About this Journal Submit a Manuscript Table of Contents
Journal of Biomedicine and Biotechnology
Volume 2009 (2009), Article ID 594738, 9 pages
http://dx.doi.org/10.1155/2009/594738
Review Article

An Evolutionary Perspective of Animal MicroRNAs and Their Targets

1Department of Cell & Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
2Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel

Received 27 March 2009; Accepted 17 June 2009

Academic Editor: Bibekanand Mallick

Copyright © 2009 Noam Shomron et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

MicroRNAs (miRNAs) are short noncoding RNAs that regulate gene expression through translational inhibition or mRNA degradation by binding to sequences on the target mRNA. miRNA regulation appears to be the most abundant mode of posttranscriptional regulation affecting 50% of the transcriptome. miRNA genes are often clustered and/or located in introns, and each targets a variable and often large number of mRNAs. Here we discuss the genomic architecture of animal miRNA genes and their evolving interaction with their target mRNAs.

1. Introduction

MicroRNAs (miRNAs) are short noncoding RNAs that regulate gene expression by binding to sequences on the target mRNA (reviewed by [17]. Gene silencing initiates when the miRNA, located within an RNA-Induced Silencing Complex (RISC), directs binding to complementary sequences on the mRNA’s untranslated region (UTR). The miRNA-mRNA recognition binding sequences are short, usually 6–8 nt [811]. Inhibition ofgene expression takes place via facilitated mRNA degradation, mRNA cleavage, or interference with translation.

2. Generation of miRNA Genes

2.1. miRNA Gene Origins

During animal evolution there were distinct, characterized phases of large scale genome duplications [1214]. miRNA origin, as well, is traced back to genomic episodes dominated by large duplication events which coincide with the advent of bilaterians, vertebrates, and (placental) mammals [15]. The current wealth of miRNA genes results, additionally, from specific duplication events of miRNA clusters [16, 17] and from mechanisms such as the integration of repetitive genetic elements [18].

2.2. The Gatekeeper of the miRNA Biogenesis

The transcription of miRNA genes is controlled by enhancer-promoter elements comparable with those of protein-coding genes [19]. Additional regulation of miRNA expression is obtained through posttranscriptional processing [20], RNA A-to-I editing [21, 22], selective export into the cytoplasm [23, 24], and subcellular localization [25] (Figure 1 (see [2, 2643]) also see review by [44]).

594738.fig.001
Figure 1: Several layers of regulation control canonical miRNA gene biogenesis: transcription activation, splicing, recognition by Drosha, postprocessing, RNA editing, subcellular localization, nuclear export, and hairpin arm selection. Selected examples are referenced within.

While several mechanisms control miRNA's expression along its biogenesis pathway, it seems that the rate limiting step in acquiring a novel miRNA is the recognition of the RNA secondary structure by Drosha. This stems from the fact that mammals express only several hundred miRNAs from myriad amounts of expressed RNA secondary structures [16, 4547]. Thus, processing of miRNA precursor by the microprocessor is probably the gatekeeper of the miRNA biogenesis pathway, which allows for only a portion of the transcribed RNA hairpins to be further processed down the miRNA biosynthetic pathway. Analysis of miR-220 recent evolution provides an intriguing example for a gene that apparently did not encode for a miRNA but became competent for Drosha-dependent microprocessing. miR-220, which contains sequences of a tubulin gene, was probably originally processed from an antisense strand (see [48, 49]) of tubulin, which folds back into a proper stem-loop structure in human but not in other vertebrates [15]. Comparative studies of the tubulin antisense strand sequence may shed light on the reasons for which human Drosha enables microprocessing while in other species it is skipped. Though canonical miRNA bioprocessing is Drosha-dependent, a novel splicing-dependent [33] mechanism was suggested recently to bypass initial steps of microprocessing [33, 50, 51]. Despite the functional robustness of miRNA secondary structures in light of accumulating mutations it is still not clear what the precise requirements for passing the gatekeeper of miRNA biogenesis are.

3. The Genomic Architecture of miRNA Genes and Their Expression

Two characteristics of miRNA genes stand out in regard to genomic organization of protein-coding genes. First, miRNA genes are often found in clusters (30%–42%) [5254]. Additionally, miRNA genes are often embedded within introns (25% or more) [27, 5559].

3.1. Chromosomal Organization of miRNA Genes

In accordance with genomic duplication events that accompany evolution of species, we see a correlation between the number of miRNA genes and chromosome length. miRNA gene number per chromosome also correlates with the protein-coding gene density (Figure 2(a) and 2(b)). This indicates that integration and/or maintenance of miRNA genes roughly follows protein-coding genes.

fig2
Figure 2: Genomic organization of miRNA genes and their expression. The number of miRNA genes correlates with chromosome length (a) and the number of protein-coding genes (b). Outliers chromosomes 14, 19, and are indicated. When removing these three chromosomes the number of miRNA genes aligns well on the regression line ( indicated). (c) Proportion of miRNA genes hosted in Introns—Intronic (based on Refseq genes), Clustered on the same genomic strand, or Stand-alone miRNAs. Overlapping intronic and clustered miRNAs are also indicated. Each row refers to 50 kb, 10 kb, and 1 kb distance between paired genes on the same strand. It was shown that clusters of size 3 kb give a large proportion of clusters (27%) with little change when increasing pairwise distance to 10 kb [54]. Diagrams are based on data from Refseq. (d) Human miRNA copy number was plotted against the average miRNA expression level of 40 Human tissues [60]. A similar plot of another dataset [55] gave comparable results (data not shown).

However, Homo Sapiens chromosomes 14, 19, and are exceptionally enriched for miRNA genes. Chromosomes 14 and 19 both possess a single miRNA cluster, accounting for 93% and 80% of the total number of miRNA genes on each chromosome, respectively [16, 61]. The cluster on chromosome 14 is located in the human imprinted domain (14 q32) where only maternally inherited miRNAs are expressed [62]. Chromosome 19 hosts the primate-specific “500” cluster [16], a recently emerging, placental-specific cluster [16]. The chromosome, on the other hand, does not have one large cluster, but it exhibits rapid emergence of smaller miRNA clusters due to frequent tandem duplications and nucleotide substitutions [17]. We note that despite the parallel evolution of miRNAs in animals and plants, miRNA clusters were observed in both kingdoms [63].

3.2. Clusters of miRNA Genes

Plausibly, employment of an already existing functional promoter by new miRNA genes is an efficient way to express new miRNAs, eliminating the need for de novo establishment of promoter-enhancer sequences upstream of the miRNA gene (such as in [6466]). This may be the rational underlying miRNA aggregation into polycistronic miRNA clusters and for their genomic preference for introns of transcribed genes (see Figure 2(c)). The consequence on the genomic level is that many miRNAs within up to 50 kb DNA fragment tend to be coexpressed [54, 55]. Amplification of an ancestral miRNA inside a cluster [54, 56] could contribute to the effective dosage of a given expressed miRNA homolog. However, at lower copy number gene dose does not seem to be a powerful predictor of expression levels (Figure 2(d)). The most likely interpretation is that the magnitude of promoter activity probably dominates regulation of miRNA expression. The correlation between miRNA gene copy number and expression level that was noted in some cases [67] may nonetheless suggest that when miRNA copy number is high ( 3; Figure 2(d)), it may also serve to impact the expression level.

3.3. Intronic miRNA Genes

At least 25% of miRNA genes are hosted in introns of both protein-coding and noncoding RNAs (Figure 2(c) also see [27, 56, 59]). This is a striking feature of noncoding RNAs (reviewed by [68]), plausibly implying that some noncoding RNAs have developed a functional relationship with their host genes [38, 69]. The use of the same promoter-enhancer system enables coupling of miRNA expression with its host gene, therefore not surprisingly frequently seen [27, 55, 70]. When derived from the same primary transcript, it appears that pri-miRNA maturation by the microprocessor and pre-mRNA editing by the Spliceosome can either coexist independently or interconnect. While some studies imply that these processes hardly interact [59], others have shown strong interactions initiating at transcription [7173]. Overall, given the tight proximity of these cellular events in time and space it is hard to imagine how these functional complexes avoid each other. Further analysis would be required to determine the extent of this interaction and whether this is true for all given transcripts [74].

3.4. Functional Expression of miRNAs and Their Host Genes

While mRNA/miRNA derived from the same transcript may simply reflect an efficient use of a promoter-enhancer cassette [59], in a subset of cases a coordinated expression of an miRNA-protein pair from the same genomic locus may reflect a genetic interaction. For example, platelets contain two cAMP phosphodiesterases (PDEs)—PDE2A and PDE3A—each regulating a specific intracellular pool of cAMP [75]. miR-139 that is hosted in an intron of the PDE2A targets PDE3A (TargetScanS, see [76]), implying that the miRNA expression from PDE2A regulates the balance between the two isoforms. Similarly, miR-208 is encoded by an intron of the cardiac-specific alpha myosin heavy chain (MHC) gene, a major cardiac contractile protein. Alpha MHC responses to stress and hypothyroidism [35, 77] partially by coexpressing miR-208. The miRNA targets and downregulates beta MHC expression [70]. Thus, the precision in regulating an miRNA and a gene product may be hardwired into the genomic organization, to promise proper balance in their opposing or collaborating functions.

4. Generation of miRNA Targets and Their Interaction with miRNAs

4.1. Reciprocal Evolutionary Interaction between miRNAs and Their Targets

Our current understanding of miRNA binding sites suggests that a stretch of 6 nucleotide “seed” region, matching between the end of the miRNA and the mRNA UTR, may suffice for regulation by miRNAs [9, 10, 76, 78]. Because changes in cis sequences often dominates rewiring of genetic networks, [79] it is likely that the UTR of mRNA targets change their repertoire of seed matches faster than the highly conserved transacting miRNAs. This can be intuitively explained merely because the large number of targets affected by mutations in any given miRNA gene acts as a stabilizing element on the miRNA itself. So given a virtually fixed population of miRNAs, targets gain and lose binding sites in a way that supports their controlled miRNA expression. This can be viewed as an evolutionary reciprocal interaction between the miRNA and its accumulating targets. After miRNA emergence, once a critical number of targets are functionally regulated by the miRNA, stabilization of its primary sequence is gained [80], while at the same time, stabilizing selection decreases variation in target seed match [28, 81, 82].

The target set size is also dramatically affected by the nucleotide composition of the new miRNA, and, as mentioned above, this characteristic affects the average selective pressure on the miRNA itself [78]. Given a set of 17 000 UTRs (“Known Genes” in the UCSC genome browser database), some 2000 UTRs would randomly have a single binding site for a heptamer seed composed of A/U residues. This number falls to only 200 seed matches with a G/C-only seed content and is somewhere in between ( 800) for a mixed nucleotide composition (equal number of A/U and G/C). Once emerged, the set of targets affected by a novel miRNA is subject to selective pressure which molds the transcriptome such that binding sites would either be acquired or lost. In fact, selective loss of seed matches, to a level below the randomly predicted baseline, dubbed “anti-targets” [9, 83], provides strong support for the evolutionary power underlying the structure of miRNA binding sites (also see [84]).

The reciprocal interaction between miRNAs and their targets gets an additional perception when looking at this relationship in viral miRNAs. Several viruses express miRNAs for controlling specific cellular genes or pathways. For this purpose, most cellular mRNA targets of viral miRNAs identified to date play a role in either regulation of apoptosis or host antiviral immune response. miRNAs are suitable for a viral genome expression as they are short and compact. In addition, they can be generated more readily than proteins against new target genes and do not elicit any antigenic response. Their evolutionary flexibility is based on the high mutation rates of the viruses. This leads to modifications in the miRNA genes themselves, and thus even the largest virus family containing miRNAs (herpesvirus) shows little conservation between their miRNAs. It also indicates that it is unlikely that host miRNA targets viral mRNAs as these would mutate away from disruptive regulation (also see [85, 86]).

4.2. The Large Variation in miRNA Target Sites

Conserved complementarities to a minimal hexamer region (matching nt 2–7 of the miRNA) [8] indicates that once a seed match emerges, it becomes functional. If the binding is preferentially beneficial, it might serve as a favorable and directional intermediate species. Within Tetrapods, the average number of predicted conserved sites per miRNA is at the range of 200 (Figure 3(a), TargetScanS, plotted for Human miRNAs). However, the number of targets is skewed to the higher values, while the upper and lower 10-percentiles regulate more than 450 or less than 50 genes, respectively (also see [87, 88]). Comparative genomics suggests that ancient miRNAs have on average twofold more targets than newly generated ones (compare 453 to 194, resp.). Some discrepancies result from misestimating miRNA antiquity or overlapping miRNA functional sites. Specifically, the age of some miRNA genes might have been misestimated, as cross-species orthologues searches are not exhausted yet. miR-761, for example, identified only in mouse [57] is in fact conserved in six other mammals (including human and opossum; see [89] also see miRviewer at http://people.csail.mit.edu/akiezun/miRviewer/). Alternatively, overlapping functional sites shared by miRNAs and other regulatory factors may bias the distribution of targets. For example, pre-existing “scaffolds” of other regulatory systems could serve as anchors for miRNA binding. In the case of miR-16, a component of the AU-rich mediated deregulation of mRNA stability [90], the miRNA is a late addition onto a mechanism that was probably functional in the common ancestor of yeast [91], before the innovation of miRNAs. In this train of thought, some transcriptional termination or pause sites [92, 93] overlap with miRNA seed-matches (miR-525 and miR-488). In human, Alu transposable elements exhibit complementarities in some of their regions to almost 30 human miRNAs [94]. In other instances, the attempt to avoid specific protein binding domains in the UTRs may expel miRNA binding sites. For example, UTRs may avoid miR-518a seed (which has only 26 predicted conserved targets) because it perfectly matches the proline and acidic rich (PAR) protein binding sequence [95]. Other miRNA interference events may involve binding to promoters via antisense transcription, which is estimated to be as common as 15% in the human genome [96]. Overlapping sequences as such might coincide with promiscuous promoter-associated functions of small RNAs [36] or increase in transcription [97]. Plausibly a selective pressure to avoid the binding of the aryl hydrocarbon receptor (AhR) [98] onto miR-521 sites (AhR and miR-521 share the same sequence) may explain how miRNAs of similar antiquity and A/U content (compare to miR-520 h) dramatically vary in their predicted numbers of conserved targets (compare 8 to 400, resp.; both miRNAs are part of the same primary transcript, BF773110). It is noteworthy that the low number of miR-521 targets cannot be explained by a conflict of expression in a broad set of tissues since miR-521 is expressed only in placenta.

fig3
Figure 3: The number of predicted conserved miRNA target sites. (a) Predicted number of conserved targets, conserved target sites and poorly conserved sites of human miRNAs (based on TargetScanS). (b) The number of predicted conserved miRNA targets was divided according to the conservation level of the miRNA itself (H, Human; P, Chimp; M, Mouse; R, Rat; D, Dog; C, Chicken; based on TargetScanS). Shaded in red/green are the regions with the largest/least number of targets (resp.). Extreme numbers of targets are boxed and are discussed in the text.
4.3. Unique Features of miRNAs with Most Number of Targets

In order to further explore the characteristics of miRNAs with extreme number of targets we compared the group of miRNAs with the largest number of targets to that with the least number of targets (Figure 3(b), shaded red and green, resp.). We found some correlation between miRNA conservation and its potential number of predicted targets. This correlation is emphasized in the conserved target sets where human-to-mouse conserved miRNAs have on average 197 predicted conserved targets; human-to-dog conserved miRNAs have 245, and human-to-chicken conserved miRNAs 453. miRNAs with the largest number of targets tend to be expressed mostly from one arm of the pre-miRNA hairpin (they do not exhibit both and arm expression) and are often expressed at higher levels and in a broader set of tissues compared to miRNAs with the least number of targets (also see [99]).

miRNAs with the largest number of targets are A/U-rich. The average A/U percentage within the seed of the top 20 miRNAs with the largest number of targets is 57%, compared to 41% for those with the least number of targets. This may be required for weaker secondary structures in the target mRNA and for ongoing accessibility [11]. Consistently, a general mutational trend (in the human genome) from G-to-A and C-to-T is more abundant than the reverse direction [100]. Analysis of human Single Nucleotide Polymorphisms (SNPs) on a representative chromosome (chromosome 1; 661 SNPs) confirms that the majority of polymorphisms generating new potential miRNA binding sites are G-to-A and C-to-T substitutions (occurring 1.7-fold more than the reverse direction). Interestingly, the two most pronounced examples of target polymorphic changes are G-to-A mutations [39, 101].

In summary, miRNA gene integration and maintenance roughly follow protein-coding genes. After emergence, the miRNA gene sequence is refined through an evolutionary reciprocal interaction with its accumulating targets, and these later stabilize the miRNA when reaching a large enough number of functional targets. Finally, overlapping functional sites shared by miRNAs and other regulatory factors may facilitate or inhibit miRNA target formation and thus influence miRNA target set size.

5. A Timescale for miRNA Target-Site Evolution

It would take several million years for a specific 7-mer binding site to evolve from a complete null binding sequence [102]. However, miRNA binding sites evolve from existing sequences, and based on these partial binding sequences, (“almost-binding” sites or “pre-seed” sites), a corrected estimated time for a miRNA binding site to emerge is 0.2 million years (Durrett R., personal communications). For example, a 5 nt pre-seed site will appear every 1024 nt ( ) or even 20 times more often since the position of the 5 nt within the 7 nt is not restricted and may also include inserts. Thus, a 1 kb UTR will contain several potential pre-seed sequences. A human specific miRNA that is absent even from the chimp genome should be roughly 6 million years old (last estimated split between human and chimp). Given 0.2 million years required for a 7-mer binding site to evolve, around 30 perfect 7-mer binding sites are expected. For an miRNA that is traced back to mouse (split more than 100 million years ago from human), about 500 conserved targets per miRNA are reasonable. This simplified calculation might indicate that, given a spontaneous mutation rate, there should be a direct correlation between the age of an miRNA and the number of targets it possesses and also to the number of duplicated events of the same miRNA site on one transcript. Eventually, it is not enough for the mutation to occur—it should also be maintained in the population after exhibiting a strong selective pressure towards a favorable regulation which can only take place when an miRNA and its targets are spatially and temporally coexpressed [83, 103]. This calculation allows us to set the general time line of events for miRNA formation. Nevertheless there are many outstanding exceptions of small and large miRNA target repertoires (also see Figure 4).

594738.fig.004
Figure 4: A possible scenario for acquiring a functional miRNA binding site.

Websites Used

Ensembl: http://www.ensembl.org/

GenBank: http://www.ncbi.nlm.nih.gov/

miRBase: http://microrna.sanger.ac.uk/

miRNAminer: http://groups.csail.mit.edu/pag/mirnaminer/

miRviewer: http://people.csail.mit.edu/akiezun/miRviewer/

Patrocles: http://www.patrocles.org/

TargetRank: http://hollywood.mit.edu/targetrank/

TargetScanS: http://www.targetscan.org/

UCSC genome browser: http://genome.ucsc.edu/.

Acknowledgments

The authors thank the following people for commenting on their manuscript: Brad Friedman, Alex Stark, Iftach Nachman, Robin Friedman, Eric Wang, Rickard Sandberg, Etgar Levy-Nissenbaum, and the Shomron lab members. They thank Rick Durrett and Deena Schmidt for assistance in statistical calculations. Work at the NS lab is supported by the Israeli Ministry of Health and the Kunz-Lion Foundation. EH is the incumbent of the Helen and Milton A. Kimmelman Career Development Chair. Work at the EH lab is supported by grants from the JDRF, ISF, ISf-Legacy, GIF, the Benoziyo Center for Neurological Disease, the Estate of Flourence Blau and the Wolfson Family Charitable trust for miRNA.

References

  1. V. Ambros, “The functions of animal microRNAs,” Nature, vol. 431, no. 7006, pp. 350–355, 2004. View at Publisher · View at Google Scholar · View at PubMed
  2. D. P. Bartel, “MicroRNAs: genomics, biogenesis, mechanism, and function,” Cell, vol. 116, no. 2, pp. 281–297, 2004. View at Publisher · View at Google Scholar
  3. L. He and G. J. Hannon, “MicroRNAs: small RNAs with a big role in gene regulation,” Nature Reviews Genetics, vol. 5, no. 7, pp. 522–531, 2004. View at Publisher · View at Google Scholar · View at PubMed
  4. T. Du and P. D. Zamore, “microPrimer: the biogenesis and function of microRNA,” Development, vol. 132, no. 21, pp. 4645–4652, 2005. View at Publisher · View at Google Scholar · View at PubMed
  5. W. Filipowicz, L. Jaskiewicz, F. A. Kolb, and R. S. Pillai, “Post-transcriptional gene silencing by siRNAs and miRNAs,” Current Opinion in Structural Biology, vol. 15, no. 3, pp. 331–341, 2005. View at Publisher · View at Google Scholar · View at PubMed
  6. R. W. Carthew, “Gene regulation by microRNAs,” Current Opinion in Genetics and Development, vol. 16, no. 2, pp. 203–208, 2006. View at Publisher · View at Google Scholar · View at PubMed
  7. R. H. A. Plasterk, “Micro RNAs in animal development,” Cell, vol. 124, no. 5, pp. 877–881, 2006. View at Publisher · View at Google Scholar · View at PubMed
  8. B. P. Lewis, I.-H. Shih, M. W. Jones-Rhoades, D. P. Bartel, and C. B. Burge, “Prediction of mammalian microRNA targets,” Cell, vol. 115, no. 7, pp. 787–798, 2003. View at Publisher · View at Google Scholar
  9. J. Brennecke, A. Stark, R. B. Russell, and S. M. Cohen, “Principles of microRNA-target recognition,” PLoS Biology, vol. 3, no. 3, article e85, 2005.
  10. A. Krek, D. Grün, M. N. Poy, et al., “Combinatorial microRNA target predictions,” Nature Genetics, vol. 37, no. 5, pp. 495–500, 2005. View at Publisher · View at Google Scholar · View at PubMed
  11. C. B. Nielsen, N. Shomron, R. Sandberg, E. Hornstein, J. Kitzman, and C. B. Burge, “Determinants of targeting by endogenous and exogenous microRNAs and siRNAs,” RNA, vol. 13, no. 11, pp. 1894–1910, 2007. View at Publisher · View at Google Scholar · View at PubMed
  12. S. Ohno, Evolution by Gene Duplication, Springer, New York, NY, USA, 1970.
  13. P. W. H. Holland, J. Garcia-Fernandez, N. A. Williams, and A. Sidow, “Gene duplications and the origins of vertebrate development,” Development, vol. 120, supplement, pp. 125–133, 1994.
  14. L.-G. Lundin, “Gene duplications in early metazoan evolution,” Seminars in Cell and Developmental Biology, vol. 10, no. 5, pp. 523–530, 1999. View at Publisher · View at Google Scholar · View at PubMed
  15. J. Hertel, M. Lindemeyer, K. Missal, et al., “The expansion of the metazoan microRNA repertoire,” BMC Genomics, vol. 7, article 25, 2006. View at Publisher · View at Google Scholar · View at PubMed
  16. I. Bentwich, A. Avniel, Y. Karov, et al., “Identification of hundreds of conserved and nonconserved human microRNAs,” Nature Genetics, vol. 37, no. 7, pp. 766–770, 2005. View at Publisher · View at Google Scholar · View at PubMed
  17. R. Zhang, Y. Peng, W. Wang, and B. Su, “Rapid evolution of an X-linked microRNA cluster in primates,” Genome Research, vol. 17, no. 5, pp. 612–617, 2007. View at Publisher · View at Google Scholar · View at PubMed
  18. J. Piriyapongsa, L. Mariño-Ramírez, and I. K. Jordan, “Origin and evolution of human microRNAs from transposable elements,” Genetics, vol. 176, no. 2, pp. 1323–1337, 2007. View at Publisher · View at Google Scholar · View at PubMed
  19. F. Ozsolak, L. L. Poling, Z. Wang, et al., “Chromatin structure analyses identify miRNA promoters,” Genes & Development, vol. 22, no. 22, pp. 3172–3183, 2008. View at Publisher · View at Google Scholar · View at PubMed
  20. J. M. Thomson, M. Newman, J. S. Parker, E. M. Morin-Kensicki, T. Wright, and S. M. Hammond, “Extensive post-transcriptional regulation of microRNAs and its implications for cancer,” Genes & Development, vol. 20, no. 16, pp. 2202–2207, 2006. View at Publisher · View at Google Scholar · View at PubMed
  21. W. Yang, T. P. Chendrimada, Q. Wang, et al., “Modulation of microRNA processing and expression through RNA editing by ADAR deaminases,” Nature Structural and Molecular Biology, vol. 13, no. 1, pp. 13–21, 2006. View at Publisher · View at Google Scholar · View at PubMed
  22. Y. Kawahara, B. Zinshteyn, P. Sethupathy, H. Iizasa, A. G. Hatzigeorgiou, and K. Nishikura, “Redirection of silencing targets by adenosine-to-inosine editing of miRNAs,” Science, vol. 315, no. 5815, pp. 1137–1140, 2007. View at Publisher · View at Google Scholar · View at PubMed
  23. E. Lund, S. Güttinger, A. Calado, J. E. Dahlberg, and U. Kutay, “Nuclear export of microRNA precursors,” Science, vol. 303, no. 5654, pp. 95–98, 2004. View at Publisher · View at Google Scholar · View at PubMed
  24. R. Yi, B. P. Doehle, Y. Qin, I. G. Macara, and B. R. Cullen, “Overexpression of exportin 5 enhances RNA interference mediated by short hairpin RNAs and microRNAs,” RNA, vol. 11, no. 2, pp. 220–226, 2005. View at Publisher · View at Google Scholar · View at PubMed
  25. H.-W. Hwang, E. A. Wentzel, and J. T. Mendell, “A hexanucleotide element directs microRNA nuclear import,” Science, vol. 315, no. 5808, pp. 97–100, 2007. View at Publisher · View at Google Scholar · View at PubMed
  26. A. Tanzer and P. F. Stadler, “Molecular evolution of a microRNA cluster,” Journal of Molecular Biology, vol. 339, no. 2, pp. 327–335, 2004. View at Publisher · View at Google Scholar · View at PubMed
  27. A. Rodriguez, S. Griffiths-Jones, J. L. Ashurst, and A. Bradley, “Identification of mammalian microRNA host genes and transcription units,” Genome Research, vol. 14, no. 10A, pp. 1902–1910, 2004. View at Publisher · View at Google Scholar · View at PubMed
  28. N. Iwai and H. Naraba, “Polymorphisms in human pre-miRNAs,” Biochemical and Biophysical Research Communications, vol. 331, no. 4, pp. 1439–1444, 2005. View at Publisher · View at Google Scholar · View at PubMed
  29. B. Weber, C. Stresemann, B. Brueckner, and F. Lyko, “Methylation of human MicroRNA genes in normal and neoplastic cells,” Cell Cycle, vol. 6, no. 9, pp. 1001–1005, 2007.
  30. S. Tan, J. Guo, Q. Huang, et al., “Retained introns increase putative microRNA targets within 3 UTRs of human mRNA,” FEBS Letters, vol. 581, no. 6, pp. 1081–1086, 2007. View at Publisher · View at Google Scholar · View at PubMed
  31. R. I. Gregory, K.-P. Yan, G. Amuthan, et al., “The Microprocessor complex mediates the genesis of microRNAs,” Nature, vol. 432, no. 7014, pp. 235–240, 2004. View at Publisher · View at Google Scholar · View at PubMed
  32. A. M. Denli, B. B. J. Tops, R. H. A. Plasterk, R. F. Ketting, and G. J. Hannon, “Processing of primary microRNAs by the microprocessor complex,” Nature, vol. 432, no. 7014, pp. 231–235, 2004. View at Publisher · View at Google Scholar · View at PubMed
  33. E. Berezikov, W.-J. Chung, J. Willis, E. Cuppen, and E. C. Lai, “Mammalian mirtron genes,” Molecular Cell, vol. 28, no. 2, pp. 328–336, 2007. View at Publisher · View at Google Scholar · View at PubMed
  34. D. J. Luciano, H. Mirsky, N. J. Vendetti, and S. Maas, “RNA editing of a miRNA precursor,” RNA, vol. 10, no. 8, pp. 1174–1177, 2004. View at Publisher · View at Google Scholar · View at PubMed
  35. R. Matsuoka, “Study of the vertebrate MHC multigene family during heart development,” Advances in Experimental Medicine and Biology, vol. 538, pp. 17–30, 2003.
  36. B. A. Janowski, S. T. Younger, D. B. Hardy, R. Ram, K. E. Huffman, and D. R. Corey, “Activating gene expression in mammalian cells with promoter-targeted duplex RNAs,” Nature Chemical Biology, vol. 3, no. 3, pp. 166–173, 2007. View at Publisher · View at Google Scholar · View at PubMed
  37. D. S. Schwarz, G. Hutvágner, T. Du, Z. Xu, N. Aronin, and P. D. Zamore, “Asymmetry in the assembly of the RNAi enzyme complex,” Cell, vol. 115, no. 2, pp. 199–208, 2003. View at Publisher · View at Google Scholar
  38. T. Hirose, M.-D. Shu, and J. A. Steitz, “Splicing-dependent and -independent modes of assembly for intron-encoded box C/D snoRNPs in mammalian cells,” Molecular Cell, vol. 12, no. 1, pp. 113–123, 2003. View at Publisher · View at Google Scholar
  39. A. Clop, F. Marcq, H. Takeda, et al., “A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep,” Nature Genetics, vol. 38, no. 7, pp. 813–818, 2006. View at Publisher · View at Google Scholar · View at PubMed
  40. A. Grishok, A. E. Pasquinelli, D. Conte, et al., “Genes and mechanisms related to RNA interference regulate expression of the small temporal RNAs that control C. elegans developmental timing,” Cell, vol. 106, no. 1, pp. 23–34, 2001. View at Publisher · View at Google Scholar
  41. G. Hutvágner, J. McLachlan, A. E. Pasquinelli, E. Bálint, T. Tuschl, and P. D. Zamore, “A cellular function for the RNA-interference enzyme dicer in the maturation of the let-7 small temporal RNA,” Science, vol. 293, no. 5531, pp. 834–838, 2001. View at Publisher · View at Google Scholar · View at PubMed
  42. R. C. Lee and V. Ambros, “An extensive class of small RNAs in Caenorhabditis elegans,” Science, vol. 294, no. 5543, pp. 862–864, 2001. View at Publisher · View at Google Scholar · View at PubMed
  43. J. W. Pham, J. L. Pellino, Y. S. Lee, R. W. Carthew, and E. J. Sontheimer, “A Dicer-2-dependent 80S complex cleaves targeted mRNAs during RNAi in Drosophila,” Cell, vol. 117, no. 1, pp. 83–94, 2004. View at Publisher · View at Google Scholar
  44. J. Winter, S. Jung, S. Keller, R. I. Gregory, and S. Diederichs, “Many roads to maturity: microRNA biogenesis pathways and their regulation,” Nature Cell Biology, vol. 11, no. 3, pp. 228–234, 2009. View at Publisher · View at Google Scholar · View at PubMed
  45. P. Bertone, V. Stolc, T. E. Royce, et al., “Global identification of human transcribed sequences with genome tiling arrays,” Science, vol. 306, no. 5705, pp. 2242–2246, 2004. View at Publisher · View at Google Scholar · View at PubMed
  46. J. S. Pedersen, G. Bejerano, A. Siepel, et al., “Identification and classification of conserved RNA secondary structures in the human genome,” PLoS Computational Biology, vol. 2, no. 4, article e33, 2006.
  47. P. Landgraf, M. Rusu, R. Sheridan, et al., “A mammalian microRNA expression atlas based on small RNA library sequencing,” Cell, vol. 129, no. 7, pp. 1401–1414, 2007. View at Publisher · View at Google Scholar · View at PubMed
  48. R. Yelin, D. Dahary, R. Sorek, et al., “Widespread occurrence of antisense transcription in the human genome,” Nature Biotechnology, vol. 21, no. 4, pp. 379–386, 2003. View at Publisher · View at Google Scholar · View at PubMed
  49. G. Lavorgna, D. Dahary, B. Lehner, R. Sorek, C. M. Sanderson, and G. Casari, “In search of antisense,” Trends in Biochemical Sciences, vol. 29, no. 2, pp. 88–94, 2004. View at Publisher · View at Google Scholar · View at PubMed
  50. K. Okamura, J. W. Hagen, H. Duan, D. M. Tyler, and E. C. Lai, “The mirtron pathway generates microRNA-class regulatory RNAs in Drosophila,” Cell, vol. 130, no. 1, pp. 89–100, 2007. View at Publisher · View at Google Scholar · View at PubMed
  51. J. G. Ruby, C. H. Jan, and D. P. Bartel, “Intronic microRNA precursors that bypass Drosha processing,” Nature, vol. 448, no. 7149, pp. 83–86, 2007. View at Publisher · View at Google Scholar · View at PubMed
  52. M. Lagos-Quintana, R. Rauhut, W. Lendeckel, and T. Tuschl, “Identification of novel genes coding for small expressed RNAs,” Science, vol. 294, no. 5543, pp. 853–858, 2001. View at Publisher · View at Google Scholar · View at PubMed
  53. E. C. Lai, P. Tomancak, R. W. Williams, and G. M. Rubin, “Computational identification of Drosophila microRNA genes,” Genome Biology, vol. 4, no. 7, article R42, 2003.
  54. Y. Altuvia, P. Landgraf, G. Lithwick, et al., “Clustering and conservation patterns of human microRNAs,” Nucleic Acids Research, vol. 33, no. 8, pp. 2697–2706, 2005. View at Publisher · View at Google Scholar · View at PubMed
  55. S. Baskerville and D. P. Bartel, “Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes,” RNA, vol. 11, no. 3, pp. 241–247, 2005. View at Publisher · View at Google Scholar · View at PubMed
  56. M. J. Weber, “New human and mouse microRNA genes found by homology search,” FEBS Journal, vol. 272, no. 1, pp. 59–73, 2005. View at Publisher · View at Google Scholar · View at PubMed
  57. E. Berezikov, G. van Tetering, M. Verheul, et al., “Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis,” Genome Research, vol. 16, no. 10, pp. 1289–1298, 2006. View at Publisher · View at Google Scholar · View at PubMed
  58. S.-L. Lin, J. D. Miller, and S.-Y. Ying, “Intronic microRNA (miRNA),” Journal of Biomedicine and Biotechnology, vol. 2006, no. 4, p. 26818, 2006. View at Publisher · View at Google Scholar · View at PubMed
  59. Y.-K. Kim and V. N. Kim, “Processing of intronic microRNAs,” The EMBO Journal, vol. 26, no. 3, pp. 775–783, 2007. View at Publisher · View at Google Scholar · View at PubMed
  60. Y. Liang, D. Ridzon, L. Wong, and C. Chen, “Characterization of microRNA expression profiles in normal human tissues,” BMC Genomics, vol. 8, article 166, 2007. View at Publisher · View at Google Scholar · View at PubMed
  61. A. Sewer, N. Paul, P. Landgraf, et al., “Identification of clustered microRNAs using an ab initio prediction method,” BMC Bioinformatics, vol. 6, article 267, 2005. View at Publisher · View at Google Scholar · View at PubMed
  62. H. Seitz, H. Royo, M.-L. Bortolin, S.-P. Lin, A. C. Ferguson-Smith, and J. Cavaillé, “A large imprinted microRNA gene cluster at the mouse Dlk1-Gtl2 domain,” Genome Research, vol. 14, no. 9, pp. 1741–1748, 2004. View at Publisher · View at Google Scholar · View at PubMed
  63. B. Zhang, X. Pan, C. H. Cannon, G. P. Cobb, and T. A. Anderson, “Conservation and divergence of plant microRNA genes,” Plant Journal, vol. 46, no. 2, pp. 243–259, 2006. View at Publisher · View at Google Scholar · View at PubMed
  64. U. Ohler, S. Yekta, L. P. Lim, D. P. Bartel, and C. B. Burge, “Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification,” RNA, vol. 10, no. 9, pp. 1309–1322, 2004. View at Publisher · View at Google Scholar · View at PubMed
  65. J. Lee, Z. Li, R. Brower-Sinning, and B. John, “Regulatory circuit of human microRNA biogenesis,” PLoS Computational Biology, vol. 3, no. 4, article e67, 2007. View at Publisher · View at Google Scholar · View at PubMed
  66. X. Zhou, J. Ruan, G. Wang, and W. Zhang, “Characterization and identification of microRNA core promoters in four model species,” PLoS Computational Biology, vol. 3, no. 3, article e37, 2007. View at Publisher · View at Google Scholar · View at PubMed
  67. B. Ason, D. K. Darnell, B. Wittbrodt, et al., “Differences in vertebrate microRNA expression,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 39, pp. 14385–14389, 2006. View at Publisher · View at Google Scholar · View at PubMed
  68. W. Filipowicz and V. Pogacic, “Biogenesis of small nucleolar ribonucleoproteins,” Current Opinion in Cell Biology, vol. 14, no. 3, pp. 319–327, 2002. View at Publisher · View at Google Scholar
  69. S. Vincenti, V. de Chiara, I. Bozzoni, and C. Presutti, “The position of yeast snoRNA-coding regions within host introns is essential for their biosynthesis and for efficient splicing of the host pre-mRNA,” RNA, vol. 13, no. 1, pp. 138–150, 2007. View at Publisher · View at Google Scholar · View at PubMed
  70. E. van Rooij, L. B. Sutherland, X. Qi, J. A. Richardson, J. Hill, and E. N. Olson, “Control of stress-dependent cardiac growth and gene expression by a microRNA,” Science, vol. 316, no. 5824, pp. 575–579, 2007. View at Publisher · View at Google Scholar · View at PubMed
  71. J. M. Pawlicki and J. A. Steitz, “Primary microRNA transcript retention at sites of transcription leads to enhanced microRNA production,” Journal of Cell Biology, vol. 182, no. 1, pp. 61–76, 2008. View at Publisher · View at Google Scholar · View at PubMed
  72. J. M. Pawlicki and J. A. Steitz, “Subnuclear compartmentalization of transiently expressed polyadenylated pri-microRNAs: processing at transcription sites or accumulation in SC35 foci,” Cell Cycle, vol. 8, no. 3, pp. 345–356, 2009.
  73. M. Morlando, M. Ballarino, N. Gromak, F. Pagano, I. Bozzoni, and N. J. Proudfoot, “Primary microRNA transcripts are processed co-transcriptionally,” Nature Structural and Molecular Biology, vol. 15, no. 9, pp. 902–909, 2008. View at Publisher · View at Google Scholar · View at PubMed
  74. N. Shomron and C. Levy, “MicroRNA-biogenesis and pre-mRNA splicing crosstalk,” Journal of Biomedicine and Biotechnology, vol. 2009, Article ID 594678, 6 pages, 2009. View at Publisher · View at Google Scholar · View at PubMed
  75. S. Butenas, R. F. Branda, C. Van't Veer, K. M. Cawthern, and K. G. Mann, “Platelets and phospholipids in tissue factor-initiated thrombin generation,” Thrombosis and Haemostasis, vol. 86, no. 2, pp. 660–667, 2001.
  76. B. P. Lewis, C. B. Burge, and D. P. Bartel, “Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets,” Cell, vol. 120, no. 1, pp. 15–20, 2005. View at Publisher · View at Google Scholar · View at PubMed
  77. E. Morkin, “Control of cardiac myosin heavy chain gene expression,” Microscopy Research and Technique, vol. 50, no. 6, pp. 522–531, 2000. View at Publisher · View at Google Scholar
  78. D. Gaidatzis, E. van Nimwegen, J. Hausser, and M. Zavolan, “Inference of miRNA targets using evolutionary conservation and pathway analysis,” BMC Bioinformatics, vol. 8, article 69, 2007. View at Publisher · View at Google Scholar · View at PubMed
  79. R. Revilla-i-Domingo and E. H. Davidson, “Developmental gene network analysis,” International Journal of Developmental Biology, vol. 47, no. 7-8, pp. 695–703, 2003.
  80. L. F. Sempere, C. N. Cole, M. A. Mcpeek, and K. J. Peterson, “The phylogenetic distribution of metazoan microRNAs: insights into evolutionary complexity and constraint,” Journal of Experimental Zoology Part B, vol. 306, no. 6, pp. 575–588, 2006. View at Publisher · View at Google Scholar · View at PubMed
  81. K. Chen and N. Rajewsky, “Natural selection on human microRNA binding sites inferred from SNP data,” Nature Genetics, vol. 38, no. 12, pp. 1452–1456, 2006. View at Publisher · View at Google Scholar · View at PubMed
  82. M. A. Saunders, H. Liang, and W.-H. Li, “Human polymorphism at microRNAs and microRNA target sites,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 9, pp. 3300–3305, 2007. View at Publisher · View at Google Scholar · View at PubMed
  83. K. K.-H. Farh, A. Grimson, C. Jan, et al., “The widespread impact of mammalian microRNAs on mRNA repression and evolution,” Science, vol. 310, no. 5755, pp. 1817–1821, 2005. View at Publisher · View at Google Scholar · View at PubMed
  84. R. C. Friedman, K. K.-H. Farh, C. B. Burge, and D. P. Bartel, “Most mammalian mRNAs are conserved targets of microRNAs,” Genome Research, vol. 19, no. 1, pp. 92–105, 2009. View at Publisher · View at Google Scholar · View at PubMed
  85. J. L. Umbach and B. R. Cullen, “The role of RNAi and microRNAs in animal virus replication and antiviral immunity,” Genes & Development, vol. 23, no. 10, pp. 1151–1164, 2009. View at Publisher · View at Google Scholar · View at PubMed
  86. B. R. Cullen, “Viral and cellular messenger RNA targets of viral microRNAs,” Nature, vol. 457, no. 7228, pp. 421–425, 2009. View at Publisher · View at Google Scholar · View at PubMed
  87. B. John, A. J. Enright, A. Aravin, T. Tuschl, C. Sander, and D. S. Marks, “Human microRNA targets,” PLoS Biology, vol. 2, no. 11, article e363, 2004. View at Publisher · View at Google Scholar · View at PubMed
  88. R. Shalgi, D. Lieber, M. Oren, and Y. Pilpel, “Global and local architecture of the mammalian microRNA-transcription factor regulatory network,” PLoS Computational Biology, vol. 3, no. 7, article e131, 2007.
  89. S. Artzi, A. Kiezun, and N. Shomron, “miRNAminer: a tool for homologous microRNA gene search,” BMC Bioinformatics, vol. 9, article 39, 2008. View at Publisher · View at Google Scholar · View at PubMed
  90. Q. Jing, S. Huang, S. Guth, et al., “Involvement of microRNA in AU-rich element-mediated mRNA instability,” Cell, vol. 120, no. 5, pp. 623–634, 2005. View at Publisher · View at Google Scholar · View at PubMed
  91. C. A. Beelman and R. Parker, “Degradation of mRNA in eukaryotes,” Cell, vol. 81, no. 2, pp. 179–183, 1995.
  92. R. Ashfield, P. Enriquez-Harris, and N. J. Proudfoot, “Transcriptional termination between the closely linked human complement genes C2 and factor B: common termination factor for C2 and c-myc?” The EMBO Journal, vol. 10, no. 13, pp. 4197–4207, 1991.
  93. M. Yonaha and N. J. Proudfoot, “Transcriptional termination and coupled polyadenylation in vitro,” The EMBO Journal, vol. 19, no. 14, pp. 3770–3777, 2000.
  94. N. R. Smalheiser and V. I. Torvik, “Alu elements within human mRNAs are probable microRNA targets,” Trends in Genetics, vol. 22, no. 10, pp. 532–536, 2006. View at Publisher · View at Google Scholar · View at PubMed
  95. K. Newcombe, T. Glassco, and C. Mueller, “Regulation of the DBP promoter by PAR proteins and in leukemic cells bearing an E2A/HLF translocation,” Biochemical and Biophysical Research Communications, vol. 245, no. 2, pp. 633–639, 1998. View at Publisher · View at Google Scholar · View at PubMed
  96. M. Lapidot and Y. Pilpel, “Genome-wide natural antisense transcription: coupling its regulation to its different regulatory mechanisms,” EMBO Reports, vol. 7, no. 12, pp. 1216–1222, 2006. View at Publisher · View at Google Scholar · View at PubMed
  97. L.-C. Li, S. T. Okino, H. Zhao, et al., “Small dsRNAs induce transcriptional activation in human cells,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 46, pp. 17337–17342, 2006. View at Publisher · View at Google Scholar · View at PubMed
  98. F. Saatcioglu, D. J. Perry, D. S. Pasco, and J. B. Fagan, “Multiple DNA-binding factors interact with overlapping specificities at the aryl hydrocarbon response element of the cytochrome P450IA1 gene,” Molecular and Cellular Biology, vol. 10, no. 12, pp. 6408–6416, 1990.
  99. K. Chen and N. Rajewsky, “The evolution of gene regulation by transcription factors and microRNAs,” Nature Reviews Genetics, vol. 8, no. 2, pp. 93–103, 2007. View at Publisher · View at Google Scholar · View at PubMed
  100. E. S. Lander, L. M. Linton, B. Birren, et al., “Initial sequencing and analysis of the human genome,” Nature, vol. 409, no. 6822, pp. 860–921, 2001. View at Publisher · View at Google Scholar · View at PubMed
  101. J. F. Abelson, K. Y. Kwan, B. J. O'Roak, et al., “Sequence variants in SLITRK1 are associated with Tourette's syndrome,” Science, vol. 310, no. 5746, pp. 317–320, 2005. View at Publisher · View at Google Scholar · View at PubMed
  102. R. Durrett and D. Schmidt, “Waiting for regulatory sequences to appear,” Annals of Applied Probability, vol. 17, no. 1, pp. 1–32, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  103. A. Stark, J. Brennecke, N. Bushati, R. B. Russell, and S. M. Cohen, “Animal microRNAs confer robustness to gene expression and have a significant impact on 3 UTR evolution,” Cell, vol. 123, no. 6, pp. 1133–1146, 2005. View at Publisher · View at Google Scholar · View at PubMed