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Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

Postal Subscription Code 80-971

Quant. Biol.    2016, Vol. 4 Issue (1) : 47-57    https://doi.org/10.1007/s40484-016-0062-5
A quantitative understanding of microRNA- mediated competing endogenous RNA regulation
Ye Yuan,Xinying Ren,Zhen Xie,Xiaowo Wang()
Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Information Science and Technology/Department of Automation, Tsinghua University, Beijing 100084, China
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Abstract

MicroRNA (miRNA) plays key roles in post-transcriptional regulations. Recently, a competing endogenous RNA (ceRNA) hypothesis has been proposed that miRNA targets could communicate and regulate each other through titrating shared miRNAs, which provides a new layer of gene regulation. Though a number of ceRNAs playing biological functions have been identified, the ceRNA hypothesis remains controversial. Recent experimental and theoretical studies argued that the modulation of a single RNA species could hardly change the expression level of competing miRNA targets through ceRNA effect under normal physiological conditions. Here, we reviewed a common framework to model miRNA regulations, and summarized the current theoretical and experimental studies for quantitative understanding ceRNA effect. By revisiting a coarse-grained ceRNA model, we proposed that network topology could significantly influence the competing effect and ceRNA regulation at protein level could be much stronger than that at RNA level. We also provided a conditional independent binding equation to describe miRNA relative repression on different target, which could be applied to quantify siRNA off-target effect.

Author Summary   

MicroRNA (miRNA) plays key roles in post-transcriptional regulations. Recently, a hypothesis of competing endogenous RNA (ceRNA) has been proposed as a new layer of gene regulation. Here, we revisit the common modelling framework and the current understanding of ceRNA effect. We propose that network topology could significantly influence it and the ceRNA effect at protein level could be much stronger than that at RNA level. We also provide a conditional independent binding equation to describe miRNA relative repression on different target, which could be applied to quantify siRNA off-target effect.

Keywords microRNA regulation      competing endogenous RNA      molecular titration      quantitative model      complex networks     
PACS:     
Fund: 
Corresponding Author(s): Xiaowo Wang   
Online First Date: 16 March 2016    Issue Date: 16 March 2016
 Cite this article:   
Ye Yuan,Xinying Ren,Zhen Xie, et al. A quantitative understanding of microRNA- mediated competing endogenous RNA regulation[J]. Quant. Biol., 2016, 4(1): 47-57.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-016-0062-5
https://academic.hep.com.cn/qb/EN/Y2016/V4/I1/47
Fig.1  Current quantitative understanding of miRNA-mediated regulation.

(A) The titrimetric chemical reaction model of miRNA-induced repression upon single target RNA species. (B) MiRNAs regulate multiple RNA species. The target RNAs could be mRNA, non-coding RNA (long and short ones), pseudogene, and circular RNA. The dots on the RNAs represent MREs. (C) High miRNA binding strength sharpens the threshold-behavior of miRNA-induced repression (M=1, and N=1) [50]. (D) MiRNA level shifts the threshold of miRNA-induced repression (M=1, and N=1) [50]. (E) Simulated relationship between free ceRNA1 level and ceRNA1 transcriptional level with ceRNA2 containing MREs under conditions of low, middle, and high level miRNA under the condition that the transcriptional level of ceRNA2 is proportional to that of ceRNA1. Here M=1, and N=1 (red curve) or 2 (blue curve). Right panel shows the fold change of free ceRNA1 between with ceRNA2 and without ceRNA2. (F) Simulated relationship between free ceRNA1 level and ceRNA1 transcriptional level with ceRNA2 containing MREs with the low, middle, and high binding strength under the condition that the transcriptional level of ceRNA2 is proportional to that of ceRNA1 (M=1, and N=1 or 2). MiRNA expression level is equal in each panel. Right panel shows the fold change of free ceRNA1 between the case with ceRNA2 (blue line) and the case without ceRNA2 (red line). (G) Simulated relationship between free ceRNA1 and miRNA transcriptional level with ceRNA2 containing MREs of no, low, high expression level (M=1, and N=1 or 2). The fold changes of miRNA level needed to cause a ten-fold change of free ceRNA1 are marked. (E), (F) and (G) were modified from our previous work [31].

ParametersValueRefs.
Cell volume2000 μm3 [52]
kR, kSVariable [53]
gR, gS~ 2.1×10-5 s-1 [53]
α0~1NA
k+~ 4.3×10-5 s-1 [3]
k-~ 4.6×10-4 s-1 [3]
gVariableNA
Tab.1  Model parameter estimation.
Fig.2  Relative repression curve.

(A) Schematic diagram of model-guided quantitative analysis of a special ceRNA system (RNAi). CeRNA2 complementary perfectly to miRNA mimics RNAi target, while ceRNA1 complementary imperfectly to miRNA mimics off-target. (B) Binding strength between target RNA and miRNA impacts the on-target-off-target repression curve. The x-axis and y-axis are the logarithm of free ratio of the two targets respectively. (C) Expression level of off-target has no impact on the on-target-off-target repression curve. The parameters of (B) and (C) were set as follow: kR1={5,10,15}×10-3,gR1=1×10-5,g1=8×10-5,a1=0.5,k1-=5×10-5,k1+={5,10,15}×10-5; kR2=5×10-3,gR2=1×10-5,g2=8×10-5,a2=0.1,k2-=5×10-5,k2+=100×10-5; gs=1×105,ks=105~10-1.

Fig.3  The difference of ceRNA regulation strength between at RNA level and at protein level.

(A) Schematic diagram of the strategy of measuring the ceRNA regulation in the minimal ceRNA regulation model. We increase the transcriptional rate of mRNA1, and measure the levels of mRNA2, including the free mRNA2 that is proportional to protein level, the complex2 containing mRNA2 and miRNA and the whole mRNA2 equaling free mRNA2 plus complex2. (B) Steady-state concentrations as function of kR1changes under the condition that gR2<g2. (C) Steady-state concentrations as function of kR1 changes under the condition that gR2>g2. (B) and (C), the left panel shows the steady-state concentrations of mRNA1 and miRNA. The right panel shows the steady-state concentrations of mRNA2. The fold changes of mRNAs concentrations caused by a ten-fold change of kR1 are marked. The parameters of (B) were set: kR1=10-5~10-1,gR1=1×10-5,g1=8×10-5,α1=0.5,k1-=5×10-5, k1+=10×10-5; kR2=5×10-3,gR2=1×10-5,g2=8×10-5,α2=0.5,k2-=5×10-5,k2+=10×10-5; gS=1×10-5,kS=4×10-3. The parameters of (C) were set the same as these of (B) with an exception that g2=0.5×10-5.

Fig.4  Network topology influences ceRNA regulation strength.

(A) One miRNA species represses three ceRNA species. (B) An indirect ceRNA regulation through ceRNA3, from ceRNA1 upon ceRNA2 is introduced. (C) Steady-state concentration as a function of kR1 of the network in (A) under the condition that gR2<g2. (D) Steady-state concentration as a function of kR1 of the network in (B) under the condition that gR2<g2. (C) and (D), the fold changes of concentrations of ceRNA2 caused by a ten-fold change of kR1 are marked on the plots. The parameters of (C) were set: kR1=10-5~10-1,gR1=1×10-5,g11=8×10-5,α11=0.5,k11-=5×10-5,k11+=10×10-5; kR2=5×10-3,gR2=1×10-5,g21=8×10-5,α21=0.5,k21-=5×10-5,k21+=10×10-5; kR3=5×10-3,gR3=1×10-5,g31=8×10-5,α31=0.5,k31-=5×10-5,k31+=10×10-5; kS1=4×10-3,gS1=1×10-5. The parameters of (D) were set: kR1=10-5~10-1,gR1=1×10-5,g11=8×10-5,α11=0.5,k11-=5×10-5,k11+=10×10-5 ; g13=8×10-5,α13=0.5,k13-=5×10-5,k13+=10×10-5 ; kR2=5×10-3,gR2=1×10-5,g21=8×10-5,α21=0.5,k21-=5×10-5,k21+=10×10-5; g22=8×10-5,α22=0.5,k22-=5×10-5,k22+=100×10-5;kR3=2×10-3,gR3=1×10-5,g32=8×10-5,α32=0.5,k32-=5×10-5,k32+=100×10-5; g33=8×10-5,α33=0.5,k33-=5×10-5,k33+=100×10-5; kS1=4×10-3,gS1=1×10-5,kS2=2×10-3,gs2=1×10-5,ks3=2×10-3,gs3=1×10-5

1 He, L. and Hannon, G. J. (2004) MicroRNAs: small RNAs with a big role in gene regulation. Nat. Rev. Genet., 5, 522–531
https://doi.org/10.1038/nrg1379 pmid: 15211354
2 Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E. and Mello, C. C. (1998) Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature, 391, 806–811
https://doi.org/10.1038/35888 pmid: 9486653
3 Wee, L. M., Flores-Jasso, C. F., Salomon, W. E. and Zamore, P. D. (2012) Argonaute divides its RNA guide into domains with distinct functions and RNA-binding properties. Cell, 151, 1055–1067
https://doi.org/10.1016/j.cell.2012.10.036 pmid: 23178124
4 Zamore, P. D., Tuschl, T., Sharp, P. A. and Bartel, D. P. (2000) RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals. Cell, 101, 25–33
https://doi.org/10.1016/S0092-8674(00)80620-0 pmid: 10778853
5 Bartel, D. P. (2009) MicroRNAs: target recognition and regulatory functions. Cell, 136, 215–233
https://doi.org/10.1016/j.cell.2009.01.002 pmid: 19167326
6 Hutvágner, G. and Zamore, P. D. (2002) A microRNA in a multiple-turnover RNAi enzyme complex. Science, 297, 2056–2060
https://doi.org/10.1126/science.1073827 pmid: 12154197
7 Agarwal, V., Bell, G. W., Nam, J. W. and Bartel, D. P. (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife, 4, 4
pmid: 26267216
8 Krek, A., Grün, D., Poy, M. N., Wolf, R., Rosenberg, L., Epstein, E. J., MacMenamin, P., da Piedade, I., Gunsalus, K. C., Stoffel, M., (2005) Combinatorial microRNA target predictions. Nat. Genet., 37, 495–500
https://doi.org/10.1038/ng1536 pmid: 15806104
9 Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U. and Segal, E. (2007) The role of site accessibility in microRNA target recognition. Nat. Genet., 39, 1278–1284
https://doi.org/10.1038/ng2135 pmid: 17893677
10 Lewis, B. P., Burge, C. B. and Bartel, D. P. (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell, 120, 15–20
https://doi.org/10.1016/j.cell.2004.12.035 pmid: 15652477
11 Jens, M. and Rajewsky, N. (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
12 Allantaz, F., Cheng, D. T., Bergauer, T., Ravindran, P., Rossier, M. F., Ebeling, M., Badi, L., Reis, B., Bitter, H., D’Asaro, M., (2012) Expression profiling of human immune cell subsets identifies miRNA-mRNA regulatory relationships correlated with cell type specific expression. PLoS One, 7, e29979
https://doi.org/10.1371/journal.pone.0029979 pmid: 22276136
13 Cajigas, I. J., Tushev, G., Will, T. J., tom Dieck, S., Fuerst, N. and Schuman, E. M. (2012) The local transcriptome in the synaptic neuropil revealed by deep sequencing and high-resolution imaging. Neuron, 74, 453–466
https://doi.org/10.1016/j.neuron.2012.02.036 pmid: 22578497
14 Gennarino, V. A., Sardiello, M., Avellino, R., Meola, N., Maselli, V., Anand, S., Cutillo, L., Ballabio, A. and Banfi, S. (2009) MicroRNA target prediction by expression analysis of host genes. Genome Res., 19, 481–490
https://doi.org/10.1101/gr.084129.108 pmid: 19088304
15 Jung, D., Kim, B., Freishtat, R. J., Giri, M., Hoffman, E. and Seo, J. (2015) miRTarVis: an interactive visual analysis tool for microRNA-mRNA expression profile data. BMC Proc, 9, S2
https://doi.org/10.1186/1753-6561-9-S6-S2 pmid: 26361498
16 Xie, P., Liu, Y., Li, Y., Zhang, M. Q. and Wang, X. (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
17 Salmena, L., Poliseno, L., Tay, Y., Kats, L. and Pandolfi, P. P. (2011) A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell, 146, 353–358
https://doi.org/10.1016/j.cell.2011.07.014 pmid: 21802130
18 Tay, Y., Rinn, J. and Pandolfi, P. P. (2014) The multilayered complexity of ceRNA crosstalk and competition. Nature, 505, 344–352
https://doi.org/10.1038/nature12986 pmid: 24429633
19 Taulli, R., Loretelli, C. and Pandolfi, P. P. (2013) From pseudo-ceRNAs to circ-ceRNAs: a tale of cross-talk and competition. Nat. Struct. Mol. Biol., 20, 541–543
https://doi.org/10.1038/nsmb.2580 pmid: 23649362
20 Cesana, M., Cacchiarelli, D., Legnini, I., Santini, T., Sthandier, O., Chinappi, M., Tramontano, A. and Bozzoni, I. (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
21 Karreth, F. A., Tay, Y., Perna, D., Ala, U., Tan, S. M., Rust, A. G., DeNicola, G., Webster, K. A., Weiss, D., Perez-Mancera, P. A., (2011) In vivo identification of tumor- suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma. Cell, 147, 382–395
https://doi.org/10.1016/j.cell.2011.09.032 pmid: 22000016
22 Sumazin, P., Yang, X., Chiu, H. S., Chung, W. J., Iyer, A., Llobet-Navas, D., Rajbhandari, P., Bansal, M., Guarnieri, P., Silva, J., (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
23 Tay, Y., Kats, L., Salmena, L., Weiss, D., Tan, S. M., Ala, U., Karreth, F., Poliseno, L., Provero, P., Di Cunto, F., (2011) Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell, 147, 344–357
https://doi.org/10.1016/j.cell.2011.09.029 pmid: 22000013
24 Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S. D., Gregersen, L. H., Munschauer, M., (2013) Circular RNAs are a large class of animal RNAs with regulatory potency. Nature, 495, 333–338
https://doi.org/10.1038/nature11928 pmid: 23446348
25 Kumar, M. S., Armenteros-Monterroso, E., East, P., Chakravorty, P., Matthews, N., Winslow, M. M. and Downward, J. (2014) HMGA2 functions as a competing endogenous RNA to promote lung cancer progression. Nature, 505, 212–217
https://doi.org/10.1038/nature12785 pmid: 24305048
26 Peng, H., Lu, M. and Selaru, F. M. (2015) The genome-wide gene expression profiling to predict competitive endogenous RNA network in hepatocellular cancer. Genom Data, 4, 93–95
https://doi.org/10.1016/j.gdata.2015.03.016 pmid: 26484188
27 Qi, X., Zhang, D. H., Wu, N., Xiao, J. H., Wang, X. and Ma, W. (2015) ceRNA in cancer: possible functions and clinical implications. J. Med. Genet., 52, 710–718
https://doi.org/10.1136/jmedgenet-2015-103334 pmid: 26358722
28 Zhang, J., Fan, D., Jian, Z., Chen, G. G. and Lai, P. B. (2015) Cancer specific long noncoding RNAs show differential expression patterns and competing endogenous RNA potential in hepatocellular carcinoma. PLoS One, 10, e0141042
https://doi.org/10.1371/journal.pone.0141042 pmid: 26492393
29 Bosson, A. D., Zamudio, J. R. and Sharp, P. A. (2014) Endogenous miRNA and target concentrations determine susceptibility to potential ceRNA competition. Mol. Cell, 56, 347–359
https://doi.org/10.1016/j.molcel.2014.09.018 pmid: 25449132
30 Mayya, V. K. and Duchaine, T. F. (2015) On the availability of microRNA-induced silencing complexes, saturation of microRNA-binding sites and stoichiometry. Nucleic Acids Res., 43, 7556–7565
https://doi.org/10.1093/nar/gkv720 pmid: 26227970
31 Yuan, Y., Liu, B., Xie, P., Zhang, M. Q., Li, Y., Xie, Z. and Wang, X. (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
32 Denzler, R., Agarwal, V., Stefano, J., Bartel, D. P. and Stoffel, M. (2014) Assessing the ceRNA hypothesis with quantitative measurements of miRNA and target abundance. Mol. Cell, 54, 766–776
https://doi.org/10.1016/j.molcel.2014.03.045 pmid: 24793693
33 Hausser, J. and Zavolan, M. (2014) Identification and consequences of miRNA-target interactions—beyond repression of gene expression. Nat. Rev. Genet., 15, 599–612
https://doi.org/10.1038/nrg3765 pmid: 25022902
34 Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., Bartel, D. P., Linsley, P. S. and Johnson, J. M. (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature, 433, 769–773
https://doi.org/10.1038/nature03315 pmid: 15685193
35 Kou, Y., Qiao, L. and Wang, Q. (2015) Identification of core miRNA based on small RNA-seq and RNA-seq for colorectal cancer by bioinformatics. Tumour Biol., 36, 2249–2255
https://doi.org/10.1007/s13277-014-2832-x pmid: 25412953
36 Wang, Z., Gerstein, M. and Snyder, M. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57–63
https://doi.org/10.1038/nrg2484 pmid: 19015660
37 Bazzini, A. A., Lee, M. T. and Giraldez, A. J. (2012) Ribosome profiling shows that miR-430 reduces translation before causing mRNA decay in zebrafish. Science, 336, 233–237
https://doi.org/10.1126/science.1215704 pmid: 22422859
38 Baek, D., Villén, J., Shin, C., Camargo, F. D., Gygi, S. P. and Bartel, D. P. (2008) The impact of microRNAs on protein output. Nature, 455, 64–71
https://doi.org/10.1038/nature07242 pmid: 18668037
39 Hausser, J., Syed, A. P., Selevsek, N., van Nimwegen, E., Jaskiewicz, L., Aebersold, R. and Zavolan, M. (2013) Timescales and bottlenecks in miRNA-dependent gene regulation. Mol. Syst. Biol., 9, 711.
https://doi.org/10.1038/msb.2013.68 pmid: 24301800
40 Selbach, M., Schwanhäusser, B., Thierfelder, N., Fang, Z., Khanin, R. and Rajewsky, N. (2008) Widespread changes in protein synthesis induced by microRNAs. Nature, 455, 58–63
https://doi.org/10.1038/nature07228 pmid: 18668040
41 Broughton, J. P. and Pasquinelli, A. E. (2013) Identifying Argonaute binding sites in Caenorhabditis elegans using iCLIP. Methods, 63, 119–125
https://doi.org/10.1016/j.ymeth.2013.03.033 pmid: 23583680
42 Chi, S. W., Zang, J. B., Mele, A. and Darnell, R. B. (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature, 460, 479–486
pmid: 19536157
43 Konig, J., Zarnack, K., Rot, G., Curk, T., Kayikci, M., Zupan, B., Turner, D. J., Luscombe, N. M. and Ule, J. (2011) iCLIP—transcriptome-wide mapping of protein-RNA interactions with individual nucleotide resolution. J. Vis. Exp., e2638
https://doi.org/10.3791/2638 pmid: 21559008
44 Loeb, G. B., Khan, A. A., Canner, D., Hiatt, J. B., Shendure, J., Darnell, R. B., Leslie, C. S. and Rudensky, A. Y. (2012) Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Mol. Cell, 48, 760–770
https://doi.org/10.1016/j.molcel.2012.10.002 pmid: 23142080
45 Sutandy, F. X., Hildebrandt, A. and König, J. (2016) Profiling the binding sites of RNA-binding proteins with nucleotide resolution using iCLIP. Methods Mol. Biol., 1358, 175–195
https://doi.org/10.1007/978-1-4939-3067-8_11 pmid: 26463384
46 Zisoulis, D. G., Lovci, M. T., Wilbert, M. L., Hutt, K. R., Liang, T. Y., Pasquinelli, A. E. and Yeo, G. W. (2010) Comprehensive discovery of endogenous Argonaute binding sites in Caenorhabditis elegans. Nat. Struct. Mol. Biol., 17, 173–179
https://doi.org/10.1038/nsmb.1745 pmid: 20062054
47 Kishore, S., Jaskiewicz, L., Burger, L., Hausser, J., Khorshid, M. and Zavolan, M. (2011) A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins. Nat. Methods, 8, 559–564
https://doi.org/10.1038/nmeth.1608 pmid: 21572407
48 Zhang, C. and Darnell, R. B. (2011) Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat. Biotechnol., 29, 607–614
https://doi.org/10.1038/nbt.1873 pmid: 21633356
49 Levine, E., Zhang, Z., Kuhlman, T. and Hwa, T. (2007) Quantitative characteristics of gene regulation by small RNA. PLoS Biol., 5, e229.
https://doi.org/10.1371/journal.pbio.0050229 pmid: 17713988
50 Mukherji, S., Ebert, M. S., Zheng, G. X., Tsang, J. S., Sharp, P. A. and van Oudenaarden, A. (2011) MicroRNAs can generate thresholds in target gene expression. Nat. Genet., 43, 854–859
https://doi.org/10.1038/ng.905 pmid: 21857679
51 Schmiedel, J. M., Klemm, S. L., Zheng, Y., Sahay, A., Blüthgen, N., Marks, D. S. and van Oudenaarden, A. (2015) MicroRNA control of protein expression noise. Science, 348, 128–132
https://doi.org/10.1126/science.aaa1738 pmid: 25838385
52 Milo, R., Jorgensen, P., Moran, U., Weber, G. and Springer, M. (2010) BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Res., 38, D750–D753
https://doi.org/10.1093/nar/gkp889 pmid: 19854939
53 Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W. and Selbach, M. (2011) Global quantification of mammalian gene expression control. Nature, 473, 337–342
https://doi.org/10.1038/nature10098 pmid: 21593866
54 Buchler, N. E. and Louis, M. (2008) Molecular titration and ultrasensitivity in regulatory networks. J. Mol. Biol., 384, 1106–1119
https://doi.org/10.1016/j.jmb.2008.09.079 pmid: 18938177
55 Ala, U., Karreth, F. A., Bosia, C., Pagnani, A., Taulli, R., Léopold, V., Tay, Y., Provero, P., Zecchina, R. and Pandolfi, P. P. (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
56 Martinez, N. J. and Gregory, R. I. (2013) Argonaute2 expression is post-transcriptionally coupled to microRNA abundance. RNA, 19, 605–612
https://doi.org/10.1261/rna.036434.112 pmid: 23485552
57 Wang, D., Zhang, Z., O’Loughlin, E., Lee, T., Houel, S., O’Carroll, D., Tarakhovsky, A., Ahn, N. G. and Yi, R. (2012) Quantitative functions of Argonaute proteins in mammalian development. Genes Dev., 26, 693–704
https://doi.org/10.1101/gad.182758.111 pmid: 22474261
58 Riba, A., Bosia, C., El Baroudi, M., Ollino, L. and Caselle, M. (2014) A combination of transcriptional and microRNA regulation improves the stability of the relative concentrations of target genes. PLoS Comput. Biol., 10, e1003490.
https://doi.org/10.1371/journal.pcbi.1003490 pmid: 24586138
59 Poliseno, L., Salmena, L., Zhang, J., Carver, B., Haveman, W. J. and Pandolfi, P. P. (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature, 465, 1033–1038
https://doi.org/10.1038/nature09144 pmid: 20577206
60 Figliuzzi, M., Marinari, E. and De Martino, A. (2013) MicroRNAs as a selective channel of communication between competing RNAs: a steady-state theory. Biophys. J., 104, 1203–1213
https://doi.org/10.1016/j.bpj.2013.01.012 pmid: 23473503
61 Nitzan, M., Steiman-Shimony, A., Altuvia, Y., Biham, O. and Margalit, H. (2014) Interactions between distant ceRNAs in regulatory networks. Biophys. J., 106, 2254–2266
https://doi.org/10.1016/j.bpj.2014.03.040 pmid: 24853754
62 Noorbakhsh, J., Lang, A. H. and Mehta, P. (2013) Intrinsic noise of microRNA-regulated genes and the ceRNA hypothesis. PLoS One, 8, e72676
https://doi.org/10.1371/journal.pone.0072676 pmid: 23991139
63 Wang, P., Zhi, H., Zhang, Y., Liu, Y., Zhang, J., Gao, Y., Guo, M., Ning, S. and Li, X. (2015) miRSponge: a manually curated database for experimentally supported miRNA sponges and ceRNAs. Database (Oxford), 2015, 2015
pmid: 26424084
64 Yu, G., Yao, W., Gumireddy, K., Li, A., Wang, J., Xiao, W., Chen, K., Xiao, H., Li, H., Tang, K., (2014) Pseudogene PTENP1 functions as a competing endogenous RNA to suppress clear-cell renal cell carcinoma progression. Mol. Cancer Ther., 13, 3086–3097
https://doi.org/10.1158/1535-7163.MCT-14-0245 pmid: 25249556
65 Jeyapalan, Z., Deng, Z., Shatseva, T., Fang, L., He, C. and Yang, B. B. (2011) Expression of CD44 3′-untranslated region regulates endogenous microRNA functions in tumorigenesis and angiogenesis. Nucleic Acids Res., 39, 3026–3041
https://doi.org/10.1093/nar/gkq1003 pmid: 21149267
66 Rutnam, Z. J. and Yang, B. B. (2012) The non-coding 3′ UTR of CD44 induces metastasis by regulating extracellular matrix functions. J. Cell. Sci., 125, 2075–2085
https://doi.org/10.1242/jcs.100818 pmid: 22637644
67 Xia, T., Chen, S., Jiang, Z., Shao, Y., Jiang, X., Li, P., Xiao, B. and Guo, J. (2015) Long noncoding RNA FER1L4 suppresses cancer cell growth by acting as a competing endogenous RNA and regulating PTEN expression. Sci Rep, 5, 13445
https://doi.org/10.1038/srep13445 pmid: 26306906
68 Jackson, A. L., Burchard, J., Schelter, J., Chau, B. N., Cleary, M., Lim, L. and Linsley, P. S. (2006) Widespread siRNA “off-target” transcript silencing mediated by seed region sequence complementarity. RNA, 12, 1179–1187
https://doi.org/10.1261/rna.25706 pmid: 16682560
69 Schafer, S., Adami, E., Heinig, M., Rodrigues, K. E., Kreuchwig, F., Silhavy, J., van Heesch, S., Simaite, D., Rajewsky, N., Cuppen, E., (2015) Translational regulation shapes the molecular landscape of complex disease phenotypes. Nat Commun, 6, 7200
https://doi.org/10.1038/ncomms8200 pmid: 26007203
70 Jiang, J., Wakimoto, H., Seidman, J. G. and Seidman, C. E. (2013) Allele-specific silencing of mutant Myh6 transcripts in mice suppresses hypertrophic cardiomyopathy. Science, 342, 111–114
https://doi.org/10.1126/science.1236921 pmid: 24092743
71 Chiu, H. S., Llobet-Navas, D., Yang, X., Chung, W. J., Ambesi-Impiombato, A., Iyer, A., Kim, H. R., Seviour, E. G., Luo, Z., Sehgal, V., (2015) Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks. Genome Res., 25, 257–267
https://doi.org/10.1101/gr.178194.114 pmid: 25378249
72 Tan, J. Y., Sirey, T., Honti, F., Graham, B., Piovesan, A., Merkenschlager, M., Webber, C., Ponting, C. P. and Marques, A. C. (2015) Extensive microRNA-mediated crosstalk between lncRNAs and mRNAs in mouse embryonic stem cells. Genome Res., 25, 655–666
https://doi.org/10.1101/gr.181974.114 pmid: 25792609
73 Xu, J., Li, Y., Lu, J., Pan, T., Ding, N., Wang, Z., Shao, T., Zhang, J., Wang, L. and Li, X. (2015) The mRNA related ceRNA-ceRNA landscape and significance across 20 major cancer types. Nucleic Acids Res., 43, 8169–8182
https://doi.org/10.1093/nar/gkv853 pmid: 26304537
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