MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression. Heterozygous loss-of-function point mutations of miRNA genes are associated with several human congenital disorders1,2,3,4,5, but neomorphic (gain-of-new-function) mutations in miRNAs due to nucleotide substitutions have not been reported. Here we describe a neomorphic seed region mutation in the chondrocyte-specific, super-enhancer-associated MIR140 gene encoding microRNA-140 (miR-140) in a novel autosomal dominant human skeletal dysplasia. Mice with the corresponding single nucleotide substitution show skeletal abnormalities similar to those of the patients but distinct from those of miR-140-null mice6. This mutant miRNA gene yields abundant mutant miR-140-5p expression without miRNA-processing defects. In chondrocytes, the mutation causes widespread derepression of wild-type miR-140-5p targets and repression of mutant miR-140-5p targets, indicating that the mutation produces both loss-of-function and gain-of-function effects. Furthermore, the mutant miR-140-5p seed competes with the conserved RNA-binding protein Ybx1 for overlapping binding sites. This finding may explain the potent target repression and robust in vivo effect by this mutant miRNA even in the absence of evolutionary selection of miRNA–target RNA interactions, which contributes to the strong regulatory effects of conserved miRNAs7,8. Our study presents the first case of a pathogenic gain-of-function miRNA mutation and provides molecular insight into neomorphic actions of emerging and/or mutant miRNAs.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

Data generated during this study are available in the Gene Expression Omnibus under accession number GSE98309. The human variant is deposited in the ClinVar database (SCV000586692.1). Human genome data from the individuals participating in the study is protected by Swedish law (2006:351), and raw Sanger sequencing data within the area of interest is available upon request. All other data will be made available upon request to the corresponding author.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Mencia, A. et al. Mutations in the seed region of human miR-96 are responsible for nonsyndromic progressive hearing loss. Nat. Genet. 41, 609–613 (2009).

  2. 2.

    Solda, G. et al. A novel mutation within the MIR96 gene causes non-syndromic inherited hearing loss in an Italian family by altering pre-miRNA processing. Hum. Mol. Genet. 21, 577–585 (2012).

  3. 3.

    Hughes, A. E. et al. Mutation altering the miR-184 seed region causes familial keratoconus with cataract. Am. J. Hum. Genet. 89, 628–633 (2011).

  4. 4.

    Iliff, B. W., Riazuddin, S. A. & Gottsch, J. D. A single-base substitution in the seed region of miR-184 causes EDICT syndrome. Invest. Ophthalmol. Vis. Sci. 53, 348–353 (2012).

  5. 5.

    Lechner, J. et al. Mutational analysis of MIR184 in sporadic keratoconus and myopia. Invest. Ophthalmol. Vis. Sci. 54, 5266–5272 (2013).

  6. 6.

    Nakamura, Y., Inloes, J. B., Katagiri, T. & Kobayashi, T. Chondrocyte-specific microRNA-140 regulates endochondral bone development and targets Dnpep to modulate bone morphogenetic protein signaling. Mol. Cell. Biol. 31, 3019–3028 (2011).

  7. 7.

    Chen, K. & Rajewsky, N. The evolution of gene regulation by transcription factors and microRNAs. Nat. Rev. Genet. 8, 93–103 (2007).

  8. 8.

    Berezikov, E. Evolution of microRNA diversity and regulation in animals. Nat. Rev. Genet. 12, 846–860 (2011).

  9. 9.

    Bonafe, L. et al. Nosology and classification of genetic skeletal disorders: 2015 revision. Am. J. Med. Genet. A 167A, 2869–2892 (2015).

  10. 10.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  11. 11.

    Suzuki, H. I., Young, R. A. & Sharp, P. A. Super-enhancer-mediated RNA processing revealed by integrative microrna network analysis. Cell 168, 1000–1014 e1015 (2017).

  12. 12.

    Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

  13. 13.

    Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

  14. 14.

    Wienholds, E. et al. MicroRNA expression in zebrafish embryonic development. Science 309, 310–311 (2005).

  15. 15.

    Tuddenham, L. et al. The cartilage specific microRNA-140 targets histone deacetylase 4 in mouse cells. FEBS Lett. 580, 4214–4217 (2006).

  16. 16.

    Miyaki, S. et al. MicroRNA-140 plays dual roles in both cartilage development and homeostasis. Genes Dev. 24, 1173–1185 (2010).

  17. 17.

    Papaioannou, G. et al. MicroRNA-140 provides robustness to the regulation of hypertrophic chondrocyte differentiation by the PTHrP-HDAC4 pathway. J. Bone Miner. Res. 30, 1044–1052 (2015).

  18. 18.

    Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

  19. 19.

    Park, J. E. et al. Dicer recognizes the 5′ end of RNA for efficient and accurate processing. Nature 475, 201–205 (2011).

  20. 20.

    Suzuki, H. I. et al. Small-RNA asymmetry is directly driven by mammalian Argonautes. Nat. Struct. Mol. Biol. 22, 512–521 (2015).

  21. 21.

    Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, e05005 (2015).

  22. 22.

    Zhang, J. et al. Loss of lysyl oxidase-like 3 causes cleft palate and spinal deformity in mice. Hum. Mol. Genet. 24, 6174–6185 (2015).

  23. 23.

    Tijchon, E. et al. Targeted deletion of btg1 and btg2 results in homeotic transformation of the axial skeleton. PLoS ONE 10, e0131481 (2015).

  24. 24.

    Napierala, D. et al. Uncoupling of chondrocyte differentiation and perichondrial mineralization underlies the skeletal dysplasia in tricho-rhino-phalangeal syndrome. Hum. Mol. Genet. 17, 2244–2254 (2008).

  25. 25.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  26. 26.

    Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5, e13984 (2010).

  27. 27.

    Wan, C. et al. Role of HIF-1α in skeletal development. Ann. N. Y. Acad. Sci. 1192, 322–326 (2010).

  28. 28.

    Grimson, A. et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91–105 (2007).

  29. 29.

    van Kouwenhove, M., Kedde, M. & Agami, R. MicroRNA regulation by RNA-binding proteins and its implications for cancer. Nat. Rev. Cancer 11, 644–656 (2011).

  30. 30.

    Ray, D. et al. A compendium of RNA-binding motifs for decoding gene regulation. Nature 499, 172–177 (2013).

  31. 31.

    Wei, W. J. et al. YB-1 binds to CAUC motifs and stimulates exon inclusion by enhancing the recruitment of U2AF to weak polypyrimidine tracts. Nucleic Acids Res. 40, 8622–8636 (2012).

  32. 32.

    Wu, S. L. et al. Genome-wide analysis of YB-1-RNA interactions reveals a novel role of YB-1 in miRNA processing in glioblastoma multiforme. Nucleic Acids Res. 43, 8516–8528 (2015).

  33. 33.

    Lyabin, D. N., Eliseeva, I. A. & Ovchinnikov, L. P. YB-1 protein: functions and regulation. Wiley Interdiscip. Rev. RNA 5, 95–110 (2014).

  34. 34.

    Goodarzi, H. et al. Endogenous tRNA-derived fragments suppress breast cancer progression via ybx1 displacement. Cell 161, 790–802 (2015).

  35. 35.

    Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).

  36. 36.

    Van Nostrand, E. L. et al. Erratum to: robust, cost-effective profiling of RNA binding protein targets with single-end enhanced crosslinking and immunoprecipitation (seCLIP). Methods Mol. Biol. 1648, E1 (2017).

  37. 37.

    de Pontual, L. et al. Germline deletion of the miR-17 approximately 92 cluster causes skeletal and growth defects in humans. Nat. Genet. 43, 1026–1030 (2011).

  38. 38.

    Henrion-Caude, A., Girard, M. & Amiel, J. MicroRNAs in genetic disease: rethinking the dosage. Curr. Gene. Ther. 12, 292–300 (2012).

  39. 39.

    Lewis, M. A. et al. An ENU-induced mutation of miR-96 associated with progressive hearing loss in mice. Nat. Genet. 41, 614–618 (2009).

  40. 40.

    Suzuki, H. I., Spengler, R. M., Grigelioniene, G., Kobayashi, T. & Sharp, P. A. Deconvolution of seed and RNA-binding protein crosstalk in RNAi-based functional genomics. Nat. Genet. 50, 657–661 (2018).

  41. 41.

    Kaminsky, E. B. et al. An evidence-based approach to establish the functional and clinical significance of copy number variants in intellectual and developmental disabilities. Genet. Med. 13, 777–784 (2011).

  42. 42.

    Niklasson, A. & Albertsson-Wikland, K. Continuous growth reference from 24th week of gestation to 24 months by gender. BMC Pediatr. 8, 8 (2008).

  43. 43.

    Kvarnung, M. et al. Genomic screening in rare disorders: new mutations and phenotypes, highlighting ALG14 as a novel cause of severe intellectual disability. Clin. Genet. 94, 528–537 (2018).

  44. 44.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  45. 45.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  46. 46.

    Okonechnikov, K., Conesa, A. & Garcia-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292–294 (2016).

  47. 47.

    Lindstrand, A. et al. Improved structural characterization of chromosomal breakpoints using high resolution custom array-CGH. Clin. Genet. 77, 552–562 (2010).

  48. 48.

    Yang, H., Wang, H. & Jaenisch, R. Generating genetically modified mice using CRISPR/Cas-mediated genome engineering. Nat. Protoc. 9, 1956–1968 (2014).

  49. 49.

    Wein, M. N. et al. SIKs control osteocyte responses to parathyroid hormone. Nat. Commun. 7, 13176 (2016).

  50. 50.

    Eyre, D. Collagen cross-linking amino acids. Methods Enzymol. 144, 115–139 (1987).

  51. 51.

    Ohba, S., He, X., Hojo, H. & McMahon, A. P. Distinct transcriptional programs underlie Sox9 regulation of the mammalian chondrocyte. Cell Rep. 12, 229–243 (2015).

  52. 52.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome. Biol. 10, R25 (2009).

  53. 53.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome. Biol. 9, R137 (2008).

  54. 54.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, (15–21 (2013).

  55. 55.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

  56. 56.

    Grant, G. R. et al. Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM). Bioinformatics 27, 2518–2528 (2011).

  57. 57.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  58. 58.

    Zamudio, J. R., Kelly, T. J. & Sharp, P. A. Argonaute-bound small RNAs from promoter-proximal RNA polymerase II. Cell 156, 920–934 (2014).

  59. 59.

    Paz, I. et al. RBPmap: a web server for mapping binding sites of RNA-binding proteins. Nucleic Acids Res. 42, W361–W367 (2014).

  60. 60.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

Download references


We thank patients and their family members for their participation in the study, M. Mannstadt, K. Leuter, M. Wein, H. Kronenberg, H. Jueppner, G. Björk, and A. Merker for advice and chemicals, J. Lundin for assistance with the human comparative genomic hybridization array databases, and Science for Life Laboratory, the National Genomics Infrastructure (NGI), Sweden, SNIC through UPPMAX under project b2014231 for providing assistance in massive parallel DNA sequencing and computational infrastructure. We also thank Center for Skeletal Research Core (NIH P30 AR066261) for access to histological analysis equipment; Harvard GMF for generation of knock-in mice; and DFCI and MGH Sequencing Cores for assistance with RNA and Sanger sequencing; the Robert A. Swanson (1969) Biotechnology Center at the Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology for technical support, specifically S. Levine and the staff of the BioMicro Center/KI Genomic Core Facility and G. Paradis, M. Jennings, and M. Saturno-Condon of the Flow Cytometry Core Facility. This project was supported by grants provided by the Stockholm County Council (ALF projects 20150143 and 20130315 to G. Grigelioniene. and A.N.) and by the National Institutes of Health (National Institute of Arthritis and Musculoskeletal and Skin Diseases grant R01-AR056645 to T.K., National Institute of General Medical Sciences grant R01-GM034277 and National Cancer Institute grant R01-CA133404 to P.A.S., and National Cancer Institute grant P30-CA14051 to the Koch Institute Core Facility). G.Grigelioniene was supported by a grant of the Sabbatical Leave Programme of the European Society for Paediatric Endocrinology through an educational grant from Eli Lilly International Corporation, travel grants from Fernström Foundation, Karolinska Institutet and Swedish Society of Medicine, scholarship from Stiftelsen Samariten, Stockholm, Sweden, Research Funds from Promobilia and Frimurare Barnhuset Stockholm, and project grant from Swedish Research Council 2018-03046. H.I.S. is supported by the Uehara Memorial Foundation Research Fellowship and the Osamu Hayaishi Memorial Scholarship for Study Abroad.

Author information

Author notes

  1. These authors contributed equally: Giedre Grigelioniene, Hiroshi I. Suzuki.


  1. Endocrine Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Giedre Grigelioniene
    • , Fatemeh Mirzamohammadi
    •  & Tatsuya Kobayashi
  2. Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden

    • Giedre Grigelioniene
    • , Fulya Taylan
    • , Anna Lindstrand
    • , Gintautas Grigelionis
    • , Anna Hammarsjö
    • , Ann Nordgren
    •  & Magnus Nordenskjöld
  3. Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden

    • Giedre Grigelioniene
    • , Anna Lindstrand
    • , Anna Hammarsjö
    • , Ann Nordgren
    •  & Magnus Nordenskjöld
  4. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Hiroshi I. Suzuki
    •  & Phillip A. Sharp
  5. Rappaport Faculty of Medicine, Technion—Israeli Institute of Technology, Medical Genetics Clinics, Assuta Medical Center, Haifa, Israel

    • Zvi U. Borochowitz
  6. Orthopaedic Research Labs, Department of Orthopaedic Surgery, Boston Children’s Hospital, Boston, MA, USA

    • Ugur M. Ayturk
    •  & Matthew L. Warman
  7. Laboratory of Molecular Medicine, Rambam Health Care Campus, Haifa, Israel

    • Shay Tzur
  8. Genomic Research Department, Emedgene Technologies, Tel Aviv, Israel

    • Shay Tzur
  9. Department for Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    • Eva Horemuzova
  10. Pediatric Endocrinology Unit, Karolinska University Hospital, Stockholm, Sweden

    • Eva Horemuzova
  11. Department of Orthopedics and Sports Medicine, University of Washington, Seattle, WA, USA

    • Mary Ann Weis
    •  & David R. Eyre
  12. Department of Otorhinolaryngology, Karolinska University Hospital, Stockholm, Sweden

    • Elin Marsk
  13. Center for Intractable Diseases, Saitama Medical University Hospital, Saitama, Japan

    • Gen Nishimura
  14. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Phillip A. Sharp


  1. Search for Giedre Grigelioniene in:

  2. Search for Hiroshi I. Suzuki in:

  3. Search for Fulya Taylan in:

  4. Search for Fatemeh Mirzamohammadi in:

  5. Search for Zvi U. Borochowitz in:

  6. Search for Ugur M. Ayturk in:

  7. Search for Shay Tzur in:

  8. Search for Eva Horemuzova in:

  9. Search for Anna Lindstrand in:

  10. Search for Mary Ann Weis in:

  11. Search for Gintautas Grigelionis in:

  12. Search for Anna Hammarsjö in:

  13. Search for Elin Marsk in:

  14. Search for Ann Nordgren in:

  15. Search for Magnus Nordenskjöld in:

  16. Search for David R. Eyre in:

  17. Search for Matthew L. Warman in:

  18. Search for Gen Nishimura in:

  19. Search for Phillip A. Sharp in:

  20. Search for Tatsuya Kobayashi in:


G.Grigelioniene, H.I.S., and T.K. designed the study and wrote the manuscript. G.Grigelioniene, H.I.S., T.K., F.T., F.M., U.M.A., S.T., A.L., M.A.W., G.Grigelionis, A.H., D.R.E., M.L.W., and P.A.S. conducted experiments and data collection. E.M., A.N., M.N., G.N., Z.U.B., and E.H. performed clinical characterization. All authors contributed to data interpretation and revised the manuscript.

Competing interests

S.T. works at Emedgene Technologies, and P.A.S. is a board member of Syros Pharmaceuticals.

Corresponding author

Correspondence to Tatsuya Kobayashi.

Extended data

  1. Extended Data Fig. 1 Human phenotype and genotype.

    ac, Fiberscopy findings in P1, 44 years of age, consistent with laryngomalacia. a, The right arytenoid cartilage is enlarged with redundant soft tissues and it prolapses antero-medially over the larynx during inspiration. b, Reduced laryngeal contraction is observed during expiration. c, A close view of the larynx and vocal folds shows dorsal narrowing of the subglottic region. d, The pathogenic variant in MIR140 (chr16:g.69967007A>G) (indicated by black arrows) in WGS and exome sequencing data (left and right panels), and its Sanger sequencing confirmation (middle panel) in individuals P1, P2, and P3 as in Fig. 1. Position of the mutation is according to GRCh37 (hg19). MP1, mother of P1; FP1, father of P1; MP3, mother of P3; FP3, father of P3. e, Evolutionarily conservation of MIR140 in different species; miRNA hairpin sequence is highlighted in gray. The second nucleotide in miR-140-5p is conserved from human to Australian ghostshark (indicated by a black arrow).

  2. Extended Data Fig. 2 The miR-140 gene-associated super-enhancers in human and mouse chondrocytes.

    a, Distribution of ChIP–seq signals at enhancers in human and mouse chondrocytes. The miR-140 gene-associated enhancers are highlighted. The miR-140 locus is associated with high ChIP–seq signals of H3K27Ac and Sox9 in mouse chondrocytes. b, Summary of association between miR-140 and super-enhancers (SEs). TE, typical enhancer. c, H3K27Ac ChIP–seq profiles at the miR-140 gene locus in human chondrocytes. The H3K27Ac profile of the donor id: 60 is shown in Fig. 1s. d, H3K27Ac and Sox9 ChIP–seq profiles of the miR-140 gene locus in mouse chondrocytes.

  3. Extended Data Fig. 3 Skeletal phenotype of miR-140 A>G mutant mice.

    a, miR-140G/G mice are smaller. The y axis and x axis indicate weight in grams and days of postnatal age, respectively. Numbers of evaluated mice with indicated genotypes are shown in parentheses. Results represent mean ± s.e.m. Statistical significance was assessed using two-way ANOVA and post hoc Tukey–Kramer test. b, Representative images from two independent experiments (at least three sections of each genotype) of in situ hybridization in epiphyseal growth plates of proximal humeri. Decreased expression of Col10a1 is observed in miR-140G/G and miR-140G/+ mice. c, Representative images of Alizarin red staining of the trachea (+/+, n = 6; G/+, n = 3; G/G, n = 5). Cartilage mineralization is absent or severely impaired in miR-140G/+ and miR-140G/G mice. d, Representative images of Alizarin red staining of the thoracic cage (+/+, n = 4; G/+, n = 7; G/G, n = 9; −/−, n = 2). Delay in chondrocyte mineralization in the rib cartilage is observed in miR-140G/G and miR-140G/+ mice. e,f, Proliferation rate assessed by BrdU labeling (e) and apoptosis assessed by TUNEL staining (f) in P7 tibial proximal growth plates. The fraction of BrdU-positive cells was calculated in the columnar proliferating chondrocytes. Results represent mean ± s.e.m. (+/+, n = 3; G/+, n = 4; G/G, n = 3). Apoptotic cells were not observed in the growth plate of miR-140G/G or miR-140+/+ mice (f). White arrow indicates apoptotic cells associated with blood vessels invaded into the epiphysis of WT mice. White dotted boxes indicate the growth plates (excluding the epiphyseal cartilage), and there were no TUNEL-positive cells in the growth plate cartilage in both miR-140+/+ and miR-140G/G mice. g, Representative images of Alizarin red staining of vertebral bodies (+/+, n = 4; G/+, n = 7; G/G, n = 9; −/−, n = 2). Vertebral bodies of miR-140G/G mice are smaller compared with miR-140G/+, miR-140+/+, and miR-140−/− mice. h, Delayed maturation of the proximal tibial epiphysis in P56-old mutant mice. Scale bars, 200 µm. Measurements of tibial growth plates of P56 mice show significant expansions in miR-140G/G and miR-140G/+ mice. The bar graphs show mean ± s.e.m. (+/+, n = 6; G/+, n = 3; G/G, n = 5). Statistical significance was assessed using Kruskal–Wallis one-way ANOVA and post hoc Steel–Dwass test. Source data

  4. Extended Data Fig. 4 Small RNA profiles in miR-140-G mutant chondrocytes.

    a, Proportion of small RNA species of mouse primary chondrocytes with indicated genotypes. b, Relative fold changes and mean expression levels of small RNAs in WT chondrocytes versus mutant chondrocytes. Note that miR-140-5p and miR-140-3p are highly abundant miRNAs in chondrocytes, reflecting super-enhancer-mediated high transcriptional activity. Red lines denote twofold cutoffs. Significant changes of other miRNAs are not observed. c, Pileup plots of miR-140-5p and miR-140-3p in WT and mutant chondrocytes. d, Comparison of Drosha and Dicer processing sites in WT and mutant chondrocytes. Drosha and Dicer cleavage sites were inferred from the 5′-ends of miR-140-5p and miR-140-3p, respectively. Results represent mean ± s.e.m. (n = 3 animals). e, Predicted effects of the A>G substitution on miR-140 5p/3p ratio. Selection of a single mature miRNA strand from miRNA duplex is determined by combination of 5′-end nucleotide identity and thermodynamic asymmetry of miRNA duplex termini20. Ago2 prefers strands with 5′-uridine or 5′-adenosine and thermodynamically unstable 5′-ends. This mechanistically explains abundant expression of miR-140-3p compared with miR-140-5p in chondrocytes. Prediction of 5p/3p ratio was performed according to our previous report20. Note that the A>G substitution is predicted to destabilize the 5′-end of the 5p arm and thus to increase the 5p/3p ratio for both 5p/3p.1 and 5p/3p.2 duplexes, consistent with the increase in miR-140-5p and decrease in miR-140-3p in chondrocytes from miR-140G/+ and miR-140G/G mice.

  5. Extended Data Fig. 5 Transcriptome analysis by RNA-seq and identification of miR-140-5p-G targets by TargetScan.

    a, Expression levels of genes within the neighborhood of the miR-140 gene locus. Results represent mean ± s.e.m. (n = 3 animals). b, Workflow of target prediction by TargetScan v7.0. c,d, Venn diagrams showing overlaps of genes with conserved sites (c) and genes with conserved and non-conserved sites (d) for miR-140-5p-WT, -5p-G, -3p.1, and -3p.2. e, Conservation frequency of target sites for miR-140-5p-WT, -5p-G, -3p.1, and -3p.2. The target sites of miR-140-5p-G are less conserved than those of WT miR-140 species. Classification of miRNAs (broadly conserved, conserved, poorly conserved, and others) is based on the TargetScan information. Center lines show medians; box limits indicate the 25th and 75th percentiles; whiskers extend to 1.5× the interquartile range. Source data

  6. Extended Data Fig. 6 Deregulation of miR-140-5p-G targets in miR-140 mutant chondrocytes.

    a, Cumulative distributions of fold changes of mRNAs with conserved and non-conserved 8mer target sites for miR-140-5p-WT, -5p-G, -3p.1, and -3p.2 between miR-140 mutant or null chondrocytes versus WT chondrocytes. P values (versus a control gene set) were calculated by one-sided Kolmogorov–Smirnov test for either direction: upregulation (U) or downregulation (D) with Bonferroni correction (control genes, n = 9,706; miR-140-5p-WT, n = 333; miR-140-5p-G, n = 608; miR-140-3p.1, n = 903; miR-140-3p.2, n = 603). b, Cumulative distributions of fold changes of mRNAs with all 8mer, 7mer-m8, 7mer-A1, and 6mer target sites for miR-140-5p-G (left) and miR-140-5p-WT (right) when comparing miR-140-G homozygous chondrocytes with WT chondrocytes. P values (versus a control gene set) were calculated by one-sided Kolmogorov–Smirnov test for either direction: upregulation (U) or downregulation (D) with Bonferroni correction. Left (miR-140-5p-G): no site, n = 7,684; 8mer, n = 608; 7mer-m8, n = 862; 7mer-A1, n = 1,741; 6mer, n = 2,497. Right (miR-140-5p-WT): no site, n = 7,453; 8mer, n = 333; 7mer-m8, n = 1,507; 7mer-A1, n = 710; 6mer, n = 3,093. c, Performance of TargetScan v7.0 for prediction of miR-140-5p-G targeting. Cumulative distribution plots (left) and box plots (right) of fold-change expression of predicted target gene sets grouped by CWCS. Expression levels of predicted target genes of miR-140-5p-G (top; no site, n = 7,684; top 128, n = 59; top 256, n = 129; top 512, n = 275; top 1,024, n = 559; top 2,048, n = 1178; all targets, n = 4,187) and miR-140-5p-WT (bottom; no site, n = 7,453; top 128, n = 68; top 256, n = 142; top 512, n = 311; top 1,024, n = 637; top 2,048, n = 1,235; all targets, n = 4,418) were analyzed. Gene expression of miR-140-G homozygous chondrocytes and WT chondrocytes was compared. All predictions were considered. High CWCS was associated with stronger target gene regulation for both miR-140-5p-G and miR-140-5p-WT, suggesting that miR-140-5p-G follows mechanisms of conventional miRNA targeting as well as other endogenous miRNAs. In box plots, center lines show medians; box limits indicate the 25th and 75th percentiles; whiskers extend to 1.5× the interquartile range. d, Box plots showing context++ scores for conserved (left) and all (right) predicted target sites for miR-140-5p-WT, -5p-G, -3p.1, and -3p.2. Consistent with the effects on target gene expression, miR-140-5p-WT and miR-140-5p-G show similar and higher predicted target site efficiency than miR-140-3p.1 and miR-140-3p.2. Center lines show medians; box limits indicate the 25th and 75th percentiles; whiskers extend to 1.5× the interquartile range. Left (conserved 8mer): miR-140-5p-WT, n = 96; miR-140-5p-G, n = 114; miR-140-3p.1, n = 222; miR-140-3p.2, n = 115. Right (conserved and non-conserved 8mer): miR-140-5p-WT, n = 524; miR-140-5p-G, n = 1,002; miR-140-3p.1, n = 1,405; miR-140-3p.2, n = 930.

  7. Extended Data Fig. 7 Deregulation of the Hif1a pathway and the cartilage markers in miR-140 mutant chondrocytes.

    a, Top: sequence alignments between miR-140-5p-G and its putative binding sites in the coding sequence of mouse and human Hif1a. Bottom: representative images from two independent experiments of western blot analysis of Hif1a protein expression in primary rib chondrocytes at the normoxic condition. Hif1a protein was detected by western blot analysis using anti-Hif1a antibody (Novus Biologicals NB100-449). Bar graphs represent mean (n = 2 animals). b, Validation of the Hif1a target site. HEK293T cells were transfected with WT or mutant mouse Hif1a expression plasmids and pri-miRNA expression plasmids, and subjected to western blot analysis. Representative images from two independent experiments are shown. A mutation of the target site is shown in a. c, Relative expression of Hif1a and its target genes was determined by qRT–PCR in primary rib chondrocytes isolated from P7 mice. Results represent mean ± s.e.m. (n = 3 animals). Statistical significance was assessed using one-way ANOVA and post hoc Tukey–Kramer test. P values indicate comparison versus WT. d, Expression of lysyl oxidases in P7 primary rib chondrocytes assessed by RNA-seq. Results represent mean ± s.e.m. (n = 3 animals). Statistical significance was assessed using one-way ANOVA and post hoc Tukey–Kramer test. P values indicate comparison versus WT. e, Reduced collagen cross-linking in miR-140G/G rib cartilage cells. Rib cartilage of four-week-old mice was subjected to collagen cross-link analysis by mass spectrometry. Hydroxylysyl pyridinoline and lysyl pyridinoline were quantified. Results represent mean ± s.e.m. (n = 4 animals). Statistical significance was assessed using one-way ANOVA and post hoc Tukey–Kramer test. P values indicate comparison versus WT. f, RNA expression of cartilage markers. The x axis and y axis indicate miR-140 genotypes and normalized expression (reads per kilobase per million mapped reads, RPKM), respectively. Each dot represents the expression level in an individual sample (n = 3 animals), and the thick bars indicate the mean value. Statistical significance was assessed using one-way ANOVA and post hoc Tukey–Kramer test. Connector lines at the top of each panel indicate significant changes in expression (P< 0.05). Source data Source data

  8. Extended Data Fig. 8 A relationship between miR-140-5p-G and Ybx1 and Ago2 and Ybx1 seCLIP analysis in mouse primary chondrocytes.

    a, Potential RBPs that are predicted to bind to complementary sequences of miR-140-5p-G (left) and miR-140-5p-WT (right). Motif analysis was performed using the CISBP-RNA database. b, Genome browser shot of YBX1 iCLIP for human HIF1A, BTG1, TRPS1, LOXL3, and EHD1 using the previously reported YBX1 iCLIP data in human glioblastoma cells32. We observed that multiple conserved miR-140-5p-G target genes were bound by YBX1 in the YBX1 iCLIP dataset. c, Expression levels of Ybx1 protein in mouse primary chondrocytes. Representative images from three independent experiments are shown. Note that Ybx1 protein expression levels normalized to those of Actb did not differ between WT and mutant chondrocytes. Normalized values of densitometric analysis are indicated. Results represent mean ± s.e.m. (+/+, n = 3; G/+, n = 3; G/G, n = 2). d, Genomic annotation of Ago2 and Ybx1 seCLIP clusters. Left pie charts shows genomic distribution of Ago2 and Ybx1 seCLIP clusters. Right panels show the fold enrichment of various genomic annotations over the background. e, Genome browser shot of the Loxl3 locus. Source data Source data

  9. Extended Data Fig. 9 Analysis of Ago2 seCLIP clusters.

    a, Hexamer enrichment analysis in all Ago2-bound seCLIP clusters including intronic clusters identified in WT (left) and mutant (right) chondrocytes (WT, n = 14,947 clusters; G/G, n = 13,873 clusters). The x axis shows z-scores of hexamer frequency. The y axis shows the negative log10 P value from two-sided Fisher’s exact test with Bonferroni correction of the hexamer enrichment above background. b, Hexamer enrichment analysis for various genomic annotations. The negative log10 P values from one-sided Fisher’s exact test with Bonferroni correction of the hexamer enrichment above background are shown. Left (Ago2 WT): 5′ UTR clusters, n = 279; CDS clusters, n = 1,315; 3′ UTR clusters, n = 1,782; intronic clusters, n = 10,402; non-coding RNA region clusters, n = 434; others, n = 735. Right (Ago2 G/G): 5′ UTR clusters, n = 303; CDS clusters, n = 2,006; 3′ UTR clusters, n = 3,229; intronic clusters, n = 7,091; non-coding RNA region clusters, n = 332; others, n = 912. c, Cumulative distributions of fold changes of mRNAs with Ago2-bound seCLIP CDS clusters with the ACCACC motif in mutant chondrocytes (red) or Ago2-bound seCLIP CDS clusters with the ACCACU motif in WT chondrocytes. P values (versus a control gene set) were calculated by one-sided Kolmogorov–Smirnov test for either direction: upregulation (U) or downregulation (D) with Bonferroni correction (control genes, n = 10,093; miR-140-5p-WT, n = 28; miR-140-5p-G, n = 100).

  10. Extended Data Fig. 10 Analysis of Ybx1 seCLIP clusters.

    a, Hexamer enrichment analysis in all Ybx1-bound seCLIP clusters including intronic clusters, identified in WT chondrocytes (n = 8,354). The x axis shows the z-scores of hexamer frequency. The y axis shows the negative log10 P value of two-sided Fisher’s exact test with Bonferroni correction of the hexamer enrichment above background. b, Hexamer enrichment analysis for various genomic annotations (5′ UTR clusters, n = 265; CDS clusters, n = 2,537; 3′ UTR clusters, n = 1,649; intronic clusters, n = 3,143; non-coding RNA region clusters, n = 191; others, n = 569). The negative log10 P values from one-sided Fisher’s exact test with Bonferroni correction of the hexamer enrichment above background are shown. c, siRNA-mediated knockdown of YBX1 in HEK293T cells for single-cell reporter analysis, confirmed by qRT–PCR analysis. Results represent mean ± s.e.m. (n = 3 biologically independent samples). d, Flow cytometry gating strategies for single-cell reporter analysis. Single-cell populations (P3) were selected by sequential gating by SSC and FSC and subjected to two-color analysis in Fig. 4i. An example of the flow cytometry gating strategy is shown. e, Cumulative distributions of fold changes of YBX1-stablized gene sets between miR-140 mutant or null chondrocytes versus WT chondrocytes. P values (versus a control gene set) were calculated by one-sided Kolmogorov–Smirnov test for downregulation (D) (other genes, n = 11,768; YBX1-regulated genes, n = 103). f,g, Systematic RBP target gene analysis using GSEA. For 94 RBPs, possible RBP target genes were predicted using RBPmap59, and used for GSEA analysis to assess which RBP activity changes in miR-140-G homozygous and miR-140-null chondrocytes relative to WT chondrocytes (n = 3 animals for each group). In f, normalized enrichment scores for 94 RBPs are shown. Among three significantly suppressed motifs in miR-140-G homozygous cells (SRSF10, YBX1, and SRSF9), only the YBX1 RBP motif gene set was selectively suppressed in miR-140-G homozygous but not miR-140-null cells. In g, GSEA enrichment plots for YBX1 RBP motif genes are shown. Enrichment analysis and statistical analysis were performed using GSEA. Source data

Supplementary information

  1. Supplementary Information

    Supplementary Tables 1 and 2

  2. Reporting Summary

  3. Supplementary Tables

    Supplementary Tables 3–9

Source data

  1. Source Data Fig. 2

    Statistics Source Data

  2. Source Data Fig. 3

    Statistics Source Data

  3. Source Data Extended Data Fig. 3

    Statistics Source Data

  4. Source Data Extended Data Fig. 5

    Statistics Source Data

  5. Source Data Extended Data Fig. 7

    Statistics Source Data

  6. Source Data Extended Data Fig. 7

    Western Blots

  7. Source Data Extended Data Fig. 8

    Statistics Source Data

  8. Source Data Extended Data Fig. 8

    Western Blots

  9. Source Data Extended Data Fig. 10

    Statistics Source Data

About this article

Publication history




Issue Date