Noise propagation through extracellular signaling leads to fluctuations in gene expression

BMC Systems Biology

Ciriyam Jayaprakash³  Fernand Hayot¹  Stuart C Sealfon¹  German Nudelman²  Omar P Tabbaa³ 

[1] Center for Translational Systems Biology, Mount Sinai School of Medicine, New York 10029, USA;Department of Neurology, Mount Sinai School of Medicine, New York 10029, USA;Department of Physics, Ohio State University, Columbus 43210, USA

关键词: Spatial heterogeneity;    Noise propagation;    Multi-scale modeling;    Dendritic cells;    Cytokine signaling;   

Others  :  1142195
DOI  :  10.1186/1752-0509-7-94

 received in 2012-12-06, accepted in 2013-09-17,  发布年份 2013

【 摘 要 】

Background

Cell-to-cell variability in mRNA and proteins has been observed in many biological systems, including the human innate immune response to viral infection. Most of these studies have focused on variability that arises from (a) intrinsic stochastic fluctuations in gene expression and (b) extrinsic sources (e.g. fluctuations in transcription factors). The main focus of our study is the effect of extracellular signaling on enhancing intrinsic stochastic fluctuations. As a new source of noise, the communication between cells with fluctuating numbers of components has received little attention. We use agent-based modeling to study this contribution to noise in a system of human dendritic cells responding to viral infection.

Results

Our results, validated by single-cell experiments, show that in the transient state cell-to-cell variability in an interferon-stimulated gene (DDX58) arises from the interplay between the spatial randomness of the cellular sources of the interferon and the temporal stochasticity of its own production. The numerical simulations give insight into the time scales on which autocrine and paracrine signaling act in a heterogeneous population of dendritic cells upon viral infection. We study the effect of different factors that influence the magnitude of the cell-to-cell-variability of the induced gene, including the cell density, multiplicity of infection, and the time scale over which the cellular sources begin producing the cytokine.

Conclusions

We propose a mechanism of noise propagation through extracellular communication and establish conditions under which the mechanism is operative. The cellular stochasticity of gene induction, which we investigate, is not limited to the specific interferon-induced gene we have studied; a broad distribution of copy numbers across cells is to be expected for other interferon-stimulated genes. This can lead to functional consequences for the system-level response to a viral challenge.

【 授权许可】

   
2013 Tabbaa et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Spiller DG, Wood CD, Rand DA, White MR: Measurement of single-cell dynamics. Nature 2010, 465:736-745.
• [2]McAdams HH, Arkin A: Stochastic mechanisms in gene expression. PNAS 1997, 94:81419-81419.
• [3]Arkin A, Ross J, McAdams HH: Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 1998, 149:1633-1648.
• [4]Pedraza JM, van Oudenaarden A: Noise propagation in gene networks. Science 2005, 307:1965-1969.
• [5]Rosenfeld N, Young JW, Alon U, Swain PS, Elowitz MB: Gene regulation at the single-cell level. Science 2005, 307:1962-1965.
• [6]Blake WJ, Balazsi G, Kohanski MA, Isaacs FJ, Murphy KF, Kuang Y, Cantor CR, Walt DR, Collins JJ: Phenotypic consequences of promoter-mediated transcriptional noise. Mol Cell 2006, 24:853-865.
• [7]Pascal V, Stulberg MJ, Anderson SK: Regulation of class I major histocompatibility complex receptor expression in natural killer cells: one promoter is not enough! Immunol Rev 2006, 214:9-21.
• [8]Volfson D, Marciniak J, Blake WJ, Ostroff N, Tsimring LS, Hasty J: Origins of extrinsic variability in eukaryotic gene expression. Nature 2006, 439:861-864.
• [9]Eldar A, Elowitz MB: Functional roles for noise in genetic circuits. Nature 2010, 467:167-173.
• [10]Hu J, Sealfon SC, Hayot F, Jayaprakash C, Kumar M, Pendleton AC, Ganee A, Fernandez-Sesma A, Morn TM, Wetmur JG: Chromosome-specific and noisy IFNb1 transcription in individual virus-infected human primary dendritic cells. NAR 2007, 35:5232-5241.
• [11]Weinberger LS, Burnett JC, Toettcher JE, Arkin AP, Schaffer DV: Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell 2005, 122:169-182.
• [12]Raj A, van Oudenaarden A: Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 2008, 135:216-226.
• [13]Balazsi G, van Oudenaarden A, Collins JC: Cellular decision-making and biological noise: from microbes to mammals. Cell 2011, 144:910-925.
• [14]Thattai M, van Oudenaarden A: Intrinsic noise in gene regulatory networks. PNAS 2001, 98:8614-8619.
• [15]Golding I, Paulsson J, Zawilski SM, Cox EC: Real-time kinetics of gene activity in individual bacteria. Cell 2005, 123:1025-1036.
• [16]Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S: Stochastic mRNA synthesis in mammalian cells. PLoS Biol 2006, 4:e309.
• [17]Elowitz MB, Levine AJ, Siggia ED, Swain PS: Stochastic gene expression in a single cell. Science 2002, 297:1183-1186.
• [18]Hu J, Nudelman G, Shimoni Y, Kumar M, Ding Y, López C, Hayot F, Wetmur JG, Sealfon SC: Role of cell-to-cell variability in activating a positive feedback antiviral response in human dendritic cells. PLoS One 2011, 6:1661-1664.
• [19]Shimoni Y, Nudelman G, Hayot F, Sealfon SC: Multi-scale stochastic simulation of diffusion-coupled agents and its application to cell culture simulation. PLoS One 2011, 6:e29298-e29298.
• [20]Park MS, Garcia-Sastre A, Cros JF, Basler CF, Palese P: Newcastle disease virus V protein is a determinant of host range restriction. J Virol 2003, 77:9522-9532.
• [21]Apostolou E, Thanos D: Virus infection induces NF-κB-dependent interchromosomal associations mediating monoallelic IFN-β gene expression. Cell 2008, 134:85-96.
• [22]Rand U, Rinas M, Schwenk J, Nöhren G, Linnes M, Kröger A, Flossdorf M, Kály-Kullai K, Hauser H, Höfer T, Köster M: Multi-layered stochasticity and paracrine signal propagation shape the type-I interferon response. Mol Syst Biol 2012, 8:584-596.
• [23]Zhao M, Zhang J, Phatnani H, Scheu S, Maniatis T: Stochastic expression of the interferon-β gene. PLoS Biol 2012, 10:1-16.
• [24]Iyer-Biswas S, Hayot F, Jayaprakash C: Stochasticity of gene products from transcriptional pulsing. Phys Rev E 2009, 79:031911.
• [25]Dar RD, Razooky BS, Singh A, Trimeloni TV, McCollum JM, Cox CD, Simpson ML, Weinberger LS: Transcriptional burst frequency and burst size are equally modulated across the human genome. PNAS 2012, 109:17454-17459.
• [26]Zaslavsky E, Hershberg U, Seto J, Pham AM, Marquez S, Duke JL, Wetmur JG, Tenoever BR, Sealfon SC, Kleinstein SH: Antiviral response dictated by choreographed cascade of transcription factors. J Immunol 2010, 184:2908-2917.
• [27]Marie I, Durbin JE, Levy DE: Differential viral induction of distinct interferon-alpha genes by positive feedback through interferon regulatory factor-7. EMBO J 1998, 17:6660-6669.
• [28]Brierley MM, Fish EN: Stats: multifaceted regulators of transcription. J Interferon Cytokine Res 2005, 25:733-744.
• [29]Pavlovic J, Haller O, Staeheli P: Human and mouse mx proteins inhibit steps of the influenza virus multiplication cycle. J Virol 1992, 66:2564-2569.
• [30]Tumpey TM, Szretter KJ, Van Hoeven N, Katz JM, Kochs G, Haller O, Garcia-Sastre A, Staeheli P: The Mx1 gene protects mice against the pandemic 1918 and highly lethal H5N1 influenza viruses. J Virol 2007, 81:10818-10821.
• [31]Carlos TS, Young D, Stertz S, Kochs G, Randall RE: Interferon-induced inhibition of parainfluenza virus type 5; the roles of MxA, PKR and oligo A synthetase/RNase. Virology 2007, 363:166-173.
• [32]Hu J, Iyer-Biswas S, Sealfon SC, Wetmur J, Jayaprakash C, Hayot F: Power-laws in interferon-b mRNA distribution in virus-infected dendritic cells. Biophys J 2009, 97:1984-1989.
• [33]Brennan K, Bowie AG: Activation of host pattern recognition receptors by viruses. Curr Opin Microbiol 2010, 13:503-507.
• [34]Theofilopoulos AN, Baccala R, Beutler B, Kono DH: Type I interferons (alpha/beta) in immunity and autoimmunity. Annu Rev Immunol 2005, 23:307-336.
• [35]Kato H, Sato S, Yoneyama M, Yamamoto M, Uematsu S, Matsui K, Tsujimura T, Takeda K, Fujita T, Takeuchi O, Akira S: Cell type-specific involvement of RIG-I in antiviral response. Immunity 2005, 23:19-28.
• [36]Kawai T, Akira S: Innate immune recognition of viral infection. Nat Immunol 2006, 7:131-137.
• [37]Goryachev AB, Toh D, Wee KB, Lee T, Zhang H, Zhang L: Transition to quorum sensing in an Agrobacterium population: a stochastic model. PLoS Comput Biol 2005, 1:e37.
• [38]Tanouchi Y, Tu D, Kim J, You Y: Noise reduction by diffusional dissipation in a minimal quorum sensing motif. PLoS Comput Biol 2008, 4:e1000167.
• [39]Bauer AL, Beauchemin CA, Perelson AS: Agent-based modeling of host-pathogen systems: the successes and challenges. Inf Sci (Ny) 2009, 179:1379-1389.
• [40]Mirsky HP, Miller MJ, Linderman JJ, Kirschner DE: Systems biology approaches for understanding cellular mechanisms of immunity in lymph nodes during infection. J Theor Biol 2011, 287:160-170.
• [41]Kepler TB, Chan C: Spatiotemporal programming of a simple inflammatory process. Immunol Rev 2007, 216:153-163.
• [42]Cilfone NA, Perry CR, Kirschner DE, Linderman JJ: Multi-scale modeling predicts a balance of tumor necrosis factor-a and interleukin-10 controls the granuloma environment during mycobacterium tuberculosis infection. PLoS One 2013, 8:e68680.
• [43]Gillespie DT: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 1976, 22:403-434.
• [44]Coppey M, Berezhkovskii AM, Sealfon SC, Shvartsman SY: Time and length scales of autocrine signals in three dimensions. Biophys J 2007, 93:1917-1922.
• [45]Munshi N, Agalioti T, Lomvardas S, Merika M, Chen G, Thanos D: Coordination of a transcriptional switch by HMGI(Y) acetylation. Science 2001, 293:1133-1136.
• [46]Panne D, Maniatis T, Harrison SC: An atomic model of the interferon-β enhanceosome. Cell 2007, 129:1111-1123.
• [47]Suter DM, Molina N, Gatfield D, Schneider K, Schibler U, Naef F: Mammalian genes are transcribed with widely different bursting kinetics. Science 2011, 332:472-474.
• [48]Honda K, Yanai H, Negishi H, Asagiri M, Sato M, Mizutani T, Shimada N, Ohba Y, Takaoka A, Yoshida N, Taniguchi T: IRF-7 is the master regulator of type-I interferon-dependent immune responses. Nature 2005, 434:772-777.