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An integrated expression atlas of miRNAs and their promoters in human and mouse

  • Nature Biotechnology volume 35, pages 872878 (2017)
  • doi:10.1038/nbt.3947
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Abstract

MicroRNAs (miRNAs) are short non-coding RNAs with key roles in cellular regulation. As part of the fifth edition of the Functional Annotation of Mammalian Genome (FANTOM5) project, we created an integrated expression atlas of miRNAs and their promoters by deep-sequencing 492 short RNA (sRNA) libraries, with matching Cap Analysis Gene Expression (CAGE) data, from 396 human and 47 mouse RNA samples. Promoters were identified for 1,357 human and 804 mouse miRNAs and showed strong sequence conservation between species. We also found that primary and mature miRNA expression levels were correlated, allowing us to use the primary miRNA measurements as a proxy for mature miRNA levels in a total of 1,829 human and 1,029 mouse CAGE libraries. We thus provide a broad atlas of miRNA expression and promoters in primary mammalian cells, establishing a foundation for detailed analysis of miRNA expression patterns and transcriptional control regions.

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Gene Expression Omnibus

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DDBJ/GenBank/EMBL

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Acknowledgements

FANTOM5 was made possible by the following grants: Research Grant for RIKEN Omics Science Center from MEXT to Y.H.; Grant of the Innovative Cell Biology by Innovative Technology (Cell Innovation Program) from the MEXT to Y.H.; Research Grant from MEXT to the RIKEN Center for Life Science Technologies; Research Grant to RIKEN Preventive Medicine and Diagnosis Innovation Program from MEXT to Y.H. K.V.-S. and A.S. were supported by the Lundbeck and Novo Nordisk Foundations. A.R.R.F. is supported by a Senior Cancer Research Fellowship from the Cancer Research Trust, funds raised by the MACA Ride to Conquer Cancer, and the Australian Research Council's Discovery Projects funding scheme (DP160101960). Y.A.M. was supported by the Russian Science Foundation, grant 15-14-30002. R.D. was supported by the Russian Science Foundation, grant 14-44-00022. We would like to thank L. Schwarzfischer for technical assistance and N. Eichner and G. Meister for sequencing RACE products. We would also like to thank GeNAS for data production.

Author information

Author notes

Affiliations

  1. Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan.

    • Derek de Rie
    • , Imad Abugessaisa
    • , Erik Arner
    • , Nicolas Bertin
    • , A Maxwell Burroughs
    • , Carsten O Daub
    • , Ruslan Deviatiiarov
    • , Alexandre Fort
    • , Jayson Harshbarger
    • , Akira Hasegawa
    • , Kosuke Hashimoto
    • , Chung Chau Hon
    • , Yuri Ishizu
    • , Takeya Kasukawa
    • , Timo Lassmann
    • , Marina Lizio
    • , Shohei Noma
    • , Jessica Severin
    • , Jay W Shin
    • , Hiroshi Tarui
    • , Kayoko Yasuzawa
    • , Hideya Kawaji
    • , Piero Carninci
    • , Alistair R R Forrest
    •  & Michiel J L de Hoon
  2. Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, Amsterdam, the Netherlands.

    • Derek de Rie
  3. Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

    • Tanvir Alam
    •  & Haitham Ashoor
  4. RIKEN Omics Science Center (OSC), Yokohama, Japan.

    • Erik Arner
    • , Nicolas Bertin
    • , A Maxwell Burroughs
    • , Carsten O Daub
    • , Alexandre Fort
    • , Jayson Harshbarger
    • , Akira Hasegawa
    • , Kosuke Hashimoto
    • , Yuri Ishizu
    • , Timo Lassmann
    • , Marina Lizio
    • , Shohei Noma
    • , Jessica Severin
    • , Jay W Shin
    • , Hiroshi Tarui
    • , Hideya Kawaji
    • , Yoshihide Hayashizaki
    • , Piero Carninci
    • , Alistair R R Forrest
    •  & Michiel J L de Hoon
  5. Department of Medicine, Karolinska Institutet at Karolinska University Hospital, Huddinge, Sweden.

    • Peter Arner
    • , Gaby Åström
    •  & Niklas Mejhert
  6. Department of Dermatology and Allergy, Charité Campus Mitte, Universitätsmedizin Berlin, Berlin, Germany.

    • Magda Babina
    •  & Sven Guhl
  7. Cancer Science Institute of Singapore, National University of Singapore, Singapore.

    • Nicolas Bertin
  8. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.

    • A Maxwell Burroughs
  9. The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.

    • Ailsa J Carlisle
    •  & Kim M Summers
  10. Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.

    • Michael Detmar
    •  & Filip Roudnicky
  11. Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.

    • Ruslan Deviatiiarov
  12. Department of Internal Medicine III, University Hospital Regensburg, Regensburg, Germany.

    • Claudia Gebhard
    •  & Michael Rehli
  13. Regensburg Centre for Interventional Immunology (RCI), Regensburg, Germany.

    • Claudia Gebhard
    •  & Michael Rehli
  14. Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.

    • Daniel Goldowitz
    • , Thomas J Ha
    • , Charles-Henri Lecellier
    • , Anthony Mathelier
    •  & Peter G Zhang
  15. Melanoma Research Center, The Wistar Institute, Philadelphia, Pennsylvania, USA.

    • Meenhard Herlyn
    •  & Rolf K Swoboda
  16. German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.

    • Peter Heutink
    •  & Patrizia Rizzu
  17. Australian Infectious Diseases Research Centre (AID), University of Queensland, Brisbane, Queensland, Australia.

    • Kelly J Hitchens
  18. The University of Melbourne Centre for Stem Cell Systems, School of Biomedical Sciences, The University of Melbourne, Victoria, Australia.

    • Edward Huang
    •  & Christine A Wells
  19. Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.

    • Edward Huang
    •  & Christine A Wells
  20. Laboratory Animal Research Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.

    • Chieko Kai
    • , Hiroki Sato
    •  & Misako Yoneda
  21. Harry Perkins Institute of Medical Research, and the Centre for Medical Research, University of Western Australia, QEII Medical Centre, Perth, Western Australia, Australia.

    • Peter Klinken
    • , Louise Winteringham
    •  & Alistair R R Forrest
  22. Telethon Kids Institute, The University of Western Australia, Subiaco, Western Australia, Australia.

    • Timo Lassmann
  23. Institute of Molecular Genetics of Montpellier, Montpellier, France.

    • Charles-Henri Lecellier
  24. Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea.

    • Weonju Lee
  25. Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.

    • Vsevolod Makeev
    •  & Yulia A Medvedeva
  26. Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.

    • Vsevolod Makeev
  27. Moscow Institute of Physics and Technology, Dolgoprudny, Russia.

    • Vsevolod Makeev
  28. IMPPC, Institute of Predictive and Personalized Medicine of Cancer, Ctra. de Can Ruti, Badalona, Spain.

    • Yulia A Medvedeva
  29. Institute of Bioengineering, Research Center of Biotechnology, Moscow, Russia.

    • Yulia A Medvedeva
  30. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

    • Christopher J Mungall
  31. Department of Biochemistry, Ohu University School of Pharmaceutical Sciences, Koriyama, Japan.

    • Mitsuhiro Ohshima
  32. Laboratory of Cell Systems, Institute for Protein Research, Osaka University, Osaka, Japan.

    • Mariko Okada-Hatakeyama
  33. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

    • Mariko Okada-Hatakeyama
    •  & Noriko Yumoto
  34. Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden.

    • Helena Persson
  35. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway.

    • Pål Sætrom
  36. Department of Clinical Molecular Genetics, School of Pharmacy, Tokyo University of Pharmacy and Life Sciences, Tokyo, Japan.

    • Hiroo Toyoda
  37. The Bioinformatics Centre, Department of Biology, and Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark.

    • Kristoffer Vitting-Seerup
    •  & Albin Sandelin
  38. Department of Biochemistry, Nihon University School of Dentistry, Tokyo, Japan.

    • Yoko Yamaguchi
  39. The SKI Stem Cell Research Facility, The Center for Stem Cell Biology and Developmental Biology Program, Sloan Kettering Institute, New York, New York, USA.

    • Susan Zabierowski
  40. Mater Research Institute—University of Queensland, Translational Research Institute, Brisbane, Australia.

    • Kim M Summers
  41. RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan.

    • Hideya Kawaji
    •  & Yoshihide Hayashizaki

Consortia

  1. The FANTOM Consortium

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Contributions

P.A., G.Å., M.B., A.J.C., M.D., D.G., S.G., T.J.H., M.H., P.H., K.J.H., C.K., P.K., W.L., N.M., M.O., M.O.-H., P.R., H.S., R.K.S., H.To., M.Y., N.Y., S.Z., P.G.Z., L.W., Y.Y., C.A.W., K.M.S., and A.R.R.F. provided RNA samples; E.A. and C.O.D. selected samples from the FANTOM5 time courses; Y.I., S.N., and H.Ta. produced the sRNA libraries; I.A., M.L., H.K., and T.K. managed the data; D.d.R., M.J.L.d.H., K.V.-S., A.M.B., T.A., H.A., A.H., T.L., H.P., C.-H.L. A.M., V.M., and M.R. carried out the bioinformatics analyses with the help of C.C.H., M.L., K.H., F.R., and J.S.; C.J.M. provided the cell ontology; K.M.S. created the Miru visualization; A.F., A.M., A.R.R.F., A.S., C.-H.L. C.A.W., D.d.R., E.H., F.R., H.P., K.V.-S., A.M.B., M.J.L.d.H., M.R., N.B., P.S., R.D., V.M., and Y.A.M. contributed to the manual miRNA promoter annotation; K.Y. and J.W.S. performed the expression validation experiments of known miRNAs; E.H. and C.A.W. performed the validation experiments of candidate miRNAs; C.G. and M.R. performed the RACE experiments; J.H. created the web visualization tool; D.d.R., A.R.R.F., and M.J.L.d.H. wrote the manuscript with the help of E.A., A.S., A.M.B., K.M.S., K.V.-S., M.R., N.B., P.C., P.S., and C.A.W.; A.R.R.F. and M.J.L.d.H. designed the study; P.C. and Y.H. supervised the FANTOM5 project.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Alistair R R Forrest or Michiel J L de Hoon.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–29 and Supplementary Note

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Short RNA data sets analyzed in this study.

  2. 2.

    Supplementary Table 2

    Novel RNA samples used. Most FANTOM5 human and mouse RNA samples used were described previously (ref. 17,18) and are therefore not included in this table.

  3. 3.

    Supplementary Table 3

    FANTOM5 RNA samples and sRNA libraries. Matching CAGE (ref. 17–19) and sRNA libraries were produced from the same RNA sample. In total, five of the CAGE libraries and two of the sRNA libraries were discarded because of their low quality; for one of the RNA samples, an sRNA library but no CAGE library was produced.

  4. 4.

    Supplementary Table 4

    Evaluation of human pre-miRNAs. For each pre-miRNA in the human robust, permissive, and candidate set, we evaluated the miRBase high-confidence criteria (Table 2), and the statistical significance of the Drosha CAGE peak as observed in the FANTOM5 and ENCODE CAGE data.

  5. 5.

    Supplementary Table 5

    Evaluation of murine pre-miRNAs. For each pre-miRNA in the murine robust, permissive, and candidate set, we evaluated the miRBase high-confidence criteria (Table 2), and the statistical significance of the Drosha CAGE peak as observed in the FANTOM5 CAGE data.

  6. 6.

    Supplementary Table 6

    Genomic locations of the candidate miRNAs predicted by miRDeep2 in human (genome assembly hg19).

  7. 7.

    Supplementary Table 7

    Genome sequence at the genomic locus of each candidate miRNA in human, the secondary structure of the predicted pre-miRNA with the corresponding ΔG, and aligning reads with their counts. Sequenced nucleotides that do not match the genome sequence are shown in lowercase.

  8. 8.

    Supplementary Table 8

    Genomic locations of the candidate miRNAs predicted by miRDeep2 in mouse (genome assembly mm9).

  9. 9.

    Supplementary Table 9

    Genome sequence at the genomic locus of each candidate miRNA in mouse, the secondary structure of the predicted pre-miRNA with the corresponding ΔG, and aligning reads with their counts. Sequenced nucleotides that do not match the genome sequence are shown in lowercase.

  10. 10.

    Supplementary Table 10

    Forward primers used for the validation of candidate miRNA expression by qPCR.

  11. 11.

    Supplementary Table 11

    Expression table of human miRNAs in the robust, permissive, and candidate set. The values shown are the (unnormalized) counts of sequence reads overlapping the mature miRNA region, and may be non-integer due to sequence reads mapping to multiple genomic locations.

  12. 12.

    Supplementary Table 12

    Expression table of murine miRNAs in the robust, permissive, and candidate set. The values shown are the (unnormalized) counts of sequence reads overlapping the mature miRNA region, and may be non-integer due to sequence reads mapping to multiple genomic locations.

  13. 13.

    Supplementary Table 13

    Cell ontology enrichment analysis. For each mature miRNA, we show the cell type specificity index, the median and maximum expression level, the RNA sample in which the miRNA was most highly expressed, the top-3 cell ontology clusters in which its expression is most enriched, with the corresponding significance value and the base-2 logarithm of the expression fold-ratio, and the top-3 cell ontology clusters in which its expression is most depleted, with the corresponding significance value and the base-2 logarithm of the expression fold-ratio.

  14. 14.

    Supplementary Table 14

    RNA samples contained in each cell ontology cluster (sRNA data).

  15. 15.

    Supplementary Table 15

    Computational miRNA promoter predictions in human. For each primary miRNA, we show the genomic location (chromosome, strand, and transcription start site; genome assembly hg19) and name of the predicted promoter, the corresponding primary miRNA, their status as intronic (if the primary miRNA transcript is coding) or intergenic (if the primary miRNA is non-coding), the pre-miRNAs contained in the primary miRNA, the average sequence conservation of the miRNA promoter, the maximum CAGE expression level, the RNA sample in which the primary miRNA promoter was most highly expressed, the top-3 cell ontology clusters in which CAGE expression of this promoter is most enriched, with the corresponding statistical significance and the base-2 logarithm of the expression fold-ratio, and the top-3 cell ontology clusters in which CAGE expression of this promoter is most depleted, with the corresponding statistical significance and the base-2 logarithm of the expression fold-ratio. The promoter loci and names were taken from the FANTOM5 permissive promoter set (ref. 17).

  16. 16.

    Supplementary Table 16

    MicroRNA promoter predictions in mouse. For each primary miRNA, we show the genomic location (chromosome, strand, and transcription start site; genome assembly mm9) and name of the predicted promoter, the corresponding primary miRNA, their status as intronic (if the primary miRNA transcript is coding) or intergenic (if the primary miRNA is non-coding), the pre-miRNAs contained in the primary miRNA, and the average sequence conservation of the miRNA promoter. The promoter loci and names were taken from the FANTOM5 permissive promoter set (ref. 17).

  17. 17.

    Supplementary Table 17

    Curated miRNA promoter predictions in human. For each primary miRNA, we show the genomic location (chromosome, strand, and transcription start site; genome assembly hg19) and name of the predicted promoter, the corresponding primary miRNA, their status as intronic (if the primary miRNA transcript is coding) or intergenic (if the primary miRNA is non-coding), the pre-miRNAs contained in the primary miRNA, the average sequence conservation of the miRNA promoter, the maximum CAGE expression level, the RNA sample in which the primary miRNA promoter was most highly expressed, the top-3 cell ontology clusters in which CAGE expression of this promoter is most enriched, with the corresponding statistical significance and the base-2 logarithm of the expression fold-ratio, and the top-3 cell ontology clusters in which CAGE expression of this promoter is most depleted, with the corresponding statistical significance and the base-2 logarithm of the expression fold-ratio. The promoter loci and names were taken from the FANTOM5 permissive promoter set (ref. 17).

  18. 18.

    Supplementary Table 18

    Outer and inner primers used for the validation of miRNA promoters by RACE.

  19. 19.

    Supplementary Table 19

    Spearman correlation across human primary cells between the mature miRNA expression, as measured by sRNA sequencing, and the miRNA promoter, as measured by CAGE.

  20. 20.

    Supplementary Table 20

    RNA samples contained in each cell ontology cluster (CAGE data).