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Well-established motifs and their functions

Much experimental work has been devoted to understanding network motifs in gene regulatory networks. These networks control which genes are expressed in the cell in response to biological signals. The network is defined such that genes are nodes, and directed edges represent the control of one gene by a transcription factor (regulatory protein that binds DNA) encoded by another gene. Thus, network motifs are patterns of genes regulating each other's transcription rate. When analyzing transcription networks, it is seen that the same network motifs appear again and again in diverse organisms from bacteria to human. The transcription network of E. coli and yeast, for example, is made of three main motif families, that make up almost the entire network. The leading hypothesis is that the network motif were independently selected by evolutionary processes in a converging manner,[1][2] since the creation or elimination of regulatory interactions is fast on evolutionary time scale, relative to the rate at which genes change,[1][2][3] Furthermore, experiments on the dynamics generated by network motifs in living cells indicate that they have characteristic dynamical functions. This suggests that the network motif serve as building blocks in gene regulatory networks that are beneficial to the organism.

The functions associated with common network motifs in transcription networks were explored and demonstrated by several research projects both theoretically and experimentally. Below are some of the most common network motifs and their associated function.


完善的模体及其功能

许多实验工作致力于理解基因调控网络中的网络模体。在响应生物信号的过程中，这些网络控制细胞中的哪些基因来表达。这样的网络以基因作为节点，有向边代表对某个基因的调控，基因调控通过其他基因编码的转录因结合在DNA上的调控蛋白子来实现。因此，网络模体是基因之间相互调控转录速率的模式。在分析转录调控网络的时候，人们发现相同的网络模体在不同的物种中不断地出现，从细菌到人类。例如，大肠杆菌和酵母的转录网络由三种主要的网络模体家族组成，它们可以构建几乎整个网络。主要的假设是在进化的过程中，网络模体是被以收敛的方式独立选择出来的。[1][2] 因为相对于基因改变的速率，转录相互作用产生和消失的时间尺度在进化上是很快的。[1][2][3] 此外，对活细胞中网络模体所产生的动力学行为的实验表明，它们具有典型的动力学功能。这表明，网络模体是基因调控网络中对生物体有益的基本单元。

一些研究从理论和实验两方面探讨和论证了转录网络中与共同网络模体相关的功能。下面是一些最常见的网络模体及其相关功能。

Negative auto-regulation (NAR)

Schematic representation of an auto-regulation motif

One of simplest and most abundant network motifs in E. coli is negative auto-regulation in which a transcription factor (TF) represses its own transcription. This motif was shown to perform two important functions. The first function is response acceleration. NAR was shown to speed-up the response to signals both theoretically [4] and experimentally. This was first shown in a synthetic transcription network[5] and later on in the natural context in the SOS DNA repair system of E .coli.[6] The second function is increased stability of the auto-regulated gene product concentration against stochastic noise, thus reducing variations in protein levels between different cells.[7][8][9]

负自反馈调控(NAR)

Schematic representation of an auto-regulation motif

负自反馈调控是大肠杆菌中最简单和最冗余的网络模体之一，其中一个转录因子抑制它自身的转录。这种网络模体有两个重要的功能，其中第一个是加速响应。人们发现在实验和理论上， [4]NAR都可以加快对信号的响应。这个功能首先在一个人工合成的转录网络中被发现，[5] 然后在大肠杆菌SOS DAN修复系统这个自然体系中也被发现。[6] 自负反馈网络的第二个功能是增强自调控基因的产物浓度的稳定性，从而抵抗随机的噪声，减少该蛋白含量在不同细胞中的差异。[7][8][9]

Positive auto-regulation (PAR)

Positive auto-regulation (PAR) occurs when a transcription factor enhances its own rate of production. Opposite to the NAR motif this motif slows the response time compared to simple regulation.[10] In the case of a strong PAR the motif may lead to a bimodal distribution of protein levels in cell populations.[11]

正自反馈调控(PAR)

正自反馈调控是指转录因子增强它自身转录速率的调控。和负自反馈调节相反，NAR模体相对于简单的调控能够延长反应时间。[10] 在强PAR的情况下，模体可能导致蛋白质水平在细胞群中呈现双峰分布。[11]

前馈回路 (FFL)

文件:Feed-forward motif.GIF
Schematic representation of a Feed-forward motif

前馈回路普遍存在于许多基因系统和生物体中。这种模体包括三个基因以及三个相互作用。目标基因C被两个转录因子（TFs）A和B调控，并且TF B同时被TF A调控。由于每个调控相互作用可以是正的或者负的，所以总共可能有八种类型的FFL模体。[12] 其中的两种：一致前馈回路的类型一（C1-FFL）（所有相互作用都是正的）和不一致前馈回路的类型一（I1-FFL）（A激活C和B，B抑制C）在大肠杆菌和酵母中相比于其他六种更频繁的出现。[12][13] 除了网络的结构外，还应该考虑来自A和B的信号被C的启动子集成的方式。在大多数情况下，FFL要么是一个与门(激活C需要A和B)，要么是或门(激活C需要A或B)，但也可以是其他输入函数。

Coherent type 1 FFL (C1-FFL)

The C1-FFL with an AND gate was shown to have a function of a ‘sign-sensitive delay’ element and a persistence detector both theoretically [12] and experimentally[14] with the arabinose system of E. coli. This means that this motif can provide pulse filtration in which short pulses of signal will not generate a response but persistent signals will generate a response after short delay. The shut off of the output when a persistent pulse is ended will be fast. The opposite behavior emerges in the case of a sum gate with fast response and delayed shut off as was demonstrated in the flagella system of E. coli.[15] De novo evolution of C1-FFLs in gene regulatory networks has been demonstrated computationally in response to selection to filter out an idealized short signal pulse, but for non-idealized noise, a dynamics-based system of feed-forward regulation with different topology was instead favored.[16]

Incoherent type 1 FFL (I1-FFL)

The I1-FFL is a pulse generator and response accelerator. The two signal pathways of the I1-FFL act in opposite directions where one pathway activates Z and the other represses it. When the repression is complete this leads to a pulse-like dynamics. It was also demonstrated experimentally that the I1-FFL can serve as response accelerator in a way which is similar to the NAR motif. The difference is that the I1-FFL can speed-up the response of any gene and not necessarily a transcription factor gene.[17] An additional function was assigned to the I1-FFL network motif: it was shown both theoretically and experimentally that the I1-FFL can generate non-monotonic input function in both a synthetic [18] and native systems.[19] Finally, expression units that incorporate incoherent feedforward control of the gene product provide adaptation to the amount of DNA template and can be superior to simple combinations of constitutive promoters.[20] Feedforward regulation displayed better adaptation than negative feedback, and circuits based on RNA interference were the most robust to variation in DNA template amounts.[20]

Multi-output FFLs

In some cases the same regulators X and Y regulate several Z genes of the same system. By adjusting the strength of the interactions this motif was shown to determine the temporal order of gene activation. This was demonstrated experimentally in the flagella system of E. coli.[21]

Single-input modules (SIM)

This motif occurs when a single regulator regulates a set of genes with no additional regulation. This is useful when the genes are cooperatively carrying out a specific function and therefore always need to be activated in a synchronized manner. By adjusting the strength of the interactions it can create temporal expression program of the genes it regulates.[22]

In the literature, Multiple-input modules (MIM) arose as a generalization of SIM. However, the precise definitions of SIM and MIM have been a source of inconsistency. There are attempts to provide orthogonal definitions for canonical motifs in biological networks and algorithms to enumerate them, especially SIM, MIM and Bi-Fan (2x2 MIM).[23]

Dense overlapping regulons (DOR)

This motif occurs in the case that several regulators combinatorially control a set of genes with diverse regulatory combinations. This motif was found in E. coli in various systems such as carbon utilization, anaerobic growth, stress response and others.[24][25] In order to better understand the function of this motif one has to obtain more information about the way the multiple inputs are integrated by the genes. Kaplan et al.[26] has mapped the input functions of the sugar utilization genes in E. coli, showing diverse shapes.

  1. ↑ 1.0 1.1 1.2 1.3 Babu MM, Luscombe NM, Aravind L, Gerstein M, Teichmann SA (June 2004). "Structure and evolution of transcriptional regulatory networks". Current Opinion in Structural Biology. 14 (3): 283–91. CiteSeerX 10.1.1.471.9692. doi:10.1016/j.sbi.2004.05.004. PMID 15193307.
  2. ↑ 2.0 2.1 2.2 2.3 Conant GC, Wagner A (July 2003). "Convergent evolution of gene circuits". Nat. Genet. 34 (3): 264–6. doi:10.1038/ng1181. PMID 12819781.
  3. ↑ 3.0 3.1 Dekel E, Alon U (July 2005). "Optimality and evolutionary tuning of the expression level of a protein". Nature. 436 (7050): 588–92. Bibcode:2005Natur.436..588D. doi:10.1038/nature03842. PMID 16049495.
  4. ↑ 4.0 4.1 Zabet NR (September 2011). "Negative feedback and physical limits of genes". Journal of Theoretical Biology. 284 (1): 82–91. arXiv:1408.1869. CiteSeerX 10.1.1.759.5418. doi:10.1016/j.jtbi.2011.06.021. PMID 21723295.
  5. ↑ 5.0 5.1 Rosenfeld N, Elowitz MB, Alon U (November 2002). "Negative autoregulation speeds the response times of transcription networks". J. Mol. Biol. 323 (5): 785–93. CiteSeerX 10.1.1.126.2604. doi:10.1016/S0022-2836(02)00994-4. PMID 12417193.
  6. ↑ 6.0 6.1 Camas FM, Blázquez J, Poyatos JF (August 2006). "Autogenous and nonautogenous control of response in a genetic network". Proc. Natl. Acad. Sci. U.S.A. 103 (34): 12718–23. Bibcode:2006PNAS..10312718C. doi:10.1073/pnas.0602119103. PMC 1568915. PMID 16908855.
  7. ↑ 7.0 7.1 Becskei A, Serrano L (June 2000). "Engineering stability in gene networks by autoregulation". Nature. 405 (6786): 590–3. doi:10.1038/35014651. PMID 10850721.
  8. ↑ 8.0 8.1 Dublanche Y, Michalodimitrakis K, Kümmerer N, Foglierini M, Serrano L (2006). "Noise in transcription negative feedback loops: simulation and experimental analysis". Mol. Syst. Biol. 2 (1): 41. doi:10.1038/msb4100081. PMC 1681513. PMID 16883354.
  9. ↑ 9.0 9.1 Shimoga V, White J, Li Y, Sontag E, Bleris L (2013). "Synthetic mammalian transgene negative autoregulation". Mol. Syst. Biol. 9: 670. doi:10.1038/msb.2013.27. PMC 3964311. PMID 23736683.
  10. ↑ 10.0 10.1 Maeda YT, Sano M (June 2006). "Regulatory dynamics of synthetic gene networks with positive feedback". J. Mol. Biol. 359 (4): 1107–24. doi:10.1016/j.jmb.2006.03.064. PMID 16701695.
  11. ↑ 11.0 11.1 Becskei A, Séraphin B, Serrano L (May 2001). "Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion". EMBO J. 20 (10): 2528–35. doi:10.1093/emboj/20.10.2528. PMC 125456. PMID 11350942.
  12. ↑ 12.0 12.1 12.2 Mangan S, Alon U (October 2003). "Structure and function of the feed-forward loop network motif". Proc. Natl. Acad. Sci. U.S.A. 100 (21): 11980–5. Bibcode:2003PNAS..10011980M. doi:10.1073/pnas.2133841100. PMC 218699. PMID 14530388.
  13. ↑ Ma HW, Kumar B, Ditges U, Gunzer F, Buer J, Zeng AP (2004). "An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs". Nucleic Acids Res. 32 (22): 6643–9. doi:10.1093/nar/gkh1009. PMC 545451. PMID 15604458.
  14. ↑ Mangan S, Zaslaver A, Alon U (November 2003). "The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks". J. Mol. Biol. 334 (2): 197–204. CiteSeerX 10.1.1.110.4629. doi:10.1016/j.jmb.2003.09.049. PMID 14607112.
  15. ↑ Kalir S, Mangan S, Alon U (2005). "A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coli". Mol. Syst. Biol. 1 (1): E1–E6. doi:10.1038/msb4100010. PMC 1681456. PMID 16729041.
  16. ↑ Xiong, Kun; Lancaster, Alex K.; Siegal, Mark L.; Masel, Joanna (3 June 2019). "Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise". Nature Communications. 10 (1): 2418. doi:10.1038/s41467-019-10388-6. PMC 6546794. PMID 31160574.
  17. ↑ Mangan S, Itzkovitz S, Zaslaver A, Alon U (March 2006). "The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli". J. Mol. Biol. 356 (5): 1073–81. CiteSeerX 10.1.1.184.8360. doi:10.1016/j.jmb.2005.12.003. PMID 16406067.
  18. ↑ Entus R, Aufderheide B, Sauro HM (August 2007). "Design and implementation of three incoherent feed-forward motif based biological concentration sensors". Syst Synth Biol. 1 (3): 119–28. doi:10.1007/s11693-007-9008-6. PMC 2398716. PMID 19003446.
  19. ↑ Kaplan S, Bren A, Dekel E, Alon U (2008). "The incoherent feed-forward loop can generate non-monotonic input functions for genes". Mol. Syst. Biol. 4 (1): 203. doi:10.1038/msb.2008.43. PMC 2516365. PMID 18628744.
  20. ↑ 20.0 20.1 Bleris L, Xie Z, Glass D, Adadey A, Sontag E, Benenson Y (2011). "Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template". Mol. Syst. Biol. 7 (1): 519. doi:10.1038/msb.2011.49. PMC 3202791. PMID 21811230.
  21. ↑ Kalir S, McClure J, Pabbaraju K, et al. (June 2001). "Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria". Science. 292 (5524): 2080–3. doi:10.1126/science.1058758. PMID 11408658.
  22. ↑ Zaslaver A, Mayo AE, Rosenberg R, et al. (May 2004). "Just-in-time transcription program in metabolic pathways". Nat. Genet. 36 (5): 486–91. doi:10.1038/ng1348. PMID 15107854.
  23. ↑ Konagurthu AS, Lesk AM (2008). "Single and Multiple Input Modules in regulatory networks". Proteins. 73 (2): 320–324. doi:10.1002/prot.22053. PMID 18433061.
  24. ↑ 引用错误：无效<ref>标签；未给name属性为she1的引用提供文字
  25. ↑ 引用错误：无效<ref>标签；未给name属性为boy1的引用提供文字
  26. ↑ Kaplan S, Bren A, Zaslaver A, Dekel E, Alon U (March 2008). "Diverse two-dimensional input functions control bacterial sugar genes". Mol. Cell. 29 (6): 786–92. doi:10.1016/j.molcel.2008.01.021. PMC 2366073. PMID 18374652.
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