A single genome gives rise to diverse tissues through complex epigenomic mechanisms, including N6-methyladenosine (m6A), a widespread RNA modification that is implicated in many biological processes. Here, to explore the global landscape of m6A in human tissues, we generated 21 whole-transcriptome m6A methylomes across major fetal tissues using m6A sequencing. These data reveal dynamic m6A methylation, identify large numbers of tissue differential m6A modifications and indicate that m6A is positively correlated with gene expression homeostasis. We also report m6A methylomes of long intergenic non-coding RNA (lincRNA), finding that enhancer lincRNAs are enriched for m6A. Tissue m6A regions are often enriched for single nucleotide polymorphisms that are associated with the expression of quantitative traits and complex traits including common diseases, which may potentially affect m6A modifications. Finally, we find that m6A modifications preferentially occupy genes with CpG-rich promoters, features of which regulate RNA transcript m6A. Our data indicate that m6A is widely regulated by human genetic variation and promoters, suggesting a broad involvement of m6A in human development and disease.
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MeRIP–seq, input RNA-seq and ChIP–seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE114150. Source data for Figs. 1–7 and Supplementary Figs. 1–7 have been provided as Supplementary Table 5. All other data supporting the findings of this study are available from the corresponding author on reasonable request.
Custom codes were written in Python and R based on published software or papers, and are available on GitHub (https://github.com/XiaLabBioinformatics/m6AMethylation/).
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We thank K. Yen for critical reading of the manuscript, and J. Chen and Z. Zuo for discussion about this project. This work was supported by the National Key R&D Program of China (2017YFA0106700 and 2018YFC1004103), the Natural Science Foundation of China (31722034, 81671466, 81870129 and 81771643), Innovation Team in Wisdom Valley of Southern China (2015CXDT06) and Pearl River S&T Nova Program of Guangzhou (201806010009).
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Integrated supplementary information
Supplementary Figure 1 Whole-transcriptome profiling profiling of m6As in major human tissues using an improved MeRIP procedure.
a. The non-specific rate (left) of MeRIP using normal or competitive washing. The non-specific IP rate was calculated from the LC/MS-MS data using the ratio of m(6)A/A in the input sample divided by that in the IP sample. Meanwhile, ssRNA with or without m(6)A was used to validated the signal to noise ratio of these two methods (right). Enrichment of IPed versus input RNA is normalize against ssRNA without m(6)A. Two independent experiments were performed with bar representing the average value (see source data in Supplementary Table 5). b. Similarity (Pearson’s correlation) of gene expression levels between each pair of samples. The FPKM of genes from input were used. The samples were hierarchically clustered (see source data in Supplementary Table 5). c. Fraction of overlapped m(6)A peaks called by MACS2 and MeTPeak/moving-window. d. Sequence logo and p value of deduced consensus motif of m(6)As in each tissue. n= 36461, 27866, 45503, 26163, 24180, 29174, 38444, 21780 m(6)As in brain, heart, kidney, liver, lung, muscle, placenta and stomach respectively, binomial distribution test (see methods for details). e. Distribution of m(6)A peaks surrounding the CDS in mRNA regions in each tissue. f. The fraction of m(6)A peaks in heart, kidney, liver, lung, muscle, placenta, and stomach in the 5′ UTR, CDS, intron, stop codon, 3′ UTR, promoter, and intergenic regions.
a. MeRIP enrichment (upper) and expression level (lower) of m(6)A-negative (blue) and positive (orange) intronic regions in liver and stomach. Both the enrichment of IPed versus input RNA and the expression level are normalized against GAPDH. MeRIP-qPCR and qPCR were performed once with two technical replicates obtained and shown (see source data in Supplementary Table 5). b. Sequence logo and p value of deduced consensus motif of 81981 intronic m(6)As with strand information. Binomial distribution test (see methods for details). c. Ratio of uniquely mapped reads in intronic regions. Biologically independent samples n=3, 3, 3, 3, 2, 2, 3, 2 in brain, heart, kidney, liver, lung, muscle, placenta and stomach, respectively (see source data in Supplementary Table 5). d. Fraction of intronic m(6)As in each tissue determined in a rigorously way as described in methods. e. Histogram of completed splicing index (coSI) values for internal exon in each tissue.
a. Circos plot of tissue-differential m(6)As across the genome in brain (orange), heart (blue), kidney (green), liver (gray), lung (acen), muscle (purple), placenta (red), and stomach (yellow), respectively.b. MeRIP enrichment (upper) and expression level (lower) of two lung-differential m(6)A peaks on ECT2L and WNT4, and one stomach-differential peak on TMEM184A. Enrichment of IPed versus input RNA and the expression level is normalize against ZNF384, which is a conserved m(6)A peak across all tissues. MeRIP-qPCR and qPCR were performed once with two technical replicates obtained and shown.NA means the RNA level is too low to be detected by qPCR (see source data in Supplementary Table 5).
a. MeRIP enrichment (upper) and expression level (lower) of m(6)A-negative and positive regions on e-lincRNA and other lincRNA in lung and stomach. Enrichment of IPed versus input RNA and the expression level is normalize against GAPDH. MeRIP-qPCR and qPCR were performed once with three technical replicates obtained and shown (see source data in Supplementary Table 5). b. Sequence logo and p value of deduced consensus motif of 226588 m(6)As with strand information resided in mRNA. Binomial distribution test. c. Number of mRNA modified by m(6)A or not in each tissue.
a. Odds ratio of eQTL SNPs enriched in m(6)As across all HapMap SNPs in corresponding tissues. n = 682487.04, 978250.18, 699224.11, 676464.39, 765139.26, 765776.76, 1391369.53, 1392390.1, 1395465.56, 1397037.35, 1394019.17, 1393978.32, 1396518.69, 1399635.18, 1404775.28, 1400517.83, 1398627.95, 1395485.53, 1392218 bases from the top down, respectively, two-sided Fisher’s exact test, P < 0.05 (see source data in Supplementary Table 5). b. Distribution of eQTL SNPs located in m(6)As in heart, liver, lung, muscle, and stomach in the 5′ UTR, CDS, intron, stop codon, 3′ UTR, promoter, and intergenic regions. c. Representative eQTL SNP on gene WNT2B (left) and GWAS SNP on gene HNF1A-AS1 (right). Normalized reads densities of MeRIP-Seq and Input-Seq of different tissues are shown. Reads of Input-Seq and MeRIP-Seq were gray and colored, respectively. All biological replicates are shown. Levels were normalized by the number of reads in each sample. Range is shown at the right side of the ‘brain-1’ track. The MeRIP-Seq was performed two or three times for each tissue type as indicated, and all replicates were shown. d. Enrichment of IPed versus input RNA of SNP-associated m(6)A peaks was normalized to GAPDH. MeRIP-qPCR was performed once with three technical replicates obtained and shown (see source data in Supplementary Table 5).
a. Western blots of FLAG or ß-ACTIN from SFB-METTL3 Hela cells before and after doxycycline induced expression. Unprocessed data is shown in Supplementary Figure 8. The western blot was performed twice with similar results, and a representative figure was shown. b. Enrichment of METTL3 occupancy in the promoters of m(6)A modified genes over unmodified genes. N = 19801, 18676, 18439, 18030, 17567, 17374, 17778 and 17303 genes in brain, heart, kidney, liver, lung, muscle, placenta and stomach, respectively. One-sided fisher’s exact test, P <2.2E-16 (see source data in Supplementary Table 5). c. Guitar plot of m(6)As distribution on genes with CpG-rich or poor promoters.
a. m(6)A distribution on mouse gene Ankrd9 and Zbtb42. Normalized reads densities of IP (orange) versus input (gray) were shown. b. RNA expression level of POU5F1/EEF1A1-Ankrd9/Zbtb42 in Hela cells. The expression level of Ankrd9 or Zbtb42 was first normalize to ACTB, and then relative to that with EEF1A1 promoters. Error bars represent mean s.d. from three independent experiments (see source data in Supplementary Table 5). c. Protein level of Ankrd9/Zbtb42, ACTB, IRF1, SMAD7 and GAPDH in promoter-displaced Hela cells. MYC tag placed before Ankrd9 or Zbtb42 coding regions was used to determine the protein level of them. Unprocessed data is shown in Supplementary Figure 8. The western blot was performed twice with similar results, and a representative figure was shown. d. Expression level of POU5F1/EEF1A1-Ankrd9/Zbtb42 in HEK293 cells. The relative expression level of Ankrd9 or Zbtb42 was judged as aforementioned. Two independent experiments were shown (see source data in Supplementary Table 5). e. Protein level of Ankrd9/Zbtb42, ACTB, IRF1 and GAPDH in promoter-displaced HEK293 cells. Unprocessed data is shown in Supplementary Figure 8. The western blot was performed twice with similar results, and a representative figure was shown. f. The relative m6A levels of ACTB, Ankrd9 and Zbtb42 in promoter displaced HEK293 cells were firstly normalized to their own inputs and then relative to GAPDH in corresponding cells. The experiments were performed twice (see source data in Supplementary Table 5). g. Lifetime of Ankrd9, Zbtb42, and IRF1 in promoter-displaced HEK293 cells after actinomycin D treatment. The plotted points represented the individual data from two independent experiments (see source data in Supplementary Table 5). h. Depth of CEBPZ and SMAD2 ChIP-Seq fragments (per bp per peak) around CpG-rich or CpG-poor TSS regions.
Supplementary Figures 1–8 and legends for Supplementary Tables 1–5
Sample information in this study.
A summary of MeRIP sequencing statistics for each sample.
Cis-eQTLs SNPs in fetal tissue m6A regions potentially affecting m6A sites in corresponding tissues.
List of primers used in this study.
Statistics source data.
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Xiao, S., Cao, S., Huang, Q. et al. The RNA N6-methyladenosine modification landscape of human fetal tissues. Nat Cell Biol 21, 651–661 (2019). https://doi.org/10.1038/s41556-019-0315-4
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