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N6-methyladenosine marks primary microRNAs for processing

Nature volume 519, pages 482–485 (2015)Cite this article

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Abstract

The first step in the biogenesis of microRNAs is the processing of primary microRNAs (pri-miRNAs) by the microprocessor complex, composed of the RNA-binding protein DGCR8 and the type III RNase DROSHA1,2,3,4. This initial event requires recognition of the junction between the stem and the flanking single-stranded RNA of the pri-miRNA hairpin by DGCR8 followed by recruitment of DROSHA, which cleaves the RNA duplex to yield the pre-miRNA product5. While the mechanisms underlying pri-miRNA processing have been determined, the mechanism by which DGCR8 recognizes and binds pri-miRNAs, as opposed to other secondary structures present in transcripts, is not understood. Here we find in mammalian cells that methyltransferase-like 3 (METTL3) methylates pri-miRNAs, marking them for recognition and processing by DGCR8. Consistent with this, METTL3 depletion reduced the binding of DGCR8 to pri-miRNAs and resulted in the global reduction of mature miRNAs and concomitant accumulation of unprocessed pri-miRNAs. In vitro processing reactions confirmed the sufficiency of the N6-methyladenosine (m6A) mark in promoting pri-miRNA processing. Finally, gain-of-function experiments revealed that METTL3 is sufficient to enhance miRNA maturation in a global and non-cell-type-specific manner. Our findings reveal that the m6A mark acts as a key post-transcriptional modification that promotes the initiation of miRNA biogenesis.

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Figure 1: The m6A mark is present in pri-miRNA regions.
Figure 2: METTL3 modulates the expression levels of miRNAs.
Figure 3: METTL3 targets pri-miRNAs for m6A methylation.
Figure 4: m6A methylation of pri-miRNAs is required for normal processing by DGCR8.

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Gene Expression Omnibus

Data deposits

RNA-seq data have been deposited in the Gene Expression Omnibus under accession number GSE60213.

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Acknowledgements

We thank members of the Tavazoie laboratory as well as S. Kurdistani for comments on previous versions of this manuscript. We thank D. Bartel for suggestions about conservation analysis. We thank L. Fish for technical advice. We thank C. Zhao and C. Lai of the Rockefeller Genomics Resource Center for assistance with next-generation RNA-seq. We thank H. Molina of the Rockefeller Proteomics Center for his input in proteomics analysis. We thank C. Eicken of LC Sciences for assistance with microarray analysis. We thank H. Chang and P. Batista for providing targeted embryonic stem cells. C.R.A. was an Anderson Cancer Center Fellow at Rockefeller University. This work was supported by an Era of Hope Department of Defense Award to S.F.T. S.F.T. is a Department of Defense Breast Cancer Collaborative Scholars and Innovators Award recipient.

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  1. Hyeseung Lee and Hani Goodarzi: These authors contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Systems Cancer Biology, Rockefeller University, 1230 York Avenue, New York, New York 10065, USA,

    Claudio R. Alarcón, Hyeseung Lee, Hani Goodarzi, Nils Halberg & Sohail F. Tavazoie

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  1. Claudio R. Alarcón
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  2. Hyeseung Lee
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Contributions

C.R.A. conceived the project and designed the experiments and S.F.T. supervised the project. C.R.A. performed most of the experiments, H.L. generated stable cell lines, performed qRT–PCR reactions and cloning, H.G. performed computational analyses and N.H. provided technical support. C.R.A. and S.F.T. wrote the manuscript.

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Correspondence to Sohail F. Tavazoie.

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Extended data figures and tables

Extended Data Figure 1 METTL3 regulates the expression levels of mature miRNAs.

a, Unbiased search for cis-regulatory elements using the FIRE algorithm. FIRE motif discovery analysis of pre-miRNAs and pri-miRNA sequences, as well as random sequences of the same length, reveals overrepresentation of the METTL3 motif in pri-miRNAs sequences (containing pre-miRNAs plus adjacent 100 bp) but not in pre-miRNAs. Yellow represents overrepresentation, and blue depicts underrepresentation of the motif. The magnitude of the over/underrepresentation is represented by the linear-scale heat map on the left. A schematic representation of a pre-miRNA and a pri-miRNA is shown on the right. b, c, qRT–PCR (b) and western blot (c) quantifications of METTL3 upon transduction with two independent shRNAs targeting METTL3 (METTL3 KD1 and METTL3 KD2) in MDA-MB-231 cells. Samples were normalized to GAPDH. Data from biological triplicates are shown. Bar graphs represent a linear scale and error bars represent s.d. P < 1 × 10−4, P < 5 × 10−4. d, A volcano plot representation of the microarray of miRNAs shown in Fig. 2a, where the y-axis represents the –log10 of the P value, and the x-axis represents the fold change (log2) between the expression levels of the miRNA from the METTL3 depletion (average of two independent shRNAs) versus the average of two control samples.

Extended Data Figure 2 Mature miRNAs are downregulated upon METTL3 depletion in MDA-MB-231 cells.

a, Quantification of representative miRNAs that were affected by METTL3 depletion in MDA-MB-231 cells as measured by qRT–PCR. Expression values were normalized to SNORD44 (also known as RNU44). b, An example of a small RNA that did not display expression level changes upon METTL3 knockdown (SNORD44, small nucleolar RNA) normalized to 18S. All experiments were conducted in biological replicates. Bar graphs represent a linear scale and error bars represent s.e.m. P < 5 × 10−4, P < 1 × 10−3, P < 5 × 10−2.

Extended Data Figure 3 Mature miRNAs are downregulated upon METTL3 depletion in multiple mammalian cell lines.

a, qRT–PCR quantification of examples of miRNAs that were modulated upon METTL3 depletion in HeLa cells. Samples were normalized to RNU44. b, Expression levels of genes used for normalization. All experiments were done in biological replicates. c, d, qRT–PCR (c) and western blot (d) quantifications of METTL3 levels upon transduction with two independent shRNAs targeting METTL3. e, Expression levels of representative miRNAs that were affected by METTL3 depletion in HUVEC cells, as measured by qRT–PCR. Normalization was done by using RNU44 as endogenous control. f, qRT–PCR quantification of METTL3 upon transduction with two independent shRNAs targeting METTL3. g, Quantification of the expression levels of control genes. h, i, Examples of miRNAs affected in mouse embryonic stem cells in which Mettl3 has been targeted using CRISPR26, whose expression levels were measured by qRT–PCR. All experiments were done in biological replicates. Bar graphs represent a linear scale and error bars represent s.d. P < 5 × 10−4, P < 1 × 10−3.

Extended Data Figure 4 Mature miRNAs are upregulated upon METTL3 over-expression in MDA-MB-231 cells.

a, qRT–PCR quantification of expression of representative miRNAs modulated upon METTL3 overexpression (METTL3 OE) in MDA-MB-231 cells. Samples were normalized to RNU44. b, qRT–PCR quantification of control RNU44 and GAPDH genes normalized to 18S. All experiments were done in biological replicates. Bar graphs represent a linear scale and error bars represent s.d. P < 5 × 10−4, P < 1 × 10−3.

Extended Data Figure 5 Quantification of mature and pri-miRNAs levels upon depletion and catalytic inactivation of METTL3 in MDA-MB-231 cells.

a, qRT–PCR quantification of representative pri-miRNAs that were impacted by METTL3 depletion using two independent hairpins in MDA-MB-231 cells. Expression levels were normalized to GAPDH. b, qRT–PCR quantification of GAPDH, endogenous control. All experiments were done in biological replicates. c, d, Quantification of mature (c) and pri-miRNAs (d) upon stable transduction of MDA-MB-231 with either wild-type or a catalytic mutant METTL3. Mature miRNA expression was normalized to RNU44 and pri-miRNAs to GAPDH expression levels. The last bar graph shows the averaged value for all individual miRNAs tested. The experiments were done in biological replicates. Bar graphs represent a linear scale and error bars represent s.d. P < 1 × 10−4, P < 5 × 10−4, P < 1 × 10− 3, P < 5 × 10−2. NS, not significant.

Extended Data Figure 6 Expression and localization of the Microprocessor upon depletion or overexpression of METTL3.

a, Western blot analysis of METTL3, DROSHA and DGCR8 obtained from nuclear and cytoplasmic fractions of cells transduced with two independent shRNAs targeting METTL3 (shMETTL3 #1 and #2) or with an shRNA control (shC). Tubulin and histone 3 were used as loading controls as well as controls for the efficiency of the fractionation. b, Same as a, but in this case, lysate from cells overexpressing (O/E) METTL3 were compared to wild-type control cells. c, In vitro pri-miRNA processing reactions. Whole-cell extracts of control cells or cells depleted of METTL3 with two independent shRNAs were used to process in vitro transcribed pri-miR-1-1 to produce pre-miR-1-1 in vitro. Pre-miR-1-1 levels were then analysed by northern blot. d, Hybridization intensities of c were quantified, normalized by their inputs and shown in a bar graph format. Bar graphs represent a linear scale and error bars represent s.d.

Extended Data Figure 7 METTL3 binding and m6A co-localization in pri-miRNA regions.

a, FIRE motif discovery analysis of the METTL3 HITS-CLIP binding sites compared to control sequences; two overrepresented versions of the METTL3 motif are shown with a z-score as indicated. The heat map represents a linear scale. b, Venn diagram representation of the overlap of miRNAs affected by METTL3 depletion and bearing the m6A and/or the METTL3 HITS-CLIP tags within 1 kb from any particular miRNA locus. The overlap of miRNAs containing both m6A and METTL3 HITS-CLIP tags is depicted in red. P = 2.4 × 10−15.

Extended Data Figure 8 m6A facilitates processing of pri-miRNAs.

a, Schematic representation of the reporters used to study the role of METTL3 in pri-miRNA processing. Represented in red is the pri-let-7e sequence and in green, the control pri-miR-1-1. The top reporter contains a wild-type (WT) sequence of pri-let-7e and the potential sites of methylations are depicted as red dots. The reporter on the bottom contains a mutant version of pri-let-7e in which the five putative adenines of the METTL3 motif were mutated. b, HEK293T cells were transfected with the reporters depicted in a, RNA was extracted, and mature miRNA expression was quantified by qRT–PCR. The bar graph represents the relative expression levels of mature let-7e normalized to mature miR-1-1. c, In vitro binding assays using immunopurified DGCR8. Samples containing in vitro transcribed pri-let-7e with N6-methyladenosine or unmodified adenosines were incubated with magnetic-bead- bound DGCR8, washed, eluted and analysed by northern blot. All reactions contained unmodified pri-miR-1-1 as endogenous control. The top panel shows pri-let-7e and the bottom panel pri-miR-1-1. On the right side of the bar graph is depicted the average intensity of pri-let-7e normalized by pri-miR-1-1 levels. The experiment was done in biological triplicate. Bar graphs represent a linear scale and error bars represent s.d. P < 5 × 10−4. NS, not significant.

Extended Data Figure 9 METTL3 depletion affects DGCR8 binding to endogenous pri-miRNAs.

Immunoprecipitation of endogenous DGCR8 crosslinked to RNA of control cells or cells depleted of METTL3 using two independent shRNAs. After immunoprecipitation, the RNA was extracted and the expression levels of a set of pri-miRNAs were quantified by qRT–PCR. The average quantification is presented in Fig. 4h. Bar graphs represent a linear scale and error bars represent s.d. P < 1 × 10−4, P < 1 × 10−3, P < 1 × 10−2, P < 5 × 10−2.

Extended Data Figure 10 Schematic depiction of m6A-seq and HITS-CLIP protocols.

a, Schematic representation of the m6A-seq protocol. b, Schematic representation of the HITS-CLIP protocol used. Both protocols are described in detail in Methods.

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Alarcón, C., Lee, H., Goodarzi, H. et al. N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482–485 (2015). https://doi.org/10.1038/nature14281

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