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The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA

Nature volume 530, pages 441–446 (2016)Cite this article

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Abstract

Gene expression can be regulated post-transcriptionally through dynamic and reversible RNA modifications. A recent noteworthy example is N6-methyladenosine (m6A), which affects messenger RNA (mRNA) localization, stability, translation and splicing. Here we report on a new mRNA modification, N1-methyladenosine (m1A), that occurs on thousands of different gene transcripts in eukaryotic cells, from yeast to mammals, at an estimated average transcript stoichiometry of 20% in humans. Employing newly developed sequencing approaches, we show that m1A is enriched around the start codon upstream of the first splice site: it preferentially decorates more structured regions around canonical and alternative translation initiation sites, is dynamic in response to physiological conditions, and correlates positively with protein production. These unique features are highly conserved in mouse and human cells, strongly indicating a functional role for m1A in promoting translation of methylated mRNA.

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Figure 1: Development of m1A-seq to map a newly identified constituent of mammalian mRNA.
Figure 2: m1A is associated with translation initiation sites (TISs) in the human transcriptome.
Figure 3: m1A occurs in GC-rich sequence contexts and in genes with structured 5′ UTRs.
Figure 4: m1A methylome conservation between human and mouse.
Figure 5: m1A in mRNA is a dynamic modification that responds to changing physiological and stress conditions, and varies between tissues.
Figure 6: m1A around the start codon correlates with higher protein levels.

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Gene Expression Omnibus

Data deposits

Data have been deposited in the NCBI Gene Expression Omnibus (GEO) and are accessible through GEO series accession number GSE70485.

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Acknowledgements

The work was supported by the National Institutes of Health HG008688 and GM71440 grants to C.H., GM113194 grant to T.P. and C.H. and grants from the Flight Attendant Medical Research Institute (FAMRI), Israel Science Foundation (ISF grant no. 1667/12), Israeli Centers of Excellence (I-CORE) Program (ISF grants no. 41/11 and no. 1796/12), Ernest and Bonnie Beutler Research Program and Kahn Family Foundation to G.R. A part of this work was funded by the Chicago Biomedical Consortium with support from the Searle Funds at The Chicago Community Trust (to D.D. and C.H.). C.H. is an investigator of the Howard Hughes Medical Institute (HHMI). G.R. is a member of the Sagol Neuroscience Network and holds the Djerassi Chair for Oncology at the Sackler Faculty of Medicine, Tel-Aviv University, Israel. D.D. is supported by a Human Frontier Science Program (HFSP) long-term fellowship. S.N. is an HHMI Fellow of the Damon Runyon Cancer Research Foundation (DRG-2215-15), previously supported by a Yen post-doctoral fellowship in interdisciplinary research. Q.D. is supported by the National Institutes of Health grant HG006699. We wish to thank S. Farage-Barhom, K. Cesarkas and E. Glick-Saar for help with deep sequencing.

Author information

Author notes

  1. Dan Dominissini, Sigrid Nachtergaele and Sharon Moshitch-Moshkovitz: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, 929 East 57th Street, Chicago, 60637, Illinois, USA

    Dan Dominissini, Sigrid Nachtergaele, Qing Dai, Dali Han, Louis C. Doré & Chuan He

  2. Howard Hughes Medical Institute, The University of Chicago, 929 East 57th Street, Chicago, 60637, Illinois, USA

    Dan Dominissini, Sigrid Nachtergaele, Qing Dai, Dali Han, Louis C. Doré & Chuan He

  3. Cancer Research Center, Chaim Sheba Medical Center, Tel Hashomer, 52621, Israel

    Sharon Moshitch-Moshkovitz, Eyal Peer, Nitzan Kol, Moshe Shay Ben-Haim, Ayelet Di Segni, Mali Salmon-Divon, Oz Solomon, Eran Eyal, Vera Hershkovitz, Ninette Amariglio & Gideon Rechavi

  4. Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel

    Eyal Peer, Moshe Shay Ben-Haim & Gideon Rechavi

  5. The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, 52900, Israel

    Oz Solomon & Ninette Amariglio

  6. Department of Biochemistry and Molecular Biology, The University of Chicago, 929 East 57th Street, Chicago, 60637, Illinois, USA

    Wesley C. Clark, Guanqun Zheng, Tao Pan & Chuan He

Authors
  1. Dan Dominissini
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Contributions

D.D. and C.H. conceived the idea. D.D. designed the experiments and analyses, and developed the method. D.D., S.N., and S.M.-M. performed experiments. A.D.S. performed PARS structural mapping. V.H., Q.D., and G.Z. helped with experiments. Q.D. synthesized deuterated standards. D.D., S.M.-M., S.N., M.S.B.-H., E.P., N.K., M.S.-D., O.S., E.E., W.C.C. and D.H. performed analyses and interpreted findings. D.D., S.M.-M., S.N. and C.H. wrote the manuscript with input from M.S.B.-H., E.P., L.C.D., W.C.C., G.Z., T.P., N.A. and G.R.

Corresponding authors

Correspondence to Dan Dominissini, Gideon Rechavi or Chuan He.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Experimental conditions for detection, quantitation and sequencing of m1A in mRNA.

a, Schematic illustration of m1A-to-m6A rearrangement (Dimroth rearrangement) under alkaline conditions at elevated temperatures. b, mRNA purification scheme before LC-MS/MS m1A quantitation (left). Corresponding RNA electrophoresis profiles obtained by Agilent 2100 Bioanalyzer (middle and right). c, Monitoring m1A-to-m6A rearrangement levels during sample preparation for LC-MS/MS. Synthetic 5-nucleotide long RNA oligonucleotides containing m1A (upper panel) or m6A (lower panel) were digested to mononucleotides, dephosphorylated (see methods) and analysed by HPLC-UV. Only minimal (<10%) m1A-to-m6A rearrangement was observed (arrow). d, Standard curves for m1A and deuterium-labelled m1A (d3-m1A) demonstrate very similar detection sensitivity in LC-MS/MS. Mean values ± s.e.m. are shown, n = 3. e, The change in m1A/A and m6A/A molar ratios (%) during the purification scheme outlined in b. Mean values ± s.e.m. are shown, n = 3. f, LC-MS/MS of mRNA isolated from HepG2 cells labelled with deuterated methionine (d3-Met) for 24 h detects d3-m1A, suggesting S-adenosyl-methionine (SAM) is the methyl donor. Mean values ± s.e.m. are shown, n = 3. g, Dot blots demonstrating high anti-m1A antibody specificity. Increasing amounts (indicated above the top blot) of synthetic RNA oligonucleotides containing m1A, m6A or unmodified A residues were spotted onto a membrane and probed with either anti-m1A or anti-m6A antibodies. Anti-m1A antibody detects m1A and does not exhibit cross-reactivity with m6A or A (upper blot); anti-m6A antibody demonstrates low, yet detectable, cross-reactivity with both m1A and A (lower blot). For blot source data, see Supplementary Fig. 1. h, Competitive dot blots were performed on separate membranes spotted with 75 pmol of synthetic m1A-containing RNA oligonucleotide. Whereas increasing concentrations of free m1A mononucleoside progressively attenuate anti-m1A binding, increasing concentrations of free m6A mononucleoside do not. For blot source data, see Supplementary Fig. 1. i, Quantitative LC-MS/MS demonstrates m1A enrichment—and m6A depletion—following immunoprecipitation (IP) with anti-m1A antibody compared to total RNA input. Mean values ± s.e.m. are shown, n = 3. j, Monitoring m1A-to-m6A rearrangement levels under different RNA fragmentation conditions for use in m1A-seq. Pure m1A mononucleoside in 1× fragmentation buffer (see Methods) was subjected to the conditions specified to the right of the chromatograms and directly analysed by injection to HPLC-UV for rearrangement to m6A. k, Comparison of competitive m1A elution and Proteinase K elution of immunoprecipitated m1A-containing RNA fragments from anti-m1A-coupled magnetic beads shows that the two elution modes are equivalent. l, Identification of the known m1A site in position 1322 of human 28S rRNA validates the accuracy of m1A-seq and the use of peak middle points as a close approximation for m1A sites. Partial m1A-to-m6A rearrangement increases the coverage around this site. m, Conditions for induced m1A-to-m6A rearrangement of RNA oligonucleotides that maintain RNA integrity for use in m1A-seq. A synthetic 5-nucleotide long RNA oligonucleotide of the sequence 5′-AC(m1A)UG-3′ was subjected to various base/heating conditions (indicated to the right of the chromatograms) and directly analysed by injection to HPLC-UV for rearrangement to 5′-AC(m6A)UG-3′. Incubation at pH 10.4, 60 °C for 1 h results in rearrangement to m6A in 40% of oligonucleotides; longer incubation times result in increased rates of rearrangement. Chromatograms of untreated RNA oligonucleotides appear above and mark the expected retention times.

Extended Data Figure 2 Characterization of human m1A-methylated genes.

a, Gene ontology (GO) analysis of methylated HeLa genes relative to all adequately expressed genes (above the 1st quartile) reveals enrichment of biological processes related to translation and RNA metabolism. Fold-enrichment and P values are indicated for each category. b, GO analysis of molecular functions reveals enrichment of structural constituents of the ribosome. Scheme based on an illustration obtained from DAVID bioinformatics website of the KEGG human ribosome pathway. Red stars indicate methylated genes in the pathway. Colouring of the boxes and ribosome constituents is according to KEGG pathway maps showing interacting proteins and hyperlinks to gene entries that can be reached through http://www.genome.jp/kegg-bin/show_pathway?hsa03010. c, The average fraction of methylated transcripts (stoichiometry) increases with gene expression level. r and P values are indicated, Pearson correlation. d, Pie chart presenting the fraction of m1A peaks in each of three non-overlapping transcript segments (5′ UTR, CDS and 3′ UTR) in HEK293 cells. e–g, The fraction of methylated genes increases with gene expression levels in HepG2 (e), HEK293 (f) and common human peaks (see Methods) (g).

Extended Data Figure 3 Metagene analyses of human m1A peaks.

a, Metagene profiles demonstrating sequence coverage along a normalized gene transcript. Sequence reads of m1A immunoprecipitation and input in HeLa cells are indicated in blue and orange, respectively. b–d, Metagene profiles of m1A peak distribution in a non-normalized window centred on the AUG start codon (b), extending downstream from the transcription start site (TSS) (c) and centred on the stop codon (d), in the indicated human cell types. e, Metagene profiles comparing the distribution of m1A peaks (blue) to that of negative peaks (black) along a normalized transcript composed of three rescaled non-overlapping segments illustrated below, in HeLa cells. f, Table demonstrating m1A peak enrichment in growing windows centred on the AUG start codon in HeLa cells. Enrichment is calculated as number of peaks in the window divided by window size (nucleotides). g, Table summarizing the overlap between m6A and m1A peaks in HepG2, HEK293 and WT mESCs. m6A peaks are sourced from Dominissini et al.1, Linder et al.67 and Geula et al.42, respectively. m1A peaks are from the current study. h, i, Metagene profiles of m1A peak distribution in a non-normalized window centred on the AUG codon (h) and extending downstream from the TSS (i). Peaks are sorted by the exon containing the AUG codon and the length of the first exon, respectively. j, Metagene profiles of m1A peak distribution in a non-normalized window centered on the nearest splice site. Peaks are sorted by the exon containing the AUG codon in that gene. k, m1A-induced reverse transcription (RT) arrests produce typical m1A peaks characterized by a central region of reduced coverage with a local minimum (m1A trough) Examples are shown. l, m1A-to-m6A rearrangement results in a reduced number of identified m1A troughs. Higher rates of rearrangement further reduce the number of identified m1A troughs (right panel). Example is shown (left panel). m, Metagene profile of m1A trough distribution along a normalized transcript composed of three rescaled non-overlapping segments illustrated below, in HepG2 cells. n, Metagene profile of m1A trough distribution in a non-normalized window centred on the AUG start codon in HepG2 cells. o, Metagene profile of m1A trough distribution along a normalized transcript in HepG2 cells. p, Metagene profile of m1A trough distribution in a non-normalized window centred on the nearest splice site in HepG2 cells. q, Pie chart presenting the fraction of m1A troughs in each of three non-overlapping transcript segments (5′ UTR, CDS and 3'UTR) in HepG2 cells. r, MEME motifs identified in 100-nucleotide windows centred on m1A troughs that lie within the AUG start codon window (±150 nucleotides) in HepG2 cells.

Extended Data Figure 4 Correlation between m1A sites and TISs in human.

a, b, The mean number of alternative TISs in methylated (m1A) versus all other (unmethylated) genes in HeLa (a) and HEK293 (b) cells. Mean values ± s.e.m. are shown. P values are indicated, Mann–Whitney U test. c, The mean number of alternative TISs per gene as a function of the number of m1A peaks per gene in HeLa and HEK293 cells. Mean values ± s.e.m. are shown; r and P values are indicated, Pearson correlation; regression line is drawn. d, e, The percentage of genes with upstream (=5′ UTR) or downstream (=CDS) m1A sites out of all genes that have either downstream or upstream TISs, compared to the expected percentage in HeLa (d) and HEK293 (e) cells. P values are indicated, χ2 test. f–h, Scatter plots showing the correlation between the locations (log2) of alternative TISs and m1A peaks with respect to the canonical AUG start codon (0) in HeLa (f), HEK293 (g) and HepG2 (h) cells: left, upstream TISs (uTIS) and 5′ UTRs m1A peaks; right, downstream TISs (dTIS) and CDS m1A peaks. r and P values are indicated, t-test.

Extended Data Figure 5 Structure and sequence features of m1A sites in human mRNA.

a, m1A peaks have a significantly higher GC content compared to negative peaks in all three transcript segments: 5′ UTR, CDS and 3′ UTR. Box limits represent 25th percentile, median and 75th percentile, whiskers represent 2.5 and 97.5 percentiles, and dots indicate outliers. P = 1.5 × 10−278, P = 8.2 × 10−259 and P = 3.3 × 10−271, respectively, t-test. b, Motifs identified in 400-nucleotide windows centred on the canonical AUG start codon in genes with m1A peaks in this window (upper table), or around m1A peaks located in the CDS, outside the AUG start codon window (lower table). c, Examples of adenosines around m1A peak middles with increased mismatch rates. Fold-enrichment values are the ratios of mismatch rates in untreated relative to rearranged samples. The top ten highest fold enrichment samples are shown. d, e, GC content (d) and minimum free energy density (MFEden) (e) of 5′ UTRs of methylated (m1A), unmethylated (Non-m1A) and all genes. Box limits represent 25th percentile, median and 75th percentile, whiskers represent 2.5 and 97.5 percentiles. P values are indicted, t-test. f, Mean GC content (upper panel), PARS score (middle panel) and free energy (ΔG, lower panel) in a 300-nucleotide window centred on the start codon of commonly methylated genes relative to non-methylated genes (see Methods). Error bars represent s.e.m. g, The PARS scores of methylated compared to all other genes in HepG2 cells in a 150-nucleotide window extending downstream from the TSS, calculated in 30-nucleotide sliding windows. Each plot represents data from an independent PARS experiment. Error bars represent s.d.

Extended Data Figure 6 Features of the mouse m1A methylome (part 1).

a, Detection of an m1A site at position 1135 of mouse 28S rRNA, in mouse liver m1A immunoprecipitation. A drop in sequence read coverage can be seen at the methylated position. b, Fold-enrichment (immunoprecipitation over input reads) identifies an m1A peak. c, High mismatch rate at the identified m1A 1135 in mouse 28S rRNA. d, Pie charts presenting the fraction of m1A peaks in each of three non-overlapping transcript segments (5′ UTR, CDS and 3′ UTR) in the indicated mouse cell types. e, Metagene profiles of m1A peak distribution in a non-normalized window centred on the AUG start codon in the indicated mouse cell types. f, Table showing m1A peak enrichment in growing windows centred on the AUG start codon in mouse liver. Enrichment is calculated as the number of peaks in the window divided by window size (nucleotides). g, h, Metagene profiles of m1A peak distribution in a non-normalized window extending downstream from the TSS (g) and centred on the stop codon (h) in the indicated mouse cell types. i, The mean number of alternative TISs in methylated (m1A) versus all other (unmethylated) genes in MEF cells. Mean values ± s.e.m. are shown; P value is indicated, Mann–Whitney U test. j, The percentage of genes with upstream (=5′ UTR) and downstream (=CDS) m1A sites out of all genes that have either downstream or upstream alternative TISs, compared to the expected percentage in MEF cells. P value is indicated, χ2 test. k, l, Scatter plots showing the correlation between the locations (log2) of upstream TISs (uTIS) and 5′ UTR m1A peaks relative to the canonical AUG start codon (0) in mouse liver (k) and MEF (l) cells. r and P values are indicated, t-test. m, Metagene profiles of mouse m1A peak distribution in a non-normalized window centred on the AUG codon. Peaks are sorted by the exon containing the AUG codon in that gene.

Extended Data Figure 7 Features of the mouse m1A methylome (part 2).

a, Mouse m1A peaks have a significantly higher GC content compared to negative peaks. Box limits represent 25th percentile, median and 75th percentile, whiskers represent 2.5 and 97.5 percentiles, and dots indicate outliers. P = 4.4 × 10−175, t-test. b, Motifs identified in 400-nucleotide windows centred on the canonical AUG start codon in genes with m1A peaks in this window in mouse liver. c, d, GC content (c) and MFEden (d) of 5′ UTRs of methylated (m1A), unmethylated (Non-m1A) and all genes. Box limits represent 25th percentile, median and 75th percentile, whiskers represent 2.5 and 97.5 percentiles. P values are indicted, t-test. e, f, A sliding window profile of mean GC content (e) and mean ΔG (f) in a 300-nucleotide window centred on the canonical AUG start codon in methylated (m1A) genes compared to all other genes in mouse liver. P values are indicated, Kolmogorov–Smirnov test and t-test. g, Representative plots of human-mouse orthologous genes with conserved m1A peaks. Plot format as in Fig. 2a.

Extended Data Figure 8 The yeast m1A methylome.

a, m1A-seq identifies the known m1A sites (645 and 2142) in S. cerevisiae 25S rRNA. A drop in sequence read coverage (indicated by purple dots) occurs around the methylated positions (indicated by dashed lines). b, Representative plots of methylated transcripts in S. cerevisiae. Plot format as in Fig. 2a. c, The percentage of methylated genes that carry 1, 2 or 3 m1A peaks per gene in S. cerevisiae. Out of 843 m1A peaks (FC ≥ 4, FDR ≤ 5%) in 778 genes, most (88.6%) are methylated only once. Unlike in mammals, m1A is distributed across the coding transcriptome without an apparent preferred location. d, m1A-seq identified the known m1A sites (670 and 2230) in S. pombe 25S rRNA. A drop in sequence read coverage (indicated by purple dots) occurs around the methylated positions (indicated by dashed lines). e, Representative plot of a methylated transcript in S. pombe. Plot format as in Fig. 2a. f, Pie charts presenting the fraction of m1A peaks in each of three non-overlapping transcript segments (5′ UTR, CDS and 3′ UTR) in S. pombe Sp1 strain under the indicated conditions. Under vegetative growth, we identified 706 m1A peaks (FC ≥ 4, FDR ≤ 5%) in 619 gene transcripts, most of which (90.4%) distributed along the CDS. Four hours after transfer to a nitrogen-source deficient ‘sporulation’ medium, 157 out of the vegetative state m1A peaks were no longer detected, and 297 new peaks appeared. Importantly, transcripts that harbour differential peaks were adequately expressed (above the 1st quartile) in both conditions. g, The percentage of methylated genes that carry 1, 2, or 3 + m1A peaks per gene in S. pombe Sp1 strain under the indicated conditions. h, Venn diagram representing differential and shared m1A peaks in S. pombe Sp1 strain under the indicated conditions. i, Representative plots of a differentially methylated transcript in S. pombe Sp1 strain under the indicated conditions. Yellow box, conserved peak; green box, differential peak.

Extended Data Figure 9 m1A in mRNA is a dynamic modification.

a, b, LC-MS/MS quantification of m1A in mRNA of untreated and amino acid (AA)-starved (a) or serum-starved (b) HepG2. Mean values ± s.e.m. are shown, n = 3, *P ≤ 0.05, ***P ≤ 0.001, unpaired t-test. c, d, Representative plots of differentially methylated transcripts in untreated and glucose-starved (c) or heat shock-treated (d) HepG2 cells. Plot formats as in Fig. 2a. e, LC-MS/MS quantification of m1A in mRNA of 293F cells overexpressing WT FLAG-ALKBH3 or an inactive mutant (D193A), presented as percentage of unmodified A. Mean values ± s.e.m. are shown, n = 3, *P ≤ 0.05, NS, P > 0.05, one-way ANOVA with Dunnet’s multiple comparison test.

Extended Data Figure 10 m1A around the start codon correlates with higher translation efficiency (TE).

a, Cumulative distribution of log2(TE) in genes methylated in a 300-nucleotide window centred on the start codon compared to all other genes, in the indicated human and mouse cell types. P values (t-test) and fold-changes (FC) of median TE values (Start m1A genes/All the rest) are indicated. b, Genes methylated in a 300-nucleotide window centred on the AUG start codon have a higher ribosome release score (RRS = TE[CDS]/TE[3′ UTR]) compared to all other genes in the indicated cell types. RRSs, which are ‘normalized’ to ribosomal drop-off in the 3′ UTR, are in line with TE scores. P values (Mann–Whitney U test) and fold-changes (FC) of median RRS values (Start m1A genes/All the rest) are indicated. c, Genes methylated in different start codon window sizes have higher TE compared to all other genes, in HeLa cells. When considering all m1A genes, including those methylated outside the start codon window, the effect is reduced. P values (t-test) and fold-changes (FC) of median TE values (Start m1A genes/All the rest) are indicated. Box limits represent 25th percentile, median and 75th percentile; whiskers extend from the limit to the highest and lowest value within 1.5 IQR (interquartile range).

Supplementary information

Supplementary Information

This file contains Supplementary notes 1-3 and Supplementary figure 1. (PDF 417 kb)

Supplementary Table 1

This file contains a list of MACS2 human m1A peaks (FC≥2, FDR≤0.05). For each peak the chromosome and middle peak position, Refseq ID, transcript segment and cell in which it was identified are indicated. (XLSX 823 kb)

Supplementary Table 2

This file contains a list of MACS2 mouse m1A peaks (FC≥2, FDR≤0.05). For each peak the chromosome and middle peak position, Refseq ID, transcript segment and cell in which it was identified are indicated. (XLSX 156 kb)

Supplementary Table 3

This file contains a list of MACS2 yeast m1A peaks (FC≥2, FDR≤0.05). For each peak the chromosome and middle peak position, Gene ID, transcript segment and cell in which it was identified are indicated. (XLSX 71 kb)

Supplementary Table 4

This file contains a summary of m1A-Seq information for all samples and cell types tested. The total number of MACS2-identified peaks and the respective information are presented. (XLSX 8 kb)

Supplementary Table 5

This file contains sites of identified mutations with higher mismatch rate in IP over input samples are shown for HepG2 and HeLa samples. Chromosome and position for each site are indicated. (XLSX 16 kb)

Supplementary Table 6

This file contains sites of 20 nt-wide troughs identified in HepG2 peaks using the PeakSplitter tool. Chromosome, start and end positions are indicated for each trough. (XLSX 39 kb)

Supplementary Table 7

This file contains data obtained from microarrays for analysis of transcript methylation stoichiometry. Data is shown for 2 replicates (A and B). SUP= immunodepleted supernatant fraction; INPUT= RNA sample prior to immunodepletion. Av=average. Columns A-G show data for genes with one m1A peak. Columns I-N show data for non-m1A genes, used for correction for technical loss along the procedure steps. (XLSX 633 kb)

Supplementary Table 8

This file contains a summary of the ANCOVA model for the contribution of m1A to protein abundance. The model tests the contribution of m1A in predicting unexplained variance, of known predictors, on protein abundance. Analysis includes 3 human and 3 mouse cell types. For each cell type, zero order P values of the bivariate correlation of each variable with protein abundance indicate the significance of its contribution in predicting protein output independently. ANCOVA model P values indicate the significance of unshared variance of m1A and all covariates included in the model. The direction of the contribution to protein output (positive in light blue or negative in pink) of each factor in each cell type (indicated in the bottom of the column) is shown. ns = non-significant p>0.05. Colour scale for P values is indicated. (XLSX 23 kb)

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Dominissini, D., Nachtergaele, S., Moshitch-Moshkovitz, S. et al. The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA. Nature 530, 441–446 (2016). https://doi.org/10.1038/nature16998

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