m6Am-seq reveals the dynamic m6Am methylation in the human transcriptome

Sun, Hanxiao; Li, Kai; Zhang, Xiaoting; Liu, Jun’e; Zhang, Meiling; Meng, Haowei; Yi, Chengqi

doi:10.1038/s41467-021-25105-5

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Published: 06 August 2021

m⁶Am-seq reveals the dynamic m⁶Am methylation in the human transcriptome

Hanxiao Sun¹^na1,
Kai Li^1,2,3^na1,
Xiaoting Zhang¹,
Jun’e Liu¹,
Meiling Zhang¹,
Haowei Meng ORCID: orcid.org/0000-0001-9695-5060¹ &
…
Chengqi Yi ORCID: orcid.org/0000-0003-2540-9729^1,3,4

Nature Communications volume 12, Article number: 4778 (2021) Cite this article

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Abstract

N⁶,2′-O-dimethyladenosine (m⁶Am), a terminal modification adjacent to the mRNA cap, is a newly discovered reversible RNA modification. Yet, a specific and sensitive tool to directly map transcriptome-wide m⁶Am is lacking. Here, we report m⁶Am-seq, based on selective in vitro demethylation and RNA immunoprecipitation. m⁶Am-seq directly distinguishes m⁶Am and 5′-UTR N⁶-methyladenosine (m⁶A) and enables the identification of m⁶Am at single-base resolution and 5′-UTR m⁶A in the human transcriptome. Using m⁶Am-seq, we also find that m⁶Am and 5′-UTR m⁶A respond dynamically to stimuli, and identify key functional methylation sites that may facilitate cellular stress response. Collectively, m⁶Am-seq reveals the high-confidence m⁶Am and 5′-UTR m⁶A methylome and provides a robust tool for functional studies of the two epitranscriptomic marks.

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Introduction

More than 160 different chemical modifications have been found so far¹. N⁶-methyladenosine (m⁶A) is the most abundant internal modification on mRNA and lncRNA in eukaryotes^2,3,4. In addition to m⁶A, there exists another reversible modification in higher eukaryotes, called N⁶, 2′-O-dimethyladenosine (m⁶Am), which is precisely located at the first transcribed nucleotide and hence adjacent to the cap structure of mRNA^5,6. A total of 50–80% of adenosine-starting mammalian mRNAs are believed to be m⁶Am modified⁶. It is catalyzed by PCIF1^7,8,9,10, a protein that interacts with the phosphorylated CTD of RNAPII¹¹, and could be removed by the m⁶A demethylase FTO^12,13. Thus, m⁶Am is dynamically regulated by FTO and PCIF1, leading to the direction of the cap epitranscriptomics.

The development of transcriptome-wide sequencing technologies for various RNA modifications has greatly facilitated the field of epitranscriptomics¹⁴. The most widely used method for m⁶Am/m⁶A mapping relies on m⁶A antibodies, which do not discriminate between the two functionally distinct modifications^15,16. While m⁶A is enriched around the stop codon, it is challenging to distinguish m⁶Am from 5′-UTR m⁶A, which constitutes a significant part of m⁶A methylome and has been shown to be functionally important in various biological processes^17,18,19. There have been efforts aiming to distinguish the two modifications^8,9,10,20,21; yet they suffer from the low efficiency of UV crosslinking, the inaccuracy of TSS annotation, and the limited activity of 5′ exonuclease, thus compromising the sensitivity and precision of m⁶Am detection. While the use of PCIF1 knockout (KO) cell lines has improved the confidence of m⁶Am detection^9,10, it is an indirect approach and is incompatible for epitranscriptome analysis in human tissues and biological samples where genetic manipulation is challenging. Thus, a sensitive and direct method for transcriptome-wide m⁶Am identification is highly desired.

Here, we develop m⁶Am-seq to investigate the prevalence, topology, and dynamics of m⁶Am in the human transcriptome. m⁶Am-seq relies on an in vitro demethylation reaction that selectively removes m⁶Am while keeps m⁶A intact, thereby discriminating genuine m⁶Am at the mRNA cap and 5′-UTR m⁶A. Using m⁶Am-seq, we identify 2166 m⁶Am sites at base resolution from 1652 peaks and 1307 5′-UTR m⁶A peaks throughout the human transcriptome. Moreover, we show that m⁶Am and 5′-UTR m⁶A respond dynamically to heat shock and hypoxia conditions, and identify hundreds of stress-induced m⁶Am and 5′-UTR m⁶A peaks that could mediate adaptive response to the stressors. Altogether, we provide a tool to directly and selectively distinguish and profile m⁶Am and 5′-UTR m⁶A; we anticipate that m⁶Am-seq will pave the way for future functional studies of m⁶Am in various biological systems.

Results

A selective demethylation reaction to distinguish m⁶Am and m⁶A

m⁶Am-seq utilizes a selective demethylation reaction to achieve specific and sensitive detection of m⁶Am. While both m⁶Am and m⁶A are recognized by antibodies, we envisioned that the unique cap-adjacent structural context of m⁶Am could be selectively read out in vitro by demethylase (Supplementary Fig. 1a). To this end, we tested ALKBH5 and FTO, two known RNA demethylases^22,23. We found that ALKBH5 does not act on m⁶Am, consistent with previous biochemical data¹³. However, it does not demethylate m⁶A to completion (~80%, Supplementary Fig. 1b), complicating subsequent analysis. In contrast, FTO prefers m⁶Am but demethylates both modifications with high efficiency (>98% and ~60% for m⁶Am and m⁶A, respectively), under recommended conditions from literature¹³ (Supplementary Fig. 1c).

Neither of the demethylation reactions would be good enough to distinguish m⁶Am and m⁶A in their present forms. The ideal condition would remove one modification to completion while leaving another intact. FTO and ALKBH5 are Fe(II)/2-KG-dependent dioxygenases, whose in vitro demethylation activity is promoted by l-ascorbic acid^22,23. As a reducing agent, it is capable of reducing redox-active metals such as iron, thereby increasing the pro-oxidant chemistry of these metals²⁴. We thus hypothesized that by omitting l-ascorbic acid from our conditions, we might be able to modulate the in vitro activity of FTO so that it only acts on the preferred, cap-adjacent m⁶Am but not the internal m⁶A. To our delight, after removing l-ascorbic acid and further optimizing the demethylation conditions, >95% of m⁶Am was removed without significantly affecting the m⁶A level (Fig. 1c). We further verified the selectivity of our demethylation condition using a dually modified probe containing both cap m⁶Am and internal m⁶A, which were 76 nt apart from each other (Supplementary Fig. 1d). Thus, we identified a demethylation reaction that is specific for m⁶Am.

**Fig. 1: m⁶Am-seq combines RNA immunoprecipitation with an optimized demethylation reaction to enrich and distinguish m⁶Am and 5′-UTR m⁶A.**

m⁶Am-seq directly detects m⁶Am

To map the m⁶Am in the human transcriptome, we developed m⁶Am-seq, which combines the selective demethylation reaction with RNA immunoprecipitation (Fig. 1). Two key RNA immunoprecipitation procedures are utilized, which are a cap-m⁷G IP and an m⁶A IP step. Because the global m⁶A level is around 20-fold higher than that of m⁶Am in total RNA (Supplementary Fig. 1e), and the vast majority of m⁶Am is localized in mRNA cap (Supplementary Fig. 1f), we used the cap-m⁷G IP step to enrich for m⁶Am and deplete m⁶A that is mostly found in the 3′-UTR. As expected, immunoprecipitation with a cap-m⁷G antibody for fragmented RNA resulted in more than 100-fold enrichment of m⁶Am and a simultaneous 2-fold reduction of m⁶A (Fig. 1b). Moreover, ~90% of peaks are located at and near 5′-UTR, suggesting 5′-end of mRNAs were successfully and specifically enriched using size selected total RNA (>200 nt) as input, without the prerequisite to purify mRNA (Supplementary Fig. 2a).

The immunoprecipitated RNA was then subjected to the selective demethylation reaction or mock treatment, which was followed by the second IP step utilizing an m⁶A antibody (Fig. 1a). The FTO (−) sample would contain both m⁶Am and 5′-UTR m⁶A signals, while the FTO (+) sample would contain only m⁶A. By comparing the methylome profiles of the two samples, we were able to detect m⁶Am, which is sensitive to demethylase treatment, from 5′-UTR m⁶A, which is insensitive and conserved in the two samples. Indeed, we observed a bimodal-like distribution of fold change of peak intensity: peaks on the right side experienced reduced intensity upon demethylase treatment, suggesting they are demethylase-sensitive m⁶Am modification; while peaks on the left were not significantly altered or even slightly increased, suggesting they are retained 5′-UTR m⁶A peaks (Supplementary Fig. 2b, c). Thus, m⁶Am-seq selectively distinguishes the two modifications.

m⁶Am-seq detects transcriptome-wide m⁶Am and 5′-UTR m⁶A

We next applied m⁶Am-seq to characterize m⁶Am and 5′-UTR m⁶A methylome in HEK293T cells. We found that m⁶Am-seq results are highly correlated between biological replicates (correlation coefficient = 0.9821, Supplementary Fig. 2d), demonstrating its robustness. We identified 1652 and 1307 high-confidence m⁶Am and 5′-UTR m⁶A peaks from 1635 and 1297 genes, exhibiting the characteristic BCA (B = C/U/G) and GGACH (H = A/C/U) motif, respectively (Fig. 2a, b and Supplementary Data 1, 2). m⁶Am shows the highest enrichment at annotated TSSs, while the summit of m⁶A is clearly within 5′-UTR; the peak density of m⁶Am is also 3-fold higher than that of 5′-UTR m⁶A, demonstrating that m⁶Am is more concentrated than m⁶A around the TSSs (Supplementary Fig. 3a, b). Representative methylation profiles are shown in Fig. 2c. In one example, the ATF5 gene has multiple isoforms with distinct TSS and alternative first exons; we observed m⁶Am modification for one transcript and 5′-UTR m⁶A for another transcript (Fig. 2c), suggesting differential cis-regulatory elements in regulating the epitranscriptomic status of different transcripts from the same gene. Gene ontology (GO) terms analysis showed that transcripts containing m⁶Am and 5′-UTR m⁶A are most strongly enriched in cell cycle and RNA metabolism, respectively, suggesting their differential functions (Supplementary Fig. 3c, d).

**Fig. 2: m⁶Am-seq uncovers the high-confidence m⁶Am and 5′-UTR m⁶A methylation in the human transcriptome.**

To further demonstrate the accuracy of m⁶Am-seq, we applied standard m⁶A IP experiments to PCIF1-KO HEK293T cells. Based on the reduction of signals compared to WT cells, we detected 1357 PCIF1-dependent peaks; notably, 1200 out of the 1357 peaks were identified by m⁶Am-seq (Fig. 2d and Supplementary Fig. 3e), suggesting the high confidence of m⁶Am-seq. The remaining peaks all exhibited m⁶Am-like property (i.e., sensitive to demethylation) in m⁶Am-seq but were not called due to the stringent cutoff we used. On the other hand, 5′-UTR m⁶A peaks identified by m⁶Am-seq were not affected upon PCIF1 depletion (Fig. 2e and Supplementary Fig. 3f), further demonstrating the specificity of m⁶Am-seq. Taken together, these results prove that m⁶Am-seq identifies m⁶Am with high confidence. It is also worth mentioning that m⁶Am-seq is applicable for m⁶Am detection in tissues or cell populations where PCIF1 KD or KO is not feasible.

Comparisons of existing m⁶Am detection methods

We then compared m⁶Am-seq results with m⁶Am-Exo-seq and miCLIP^9,10. Under normalized sequencing depth, the averaged peak coverage of m⁶Am-seq (~387) was ~4× and 21× higher than that of m⁶Am-Exo-seq (~95) and miCLIP (~18) (Fig. 3a). In addition, m⁶Am peaks by m⁶Am-seq exhibit a strong tendency towards the annotated TSS, in comparison to a modest enrichment within 5′-UTR by miCLIP and m⁶Am-Exo-Seq (Fig. 3b). Thus, m⁶Am-seq outperforms existing methods in m⁶Am detection.

**Fig. 3: Comparison of m⁶Am methylome detected by m⁶Am-seq and existing m⁶Am detection methods.**

Because m⁶Am-seq and miCLIP can both detect m⁶Am and m⁶A, we performed comparisons of the two methods^10,20 (Fig. 3c and Supplementary Fig. 4a, b). We used the recent miCLIP data relying on PCIF1-KO for detailed comparison¹⁰. Approximately 21% (456/2217) of m⁶Am marked genes by miCLIP overlap with that of m⁶Am-seq (Fig. 3c). We found that miCLIP data sets showed very low coverage, providing an explanation for the limited sensitivity of miCLIP (Fig. 3d, e). While it is possible that the stringent cutoff of m⁶Am-seq may favor specificity over sensitivity, a significant portion of miCLIP-identified m⁶Am peaks could be mis-annotated and are instead 5′-UTR m⁶A peaks (Fig. 3f).

It was proposed that m⁶Am controls mRNA stability¹², based on the m⁶Am methylome identified by miCLIP. Given the observation that miCLIP may mis-annotate the two modifications, we compared the expression of m⁶Am-containing genes, which are identified by m⁶Am-seq, in wild-type and PCIF1-KO cells. Different from the previous conclusion¹², we found that the loss of m⁶Am did not alter the expression of their host mRNA (Supplementary Fig. 4c).

m⁶Am-seq detects m⁶Am at single-base resolution

Because m⁶Am is the first transcribed nucleotide, one would expect to identify m⁶Am at base resolution by searching for A-starting transcripts (i.e., those showing sudden drop-off of sequencing reads at the 5′ end). However, even when we used a library preparation procedure that preserves the first transcribed nucleotide, we did not observe an enrichment of A nucleotide in the m⁶Am/m⁶A enriched FTO (−) data set (Fig. 4a), suggesting that its TSS location alone is not sufficient to detect m⁶Am sites. Thus, we defined a “start rate difference” score (SRD score) for each nucleotide to quantify sequencing reads beginning from it (see “Methods” section). Application of these metrics immediately resulted in accurate identification of the m⁶Am site in synthetic spike-in RNAs (Supplementary Fig. 5a) and ~95.8% of A nucleotides in the m⁶Am/m⁶A enriched FTO (−) data set (Fig. 4a). In total, we identified 2166 m⁶Am sites from 1459 genes (Supplementary Data 3). For instance, mRNA-coding gene PRR11 and a lncRNA LOC101929147 both have alternative TSSs that start with an A nucleotide, and we showed that all these transcripts are marked by m⁶Am (Fig. 4b and Supplementary Fig. 5b). In addition, while m⁶Am is associated with a BCA motif, our single-base m⁶Am methylome revealed ~34% non-BCA m⁶Am sites (Supplementary Fig. 6a). We found also a weaker signal of GRO-seq and H3K27ac ChIP-seq for the non-BCA m⁶Am sites, but no difference in H3K4me3, H3K4me1, and H3K9me3 signals (Supplementary Fig. 6b–f). Altogether, these data indicate that m⁶Am-seq enables m⁶Am detection at single-base resolution.

**Fig. 4: m⁶Am-seq detects m⁶Am methylome at single-base resolution.**

To further characterize the accuracy of the identified m⁶Am sites, we performed the cap-analysis of gene expression (CAGE) experiments and compared the m⁶Am sites with annotated and CAGE-identified TSSs. We found that most of the m⁶Am sites are present downstream of the RefSeq-annotated TSSs, with only 14% of m⁶Am sites mapped at the annotated TSSs (Supplementary Fig. 7a). This observation suggested that existing methods based on annotated TSSs information can be inaccurate for m⁶Am mapping. For instance, the m⁶Am site of TMET241 would have been located internally within 5′-UTR if the annotated TSSs were used (Supplementary Fig. 7b); and two m⁶Am peaks are clearly identified for RAB5A, which has only one annotated TSS (Supplementary Fig. 7c). We next plotted the distance from m⁶Am sites to their nearest CAGE TSS sites, and observed that the vast majority of m⁶Am sites overlapped with CAGE TSSs (Fig. 4c), suggesting that these m⁶Am sites are indeed TSSs. Compared with miCLIP^10,20, m⁶Am sites by m⁶Am-seq were much closer to the CAGE TSSs (Fig. 4c). These data highlight the reliability of m⁶Am-seq in obtaining high-confidence m⁶Am sites.

Heat shock induces dynamic m⁶Am and 5′-UTR m⁶A methylations

Stress-induced RNA modification is an important feature of dynamic epitranscriptomic regulation under physiological conditions^15,25,26,27. For instance, it has been shown that in response to heat shock stress, m⁶A deposited to the 5′-UTR of newly transcribed mRNAs promotes cap-independent translation initiation, providing a mechanism of selective mRNA translation during general translation inhibition^17,19. In light of this, we applied m⁶Am-seq to characterize the dynamic m⁶Am and 5′-UTR m⁶A methylome under heat shock conditions. We identified 45 upregulated m⁶Am peaks and 90 5′-UTR m⁶A peaks, as well as 36 and 67 downregulated m⁶Am and 5′-UTR m⁶A peaks (Fig. 5a, b and Supplementary Fig. 8a, b and Supplementary Fig. 9a–d and Supplementary Data 4). Among them, we found key heat shock-associated transcripts with elevated 5′-UTR m⁶A: notable examples include HSPA1A, HSPA1B, and DNAJB1 (Supplementary Fig. 10a, b), whose transcripts undergo m⁶A-dependent but cap-independent selective translation¹⁷. These results by m⁶Am-seq accurately recapitulated heat shock-induced, functional 5′-UTR m⁶A methylation.

**Fig. 5: m⁶Am and 5′-UTR m⁶A are dynamically regulated by different stress conditions.**

Unexpectedly, a subset of transcripts previously proposed to rely on m⁶A-dependent translation under heat shock¹⁷ did not contain m⁶A modification in the 5′-UTR; instead, m⁶Am-seq reveals that they are dynamically modified by m⁶Am (Supplementary Fig. 10c). For instance, using a method that does not distinguish between m⁶Am and 5′-UTR m⁶A, upregulation of 5′-UTR m⁶A level in EGR1 was believed to mediate the stress-induced selective translation. However, using m⁶Am-seq, we found that EGR1 contained m⁶Am rather than 5′-UTR m⁶A (Supplementary Fig. 10d). Therefore, cap-independent translation of such transcripts including EGR1 is unlikely to be mediated by m⁶A. Whether or not m⁶Am, which is located at TSS and immediately after the m⁷G cap, can also promote stress-induced selective translation would be of interest for future studies. Overall, m⁶Am-seq provides accurate methylome maps to enable the study of the physiological roles of the two RNA modifications.

Dynamic m⁶Am and 5′-UTR m⁶A methylome under hypoxia conditions

We next determined whether m⁶Am and 5′-UTR m⁶A methylation can be dynamically regulated by hypoxia. We were able to identify 362 and 638 elevated m⁶Am and 5′-UTR m⁶A peaks respectively, with 31 and 63 decreased m⁶Am and 5′-UTR m⁶A peaks (Fig. 5c, d and Supplementary Fig. 8c, d and Supplementary Data 5). While some of the hypoxia-specific genes show inducible m⁶Am and 5′-UTR m⁶A modifications, many transcripts with unaltered expression levels are also dynamically modified (Supplementary Fig. 9e–h). Overall, we observed a more evident increase of m⁶Am and m⁶A methylome under hypoxia conditions than heat shock conditions.

To cope with hypoxia, mammals have evolved key adaptive mechanisms including cellular adaptations of protein synthesis, energy metabolism, mitochondrial respiration, lipid, and carbon metabolism as well as nutrient acquisition²⁸. Given the dramatic change of m⁶Am level, we examined whether dynamic m⁶Am may participate in the adaptive mechanisms during the hypoxia condition. Intriguingly, our GO analysis showed that genes with elevated m⁶Am levels are linked to the endoplasmic reticulum (ER) stress-response regulation (Supplementary Fig. 11a). For instance, it has been documented that transcriptional and translational upregulation of both CHOP and ATF3 are important to activate the expression of downstream genes in response to stress^29,30; in line with this, we observed significantly elevated m⁶Am levels in their mRNAs (Fig. 5e and Supplementary Data 5). In addition to ER stress, oxygen deprivation also reprograms intracellular metabolism³¹. For instance, hypoxia can enhance the mRNA and protein levels of HIF1 target gene GLUT1 to achieve glucose transport³¹; in agreement with this, we also observed increased m⁶Am level in the GLUT1 mRNA (Fig. 5f). Thus, we identified a dynamic m⁶Am program in key transcripts that are utilized by cells to adapt to hypoxia.

Distinct from the m⁶Am-marked genes, GO analysis of genes with elevated 5′-UTR m⁶A indicated an enrichment of the p53 signaling pathway (Supplementary Fig. 11b). In fact, hypoxia has frequently been described to be a p53 inducer³². Along this line, p53 and many of its downstream genes, such as MDM2, PTEN, PIDD1, etc., were found to contain elevated 5′-UTR m⁶A under hypoxia stress (Fig. 5g, h and Supplementary Data 5). Since induced 5′-UTR m⁶A by heat shock has been shown to enable cap-independent translation, it is possible that protein synthesis of the p53 signaling pathway could be modulated by the stress-induced m⁶A. Altogether, our results suggest that dynamic m⁶Am and m⁶A as key components of the adaptive response to hypoxia.

Discussion

More than 40 years have passed since the initial documentation of m⁶Am was made^5,6. The recent discovery of its “eraser” and “writer” has led to a renewed interest in this reversible modification³³. Yet its biological functions remain enigmatic, due to a lack of sensitive methods that detect the modification at the transcriptome-wide level. Here, we report m⁶Am-seq, which enables specific and robust profiling of m⁶Am in the human transcriptome.

The development of m⁶Am-seq is inspired by existing epigenomic and epitranscriptomic methods utilizing biologically important demethylases. For instance, TAB-Seq³⁴ and TAPS³⁵ rely on the TET proteins, which oxidize 5-methylcytosine (5mC), to detect 5mC and its oxidative derivatives in the genome. In DM-tRNA-seq³⁶, ARM-seq³⁷, and m¹A-MAP³⁸, the RNA/DNA demethylase AlkB has been used to map N¹-methyladenosine (m¹A) in tRNA and mRNA. In this study, we expand the available toolbox by repurposing FTO to detect m⁶Am and m⁶A, two dynamic and reversible epitranscriptomic marks. Different from the existing methods that use demethylases to indiscriminately remove all substrates, we manipulated the in vitro activity of FTO to selectively demethylate just one of the two cognate substrates. This is achieved by the discovery of a controlled in vitro demethylation reaction, which omits the key cofactor l-ascorbic acid from the biochemical condition. Owing to its capacity to reduce Fe³⁺ to the catalytically active Fe²⁺, l-ascorbic acid can enhance the in vitro demethylation activity^24,39. By leaving out l-ascorbic acid from the in vitro reactions, FTO still possesses a high m⁶Am demethylation activity while minimally affects the level of m⁶A, hence allowing selective detection of m⁶Am.

Interestingly, the in vivo bioavailability of ascorbate, the dominant form of vitamin C under physiological pH conditions, has been demonstrated to impact the epigenome via TETs and JmjC domain-containing demethylases, ultimately leading to phenotypic changes in development, aging, cancer and other diseases²⁴. It remains to be seen whether or not the deficiency of vitamin C might alter m⁶Am or m⁶A methylome and hence epitranscriptome reprogramming inside of cells.

Except for the selective demethylation condition omitting l-ascorbic acid, major steps of m⁶Am-seq are well-known meRIP protocols. Hence, m⁶Am-seq is straightforward and highly reproducible. Its high sensitivity and accuracy are also enabled by the combination of cap-m⁷G IP and m⁶A IP, which builds on the knowledge that m⁶Am is adjacent to the m⁷G cap. It should be noted that m⁶Am-seq does not detect internal m⁶Am, although the amount of the internal m⁶Am in mRNAs seems negligible. Only one internal m⁶Am site in U2 snRNA has been documented so far. In m⁶Am-seq, we used a demethylation reaction to distinguish m⁶Am from m⁶A, other selective chemistry could be exploited for the determination of additional modifications. For instance, dNTP-sensitivity during reverse transcription⁴⁰ and peroxidate oxidation⁴¹ could be combined with cap-m⁷G IP to detect ribose methylation at the first and second transcribed nucleotides of mRNA. In addition, given the diversity of RNA cap structures^42,43, including NAD+, NADH, desphospho-CoA, and 2,2,7-trimethylguanosine, tailored combination of IP enrichment and a discrimination procedure, as demonstrated in m⁶Am-seq, could have broad applications in analyzing the epitranscriptomic state of the transcriptome.

m⁶Am-seq distinguishes m⁶Am from m⁶A and provides a high-confidence m⁶Am methylome. While a previous study used miCLIP for m⁶Am identification and has indicated a role of m⁶Am in controlling mRNA stability¹², this conclusion was disputed by more recent studies led by independent groups^7,9,13. Using m⁶Am-seq, we corroborated the latter studies and found that m⁶Am does not affect mRNA stability. Because miCLIP does not separate well m⁶Am from m⁶A, it is likely that the observed effect in mRNA stability is caused by m⁶A¹³, which has been shown to increase mRNA stability when recognized by certain m⁶A readers⁴⁴. Nevertheless, such disagreement underscores the importance of a specific method to accurately map m⁶Am for future functional studies.

We showed that m⁶Am is dynamically regulated by heat shock and hypoxia, hinting that m⁶Am is important for cellular stress response. While a specific role of m⁶Am remains to be elucidated, we found that many well-known stress-induced genes exhibiting elevated protein levels showed increased m⁶Am methylation levels upon stimuli. Such a positive correlation appears to be the opposite in normal human tissues²¹; nevertheless, this does not rule out the possibility of a unique role of m⁶Am under stress. Consistent with the positive association, ribosome profiling experiments also revealed a positive role of m⁶Am in translation⁷. Given that 5′-UTR m⁶A has been demonstrated to mediate cap-independent translation^17,19, it is tempting to speculate that m⁶Am plays a role in stress-induced mRNA translation.

Whether or not m⁶Am may participate in cap-independent translation is an open question. It is known that the mRNA cap structure plays an important role in translation initiation. Akichika et al. showed that m⁶Am does not modulate the binding affinity of eIF4E to the cap structure, suggesting that other cap-binding factors could be involved⁷. For instance, it has been shown that eIF3 and ABCF1 can selectively recognize 5′-UTR m⁶A and mediate the cap-independent translation^18,19. Thus, future identification of potential m⁶Am readers, particularly those in the integrated stress response, could help elucidate the detailed function and mechanism of m⁶Am.

In summary, we report a tool for transcriptome-wide mapping of m⁶Am and 5′-UTR m⁶A, and reveal the dynamic features of the two epitranscriptomic marks under stress conditions. We anticipate that m⁶Am-seq will lead to new territories of RNA biology and open opportunities in the field of “cap epitranscriptomics”.

Methods

Antibodies and cell culture

Rabbit anti-m⁶A antibody was purchased from EMD Millipore (ABE572). Anti-m⁷G-cap monoclonal antibody was obtained from MBL (RN016M). HEK293T cells were cultured at 37 °C in DMEM medium supplemented with 10% FBS, 1% GlutaMAX and 0.5% penicillin/streptomycin with 5% CO₂. For heat shock, cells were grown to a confluence of 80% in 10-cm plastic dishes, placed in a 43 °C water bath for 2 h, and then recover for 6 h in normal growing conditions. For hypoxia treatment, cells were grown to 80%, carried out in Hypoxia Hood which was maintained at 37 °C, 5% CO₂, 1% oxygen for 24 h.

RNA isolation

TRIzol was used for total RNA isolation according to the manufacturer’s protocol. <200 nt RNA was deleted using the MEGAclear Transcription Clean-Up kit (Ambion, AM1908).

Quantification of m⁶A and m⁶Am by LC–MS/MS

For m⁶Am, 150 ng RNA was decapped by 10 units of RppH (NEB, M0356S) in Thermopol buffer for 5 h at 37 °C. RNA was purified by RNA clean & concentrator kit (Zymo Research, R1015). Purified RNA was digested by nuclease P1 (Sigma, N8630) with 10 mM ammonium acetate at 42 °C for 6 h, then mixed with 50 mM MES buffer (pH 6.5) and 0.5 U Shrimp Alkaline Phosphatase (NEB, M0371S), and incubated at 37 °C for another 6 h. For m⁶A, 150 ng RNA was digested into single nucleosides by nuclease P1 with 10 mM ammonium acetate at 42 °C for 6 h, then incubated with 50 mM MES buffer (pH 6.5) and 0.5 U Shrimp Alkaline Phosphatase for another 6 h at 37 °C.

The digested nucleosides were separated by the ultra-performance liquid chromatography on C18 column, and detected by the triple-quadrupole mass spectrometer (AB SCIEX QTRAP 6500). The multiple reaction-monitoring (MRM) mode was monitored: m/z 268.0–136.0 (A), m/z 245.0–113.1 (U), m/z 282.0–150.1 (m⁶A), m/z 296.0–150.0 (m⁶Am). The concentrations of m⁶A and m⁶Am levels in RNA samples were calculated from the standard curves.

Synthesis of m⁶Am/Am and m⁶A probes

cap-m⁶Am/Am RNA: template was obtained by PCR, which contains the T7 promoter sequence and ~150 bp DNA sequence. Primers used to amplify the spike-in template was provided (Supplementary Data 6). In vitro transcription reaction contained: reaction buffer (ThermoFisher Scientific, AM1354), 800 ng template DNA, T7 Enzyme Mix (ThermoFisher Scientific, AM1354), 15 mM ATP/UTP/CTP/GTP, and 15 mM cap analogs (Trilink, N-7113 or N-7102, N-7102 was ordered via email addressed to orders@trilinkbiotech.com). The reaction was incubated overnight at 37 °C. After incubation, 1 μl of TURBO DNase (ThermoFisher Scientific, AM1354) was added to the reaction, and the reaction was incubated at 37 °C for 1 h. The RNA was purified by phenol–chloroform isolation and ethanol precipitation. The precise RNA was purified from 7 M urea/15% acrylamide gels using elution buffer (0.3 M sodium acetate, 1 mM EDTA, 0.05% SDS) at 37 °C for 12 h. Next, the RNA was precipitated with ethanol. The probes were used as spike-ins and for probe demethylation assay. The sequence of cap-m⁶Am/Am RNA probes was list as follows:

m⁶Am(Am)GGAGAAAAAUCACUCAGGGUCAAUGCCAGCGCUUCGUUAAUACAGAUGUAGGUGUUCCACAGGGUAGCCAGCAGCAUCCUGCGAUGCAGAUCCGGAACAUAAUGGUGCAGGGCGCUGACUUCCGCGUUUCCAGACUUUACGAAACACGGA.

The sequence of m⁶A probe was list as follows:

AUCUACCUGUCCAGUAGCCUUCAGGAUCAUGCUGUCUGACUUGCUGGm⁶ACAUCAUUCUAGUGCCAUAACUUCAGC.

Synthesize the dually modified m⁶Am&m⁶A probe

The dually modified m⁶Am & m⁶A probe was obtained by utilizing T4 RNA ligase 2 to ligate the 3′ end of RNA probe to the 5′ end of another RNA probe. The 30nt for m⁶Am probe was obtained by in vitro transcription reaction. For phosphorylation of 5′end of m⁶A probe, RNA was treated by PNK (NEB, M0201S) with incubation for 1 h at 37 °C. 1 μl of 20 μM m⁶Am probe, 2 μl of 20 μM m⁶A probe, 1.5 μl of 20 μM splint DNA (CTACTGGACAGGTAGATAGTCTTGAAGGATTC), 1 μl 10× T4 DNA ligase buffer and 3 μl nuclease-free water were heated at 65 °C for 3 min, followed by 5 min at 25 °C in a thermocycler. Add 1 μl of 10 units/μl T4 RNA ligase 2 (NEB, M0239S) and 0.5 μl RiboLock RNase inhibitor (ThermoFisher Scientific, EO0381) into the reaction mixture and incubate at 37 °C for 2 h. Synthetic m⁶Am & m⁶A probe was gel purified and sliced in the desired size. The sequence of the m⁶Am & m⁶A probe was listed as follows:

Capm⁶AmGGAUAGCAGGCAUGGAAUCCUUCAAGACUAUCUACCUGUCCAGUAGCCUUCAGGAUCAUGCUGUCUGACUUGCUGGm⁶ACAUCAUUCUAGUGCCAUAACUUCAGC.

In vitro probe assay

To investigate the FTO and ALKBH5 demethylation activity for m⁶Am and m⁶A, we performed in vitro demethylation assay using purified demethylases and synthesized probes contained m⁶A and m⁶Am. The demethylation assay was performed in 20 μl of mixture containing 200 ng probe, 50 mM MES (pH 6.5), 100 mM KCl, 2 mM MgCl₂, 100 μM (NH₄)₂Fe(SO₄)₂·6H₂O, 300 μM 2-ketoglutarate, 0 or 2 mM l-ascorbic acid, 0.4 U/ml SUPERase·In RNase inhibitor (Invitrogen, AM2694) and 2 μM purified FTO. The reaction mixture was incubated at 37 °C for 20 min. As the control, inactive demethylases were using for incubation with RNA.

CAGE-seq library preparation

CAGE allows mapping of the transcriptional start sites of capped RNAs. 5 μg of total RNA purified from HEK293T cells was used for the CAGE-seq⁴⁵. Reverse transcription was carried out using PrimeScript reverse transcriptase (Takara, 2680A) with the RT-N15-EcoP primer. The RNA-cDNA hybrid was diol oxidized with NaIO₄ and then the 5′ Cap and 3′ end of RNA were biotinylated. The noncapped biotinylated RNA-cDNA hybrid was eliminated by RNase digestion and the biotin-trapping step. The complete cDNA was released from RNA and then the Bar-coded 5′ linker was ligated to the single-stranded cDNA. The second-strand cDNA was synthesized using the biotin-modified primer. The cDNA was digested with EcoP15I (NEB, R0646S) and the 3′ linker was ligated to the 3′ end of cDNA. The cDNA was performed PCR amplification. The CAGE library was sequenced on Illumina HiSeq 2000. The sequences of primers and linkers were provided (Supplementary Data 6).

CAGE data analysis

CAGE tags were mapped to the human genome (hg19, UCSC Genome Browser) using TopHat2 (version 2.0.13) with default parameters that allow 0 mismatches per seed (22 nt), and CTSS pipeline was used to identify CAGE TSS⁴⁵. Only uniquely mapped reads were used in downstream analysis within R and custom scripts. The mapped reads were sorted and the TSS tag was calculated. All unique 5′ ends of CAGE tag-supported TSS and the number of tags in each CTSS represents expression levels. Tag counts in each CTSS were normalized using tag pre million reads (TPMs). We also used Deeptools (version 3.5.0) to analyze the enrichment of m⁶Am in the −2 Kb to +2 Kb region around the CAGE TSS. The miCLIP-2015 m⁶Am list and miCLIP-2019 m⁶Am list were obtained from previous studies^10,13.

Cloning, expression, and purification of FTO and ALKBH5

Wild-type human FTO gene and ALKBH5 gene with deletion of the 66 amino acids were cloned into pET-28a. The recombinant plasmids were then transduced into E. coli BL21 (DE3). FTO and ALKBH5 proteins were purified following the procedures as previously described⁴⁶. Bacteria were grown at 37 °C until the OD₆₀₀ reached 0.8–1, and then induced with 0.5 mM IPTG overnight at 15 °C. Cells were harvested by centrifugation and then resuspended in ice-cold lysis buffer (25 mM Tris-HCl pH 8.0, 150 mM NaCl, 10 mM imidazole), disrupted on ice, and centrifuged at 11,000×g for 1 h at 4 °C. The supernatant was purified on HiTrap His column (GE Healthcare) and eluted with buffer containing 25 mM Tris-HCl pH 8.0, 150 mM NaCl, 500 mM imidazole. Next, the fractions were purified by HiTrap Q column (GE Healthcare). Finally, the fractions were purified by Gel-filtration chromatography (HiLoad 16/600 Superdex 200 pg column, GE Healthcare) equilibrated in storage buffer containing 25 mM Tris-HCl pH 8.0, 150 mM NaCl, and 3 mM dithiothreitol (DTT). Purified protein was stored at −80 °C with the addition of 20% glycerol.

m⁶Am-seq

cap-m⁷G RNA immunoprecipitation

The total RNAs were extracted from control, heat shock-treated, and hypoxia-treated cells. 100 μg >200 nt RNA was fragmented into ~150 nt-long fragments using RNA fragmentation buffer (NEB, E6150S). Adding fragment stop solution buffer stopped the reaction, and fragments were purified and concentrated by ethanol precipitation. 10 ng of fragments were used as “input” and the remaining fragments were used for followed treatments. The fragmented RNA was denatured at 65 °C for 5 min, followed by chilling in ice. The denatured RNA was incubated overnight at 4 °C with 2 μg cap-m⁷G antibody (1:100 dilution) and 5 μl RiboLock RNase inhibitor in IPP buffer (10 mM Tris-HCl, pH 7.4, 150 mM NaCl, 0.1% NP-40). 20 μl of protein A/G UltraLink Resin (ThermoFisher Scientific, 53132) were washed twice with IPP buffer, and then resuspended in a 200 μl IPP buffer contained 5 μl RiboLock RNase inhibitor. The antibody-RNA mixture was then incubated with protein A/G UltraLink Resin at 4 °C for 3 h. The Resin was washed twice with 1 ml of IPP buffer, once with 1 ml of low-salt IP buffer (10 mM Tris-HCl, pH 7.4, 75 mM NaCl, 0.05% NP-40), once with 1 ml of high-salt IP buffer (10 mM Tris-HCl, pH 7.4, 200 mM NaCl, 0.05% NP-40) and twice with 1 ml TET buffer (10 mM Tris-HCl, pH 7.4, 1 mM EDTA, 0.05% NP-40) at 4 °C. The beads-antibody-RNA was resuspended in 1 ml Trizol and rotated for 10 min at room temperature. cap-m⁷G-containing RNA fragments were extracted and precipitated. Around 150 ng cap-m⁷G-immunoprecipitated RNA fragments could be obtained.

In vitro demethylation treatment

1 ng of the cap-m⁷G-immunoprecipitated RNA was used as “m⁷G-IP”. RNA was subjected to [FTO (+)] or [FTO (−)] treatment. About 100 ng of RNA was used for [FTO (+)] treatment. RNA was denatured at 65 °C for 5 min, followed by chilling in ice. And then, the demethylation assay was performed in a 20 μl mixture which contained 50 mM MES (pH 6.5), 100 mM KCl, 2 mM MgCl₂, 100 μM (NH₄)₂Fe(SO₄)₂·6H₂O, 300 μM 2-ketoglutarate, 0.4 U/ml SUPERase·In RNase inhibitor and 1 μM purified FTO. After incubation at 37 °C for 20 min, the demethylated RNA was purified by phenol–chloroform extraction. The remaining RNA (around 50 ng) directly performed m⁶A RNA immunoprecipitation.

m⁶A RNA immunoprecipitation

The [FTO (−)] and [FTO (+)] samples were immunoprecipitated with m⁶A antibody. The m⁶A RNA immunoprecipitation was performed following the procedures as previously described²¹. 10 μl of protein A magnetic beads (ThermoFisher Scientific, 10002D) and 10 μl of protein G magnetic beads (ThermoFisher Scientific, 10004D) were mixed and washed twice with 500 μl of IPP buffer (10 mM Tris-HCl, pH 7.4,150 mM NaCl, 0.1% NP-40). The beads were resuspended in 500 μl of IPP buffer containing 2 μg of anti-m⁶A antibody (Millipore, ABE572). The mixture was incubated at 4 °C for at least 6 h. The beads-antibody mixture was washed twice in 500 μl of IPP buffer and resuspended with 200 μl mixture containing 40 μl of 5× IPP buffer, denatured RNA (the [FTO (−)] control and [FTO (+)] treated samples) and 5 μl of RNasin Plus RNase Inhibitor (Promega, N2615) at 4 °C for 2 h (1:100 dilution of anti-m⁶A antibody). The beads-antibody-RNA mixture was washed twice with 500 μl of IPP buffer, twice with 500 μl of low-salt IP buffer (10 mM Tris-HCl, pH 7.5, 50 mM NaCl, 0.1% NP-40), and twice with 500 μl of high-salt IP buffer (10 mM Tris-HCl, pH 7.5, 500 mM NaCl,0.1% NP-40). And then, bound fragments were eluted from beads with 200 μl of IPP buffer containing 6.7 mM N⁶-methyladenosine (Sigma-Aldrich, M2780) and 5 μl of RiboLock RNase inhibitor. An additional phenol–chloroform isolation and ethanol precipitation were used to purify the RNA. The bound RNA was called [FTO (+) m⁶A-IP] and [FTO (−) m⁶A-IP].

Library preparation and sequencing

The “input”, “m⁷G-IP”, [FTO (+) m⁶A-IP] and [FTO (−) m⁶A-IP] were subjected to library construction using SMARTer® Stranded Total RNA-Seq Kit v2—Pico Input Mammalian (634413, Takara – Clontech, Japan) according to the manufacturer’s protocol. The libraries were sequenced on Illumina HiSeq X10 with PE150.

Reads pre-processing and alignment

Strand orientation of the original RNA was preserved on the process of library construction and reads R2 yields sequences sense to the original RNA. Thus, only reads R2 was used in our study. Raw sequencing reads were firstly subjected to Trim_galore (version 0.6.6, http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and cutadapt software (version 1.18) for quality control and trimming adaptor. The quality threshold was set to 30, and the minimum length required for reads after trimming was 20 nt. Processed reads were mapped to genome (hg19, UCSC Genome Browser) using HISAT2 (version 2.1.0)⁴⁷ with default parameters, separated by strand with in-house scripts, and reads coverage were showed by IGV. The mpileup files are generated from mapped BAM files using samtools mpileup command (version 1.9).

Transcriptome-wide identification of 5′-UTR peaks in m⁶A-IP sample

Mapped reads were subjected to the RNA methylation peak caller exomePeak⁴⁸, the FOLD_ENRICHMENT was set to 3, the GENE_ANNO_GTF were set RefSeq_hg19 for peak calling. For genome-base peak caller MACS2 (version 2.1.1)⁴⁹, the effective genome size was set to 2.7*10⁹ for human, under the option of -nomodel and q-value cutoff 0.01. In order to identify high-confidence peak in m⁶A-IP sample, peak must meet the following conditions:(1) peak was overlapped between m⁶A-IP and m⁷G-IP samples, and (2) fold enrichment in m⁶A-IP higher than m⁷G-IP samples. Only peaks that can meet both requirements were used for m⁶A/m⁶Am signal identification in our study.

Definition and calculation of the demethylase-sensitivity peaks

In order to more accurately extract the m⁶Am peak and 5′-UTR m⁶A peak, we identify the demethylase-sensitivity peak between the FTO (+) and FTO (−) sample. Firstly, 5′-UTR peak defined in the previous section of “Transcriptome-wide identification of 5′-UTR peaks in m⁶A-IP sample” were used for analysis. Secondly, differential methylation peak between FTO (+) and FTO (−) samples were identified using exomePeak (P < 0.01) and DESeq2 (version 3.11)⁵⁰ in the R environment (version 3.6). Thirdly, when the ratio of FTO (−) to FTO (+) is >2, is defined as demethylase-sensitivity peak.

Identification of m⁶Am and 5′-UTR m⁶A peaks

An m⁶Am peak was identified when: (1) peak was high-confidence in m⁶A-IP sample; (2) peak was demethylase sensitivity between the FTO (+) and FTO (−) sample. The 5′-UTR m⁶A peak was identified when: (1) peak was high-confidence in m⁶A-IP sample; (2) peak was demethylase insensitivity between the FTO (+) and FTO (−) sample, and was not decrease after FTO treatment.

Identification of m⁶Am site

To identify the potential m⁶Am site, we defined a “start rate difference” score (SRD score) for each nucleotide within an m⁶Am peak. SRD score takes into account normalized sequencing start rate in untreated samples m1 = [FTO (−) start reads]/[FTO (−) depth] and the reads coverage difference within the FTO (+) and FTO (−) samples m2 = [FTO (−) depth − FTO (+) depth]/[FTO (−) depth]. m1 reflects the fact that m⁶Am site is the first transcribed nucleotide, while m2 is a measure for demethylase sensitivity. Because the start rate in FTO (+) samples could be very low and hence prone to the high background, we used FTO (+) coverage in m2 instead of FTO (+) start rate.

$${{{\rm{SRD}}}}\,{{{\rm{score}}}}={{{\rm{m1}}}}+{{{\rm{m2}}}}$$

(1)

The SRD score was used to identify the m⁶Am site. The defined a position i was defined to be m⁶Am when the following criteria were met: (1) position i must be located within m⁶Am peak and carry adenosine residue; (2) the start reads of position i are not <20 in FTO (−) sample; (3) the start rate of position i in FTO (−) sample is greater than that in m⁷G-IP sample; (4) the start reads coverage in m⁷G-IP sample is greater than that in input; (5) the position i possesses the top 3 of SRD scores (SRD score > 1).

Identification of stress-induced peak

In order to identify the stress-induced peak, differential methylation peaks between the untreated and stress sample were calculate using exomePeak (version 2.13, P < 0.01) and DESeq2. The stress-induced peak was defined, when the difference between the stress and untreated sample is >2.

Identification of PCIF1-dependent peak

The m⁶Am peak in WT and PCIF1-KO sample was calculated in section “Identification of m⁶Am and 5′UTR m⁶A peak”. To identification, the PCIF1-dependent peak, differential methylation peaks between WT and PCIF1-KO samples were calculate using exomePeak and DESeq2. The PCIF1-dependent peak was extracted, when the differential methylation ratio of WT to PCIF1-KO sample is >2 and P-value < 0.05.

Analysis of RNA-seq data

Paired-end, adapter-clean reads were aligned to the human genome (hg19, UCSC Genome Browser) using TopHat2 (version 2.0.13) with default parameters. The gene expression level was quantified as FPKM by Cufflinks (version 2.2.1)⁵¹.

Correlations analysis of m⁶Am site with histone modification and TSS

H3K27ac, H3K4me3, H3K4me1 and H3K9me3 ChIP-seq data was downloaded from ENCODE portal⁵² (https://www.encodeproject.org/) with the following identifiers: ENCSR000DTU, ENCSR000FCJ, ENCSR000FCH, ENCSR000FCG. GRO-seq data were downloaded from the GEO database (GSE92375)⁵³. We divided the m⁶Am sites into two parts with and without BCA motif, m⁶Am sites was extended 500 bp for each site, the overlapped site was removed. Deeptools were used to analyze ChIP-seq enrichment in −5 Kb to +5 Kb region around the m⁶Am sites.

Motif discovery and GO enrichment analysis

Peaks were annotated by homer software (version 4.10) with hg19 reference genome to annotate the marked genes. For the analysis of sequence consensus, the top 1000 peaks were chosen for de novo motif analysis with MEME (version 4.12.0)⁵⁴, with 100 nt-long peak summit centered sequences as input. Gene Ontology (GO) enrichment analyses were performed using DAVID web-based tool (version 6.8)⁵⁵.

Statistical analysis and quantification

For the statistical analysis of results, statistical evaluation was performed by unpaired two-sided Mann–Whitney U-test.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The sequence data generated in this study have been deposited in the NCBI GEO, under accession code GSE180253 that is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180253. For H3K27ac, H3K4me3, H3K4me1, and H3K9me3 ChIP-seq public data were downloaded from ENCODE portal⁵² (https://www.encodeproject.org/) with the following identifiers: ENCSR000DTU, ENCSR000FCJ, ENCSR000FCH, and ENCSR000FCG. GRO-seq data were downloaded from the GEO database (GSE92375)⁵³. Source data are provided with this paper.

Code availability

Custom code used for m⁶Am-seq analysis is available on GitHub (https://github.com/KaiLi-bioinfo/m6Am_seq/).

References

Boccaletto, P. et al. MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res. 46, D303–D307 (2018).
Article CAS PubMed Google Scholar
Roundtree, I. A., Evans, M. E., Pan, T. & He, C. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).
Article CAS PubMed PubMed Central Google Scholar
Frye, M., Jaffrey, S. R., Pan, T., Rechavi, G. & Suzuki, T. RNA modifications: what have we learned and where are we headed? Nat. Rev. Genet. 17, 365–372 (2016).
Article CAS PubMed Google Scholar
Gilbert, W. V., Bell, T. A. & Schaening, C. Messenger RNA modifications: form, distribution, and function. Science 352, 1408–1412 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
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).
Article ADS CAS PubMed Google Scholar
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).
Article CAS PubMed Google Scholar
Akichika, S. et al. Cap-specific terminal N (6)-methylation of RNA by an RNA polymerase II-associated methyltransferase. Science 363, eaav0080 (2019).
Article CAS PubMed Google Scholar
Sun, H., Zhang, M., Li, K., Bai, D. & Yi, C. Cap-specific, terminal N(6)-methylation by a mammalian m(6)Am methyltransferase. Cell Res. 29, 80–82 (2019).
Article CAS PubMed Google Scholar
Sendinc, E. et al. PCIF1 catalyzes m(6)Am mRNA methylation to regulate gene expression. Mol. Cell 75, 620–630.e629 (2019).
Article CAS PubMed PubMed Central Google Scholar
Boulias, K. et al. Identification of the m(6)Am methyltransferase PCIF1 reveals the location and functions of m(6)Am in the transcriptome. Mol. Cell 75, 631–643 e638 (2019).
Article CAS PubMed PubMed Central Google Scholar
Fan, H. et al. PCIF1, a novel human WW domain-containing protein, interacts with the phosphorylated RNA polymerase II. Biochem. Biophys. Res. Commun. 301, 378–385 (2003).
Article CAS PubMed Google Scholar
Mauer, J. et al. Reversible methylation of m(6)Am in the 5′ cap controls mRNA stability. Nature 541, 371–375 (2017).
Article ADS CAS PubMed Google Scholar
Wei, J. et al. Differential m(6)A, m(6)Am, and m(1)A demethylation mediated by FTO in the cell nucleus and cytoplasm. Mol. Cell 71, 973–985.e975 (2018).
Article CAS PubMed PubMed Central Google Scholar
Li, X., Xiong, X. & Yi, C. Epitranscriptome sequencing technologies: decoding RNA modifications. Nat. Methods 14, 23–31 (2016).
Article PubMed CAS Google Scholar
Dominissini, D. et al. Topology of the human and mouse m(6)A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).
Article ADS CAS PubMed Google Scholar
Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).
Article CAS PubMed PubMed Central Google Scholar
Zhou, J. et al. Dynamic m(6)A mRNA methylation directs translational control of heat shock response. Nature 526, 591–594 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Meyer, K. D. et al. 5′ UTR m(6)A promotes cap-independent translation. Cell 163, 999–1010 (2015).
Article CAS PubMed PubMed Central Google Scholar
Coots, R. A. et al. m(6)A facilitates eIF4F-independent mRNA translation. Mol. Cell 68, 504–514 e507 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Linder, B. et al. Single-nucleotide-resolution mapping of m(6)A and m(6)Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).
Article CAS PubMed PubMed Central Google Scholar
Liu, J. et al. Landscape and regulation of m(6)A and m(6)Am methylome across human and mouse tissues. Mol. Cell 77, 426–440 e426 (2020).
Article CAS PubMed Google Scholar
Jia, G. et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat. Chem. Biol. 7, 885–887 (2011).
Article CAS PubMed PubMed Central Google Scholar
Zheng, G. et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell 49, 18–29 (2013).
Article CAS PubMed Google Scholar
Young, J. I., Zuchner, S. & Wang, G. Regulation of the epigenome by vitamin C. Annu. Rev. Nutr. 35, 545–564 (2015).
Article CAS PubMed PubMed Central Google Scholar
Li, X. et al. Chemical pulldown reveals dynamic pseudouridylation of the mammalian transcriptome. Nat. Chem. Biol. 11, 592–597 (2015).
Article CAS PubMed Google Scholar
Li, X. et al. Transcriptome-wide mapping reveals reversible and dynamic N(1)-methyladenosine methylome. Nat. Chem. Biol. 12, 311–316 (2016).
Article CAS PubMed Google Scholar
Chan, C. T. et al. A quantitative systems approach reveals dynamic control of tRNA modifications during cellular stress. PLoS Genet. 6, e1001247 (2010).
Article CAS PubMed PubMed Central Google Scholar
Lee, P., Chandel, N. S. & Simon, M. C. Cellular adaptation to hypoxia through hypoxia inducible factors and beyond. Nat. Rev. Mol. Cell Biol. 21, 268–283 (2020).
Article CAS PubMed PubMed Central Google Scholar
Blais, J. D. et al. Activating transcription factor 4 is translationally regulated by hypoxic stress. Mol. Cell. Biol. 24, 7469–7482 (2004).
Article CAS PubMed PubMed Central Google Scholar
Bi, M. et al. ER stress-regulated translation increases tolerance to extreme hypoxia and promotes tumor growth. EMBO J. 24, 3470–3481 (2005).
Article CAS PubMed PubMed Central Google Scholar
Semenza, G. L. Hypoxia-inducible factors in physiology and medicine. Cell 148, 399–408 (2012).
Article CAS PubMed PubMed Central Google Scholar
An, W. G. et al. Stabilization of wild-type p53 by hypoxia-inducible factor 1alpha. Nature 392, 405–408 (1998).
Article ADS CAS PubMed Google Scholar
Zhao, L. Y., Song, J., Liu, Y., Song, C. X. & Yi, C. Mapping the epigenetic modifications of DNA and RNA. Protein Cell 11, 792–808 (2020).
Article CAS PubMed PubMed Central Google Scholar
Yu, M. et al. Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149, 1368–1380 (2012).
Article CAS PubMed PubMed Central Google Scholar
Liu, Y. et al. Bisulfite-free direct detection of 5-methylcytosine and 5-hydroxymethylcytosine at base resolution. Nat. Biotechnol. 37, 424–429 (2019).
Article CAS PubMed Google Scholar
Zheng, G. et al. Efficient and quantitative high-throughput tRNA sequencing. Nat. Methods 12, 835–837 (2015).
Article CAS PubMed PubMed Central Google Scholar
Cozen, A. E. et al. ARM-seq: AlkB-facilitated RNA methylation sequencing reveals a complex landscape of modified tRNA fragments. Nat. Methods 12, 879–884 (2015).
Article CAS PubMed PubMed Central Google Scholar
Li, X. et al. Base-resolution mapping reveals distinct m(1)A methylome in nuclear- and mitochondrial-encoded transcripts. Mol. Cell 68, 993–1005.e1009 (2017).
Article CAS PubMed PubMed Central Google Scholar
Blaschke, K. et al. Vitamin C induces Tet-dependent DNA demethylation and a blastocyst-like state in ES cells. Nature 500, 222–226 (2013).
Article ADS CAS PubMed PubMed Central Google Scholar
Incarnato, D. et al. High-throughput single-base resolution mapping of RNA 2′-O-methylated residues. Nucleic Acids Res. 45, 1433–1441 (2017).
Article CAS PubMed Google Scholar
Dai, Q. et al. Nm-seq maps 2′-O-methylation sites in human mRNA with base precision. Nat. Methods 14, 695–698 (2017).
Article CAS PubMed PubMed Central Google Scholar
Bird, J. G. et al. The mechanism of RNA 5′ capping with NAD+, NADH and desphospho-CoA. Nature 535, 444–447 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Karijolich, J. & Yu, Y. T. Spliceosomal snRNA modifications and their function. RNA Biol. 7, 192–204 (2010).
Article CAS PubMed Google Scholar
Huang, H. et al. Recognition of RNA N(6)-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat. Cell Biol. 20, 285–295 (2018).
Article CAS PubMed PubMed Central Google Scholar
Takahashi, H., Lassmann, T., Murata, M. & Carninci, P. 5′ end-centered expression profiling using cap-analysis gene expression and next-generation sequencing. Nat. Protoc. 7, 542–561 (2012).
Article CAS PubMed PubMed Central Google Scholar
Jia, G. et al. Oxidative demethylation of 3-methylthymine and 3-methyluracil in single-stranded DNA and RNA by mouse and human FTO. FEBS Lett. 582, 3313–3319 (2008).
Article CAS PubMed PubMed Central Google Scholar
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Article CAS PubMed PubMed Central Google Scholar
Meng, J. et al. A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods 69, 274–281 (2014).
Article CAS PubMed PubMed Central Google Scholar
Feng, J. X., Liu, T., Qin, B., Zhang, Y. & Liu, X. S. Identifying ChIP-seq enrichment using MACS. Nat. Protoc. 7, 1728–1740 (2012).
Article CAS PubMed Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article PubMed PubMed Central CAS Google Scholar
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
Sloan, C. A. et al. ENCODE data at the ENCODE portal. Nucleic Acids Res. 44, D726–D732 (2016).
Article CAS PubMed Google Scholar
Bouvy-Liivrand, M. et al. Analysis of primary microRNA loci from nascent transcriptomes reveals regulatory domains governed by chromatin architecture. Nucleic Acids Res. 45, 12054 (2017).
Article CAS PubMed PubMed Central Google Scholar
Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).
Article CAS PubMed PubMed Central Google Scholar
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Article CAS Google Scholar

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Acknowledgements

We would like to thank Prof. Guifang Jia and Mr. Wei Zhang for their assistance with recombinant FTO and ALKBH5 plasmids, and Xiaoyu Li and Xushen Xiong for discussions. We thank the National Center for Protein Sciences at Peking University in Beijing, China for assistance with RNA quality control and High Performance Computing Platform of the Center for Life Science for assistance with the analysis. This work was supported by the National Key R&D Program of China (Nos. 2019YFA0110900 and 2019YFA0802200), the National Natural Science Foundation of China (Nos. 91940304, 21825701, 31861143026, and 91740112 to C.Y.).

Author information

These authors contributed equally: Hanxiao Sun, Kai Li.

Authors and Affiliations

State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
Hanxiao Sun, Kai Li, Xiaoting Zhang, Jun’e Liu, Meiling Zhang, Haowei Meng & Chengqi Yi
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
Kai Li
Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
Kai Li & Chengqi Yi
Department of Chemical Biology and Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
Chengqi Yi

Authors

Hanxiao Sun
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Kai Li
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Xiaoting Zhang
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Jun’e Liu
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Meiling Zhang
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Haowei Meng
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Chengqi Yi
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Contributions

H.S. and C.Y. conceived the project, designed the experiments; H.S., K.L., and C.Y. wrote the manuscript; H.S. performed the experiments with the help of M.Z. and J.L.; K.L. designed and performed the bioinformatics analysis with the help of X.Z. and H.M.; J.L. performed the CAGE experiment; M.Z. performed the PCIF1-KO cell line and participated in the discussion.

Corresponding author

Correspondence to Chengqi Yi.

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Sun, H., Li, K., Zhang, X. et al. m⁶Am-seq reveals the dynamic m⁶Am methylation in the human transcriptome. Nat Commun 12, 4778 (2021). https://doi.org/10.1038/s41467-021-25105-5

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Received: 17 March 2021
Accepted: 20 July 2021
Published: 06 August 2021
DOI: https://doi.org/10.1038/s41467-021-25105-5

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