Russian version English version
About the Journal
Editorial Board
Instructions for Authors
Information for Readers and Authors
Current Issue
Archive
Contact Us
Search

Send manuscript
 


Volume 14   Issue 1   Year 2019
Voropaeva O.F., Lisachev P.D., Senotrusova S.D., Shokin Y.I.

Hyperactivation of the p53–MicroRNA Signaling Pathway: Mathematical Model of Variants of Antitumor Therapy

Mathematical Biology & Bioinformatics. 2019;14(1):355-372.

doi: 10.17537/2019.14.355.

References

 

  1. Vousden K.H., Prives C. Blinded by the light: The growing complexity of p53. Cell. 2009;137:413–431. doi: 10.1016/j.cell.2009.04.037
  2. Zheltukhin A.O. Chumakov P.M. Constitutive and induced functions of the p53 gene. Biochemistry (Moscow). 2010;75(13):1692–1721.
  3. Muller P.A., Vousden K.H. p53 mutations in cancer. Nat. Cell Biol. 2013;15:2–8. doi: 10.1038/ncb2641
  4. Liu J., Zhang C., Hu W., Feng Z. Tumor suppressor p53 and its mutants in cancer metabolism. Cancer Lett. 2015;356:197–203. doi: 10.1016/j.canlet.2013.12.025
  5. Almazov V.P., Kochetkov D.V., Chumakov P.M. Use of p53 for therapy of human cancer. Molecular Biology. 2007;41(6):863–877. doi: 10.1134/S0026893307060015
  6. Liu J., Zhang C., Zhao Y., Feng Z. MicroRNA control of p53. J. Cell. Biochem. 2017;118:7–14. doi: 10.1002/jcb.25609
  7. He L., He X., Lim L.P., Stanchina E.D., Xuan Z., Liang Y., Xue W., Zender L., Magnus J., Ridzon D., et al. A microRNA component of the p53 tumour suppressor network. Nature. 2007;447:1130–1134. doi: 10.1038/nature05939
  8. Tarasov V., Jung P., Verdoodt B., Lodygin D., Epanchintsev A., Menssen A., Meister G., Hermeking H. Differential regulation of microRNAs by p53 revealed by massively parallel sequencing: miR-34a is a p53 target that induces apoptosis and G1-arrest. Cell Cycle. 2007;6(13):1586–1593. doi: 10.4161/cc.6.13.4436
  9. Shin S., Lee E.M., Cha H.J., Bae S., Jung J. H., Lee S.M., Yoon Y., Lee H., Kim S., Kim H. et al. MicroRNAs that respond to histone deacetylase inhibitor SAHA and p53 in HCT116 human colon carcinoma cells. Int. J. Oncol. 2009;35:1343–1352. doi: 10.3892/ijo_00000452
  10. Bisio A., De Sanctis V., Del Vescovo V., Denti M.A., Jegga A.G., Inga A., Ciribilli Y. Identification of new p53 target microRNAs by bioinformatics and functional analysis. BMC Cancer. 2013;13. Article No. 552. doi: 10.1186/1471-2407-13-552
  11. Ren Z.J., Nong X.Y., Lv Y.R., Sun H.H., An P.P., Wang F., Li X., Liu M., Tang H. Mir-509-5p joins the Mdm2/p53 feedback loop and regulates cancer cell growth. Cell Death Dis. 2014;5. Article No. e1387. doi: 10.1038/cddis.2014.327
  12. Selbach M., Schwanhausser B., Thierfelder N., Fang Z., Khanin R., Rajewsky N. Widespread changes in protein synthesis induced by microRNAs. Nature. 2008;455:58–63. doi: 10.1038/nature07228
  13. Bartel D.P. Metazoan microRNAs. Cell. 2018;173:20–51. doi: 10.1016/j.cell.2018.03.006
  14. Vasudevan S. Posttranscriptional upregulation by microRNAs. WIREs RNA. 2012;3:311–330. doi: 10.1002/wrna.121
  15. Suzuki H.I., Yamagata K., Sugimoto K., Iwamoto T., Kato S., Miyazono K. Modulation of microRNA processing by p53. Nature. 2009;460:529–533. doi: 10.1038/nature08199
  16. Harris S.L., Levine A.J. The p53 pathway: positive and negative feedback loops. Oncogene. 2005;24:2899–2908. doi: 10.1038/sj.onc.1208615
  17. Chumakov P.M. Versatile functions of p53 protein in multicellular organisms. Biochemistry (Moscow). 2007;72:1399–1421. doi: 10.1134/S0006297907130019
  18. Lu X. Tied up in loops: positive and negative autoregulation of p53. Cold Spring Harb. Perspect. Biol. 2010;2. Article No. a000984. doi: 10.1101/cshperspect.a000984
  19. Tricoli JV, Jacobson JW. MicroRNA: Potential for cancer detection, diagnosis, and prognosis. Cancer Res. 2007;67:4553–4555. doi: 10.1158/0008-5472.CAN-07-0563
  20. Xie C., Chen W., Zhang M., Cai Q., Xu W., Li X., Jiang S. MDM4 regulation by the let-7 miRNA family in the DNA damage response of glioma cells. FEBS Lett. 2015;589:1958–1965. doi: 10.1016/j.febslet.2015.05.030
  21. Rahman M., Lovat F., Romano G., Calore F., Acunzo M., Bell E.H., Nana-Sinkam P. miR-15b/16-2 regulates factors that promote p53 phosphorylation and augments the DNA damage response following radiation in the lung. J. Biol. Chem. 2014;289:26406–26416. doi: 10.1074/jbc.M114.573592
  22. Zhang X., Wan G., Mlotshwa S., Vance V., Berger F.G., Chen H., Lu X. Oncogenic Wip1 phosphatase is inhibited by miR-16 in the DNA damage signaling pathway. Cancer Res. 2010;70:7176–7186. doi: 10.1158/0008-5472.CAN-10-0697
  23. Issler M.V.C., Mombach J.C.M. MicroRNA-16 feedback loop with p53 and Wip1 can regulate cell fate determination between apoptosis and senescence in DNA damage response. PLoS ONE. 2017;12. Article No. e0185794. doi: 10.1371/journal.pone.0185794
  24. Ugalde A.P., Ramsay A.J., de la Rosa J., Varela I., Mariño G., Cadiñanos J., Lu J., Freije J.M., López-Otín C. Aging and chronic DNA damage response activate a regulatory pathway involving miR-29 and p53. EMBO J. 2011;30:2219–2232. doi: 10.1038/emboj.2011.124
  25. Wang B., Li D., Sidler C., Rodriguez-Juarez R., Singh N., Heyns M., Ilnytskyy Y., Bronson R.T., Kovalchuk O. A suppressive role of ionizing radiation-responsive miR-29c in the development of liver carcinoma via targeting WIP1. Oncotarget. 2015;6:9937–9950. doi: 10.18632/oncotarget.3157
  26. Bommer G.T., Gerin I., Feng Y., Kaczorowski A.J., Kuick R., Love R.E., Zhai Y., Giordano T.J., Qin Z.S., Moore B.B. et al. p53-mediated activation of miRNA34 candidate tumor-suppressor genes. Curr. Biol. 2007;17:1298–1307. doi: 10.1016/j.cub.2007.06.068
  27. Yamakuchi M., Lowenstein C.J. MiR-34, SIRT1, and p53: The feedback loop. Cell Cycle. 2009;8:712–715. doi: 10.4161/cc.8.5.7753
  28. Neault M., Couteau F., Bonneau É., De Guire V., Mallette F.A. Molecular regulation of cellular senescence by microRNAs: implications in cancer and age-related diseases. Int. Rev. Cell. Mol. Biol. 2017;334:27–98. doi: 10.1016/bs.ircmb.2017.04.001
  29. Zhang J., Sun Q., Zhang Z., Ge S., Han Z.G., Chen W.T. Loss of microRNA-143/145 disturbs cellular growth and apoptosis of human epithelial cancers by impairing the Mdm2–p53 feedback loop. Oncogene. 2013;32:61–69. doi: 10.1038/onc.2012.28
  30. Pichiorri F., Suh S.S., Rocci A., De Luca L., Taccioli C., Santhanam R., Zhou W., Benson D.M. Jr, Hofmainster C., Alder H. et al. Downregulation of p53-inducible microRNAs 192, 194, and 215 impairs the p53/MDM2 autoregulatory loop in multiple myeloma development. Cancer Cell. 2010;18:367–381. doi: 10.1016/j.ccr.2010.09.005
  31. Fornari F., Milazzo M., Galassi M., Callegari E., Veronese A., Miyaaki H., Sabbioni S., Mantovani V., Marasco E., Chieco P. et al. p53/mdm2 feedback loop sustains miR-221 expression and dictates the response to anticancer treatments in hepatocellular carcinoma. Mol. Cancer Res. 2014;12:203–216. doi: 10.1158/1541-7786.MCR-13-0312-T
  32. Scarola M., Schoeftner S., Schneider C., Benetti R. miR-335 directly targets Rb1 (pRb/p105) in a proximal connection to p53-dependent stress response. Cancer Res. 2010;70:6925–6933. doi: 10.1158/0008-5472.CAN-10-0141
  33. Xiao J., Lin H., Luo X., Luo X., Wang Z. miR-605 joins p53 network to form a p53:miR-605:Mdm2 positive feedback loop in response to stress. EMBO J. 2011;30:524–532. doi: 10.1038/emboj.2010.347
  34. Batchelor E., Loewer A. Recent progress and open challenges in modeling p53 dynamics in single cells. Curr. Opin. Syst. Biol. 2017;3:54–59. doi: 10.1016/j.coisb.2017.04.007
  35. Voropaeva O.F., Shokin Yu.I. Numerical simulation of feedback p53 - Mdm2 in biological process of apoptosis. Computational Technologies. 2012;17(6):47–63.(in Russ.).
  36. Voropaeva O.F., Shokin Yu.I., Nepomnyashchikh L.M., Senchukova S.R. Matematicheskoe modelirovanie funktsionirovaniia i reguliatsii biologicheskoi sistemy p53-Mdm2 (Mathematical modeling of functioning and regulation of biological system p53-Mdm2). Moscow; 2014. 176 p.(in Russ.).
  37. Voropaeva O.F., Senotrusova S.D., Shokin Yu.I. Deregulation of p53-dependent microRNAs: the results of mathematical modeling. Mathematical Biology and Bioinformatics. 2017;12(1):151–175 (in Russ.). doi: 10.17537/2017.12.151
  38. Zhao C., Zhang Y., Popel A.S. Mechanistic computational models of microRNA-mediated signaling networks in human diseases. Int. J. Mol. Sci. 2019;20(2). doi: 10.3390/ijms20020421
  39. Khanin R., Vinciotti V. Computational Modeling of Post-Transcriptional Gene Regulation by MicroRNAs. J. Computational Biology. 2008;15(3):305–316. doi: 10.1089/cmb.2007.0184
  40. Nissan T., Parker R. Computational analysis of miRNA-mediated repression of translation: Implications for models of translation initiation inhibition. RNA. 2008;14(8):1480–1491. doi: 10.1261/rna.1072808
  41. Zinovyev A., Morozova N., Nonne N., Barillot E., Harel-Bellan A., Gorban A.N. Dynamical modeling of microRNA action on the protein translation process. BMC Systems Biology. 2010;4(13). doi: 10.1186/1752-0509-4-13
  42. Zinovyev A., Morozova N., Gorban A., Harel-Belan A. Mathematical modeling of microRNA-mediated mechanisms of translation repression. Adv. Exp. Med. Biol. 2013;774:189–224. doi: 10.1007/978-94-007-5590-1_11
  43. Zhdanov V.P. Effect of non-coding RNA on bistability and oscillations in mRNA-protein interplay. Biophys. Rev. Lett. 2010;5(2):89–107. doi: 10.1142/S1793048010001159
  44. Zhdanov V.P. Kinetic models of gene expression including non-coding RNAs. Physics Reports. 2011;500(1):1–42. doi: 10.1016/j.physrep.2010.12.002
  45. Zhdanov V.P. Intracellular miRNA or siRNA delivery and function. BioSystems. 2018;171:20–25. doi: 10.1016/j.biosystems.2018.05.007
  46. Kang H.-W., Crawford M., Fabbri M., Nuovo G., Garofalo M., Nana-Sinkam S.P., Friedman A. A mathematical model for microRNA in lung cancer. PLoS ONE. 2013;8(1). Article No. e53663. doi: 10.1371/journal.pone.0053663
  47. Nikolova E., Jordanov I., Vitanov N.K. Dynamical features of the quasi-stationary microRNA-mediated protein translation process supported by eIF4F translation initiation factors. Computers and Mathematics with Applications. 2013;66:1716–1725. doi: 10.1016/j.camwa.2013.04.021
  48. Schmitz U., Wolkenhauer O., Vera Ju. MicroRNA cancer regulation: advanced concepts, bioinformatics and systems biology tools. Advances in experimental medicine and biology. 2013;774. doi: 10.1007/978-94-007-5590-1
  49. Vera Ju., Schmitz U., Lai X., Engelmann D., Khan F. M., Wolkenhauer O., Putzer B. M. Kinetic modeling–based detection of genetic signatures that provide chemoresistance via the E2F1–p73/DNp73–miR-205 network. Cancer Research. 2013;73(12):3511–3524. doi: 10.1158/0008-5472.CAN-12-4095
  50. Lai X., Schmitz U., Gupta S. K., Bhattacharya A., Kunz M., Wolkenhauer O., Vera Ju. Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs. Nucleic Acids Research. 2012;40(18):8818–8834. doi: 10.1093/nar/gks657
  51. Nikolov S., Vera Ju., Schmitz U., Wolkenhauer O. A model-based strategy to investigate the role of microRNA regulation in cancer signalling networks. Theory in Biosciences. 2011;130(1):55–69. doi: 10.1007/s12064-010-0109-5
  52. Lai X., Wolkenhauer O., Vera Ju. Modeling miRNA regulation in cancer signaling systems: mir-34a regulation of the p53/Sirt1 signaling module. Computational Modeling of Signaling Networks. Methods in Molecular Biology. 2012;880:87–108. doi: 10.1007/978-1-61779-833-7_6
  53. Lai X., Wolkenhauer O., Vera Ju. Understanding microRNA-mediated gene regulatory networks through mathematical modelling. Nucleic Acids Research. 2016;44(13):6019–6035. doi: 10.1093/nar/gkw550
  54. Lai X., Bhattacharya A., Schmitz U., Kunz M., Vera Ju., Wolkenhauer O. A systems’ biology approach to study microRNA-mediated gene regulatory networks. BioMed Research International. 2013;2013. Article No. 703849. doi: 10.1155/2013/703849
  55. Luo Z., Azencott R., Zhao Y. Modeling miRNA-mRNA interactions: fitting chemical kinetics equations to microarray data. BMC Systems Biology. 2014;8(19). doi: 10.1186/1752-0509-8-19
  56. Ooi H.K., Ma L. Integral control feedback circuit for the reactivation of malfunctioning p53 pathway. arXiv: 1510.04136 [q-bio.MN]. 2015. arxiv.org/abs/1510.04136 (accessed 17.03.2019).
  57. Azam M. R., Fazal S., Ullah M., Bhatti A. I. System-based strategies for p53 recovery. IET Syst. Biol. 2018;12(3):101–107. doi: 10.1049/iet-syb.2017.0025
  58. Moore R., Ooi H.K., Kang T., Bleris L., Ma L. MiR-192-mediated positive feedback loop controls the robustness of stress-induced p53 oscillations in breast cancer cells. PLoS Computational Biology. 2015;11(12). Article No. e1004653. doi: 10.1371/journal.pcbi.1004653
  59. Jonak K., Kurpas M., Szoltysek K., Janus P., Abramowicz A., Puszynski K. A novel mathematical model of ATM/p53/NF-κB pathways points to the importance of the DDR switch-off mechanisms. BMC Systems Biology. 2016;10(75). doi: 10.1186/s12918-016-0293-0
  60. Liu Z., Shen J., Cai S., Yan F. MicroRNA regulatory network: structure and function. Springer, 2018. 231 p. doi: 10.1007/978-94-024-1577-3
  61. Zhang T., Brazhnik P., Tyson J.J. Exploring mechanisms of the DNA-damage response: p53 pulses and their possible relevance to apoptosis. Cell Cycle. 2007;6(1):85–94. doi: 10.4161/cc.6.1.3705
  62. Gupta S., Silveira D.A., Mombach J.C.M. Modeling the role of microRNA-449a in the regulation of the G2/M cell cycle checkpoint in prostate LNCaP cells under ionizing radiation. PLoS ONE. 2018;13(7). Article No. e0200768. doi: 10.1371/journal.pone.0200768
  63. Tiana G., Jensen M.H., Sneppen K. Time delay as a key to apoptosis induction in the p53 network. Eur. Phys. J. B. 2002(29):135–140. doi: 10.1140/epjb/e2002-00271-1
  64. Voropaeva O.F., Kozlova A.O., Senotrusova S.D. Numerical analysis of the transition from the equation with retarded argument to the ODE system in a mathematical model of the tumor markers network. Computational Technologies. 2016;21(2):12–25 (in Russ.).
  65. Voropaeva O.F., Senotrusova S.D. he passage from delay equation to ODE system in the model of the tumor markers network. Matem. Mod. 2017;29(9):135–154 (in Russ.).
  66. Senotrusova S.D., Voropaeva O.F. Sib. Zh. Vychisl. Mat. 2019;22(3):17–34 (in Russ.).
  67. Kitadate A., Ikeda S., Teshima K., Ito M., Toyota I., Hasunuma N., Takahashi N., Miyagaki T., Sugaya M., Tagawa H. MicroRNA-16 mediates the regulation of a senescence-apoptosis switch in cutaneous T-cell and other non-Hodgkin lymphomas. Oncogene. 2016;35(28):3692–3704. doi: 10.1038/onc.2015.435
  68. Munk R., Panda A.C., Grammatikakis I., Gorospe M., Abdelmohsen K. Senescence-associated microRNAs. International Review of Cell and Molecular Biology. 2017;334:177–205. doi: 10.1016/bs.ircmb.2017.03.008
  69. Batchelor E., Mock C.S., Bhan I., Loewer A., Lahav G. Recurrent initiation: A mechanism for triggering p53 pulses in response to DNA damage. Molecular Cell. 2008;30(3):277–289. doi: 10.1016/j.molcel.2008.03.016
  70. Yang R., Huang B., Zhu Y., Li Y., Liu F., Shi J. Cell type–dependent bimodal p53 activation engenders a dynamic mechanism of chemoresistance. Science Advances. 2018;4. ¹. 12. Article No. eaat5077. doi: 10.1126/sciadv.aat5077
  71. Okada N., Lin C.-P., Ribeiro M.C., Biton A., Lai G., He X., Bu P., Vogel H., Jablons D.M., Keller A.C. et al. A positive feedback between p53 and miR-34 miRNAs mediates tumor suppression. Genes & Development. 2014;28:438–450. doi: 10.1101/gad.233585.113
  72. Concepcion C.P., Han Y.-C., Mu P., Bonetti C., Yao E., D’Andrea A., Vidigal J.A., Maughan W.P., Ogrodowski P., Ventura A. Intact p53-dependent responses in mir-34–deficient mice. PLoS Genetics. 2012;8(7). Article No. e1002797. doi: 10.1371/journal.pgen.1002797
  73. Navarro F., Lieberman J. miR-34 and p53: new insights into a complex functional relationship. PLoS ONE. 2015;10. Article No. e0132767. doi: 10.1371/journal.pone.0132767
  74. Yu J., Baron V., Mercola D., Mustelin T., Adamson E.D. A network of p73, p53 and Egr1 is required for effcient apoptosis in tumor cells. Cell Death and Differentiation. 2007;14:436–446. doi: 10.1038/sj.cdd.4402029
  75. Kato R., Mizuno S., Kadowaki M., Shiozaki K., Akai M., Nakagawa K., Oikawa T., Iguchi M., Osanai K., Ishizaki T. et al. Sirt1 expression is associated with CD31 expression in blood cells from patients with chronic obstructive pulmonary disease. Respiratory Research. 2016;17. Article No. 139. doi: 10.1186/s12931-016-0452-2
  76. Castro R.E., Ferreira D.M.S., Afonso M.B., Borralho P.M., Machado M.V., Cortez-Pinto H., Rodrigues C.M. miR-34a/SIRT1/p53 is suppressed by ursodeoxycholic acid in the rat liver and activated by disease severity in human non-alcoholic fatty liver disease. J. Hepatology. 2013;58(1):119–125. doi: 10.1016/j.jhep.2012.08.008
  77. Baker J.R., Vuppusetty C., Colley T., Papaioannou A.I., Fenwick P., Donnelly L., Ito K., Barnes P.J. Oxidative stress dependent microRNA-34a activation via PI3Kα reduces the expression of sirtuin-1 and sirtuin-6 in epithelial cells. Scientific Reports. 2016;6. Article No. 35871. doi: 10.1038/srep35871
Table of Contents
Original Article
Math. Biol. Bioinf.
2019;14(1):355-372
doi: 10.17537/2019.14.355
published in Russian

Abstract (rus.)
Abstract (eng.)
Full text (rus., pdf)
References

 
  Copyright IMPB RAS © 2005-2024