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Exploring the Influence of Parameters on the p53 Response When Single-Stranded Breaks and Double-Stranded Breaks Coexist

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Abstract

The p53 response to DNA damage is closely related to cell fate decisions. P53 preferentially responds to single-stranded breaks (SSBs) exhibiting a graded response when single-stranded breaks (SSBs) and double-stranded breaks (DSBs) coexist. However, how p53 natural preferential response is affected by kinetic parameters remains to be elucidated. Here, based on the hybrid model I, we computationally searched all the parameters and parameter combinations in the parameter space to identify those that could alter the natural preferential response of p53 when SSBs and DSBs coexist. Firstly, when a single parameter is changed, the parameters that can alter graded response to produce p53 pulse response are production rate of ATM- and Rad3-related kinase(ATR) (beta2), ATR degradation rate (alf2) and ATR-dependent p53 production rate (beta31). Secondly, when double parameters are changed, the combinations of beta2/alf2/beta31 and any other parameters are capable of altering the p53 natural preferential response, and the combination of ataxia-telangiectasia mutated kinase (ATM)-dependent p53 production rate (beta3) and Wip1-dependent p53 degradation rate (alf35) is also capable of altering the p53 natural preferential response. Thirdly, we analyzed the sensitivity of both pulse amplitude and apoptosis to kinetic parameters. We find that pulse amplitude is most sensitive to ATM-dependent p53 production rate (beta3), and apoptosis is more sensitive to damage-dependent ATM production rate (beta1), wip1-dependent ATM degradation rate (alf15), wip1 production rate (beta5) and wip1 degradation rate (alf5). What is more, the smaller the value of alf15/beta5 or the larger the value of beta1/alf5, the more susceptible the cells are to apoptosis. These results provide clues to design more effective and less toxic targeted treatments for cancer.

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References

  1. Horn HF, Vousden KH (2007) Coping with stress: multiple ways to activate p53. Oncogene 26:1306–1316

    Article  CAS  PubMed  Google Scholar 

  2. Vousden KH, Lane DP (2007) p53 in health and disease. Nat Rev Mol Cell Biol 8:275–283

    Article  CAS  PubMed  Google Scholar 

  3. Vogelstein B, Lane D, Levine AJ (2000) Surfing the p53 network. Nature 408(6810):307–310

    Article  CAS  PubMed  Google Scholar 

  4. Reinhardt HC, Schumacher B (2012) The p53 network: cellular and systemic dna damage responses in aging and cancer. Trends Genet 28(3):128–136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Meek DW (2004) The p53 response to dna damage. DNA Repair 3(8):1049–1056

    Article  CAS  PubMed  Google Scholar 

  6. Muller PAJ, Vousden KH (2013) P53 mutations in cancer. Nat Cell Biol 15(1):2–8

    Article  CAS  PubMed  Google Scholar 

  7. Brosh R, Rotter V (2009) When mutants gain new powers: news from the mutant p53 field. Nat Rev Cancer 9(10):701–713

    Article  CAS  PubMed  Google Scholar 

  8. Vazquez A, Bond EE, Levine AJ, Bond GL (2008) The genetics of the p53 pathway, apoptosis and cancer therapy. Nat Rev Drug Discov 7:979–987

    Article  CAS  PubMed  Google Scholar 

  9. Batchelor E, Loewer A, Mock C, Lahav G (2011) Stimulus-dependent dynamics of p53 in single cells. Mol Syst Biol 7(1):488

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lev Bar-Or R, Maya R, Segel LA, Alon U, Levine AJ, Oren M (2000) Generation of oscillations by the p53-Mdm2 feedback loop: a theoretical and experimental study. Proc Natl Acad Sci USA 97:11250–11255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lahav G, Rosenfeld N, Sigal A, Geva-Zatorsky N, Levine AJ, Elowitz MB, Alon U (2004) Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat Genet 36:147–150

    Article  CAS  PubMed  Google Scholar 

  12. Batchelor E, Mock C, Bhan I, Loewer A, Lahav G (2008) Recurrent initiation: a mechanism for triggering p53 pulses in response to dna damage. Mol Cell 30(3):277–289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Aiqing M, Xianhua D (2018) The relationship between DNA single stranded damage response and double-stranded damage response. Cell Cycle 17(1):73–79. https://doi.org/10.1080/15384101.2017.1403681

    Article  CAS  Google Scholar 

  14. Kent E, Neumann S, Kummer U, Mendes P (2013) What can we learn from global sensitivity analysis of biochemical systems. Plos One 8(11):e79244–e79244

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Bi YH, Yang ZQ, He XY (2016) Global dynamics and stability of p53-mdm2 oscillator mediated by mdm2 production rate. Acta Phys Sin 65(2):028701

    Google Scholar 

  16. Ingalls BP, Sauro HM (2003) Sensitivity analysis of stoichiometric networks: an extension of metabolic control analysis to non-steady state trajectories. J Theor Biol 222(1):23–36

    Article  PubMed  Google Scholar 

  17. Marino S, Hogue IB, Ray CJ, Kirschner DE (2008) A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol 254:178–196

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cuba CE, Valle AR, Ayalacharca G, Villota ER, Coronado AM (2015) Influence of parameter values on the oscillation sensitivities of two p53–mdm2 models. Syst Synth Biol 9(3):1–8

    Article  Google Scholar 

  19. Geva-Zatorsky N, Rosenfeld N, Itzkovitz S, Milo R, Sigal A, Dekel E et al (2006) Oscillations and variability in the p53 system. Mol Syst Biol 2(1):2006.0033

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Loewer A, Batchelor E, Gaglia G, Lahav G (2010) Basal dynamics of p53 reveals transcriptionally attenuated pulses in cycling cells. Cell 142(1):89–100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wilkins AK, Tidor B, White J, Barton PI (2009) Sensitivity analysis for oscillating dynamical systems. SIAM J Sci Comput 31(4):2706–2732

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhang LW, Cheng YM, Liew KM (2014) A mathematical study of the robustness of g2/m regulatory network in response to dna damage with parameters sensitivity. Appl Math Comput 232:365–374

    Google Scholar 

  23. Das S, Raj L, Zhao B, Kimura Y, Bernstein A, Aaronson SA et al (2007) Hzf determines cell survival upon genotoxic stress by modulating p53 transactivation. Cell 130(4):624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Samuelslev Y, O’Connor DJ, Bergamaschi D, Trigiante G, Hsieh JK, Zhong S et al (2001) Aspp proteins specifically stimulate the apoptotic function of p53. Mol Cell 8(4):781

    Article  CAS  Google Scholar 

  25. Tang Y, Luo J, Zhang W, Gu W (2006) Tip60-dependent acetylation of p53 modulates the decision between cell-cycle arrest and apoptosis. Mol Cell 24(6):827

    Article  CAS  PubMed  Google Scholar 

  26. Paek AL, Liu JC, Loewer A, Forrester WC, Lahav G (2016) Cell-to-cell variation in p53 dynamics leads to fractional killing. Cell 165(3):631–642

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhang XP, Liu F, Cheng Z, Wang W (2009) Cell fate decision mediated by p53 pulses. Proc Natl Acad Sci USA 106(30):12245–12250

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shreeram Sathyavageeswaran, Demidov Oleg, Hee N, Weng K et al (2006) Wip1 phosphatase modulates atm-dependent signaling pathways. Mol Cell 23(5):757–764

    Article  CAS  PubMed  Google Scholar 

  29. Shreeram S, Hee WK, Demidov ON, Kek C, Yamaguchi H, Jr FA et al (2006) Regulation of atm/p53-dependent suppression of myc-induced lymphomas by wip1 phosphatase. J Exp Med 203(13):2793–2799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gilmartin AG, Faitg TH, Richter M, Groy A, Seefeld MA, Darcy MG et al (2014) Allosteric wip1 phosphatase inhibition through flap-subdomain interaction. Nat Chem Biol 10(3):181–187

    Article  CAS  PubMed  Google Scholar 

  31. Li J, Yang Y, Peng Y, Austin RJ, van Eyndhoven WG, Nguyen KC et al (2002) Oncogenic properties of ppm1d located within a breast cancer amplification epicenter at 17q23. Nat Genet 31(2):133–134

    Article  CAS  PubMed  Google Scholar 

  32. Saitoohara F, Imoto I, Inoue J, Hosoi H, Nakagawara A, Sugimoto T et al (2003) Ppm1d is a potential target for 17q gain in neuroblastoma. Cancer Res 63(8):1876–1883

    CAS  Google Scholar 

  33. Mendrzyk F, Radlwimmer B, Joos S, Kokocinski F, Benner A, Stange DE et al (2015) Genomic and protein expression profiling identifies cdk6 as novel independent prognostic marker in medulloblastoma. J Clin Oncol 23(34):8853–8862

    Article  CAS  Google Scholar 

  34. Rauta J, Alarmo EL, Kauraniemi P, Karhu R, Kuukasjärvi T, Kallioniemi A (2006) The serine-threonine protein phosphatase ppm1d is frequently activated through amplification in aggressive primary breast tumours. Breast Cancer Res Treat 95(3):257–263

    Article  CAS  PubMed  Google Scholar 

  35. Fuku T, Semba S, Yutori H, Yokozaki H (2007) Increased wild-type p53-induced phosphatase 1 (wip1 or ppm1d) expression correlated with downregulation of checkpoint kinase 2 in human gastric carcinoma. Pathol Int 57(9):566–571

    Article  CAS  PubMed  Google Scholar 

  36. Tan DSP, Lambros MBK, Rayter S, Natrajan R, Vatcheva R, Gao Q et al (2009) Ppm1d is a potential therapeutic target in ovarian clear cell carcinomas. Clin Cancer Res 15(7):2269–2280

    Article  CAS  PubMed  Google Scholar 

  37. Yoda A, Toyoshima K, Watanabe Y, Onishi N, Hazaka Y, Tsukuda Y et al (2008) Arsenic trioxide augments chk2/p53-mediated apoptosis by inhibiting oncogenic wip1 phosphatase. J Biol Chem 283(27):18969–18979

    Article  CAS  PubMed  Google Scholar 

  38. Bulavin DV, Demidov ON, Saito S, Kauraniemi P, Phillips C, Amundson SA et al (2002) Amplification of ppm1d in human tumors abrogates p53 tumor-suppressor activity. Nat Genet 31(2):210–215

    Article  CAS  PubMed  Google Scholar 

  39. Castellino RC, Bortoli MD, Lu X, Moon SH, Nguyen TA, Shepard MA et al (2008) Medulloblastomas overexpress the p53-inactivating oncogene wip1/ppm1d. J Neurooncol 86(3):245–256

    Article  CAS  PubMed  Google Scholar 

  40. Zhang X, Wan G, Mlotshwa S, Vance V, Berger FG, Chen H et al (2010) Oncogenic wip1 phosphatase is inhibited by mir-16 in the dna damage signaling pathway. Cancer Res 70(18):7176–7186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Baxter EW, Milner J (2010) P53 regulates lif expression in human medulloblastoma cells. J Neurooncol 97(3):373–382

    Article  CAS  PubMed  Google Scholar 

  42. Liu B, Bhatt D, Oltvai ZN, Greenberger JS, Bahar I (2014) Significance of p53 dynamics in regulating apoptosis in response to ionizing radiation, and polypharmacological strategies. Sci Rep 4(1):6245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wu M, Hui Y, Tang Z, Chang S, Lu G, Chen B et al (2017) P53 dynamics orchestrates with binding affinity to target genes for cell fate decision. Cell Death Dis 8(10):e3130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ma W, Trusina A, El-Samad H, Lim WA, Tang C (2009) Defining networktopologies that can achieve biochemical adaptation. Cell 138(4):760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Purvis JE, Karhohs KW, Mock C, Batchelor E, Loewer A, Lahav G (2012) P53 dynamics control cell fate. Science 336(6087):1440–1444

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Aiqing Ma and X. H. Dai designed and implemented the study. Aiqing Ma accomplished the numerical analysis. Aiqing Ma also analyzed the results and drafted the manuscript. All of them participated in the discussion and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant no. 61872396).

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  1. School of Electronics and Information Technology, Sun Yat-Sen University, No. 132 East Outer Ring Road, Guangzhou, 510006, China

    Aiqing Ma & Xianhua Dai

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Correspondence to Xianhua Dai.

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Ma, A., Dai, X. Exploring the Influence of Parameters on the p53 Response When Single-Stranded Breaks and Double-Stranded Breaks Coexist. Interdiscip Sci Comput Life Sci 11, 679–690 (2019). https://doi.org/10.1007/s12539-019-00332-z

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  • DOI: https://doi.org/10.1007/s12539-019-00332-z

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