Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

m6A-LAIC-seq reveals the census and complexity of the m6A epitranscriptome

Nature Methods volume 13, pages 692–698 (2016)Cite this article

Subjects

Abstract

N6-Methyladenosine (m6A) is a widespread, reversible chemical modification of RNA molecules, implicated in many aspects of RNA metabolism. Little quantitative information exists as to either how many transcript copies of particular genes are m6A modified ('m6A levels') or the relationship of m6A modification(s) to alternative RNA isoforms. To deconvolute the m6A epitranscriptome, we developed m6A-level and isoform-characterization sequencing (m6A-LAIC-seq). We found that cells exhibit a broad range of nonstoichiometric m6A levels with cell-type specificity. At the level of isoform characterization, we discovered widespread differences in the use of tandem alternative polyadenylation (APA) sites by methylated and nonmethylated transcript isoforms of individual genes. Strikingly, there is a strong bias for methylated transcripts to be coupled with proximal APA sites, resulting in shortened 3′ untranslated regions, while nonmethylated transcript isoforms tend to use distal APA sites. m6A-LAIC-seq yields a new perspective on transcriptome complexity and links APA usage to m6A modifications.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Development and validation of m6A-LAIC-seq protocol.
Figure 2: m6A-LAIC-seq reveals multiple properties of m6A levels in H1-ESC.
Figure 3: Cell-type comparison of m6A levels by m6A-LAIC-seq.
Figure 4: m6A is associated with differential 3′-UTR usage.
Figure 5: APA usage between methylated versus nonmethylated fractions is associated with differential miRNA and RBP target densities in 3′ UTR.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Fu, Y. & He, C. Nucleic acid modifications with epigenetic significance. Curr. Opin. Chem. Biol. 16, 516–524 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Sibbritt, T., Patel, H.R. & Preiss, T. Mapping and significance of the mRNA methylome. Wiley Interdiscip. Rev. RNA 4, 397–422 (2013).

    CAS  PubMed  Google Scholar 

  3. Meyer, K.D. & Jaffrey, S.R. The dynamic epitranscriptome: N6-methyladenosine and gene expression control. Nat. Rev. Mol. Cell Biol. 15, 313–326 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Klungland, A. & Dahl, J.A. Dynamic RNA modifications in disease. Curr. Opin. Genet. Dev. 26, 47–52 (2014).

    CAS  PubMed  Google Scholar 

  5. Pan, T. N6-methyl-Adenosine modification in messenger and long non-coding RNA. Trends Biochem. Sci. 38, 204–209 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Schwartz, S. et al. High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell 155, 1409–1421 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang, Y. et al. N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang, X. et al. m6A-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

    PubMed  Google Scholar 

  9. Meyer, K.D. et al. 5′ UTR m6A promotes cap-independent translation. Cell 163, 999–1010 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhou, J. et al. Dynamic m6A mRNA methylation directs translational control of heat shock response. Nature 526, 591–594 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Wang, X. et al. N6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Bokar, J.A., Shambaugh, M.E., Polayes, D., Matera, A.G. & Rottman, F.M. Purification and cDNA cloning of the AdoMet-binding subunit of the human mRNA (N6-adenosine)-methyltransferase. RNA 3, 1233–1247 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Liu, J. et al. A METTL3–METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014).

    CAS  PubMed  Google Scholar 

  14. Ping, X.L. et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 24, 177–189 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Schwartz, S. et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5′ sites. Cell Rep. 8, 284–296 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Zheng, G. et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell 49, 18–29 (2013).

    CAS  PubMed  Google Scholar 

  17. Jia, G. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat. Chem. Biol. 7, 885–887 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Batista, P.J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Geula, S. et al. Stem cells. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 347, 1002–1006 (2015).

    CAS  PubMed  Google Scholar 

  20. Dominissini, D., Moshitch-Moshkovitz, S., Amariglio, N. & Rechavi, G. Transcriptome-wide mapping of N6-methyladenosine by m6A-seq. Methods Enzymol. 560, 131–147 (2015).

    CAS  PubMed  Google Scholar 

  21. Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).

    CAS  PubMed  Google Scholar 

  22. Liu, N. et al. Probing N6-methyladenosine RNA modification status at single nucleotide resolution in mRNA and long noncoding RNA. RNA 19, 1848–1856 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Horowitz, S., Horowitz, A., Nilsen, T.W., Munns, T.W. & Rottman, F.M. Mapping of N6-methyladenosine residues in bovine prolactin mRNA. Proc. Natl. Acad. Sci. USA 81, 5667–5671 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Neff, A.T., Lee, J.Y., Wilusz, J., Tian, B. & Wilusz, C.J. Global analysis reveals multiple pathways for unique regulation of mRNA decay in induced pluripotent stem cells. Genome Res. 22, 1457–1467 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Wei, C., Gershowitz, A. & Moss, B. N6, O2′-dimethyladenosine a novel methylated ribonucleoside next to the 5′ terminal of animal cell and virus mRNAs. Nature 257, 251–253 (1975).

    CAS  PubMed  Google Scholar 

  27. Kruse, S. et al. A novel synthesis and detection method for cap-associated adenosine modifications in mouse mRNA. Sci. Rep. 1, 126 (2011).

    PubMed  PubMed Central  Google Scholar 

  28. Keith, J.M., Ensinger, M.J. & Moss, B. HeLa cell RNA (2′-O-methyladenosine-N6-)-methyltransferase specific for the capped 5′-end of messenger RNA. J. Biol. Chem. 253, 5033–5039 (1978).

    CAS  PubMed  Google Scholar 

  29. Robins, M.J., Naik, S.R. & Lee, A.S. Nucleic acid related compounds. 12. The facile and high-yield stannous chloride catalyzed monomethylation of the cis-glycol system of nucleosides by diazomethane. J. Org. Chem. 39, 1891–1899 (1974).

    CAS  PubMed  Google Scholar 

  30. Zhao, X. et al. FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res. 24, 1403–1419 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Liu, N. et al. N6-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560–564 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Spitale, R.C. et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519, 486–490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Shen, S. et al. MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-seq data. Nucleic Acids Res. 40, e61 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl. Acad. Sci. USA 111, E5593–E5601 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Masamha, C.P. et al. CFIm25 links alternative polyadenylation to glioblastoma tumour suppression. Nature 510, 412–416 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Ke, S. et al. A majority of m6A residues are in the last exons, allowing the potential for 3′ UTR regulation. Genes Dev. 29, 2037–2053 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Di Giammartino, D.C., Nishida, K. & Manley, J.L. Mechanisms and consequences of alternative polyadenylation. Mol. Cell 43, 853–866 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Berkovits, B.D. & Mayr, C. Alternative 3′ UTRs act as scaffolds to regulate membrane protein localization. Nature 522, 363–367 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Misra, A., Ou, J., Zhu, L.J. & Green, M.R. Global promotion of alternative internal exon usage by mRNA 3′ end formation factors. Mol. Cell 58, 819–831 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Shi, Y. & Manley, J.L. The end of the message: multiple protein-RNA interactions define the mRNA polyadenylation site. Genes Dev. 29, 889–897 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Nelles, D.A. et al. Programmable RNA tracking in live cells with CRISPR/Cas9. Cell 165, 488–496 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Chen, K., Zhao, B.S. & He, C. Nucleic acid modifications in regulation of gene expression. Cell Chem. Biol. 23, 74–85 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Lianoglou, S., Garg, V., Yang, J.L., Leslie, C.S. & Mayr, C. Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression. Genes Dev. 27, 2380–2396 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Chan, C.T. et al. Identification of N6,N6-dimethyladenosine in transfer RNA from Mycobacterium bovis Bacille Calmette-Guérin. Molecules 16, 5168–5181 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Livak, K.J. & Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-ΔΔCT) method. Methods 25, 402–408 (2001).

    CAS  PubMed  Google Scholar 

  46. Sigova, A.A. et al. Divergent transcription of long noncoding RNA/mRNA gene pairs in embryonic stem cells. Proc. Natl. Acad. Sci. USA 110, 2876–2881 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Huang da, W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    PubMed  Google Scholar 

  50. Müller, S. et al. APADB: a database for alternative polyadenylation and microRNA regulation events. Database (Oxford) 2014, bau076 (2014).

    Google Scholar 

  51. Roost, C. et al. Structure and thermodynamics of N6-methyladenosine in RNA: a spring-loaded base modification. J. Am. Chem. Soc. 137, 2107–2115 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Lewis, B.P., Burge, C.B. & Bartel, D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

    CAS  PubMed  Google Scholar 

  53. Ule, J. et al. An RNA map predicting Nova-dependent splicing regulation. Nature 444, 580–586 (2006).

    CAS  PubMed  Google Scholar 

  54. Dittmar, K.A. et al. Genome-wide determination of a broad ESRP-regulated posttranscriptional network by high-throughput sequencing. Mol. Cell. Biol. 32, 1468–1482 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Anderson, E.S. et al. The cardiotonic steroid digitoxin regulates alternative splicing through depletion of the splicing factors SRSF3 and TRA2B. RNA 18, 1041–1049 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was supported by an MGH startup and ECOR grants to C.C.G. This study was also supported by National Institutes of Health (NIH) grant R01GM088342 and an Eli & Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA and Rose Hills Foundation Research Award to Y.X. Y.X. is supported by an Alfred Sloan Research Fellowship. P.D. was supported by the National Research Foundation of Singapore through the Singapore–MIT Alliance for Research and Technology, National Institute of Environmental Health Science grants ES002109 and ES024615, and National Science Foundation grant CHE-1308839. A.C.M. was supported by NIH grant DK090122. We thank D. Mirsky for copy editing the manuscript. We thank J. Wan for technical support on data analyses. We thank H. Chang, P. Batista, and K. Jeffrey for reading the manuscript and providing helpful comments.

Author information

Author notes

  1. Benoit Molinie and Jinkai Wang: These authors contributed equally to this work.

Authors and Affiliations

  1. Gastrointestinal Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Benoit Molinie, Nicholas Van Wittenberghe, Benjamin D Howard, Kaveh Daneshvar, Alan C Mullen & Cosmas C Giallourakis

  2. Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, California, USA

    Jinkai Wang, Zhi-xiang Lu & Yi Xing

  3. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Kok Seong Lim, Roman Hillebrand & Peter Dedon

  4. Harvard Stem Cell Institute, Cambridge, Massachusetts, USA

    Alan C Mullen & Cosmas C Giallourakis

  5. Center for Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Cosmas C Giallourakis

Authors
  1. Benoit Molinie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinkai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kok Seong Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roman Hillebrand
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhi-xiang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicholas Van Wittenberghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Benjamin D Howard
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kaveh Daneshvar
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alan C Mullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Peter Dedon
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yi Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Cosmas C Giallourakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.C.G., Y.X., B.M., and J.W. conceived of the project, analyzed the data, and wrote the manuscript with input from all authors. B.M. did the experimental work related to m6A-LAIC-seq along with Z.L., N.V.W., B.D.H., K.D., and A.C.M. Y.X. and J.W. did the computational analyses of data with input from C.C.G. and B.M. P.D., K.S.L., and R.H. synthesized m6Am and performed RNA mass spectrometry.

Corresponding authors

Correspondence to Yi Xing or Cosmas C Giallourakis.

Ethics declarations

Competing interests

C.C.G., Y.X., B.M., and J.W. are in the process of filing a patent application for m6A-LAIC-seq.

Integrated supplementary information

Supplementary Figure 1 Anti-m6A RIP is not at antibody saturation and anti-m6A RIP efficiency is independent of number of m6A modifications per transcript.

(a) anti-m6A RNA immunoprecipitation (RIP) conditions are not at saturation. Schematic of approach to evaluate the saturation of anti-m6A RIP. Total RNA was spiked with m6A methylated GFP transcript then subjected to anti-m6A RNA immunoprecipitation (RIP) using 500ng of anti-m6A antibody per mg of starting RNA. The resulting m6A negative fraction (supernatant) was subjected to a 2nd round of anti-m6A RIP. (b) Bioanalyzer traces of all indicated fractions from 1st and 2nd rounds of anti-m6A RIP shown. (c) Results of anti-m6A dot blot of all fractions from 1st and 2nd rounds of anti-m6A RIP shown. Indicated RNAs were spotted on nylon membrane and probed with anti-m6A antibody followed by HRP conjugated secondary antibody as well as, results of qRT-PCR for m6A modified GFP spike-in all indicated fractions from 1st and 2nd rounds of anti-m6A RIP shown. (d) anti-m6A RNA immunoprecipitation efficiency is independent of the number of m6A modifications per transcript. Schematic of experimental setup showing in vitro-synthesized RNAs with different percentage of modified RNAs spiked separately into RNA, then subjected to anti-m6A RNA RIP. (e) Results of qRT-PCR for GFP from different starting/input RNA and resulting m6A positive (eluate) and m6A negative (supernatant) fractions indicated in schematic of panel d (n=2).

Supplementary Figure 2 m6A-LAIC-seq results with respect to cell type, coding potential, RNA half-life and m6A-seq results.

(a) The relationship between m6A peak numbers and m6A levels. Box plots of m6A levels based on m6A-LAIC-seq (y-axis) are stratified by number of m6A peaks detected by m6A-seq. (b) m6A levels and m6A peak intensities are only modestly correlated. Correlation plot between m6A-LAIC-seq–derived m6A levels (x-axis) and m6A-seq–derived maximal peak intensities (y-axis) (Pearson correlation coefficient r = 0.6, P < 2.2x10-16). (c) Analyses of relationship between m6A levels and m6A peak detection using m6A-LAIC-seq and m6A-seq, respectively. The plotted curve represents the percentage of genes with an m6A peak identified by m6A-seq for each range of m6A levels, employing peak calling algorithm previously described1. (d) Cross-validation of m6A levels. qRT-PCR results of m6A levels of indicated genes (n=2 biological replicates) compared to m6A levels determined by m6A-LAIC-seq. Dots and diamonds represent each of the two replicates. The Pearson correlation coefficient and P value are indicated at bottom right corner. (e) m6A-LAIC-seq example data tracks. UCSC browser tracks of m6A level results showing representative examples of low (e.g. POU5F1/OCT4), medium (e.g. NANOG) and high (e.g. SOX2) m6A levels. RNA-seq tracks of input (black), m6A positive (eluate; red bottom row) and m6A negative (supernatant; blue) of m6A-LAIC-seq shown along with m6A-seq position analyses of previously published H1-ESC data (red, top row). The m6A positive and m6A negative are both normalized using ERCC such that the read coverage ratio between them reflects the calculated m6A levels (i.e. equal signals in m6A positive (eluate) and m6A negative (supernatant) indicate m6A levels of 50%; see Material and Methods), while the input and m6A-seq tracks are shown for optimal viewing. Note: POU5F1 represents counts of uniquely and non-uniquely mapped reads at this locus on chr6 to overcome the mapping difficulties due multiple pseudogenes in human. (f) m6A levels are inversely correlated with steady-state levels. Plot of m6A levels (x-axis) defined by m6A-LAIC-seq versus steady-state input RNA-seq levels (y-axis; FPKM) (r = -0.38; P < 2.2x10-16). (g) m6A levels show significant correlation with RNA half-lives. Box plot representing the RNA half-life of published human-induced pluripotent stem cells (iPSCs), with RNAs separated according to m6A levels. (h) m6A levels stratified by types of RNA. Box plots comparing levels of different classes of RNA (mRNA genes versus lincRNA: P = 3.0×10-16; two-sided Wilcoxon test; mRNA versus pseudogene: P = 9.4×10-16; two-sided Wilcoxon test). (i) m6A levels of GM12878 replicated are reproducible. m6A levels obtained by m6A-LAIC-seq are highly correlated between replicates of GM12878 with levels of key B-cell genes indicated. Pearson correlation coefficient and P value are indicated at bottom right corner. (j) Frequency distribution of m6A levels of genes in GM12878.

Supplementary Figure 3 m6A-LAIC-seq results with respect to potential m6Am and quantification of m6Am and m6A levels.

(a) Violin plots representing distributions of m6A-LAIC-seq calculated m6A levels for all genes and genes without m6A peak in the first 200 nt in H1-ESC and GM12878 respectively. (b) The upper panels show the standard curves for m6A and m6Am abundance measurements using LC-MS. The lower panel shows the distribution of the LC-MS reported retention times, which clearly separate m6A and m6Am. (c) Bar plots representing the fractions (left panel) and fold changes (right panel) of LC-MS measured m6A and m6Am abundance in each replicate of H1-ESC and GM12878.

Supplementary Figure 4 m6A-LAIC-seq identifies differential splicing of exons between m6A positive and negative transcripts of genes.

(a) Distribution of various types of indicated splicing events identified by rMATs algorithm as significantly different in m6A positive (eluate) and negative (supernatant) fractions of m6A-LIAC-seq in either H1-ESC or GM12878. (b) Skipped exons (SE) with m6A peak as compared to exons without peaks are more likely to show significant changes between supernatant (m6A negative) and eluate (m6A positive) (P = 2.7x10-17; test) with calculation table shown on right. (c) Gel electrophoresis showing exon inclusion of an exon in CCDC50 gene in GM12878 in m6A positive (retained) and m6A negative fraction (excluded) in two replicates. UCSC genome browser tracks of m6A-LAIC-seq results of CCDC50 gene, showing exon inclusion difference between m6A positive and negative fractions.

Supplementary Figure 5 m6A is associated with differential 3’UTR usage.

(a) Density distribution of various classes of genes with different or same proximal versus distal alternative polyadenylation (APA) usage in methylated and nonmethylated fractions in GM12878. (b) Examples of m6A-LAIC-seq data tracks, showing genes with differing APA usage in methylated and nonmethylated transcripts. UCSC browser tracks of m6A-LAIC-seq results for example genes of each indicated class of genes. RNA-seq tracks of input (black), m6A positive (eluate; red bottom row) and m6A negative (supernatant; blue) of m6A-LAIC-seq are shown along with overlay tracks of m6A-seq position analyses of previously published H1-ESC data (red for RIP and gray for input with black squares above indicating a peak call; top row). Diamonds indicate poly(A) sites based on previously published hESC 3p-seq data. Also shown are Ensembl gene models. The distal polyadenylation (dPAS) usage index called DPDUI and calculated utilizing qRT-PCR is shown above each example gene. (c, d) Density distribution of m6A levels of various indicated classes of genes in H1-ESC (c) and GM12878 (d). Genes with proximal APA usage in m6A positive fraction greater than that in m6A negative fraction (P/D m6A+ > m6A-) show significantly higher m6A levels, compared to other classes of genes (Class I vs II, P = 4.9×10-34, Wilcoxon test; Class I vs III, P = 2.6×10-151, Wilcoxon test). (e) Box plot representing the m6A levels of genes, with genes separated according to their 3’-UTR length. (f) Genes with or without differing APA usage in m6A positive and negative fractions exhibit significantly different 3’-UTR lengths. Plot of cumulative fraction (y-axis) of log2 3’-UTR lengths (x-axis) in the indicated classes of genes. P values of two-sided Wilcoxon tests are indicated at bottom right corner. (g, h) Genes with or without differing APA usage in m6A positive and negative fractions exhibit significantly different P/D ratio in steady state RNAs in both H1-ESC (g) and GM12878 (h). Plot of cumulative fraction (y-axis) of log2 (P/D+1) of input RNAs (x-axis) in the indicated classes of genes. P values of two-sided Wilcoxon tests are indicated at bottom right corner.

Supplementary Figure 6 Potential presence of m6Am does not influence the m6A associated APA usage.

(a) Density distribution of various indicted classes of genes without m6A peak in the first 200 nt of transcripts but with different or same proximal versus distal alternative polyadenylation (APA) usage in methylated and nonmethylated fractions in H1-ESC (left panel) and GM12878 (right panel) respectively. (b) Bar plots representing the fractions of the three indicated classes of genes with different or same proximal versus distal alternative polyadenylation (APA) usage in methylated and nonmethylated fractions stratified by whether the Ensembl (left panel) or CAGE (right panel) annotated TSSs are A in H1-ESC and GM12878 respectively.

Supplementary Figure 7 Linear regression of read pairs of ERCCs for supernatant and eluate.

Linear regression plots of log2 ERCCs read pairs in eluate and supernatant are shown for H1-ESC sp1 (a), H1-ESC sp2 (b), GM12878 sp1 (c), and GM12878 sp2 (d) respectively. Red circles represent ERCC RNAs. Solid lines and the two parallel dashed lines indicate the regression line and ±0.5 apart. Vertical and horizontal dotted lines indicates the cutoff of read pair > 100 used for filtering out low-concentration RNAs before modeling. Regression function is indicated at the top of each panel.

Supplementary Figures and Tables References

1. Batista, P.J. et al. m(6)A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell stem cell 15, 707-719 (2014).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1-7 (PDF 2490 kb)

Supplementary Table 1

m6A-LAIC-seq generated genome-wide m6A levels in H1-ESC and GM12878 cell lines. The m6A level and mapped read pairs from m6A-LAIC-seq are shown for each gene. (XLSX 7894 kb)

Supplementary Table 2

Hypermethylated lincRNAs. The lincRNAs with m6A levels greater than 0.6 in H1-ESC and GM12878 are shown respectively. (XLSX 77 kb)

Supplementary Table 3

Splicing changes between supernatant and eluate in H1-ESC and GM12878 cell lines. The information of the significantly changed alternative splicing events between supernatant and eluate in either H1-ESC or GM12878 is shown. (XLSX 96 kb)

Supplementary Table 4

Alternative poly(A) changes between supernatant and eluate in H1-ESC and GM12878. The information of the significantly changed alternative poly(A) sites between supernatant and eluate in H1-ESC and GM12878 is shown respectively. (XLSX 484 kb)

Supplementary Table 5

Summary of APA changes caused by knockdown of m6A writers in different cell lines. (XLSX 35 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Molinie, B., Wang, J., Lim, K. et al. m6A-LAIC-seq reveals the census and complexity of the m6A epitranscriptome. Nat Methods 13, 692–698 (2016). https://doi.org/10.1038/nmeth.3898

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3898

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing