MicroRNAs coordinately regulate protein complexes
- Steffen Sass†1,
- Sabine Dietmann†1, 2,
- Ulrike C Burk3,
- Simone Brabletz3,
- Dominik Lutter1,
- Andreas Kowarsch1,
- Klaus F Mayer1,
- Thomas Brabletz3,
- Andreas Ruepp1,
- Fabian J Theis1Email author and
- Yu Wang1, 4Email author
© Sass et al; licensee BioMed Central Ltd. 2011
Received: 14 February 2011
Accepted: 25 August 2011
Published: 25 August 2011
In animals, microRNAs (miRNAs) regulate the protein synthesis of their target messenger RNAs (mRNAs) by either translational repression or deadenylation. miRNAs are frequently found to be co-expressed in different tissues and cell types, while some form polycistronic clusters on genomes. Interactions between targets of co-expressed miRNAs (including miRNA clusters) have not yet been systematically investigated.
Here we integrated information from predicted and experimentally verified miRNA targets to characterize protein complex networks regulated by human miRNAs. We found striking evidence that individual miRNAs or co-expressed miRNAs frequently target several components of protein complexes. We experimentally verified that the miR-141-200c cluster targets different components of the CtBP/ZEB complex, suggesting a potential orchestrated regulation in epithelial to mesenchymal transition.
Our findings indicate a coordinate posttranscriptional regulation of protein complexes by miRNAs. These provide a sound basis for designing experiments to study miRNA function at a systems level.
Hundreds of microRNA (miRNA) genes have been identified in mammalian genomes . Each miRNA may repress the translation of, and/or destabilize numerous messenger RNAs (mRNAs). Moreover, miRNA genes are frequently organized into genomic clusters [2–4], which are transcribed from a common promoter as polycistronic primary transcripts, and whose coordinate functional roles remain to be investigated . Recent large-scale, quantitative proteomics studies have demonstrated that some miRNAs probably participate in fine-tuning the production of their targets, both at the messenger RNA and the protein level [6, 7]. However, the overall effect of miRNAs on many of their target proteins is often intriguingly modest. It remains unclear how these marginal effects can convey the necessary regulatory information for proper cellular activities .
We applied a network-based strategy to systematically map coordinate regulatory interactions of single and co-expressed (including clustered) miRNAs. Previous works [9–12] have demonstrated that the targets of single miRNAs are more connected in the protein-protein interaction network than expected by chance. The use of protein-protein interaction (PPI) data provides only a rough overall picture of miRNA target interactions. It is not easy to evaluate the regulatory effects of miRNAs on such large-scaled PPI networks. Instead, as the basic functional units of the cellular machinery, experimentally verified protein complexes are natural subsets of PPI networks for investigating miRNA target interactions. Several components of protein complexes may be regulated simultaneously by a single miRNA or by several co-expressed miRNAs. Thus, although the regulation of protein synthesis is marginal for some of the miRNA targets, a cumulative effect for substantial phenotypic consequence may be achieved for those targets, which are members of the same protein complexes.
To test this hypothesis, we developed a robust computational framework to select protein complexes, of which several distinct components are simultaneously regulated by either single miRNAs or co-expressed miRNAs. We applied the framework to characterize the protein complex networks, which consist of 722 experimentally verified protein complexes and protein-protein interactions. These protein complex networks are regulated by 677 miRNAs and 154 known miRNA clusters in humans. We find that our framework has several advantages over previous analyses of miRNA targets and their interactions. First, high-confidence miRNA target predictions allowed us to characterize the overall functional spectrum of miRNA-regulated protein complexes. Second, we demonstrated that miRNAs, which target the same protein complexes, are frequently co-expressed. Finally, we experimentally verified that the miR141-200c cluster simultaneously targets several protein components of the CtBP/ZEB complex, implying an efficient regulation of a protein complex by a cluster of miRNAs.
miRNA targets and target interaction networks
Recent studies showed a high reliability of miRNA targets predicted by TargetScan . Therefore we selected the targets for all human miRNAs listed in the TargetScan database. We obtained a set of 677 miRNAs and 18,880 unique target proteins. The resulting miRNA-protein network contained 224,316 interactions. To predict miRNA targets based on PAR-CLIP data, the crosslink-centered regions (CCRs) from combined AGO-PAR-CLIP libraries  were used. Target site prediction for all CCRs was done with the program RNAhybrid  with the default parameters. From the resulting list we filtered all predictions with a p-value below 0.02 and an energy score below the 25% quantlile. This resulted in a final miRNA- mRNA list of 50,160 predicted interactions.
Association of protein complexes with miRNA target sets - test for statistical significance
We used the Fisher's exact test for assigning the significance of the association with protein complexes for each miRNA target set. The hypergeometric P-value is given as the probability under which we could expect at least N c miRNA targets by chance in a protein complex, if we randomly select N t (total number of miRNA targets) proteins out of the total set of proteins N consisting of all miRNA targets N T and all proteins in complexes N C . P-values were corrected for multiple testing of 677 miRNAs using the Holm-Bonferroni correction method. We assigned the association of complexes and miRNA clusters by using the union of targets from all miRNAs within one cluster. Here, we tested for significant overlaps of these unified sets between the components of a complex in the same way as for single miRNA target sets.
Enrichment of biological processes
In order to test for significant enrichment of biological functions based on Gene Ontology (GO)  and KEGG  pathways within the set of targets in protein complexes, the R package GOstats  was used. A set of targeted components of 722 targeted protein complexes was extracted and compared to a set of proteins which consisted of all components of these complexes.
Comparison of fold change distributions
We used fold change measurements after over-expression of selected miRNAs from recent proteomics studies [6, 7]. We selected for every of these miRNAs the protein complexes consisting of at least one of its targets. A set of components of these protein complexes was built. Within this set, we compared the fold changes of components that are targets of the specific miRNA with the fold changes of the non-target components. This was done by performing a one sided Kolmogorov-Smirnov test for each of the miRNAs that were investigated in the proteomics studies.
PANC-1 cells were purchased from ATCC (Manassas, VA, USA). PANC-1 stable clones for miR-141 or miR-200c were obtained with sequence verified pRetroSuper-miRNA plasmids. Cell lines were cultivated under standard conditions in DMEM + 10% fetal bovine serum + 2 μg/ml puromycin. For transient knock down PANC-1 were transfected with siRNA targeting ZEB1 (r(aga uga uga aug cga guc g)d(TT)), CtBP2 (1: r(cuuuggauucagcgucaua)d(TT), 2: r(cuuuguaacugauucugga)d(TT)) or GFP (r(gcu acc ugu ucc aug gcc a)d(TT). All transfections and reporter assays were performed as described previously .
Specific assay for miRNA modulation
RNA from cultured cells was extracted using the mirVana™ miRNA Isolation Kit (Ambion, Austin, TX, USA). mRNA expression values were measured in triplicate using the Roche LightCycler 480 and normalized to b-actin expression as a housekeeping control. Expression values were calculated according to ref..
were performed using modified standard protocols. In brief, whole cell extracts were made of the cells in Triple Lysis Buffer [50 mM Tris-HCl pH8, 150 mM NaCl, 0,02% (w/v) NaN3, 0,5% (w/v) NaDeoxycholate, 0,1% SDS, 1% (v/v) NP40]. Extracts (10 μg/lane) were separated on a 10% SDS-polyacrylamide gel, blotted onto a PVDF membrane, and incubated with the indicated primary antibodies diluted in blocking buffer (5% nonfat dry milk) over night at 4°C. After washing and incubation with peroxidase-coupled species-specific secondary antibodies, the signal was developed using SuperSignal West PICO Chemiluminescent Substrate (Perbio Science, Bonn, Germany) according to manufacturer's protocol. CtBP2, CDYL, RCOR3, β-actin and ZEB1 were immunodetected with the following primary antibodies: anti-CtBP2 mouse monoclonal antibody (1:8.000, BD Transduction Laboratories™, Franklin Lakes, NJ, USA), anti-CDYL rabbit polyclonal antibody (1:500, Abcam, Cambridge, UK), anti-RCOR3 rabbit polyclonal antibody (1:1000Abcam, Cambridge, UK) anti-β-actin mouse monoclonal antibody (1:5.000, Sigma-Aldrich Chemie GmbH, Munich, Germany). The anti-ZEB1 rabbit polyclonal antibody (1:20.000) was a gift of D.S. Darling, University of Louisville, Louisville, KY, USA.
Top ranking single miRNAs targeting protein complexes
TGF-beta receptor II-TGF-beta receptor I-TGF-beta3 complex
CREBBP-SMAD3-SMAD4 pentameric complex
CREBBP-SMAD2-SMAD4 pentameric complex
Top ranking miRNA clusters targeting protein complexes
TGF-beta receptor II-TGF-beta receptor I-TGF-beta3 complex
CREBBP-SMAD2-SMAD4 pentameric complex
Functional spectrum of miRNA-regulated protein complexes
These observations correspond with the overrepresentation of targeted genes contained in pathways from KEGG (see Figure 1b). A high overrepresentation of genes could be observed in "Pathways in cancer". Also many signaling pathways are overrepresented, namely Wnt signaling, TGF-beta signaling, Insulin signaling, Notch signaling, ErbB signaling, MAPK signaling, T and B cell receptor signaling and Chemokine signaling. Genes involved in house-keeping functions were underrepresented also in KEGG pathways, namely RNA polymerase, RNA transport, Proteasome, Oxidative phosphorylation and Ribosome.
Validating predicted miRNA targets in protein complexes
Significance of miRNA target downregulation
PAR-CLIP (Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immuno-precipitation) is a powerful tool to detect segments of RNA bound by RNA-binding proteins (RBPs) and ribonucleoprotein complexes (RNPs). We corroborated the miRNA target sites identified by PAR-CLIP  with the proteomics data [6, 7]. 55% of the proteins with miRNA targets sites predicted based on PAR-CLIP data were moderately down-regulated (log2-fold change < -0.1). 413 protein complexes contained miRNA target sites in at least two subunits (Additional file 5, Table S5 online). Interestingly, of the 5,185 unique proteins with miRNA target sites identified based on PAR-CLIP data, 607 (12%) are members of protein complexes (with at least two distinct targets of one miRNA in the same protein complex). For comparison, the manually curated collection of human protein complexes in the CORUM database covers 2,780 unique proteins (2% of UniProt proteins). This implies miRNA targets identified from PAR-CLIP data are more likely to be in a protein complex from the CORUM database (12%) as compared to proteins in general (2%). While miRNAs frequently target multiple genes with isolated functions, these independent data, though only by a simple estimate, suggest that there is also a significant proportion of miRNA targets, which are distinct members of protein complexes (hypergeometic P-value 1.23e-11).
Protein complexes and miRNA expression
We next tested whether miRNAs, which target different components of the same protein complex, are more likely to be co-expressed. The average expression correlation (Co-expression as calculated by Pearson correlation coefficients, hereafter termed PC values) of miRNAs was examined based on pairwise correlation calculations of miRNA expression profiles obtained for 26 different organ systems and cell types . To test for statistical significance, we combined all pairwise PC values obtained from the sets of miRNAs which significantly target the same complex. These PC values were then compared to all other pairwise PC values that were present in the data set from . We performed a one-sided Kolmogorov-Smirnov (KS) test for the two PC value distributions and obtained a significantly (P-value 6.106e-24) higher co-expression within the sets of miRNAs that target the same complex. Since we are interested in coexpression of miRNAs that are not in one transcription unit, we also tested for increased correlation only for miRNAs of different transcription units. Only a few (3.3%) of the correlated miRNAs were actually contained in one transcription unit. Therefore, the result remains highly significant (P-value 2.11e-18). Another bias of our results might occur due to fact that all miRNAs from one family must target the same complex since they target the same set of mRNA. We compared only miRNAs within one complex that belong to different families. The KS test resulted in a P-value of 0.0058. Taken together, our statistical test indicates that miRNAs targeting different components of a protein complex are significantly co-expressed. The average Pearson correlations of miRNAs that simultaneously target a specific complex can be found in Additional file 6, Table S6 online1).
Protein complex networks co-ordinately regulated by clusters of miRNAs
CtBP/ZEB complex regulated by the miR-141-200c cluster
Top ranking miRNA clusters with interconnected target sets
Ppis [#| P-value]
Ppis [miR-miR] [# |P-value]
Very recent reports have shown that the miR-200 family regulates epithelial to mesenchymal transition (EMT) by targeting the transcriptional repressor zinc-finger E-box binding homebox 1 (ZEB1) and ZEB2[4, 32–35]. During EMT, the miR-141-200c cluster and the tumor invasion suppressor gene E-cadherin are downregulated by ZEB1/2. ZEB1 and ZEB2 repress transcription through interaction with corepressor CtBP (C-terminal binding protein) . Interestingly, several essential components of the CtBP/ZEB complex, namely ZEB1/2, CtBP2, RCOR3 (REST corepressor 3) and CDYL (Chromodomain Y-like protein), are predicted targets of the miR-141-200c cluster. CtBP2 has one miR-141 target site and one miR-200c target site, while ZEB1 and CDYL have two miR-200c target sites. RCOR3 has one miR-141 target site. The CtBP/ZEB complex mediates the transcriptional repression of its target genes by binding to their promotors and altering the histone modification .
MicroRNAs and their functions have been a fascinating research topic in recent years [8, 39, 40]. In animals, miRNA-guided regulations of gene expression are likely to involve hundreds of miRNAs and their targets. Genetic studies have successfully elucidated some miRNA activities, termed genetic switches, which have intrinsic phenotypic consequences [8, 40]. miRNA activities can be classified based on whether their major effect is conveyed through one, a few or many targets (from tens to hundreds). All genetic switches discovered so far belong to the former class (a few targets). It is unclear how the latter class, termed target battery , which might be subtly regulated on the protein level [6, 7], contributes to proper phenotypes.
In this study, we completed a comprehensive analysis of human protein complexes, which might be co-ordinately regulated by miRNAs. When this paper was under review, Tsang et al.  predicted human microRNA functions by miRBridge to assess the statistical enrichment of microRNA-targeting signatures in annotated gene sets, including our CORUM protein complexes . These protein complexes can be considered as examples of "target battery" . Our statistical analysis suggests that, by simultaneously targeting several components of protein complexes, a single miRNA or co-expressed miRNAs may have cumulative effects. To demonstrate this, we experimentally verified that the miR141-200c cluster interacts with four different components of the CtBP/ZEB complex. Interestingly, although Tsang et al. used their own miRNA target predition, which is different from TargetScan prediction, their protein complex result also included the interaction of the miR200 family and CtBP complex  which includes miR-200c. This supports our finding that the miR141-200c cluster also interacts with the CtBP complex. The functional analysis of the miRNA-regulated protein complexes revealed a clear bias towards transcriptional regulation, signal transduction, cell cycle and chromatin regulation, for which confirmation has been reported only by individual experimental studies of selected miRNAs. Our approach provides improved candidate miRNA target lists to the experimentalist, as demonstrated by a benchmark against large-scale, quantitative proteomics data.
Some ancient miRNA genes are deeply conserved in the kingdom Animalia [37, 38] or in the kingdom Plantae  while during the evolution, novel miRNA genes were constantly created, fixed or lost [42–45]. Interestingly, the genomic organization of some miRNA clusters were well preserved for millions of years, implying a functional incentive to keep such configurations [5, 46]. The evolution of homogeneous miRNA clusters can be easily explained by the classical gene duplication theory . The regulatory effect of such clusters might merely be an increase of dosage. The evolution of hetergeneous miRNA clusters is more complicated. Two different miRNAs can be located near each other by various genomic events, such as recombination, transposon insertion, etc. Or large number hairpin repeats might evolve into miRNAs of different families. For example, the largest human miRNA cluster miR-379-656  consists of different miRNA families, which evolved by tandem duplication of an ancient hairpin sequence. Once a newly formed miRNA cluster proves to provide a functional advantage, which might be co-ordinate regulation of protein complexes, the genomic organization of such a cluster could be fixed by evolution .
In eukaryotic cells, RNA operons, mostly sequence-specific RNA binding proteins, may co-ordinately regulate functionally related mRNAs to aid the formation of macromolecular protein complexes . In such a scenario, mRNAs of different components of a protein complex are brought together by associating with specific RNA operons. The localization of these mRNAs might also facilitate the simultaneous interaction of miRNAs and their corresponding target mRNAs. Interestingly, RNA operons bind to motifs, which are sometimes located in the 3'UTRs of mRNAs. Thus, the competition or cooperation between miRNA binding and RNA operon binding might be a research topic worth pursuing.
The results presented here can be used as a starting point for experimentalists to systematically evaluate miRNAs and targets interactions at a systems level. The concept that coexpressed small RNAs may synergistically target protein complexes for a more efficient regulation is of course not limited to animal miRNAs.
SS, DL, AK and FT are supported by the Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology (project CoReNe). The authors thank Peter Brodersen and Hans-Werner Mewes for their critical reading of the manuscript and Ivan Kondofersky for his statistical support.
- Griffiths-Jones S, Saini HK, Van Dongen S, Enright AJ: "miRBase: tools for microRNA genomics, ". Nucleic Acids Research. 2008, 36: D154-158. 10.1093/nar/gkn221PubMed CentralView ArticlePubMedGoogle Scholar
- Bonci D, Coppola V, Musumeci M, Addario A, Giuffrida R, Memeo L, D'Urso L, Pagliuca A, Biffoni M, Labbaye C, Bartucci M, Muto G, Peschle C, De Maria R: "The miR-15a-miR-16-1 cluster controls prostate cancer by targeting multiple oncogenic activities, ". Nature Medicine. 2008, 14: 1271-1277. 10.1038/nm.1880View ArticlePubMedGoogle Scholar
- Mendell JT: "miRiad roles for the miR-17-92 cluster in development and disease, ". Cell. 2008, 133: 217-222. 10.1016/j.cell.2008.04.001PubMed CentralView ArticlePubMedGoogle Scholar
- Nakada C, Matsuura K, Tsukamoto Y, Tanigawa M, Yoshimoto T, Narimatsu T, Nguyen LT, Hijiya N, Uchida T, Sato F, Mimata H, Seto M, Moriyama M: "Genome-wide microRNA expression profiling in renal cell carcinoma: significant down-regulation of miR-141 and miR-200c, ". The Journal of Pathology. 2008, 216: 418-427. 10.1002/path.2437View ArticlePubMedGoogle Scholar
- Ambros V: "The evolution of our thinking about microRNAs, ". Nature Medicine. 2008, 14: 1036-1040. 10.1038/nm1008-1036View ArticlePubMedGoogle Scholar
- Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N: "Widespread changes in protein synthesis induced by microRNAs, ". Nature. 2008, 455: 58-63. 10.1038/nature07228View ArticlePubMedGoogle Scholar
- Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP: "The impact of microRNAs on protein output, ". Nature. 2008, 455: 64-71. 10.1038/nature07242PubMed CentralView ArticlePubMedGoogle Scholar
- Flynt AS, Lai EC: "Biological principles of microRNA-mediated regulation: shared themes amid diversity, ". Nature Reviews Genetics. 2008, 9: 831-842.PubMed CentralView ArticlePubMedGoogle Scholar
- Liang H, Li W-H: "MicroRNA regulation of human protein protein interaction network, ". RNA (New York, NY). 2007, 13: 1402-1408.View ArticleGoogle Scholar
- Hsu C-W, Juan H-F, Huang H-C: "Characterization of microRNA-regulated protein-protein interaction network, ". Proteomics. 2008, 8: 1975-1979. 10.1002/pmic.200701004View ArticlePubMedGoogle Scholar
- Yuan X, Liu C, Yang P, He S, Liao Q, Kang S, Zhao Y: "Clustered microRNAs' coordination in regulating protein-protein interaction network, ". BMC Systems Biology. 2009, 3: 65- 10.1186/1752-0509-3-65PubMed CentralView ArticlePubMedGoogle Scholar
- Tsang JS, Ebert MS, van Oudenaarden A: "Genome-wide dissection of microRNA functions and cotargeting networks using gene set signatures, ". Mol Cell. 2010, 38: 140-153. 10.1016/j.molcel.2010.03.007PubMed CentralView ArticlePubMedGoogle Scholar
- Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jungkamp A-C, Munschauer M, Ulrich A, Wardle GS, Dewell S, Zavolan M, Tuschl T: "Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP, ". Cell. 2010, 141: 129-141. 10.1016/j.cell.2010.03.009PubMed CentralView ArticlePubMedGoogle Scholar
- Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R: "Fast and effective prediction of microRNA/target duplexes, ". RNA (New York, NY). 2004, 10: 1507-1517.View ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: "Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, ". Nature Genetics. 2000, 25: 25-29. 10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: "KEGG for representation and analysis of molecular networks involving diseases and drugs, ". Nucleic Acids Res. 2010, 38: D355-360. 10.1093/nar/gkp896PubMed CentralView ArticlePubMedGoogle Scholar
- Falcon S, Gentleman R: "Using GOstats to test gene lists for GO term association, ". Bioinformatics. 2007, 23: 257-258. 10.1093/bioinformatics/btl567View ArticlePubMedGoogle Scholar
- Brabletz T, Jung A, Hlubek F, Löhberg C, Meiler J, Suchy U, Kirchner T: "Negative regulation of CD4 expression in T cells by the transcriptional repressor ZEB, ". International Immunology. 1999, 11: 1701-1708. 10.1093/intimm/11.10.1701View ArticlePubMedGoogle Scholar
- Pfaffl MW: "A new mathematical model for relative quantification in real-time RT-PCR, ". Nucleic Acids Researchi. 2001, 29: e45-10.1093/nar/29.9.e45.View ArticleGoogle Scholar
- Ruepp A, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Stransky M, Waegele B, Schmidt T, Doudieu ON, Stümpflen V, Mewes HW: "CORUM: the comprehensive resource of mammalian protein complexes, ". Nucleic Acids Research. 2008, 36: D646-650.PubMed CentralView ArticlePubMedGoogle Scholar
- Cui Q, Yu Z, Purisima EO, Wang E: "Principles of microRNA regulation of a human cellular signaling network, ". Molecular Systems Biology. 2006, 2: 46-PubMed CentralView ArticlePubMedGoogle Scholar
- Friggi-Grelin F, Lavenant-Staccini L, Therond P: "Control of antagonistic components of the hedgehog signaling pathway by microRNAs in Drosophila, ". Genetics. 2008, 179: 429-439. 10.1534/genetics.107.083733PubMed CentralView ArticlePubMedGoogle Scholar
- Silver SJ, Hagen JW, Okamura K, Perrimon N, Lai EC: "Functional screening identifies miR-315 as a potent activator of Wingless signaling, ". Proceedings of the National Academy of Sciences of the United States of America. 2007, 104: 18151-18156. 10.1073/pnas.0706673104PubMed CentralView ArticlePubMedGoogle Scholar
- Martello G, Zacchigna L, Inui M, Montagner M, Adorno M, Mamidi A, Morsut L, Soligo S, Tran U, Dupont S, Cordenonsi M, Wessely O, Piccolo S: "MicroRNA control of Nodal signalling, ". Nature. 2007, 449: 183-188. 10.1038/nature06100View ArticlePubMedGoogle Scholar
- Li X, Carthew RW: "A microRNA mediates EGF receptor signaling and promotes photoreceptor differentiation in the Drosophila eye, ". Cell. 2005, 123: 1267-1277. 10.1016/j.cell.2005.10.040View ArticlePubMedGoogle Scholar
- Flynt AS, Li N, Thatcher EJ, Solnica-Krezel L, Patton JG: "Zebrafish miR-214 modulates Hedgehog signaling to specify muscle cell fate, ". Nature Genetics. 2007, 39: 259-263. 10.1038/ng1953PubMed CentralView ArticlePubMedGoogle Scholar
- Neumüller RA, Betschinger J, Fischer A, Bushati N, Poernbacher I, Mechtler K, Cohen SM, Knoblich JA: "Mei-P26 regulates microRNAs and cell growth in the Drosophila ovarian stem cell lineage, ". Nature. 2008, 454: 241-245. 10.1038/nature07014PubMed CentralView ArticlePubMedGoogle Scholar
- Carleton M, Cleary MA, Linsley PS: "MicroRNAs and cell cycle regulation, ". Cell Cycle (Georgetown, Tex). 2007, 6: 2127-2132. 10.4161/cc.6.17.4641.View ArticleGoogle Scholar
- Volinia S, Calin GA, Liu C-G, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM: "A microRNA expression signature of human solid tumors defines cancer gene targets, ". Proceedings of the National Academy of Sciences of the United States of America. 2006, 103: 2257-2261. 10.1073/pnas.0510565103PubMed CentralView ArticlePubMedGoogle Scholar
- Mahajan MC, Narlikar GJ, Boyapaty G, Kingston RE, Weissman SM: "Heterogeneous nuclear ribonucleoprotein C1/C2, MeCP1, and SWI/SNF form a chromatin remodeling complex at the beta-globin locus control region, ". Proceedings of the National Academy of Sciences of the United States of America. 2005, 102: 15012-15017. 10.1073/pnas.0507596102PubMed CentralView ArticlePubMedGoogle Scholar
- Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, et al.: "A mammalian microRNA expression atlas based on small RNA library sequencing, ". Cell. 2007, 129: 1401-1414. 10.1016/j.cell.2007.04.040PubMed CentralView ArticlePubMedGoogle Scholar
- Gregory PA, Bert AG, Paterson EL, Barry SC, Tsykin A, Farshid G, Vadas MA, Khew-Goodall Y, Goodall GJ: "The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1, ". Nature Cell Biology. 2008, 10: 593-601. 10.1038/ncb1722View ArticlePubMedGoogle Scholar
- Park S-M, Gaur AB, Lengyel E, Peter ME: "The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2, ". Genes & Development. 2008, 22: 894-907. 10.1101/gad.1640608View ArticleGoogle Scholar
- Korpal M, Lee ES, Hu G, Kang Y: "The miR-200 family inhibits epithelial-mesenchymal transition and cancer cell migration by direct targeting of E-cadherin transcriptional repressors ZEB1 and ZEB2, ". The Journal of Biological Chemistry. 2008, 283: 14910-14914. 10.1074/jbc.C800074200PubMed CentralView ArticlePubMedGoogle Scholar
- Burk U, Schubert J, Wellner U, Schmalhofer O, Vincan E, Spaderna S, Brabletz T: "A reciprocal repression between ZEB1 and members of the miR-200 family promotes EMT and invasion in cancer cells, ". EMBO Reports. 2008, 9: 582-589. 10.1038/embor.2008.74PubMed CentralView ArticlePubMedGoogle Scholar
- Postigo AA, Dean DC: "ZEB represses transcription through interaction with the corepressor CtBP, ". Proceedings of the National Academy of Sciences of the United States of America. 1999, 96: 6683-6688. 10.1073/pnas.96.12.6683PubMed CentralView ArticlePubMedGoogle Scholar
- Shi Y, Sawada J-ichi, Sui G, Affar EB, Whetstine JR, Lan F, Ogawa H, Luke MP-S, Nakatani Y, Shi Y: "Coordinated histone modifications mediated by a CtBP co-repressor complex, ". Nature. 2003, 422: 735-738. 10.1038/nature01550View ArticlePubMedGoogle Scholar
- Bienz M: "beta-Catenin: a pivot between cell adhesion and Wnt signalling, ". Current Biology CB. 2005, 15: R64-67. 10.1016/j.cub.2004.12.058View ArticlePubMedGoogle Scholar
- Filipowicz W, Bhattacharyya SN, Sonenberg N: "Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight?, ". Nature Reviews Genetics. 2008, 9: 102-114.View ArticlePubMedGoogle Scholar
- Bushati N, Cohen SM: "microRNA functions, ". Annual Review of Cell and Developmental Biology. 2007, 23: 175-205. 10.1146/annurev.cellbio.23.090506.123406View ArticlePubMedGoogle Scholar
- Axtell MJ, Snyder JA, Bartel DP: "Common functions for diverse small RNAs of land plants, ". The Plant Cell. 2007, 19: 1750-1769. 10.1105/tpc.107.051706PubMed CentralView ArticlePubMedGoogle Scholar
- Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, Carrington JC: "High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes, ". PloS One. 2007, 2: e219- 10.1371/journal.pone.0000219PubMed CentralView ArticlePubMedGoogle Scholar
- Lu J, Fu Y, Kumar S, Shen Y, Zeng K, Xu A, Carthew R, Wu C-I: "Adaptive evolution of newly emerged micro-RNA genes in Drosophila, ". Molecular Biology and Evolution. 2008, 25: 929-938. 10.1093/molbev/msn040PubMed CentralView ArticlePubMedGoogle Scholar
- Lu J, Shen Y, Wu Q, Kumar S, He B, Shi S, Carthew RW, Wang SM, Wu C-I: "The birth and death of microRNA genes in Drosophila, ". Nature Genetics. 2008, 40: 351-355. 10.1038/ng.73View ArticlePubMedGoogle Scholar
- Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: "A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana, ". Genes & Development. 2006, 20: 3407-3425. 10.1101/gad.1476406View ArticleGoogle Scholar
- Glazov EA, McWilliam S, Barris WC, Dalrymple BP: "Origin, evolution, and biological role of miRNA cluster in DLK-DIO3 genomic region in placental mammals, ". Molecular Biology and Evolution. 2008, 25: 939-948. 10.1093/molbev/msn045View ArticlePubMedGoogle Scholar
- Altuvia Y, Landgraf P, Lithwick G, Elefant N, Pfeffer S, Aravin A, Brownstein MJ, Tuschl T, Margalit H: "Clustering and conservation patterns of human microRNAs, ". Nucleic Acids Research. 2005, 33: 2697-2706. 10.1093/nar/gki567PubMed CentralView ArticlePubMedGoogle Scholar
- Keene JD: "RNA regulons: coordination of post-transcriptional events, ". Nature Reviews Genetics. 2007, 8: 533-543. 10.1038/nrg2111View ArticlePubMedGoogle Scholar