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Global profiling of miRNAs and the hairpin precursors: insights into miRNA processing and novel miRNA discovery

Li, Na, You, Xintian, Chen, Tao, Mackowiak, Sebastian D., Friedländer, Marc R., Weigt, Martina, Du, Hang, Gogol-Döring, Andreas, Chang, Zisong, Dieterich, Christoph, Hu, Yuhui, Chen, Wei·2013·Nucleic Acids Research
DOI 10.1093/nar/gkt072PMC PMC3616697
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Figure 1. Genome annotation of sequencing reads from 10 mouse tissues. The pie charts depicted the percentage of 10–40 nt short reads (A) and 50–100 nt long reads (B) that were mapped to the indicated genome features based on miRBase v16.0 (34) and Ensembl Genes 59 (www.biomart.org) (37). ‘Other ncRNA’ included rRNA, tRNA, scRNA, snRNA and srpRNA.
Figure 1. Genome annotation of sequencing reads from 10 mouse tissues. The pie charts depicted the percentage of 10–40 nt short reads (A) and 50–100 nt long reads (B) that were mapped to the indicated genome features based on miRBase v16.0 (34) and Ensembl Genes 59 (www.biomart.org) (37). ‘Other ncRNA’ included rRNA, tRNA, scRNA, snRNA and srpRNA.CC BY-NC · PubMed Central
Figure 2. Distribution of the miRNA and pre-miRNA abundance. (A) Histogram of the log2 transformed expression level (sum of RPMs from 10 tissues, RPM: Reads Per Million total miRNA reads) was shown for the known miRNAs. The miRNAs with precursor detected (black) expressed at a higher level than those without (grey). (B) Comparison of the miRNA and pre-miRNA abundance. Each circle represented one pre-miRNA locus. X-axis denoted the log2 transformed sum of miRNA/miRNA* RPM derived from the pre-miRNA locus in 10 tissues, Y-axis denoted the log2 transformed number of pre-miRNA sequencing reads.
Figure 2. Distribution of the miRNA and pre-miRNA abundance. (A) Histogram of the log2 transformed expression level (sum of RPMs from 10 tissues, RPM: Reads Per Million total miRNA reads) was shown for the known miRNAs. The miRNAs with precursor detected (black) expressed at a higher level than those without (grey). (B) Comparison of the miRNA and pre-miRNA abundance. Each circle represented one pre-miRNA locus. X-axis denoted the log2 transformed sum of miRNA/miRNA* RPM derived from the pre-miRNA locus in 10 tissues, Y-axis denoted the log2 transformed number of pre-miRNA sequencing reads.CC BY-NC · PubMed Central
Figure 3. Probabilistic score distribution of pre-miRNA candidates. Histogram showed the probabilistic score distribution of the potential pre-miRNA candidates using a Naïve Bayesian classifier with six features of known miRNA, including pre-miRNA minimal folding free energy, thermodynamic stability, 5′ and 3′ end duplex overhang as well as pre-miRNA and mature miRNA length. Known pre-miRNAs were shown in black, the remaining in grey.
Figure 3. Probabilistic score distribution of pre-miRNA candidates. Histogram showed the probabilistic score distribution of the potential pre-miRNA candidates using a Naïve Bayesian classifier with six features of known miRNA, including pre-miRNA minimal folding free energy, thermodynamic stability, 5′ and 3′ end duplex overhang as well as pre-miRNA and mature miRNA length. Known pre-miRNAs were shown in black, the remaining in grey.CC BY-NC · PubMed Central
Figure 4. Abundance of known and novel mouse miRNAs. Histogram of the log2 transformed expression level (sum of sequencing reads in 10 mouse tissues) of known miRNAs (grey) and novel miRNAs (black) in 10 mouse tissues. Compared with known miRNAs, novel miRNAs expressed at a much lower level.
Figure 4. Abundance of known and novel mouse miRNAs. Histogram of the log2 transformed expression level (sum of sequencing reads in 10 mouse tissues) of known miRNAs (grey) and novel miRNAs (black) in 10 mouse tissues. Compared with known miRNAs, novel miRNAs expressed at a much lower level.CC BY-NC · PubMed Central
Figure 5. Examples of novel pre-miRNAs discovered in this study. (A) A novel mouse pre-miRNA within the intron of the protein-coding gene Camk2a. (B) A novel mouse 5′ tailed mirtron derived from the protein-coding gene Ankrd23. (C) A novel C. elegans pre-miRNA from the 5′UTR of the protein-coding gene F35G12.2.
Figure 5. Examples of novel pre-miRNAs discovered in this study. (A) A novel mouse pre-miRNA within the intron of the protein-coding gene Camk2a. (B) A novel mouse 5′ tailed mirtron derived from the protein-coding gene Ankrd23. (C) A novel C. elegans pre-miRNA from the 5′UTR of the protein-coding gene F35G12.2.CC BY-NC · PubMed Central
Figure 6. Novel miRNAs are dependent on Dicer for expression. RNA interference was used to silence Dicer in a mouse N2a cell line. Log2 fold-changes of small RNA expression after Dicer knockdown were noted for known miRNAs (A), novel miRNAs (B), tRNAs (C) and rRNAs (D).
Figure 6. Novel miRNAs are dependent on Dicer for expression. RNA interference was used to silence Dicer in a mouse N2a cell line. Log2 fold-changes of small RNA expression after Dicer knockdown were noted for known miRNAs (A), novel miRNAs (B), tRNAs (C) and rRNAs (D).CC BY-NC · PubMed Central
Figure 7. Novel miRNAs show similar Ago2-binding profile as known miRNAs. X-axis denoted the abundance (log10 transformed RPM value) of known miRNAs (light blue dots), novel miRNAs (red dots), tRNAs (black solid squares) and rRNAs (dark blue solid triangles) in the input sample. y-axis denoted their abundance in Ago2 IP sample. Novel miRNAs showed similar Ago2-binding profile as known miRNAs, whereas both tRNAs and rRNAs were significantly depleted in Ago2 IP sample.
Figure 7. Novel miRNAs show similar Ago2-binding profile as known miRNAs. X-axis denoted the abundance (log10 transformed RPM value) of known miRNAs (light blue dots), novel miRNAs (red dots), tRNAs (black solid squares) and rRNAs (dark blue solid triangles) in the input sample. y-axis denoted their abundance in Ago2 IP sample. Novel miRNAs showed similar Ago2-binding profile as known miRNAs, whereas both tRNAs and rRNAs were significantly depleted in Ago2 IP sample.CC BY-NC · PubMed Central
Figure 8. Experimental validation of the two novel miRNA candidates. (A) The RNA with size of 10–200 nt was separated using flashPAGE Fractionator (Ambion) and loaded onto the denaturing PAGE gel for northern blot. Northern blot of two novel miRNAs (CandidateMMU32 and CandidateMMU33) revealed bands corresponding to the mature miRNA (marked as *). (B) In vitro rDicer processing. 32P-labelled CandidateMMU32 pre-miRNA (lane 3–5) and CandidateMMU33 pre-miRNA (lane 8–10) were incubated with rDicer for an increasing time. Lane 1 and 6 showed the size marker. Lane 2 and 7 were templates without adding rDicer. Both candidates could be efficiently processed by rDicer. (C) Luciferase assay. The sequences complementary to candidate miRNAs (Target) or scrambled control sequences (Scramble) were inserted into the 3′UTR of the Renilla luciferase gene in psiCHECK-2 plasmid. HEK293 cells were transfected with the plasmid alone or together with candidate pre-miRNA in vitro transcripts. All of the experiments were preformed in triplicates. Compared with scrambled controls, presence of target sequences of the candidate miRNAs in 3′UTR resulted in significant reduction of protein translation.
Figure 8. Experimental validation of the two novel miRNA candidates. (A) The RNA with size of 10–200 nt was separated using flashPAGE Fractionator (Ambion) and loaded onto the denaturing PAGE gel for northern blot. Northern blot of two novel miRNAs (CandidateMMU32 and CandidateMMU33) revealed bands corresponding to the mature miRNA (marked as *). (B) In vitro rDicer processing. 32P-labelled CandidateMMU32 pre-miRNA (lane 3–5) and CandidateMMU33 pre-miRNA (lane 8–10) were incubated with rDicer for an increasing time. Lane 1 and 6 showed the size marker. Lane 2 and 7 were templates without adding rDicer. Both candidates could be efficiently processed by rDicer. (C) Luciferase assay. The sequences complementary to candidate miRNAs (Target) or scrambled control sequences (Scramble) were inserted into the 3′UTR of the Renilla luciferase gene in psiCHECK-2 plasmid. HEK293 cells were transfected with the plasmid alone or together with candidate pre-miRNA in vitro transcripts. All of the experiments were preformed in triplicates. Compared with scrambled controls, presence of target sequences of the candidate miRNAs in 3′UTR resulted in significant reduction of protein translation.CC BY-NC · PubMed Central
Figure 9. Northern Blot validation of six novel miRNAs in C. sp. 11. *Signals denoted the putative mature miRNAs. Note that CandiCsp11_37 and CandiCsp11_49 have corresponding homologues in C. elegans as Cel-mir-58 and Cel-let-7, respectively.
Figure 9. Northern Blot validation of six novel miRNAs in C. sp. 11. *Signals denoted the putative mature miRNAs. Note that CandiCsp11_37 and CandiCsp11_49 have corresponding homologues in C. elegans as Cel-mir-58 and Cel-let-7, respectively.CC BY-NC · PubMed Central

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Cites

Research works printed but not linked: 52

  1. Bartel, 2004, MicroRNAs: genomics, biogenesis, mechanism, and function, Cell, 116, 281, 10.1016/S0092-8674(04)00045-5 · DOI
  2. Gregory, 2004, The microprocessor complex mediates the genesis of microRNAs, Nature, 432, 235, 10.1038/nature03120 · DOI
  3. Han, 2004, The Drosha-DGCR8 complex in primary microRNA processing, Genes Dev., 18, 3016, 10.1101/gad.1262504 · DOI
  4. Berezikov, 2007, Mammalian mirtron genes, Mol. Cell, 28, 328, 10.1016/j.molcel.2007.09.028 · DOI
  5. Okamura, 2007, The mirtron pathway generates microRNA-class regulatory RNAs in Drosophila, Cell, 130, 89, 10.1016/j.cell.2007.06.028 · DOI
  6. Ruby, 2007, Intronic microRNA precursors that bypass Drosha processing, Nature, 448, 83, 10.1038/nature05983 · DOI · Paper page
  7. Babiarz, 2008, Mouse ES cells express endogenous shRNAs, siRNAs, and other microprocessor-independent, Dicer-dependent small RNAs, Genes Dev., 22, 2773, 10.1101/gad.1705308 · DOI
  8. Flynt, 2010, MicroRNA biogenesis via splicing and exosome-mediated trimming in Drosophila, Mol. Cell, 38, 900, 10.1016/j.molcel.2010.06.014 · DOI
  9. Yang, 2011, Alternative miRNA biogenesis pathways and the interpretation of core miRNA pathway mutants, Mol. Cell, 43, 892, 10.1016/j.molcel.2011.07.024 · DOI
  10. Ender, 2008, A human snoRNA with microRNA-like functions, Mol. Cell, 32, 519, 10.1016/j.molcel.2008.10.017 · DOI
  11. Saraiya, 2008, snoRNA, a novel precursor of microRNA in Giardia lamblia, PLoS Pathog, 4, 10.1371/journal.ppat.1000224 · DOI
  12. Scott, 2009, Human miRNA precursors with box H/ACA snoRNA features, PLoS Comput. Biol., 5, 10.1371/journal.pcbi.1000507 · DOI
  13. Taft, 2009, Small RNAs derived from snoRNAs, RNA, 15, 1233, 10.1261/rna.1528909 · DOI
  14. Berezikov, 2011, Deep annotation of Drosophila melanogaster microRNAs yields insights into their processing, modification, and emergence, Genome Res., 21, 203, 10.1101/gr.116657.110 · DOI
  15. Brameier, 2011, Human box C/D snoRNAs with miRNA like functions: expanding the range of regulatory RNAs, Nucleic Acids Res., 39, 675, 10.1093/nar/gkq776 · DOI
  16. Ono, 2011, Identification of human miRNA precursors that resemble box C/D snoRNAs, Nucleic Acids Res., 39, 3879, 10.1093/nar/gkq1355 · DOI
  17. Lund, 2004, Nuclear export of microRNA precursors, Science, 303, 95, 10.1126/science.1090599 · DOI
  18. Okada, 2009, A high-resolution structure of the pre-microRNA nuclear export machinery, Science, 326, 1275, 10.1126/science.1178705 · DOI
  19. Hutvagner, 2001, A cellular function for the RNA-interference enzyme Dicer in the maturation of the let-7 small temporal RNA, Science, 293, 834, 10.1126/science.1062961 · DOI · Paper page
  20. Gregory, 2005, Human RISC couples microRNA biogenesis and posttranscriptional gene silencing, Cell, 123, 631, 10.1016/j.cell.2005.10.022 · DOI · Paper page
  21. Leuschner, 2006, Cleavage of the siRNA passenger strand during RISC assembly in human cells, EMBO Rep., 7, 314, 10.1038/sj.embor.7400637 · DOI
  22. Hutvagner, 2008, Argonaute proteins: key players in RNA silencing, Nat. Rev. Mol. Cell Biol., 9, 22, 10.1038/nrm2321 · DOI
  23. Krek, 2005, Combinatorial microRNA target predictions, Nat. Genet., 37, 495, 10.1038/ng1536 · DOI · Paper page
  24. Lewis, 2005, Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets, Cell, 120, 15, 10.1016/j.cell.2004.12.035 · DOI · Paper page
  25. Lagos-Quintana, 2001, Identification of novel genes coding for small expressed RNAs, Science, 294, 853, 10.1126/science.1064921 · DOI · Paper page
  26. Lau, 2001, An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans, Science, 294, 858, 10.1126/science.1065062 · DOI · Paper page
  27. Lee, 2001, An extensive class of small RNAs in Caenorhabditis elegans, Science, 294, 862, 10.1126/science.1065329 · DOI · Paper page
  28. Mardis, 2008, The impact of next-generation sequencing technology on genetics, Trends Genet., 24, 133, 10.1016/j.tig.2007.12.007 · DOI
  29. Shendure, 2008, Overview of DNA sequencing strategies, Curr. Protoc. Mol. Biol., 10.1002/0471142727.mb0701s81 · DOI
  30. Newman, 2011, Deep sequencing of microRNA precursors reveals extensive 3′ end modification, RNA, 17, 1795, 10.1261/rna.2713611 · DOI
  31. Burroughs, 2011, pre-miRNA profiles obtained through application of locked nucleic acids and deep sequencing reveals complex 5′/3′ arm variation including concomitant cleavage and polyuridylation patterns, Nucleic Acids Res., 40, 1424, 10.1093/nar/gkr903 · DOI
  32. Livak, 2001, Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method, Methods, 25, 402, 10.1006/meth.2001.1262 · DOI · Paper page
  33. Beitzinger, 2011, Experimental identification of microRNA targets by immunoprecipitation of Argonaute protein complexes, Methods Mol. Biol., 732, 153, 10.1007/978-1-61779-083-6_12 · DOI
  34. Kozomara, 2011, miRBase: integrating microRNA annotation and deep-sequencing data, Nucleic Acids Res., 39, D152, 10.1093/nar/gkq1027 · DOI · Paper page
  35. Li, 2008, SOAP: short oligonucleotide alignment program, Bioinformatics, 24, 713, 10.1093/bioinformatics/btn025 · DOI · Paper page
  36. Li, 2009, SOAP2: an improved ultrafast tool for short read alignment, Bioinformatics, 25, 1966, 10.1093/bioinformatics/btp336 · DOI
  37. Kinsella, 2011, Ensembl BioMarts: a hub for data retrieval across taxonomic space, Database (Oxford), 2011, 10.1093/database/bar030 · DOI
  38. Hofacker, 2006, Memory efficient folding algorithms for circular RNA secondary structures, Bioinformatics, 22, 1172, 10.1093/bioinformatics/btl023 · DOI
  39. Bonnet, 2004, Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences, Bioinformatics, 20, 2911, 10.1093/bioinformatics/bth374 · DOI
  40. Underwood, 2010, FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing, Nat. Methods, 7, 995, 10.1038/nmeth.1529 · DOI
  41. Adamidi, 2011, De novo assembly and validation of planaria transcriptome by massive parallel sequencing and shotgun proteomics, Genome Res., 21, 1193, 10.1101/gr.113779.110 · DOI
  42. Diederichs, 2007, Dual role for argonautes in microRNA processing and posttranscriptional regulation of microRNA expression, Cell, 131, 1097, 10.1016/j.cell.2007.10.032 · DOI
  43. Nishikura, 2010, Functions and regulation of RNA editing by ADAR deaminases, Annu. Rev. Biochem., 79, 321, 10.1146/annurev-biochem-060208-105251 · DOI · Paper page
  44. Kawahara, 2007, Redirection of silencing targets by adenosine-to-inosine editing of miRNAs, Science, 315, 1137, 10.1126/science.1138050 · DOI · Paper page
  45. Chiang, 2010, Mammalian microRNAs: experimental evaluation of novel and previously annotated genes, Genes Dev., 24, 992, 10.1101/gad.1884710 · DOI · Paper page
  46. Smalheiser, 2005, Mammalian microRNAs derived from genomic repeats, Trends Genet., 21, 322, 10.1016/j.tig.2005.04.008 · DOI · Paper page
  47. Kim, 2009, Biogenesis of small RNAs in animals, Nat. Rev. Mol. Cell Biol., 10, 126, 10.1038/nrm2632 · DOI · Paper page
  48. Friedlander, 2008, Discovering microRNAs from deep sequencing data using miRDeep, Nat. Biotechnol., 26, 407, 10.1038/nbt1394 · DOI
  49. Hackenberg, 2009, miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments, Nucleic Acids Res., 37, W68, 10.1093/nar/gkp347 · DOI
  50. Hendrix, 2010, miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data, Genome Biol., 11, R39, 10.1186/gb-2010-11-4-r39 · DOI
  51. Gerlach, 2009, miROrtho: computational survey of microRNA genes, Nucleic Acids Res., 37, D111, 10.1093/nar/gkn707 · DOI
  52. Kiontke, 2011, A phylogeny and molecular barcodes for Caenorhabditis, with numerous new species from rotting fruits, BMC Evol. Biol., 11, 339, 10.1186/1471-2148-11-339 · DOI

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Metadata

Authors
Li, N., You, X., Chen, T., Mackowiak, S. D., Friedländer, M. R., Weigt, M., Du, H., Gogol-Döring, A., Chang, Z., Dieterich, C., Hu, Y., & Chen, W.
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2013-02-08
Volume
41
Issue
6
Pages
3619-3634
ISSN
  • 1362-4962
  • 0305-1048
DOI
10.1093/nar/gkt072
PMC
PMC3616697
Cited by
57
References
1
Language
en
Licence
creativecommons.org/licenses/by-nc/3.0/ (vor, 1554 d)

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