single node, the transcription factor p53. Yet, the pathway is flexible enough to produce different downstream gene expression patterns in response to different stresses.
We construct a mathematical model of the negative feedback loop involving p53 and its inhibitor, Mdm2, at the core of this pathway, and use it to examine the effect of different stresses that trigger p53. In response to DNA damage, hypoxia, etc., the model exhibits a wide variety of specific output behaviour - steady states with low or high levels of p53 and Mdm2, as well as spiky oscillations with low or high average p53 levels.
We show that even a simple negative feedback loop is capable of exhibiting the kind of flexible stress-specific response observed in the p53 system. Further, our model provides a framework for predicting the differences in p53 response to different stresses and single nucleotide polymorphisms.
The tumor suppressor protein, p53, is a transcription factor that regulates the activity of hundreds of genes involved in cell growth and death [1, 2]. Over 50% of human cancer cells contain mutations in p53, because of which it has become a key target in cancer research . A wide variety of stress conditions result in the accumulation and activation of p53 - among others: DNA damage, hypoxia, heat shock, nutrient deprivation and oncogene activation. Despite the fact that all these inputs are integrated into a single node, p53, the expression pattern of downstream genes (and hence the physiological response) appears to be specific to each stress. For example, hypoxia invariably leads to apoptosis , whereas ribonucleotide depletion leads to reversible cell cycle arrest , and UV irradiation can result in either cell cycle arrest or apoptosis depending on the intensity of the damage .
How does the regulatory network around p53 retain this exibility even though all inputs converge at a single node? We argue in this paper that the particular design of the p53-Mdm2 feedback loop at the core of this network could be the source of this flexibility. p53 is regulated by other proteins at two levels: its stability (e.g., Pirh2, COP1, Mdm2 decrease its half-life [7–9]), and its activity as a transcription factor (e.g., MdmX, Mdm2 retard its activity ). We focus on Mdm2 because (a) Mdm2 null mutants are lethal in early development in mice , and (b) Mdm2 directly regulates both activity and stability of p53. Mdm2 is an E3 ligase that binds to p53. Mono-ubiquitination of p53 by Mdm2 inhibits its transcriptional activity, while poly-ubiquitination triggers its degradation . In turn, the mdm2 gene is activated by p53, thus forming a negative feedback loop . We use a mathematical model of the p53-Mdm2 feedback loop to demonstrate how multiple inputs can be integrated with sufficient discrimination in such a feedback loop to allow diverse, yet specific, output behaviour. Using the model, we can predict which input stresses will produce the stronger p53 response, as well as the effect of single nucelotide polymorphisms (in particular the SNP309 on mdm2) on the p53 response.
A model of the p53-Mdm2 negative feedback loop
Effect of stresses
Ribonucleotide depletion ↗
degradation/deactivation of p53
DNA damage, Hypoxia,
degradation/deactivation of p53
Oncogene, Nitric Oxide: all ↘
Mdm2 mRNA degradation
degradation/deactivation of Mdm2
DNA damage ↗
k D = k b /k f
p53-Mdm2 dissociation constant
Nutlin ↗, DNA damage ↗
Most previous models have used an explicit time delay to model transcription and translation (for example, see [16–18]). In these models, the time delays are essential for producing oscillatory behaviour of p53 concentration. Mathematically, the use of explicit time delays converts the equations into delay differential equations which have effectively infinite dimensions and are well known to often exhibit oscillatory behaviour. In contrast our model has no explicit time delay. Thus, the cause of oscillations in our model is completely different; they occur due to the nonlinearities introduced by complex formation between p53 and Mdm2. Other models [19, 20] have avoided explicit time delays but used multiple feedback loops, whereas our model uses a single negative feedback loop.  has explored a range of different models to reproduce the behaviour under gamma irradiation. Of these, one model, IV, is closest to our model in that it uses a nonlinear degradation of p53 instead of explicit time delays to produce oscillations. However, the molecular mechanism behind this nonlinearity was not discussed. Our model shows that the complex formation between p53 and Mdm2 is sufficient for generating nonlinearities that lead to oscillations. Finally, the main purpose of this paper, to investigate response specificity to different stresses has not, to our knowledge, been studied in any previous model.
Results and Discussion
p53 dynamics in the presence and absence of stress
Ideally, we would like to correlate each of these output states of the pathway to specific physiological responses like cell cycle arrest or apoptosis. Clearly, the level of p53 is an important determinant of the response [6, 22, 23], and the presence or absence of oscillations is also likely to be related to the physiological behaviour [2, 24]. When there are oscillations, some downstream genes may respond to the peak p53 level, while others may sense the average level. This depends on the association and dissociation rates of p53 to the relevant operators (this has been discussed in the context of the transcription factor NF-kB in ref. , and the same principle would apply to p53). Further, in some cases the response may depend on the activity of p53 as well as its level [22, 26]. In sum, not enough information exists to make a precise link between the molecular state and the physiological response. However, it is reasonable to expect that large increases in p53 levels would correlate with a higher incidence of apoptosis, whereas low or moderate increases would correlate with less drastic responses such as cell cycle arrest. Therefore, we have elected to discuss the response in terms of the p53 level. In the figures below we have shown the peak p53 level. Similar figures with average p53 level, and the ratio between free and bound p53 are shown in Figures S3 and S4 of additional file 2.
In the absence of stress, p53 levels are typically maintained quite low. For this, a sufficient Mdm2 level is required to keep the half-life of p53 short. Thus, in a typical "resting" state there is a fairly high turnover of p53. The area shaded in green and blue in Figures 2B-D shows parameter combinations which satisfy these conditions - a low level of p53 and no oscillations. The white dot, the default resting state of the cell, before it is subjected to any stress, was chosen to lie within this blue-green region of parameter space (see Table 1 for the corresponding parameter values). Of course, the precise levels of concentration, and turnover rates, in the resting state can vary from cell to cell, both because of variability in levels of various proteins, as well as the presence of mutations, such as single-nucleotide polymorphisms. We will return to this point later in the paper.
Specific response to four stresses
The system can be triggered by numerous stresses. We model different stresses as affecting different parameter combinations, as shown in Table 1. Figure 2A shows the diversity in response to different stresses, starting from the same resting state. From Figures 2B-D it already becomes apparent that the level of p53 is more sensitive to changes in δ and k t than to the p53-Mdm2 dissociation constant k D = k b /k f . Most stresses, however, affect more than one parameter.
A particularly simple, though "artificial", stress is the introduction of Nutlin. Nutlin reduces the binding of Mdm2 to p53, while leaving its other properties unchanged. Nutlin treatment can trigger cell cycle arrest, but not apoptosis [27, 28]. This is consistent with our model's prediction that increasing k D (weakening the binding) alone causes a very modest increase in p53 levels (Figures 2A and 2D).
A more common real-world stress is DNA damage, which can trigger processes that result in (a) increased auto-ubiquitination of Mdm2, (b) decreased ubiquitination of p53 by Mdm2 and (c) weaker binding of p53-Mdm2 [15, 29, 30], corresponding in our model to increasing γ, decreasing δ, and increasing k D . Single-cell experiments have found that irradiation of various types triggers oscillations in p53 levels with a period of 5-6 hours. The parameter changes used to mimic DNA damage stress were chosen such that the response matches the observations of Ref.  which found that, in response to ionizing radiation, the first p53 peak occured at around 30 min, the second at 6 hours and the third between 9 and 13 hours. The damping of the amplitude also matches the observations which found the second peak to be around half as high as the first, and the third to be around 2.5 times lower than the first . A similar response is seen when gamma radiation is used to induce DNA damage . When we increase k D and γ, while lowering δ, corresponding to the molecular processes described above, our model produces an oscillatory solution in accordance with the experimental observations (see Figure 2A).
Hypoxia is another stress that increases p53 levels. It is known that under hypoxic conditions, even though p53 accumulates, it does not possess its transactivation property [4, 32], i.e., k t is decreased. This means that Mdm2 is down-regulated. Furthermore, hypoxia induces HIF which binds to p53 and prevents degradation , which we mimic by decreasing δ. Hypoxia does not lead to cell-cycle arrest, suggesting that it typically results in much higher levels of p53. Consistent with this picture, our model yields a stronger response (i.e., oscillations with a bigger amplitude and larger average p53 level) when we apply a hypoxic stress when compared to other stresses with similar fold-changes in parameter values (see Figure 2A).
Deregulated oncogenes are another signal that can trigger the p53 pathway. They lead to increased transcription of ARF, which binds to Mdm2 and inhibits its E3 ligase activity . This corresponds to decreasing δ, the Mdm2-dependent degradation of p53. The response to this, in our model, is oscillations in p53 but weaker than the response to DNA damage or hypoxia (see Figure 2).
Predicting the relative strength of the response to different stresses
The average free p53 level is in general much more sensitive to changes in γ, δ, k t and ι than to changes in α and k D . The sensitivity with respect to variation of γ appears to be very little for values in the range of 1-to 2-fold the default value. This coincides with the onset of oscillations. In contrast, the peak p53 level retains its sensitivity (Figure S4, additional file 2). That is, while the amplitude of oscillations increases significantly the average does not, a feature that arises due to the spikyness of the oscillations. The physiological significance of this is unclear.
Overall, it is clear that stresses that affect only α or k D , such as Nutlin, will have the least impact on average p53 level. For other stresses, the relative impact depends on how many of the sensitive parameters they affect. Thus, DNA damage and hypoxia, which each affect two sensitive parameters, result in a relatively stronger response than oncogene deregulation, which only affects one parameter.
The effect of single nucleotide polymorphisms
Variability in the p53 response
The variability in p53 response observed in [21, 31] must originate from sources other than stochasticity in the production and degradation of molecules, because the numbers of involved molecules are rather large.
Increasing amounts of noise are likely to introduce more variability in the position of later p53 peaks than in earlier peaks, as observed.
However, a proper analysis of these hypotheses requires a better knowledge of which sources of noise underly the variability observed, so that they can be modelled accurately.
Predictions from the model
The sensitivity analysis in Figure 3 shows which parameters most affect the p53 level in our model. Combining this information with a knowledge of which parameters are affected by different stresses provides predictions about which stresses will affect the p53 level the most. One specific prediction is that around the onset of oscillations, changes in γ result in large changes in peak p53 levels but hardly any change in average p53 levels.
The analysis also leads to a prediction of reduced p53-dependent apoptosis in populations which have an increased frequency of the G allele of the mdm2 SNP309 - a prediction that is confirmed by observations. In addition, if the increase in k t due to the SNP is sufficient, then although p53 will be upregulated in response to stress, oscillations will not occur (as can be seen from Figure 4). This effect has also been observed experimentally . The same analysis method can be used to predict the effect of other SNPs as soon as one knows which parameters (i.e., which molecular processes) they affect.
Finally, we note that the temporal dynamics of the p53 response to different stresses are also predictions of the model that can be tested experimentally. To our knowledge, single cell experiments examining the p53 dynamics in response to hypoxia or oncogene deregulation have not been done. Our model predicts that oscillations should be observed in both cases, which tend to have longer time periods than in response to DNA damage and with a particularly distinct time delay (and reduction of amplitude) between the first and second peaks.
Extending the model
Our model could eventually be extended to cover other stresses that trigger a p53 response as more data becomes available. Nitric oxide (NO) is a free radical produced in inflamed tissue which can trigger the p53 pathway by phosphorylating p53 and thereby inhibiting its Mdm2-mediated degradation . Another example is ribonucleotide depletion: cells suffering this undergo a reversible p53-dependent cell cycle arrest . How this happens has not been fully worked out, but a hypothesis exists: the depletion could cause a redistribution of p53 from cytoplasm to the nucleus, where it can be transcriptionally active . Finally, heat shock can also trigger p53 but the picture is rather complex and indecisive, involving various chaperones and heat shock proteins [41–43]. Other directions to extend the model are of course to include other feedback loops and essential players in p53 regulation, such as Wip1  and MdmX , and to model the connection between p53 levels and physiological behaviour more accurately as has been done for cell cycle arrest in ref. .
Overall, we have shown that this kind of negative feedback loop, consisting of a relatively slow transciptional activation on one leg of the loop, and an inhibition based on fast complex formation on the other, can be designed to respond specifically to a number of different input triggers. This kind of negative feedback loop also occurs in another important signalling pathway that is triggered by hundreds of input signals, namely NF-κ B signalling in the immune system . NF-κ B is a transcription factor that controls hundreds of downstream genes. It activates production of Iκ Bα, which binds to and inhibits the action of NF-κ B . The resultant negative feedback loop exhibits spiky oscillations [25, 47] similar to the kind we observe in the model presented here. Thus, our results might also have relevance beyond p53.
This work was funded by the Danish National Research Foundation.
- Levine AJ, Hu W, Feng Z: The P53 pathway: what questions remain to be explored?. Cell Death Differ. 2006, 13 (6): 1027-36. 10.1038/sj.cdd.4401910View ArticlePubMedGoogle Scholar
- Batchelor E, Loewer A, Lahav G: The ups and downs of p53: understanding protein dynamics in single cells. Nat Rev Cancer. 2009, 9: 371-377. 10.1038/nrc2604PubMed CentralView ArticlePubMedGoogle Scholar
- Levine AJ: p53, the Cellular Gatekeeper for Growth and Division. Cell. 1997, 88: 323-331. 10.1016/S0092-8674(00)81871-1View ArticlePubMedGoogle Scholar
- Koumenis C, Alarcon R, Hammond E, Sutphin P, Hoff-man W, Murphy M, Derr J, Taya Y, Lowe SW, Kastan M, Giaccia A: Regulation of p53 by hypoxia: dissociation of transcriptional repression and apoptosis from p53-dependent transactivation. Mol Cell Biol. 2001, 21 (4): 1297-310. 10.1128/MCB.21.4.1297-1310.2001PubMed CentralView ArticlePubMedGoogle Scholar
- Linke SP, Clarkin KC, Di Leonardo A, Tsou A, Wahl GM: A reversible, p53-dependent G0/G1 cell cycle arrest induced by ribonucleotide depletion in the absence of detectable DNA damage. Genes & Development. 1996, 10 (8): 934-View ArticleGoogle Scholar
- Sionov RV, Haupt Y: The cellular response to p53: the decision between life and death. Oncogene. 1999, 18: 6145-6157. 10.1038/sj.onc.1203130View ArticlePubMedGoogle Scholar
- Leng RP, Lin Y, Ma W, Wu H, Lemmers B, Chung S, Parant JM, Lozano G, Hakem R, Benchimol S: Pirh2, a p53-Induced Ubiquitin-Protein Ligase, Promotes p53 Degradation. Cell. 2003, 112: 779-791. 10.1016/S0092-8674(03)00193-4View ArticlePubMedGoogle Scholar
- Dornan D, Wertz I, Shimizu H, Arnott D, Frantz GD, Dowd P, O'Rourke K, Koeppen H, Dixit VM: The ubiq-uitin ligase COP1 is a critical negative regulator of p53. Nature. 2004, 429 (6987): 86-92. 10.1038/nature02514View ArticlePubMedGoogle Scholar
- Barboza JA, Iwakuma T, Terzian T, El-Naggar AK, Lozano G: Mdm2 and Mdm4 loss regulates distinct p53 activities. Mol Cancer Res. 2008, 6: 947-954. 10.1158/1541-7786.MCR-07-2079PubMed CentralView ArticlePubMedGoogle Scholar
- Shvarts A, Steegenga WT, Riteco N, van Laar T, Dekker P, Bazuine M, van Ham RC, van der Houven van Oordt W, Hateboer G, van der Eb AJ, Jochemsen AG: MDMX: a novel p53-binding protein with some functional properties of MDM2. EMBO J. 1996, 15 (19): 5349-5357.PubMed CentralPubMedGoogle Scholar
- Montes de Oca Luna R: Rescue of early embrionic lethality in mdm2-deficient mice by deletion of p53. Nature. 1995, 378: 203-206. 10.1038/378203a0View ArticlePubMedGoogle Scholar
- Li M, Brooks CL, Wu-Baer F, Chen D, Baer R, Gu W: Mono-Versus Polyubiquitination: Differential Control of p53 Fate by Mdm2. Science. 2003, 302: 1972-1975. 10.1126/science.1091362View ArticlePubMedGoogle Scholar
- Harris SL, Levine AJ: The p53 pathway: positive and negative feedback loops. Oncogene. 2005, 24 (17): 2899- 10.1038/sj.onc.1208615View ArticlePubMedGoogle Scholar
- Kaku S, Iwahashi Y, Kuraishi A, Albor A, Yamagishi T, Nakaike S, Kulesz-Martin M: Binding to the naturally occurring double p53 binding site of the Mdm2 promoter alleviates the requirement for p53 C-terminal activation. Nucl Acids Res. 2001, 29: 1989-1993. 10.1093/nar/29.9.1989PubMed CentralView ArticlePubMedGoogle Scholar
- Itahana K, Mao H, Jin A, Itahana Y, Clegg HV, Lind-strom MS, Bhat KP, Godfrey VL, Evan GI, Zhang Y: Targeted inactivation of Mdm2 RING finger E3 ubiquitin ligase activity in the mouse reveals mechanistic insights into p53 regulation. Cancer Cell. 2007, 12: 355-366. 10.1016/j.ccr.2007.09.007View ArticlePubMedGoogle Scholar
- Tiana G, Sneppen K, Jensen MH: Time delay as a key to apoptosis induction in the p53 network. Euro-phys J B. 2002, 29: 135-140. 10.1140/epjb/e2002-00271-1.View ArticleGoogle Scholar
- Ma L, Wagner J, Rice JJ, Hu W, Levine AJ, Stolovitzky G: A plausible model for the digital response of p53 to DNA damage. Proc Natl Acad Sci (USA). 2005, 102: 14266-14271. 10.1073/pnas.0501352102.View ArticleGoogle Scholar
- Wagner J, Ma L, Rice JJ, Hu W, Levine AJ, Stolovitzky G: p53-Mdm2 loop controlled by a balance of its feedback strength and effective dampening using ATM and delayed feedback. IEE Proc-Syst Biol. 2005, 152: 109-118. 10.1049/ip-syb:20050025.View ArticleGoogle Scholar
- Ciliberto A, Novak B, Tyson JJ: Steady states and oscillations in the p53/Mdm2 network. Cell Cycle. 2005, 4: 488-493.View ArticlePubMedGoogle Scholar
- Bacthelor E, Mock CS, Bhan I, Loewer A, Lahav G: Recurrent initiation: A mechanism for triggering p53 pulses in response to DNA damage. Mol Cell. 2008, 30: 277-289. 10.1016/j.molcel.2008.03.016View ArticleGoogle Scholar
- Geva-Zatorsky N, Rosenfeld N, Itzkovitz S, Milo R, Si-gal A, Dekel E, Yarnithky T, Polak P, Liron Y, Kam Z, Lahav G, Alon U: Oscillations and variability in the p53 system. Molecular Systems Biology. 2006, 2:Google Scholar
- Chen X, Ko LJ, Jayaraman L, Prives C: p53 levels, functional domains, and DNA damage determine the extent of the apoptotic response of tumor cells. Genes Dev. 1996, 10: 2438-2451. 10.1101/gad.10.19.2438View ArticlePubMedGoogle Scholar
- Inga A, Storici F, Darden TA, Resnick MA: Differential transactivation by the p53 transcription factor is highly dependent on p53 level and promoter target sequence. Mol Cell Biol. 2002, 22: 8612-8625. 10.1128/MCB.22.24.8612-8625.2002PubMed CentralView ArticlePubMedGoogle Scholar
- Tyson JJ: Another turn for p53. Mol Sys Biol. 2006, 2: 0032-Google Scholar
- Krishna S, Jensen MH, Sneppen K: Minimal model of spiky oscillations in NF-kB signalling. Proc Natl Acad Sci (USA). 2006, 103: 10840-10845. 10.1073/pnas.0604085103.View ArticleGoogle Scholar
- Lu X, Burbidge SA, Griffn S, Smith HM: Discordance between accumulated p53 protein level and its transcriptional activity in response to u.v. radiation. Oncogene. 1996, 13: 413-418.PubMedGoogle Scholar
- Bond GL, Hu W, Levine AJ: MDM2 is a Central Node in the p53 Pathway: 12 Years and Counting. Current Cancer Drug Targets. 2005, 5: 3-8. 10.2174/1568009053332627View ArticlePubMedGoogle Scholar
- Vassilev LT, Vu BT, Graves B, Carvajal D, Podlaski F, Filipovic Z, Kong N, Kammlott U, Lukacs C, Klein C, Fotouhi N, Liu EA: In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science. 2004, 303 (5659): 844-8. 10.1126/science.1092472View ArticlePubMedGoogle Scholar
- Michael D, Oren M: The p53-Mdm2 module and the ubiquitin system. Seminars in Cancer Biology. 2003, 13: 49-58. 10.1016/S1044-579X(02)00099-8View ArticlePubMedGoogle Scholar
- Stommel JM, Wahl GM: Accelerated MDM2 auto-degradation induced by DNA-damage kinases is required for p53 activation. EMBO J. 2004, 23 (7): 1547-56. 10.1038/sj.emboj.7600145PubMed CentralView ArticlePubMedGoogle Scholar
- Hamstra DA, Bhojani MS, Griffn LB, Laxman B, Ross BD, Rehemtulla A: Real-time Evaluation of p53 Oscillatory Behavior In vivo Using Bioluminescent Imaging. Cancer Res. 2006, 66: 7482-7489. 10.1158/0008-5472.CAN-06-1405View ArticlePubMedGoogle Scholar
- Hammond EM, Giaccia AJ: The role of p53 in hypoxia-induced apoptosis. Biochem Biophys Res Commun. 2005, 331 (3): 718-25. 10.1016/j.bbrc.2005.03.154View ArticlePubMedGoogle Scholar
- Greijer AE, van der Wall E: The role of hypoxia inducible factor 1 (HIF-1) in hypoxia induced apoptosis. J Clin Pathol. 2004, 57 (10): 1009-14. 10.1136/jcp.2003.015032PubMed CentralView ArticlePubMedGoogle Scholar
- Moll UM, Petrenko O: The MDM2-p53 Interaction. Molecular Cancer Research. 2003, 1: 1001-1008.PubMedGoogle Scholar
- Hu W, Feng Z, Ma L, Wagner J, Rice JJ, Stolovitzky G, Levine AJ: A single nucleotide polymorphism in the Mdm2 gene disrupts the oscillation of p53 and Mdm2 levels in cells. Cancer Res. 2007, 67 (6): 2757-2765. 10.1158/0008-5472.CAN-06-2656View ArticlePubMedGoogle Scholar
- Harris SL, Gil G, Robins H, Hu W, Hirshfield K, Bond E, Bond G, Levine AJ: Detection of functional single-nucleotide polymorphisms that affect apoptosis. Proc Natl Acad Sci USA. 2005, 102 (45): 16297-302. 10.1073/pnas.0508390102PubMed CentralView ArticlePubMedGoogle Scholar
- Wang YV, Wade M, Wong E, Li YC, Rodewald LW, Wahl GM: Quantitative analyses reveal the importance of regulated Hdmx degradation for p53 activation. Proc Natl Acad Sci (USA). 2007, 104: 12365-12370. 10.1073/pnas.0701497104.View ArticleGoogle Scholar
- Gillespie DT: Exact Stochastic Simulation of Coupled Chemical Reactions. J Phys Chem. 1977, 81: 2340-2361. 10.1021/j100540a008.View ArticleGoogle Scholar
- Proctor CJ, Gray DA: Explaining oscillations and variability in the p53-Mdm2 system. BMC Sys Biol. 2008, 2: 75-10.1186/1752-0509-2-75.View ArticleGoogle Scholar
- Hofseth LJ, Saito S, Hussain SP, Espey MG, Miranda KM, Araki Y, Jhappan C, Higashimoto Y, He P, Linke SP, Quezado MM, Zurer I, Rotter V, Wink DA, Appella E, Harris CC: Nitric oxide-induced cellular stress and p53 activation in chronic inflammation. Proc Natl Acad Sci USA. 2003, 100 (1): 143-8. 10.1073/pnas.0237083100PubMed CentralView ArticlePubMedGoogle Scholar
- King FW, Wawrzynow A, Höhfeld J, Zylicz M: Co-chaperones Bag-1, Hop and Hsp40 regulate Hsc70 and Hsp90 interactions with wild-type or mutant p53. EMBO J. 2001, 20 (22): 6297-6305. 10.1093/emboj/20.22.6297PubMed CentralView ArticlePubMedGoogle Scholar
- Nitta M, Okamura H, Aizawa S, Yamaizumi M: Heat shock induces transient p53-dependent cell cycle arrest at G1/S. Oncogene. 1997, 15: 561-568. 10.1038/sj.onc.1201210View ArticlePubMedGoogle Scholar
- Zyclicz M, King FW, Wawrzynow A: Hsp70 interactions with the p53 tumour supporessor protein. EMBO J. 2001, 20 (17): 4634-4638. 10.1093/emboj/20.17.4634View ArticleGoogle Scholar
- Toettcher JE, Loewer A, Ostheimer GJ, Yaffe MB, Tidor B, Lahav G: Distinct mechanisms act in concert to mediate cell cycle arrest. Proc Natl Acad Sci (USA). 2009, 106: 785-790. 10.1073/pnas.0806196106.View ArticleGoogle Scholar
- Pahl HL: Activators and target genes of Rel/NF-kB transcription factors. Oncogene. 1999, 18: 6853-6866. 10.1038/sj.onc.1203239View ArticlePubMedGoogle Scholar
- Hoffmann A, Levchenko A, Scott ML, Baltimore D: The IkB-NF-kB signaling module: temporal control and selective gene activation. Science. 2002, 298: 1241-1245. 10.1126/science.1071914View ArticlePubMedGoogle Scholar
- Nelson DE, et al.: Oscillations in NF-kB signaling control the dynamics of gene expression. Science. 2004, 306: 704-708. 10.1126/science.1099962View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.