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

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.

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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.

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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

Authors and Affiliations

Authors

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.

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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)

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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

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