期刊论文详细信息
BMC Systems Biology
Modeling heterogeneous responsiveness of intrinsic apoptosis pathway
Lan Ma1  Hsu Kiang Ooi1 
[1] Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Rd, Richardson, TX 75080, USA
关键词: Extrinsic noise;    Intrinsic noise;    Stochastic model;    Intrinsic apoptosis pathway;   
Others  :  1142620
DOI  :  10.1186/1752-0509-7-65
 received in 2012-12-19, accepted in 2013-07-19,  发布年份 2013
PDF
【 摘 要 】

Background

Apoptosis is a cell suicide mechanism that enables multicellular organisms to maintain homeostasis and to eliminate individual cells that threaten the organism’s survival. Dependent on the type of stimulus, apoptosis can be propagated by extrinsic pathway or intrinsic pathway. The comprehensive understanding of the molecular mechanism of apoptotic signaling allows for development of mathematical models, aiming to elucidate dynamical and systems properties of apoptotic signaling networks. There have been extensive efforts in modeling deterministic apoptosis network accounting for average behavior of a population of cells. Cellular networks, however, are inherently stochastic and significant cell-to-cell variability in apoptosis response has been observed at single cell level.

Results

To address the inevitable randomness in the intrinsic apoptosis mechanism, we develop a theoretical and computational modeling framework of intrinsic apoptosis pathway at single-cell level, accounting for both deterministic and stochastic behavior. Our deterministic model, adapted from the well-accepted Fussenegger model, shows that an additional positive feedback between the executioner caspase and the initiator caspase plays a fundamental role in yielding the desired property of bistability. We then examine the impact of intrinsic fluctuations of biochemical reactions, viewed as intrinsic noise, and natural variation of protein concentrations, viewed as extrinsic noise, on behavior of the intrinsic apoptosis network. Histograms of the steady-state output at varying input levels show that the intrinsic noise could elicit a wider region of bistability over that of the deterministic model. However, the system stochasticity due to intrinsic fluctuations, such as the noise of steady-state response and the randomness of response delay, shows that the intrinsic noise in general is insufficient to produce significant cell-to-cell variations at physiologically relevant level of molecular numbers. Furthermore, the extrinsic noise represented by random variations of two key apoptotic proteins, namely Cytochrome C and inhibitor of apoptosis proteins (IAP), is modeled separately or in combination with intrinsic noise. The resultant stochasticity in the timing of intrinsic apoptosis response shows that the fluctuating protein variations can induce cell-to-cell stochastic variability at a quantitative level agreeing with experiments. Finally, simulations illustrate that the mean abundance of fluctuating IAP protein is positively correlated with the degree of cellular stochasticity of the intrinsic apoptosis pathway.

Conclusions

Our theoretical and computational study shows that the pronounced non-genetic heterogeneity in intrinsic apoptosis responses among individual cells plausibly arises from extrinsic rather than intrinsic origin of fluctuations. In addition, it predicts that the IAP protein could serve as a potential therapeutic target for suppression of the cell-to-cell variation in the intrinsic apoptosis responsiveness.

【 授权许可】

   
2013 Ooi and Ma; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150328100758161.pdf 3099KB PDF download
Figure 9. 174KB Image download
Figure 8. 47KB Image download
Figure 7. 96KB Image download
Figure 6. 162KB Image download
Figure 5. 100KB Image download
Figure 4. 75KB Image download
Figure 3. 43KB Image download
Figure 2. 50KB Image download
Figure 1. 45KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

【 参考文献 】
  • [1]Vousden K, Lu X: Live or Let Die: The Cell’s Response to p53. Nat Rev Cancer 2002, 2:594-604.
  • [2]Taylor R, Cullen S, Martin S: Apoptosis: controlled demolition at the cellular level. Nat Rev Mol Cell Biol 2008,, 9:231-41.
  • [3]Spencer S, Sorger P: Measuring and Modeling Apoptosis in Single Cells. Cell 2011, 144:926-939.
  • [4]Xu G, Shi Y: Apoptosis signaling pathways and lymphocyte homeostasis. Cell Res 2007, 17:759-771.
  • [5]Fuchs Y, Steller H: Programmed cell death in animal development and disease. Cell 2011, 147(4):742-758.
  • [6]Elmore S: Apoptosis: a review of programmed cell death. Toxicol Pathol 2007, 35(4):495-516.
  • [7]Wang J, Zheng L, Lobito A, Chan F, Dale J: Inherited human Caspase 10 mutations underlie defective lymphocyte and dendritic cell apoptosis in autoimmune lymphoproliferative syndrome type II. Cell 1999, 98:47-48.
  • [8]Green D: A matter of life and death. Cancer Cell 2002, 1:19-30.
  • [9]Chipuk J, Green D: Dissecting p53-dependent Apoptosis. Nat Rev Cell Death Differ 2006, 13:994-1002.
  • [10]Riedl S, Salvesen G: The apoptosome: signaling platform of cell death. Nat Rev Mol Cell Biol 2007, 8:405-413.
  • [11]Fuentes-Prior P, Salvesen G: The protein structures that shape caspase activity, specificity, activation and inhibition. Biochem J 2004, 384:201-232.
  • [12]Jiang S, Chow S, Nicotera P, Orrenius S: Intracellular Ca2+ signals activate apoptosis in thymocytes: studies using the Ca2+−ATPase inhibitor thapsigargin. Exp Cell Res 1994, 212:84-92.
  • [13]Rehm M, Dussmann H, Janicke R, Tavare J, Kogel D, Prehn J: Single-cell fluorescence resonance energy transfer analysis demonstrates that caspase activation during apoptosis is a rapid process. Role of caspase-3. J Biol Chem 2002, 277(27):24506-24514.
  • [14]Albeck J, Burke J, Aldridge B, Zhang M, Lauffenburger D, Sorger P: Quantitative analysis of pathways controlling extrinsic apoptosis in single cells. Mol Cell 2008, 30:11-25.
  • [15]Albeck J, Burke J, Spencer S, Lauffenburger D, Sorger P: Modeling a snap-action, variable-delay switch controlling extrinsic cell death. PLoS Biol 2008, 6(12):2831-2852.
  • [16]Spencer S, Gaudet S, Albeck J, Burke J, Sorger P: Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 2009, 459:428-433.
  • [17]Bentele M, Lavrik I, Ulrich M, Stober S, Heermann D, Kalthoff H, Krammer P, Eils R: Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J Cell Biol 2004, 166:839-851.
  • [18]Eissing T, Conzelmann H, Gilles E, Allogowert F, Bullinger E, Scheurich P: Bistability analyses of a caspase activation model for receptor-induced apoptosis. J Biol Chem 2004, 279(35):36892-36897.
  • [19]Huber H, Duessmann H, Wenus J, Kilbride S, Prehn J: Mathematical modelling of the mitochondrial apoptosis pathway. Biochimica et Biophysica Acta 2011, 1813(4):608-615.
  • [20]Fussenegger M, Bailey J, Varner J: A mathematical model of caspase function in apoptosis. Nat Biotechnol 2000, 18:768-774.
  • [21]Nair V, Yuen T, Olanow C, Sealfon S: Early single cell bifurcation of pro- and antiapoptotic states during oxidative stress. J Biol Chem 2004, 279(26):27494-27501.
  • [22]Skommer J, Brittain T, Raychaudhuri S: Bcl-2 inhibits apoptosis by increasing the time-to-death and intrinsic cell-to-cell variations in the mitochondrial pathway of cell death. Apoptosis 2010, 15:1223-1233.
  • [23]Raychaudhuri S: A minimal model of signaling network elucidates cell-to-cell stochastic variability in apoptosis. PLoS ONE 2010, 5(8):1-7.
  • [24]Eissing T, Allogowert F, Bullinger E: Robustness properties of apoptosis models with respect to parameter variations and intrinsic noise. IEEE Proc Syst Biol 2005, 152(4):221-228.
  • [25]Huber H, Bullinger E, Rehm M: System biology approaches to the study of apoptosis. In Essentials of Apoptosis,. Edited by Yin XM, Dong Z. New York: Humana Press; 2009:283-297.
  • [26]Aldridge B, Haller G, Sorger P, Lauffenburger D: Direct Lyapunov exponent analysis enables parametric study of transient signalling governing cell behaviour. Syst Biol (Stevenage) 2006, 153(6):425-432.
  • [27]Hua F, Cornejo M, Cardone M, Stokes C, Lauffenburger D: Effects of Bcl-2 levels on Fas signaling-induced caspase-3 activation: molecular genetic tests of computational model predictions. J Immunol 2005, 175:985-995.
  • [28]Legewie S, Bluthgen N, Herzel H: Mathematical modeling identifies inhibitors of apoptosis as mediators of positive feedback and bistability. PLoS Comput Biol 2006, 2(9):1061-1073.
  • [29]Zhang T, Brazhnik P, Tyson J: Computational analysis of dynamical responses to the intrinsic pathway of programmed cell death. Biophys J 2009, 97:415-434.
  • [30]Bagci E, Vodovotz Y, Billiar T, Ermentrout G, Bahar I: Bistability in apoptosis: Roles of Bax, Bcl-2, and mitochondrial permeability transition pores. Biophys J 2006, 90(5):1546-1559.
  • [31]Stucki J, Simon H: Mathematical modeling of the regulation of caspase-3 activation and degradation. J Theor Biol 2005, 234:123-131.
  • [32]Nakabayashi J, Sasaki A: A mathematical model for apoptosome assembly: the optimal cytochrome c / Apaf-1 ratio. J Theor Biol 2006, 242(2):280-287.
  • [33]Harrington H, Ho K, Ghosh S, Tung K: Construction and analysis of a modular model of caspase activation in apoptosis. Theor Biol Med Model 2008, 5(26):1-15.
  • [34]Kutumova E, Zinovyev A, Sharipov R, Kolpakov F: Modeling composition through model reduction: a combined model of CD95 and NF-kB signaling pathways. BMC Syst Biol 2013, 7:13. BioMed Central Full Text
  • [35]Elowitz M, Levine A, Siggia E, Swain P: Stochastic gene expression in a single cell. Science 2002, 297:1183-1186.
  • [36]Swain P, Elowitz M, Siggia E: Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci USA 2002, 99:12795-12800.
  • [37]Samoilov M, Arkin A: Deviant effects in molecular reaction pathways. Nat Biotech 2006, 24:1235-1240.
  • [38]Raser J, O’Shea E: Control of stochasticity in eukaryotic gene expression. Science 2004, 304:1811-1814.
  • [39]Johnston I: Mitochondrial variability as a source of extrinsic cellular noise. PLoS Comput Biol 2012, 8(3):1-14.
  • [40]J P: Summing up the noise in gene networks. Nature 2004, 427(6973):415-418.
  • [41]J P: Models of stochastic gene expression. Physics Life Rev 2005, 2:157-175.
  • [42]Chalancon G, Ravarani C, Balaji S, Martinez-Arias A, Aravind L, Jothi R, Babu M: Interplay between gene expression noise and regulatory network architecture. Trends Genet 2012, 28(5):221-232.
  • [43]EA A: Determining biological noise via single cell analysis. Anal Bioanal Chem 2009, 393:73-80.
  • [44]Thattai M, Oudenaarden A: Intrinsic noise in gene regulatory networks. Proc Natl Acad Sci U S A 2001, 98(15):8614-8619.
  • [45]Ozbudak E, Thattai M, Kurtser I, Grossman A, van Oudenaarden A: Regulation of noise in the expression of a single gene. Nat Genet 2002, 31:69-73.
  • [46]Tao Y: Intrinsic and external noise in an auto-regulatory genetic network. J Theor Biol 2004, 229(2):147-156.
  • [47]Rosenfeld N, Young J, Alon U, Swain P, Elowitz M: Gene Regulation at the Single-Cell Level. Science 2005, 307(5717):1962-1965.
  • [48]Newman J, Ghaemmaghami S, Ihmels J, Breslow D, Noble M, DeRisi J, Weissman J: Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 2006, 441(7095):840-846.
  • [49]Shahrezaei V, Ollivier J, Swain P: Colored extrinsic fluctuations and stochastic gene expression. Mol Syst Biol 2008., 4(196)
  • [50]Stekel D, Jenkins D: Strong negative self regulation of prokaryotic transcription factors increases the intrinsic noise of protein expression. BMC Syst Biol 2008, 2:6. BioMed Central Full Text
  • [51]Kar S, Baumann W, Paul M, Tyson J: Exploring the roles of noise in the eukaryotic cell cycle. Proc Natl Acad Sci U S A 2009, 106(16):6471-6476.
  • [52]Thomas P, Matuschek H, Grima R: Intrinsic intrinsic noise analyzer: a software package for the exploration of stochastic biochemical kinetics using the system size expansion. PLoS ONE 2012, 7(6):e38518.
  • [53]Singh A, Razooky B, Dar R, Weinberger L: Dynamics of protein noise can distinguish between alternate sources of gene-expression variability. Mol Syst Biol 2012, 8:607.
  • [54]Raychaudhuri S, Willgohs E, Nguyen T, Khan E, Goldkorn T: Monte Carlo simulation of cell death signaling predicts large cell-to-cell stochastic fluctuations through the type 2 pathway of apoptosis. Biophys J 2008, 95:3559-3562.
  • [55]Raychaudhuri S: How can we kill cancer cells: Insights from the computational models of apoptosis. World J Clin Oncol 2010, 1:24-28.
  • [56]Calzolari D, Paternostro G, Harrington PJ, Piermarocchi C, Duxbury P: Selective control of the apoptosis signaling network in heterogeneous cell populations. PLoS ONE 2007, 2(6):e547.
  • [57]Goldbeter A, Koshland D: An amplified sensitivity arising from covalent modification in biological systems. Proc Natl Acad Sci U S A 1981, 78:6840-6844.
  • [58]Shah N, Sarkar C: Robust network topologies for generating switch-like cellular responses. PLoS Comput Biol 2011, 7(6):e1002085.
  • [59]Srinivasula S, Ahmad M, Fernandes-Alnemri T, Alnemri E: Autoactivation of Procaspase-9 by Apaf-1-Mediated Oligomerization. Mol Cell 1998, 1:949-957.
  • [60]Creagh E, Martin S: Caspase: cellular demolition experts. Biochem Soc Trans 2001, 29(6):696-702.
  • [61]Budihardjo I, Oliver H, Lutter M, Luo X, Wang X: Biochemical pathways of caspase activation during apoptosis. Annu Rev Cell Dev Biol 1999, 15:269-290.
  • [62]Lancker JLV: Apoptosis, Genomic Integrity, and Cancer. Massachusetts: Jones and Bartlett Publishers; 2006.
  • [63]Hill M, Adrain C, Duriez P, Creagh E, Martin S: Analysis of the composition, assembly kinetics and activity of native Apaf-1 apoptosomes. Eur Mol Biol Organ J 2004, 23:2134-2145.
  • [64]Gillespie D: Exact stochastic simulation of coupled chemical reactions. J Phys Chem 1977, 81:2340-61.
  • [65]Gillespie D: Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 2007, 58:35-55.
  • [66]Svingen P, Loegering D, Rodriquez J, Meng X, Mesner PJ, Holbeck S, Monks A, Krajewski S, Scudiero D, Sausville E, Reed J, Lazebnik Y, Kaufmann S: Components of the cell death machine and drug sensitivity of the national cancer institute cell line panel. Clin Cancer Res 2004, 10(20):6807-6820.
  • [67]Song C, Phenix H, Abedi V, Scott M, Ingalls B, Kaern M, Perkins T: Estimating the stochastic bifurcation structure of cellular networks. PLoS Comput Biol 2010, 6(3):e1000699.
  • [68]Kim J, Heslop-Harrison P, Postlethwaite I, Bates D: Stochastic noise and synchronisation during dictyostelium aggregation make cAMP oscillations robust. PLoS Comput Biol 2007, 3(11):e218.
  • [69]Kim D, Debusschere B, Najm H: Spectral methods for parametric sensitivity in stochastic dynamical systems. Biophys J 2007, 92(2):379-393.
  • [70]Niepel M, Spencer S, Sorger P: Non-genetic cell-to-cell variability and the consequences for pharmacology. Curr Opin Chem Biol 2009, 13(5–6):556-561.
  • [71]Sigal A, Milo R, Cohen A, Klein Y, Liron Y, Rosenfeld N, Danon T, Perzov N, Alon U, Geva-Zatorsky N: Variability and memory of protein levels in human cells. Nature 2006, 444(30):643-646.
  • [72]Potts P, Singh S, Knezek M, Thompson C, Deshmukh M: Critical function of endogenous XIAP in regulating caspase activation during sympathetic neuronal apoptosis. J Cell Biol 2003, 163:789-799.
  • [73]Hu Y, Cherton-Horvat G, Dragowska V, Baird S, Korneluk R: Antisense oligonucleotides targeting XIAP induce apoptosis and enhance chemotherapeutic activity against human lung cancer cells in vitro and in vivo. Clin Cancer Res 2003, 9:2826-2836.
  • [74]Fulda S, Vucic D: Targeting IAP proteins for therapeutic intervention in cancer. Nat Rev Drug Discov 2012, 11(2):109-124.
  • [75]Almendro V, Marusyk A, Polyak K: Cellular heterogeneity and molecular evolution in cancer. Annu Rev Pathol Mech Dis 2012, 8:277-302.
  • [76]Bagci E, Sen S, Camurdan M: Analysis of a mathematical model of apoptosis: individual differences and malfunction in programmed cell death. J Clin Monit Comput 2013, 27(4):465-479.
  • [77]Ermentrout B: Simulating, Analyzing, and Animating Dynamical Systems: A Guide to XPPAUT for Researchers and Students. Philadelphia: Society for Industrial and Applied Mathematics; 2002.
  • [78]Gonze D, Halloy J, Goldbeter A: Deterministic versus stochastic models for circadian rhythms. J Biol Phys 2002, 28:637-653.
  • [79]Gonze D, Halloy J, Jean-Christophe L, Goldbeter A: Stochastic models for circadian rhythms: effect of molecular noise on periodic and chaotic behaviour. C R Biol 2003, 326:189—203.
  • [80]Kim H, Gelenbe E: Stochastic gene expression modeling with hill function for switch-like gene responses. IEEE/ACM Trans Comput Biol Bioinformatics 2012, 9:973-979.
  • [81]Smolen P, Baxter D, Byrne J: Interlinked dual-time feedback loops can enhance robustness to stochasticity and persistence of memory. Phys Rev 2009, 79:031902-1–11.
  • [82]Arnoult D, Gaume B, Karbowski M, Sharpe J, Cecconi F, Youle R: Mitochondrial release of AIF and EndoG requires caspase activation downstream of Bax/Bak-mediated permeabilization. EMBO J 2003, 22(17):4385-4399.
  文献评价指标  
  下载次数:65次 浏览次数:14次