Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2022 Nov 18;17(12):3306–3312. doi: 10.1021/acschembio.2c00792

m7G-quant-seq: Quantitative Detection of RNA Internal N7-Methylguanosine

Li-Sheng Zhang †,‡,*, Cheng-Wei Ju §, Chang Liu †, Jiangbo Wei †, Qing Dai †, Li Chen †, Chang Ye †, Chuan He †,∥,*
PMCID: PMC9764283  PMID: 36398936

Abstract

graphic file with name cb2c00792_0005.jpg

Methods for the precise detection and quantification of RNA modifications are critical to uncover functional roles of diverse RNA modifications. The internal m7G modification in mammalian cytoplasmic tRNAs is known to affect tRNA function and impact embryonic stem cell self-renewal, tumorigenesis, cancer progression, and other cellular processes. Here, we introduce m7G-quant-seq, a quantitative method that accurately detects internal m7G sites in human cytoplasmic tRNAs at single-base resolution. The efficient chemical reduction and mild depurination can almost completely convert internal m7G sites into RNA abasic sites (AP sites). We demonstrate that RNA abasic sites induce a mixed variation pattern during reverse transcription, including G → A or C or T mutations as well as deletions. We calculated the total variation ratio to quantify the m7G modification fraction at each methylated site. The calibration curves of all relevant motif contexts allow us to more quantitatively determine the m7G methylation level. We detected internal m7G sites in 22 human cytoplasmic tRNAs from HeLa and HEK293T cells and successfully estimated the corresponding m7G methylation stoichiometry. m7G-quant-seq could be applied to monitor the tRNA m7G methylation level change in diverse biological processes.

Introduction

Transfer RNA (tRNA), as one of the abundant noncoding RNAs, is subject to numerous post-transcriptional modifications that regulate tRNA biogenesis, structural folding, stability, and function.1,2 Dysregulation of tRNA modification is linked to neurological disorders, mitochondrial disease, type 2 diabetes, and cancer.3,4 Internal N7-methylguanosine (m7G) at the nucleotide position 46 of tRNA (m7G46) is one of the most prevalent tRNA modifications, which is responsible for the tertiary base-pairing with C13-G22 that stabilizes the tRNA structure.5,6

A heterodimer of METTL1/WDR4 was identified as a “writer” machinery that installs a tRNA m7G modification in eukaryotes.7−10 The mutation in human WDR4 impairs tRNA m7G methylation and causes microcephalic primordial dwarfism.11 tRNA m7G methylation also affects embryonic stem cell self-renewal and differentiation.12,13 METTL1 is frequently overexpressed in human cancers and is associated with poor patient survival in some cancers; METTL1 depletion inhibits oncogenicity and tumor growth in many cancer types.14 For example, the METTL1-mediated tRNA m7G was shown to promote cancer progression in hepatocellular carcinoma and intrahepatic cholangiocarcinoma15,16 and enhance esophageal squamous cell carcinoma tumorigenesis.17

To study internal m7G profiles in various human tRNAs by high-throughput sequencing, chemical-assisted approaches have been proposed to detect tRNA m7G modification at base resolution. In 2018, Marchand et al. reported alkaline hydrolysis and aniline cleavage sequencing (AlkAniline-Seq) to measure internal m7G and m3C in one pot, based on truncation signatures generated during reverse transcription (RT).18 In the same year, using a similar principle, Lin et al. developed tRNA reduction and cleavage sequencing (TRAC-Seq) to globally map tRNA m7G at single-nucleotide resolution, via NaBH4-induced reduction and aniline-promoted RNA cleavage.12,19 Different from such chemical cleavage-mediated detection, in 2019 our lab reported m7G-seq by the application of chemical reduction and depurination, which selectively converts internal m7G sites into biotinylated abasic sites (AP sites).20,21 Using human immunodeficiency virus (HIV) reverse transcriptase, m7G-seq successfully detects internal m7G sites on human tRNA as RT misincorporation signatures, at 20–60% mutation ratios after biotin pulldown.20 The method also revealed notable m7G methylation on mRNA from several cancer cell lines. In a back-to-back report, Pandolfini et al. employed a similar principle and discovered internal m7G methylation within human let-7 miRNA, which is installed by METTL1.22 In the same year, Enroth et al. published m7G-MaP-seq, which directly converts m7G-modified positions into abasic sites by one-step NaBH4 reduction and read out as cDNA mutations.23 However, the <8% misincorporation levels at tRNA m7G sites revealed in m7G-MaP-seq are a bit too low to monitor methylation level change at all m7G sites in human tRNA.

Despite these recent advances in m7G sequencing method development, we still lack a quantitative method that maps m7G sites with stoichiometric information and high sensitivity, mostly because of incomplete reduction, cleavage, or depurination at internal m7G sites. In addition, the presence of multiple heavily modified bases on tRNA and its extensive secondary structure also challenge the direct sequencing of tRNA m7G.24 Our recent work on RT-misincorporation-based m1A-quant-seq25 and m6A-SAC-seq26 to achieve more accurate sequencing of m1A and m6A methylations in mRNA inspired us to continue optimizing the RT-misincorporation-based m7G-seq. Also, the recent successes in quantification of m1A, m3C, m1G, and m22G methylations in cytoplasmic and mitochondrial tRNAs with excellent read-through across the methylated regions, such as DAMM-seq,27 PANDORA-seq,28 and AQRNA-seq,29 provided strategies to overcome challenges in tRNA sequencing. Here, we introduce m7G-quant-seq, a method that detects abasic sites derived from internal m7G modifications on human tRNA at single-base resolution with stoichiometry information.

Results

m7G-quant-seq Detects Human 18S rRNA m7G1639 in High Variation Ratios

Although m7G-seq can estimate the modification fraction of internal m7G sites on human tRNAs in the absence of biotin pulldown, the ∼10–20% misincorporation ratios are not high enough for either accurate quantification of m7G stoichiometry or sensitive measurement of m7G methylation fraction change.20,21 We attributed this to incomplete conversion of internal m7G sites into RNA abasic sites (AP sites), under the m7G-seq chemical treatment conditions. To elevate the misincorporation ratios at an internal m7G site, we optimized and performed two improvements (Figure 1A), including high-efficiency reduction and depurination. First, we utilized KBH4-mediated reduction with high BH4– concentration (∼800 mM) at room temperature for 4 h to completely convert m7G into its reduced form. We synthesized a single-stranded RNA (ssRNA) oligo probe containing a single internal m7G site, which was designed to be free of secondary structures nearby. Both m7G-quant-seq KBH4 treatment and m7G-seq NaBH4 treatment20,21 gave a ∼99% reduction efficiency at the internal m7G site within the synthetic RNA probe (Figure S1A). We then tested the fragmented HeLa total RNA, which may contain some secondary structures around the internal m7G site. While m7G-seq NaBH4 treatment20,21 led to ∼77% reduction of m7G, m7G-quant-seq KBH4 treatment converted ∼97% m7G into reduced m7G (Figures 1A and S1B). Taken together, m7G-quant-seq KBH4 treatment showed a much higher reduction efficiency. Second, we employed a mild depurination condition at pH = 2.9 (100 mM NaOAc/AcOH buffer) with heating at 45 °C for 4 h to generate a stable RNA abasic site at the reduced m7G site, with two resonant structures that could further induce misincorporation or deletion signatures during reverse transcription (Figure 1A).

Figure 1.

Figure 1

Open in a new tab

Development of m7G-quant-seq. (A) Chemical structures of reduced m7G and RNA abasic site, generated under m7G-quant-seq treatment. KBH4-mediated reduction at room temperature for 4 h, followed by a mild depurination (pH = 2.9) at 45 °C for 4 h. (B) A flowchart of library construction in m7G-quant-seq, revealing m7G methylation fraction by the sum of variation signatures. (C) The variation signatures at the AP site generated from HeLa 18S rRNA m7G1639, under m7G-quant-seq treatment and with different RTs. (D) The variation signatures at the AP site generated from HeLa 18S rRNA m7G1639, under m7G-quant-seq treatment with wild-type HIV RT and adjusted dNTP/dATP ratios.

Based on the new chemical treatment we measured variation signals generated at AP site during reverse transcription to comprehensively detect and quantify internal m7G sites in biological samples (Figure 1B).

To validate the improvement, we applied m7G-quant-seq to HeLa total RNA with the wild-type HIV RT and captured a mixed variation pattern at the AP site generated from 18S rRNA m7G1639, which includes 4.3% G → A mutation, 10.8% G → C mutation, 32.1% G → T mutation, and 19.1% deletion (∼66% variation in total, Figure 1C). This is a dramatic improvement compared with the ∼30% mutation ratio (without biotin pulldown enrichment) previously observed in m7G-seq.20 We then screened three commercially available RTs plus one engineered RT and obtained 72.3%, 74.6%, 82.6%, and 64.0% variation ratios for ProtoScript II RT, SuperScript II RT, SuperScript IV RT, and RT1306,25 respectively (Figure 1C). Overall, at this 18S rRNA m7G site (in AG(m7G)AA motif), G → T mutation is the major variation type for HIV RT, ProtoScript II RT, SuperScript II RT, and RT1306, while SuperScript IV RT shows G → A mutation plus deletion as its main variation signature (Figure 1C). The variation signatures are nearly the same between SuperScript II RT and ProtoScript II RT (Figure 1C).

Note that, in m7G-seq,20,21 we only observed a ∼30% misincorporation ratio at m7G1639 (within an AG(m7G)AA motif) in wild-type HeLa 18S rRNA. Although m7G-quant-seq revealed an ∼66% total mutation and deletion ratio at the 18S m7G1639 site, which corresponds to at least 85% m7G methylation fraction in wild-type HeLa cells (Figure S1C), we detected a ∼79% total mutation and deletion ratio, corresponding to ∼99% m7G methylation fraction, at the 18S m7G1639 site in total RNA from HeLa shControl cells. This measured total mutation and deletion rate is close to the ∼83% variation ratio obtained from the synthetic RNA oligo with the AG(abasic site)AA motif (Figure S1C). These results suggested the almost complete conversion of the internal m7G site into an RNA abasic site after two steps of chemical treatment in m7G-quant-seq.

Because G → T mutation is the major variation type for most RTs, we next tested different deoxyribonucleoside triphosphate (dNTP)/deoxyadenosine triphosphate (dATP) ratios to further elevate the variation rates and to tune the variation pattern at the AP site generated from 18S m7G1639. Starting with HIV RT, we observed obviously elevated G → T mutation ratios (as 64.0%, 68.6%, and 73.3%) and globally increased total variation ratios (as 73.2%, 76.9%, and 79.2%) when adjusting dNTP/dATP ratios to 100, 50, and 25 μM/1 mM, respectively (Figure 1D). These results reveal that elevated dATP/dNTP ratios can largely improve the overall variation rate at internal m7G sites and can enhance the variation signature of the G → T mutation. We observed similar effects when testing RT1306, ProtoScript II, and SuperScript II (Figure S1D–F). Because SuperScript IV does not display the G → T mutation as its major variation type, the dNTP/dATP ratio change did not show any improvements on its corresponding variation signature at this AP site generated from 18S rRNA m7G1639 (Figure S1G).

m7G-quant-seq Detects Human tRNA m7G46 in High Variation Ratios

However, we found that the elevation of the dATP/dNTP ratio in the RT buffer led to dramatic RT stops at internal m7G sites when using any of these 5 RTs, although the adjustment in the dATP/dNTP ratio could induce a higher variation ratio for internal m7G detection. The RT stop caused by the higher dATP amount could impact the read-through of RNA fragments generated from tRNA. We previously found that the wild-type HIV RT with 1 mM dNTP could work very well on read-through of human tRNAs and reveal multiple tRNA methylations (such as m1A, m3C, m1G, m22G) as misincorporation signatures in one pot in DAMM-seq.27 We decided to focus on the “1 mM dNTP” condition in m7G-quant-seq.

We performed the standard m7G-quant-seq protocol (Figure 1B) with four different RTs (under 1 mM dNTP) and sequenced each tRNA library at ∼10-20 M read depth. We indeed successfully detected strong signals for internal m7G sites in 22 human tRNAs, at a range of 54–96% total variation rates when using the wild-type HIV RT (Figure 2A), in which all tRNA m7G sites displayed a mixed variation pattern of mutations and deletions. These results are much better than those in m7G-seq, in the absence of biotin pulldown enrichment.20 We also screened the engineered RT130625 under 1 mM dNTP and observed 50–73% total variation rates (Figure 2B), which are notably lower than the ratios in wild-type HIV RT. Although SuperScript II RT and SuperScript IV RT gave high variation ratios at 18S rRNA m7G site (Figure 1C), these two RTs displayed poor variation rates at all tRNA m7G sites (Figure 2C,D). Based on these data from tRNA m7G methylomes, we conclude that the wild-type HIV RT is most suitable for the tRNA m7G methylation measurement.

Figure 2.

Figure 2

Open in a new tab

m7G-quant-seq detects 22 tRNA m7G sites in human cytoplasmic tRNAs, as a mixed pattern of mutations and deletion. (A) The variation signatures at the AP site generated from HeLa tRNA m7G46 under m7G-quant-seq treatment with wild-type HIV RT and 1 mM dNTP. (B) The variation signatures at the AP site generated from HeLa tRNA m7G46 under m7G-quant-seq treatment with engineered RT1306 and 1 mM dNTP. (C) The variation signatures at the AP site generated from HeLa tRNA m7G46 under m7G-quant-seq treatment with SuperScript II RT and 1 mM dNTP. (D) The variation signatures at the AP site generated from HeLa tRNA m7G46 under m7G-quant-seq treatment with SuperScript IV RT and 1 mM dNTP.

We noticed that, when using wild-type HIV RT, the pattern of misincorporation and deletion at the m7G46 site are different in 22 tRNAs from HeLa and HEK293T cells (Figure 2A), which can be attributed to sequence context difference around the m7G46 site. tRNA m7G46 is deposited in several different 5-mer motif contexts, such as AG(m7G)CC, AG(m7G)CU, AG(m7G)GU, AG(m7G)UA, AG(m7G)UC, AG(m7G)UU, and GG(m7G)UC; sequence differences contribute to different misincorporation and deletion ratios. While there is no notable difference in other RNA modifications around the m7G46 site (Figure S2), the sequence context beyond the 5-mer motif may also contribute to misincorporation and deletion changes during RT using HIV RT enzyme.

m7G-quant-seq Calibration Curves to Determine m7G Stoichiometry

The internal m7G at position 1639 of 18S rRNA and position 46 of cytoplasmic tRNAs possess eight motif contexts, including AG(m7G)GA, AG(m7G)CC, AG(m7G)CU, AG(m7G)GU, AG(m7G)UA, AG(m7G)UC, AG(m7G)UU, and GG(m7G)GC. The synthetic challenges make it difficult to build RNA oligo probes that carry internal m7G within all these motifs. Instead, we synthesized RNA oligos containing an AP site at the expected m7G position because we are measuring the AP sites generated from m7G.

We mixed oligo probes containing NN(AP-site)NN and NNGNN (as controls) to plot calibration curves for all sequence contexts. We obtained either linear curves or hyperbola curves for the eight motif contexts around internal m7G sites in human rRNA and tRNA (Figure 3A). For instance, based on the calibration curve of AG(m7G)AA, the fraction of the m7G site in 18S rRNA was calculated to be at least 85% in HeLa and HEK293T cells (Figure 3B), generally consistent with those measured by mass spectrometry.30 The methylation fraction of m7G in 18S rRNA seems to be slightly lower in mouse tissues, compared with cultured cells (Figure 3B). Notably, m7G-quant-seq only detected one m7G candidate site at position 1639 among hundreds of guanosine sites in HeLa 18S rRNA (Figure S3A), when applying several cutoffs for internal m7G detection, including: (1) variation (misincorporation and deletion) ratio above 5% in m7G-quant-seq libraries; (2) variation ratio below 5% in “Input” libraries; (3) total reads coverage depth above 20 in both m7G-quant-seq and “Input” libraries; (4) variation ratio in m7G-quant-seq libraries greater than fivefold over that in “input” libraries; (5) variation ratio in m7G-quant-seq libraries greater than fivefold over the background in any given sequence motif (defined as the variation rates detected from RNA probes containing unmodified NNGNN after m7G-quant-seq treatment). Additionally, all misincorporation and deletion signatures must occur at the internal positions of the reads, instead of reads end.

Figure 3.

Figure 3

Open in a new tab

m7G-quant-seq calibration curves for motif contexts around internal m7G sites in human tRNAs and 18S rRNA. (A) Representative sequence-context-dependent calibration curves (total variation rate vs methylation fraction) for m7G fraction quantification, including eight major motif contexts around internal m7G sites in human rRNA and tRNA, under m7G-quant-seq treatment with HIV RT (1 mM dNTP). (B) The m7G methylation fractions of 18S rRNA m7G1639 in HeLa cells, HEK293T cells, mouse heart, mouse brain, and mouse kidney, respectively.

m7G-quant-seq Estimates the Stoichiometry of Internal m7G Sites in 22 Cytoplasmic tRNAs

With the calibration curves in hand, we were able to measure the methylation fractions at internal m7G sites on human cytoplasmic tRNAs. We applied m7G-quant-seq to cellular small RNAs (<200 nt) from HeLa and HEK293T cells. In both cell lines, we observed strong variation signatures at internal m7G sites in 22 cytoplasmic tRNAs, at a range of 50–97% total variation rates (Figure 4A). Internal m7G methylomes were not detected in human mitochondrial tRNAs, which is consistent with the findings in m7G-seq.20

Figure 4.

Figure 4

Open in a new tab

m7G-quant-seq measures the stoichiometry of internal m7G sites in 22 human tRNAs. (A) The total variation rate at the AP site generated from m7G46 of 22 cytoplasmic tRNAs in HeLa and HEK293T cells, under m7G-quant-seq treatment with HIV RT. Data dots from two biological replicates are shown. (B) The m7G methylation fraction at m7G46 of 22 cytoplasmic tRNAs in HeLa and HEK293T cells, respectively. Data dots from two biological replicates are shown.

Based on the calibration curves of the seven major m7G motifs in human tRNA, we calculated the modification fractions of these m7G sites according to the corresponding variation ratios. We found that the m7G stoichiometry in human tRNAs ranges around 60–85% at least, and most tRNA m7G sites display at least 70% methylation fraction in these two human cell lines (Figure 4B). Highly consistent results indicate a superb performance of the current protocol. This method should allow accurate determination of m7G stoichiometry changes under different stress or different cellular contexts as well as in other RNA species.

Although TRAC-seq is based on RT truncation signatures and cannot provide stoichiometry information on m7G methylation, it detected 25, 22, 17, 21, and 19 tRNA m7G sites in LNZ308, HuCCT1, MHCC97H, NBL, and ESCC cells, respectively (Figure S3B).14−17,31 In m7G-quant-seq and TRAC-seq, m7G sites on 11 tRNAs are consistent among all aforementioned human cell lines, including Ala-TGC, Arg-TCT, Cys-GCA, Lys-TTT, Lys-CTT, Met-CAT, Phe-GAA, Thr-TGT, Trp-CCA, Val-CAC, and Val-AAC. However, m7G sites on other tRNAs showed more dynamic changes in different cell lines, indicating differential methylation fractions at these m7G tRNA sites in different cancer cells (Figure S3B).

Discussion

We report here m7G-quant-seq based on the chemistry principle of RT-misincorporation-based m7G-seq 20. m7G-quant-seq employs new conditions to achieve highly efficient reduction and depurination at the internal m7G site, leading to the almost complete conversion of internal m7G sites into RNA abasic sites. We screened RT enzymes and RT conditions to achieve maximum mutation and deletion changes from the RNA abasic sites. The variation signals in the new procedure increased by at least twofold compared to the previous procedure. We also include a brief and fast protocol for library construction, which starts with ∼200 ng of RNA (Supporting Information). The 4 h KBH4 reduction step (at room temperature) and the 4 h acidic depurination step (at 45 °C) are easy to handle and can be robustly repeated.

We observed that, under multiple different RTs, an RNA abasic site induces a mixed variation pattern during reverse transcription, including G → A or C or T mutations and meanwhile deletions (Figure 1C). In this study, we conducted a comprehensive study on variation patterns at RNA abasic sites generated from internal m7G sites. In addition to m7G mapping, this method could potentially benefit future research on RNA abasic sites and abasic sites generated from other RNA modifications as well. In HeLa and HEK293T cells, m7G-quant-seq detected internal m7G sites in 22 human cytoplasmic tRNAs with high variation ratios (Figures 2A and 4A). Using proper calibration curves these high variation rates enabled us to successfully estimate the corresponding m7G methylation stoichiometry (Figure 4B), without any pulldown enrichment of these variation signatures.

In summary, m7G-quant-seq could be broadly applied to quantitatively monitor tRNA m7G methylation level change in diverse biological processes, such as gene knockdown, cellular stress, heat shock, cancer progression, etc. Considering the METTL1-mediated tRNA m7G regulates tumorigenesis and cancer progression in many cancer types, m7G-quant-seq could potentially facilitate a series of future investigations on METTL1 functions in cancer biology and cancer therapy.

Acknowledgments

We thank P. W. Faber and his team in Genomics Facility of the University of Chicago for help on high-throughput sequencing. We thank X. Feng for her help in building data figures. We thank B. Gao for her help in RNA experiments.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acschembio.2c00792.

  • A complete description of Methods for cell culture, RNA isolation, calibration curves, m7G-quant-seq library construction, and sequencing data analysis (PDF)

Author Contributions

⊥ These authors contributed equally. The manuscript was written through the contributions of all authors. All authors have approved the final version of the manuscript.

This work was supported by National Institutes of Health (NIH) Grant Nos. R01 HL155909 (C.H.) and RM1 HG008935 (C.H.)

The authors declare the following competing financial interest(s): C. He is a scientific founder, a member of the scientific advisory board and equity holder of Aferna Green, Inc. and AccuaDX Inc., and a scientific co-founder and equity holder of Accent Therapeutics, Inc.

Supplementary Material

cb2c00792_si_001.pdf (1.4MB, pdf)

References

  1. Frye M.; Harada B. T.; Behm M.; He C. RNA modifications modulate gene expression during development. Science 2018, 361, 1346–1349. 10.1126/science.aau1646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Roundtree I. A.; Evans M. E.; Pan T.; He C. Dynamic RNA modifications in gene expression regulation. Cell 2017, 169, 1187–1200. 10.1016/j.cell.2017.05.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Suzuki T. The expanding world of tRNA modifications and their disease relevance. Nat. Rev. Mol. Cell Biol. 2021, 22, 375–392. 10.1038/s41580-021-00342-0. [DOI] [PubMed] [Google Scholar]
  4. Torres A. G.; Batlle E.; Ribas de Pouplana L. Role of tRNA modifications in human diseases. Trends Mol. Med. 2014, 20 (6), 306–314. 10.1016/j.molmed.2014.01.008. [DOI] [PubMed] [Google Scholar]
  5. Tomikawa C. 7-Methylguanosine Modifications in Transfer RNA (tRNA). Int. J. Mol. Sci. 2018, 19 (12), 4080. 10.3390/ijms19124080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Lorenz C.; Lünse C. E.; Mörl M. tRNA Modifications: Impact on Structure and Thermal Adaptation. Biomolecules. 2017, 7 (2), 35. 10.3390/biom7020035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Alexandrov A.; Martzen M. R.; Phizicky E. M. Two proteins that form a complex are required for 7-methylguanosine modification of yeast tRNA. RNA 2002, 8, 1253–1266. 10.1017/S1355838202024019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Alexandrov A.; Grayhack E. J.; Phizicky E. M. tRNA m7G methyl-transferase Trm8p/Trm82p: evidence linking activity to a growth phenotype and implicating Trm82p in maintaining levels of active Trm8p. RNA 2005, 11, 821–830. 10.1261/rna.2030705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Guy M. P.; Phizicky E. M. Two-subunit enzymes involved in eukaryotic post-transcriptional tRNA modification. RNA Biol. 2014, 11, 1608–1618. 10.1080/15476286.2015.1008360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Leulliot N.; Chaillet M.; Durand D.; Ulryck N.; Blondeau K.; van Tilbeurgh H. Structure of the yeast tRNA m7G methylation complex. Structure 2008, 16, 52–61. 10.1016/j.str.2007.10.025. [DOI] [PubMed] [Google Scholar]
  11. Shaheen R.; Abdel-Salam G. M. H.; Guy M. P.; Alomar R.; Abdel-Hamid M. S.; Afifi H. H.; Ismail S. I.; Emam B. A.; Phizicky E. M.; Alkuraya F. S. Mutation in WDR4 impairs tRNA m7G46 methylation and causes a distinct form of microcephalic primordial dwarfism. Genome Biol. 2015, 16, 210. 10.1186/s13059-015-0779-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Lin S.; Liu Q.; Lelyveld V. S.; Choe J.; Szostak J. W.; Gregory R. I. Mettl1/Wdr4-mediated m7G tRNA methylome is required for normal mRNA translation and embryonic stem cell self-renewal and differentiation. Mol. Cell 2018, 71, 244–255. 10.1016/j.molcel.2018.06.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Deng Y.; Zhou Z.; Ji W.; Lin S.; Wang M. METTL1-mediated m7G methylation maintains pluripotency in human stem cells and limits mesoderm differentiation and vascular development. Stem Cell Res. Ther. 2020, 11, 306. 10.1186/s13287-020-01814-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Orellana E. A.; Liu Q. D.; Yankova E.; Pirouz M.; De Braekeleer E.; Zhang W.; Lim J.; Aspris D.; Sendinc E.; Garyfallos D.; Gu M.; Ali R.; Gutierrez A.; Mikutis S.; Bernardes G.; Fischer E.; Bradley A.; Vassiliou A.; Slack F.; Tzelepis K.; Gregory R. METTL1-mediated m7G modification of Arg-TCT tRNA drives oncogenic transformation. Mol. Cell 2021, 81, 3323–3338. 10.1016/j.molcel.2021.06.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dai Z.; Liu H.; Liao J.; Huang C.; Ren X.; Zhu W.; Zhu S.; Peng B.; Li S.; Lai J.; Liang L.; Xu L.; Peng S.; Lin S.; Kuang M. N7-Methylguanosine tRNA modification enhances oncogenic mRNA translation and promotes intrahepatic cholangiocarcinoma progression. Mol. Cell 2021, 81, 3339–3355. 10.1016/j.molcel.2021.07.003. [DOI] [PubMed] [Google Scholar]
  16. Chen Z.; Zhu W.; Zhu S.; Sun K.; Liao J.; Liu H.; Dai Z.; Han H.; Ren X.; Yang Q.; Zheng S.; Peng B.; Peng S.; Kuang M.; Lin S. METTL1 promotes hepatocarcinogenesis via m7G tRNA modification-dependent translation control. Clin. Transl. Med. 2021, 11 (12), e661 10.1002/ctm2.661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Han H.; Yang C.; Ma J.; Zhang S.; Zheng S.; Ling R.; Sun K.; Guo S.; Huang B.; Liang Y.; Wang L.; Chen S.; Wang Z.; Wei W.; Huang Y.; Peng H.; Jiang Y.; Choe J.; Lin S. N7-methylguanosine tRNA modification promotes esophageal squamous cell carcinoma tumorigenesis via the RPTOR/ULK1/autophagy axis. Nat. Commun. 2022, 13, 1478. 10.1038/s41467-022-29125-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Marchand V.; Ayadi L.; Ernst F. G. M.; Hertler J.; Bourguignon-Igel V.; Galvanin A.; Kotter A.; Helm M.; Lafontaine D. L. J.; Motorin Y. AlkAniline-Seq: Profiling of m7G and m3C RNA Modifications at Single Nucleotide Resolution. Angew. Chem., Int. Ed. Engl. 2018, 57 (51), 16785–16790. 10.1002/anie.201810946. [DOI] [PubMed] [Google Scholar]
  19. Lin S.; Liu Q.; Jiang Y.; Gregory R. I. Nucleotide resolution profiling of m7G tRNA modification by TRAC-Seq. Nat. Protoc. 2019, 14, 3220–3242. 10.1038/s41596-019-0226-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Zhang L.-S.; Liu C.; Ma H.; Dai Q.; Sun H.-L.; Luo G.; Zhang Z.; Zhang L.; Hu L.; Dong Z.; He C. Transcriptome-wide mapping of internal N7-methylguanosine methylome in mammalian mRNA. Mol. Cell 2019, 74 (6), 1304–1316. 10.1016/j.molcel.2019.03.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Zhang L.-S.; Liu C.; He C. Transcriptome-Wide Detection of Internal N7-Methylguanosine. Methods Mol. Biol. 2021, 2298, 97–104. 10.1007/978-1-0716-1374-0_6. [DOI] [PubMed] [Google Scholar]
  22. Pandolfini L.; Barbieri I.; Bannister A. J.; Hendrick A.; Andrews B.; Webster N.; Murat P.; Mach P.; Brandi R.; Robson S. C.; Migliori V.; Alendar A.; d’Onofrio M.; Balasubramanian S.; Kouzarides T. METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation. Mol. Cell 2019, 74, 1278–1290. 10.1016/j.molcel.2019.03.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Enroth C.; Poulsen L. D.; Iversen S.; Kirpekar F.; Albrechtsen A.; Vinther J. Detection of internal N7-methylguanosine (m7G) RNA modifications by mutational profiling sequencing. Nucleic Acids Res. 2019, 47 (20), e126 10.1093/nar/gkz736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Zheng G.; Qin Y.; Clark W. C.; Dai Q.; Yi C.; He C.; Lambowitz A. M.; Pan T. Efficient and quantitative high-throughput tRNA sequencing. Nat. Methods 2015, 12, 835–837. 10.1038/nmeth.3478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Zhou H.; Rauch S.; Dai Q.; Cui X.; Zhang Z.; Nachtergaele S.; Sepich C.; He C.; Dickinson B. C. Evolution of reverse transcriptase to map N1-methyladenosine in human messenger RNA. Nat. Methods 2019, 16 (12), 1281–1288. 10.1038/s41592-019-0550-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hu L.; Liu S.; Peng Y.; Ge R.; Su R.; Senevirathne R.; Harada B. T.; Dai Q.; Wei J.; Zhang L.-S.; Hao Z.; Luo L.; Wang H.; Wang Y.; Luo M.; Chen M.; Chen J.; He C. m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat. Biotechnol. 2022, 40 (8), 1210–1219. 10.1038/s41587-022-01243-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Zhang L.-S.; Xiong Q.-P.; Pena Perez S.; Liu C.; Wei J.; Le C.; Zhang L.; Harada B. T.; Dai Q.; Feng X.; Hao Z.; Wang Y.; Dong X.; Hu L.; Wang E.-D.; Pan T.; Klungland A.; Liu R.-J.; He C. ALKBH7-mediated demethylation regulates mitochondrial polycistronic RNA processing. Nat. Cell Biol. 2021, 23, 684–691. 10.1038/s41556-021-00709-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Shi J.; Zhang Y.; Tan D.; Zhang X.; Yan M.; Zhang Y.; Franklin R.; Shahbazi M.; Mackinlay K.; Liu S.; Kuhle B.; James E. R.; Zhang L.; Qu Y.; Zhai Q.; Zhao W.; Zhao L.; Zhou C.; Gu W.; Murn J.; Guo J.; Carrell D. T.; Wang Y.; Chen X.; Cairns B. R.; Yang X.-l.; Schimmel P.; Zernicka-Goetz M.; Cheloufi S.; Zhang Y.; Zhou T.; Chen Q. PANDORA-seq expands the repertoire of regulatory small RNAs by overcoming RNA modifications. Nat. Cell Biol. 2021, 23, 424–436. 10.1038/s41556-021-00652-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hu J. F.; Yim D.; Ma D.; Huber S. M.; Davis N.; Bacusmo J. M.; Vermeulen S.; Zhou J.; Begley T. J.; DeMott M. S.; Levine S. S.; de Crécy-Lagard V.; Dedon P. C.; Cao B. Quantitative mapping of the cellular small RNA landscape with AQRNA-seq. Nat. Biotechnol. 2021, 39, 978–988. 10.1038/s41587-021-00874-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Taoka M.; Nobe Y.; Yamaki Y.; Sato K.; Ishikawa H.; Izumikawa K.; Yamauchi Y.; Hirota K.; Nakayama H.; Takahashi N.; Isobe T. Landscape of the complete RNA chemical modifications in the human 80S ribosome. Nucleic Acids Res. 2018, 46, 9289–9298. 10.1093/nar/gky811. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Huang Y.; Ma J.; Yang C.; Wei P.; Yang M.; Han H.; Chen H. D.; Yue T.; Xiao S.; Chen X.; Li Z.; Tang Y.; Luo J.; Lin S.; Huang L. METTL1 promotes neuroblastoma development through m7G tRNA modification and selective oncogenic gene translation. Biomark Res. 2022, 10, 68. 10.1186/s40364-022-00414-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

cb2c00792_si_001.pdf (1.4MB, pdf)

Articles from ACS Chemical Biology are provided here courtesy of American Chemical Society

RESOURCES