Regulation by competition: a hidden layer of gene regulatory network
Lei Wei1,2, Ye Yuan1,2, Tao Hu1,2, Shuailin Li3, Tianrun Cheng1,2, Jinzhi Lei4, Zhen Xie1,2, Michael Q. Zhang1,2,5,6, Xiaowo Wang1,2()
1. Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China 2. Beijing National Research Center for Information Science and Technology, Beijing 100084, China 3. School of Life Sciences, Tsinghua University, Beijing 100084, China 4. Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing 100084, China 5. Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China 6. Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
Background: Molecular competition brings about trade-offs of shared limited resources among the cellular components, and thus introduces a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. Several molecular competition scenarios have been observed recently, but there is still a lack of systematic quantitative understanding to reveal the essence of molecular competition.
Methods: Here, by abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically integrate experimental evidences in these processes and analyzed general properties shared behind them from steady-state behavior to dynamic responses.
Results: We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets.
Conclusions: This work uncovered the complexity and generality of molecular competition effect as a hidden layer of gene regulatory network, and therefore provided a unified insight and a theoretical framework to understand and employ competition in both natural and synthetic systems.
T. W.Schoener, (1983) Field experiments on interspecific competition. Am. Nat., 122, 240–285 https://doi.org/10.1086/284133.
3
J. H.Connell, (1983) On the prevalence and relative importance of interspecific competition: evidence from field experiments. Am. Nat., 122, 661–696 https://doi.org/10.1086/284165.
4
M. H.Zwietering, , I.Jongenburger, , F. M.Rombouts, and K.van ’t Riet, (1990) Modeling of the bacterial growth curve. Appl. Environ. Microbiol., 56, 1875–1881
pmid: 16348228.
R.Svanbäck, and D. I.Bolnick, (2007) Intraspecific competition drives increased resource use diversity within a natural population. Proc. Biol. Sci., 274, 839–844 https://doi.org/10.1098/rspb.2006.0198.
pmid: 17251094
7
A.Khare, and G. Shaulsky, (2006) First among equals: competition between genetically identical cells. Nat. Rev. Genet., 7, 577–583 https://doi.org/10.1038/nrg1875.
pmid: 16702983
C. H.Chang, , J. Qiu, , D.O’Sullivan, , M. D.Buck, , T.Noguchi, , J. D.Curtis, , Q.Chen, , M.Gindin, , M. M.Gubin, , G. J.van der Windt, , et al.et al. (2015) Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell, 162, 1229–1241 https://doi.org/10.1016/j.cell.2015.08.016.
pmid: 26321679
11
M.Scott, , C. W. Gunderson, , E. M.Mateescu, , Z.Zhang, and T.Hwa, (2010) Interdependence of cell growth and gene expression: origins and consequences. Science, 330, 1099–1102 https://doi.org/10.1126/science.1192588.
pmid: 21097934
12
A. Y.Weiße, , D. A.Oyarzún, , V.Danos, and P. S.Swain, (2015) Mechanistic links between cellular trade-offs, gene expression, and growth. Proc. Natl. Acad. Sci. USA, 112, E1038–E1047 https://doi.org/10.1073/pnas.1416533112.
pmid: 25695966
13
S.Hui, , J. M. Silverman, , S. S.Chen, , D. W.Erickson, , M.Basan, , J.Wang, , T.Hwa, and J. R.Williamson, (2015) Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol. Syst. Biol., 11, e784 https://doi.org/10.15252/msb.20145697.
pmid: 25678603
14
R. C.Brewster, , F. M.Weinert, , H. G.Garcia, , D.Song, , M.Rydenfelt, and R.Phillips, (2014) The transcription factor titration effect dictates level of gene expression. Cell, 156, 1312–1323 https://doi.org/10.1016/j.cell.2014.02.022.
pmid: 24612990
15
A. A.Sigova, , B. J.Abraham, , X.Ji, , B. Molinie, , N. M.Hannett, , Y. E.Guo, , M.Jangi, , C. C.Giallourakis, , P. A.Sharp, and R. A.Young, (2015) Transcription factor trapping by RNA in gene regulatory elements. Science, 350, 978–981 https://doi.org/10.1126/science.aad3346.
pmid: 26516199
16
Q.Zheng, , C. Bao, , W.Guo, , S.Li, , J. Chen, , B.Chen, , Y.Luo, , D.Lyu, , Y.Li, , G. Shi, , et al.et al. (2016) Circular RNA profiling reveals an abundant circHIPK3 that regulates cell growth by sponging multiple miRNAs. Nat. Commun., 7, 11215 https://doi.org/10.1038/ncomms11215.
pmid: 27050392
17
M.Cesana, , D. Cacchiarelli, , I.Legnini, , T.Santini, , O.Sthandier, , M.Chinappi, , A.Tramontano, and I.Bozzoni, (2011) A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell, 147, 358–369 https://doi.org/10.1016/j.cell.2011.09.028.
pmid: 22000014
18
P.Sumazin, , X. Yang, , H. S.Chiu, , W. J.Chung, , A.Iyer, , D.Llobet-Navas, , P.Rajbhandari, , M.Bansal, , P.Guarnieri, , J.Silva, , et al.et al. (2011) An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell, 147, 370–381 https://doi.org/10.1016/j.cell.2011.09.041.
pmid: 22000015
19
S.Saha, , C. A. Weber, , M.Nousch, , O.Adame-Arana, , C.Hoege, , M. Y.Hein, , E.Osborne-Nishimura, , J.Mahamid, , M.Jahnel, , L.Jawerth, , et al. (2016) Polar positioning of phase-separated liquid compartments in cells regulated by an mRNA competition mechanism. Cell 166, 1572–1584
20
H.-S.Chiu, , S. Somvanshi, , E.Patel, , T.-W.Chen, , V. P.Singh, , B.Zorman, , S. L.Patil, , Y.Pan, , S. S.Chatterjee, , A. K.Sood, , et al.et al. (2018) Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context. Cell Reports, 23, 297–312.e12 https://doi.org/10.1016/j.celrep.2018.03.064.
pmid: 29617668
21
S.Cardinale, and A. P.Arkin, (2012) Contextualizing context for synthetic biology‒identifying causes of failure of synthetic biological systems. Biotechnol. J., 7, 856–866 https://doi.org/10.1002/biot.201200085.
pmid: 22649052
22
G.Wu, , Q. Yan, , J. A.Jones, , Y. J.Tang, , S. S.Fong, and M. A. G.Koffas, (2016) Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol., 34, 652–664 https://doi.org/10.1016/j.tibtech.2016.02.010.
pmid: 26996613
23
Y.Qian, , H. H. Huang, , J. I.Jiménez, and D.Del Vecchio, (2017) Resource competition shapes the response of genetic circuits. ACS Synth. Biol., 6, 1263–1272 https://doi.org/10.1021/acssynbio.6b00361.
pmid: 28350160
24
P.Jiang, , A. C. Ventura, , E. D.Sontag, , S. D.Merajver, , A. J.Ninfa, and D.Del Vecchio, (2011) Load-induced modulation of signal transduction networks. Sci. Signal., 4, ra67 https://doi.org/10.1126/scisignal.2002152.
pmid: 21990429
25
S.Jayanthi, , K. S. Nilgiriwala, and D.Del Vecchio, (2013) Retroactivity controls the temporal dynamics of gene transcription. ACS Synth. Biol., 2, 431–441 https://doi.org/10.1021/sb300098w.
pmid: 23654274
26
U.Ala, , F. A. Karreth, , C.Bosia, , A.Pagnani, , R.Taulli, , V.Léopold, , Y.Tay, , P.Provero, , R.Zecchina, and P. P.Pandolfi, (2013) Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments. Proc. Natl. Acad. Sci. USA, 110, 7154–7159 https://doi.org/10.1073/pnas.1222509110.
pmid: 23536298
27
H.-S.Chiu, , M. R. Martínez, , E. V.Komissarova, , D.Llobet-Navas, , M.Bansal, , E. O.Paull, , J.Silva, , X.Yang, , P.Sumazin, and A.Califano, (2018) The number of titrated microRNA species dictates ceRNA regulation. Nucleic Acids Res., 46, 4354–4369 https://doi.org/10.1093/nar/gky286.
pmid: 29684207
28
Y.Yuan, , B. Liu, , P.Xie, , M. Q.Zhang, , Y.Li, , Z. Xie, and X.Wang, (2015) Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc. Natl. Acad. Sci. USA, 112, 3158–3163 https://doi.org/10.1073/pnas.1413896112.
pmid: 25713348
29
T. E.Gorochowski, , I.Avcilar-Kucukgoze, , R. A.Bovenberg, , J. A.Roubos, and Z.Ignatova, (2016) A minimal model of ribosome allocation dynamics captures trade-offs in expression between endogenous and synthetic genes. ACS Synth. Biol., 5, 710–720 https://doi.org/10.1021/acssynbio.6b00040.
pmid: 27112032
30
W. H.Mather, , N. A.Cookson, , J.Hasty, , L. S.Tsimring, and R. J.Williams, (2010) Correlation resonance generated by coupled enzymatic processing. Biophys. J., 99, 3172–3181 https://doi.org/10.1016/j.bpj.2010.09.057.
pmid: 21081064
31
N. A.Cookson, , W. H.Mather, , T.Danino, , O.Mondragón-Palomino, , R. J.Williams, , L. S.Tsimring, and J.Hasty, (2014) Queueing up for enzymatic processing: correlated signaling through coupled degradation. Mol. Syst. Biol., 7, 561 https://doi.org/10.1038/msb.2011.94.
pmid: 22186735
32
A.Gyorgy, , J. I. Jiménez, , J.Yazbek, , H. H.Huang, , H.Chung, , R.Weiss, and D.Del Vecchio, (2015) Isocost lines describe the cellular economy of genetic circuits. Biophys. J., 109, 639–646 https://doi.org/10.1016/j.bpj.2015.06.034.
pmid: 26244745
33
M.Carbonell-Ballestero, , E.Garcia-Ramallo, , R.Montañez, , C.Rodriguez-Caso, and J.Macía, (2016) Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm’s law. Nucleic Acids Res., 44, 496–507 https://doi.org/10.1093/nar/gkv1280.
pmid: 26656950
S.Mukherji, , M. S. Ebert, , G. X. Y.Zheng, , J. S.Tsang, , P. A.Sharp, and A.van Oudenaarden, (2011) MicroRNAs can generate thresholds in target gene expression. Nat. Genet., 43, 854–859 https://doi.org/10.1038/ng.905.
pmid: 21857679
36
T.Quarton, , K. Ehrhardt, , J.Lee, , S.Kannan, , Y.Li, , L. Ma, , & L.Bleris, (2018) Mapping the operational landscape of microRNAs in synthetic gene circuits. NPJ Syst. Biol. Appl., https://doi.org/10.1038/s41540-017-0043-y
37
T. H.Lee, and N.Maheshri, (2012) A regulatory role for repeated decoy transcription factor binding sites in target gene expression. Mol. Syst. Biol., 8, 576 https://doi.org/10.1038/msb.2012.7.
pmid: 22453733
38
M.Jens, and N. Rajewsky, (2015) Competition between target sites of regulators shapes post-transcriptional gene regulation. Nat. Rev. Genet., 16, 113–126 https://doi.org/10.1038/nrg3853.
pmid: 25488579
39
Y.Yuan, , X. Ren, , Z.Xie, and X.Wang, (2016) A quantitative understanding of microRNA-mediated competing endogenous RNA regulation. Quant. Biol., 4, 47–57 https://doi.org/10.1007/s40484-016-0062-5.
40
D.Mishra, , P. M. Rivera, , A.Lin, , D.Del Vecchio, and R.Weiss, (2014) A load driver device for engineering modularity in biological networks. Nat. Biotechnol., 32, 1268–1275 https://doi.org/10.1038/nbt.3044.
pmid: 25419739
41
A.Burger, , A. M. Walczak, and P. G.Wolynes, (2010) Abduction and asylum in the lives of transcription factors. Proc. Natl. Acad. Sci. USA, 107, 4016–4021 https://doi.org/10.1073/pnas.0915138107.
pmid: 20160109
M.Chen, , L. Wang, , C. C.Liu, and Q.Nie, (2013) Noise attenuation in the ON and OFF states of biological switches. ACS Synth. Biol., 2, 587–593 https://doi.org/10.1021/sb400044g.
pmid: 23768065
44
E.Schrödinger, (1944) What is Life? Cambridge: Cambridge University Press
45
J. M.Schmiedel, , S. L.Klemm, , Y.Zheng, , A.Sahay, , N.Blüthgen, , D. S.Marks, and A.van Oudenaarden, (2015) MicroRNA control of protein expression noise. Science, 348, 128–132 https://doi.org/10.1126/science.aaa1738.
pmid: 25838385
46
C.Bosia, , F. Sgrò, , L.Conti, , C.Baldassi, , D.Brusa, , F.Cavallo, , F. D.Cunto, , E.Turco, , A.Pagnani, and R.Zecchina, (2017) RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells. Genome Biol., 18, 37 https://doi.org/10.1186/s13059-017-1162-x.
pmid: 28219439
T.-C.Chou, and P.Talaly, (1977) A simple generalized equation for the analysis of multiple inhibitions of Michaelis-Menten kinetic systems. J. Biol. Chem., 252, 6438–6442
pmid: 893418.
49
L.Bintu, , N. E. Buchler, , H. G.Garcia, , U.Gerland, , T.Hwa, , J.Kondev, and R.Phillips, (2005) Transcriptional regulation by the numbers: models. Curr. Opin. Genet. Dev., 15, 116–124 https://doi.org/10.1016/j.gde.2005.02.007.
pmid: 15797194
50
P.Xie, , Y. Liu, , Y.Li, , M. Q.Zhang, and X.Wang, (2014) MIROR: a method for cell-type specific microRNA occupancy rate prediction. Mol. Biosyst., 10, 1377–1384 https://doi.org/10.1039/C3MB70610A.
pmid: 24668253
J.Kim, , J. Hopfield, and E.Winfree, (2004) Neural network computation by in vitro transcriptional circuits. In Advances in Neural Information Processing Systems 17, 681–688. Vancouver, Canada
53
A. J.Genot, , T. Fujii, and Y.Rondelez, (2013) Scaling down DNA circuits with competitive neural networks. J. R. Soc. Interface, 10, 20130212 https://doi.org/10.1098/rsif.2013.0212.
pmid: 23760296
L.Potvin-Trottier, , N. D.Lord, , G.Vinnicombe, and J.Paulsson, (2016) Synchronous long-term oscillations in a synthetic gene circuit. Nature, 538, 514–517 https://doi.org/10.1038/nature19841.
pmid: 27732583
58
C.Liao, , A. E. Blanchard, and T.Lu, (2017) An integrative circuit-host modelling framework for predicting synthetic gene network behaviours. Nat. Microbiol., 2, 1658–1666 https://doi.org/10.1038/s41564-017-0022-5.
pmid: 28947816
59
O. S.Venturelli, , M.Tei, , S.Bauer, , L. J. G.Chan, , C. J.Petzold, and A. P.Arkin, (2017) Programming mRNA decay to modulate synthetic circuit resource allocation. Nat. Commun., 8, 15128 https://doi.org/10.1038/ncomms15128.
pmid: 28443619
60
L.Nissim, , M. R. Wu, , E.Pery, , A.Binder-Nissim, , H. I.Suzuki, , D.Stupp, , C.Wehrspaun, , Y.Tabach, , P. A.Sharp, , and T. K.Lu, (2017) Synthetic RNA-based immunomodulatory gene circuits for cancer immunotherapy. Cell 171,1138–1150e1115 https://doi.org/https://doi.org/10.1016/j.cell.2017.09.049