南京大学学报(自然科学版) ›› 2020, Vol. 56 ›› Issue (3): 418–429.doi: 10.13232/j.cnki.jnju.2020.03.010

• • 上一篇    下一篇

基因转录爆发的建模研究

李佳云,吴人杰()   

  1. 南京大学物理学院,南京,210093
  • 收稿日期:2020-05-14 出版日期:2020-05-30 发布日期:2020-06-03
  • 通讯作者: 吴人杰 E-mail:15995920872@163.com
  • 基金资助:
    国家自然科学基金(11874209)

Modeling of transcriptional bursting

Jiayun Li,Renjie Wu()   

  1. School of Physics,Nanjing University,Nanjing,210093,China
  • Received:2020-05-14 Online:2020-05-30 Published:2020-06-03
  • Contact: Renjie Wu E-mail:15995920872@163.com

摘要:

基因转录是细胞最重要的活动之一,涉及众多分子事件,且不同基因间存在显著差异性.建立基因转录的模型有助于理解复杂的转录动力学和调控机制.如何构建合适的转录模型依然具有大的挑战性.近年来的实验发现,转录爆发是一类普遍的转录模式,揭示其特征(如转录爆发的频率和大小以及激活态和沉默态的持续时间等)和调控机制是当前的研究热点.人们相继提出两态模型和多态模型来理解转录现象.有些模型不再是简单的唯象模型,而是考虑了转录的分子过程,能够深入研究转录的内在机理.结合最近的实验和理论研究,综述不同转录模型的特点、合理性及其适用范围,特别比较了各个模型的优缺点,有助于在研究中选取合适的转录模型.随着单细胞实验技术的发展,构建基因转录的定量模型将起到越来越重要的作用.

关键词: 基因转录, 转录爆发, 两态模型, 多态模型, 适用范围

Abstract:

Gene transcription is one of the most important cellular activities,involving various molecular events and exhibiting great variability among genes. Modeling of gene transcription can promote our understanding of the complex mechanisms for transcriptional kinetics and regulation. It is still challenging to construct suitable models under different conditions. It is established that transcriptional bursting has been a ubiquitous mode; it is essential to unravel the features of transcriptional bursting (such as burst frequency and size,as well as the duration of active and inactive gene states) and underlying regulatory mechanisms. Two?state and multi?state models have been proposed to investigate transcriptional bursting. Some models are no longer simple phenomenological ones; instead,they take into account molecular events involved in transcription and thus can be used to explore the intrinsic mechanisms for transcription. Integrating recent experimental and theoretical studies,the current work reviews widely used models of transcriptional bursting in the literature,including the two?state,continuum,multi?scale,and Wang?Liu?Wang (WLW) models. We analyze the essential features,rationality and applicability of models. Specifically,we list the advantages and disadvantages of these models to facilitate choosing an appropriate model in a special situation. With the advancement of single?cell technology,building quantitative models of gene transcription will play an increasingly important role.

Key words: gene transcription, transcriptional bursting, two?state model, multi?state model, scope of application

中图分类号: 

  • Q615

图 1

单细胞中的转录爆发"

图2

转录爆发的典型特征"

图3

转录模型"

表1

转录模型的适用性"

模型

单态模型

图3a)

两态模型

图3b)

多态模型(图3c至图3i)

多ON模型

(含连续性模型)

多OFF模型

(含棘轮模型和多尺度模型)

WLW模型
激活态的持续时间分布/图2a主要时图2a,少部分情况下为峰值不为0的单峰分布,但是也接近图2a图2a主要时图2a,少部分情况下为峰值不为0的单峰分布,但是也接近图2a
沉默态的持续时间分布/图2c图2c图2b,图2c图2b,图2c
mRNA数量分布图2e图2d至图2f图2d至图2g图2d至图2g图2d至图2g

细胞群体水平的平均mRNA信息
mRNA的噪声强度、爆发频率和大小、激活态和沉默态的平均持续时间
mRNA分布性质
激活态持续时间分布沉默态持续时间分布

激活态和沉默态的

持续时间分布

精确度中等较高较高
参数很少,可由实验测得少,可由实验测得中等,部分可由实验测得,部分需要假设且要与实验相符中等,部分可由实验测得,部分需要假设且要与实验相符多,主要步骤可由实验测得,部分未明确的机制需要假设
计算量很小中等中等
适用情况mRNA或蛋白质的CV较小,不考虑单细胞中mRNA的时变研究单细胞转录产物数量变化,基因状态切换以及转录调控等mRNA数量呈多峰分布、激活态时长呈单峰分布,或者研究转录调控和信号传导时采用多ON态模型 。在研究相分离对转录的影响时可用连续性模型,侧重于转录因子或者聚合酶的局部时空变化对转录的影响研究周期性和能耗时,可采用棘轮模型。研究温度、启动子序列、转录因子等对转录的影响时用多尺度,侧重于脚手架结构对转录的影响研究转录因子自身生物功能对转录过程的影响
1 Jonkers I,Kwak H,Lis J T. Genome?wide dynamics of Pol II elongation and its interplay with promoter proximal pausing,chromatin,and exons. eLife,2014,3:e02407.
2 Stasevich T J,Hayashi?Takanaka Y,Sato Y,et al. Regulation of RNA polymerase II activation by histone acetylation in single living cells. Nature,2014,516(7530):272-275.
3 Senecal A,Munsky B,Proux F,et al. Transcription factors modulate c?Fos transcriptional bursts. Cell Reports,2014,8(1):75-83.
4 Voss T C,Hager G L. Dynamic regulation of transcriptional states by chromatin and transcription factors. Nature Reviews Genetics,2014,15(2):69-81.
5 Brown C R,Mao C,Falkovskaia E,et al. Linking stochastic fluctuations in chromatin structure and gene expression. PLoS Biology,2013,11(8):e1001621.
6 Nicolas D,Zoller B,Suter D M,et al. Modulation of transcriptional burst frequency by histone acetylation. Proceedings of the National Academy of Sciences of the United States of America,2018,115(27):7153-7158.
7 Muramoto T,Müller I,Thomas G,et al. Methylation of H3K4 is required for inheritance of active transcriptional states. Current Biology,2010,20(5):397-406.
8 Sanchez A,Choubey S,Kondev J. Stochastic models of transcription:from single molecules to single cells. Methods,2013,62(1):13-25.
9 Chubb J R,Trcek T,Shenoy S M,et al. Transcriptional pulsing of a developmental gene. Current Biology,2006,16(10):1018-1025.
10 Golding I,Paulsson J,Zawilski S M,et al. Real?time kinetics of gene activity in individual bacteria. Cell,2005,123(6):1025-1036.
11 Raj A,Peskin C S,Tranchina D,et al. Stochastic mRNA synthesis in mammalian cells. PLoS Biology,2006,4(10):e309.
12 Suter D M,Molina N,Gatfield D,et al. Mammalian genes are transcribed with widely different bursting kinetics. Science,2011,332(6028):472-474.
13 Wang Y,Ni T,Wang W,et al. Gene transcription in bursting:a unified mode for realizing accuracy and stochasticity. Biological Reviews,2019,94(1):248-258.
14 Ko M S H. Induction mechanism of a single gene molecule:Stochastic or deterministic? BioEssays,1992,14(5):341-346.
15 Zhang J,Zhou T. Promoter?mediated tran?scriptional dynamics. Biophysical Journal,2014,106(2):479-488.
16 Zhang J,Chen L,Zhou T. Analytical distribution and tunability of noise in a model of promoter progress. Biophysical Journal,2012,102(6):1247-1257.
17 Zhou T,Zhang J. Analytical results for a multistate gene model. SIAM Journal on Applied Mathematics,2012,72(3):789-818.
18 Bertrand E,Chartrand P,Schaefer M,et al. Localization of ASH1 mRNA particles in living yeast. Molecular Cell,1998,2(4):437-445.
19 Femino A M,Fay F S,Fogarty K,et al. Visualization of single RNA transcripts in situ. Science,1998,280(5363):585-590.
20 Zenklusen D,Larson D R,Singer R H. Single?RNA counting reveals alternative modes of gene expression in yeast. Nature Structural & Molecular Biology,2008,15(12):1263-1271.
21 Gillespie D T. Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry,1977,81(25):2340-2361.
22 Pedraza J M,Paulsson J. Effects of molecular memory and bursting on fluctuations in gene expression. Science,2008,319(5861):339-343.
23 Zoller B,Nicolas D,Molina N,et al. Structure of silent transcription intervals and noise characteristics of mammalian genes. Molecular Systems Biology,2015,11(7):823.
24 Sepúlveda L A,Xu H,Zhang J,et al. Measurement of gene regulation in individual cells reveals rapid switching between promoter states. Science,2016,351(6278):1218-1222.
25 Fritzsch C,Baumg?rtner S,Kuban M,et al. Estrogen-dependent control and cell?to?cell varia?bility of transcriptional bursting. Molecular Systems Biology,2018,14(2):e7678.
26 Zoller B,Little S C,Gregor T. Diverse spatial expression patterns emerge from unified kinetics of transcriptional bursting. Cell,2018,175(3):835-847.e5.
27 Ochab?Marcinek A,Tabaka M. Bimodal gene expression in noncooperative regulatory systems. Proceedings of the National Academy of Sciences of the United States of America,2010,107(51):22096-22101.
28 To T L,Maheshri N. Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science,2010,327(5969):1142-1145.
29 Raj A,van Oudenaarden A. Nature,nurture,or chance:stochastic gene expression and its consequences. Cell,2008,135(2):216-226.
30 Peccoud J,Ycart B. Markovian modeling of gene?product synthesis. Theoretical Population Biology,1995,48(2):222-234.
31 Dar R D,Razooky B S,Singh A,et al. Transcriptional burst frequency and burst size are equally modulated across the human genome. Proceedings of the National Academy of Sciences of the United States of America,2012,109(43):17454-17459.
32 Rodriguez J,Ren G,Day C R,et al. Intrinsic dynamics of a human gene reveal the basis of expression heterogeneity. Cell,2018,176(1-2):213-226.e18.
33 Harper C V,Finkenst?dt B,Woodcock D J,et al. Dynamic analysis of stochastic transcription cycles. PLoS Biology,2011,9(4):e1000607.
34 Corrigan A M,Tunnacliffe E,Cannon D,et al. A continuum model of transcriptional bursting. eLife,2016,5:e13051.
35 Tantale K,Mueller F,Kozulic?Pirher A,et al. A single?molecule view of transcription reveals convoys of RNA polymerases and multi?scale bursting. Nature Communications,2016,7:12248.
36 Krasnov A N,Mazina M Y,Nikolenko J V,et al. On the way of revealing coactivator complexes cross?talk during transcriptional activation. Cell & Bioscience,2016,6:15.
37 Lemaire V,Lee C F,Lei J,et al. Sequential recruitment and combinatorial assembling of multiprotein complexes in transcriptional activation. Physical Review Letters,2006,96(19):198102.
38 Wang Y,Liu F,Li J,et al. Reconciling the concurrent fast and slow cycling of proteins on gene promoters. Journal of the Royal Society Interface,2014,11(96):20140253.
39 Wang Y,Liu F,Wang W. Dynamic mechanism for the transcription apparatus orchestrating reliable responses to activators. Scientific Reports,2012,2:422.
40 Kornberg R D. Mediator and the mechanism of transcriptional activation. Trends in Biochemical Sciences,2005,30(5):235-239.
41 Malik S,Roeder R G. Dynamic regulation of pol II transcription by the mammalian Mediator complex. Trends in Biochemical Sciences,2005,30(5):256-263.
42 Huh D,Paulsson J. Random partitioning of molecules at cell division. Proceedings of the National Academy of Sciences of the United States of America,2011,108(36):15004-15009.
43 Peterson J R,Cole J A,Fei J,et al. Effects of DNA replication on mRNA noise. Proceedings of the National Academy of Sciences of the United States of America,2015,112(52):15886-15891.
44 Padovan?Merhar O,Nair G P,Biaesch A G,et al. Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. Molecular Cell,2015,58(2):339-352.
45 Chen J,Zhang Z,Li L,et al. Single?molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell,2014,156(6):1274-1285.
46 Paakinaho V,Presman D M,Ball D A,et al. Single?molecule analysis of steroid receptor and cofactor action in living cells. Nature Communications,2017,8:15896.
47 Grimm J B,English B P,Chen J J,et al. A general method to improve fluorophores for live?cell and single-molecule microscopy. Nature Methods,2015,12(3):244-250.
48 Phillips R,Belliveau N M,Chure G,et al. Figure 1 theory meets figure 2 experiments in the study of gene expression. Annual Reviews of Biophysics,2019,48:121-163.
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