Relatively slow stochastic gene-state switching in the presence of positive feedback significantly broadens the region of bimodality through stabilizing the uninduced phenotypic state

Within an isogenic population, even in the same extracellular environment, individual cells can exhibit various phenotypic states. The exact role of stochastic gene-state switching regulating the transition among these phenotypic states in a single cell is not fully understood, especially in the presence of positive feedback. Recent high-precision single-cell measurements showed that, at least in bacteria, switching in gene states is slow relative to the typical rates of active transcription and translation. Hence using the lac operon as an archetype, in such a region of operon-state switching, we present a fluctuating-rate model for this classical gene regulation module, incorporating the more realistic operon-state switching mechanism that was recently elucidated. We found that the positive feedback mechanism induces bistability (referred to as deterministic bistability), and that the parameter range for its occurrence is significantly broadened by stochastic operon-state switching. We further show that in the absence of positive feedback, operon-state switching must be extremely slow to trigger bistability by itself. However, in the presence of positive feedback, which stabilizes the induced state, the relatively slow operon-state switching kinetics within the physiological region are sufficient to stabilize the uninduced state, together generating a broadened parameter region of bistability (referred to as stochastic bistability). We illustrate the opposite phenotype-transition rate dependence upon the operon-state switching rates in the two types of bistability, with the aid of a recently proposed rate formula for fluctuating-rate models. The rate formula also predicts a maximal transition rate in the intermediate region of operon-state switching, which is validated by numerical simulations in our model. Overall, our findings suggest a biological function of transcriptional “variations” among genetically identical cells, for the emergence of bistability and transition between phenotypic states.

[1]  E A Matzke,et al.  Functional role of arginine 302 within the lactose permease of Escherichia coli. , 1992, The Journal of biological chemistry.

[2]  H. Qian,et al.  Mesoscopic biochemical basis of isogenetic inheritance and canalization: stochasticity, nonlinearity, and emergent landscape. , 2012, Molecular & cellular biomechanics : MCB.

[3]  Nacho Molina,et al.  Mammalian Genes Are Transcribed with Widely Different Bursting Kinetics , 2011, Science.

[4]  L. Hood,et al.  Calculating biological behaviors of epigenetic states in the phage λ life cycle , 2004, Functional & Integrative Genomics.

[5]  X. Xie,et al.  When does the Michaelis-Menten equation hold for fluctuating enzymes? , 2006, The journal of physical chemistry. B.

[6]  Hong Qian,et al.  Thermodynamic limit of a nonequilibrium steady state: Maxwell-type construction for a bistable biochemical system. , 2009, Physical review letters.

[7]  J. Doob Markoff chains—denumerable case , 1945 .

[8]  Jayajit Das,et al.  Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities , 2007, Proceedings of the National Academy of Sciences.

[9]  Ido Golding,et al.  Measurement of gene regulation in individual cells reveals rapid switching between promoter states , 2016, Science.

[10]  D. Gillespie A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[11]  Jerome T. Mettetal,et al.  Stochastic switching as a survival strategy in fluctuating environments , 2008, Nature Genetics.

[12]  Gene-Wei Li,et al.  Central dogma at the single-molecule level in living cells , 2011, Nature.

[13]  O. Berg A model for the statistical fluctuations of protein numbers in a microbial population. , 1978, Journal of theoretical biology.

[14]  J M Rosenberg,et al.  Kinetic studies of inducer binding to lac repressor.operator complex. , 1980, The Journal of biological chemistry.

[15]  G. K. Ackers,et al.  Quantitative model for gene regulation by lambda phage repressor. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[16]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[17]  Margaret Ann Shea,et al.  Quantitative model for gene regulation by ? phage repressor , 1997 .

[18]  Ertugrul M. Ozbudak,et al.  Multistability in the lactose utilization network of Escherichia coli , 2004, Nature.

[19]  Tsz-Leung To,et al.  Noise Can Induce Bimodality in Positive Transcriptional Feedback Loops Without Bistability , 2010, Science.

[20]  Erik Aurell,et al.  Quasi-potential landscape in complex multi-stable systems , 2012, Journal of The Royal Society Interface.

[21]  M. Ehrenberg,et al.  Random signal fluctuations can reduce random fluctuations in regulated components of chemical regulatory networks. , 2000, Physical review letters.

[22]  Hong Qian,et al.  Stochastic phenotype transition of a single cell in an intermediate region of gene state switching. , 2013, Physical review letters.

[23]  J. Onuchic,et al.  Absolute rate theories of epigenetic stability. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Rob Phillips,et al.  Operator sequence alters gene expression independently of transcription factor occupancy in bacteria. , 2012, Cell reports.

[25]  Zaida Luthey-Schulten,et al.  Determining the stability of genetic switches: explicitly accounting for mRNA noise. , 2011, Physical review letters.

[26]  A. Novick,et al.  ENZYME INDUCTION AS AN ALL-OR-NONE PHENOMENON. , 1957, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Kirsten L. Frieda,et al.  A Stochastic Single-Molecule Event Triggers Phenotype Switching of a Bacterial Cell , 2008, Science.

[28]  T. W. Barrett,et al.  Catastrophe Theory, Selected Papers 1972-1977 , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  James E. Ferrell,et al.  Bistability in cell signaling: How to make continuous processes discontinuous, and reversible processes irreversible. , 2001, Chaos.

[30]  D R Rigney,et al.  Note on the kinetics and stochastics of induced protein synthesis as influenced by various models for messenger RNA degradation. , 1979, Journal of theoretical biology.

[31]  Takamasa Kudo,et al.  Controlling low rates of cell differentiation through noise and ultrahigh feedback , 2014, Science.

[32]  Hong Qian,et al.  Nonequilibrium thermodynamics and nonlinear kinetics in a cellular signaling switch. , 2005, Physical review letters.

[33]  I. Bose,et al.  Graded and binary responses in stochastic gene expression , 2004, Physical biology.

[34]  Michael A Savageau,et al.  Distinctive topologies of partner-switching signaling networks correlate with their physiological roles. , 2007, Journal of molecular biology.

[35]  B. Müller-Hill,et al.  The three operators of the lac operon cooperate in repression. , 1990, The EMBO journal.

[36]  N. Friedman,et al.  Stochastic protein expression in individual cells at the single molecule level , 2006, Nature.

[37]  Ertugrul M. Ozbudak,et al.  Predicting stochastic gene expression dynamics in single cells. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[38]  H. Feng,et al.  Adiabatic and non-adiabatic non-equilibrium stochastic dynamics of single regulating genes. , 2011, The journal of physical chemistry. B.

[39]  Sui Huang,et al.  The potential landscape of genetic circuits imposes the arrow of time in stem cell differentiation. , 2010, Biophysical journal.

[40]  Youfang Cao,et al.  Nonlatching positive feedback enables robust bimodality by decoupling expression noise from the mean , 2017, bioRxiv.

[41]  M. Elowitz,et al.  Functional roles for noise in genetic circuits , 2010, Nature.

[42]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells , 2010, Science.

[43]  Klaus Aktories,et al.  Noise Can Induce Bimodality in Positive Transcriptional Feedback Loops Without Bistability , 2010 .

[44]  H. Qian Cellular Biology in Terms of Stochastic Nonlinear Biochemical Dynamics: Emergent Properties, Isogenetic Variations and Chemical System Inheritability , 2010 .

[45]  H. Riezman,et al.  Transcription and translation initiation frequencies of the Escherichia coli lac operon. , 1977, Journal of molecular biology.

[46]  M C Mackey,et al.  Origin of bistability in the lac Operon. , 2007, Biophysical journal.

[47]  H. Qian,et al.  A perturbation analysis of rate theory of self-regulating genes and signaling networks. , 2011, The Journal of chemical physics.

[48]  A. Faggionato,et al.  Non-equilibrium Thermodynamics of Piecewise Deterministic Markov Processes , 2009 .

[49]  A. Riggs,et al.  Interaction of effecting ligands with lac repressor and repressor-operator complex. , 1975, Biochemistry.

[50]  S. Leibler,et al.  Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments , 2005, Science.

[51]  Johannes Berg,et al.  What makes the lac-pathway switch: identifying the fluctuations that trigger phenotype switching in gene regulatory systems , 2013, Nucleic acids research.

[52]  Robert Ahrends,et al.  Consecutive Positive Feedback Loops Create a Bistable Switch that Controls Preadipocyte-to-Adipocyte Conversion , 2012, Cell reports.

[53]  Gregory Stephanopoulos,et al.  On physiological multiplicity and population heterogeneity of biological systems , 1996 .

[54]  M. Delbrück Statistical Fluctuations in Autocatalytic Reactions , 1940 .

[55]  E. Aurell,et al.  Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape , 2016, Proceedings of the National Academy of Sciences.

[56]  Gabriel S. Eichler,et al.  Cell fates as high-dimensional attractor states of a complex gene regulatory network. , 2005, Physical review letters.

[57]  J. Onuchic,et al.  Self-regulating gene: an exact solution. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[58]  Tiejun Li,et al.  Construction of the landscape for multi-stable systems: Potential landscape, quasi-potential, A-type integral and beyond. , 2016, The Journal of chemical physics.

[59]  Paul J. Choi,et al.  Stochastic switching in gene networks can occur by a single-molecule event or many molecular steps. , 2010, Journal of molecular biology.

[60]  Hannah H. Chang,et al.  Transcriptome-wide noise controls lineage choice in mammalian progenitor cells , 2008, Nature.

[61]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[62]  H. Stanley,et al.  Spontaneous recovery in dynamical networks , 2013, Nature Physics.

[63]  K. Sneppen,et al.  Epigenetics as a first exit problem. , 2001, Physical review letters.

[64]  J. Elf,et al.  Probing Transcription Factor Dynamics at the Single-Molecule Level in a Living Cell , 2007, Science.

[65]  Guy S. Salvesen,et al.  SnapShot: Caspases , 2011, Cell.

[66]  X. Xie,et al.  Probing Gene Expression in Live Cells, One Protein Molecule at a Time , 2006, Science.

[67]  Fangting Li,et al.  Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise , 2014, PloS one.

[68]  A. B. Bortz,et al.  A new algorithm for Monte Carlo simulation of Ising spin systems , 1975 .