The catalytic efficiency of METTL16 affects cellular processes by governing the intracellular S-adenosylmethionine setpoint

Juliana N Flaherty; Enakshi Sivasudhan; Matthew Tegowski; Zheng Xing; Madeline M McGinnis; Olga V Hunter; Kyah M Featherston; Komal Sethia; Benjamin P Tu; Kate D Meyer; Nicholas K Conrad

doi:10.1016/j.celrep.2025.115966

. Author manuscript; available in PMC: 2025 Aug 7.

Published in final edited form as: Cell Rep. 2025 Jul 10;44(7):115966. doi: 10.1016/j.celrep.2025.115966

The catalytic efficiency of METTL16 affects cellular processes by governing the intracellular S-adenosylmethionine setpoint

Juliana N Flaherty ¹, Enakshi Sivasudhan ², Matthew Tegowski ², Zheng Xing ³, Madeline M McGinnis ³, Olga V Hunter ¹, Kyah M Featherston ¹, Komal Sethia ¹, Benjamin P Tu ³, Kate D Meyer ^2,⁴, Nicholas K Conrad ^1,^5,^*

PMCID: PMC12330366 NIHMSID: NIHMS2099528 PMID: 40644296

SUMMARY

The methyl donor S-adenosylmethionine (SAM) regulates many cellular processes. The N6-methyladenosine (m⁶A) methyltransferase METTL16 regulates the expression of the SAM synthetase MAT2A, but the consequences of this regulation are not well documented. Here, we used a degron and complementation strategy in HCT116 cells to demonstrate that disruption of MAT2A regulation by METTL16 influences SAM-dependent processes including histone methylation, translation, and RNA methylation. We also identify U6 snRNA pseudogenes as METTL16 substrates. Complementation by a catalytically hyperactive METTL16 complements its methyltransferase activities but decreases intracellular SAM concentrations by abrogating MAT2A regulation. Moreover, these cells are hypersensitive to treatment with a MAT2A inhibitor and to deletion of the MTAP gene, which is lost in ~15% of cancers. These findings support the conclusion that the catalytic efficiency of METTL16 helps establish the SAM setpoint in cells and suggest that this function could be exploited as a treatment for MTAP-deficient cancers.

In brief

Flaherty et al. demonstrate that the catalytic efficiency of METTL16 regulates expression of MAT2A to determine the intracellular SAM setpoint. Their data indicate that this is required for several SAM-dependent processes. Moreover, they show that hyperactive METTL16 is synthetically lethal with MTAP deletion, a common genetic abnormality in cancer.

Graphical Abstract

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INTRODUCTION

S-Adenosylmethionine (SAM) is the methyl donor for nearly all cellular methylation events. Furthermore, SAM is a component of the methionine (Met) cycle, which feeds into metabolic pathways including nucleotide synthesis, glutathione synthesis, polyamine metabolism, and Met salvage.^1–4 Therefore, it is not surprising that control of SAM has important implications in human disease and treatment.^5–11 For example, MTAP is a passenger deletion in 15% of all cancers.^12–16 In MTAP-deficient cells, its substrate MTA accumulates and competes with SAM binding to the essential symmetric dimethylarginine (SDMA) methyltransferase PRMT5. As a result, MTAP-deleted cancer cells are hypersensitive to SAM loss. These observations have stimulated considerable interest in finding inhibitors of the primary SAM-synthetase in mammals, MAT2A.^17–20 However, deeper understanding of the cellular mechanisms that regulate MAT2A may suggest additional therapeutic strategies and inform basic understanding of SAM homeostasis.

Cells posttranscriptionally regulate MAT2A mRNA levels through a METTL16-mediated two-tiered SAM feedback mechanism (Figure 1A).^21–25 METTL16 is an essential RNA N6-methyladenosine (m⁶A) methyltransferase that methylates six conserved hairpins (hp1-hp6) in the 3′ UTR of MAT2A. In low SAM conditions, METTL16 binds hp1 and induces splicing of the final intron of MAT2A using the CFI_m complex. In high SAM, the last intron of the MAT2A RNA is detained and the transcript is degraded, presumably due to shorter METTL16 occupancy on hp1. In addition, METTL16 regulates MAT2A stability by methylating hp1–6 to destabilize the mRNA in high SAM. Conversely, lack of methylation of the hairpins stabilizes the transcript. In both tiers, METTL16 serves as the SAM sensor in a feedback loop that affects MAT2A expression in response to SAM levels. However, the impact of METTL16’s control of MAT2A expression has been unknown.

It is critical for METTL16 to sense losses in SAM at concentrations that do not affect other methyltransferases. Indeed, the K_m of METTL16 for SAM is 132–400 μM, while the K_m for SAM of the common m⁶A methyltransferase METTL3/14 is 102 nM.^26–28 METTL16 lysine 163 is part of the “auto-regulatory K-loop” that occludes the SAM binding pocket of METTL16.^29,30 Mutating K163 to an alanine (K163A) creates a hyperactive methyltransferase, presumably due to higher SAM affinity.²⁹ K163A is therefore predicted to have two effects on MAT2A expression. First, its catalytic activity should be less sensitive to drops in SAM levels. As a result, K163A METTL16 would have a shorter dwell time on hp1 of MAT2A even in low SAM, leading to increased intron detention. Second, K163A will efficiently methylate all the hps in the MAT2A 3′ UTR and destabilize MAT2A mRNA even after SAM reduction. These predictions were supported in reporter assays,²⁹ but the consequences of K163A on MAT2A, SAM levels, and downstream phenotypes have yet to be examined.

METTL16 regulation of MAT2A is likely restricted to metazoans, but its evolutionarily more ancient function is to methylate U6 snRNA.^{21,24,31–35} Approximately 95% of U6 snRNA is methylated in humans, and its methylation dictates 5′ splice site selection.^32,35–38 METTL16 also binds MALAT1 and XIST, although it does not appear to methylate either of these RNAs.^24,26,30,39 Multiple m⁶A-seq studies found thousands of m⁶A sites that decrease after METTL16 knockdown, but it is difficult to know which of these sites are direct substrates and which are indirect consequences of METTL16’s role in MAT2A and SAM homeostasis.^21,40–43 Independent from its role as a methyltransferase, METTL16 has also been reported to inhibit DNA repair.^44,45 Moreover, it has been suggested to promote translation by two distinct catalysis-independent mechanisms.^41,46

Here, we tagged METTL16 with an auxin-inducible degron (AID) to allow for rapid degradation and complementation by exogenous expression of K163A METTL16 or MAT2A. This allows us to genetically separate METTL16’s function in MAT2A regulation from U6 snRNA methylation, and it provides insights into how the catalytic activity of METTL16 controls the intracellular SAM setpoint. Cells expressing METTL16 K163A are viable but are hypersensitive to SAM depletion. Furthermore, histone methylation patterns change upon manipulation of METTL16 activity, but were rescued by MAT2A overexpression. Short-term depletion of METTL16 had no effect on translation efficiency, but long-term depletion decreased global translation, suggesting an indirect effect on translation. Indeed, the long-term effects of METTL16 on translation could be rescued by MAT2A overexpression. Similarly, m⁶A-seq and GLORI-seq suggest that most changes in RNA methylation are secondary consequences of METTL16 depletion. However, GLORI-seq revealed that a number of U6 snRNA pseudogenes are methylated by METTL16 at sites with variations in the consensus sequence. Finally, K163A METTL16 is synthetic lethal with MTAP deletion, suggesting that this pathway provides a therapeutic target for MTAP-deleted cancers. In summary, the work here demonstrates that abrogation of METTL16’s regulation of MAT2A has widespread consequences for SAM-dependent cellular processes, supporting the conclusion that METTL16 is a critical contributor to cellular maintenance of SAM homeostasis.

RESULTS

A METTL16 rapid degron system confirms METTL16 is essential for viability, MAT2A splicing induction, and SAM maintenance

We generated an HCT116-derived clonal cell line that has both alleles of METTL16 AID-tagged⁴⁷ and introduced the auxin-dependent E3 ligase TIR1 at the AAVS1 safe harbor locus under a doxycycline (dox)-inducible promoter to control for auxin-independent degradation.⁴⁸ Except where noted, all auxin-treated conditions refer to co-treatment of auxin and dox. We chose a degron system for several reasons. METTL16’s essentiality prohibits knockouts, and the system supports genetic complementation of METTL16 loss. Most importantly, this system supports both long-term and short-term depletions of METTL16 that are not feasible with knockdown strategies. In these cells, we observed 95% reduction of METTL16 within 2 h (Figure 1B), while auxin treatment has no effect on METTL16 protein in parental cells (Figure S1A). Moreover, auxin treatment had no effect on the parental cell viability, but it led to a loss of viability in our METTL16-AID cell line (Figure 1C).

We next tested whether depletion of METTL16 prevents MAT2A splicing induction. We pre-treated cells with auxin to deplete METTL16 for 2 h, then depleted Met from the medium to reduce SAM levels.²¹ We analyzed MAT2A RNA by northern blot to detect both the detained intron (DI) and the fully spliced mRNA isoforms (Figure 1D). We approximate splicing by measuring the percent DI out of the total MAT2A RNA levels. Isoform stability will also contribute to this value, but it provides a reasonable approximation of METTL16 activity with respect to MAT2A expression.^21,23,25 As expected, Met removal induces splicing, but METTL16 depletion abrogates this effect (Figure 1D). Furthermore, auxin treatment decreases steady-state levels of MAT2A mRNA in normal media within a matter of hours (Figures 1D, t = 0 and 1E). This suggests that METTL16 is essential for fine-tuning MAT2A levels in SAM-replete conditions as well as inducing MAT2A after SAM loss. Finally, we tested SAM levels and observed an 80% drop in SAM 24 h after auxin addition (Figure 1F). Together, these observations confirm that METTL16 impacts SAM levels by regulating MAT2A expression, and they validate our METTL16-AID degron line.

Hyperactive METTL16 supports viability but changes MAT2A and SAM levels in cells

We used the METTL16-AID line to test the prediction that K163A lowers the SAM setpoint in cells. We stably integrated dox-inducible N-terminally myc-tagged METTL16 wild-type (WT), hyperactive (K163A), catalytic dead (N184A), and empty vector (EV) plasmids into our METTL16-AID cell line and found that each was expressed to similar levels in G418-selected pools (Figures S1B and S1C). However, exogenous METTL16 was less expressed than the endogenous METTL16-AID, suggesting that a subpopulation of cells does not express the transgene (Figure S1C). We tested which mutants rescued viability by following growth over 12 days after auxin treatment (Figure 2A). As expected, EV cells were not viable, but cells expressing WT METTL16 survived. Cells that expressed N184A were not viable, suggesting an essential function for METTL16 m⁶A methyltransferase activity. However, it is formally possible that N184A led to toxic overproduction of SAM from elevated MAT2A mRNA. Most importantly, cells expressing hyperactive K163A were viable, suggesting that METTL16’s role in regulating MAT2A expression is not essential in normal cell culture conditions or that it retains enough activity to support viability. Notably, after 12 days in auxin, exogenous WT and K163A METTL16 were expressed at similar levels as endogenous METTL16-AID, presumably due to loss of cells within the pool that did not express the transgene (Figure S1D). We therefore used these auxin-selected WT and K163A pools for subsequent experiments.

Figure 2. — (A) CellTiter Glo (CTG) assays in complemented METTL16-AID cells after auxin treatment. Day 0 is set to 1 (n = 2).

(B) Northern blot of total RNA after 24 h of auxin and/or MAT2Ai treatment in EV, WT, or K163A cells. GAPDH is a loading control. One-way ANOVAs comparing the +auxin samples with WT+ for both − and + MAT2Ai were performed. ***p = 0.0009 (EV+ to WT+, − MAT2Ai), **p = 0.0020, ****p < 0.0001, and ***p = 0.0007 (WT+ to K163A+, +MAT2Ai) (n = 3).

(C) SAM levels measured by HPLC-tandem mass spectrometry ±auxin for 48 h in EV, WT, or K163A cells. SAM levels were normalized to total ion count (TIC) for loading control, with EV −auxin set to 1. One-way ANOVA comparing the +auxin samples with WT+ was performed. p = 0.0004 and p = 0.0007 (n = 3). The data for EV are from the 48 h data shown in Figure 1F.

(D) CTG assays in WT or K163A complemented cells after auxin treatment. Cells were conditioned in ±auxin medium for 5–7 days prior to seeding. Day 0 is set to 1 (n = 3). Unpaired two-tailed t tests were performed between K163A and WT +auxin. For day 4, p = 0.0291. For day 5, p = 0.0039.

(E) CTG assays of WT or K163A cells after auxin and MAT2Ai treatment. Five days at 0 nM MAT2Ai is set to 1 for each cell line (n = 3).

(F) Western blot of protein from WT or K163A cells after 5 days of auxin treatment with MAT2Ai. MAT2A protein was normalized to actin and WT, 1,000 nM MAT2Ai was set to 1 (n = 2). Protein was harvested from the same cells used on day 5 of the CTG assays in (E).

(G) SAM levels measured by HPLC-tandem mass spectrometry after auxin and/or MAT2Ai treatment for 5 days in WT or K163A cells. SAM levels were normalized to TIC for loading. The −auxin/−MAT2Ai sample was set to 1 for each cell line. Unpaired two-tailed t test comparing WT with K163A for +auxin/−MAT2Ai was performed (n = 3).

(H) Same data as (G) but omitting the −MAT2Ai samples for better visualization of +MAT2Ai data.

All error bars represent standard deviation from the mean.

The regulatory model predicts that K163A cells will have defects in MAT2A expression and SAM homeostasis. To test MAT2A induction, we treated cells with a MAT2A inhibitor AGI-24512 (MAT2Ai)^17,19 and simultaneously treated them ±auxin for 24 h (Figure 2B). As expected, K163A and EV did not rescue MAT2A splicing induction, but WT partially rescued splicing (Figure 2B, right). Even in the absence of MAT2Ai, auxin treatment decreases MAT2A splicing in K163A-expressing cells (Figure 2B, left). We next measured SAM levels after 48 h of METTL16 depletion and observed an ~80% reduction in SAM in EV and K163A, while there is only an ~52% reduction in SAM levels in WT (Figure 2C). We do not know why the WT allele does not fully rescue METTL16 depletion but speculate that the N-terminal myc-tag may impede its function. Nonetheless, these data support the conclusion that the hyperactive K163A allele supports viability but lowers the intracellular SAM setpoint by abrogating MAT2A expression.

METTL16 K163A mutant cells are hypersensitive to MAT2A inhibition

To test whether there are any growth defects in K163A cells, we conditioned the WT and K163A cells in auxin over several days and then compared growth rates. K163A produced a modest, but significant, growth defect after auxin treatment (Figure 2D). We hypothesized that depleting SAM would exacerbate phenotypes resulting from K163A-expressing cells’ inability to induce MAT2A splicing. Indeed, K163A cells stop growing after treatment with 1 μM MAT2Ai, while WT cells tolerate this treatment (Figure 2E). This suggests that cells with low intracellular SAM levels require METTL16’s ability to restore SAM by enhancing MAT2A expression.

Consistent with this, we observed a dose-dependent increase in MAT2A protein in the WT cells after 5 days of auxin treatment and MAT2Ai, but this increase was reduced in the K163A cells (Figure 2F). We also observed an ~5-fold decrease in SAM levels in K163A cells, and WT partially rescued this loss (only a 1.6-fold loss) after 5 days. We observed similar ~5-fold losses in SAM after 5 days of MAT2Ai treatment in the absence of auxin. Remarkably, co-treatment with MAT2Ai and auxin has a synergistic effect on SAM levels. Co-treatment leads to a 12-fold decrease of SAM in WT-expressing cells but leads to a 46-fold decrease of SAM in K163A-expressing cells. These data further support the conclusion that cells require METTL16-dependent upregulation of MAT2A to control SAM homeostasis in a SAM-limiting environment.

We estimated the intracellular SAM levels and found that untreated cells have ~25–35 μM SAM (Figures S1E and S1F), but co-treatment with auxin and MAT2Ai led to ~2 and 0.7 μM SAM in WT and K163A cells, respectively. While WT cells grew slowly in these conditions, K163A cells showed a near complete loss of growth (Figure 2E). These data suggest that the minimal intracellular concentration of SAM required for growth in HCT116 cells is between 0.7 and 2 μM.

METTL16 K163A rescues U6 snRNA methylation

K163A should separate U6 snRNA methylation from MAT2A regulation as it retains methyltransferase activity. To test this, we performed an immunoprecipitation (IP) using an anti-m⁶A antibody, then analyzed the RNA by northern blot for U6 snRNA. Because the m⁶A site is masked by a stem loop formed with the 3′ end of U6 snRNA, we target the 3′ terminus of the RNA for degradation with a DNA oligonucleotide and RNaseH prior to IP.²¹ U2 snRNA serves as a positive control, because it has a METTL16-independent m⁶A mark. After 24 h of auxin treatment, m⁶A IP of U6 snRNA was reduced 67% (Figure S2A). U6 snRNA is stable, so the immunoprecipitated U6 snRNA observed after METTL16 depletion could represent pre-existing methylated U6 snRNA or result from residual METTL16 activity.⁴⁹ To test this, we treated cells for 6 h with auxin, then added 4-thiouridine (4SU) to label newly made RNA. We isolated 4SU-labeled transcripts and performed m⁶A IP (Figure S2B). We observed that m⁶A IP of newly made U6 snRNA is reduced by 84%. This suggests that pre-existing methylated U6 snRNA contributes to the remaining m⁶A-containing U6 snRNA but that there also may be some residual METTL16 activity. Finally, we tested if our complementation pools rescued U6 snRNA methylation. As expected, both WT and K163A rescued U6 snRNA methylation, while EV did not (Figure S2C). Thus, we expect that phenotypes in K163A-expressing cells are likely due to abrogated MAT2A regulation, not U6 snRNA methylation.

MAT2A inhibition and METTL16 depletion synergistically decrease histone methylation due to decreased SAM

We next tested potential downstream effects of METTL16 on SAM-dependent processes. Initially, we examined histone methylation because misregulation of SAM directly affects gene expression through histone methylation and recent reports suggest that histones serve as methyl sinks.^50–55 We assayed the levels of H3K4me3, H3K36me3, H3K9me3, and H3K27me3 after 48 h of auxin and MAT2Ai treatment (Figures 3A and 3B). We observed little to no decrease in these marks after either auxin or MAT2Ai treatment alone, but co-treatment produced a synergistic decrease of H3K4me3, H3K36me3, H3K9me3, and H3K27me3 in EV cells (Figures 3C–3F, red bars). Importantly, K163A does not rescue this effect, suggesting that decreased histone methylation is a consequence MAT2A regulation by METTL16 (Figures 3C–3F, orange bars). Indeed, MAT2A protein increased after MAT2A inhibition alone when endogenous METTL16 was present, but EV and K163A failed to increase MAT2A protein after co-treatment (Figures 3A and 3B). Thus, METTL16-dependent control of MAT2A is required for maintenance of normal histone methylation patterns, particularly in SAM-limiting environments.

Figure 3. — (A) Western blots of total protein from EV, K163A, WT, GFP, or MAT2A (M2A) cells after 48 h of auxin and/or MAT2Ai treatment. Protein was run in parallel for H3K4me3, H3K36me3, H3K9me3, H3K27me3, and MAT2A. Actin, H3, and PABPN1 are loading controls.

(B) MAT2A protein levels were normalized to actin, with −auxin, +MAT2Ai set to 1. Unpaired two-tailed t tests were performed for the indicated comparisons (n = 3).

(C) H3K4me3 protein levels were normalized to actin, with −auxin, −MAT2Ai set to 1, except for GFP and MAT2A in which −auxin, +MAT2Ai was set to 1. Unpaired two-tailed t tests were performed for the indicated comparisons, with p values noted in the figure (n = 3).

(D) Quantification of H3K36me3 levels as in (C) (n = 3).

(E) Quantification of H3K9me3 levels as in (C) but normalized to H3 (n = 3).

(F) Quantification of H3K27me3 levels as in (C) but normalized to PABPN1 (n = 3).

All error bars represent standard deviation from the mean.

Interestingly, the degree of histone methylation rescued in WT cells and the extent of methylation loss varied by histone mark (Figures 3C–3F, blue bars). For example, in EV, there is a 4-fold decrease in H3K4me3 after co-treatment compared with an 11-fold decrease in H3K36me3. Furthermore, WT fully rescues H3K4me3 but does not restore H3K36me3 to untreated levels. This may reflect the proposed role of histone H3K36me3 as a methyl “sink” (see discussion).

To rescue SAM levels after loss of METTL16, we overexpressed MAT2A cDNA by lentiviral transduction. Attempts to express a dox-inducible MAT2A in the existing METTL16-AID cell lines were unfruitful due to poor transgene expression. Therefore, we generated a Tet-Off METTL16-AID cell line that has efficient TIR1 and MAT2A transgene expression after dox removal. After lentiviral transduction of MAT2A (M2A) or GFP as a negative control, the cells were both auxin responsive and expressed the MAT2A transgene (Figures S1G and 3B, purple). As expected, METTL16-AID cells complemented with GFP were unable to induce MAT2A expression after auxin treatment, while the MAT2A cells overexpressed MAT2A (Figures 3A and 3B, green and purple bars). We then tested histone methylation in the context of MAT2Ai treatment ±auxin and dox removal. As expected, histone methylation was reduced after auxin treatment in GFP control cells (Figures 3C–3F, green). In contrast, the MAT2A transgene fully rescued the methylation of histones after auxin treatment (Figures 3A and 3C–3F, purple). In fact, overexpression of MAT2A in these cells increases histone methylation in some samples. These data confirm that METTL16 depletion reduces histone methylation by reducing MAT2A expression.

METTL16 decreases translation by impacting SAM levels

METTL16 has been proposed to promote global translation in a catalytic-independent fashion by direct recruitment of either eIF3 or eIF4E2.^41,46 Since both mechanisms require stoichiometric interactions of METTL16 with initiation factors, these models predict that METTL16 depletion will lead to an immediate inhibition of new translation. To test this, we used SUnSET assays to monitor new translation by puromycin labeling. We depleted METTL16 with auxin for 6 h then added puromycin for 15 min and observed no change in translation (Figure 4A). Since METTL16 is over 90% depleted by 6 h, these data are inconsistent with a direct recruitment model. We did observe a modest decrease in translation after 48 h of auxin treatment, and a further decrease after 96 h, consistent with a downstream effect of METTL16 on translation.

Figure 4. — (A) SUnSET assay (left) and quantification (right) of puromycin signal from METTL16-AID cells after auxin treatment followed by 15 min of puromycin treatment. Cycloheximide (CHX) was used as a negative control. Puromycin-labeled proteins were normalized to PABPN1, with −auxin set to 1. Unpaired two-tailed t tests were performed for the indicated comparisons, using Welch’s correction when a sample was set to 1 (n = 3).

(B) SUnSET assay as in (A) using GFP and MAT2A complemented METTL16-AID cells after 48 h of auxin and MAT2Ai treatment followed by 15 min of puromycin. Puromycin-labeled protein levels were normalized to PABPN1, with −auxin, +MAT2Ai for each cell line set to 1. Unpaired two-tailed t tests were performed for the indicated comparisons, using Welch’s correction when a sample was set to 1, with p values noted in the figure (n = 3). Both images are from the same blot and brightness was adjusted prior to cropping.

All error bars represent standard deviation from the mean.

SAM levels have also been reported to affect translation.^56,57 To test if METTL16’s long-term impact on translation is due to its role in SAM maintenance, we performed SUnSET assays in GFP- or MAT2A-overexpressing METTL16-AID cells treated with auxin and MAT2Ai for 48 h (Figure 4B). In GFP cells, METTL16 depletion led to a 38% decrease in translation, and MAT2A overexpression rescued this phenotype. These data support the conclusion that decreased translation following loss of METTL16 in HCT116 cells is caused by SAM reductions.

METTL16 knockdown reduces m⁶A levels at sites enriched for METTL3 motifs

We previously showed that siRNA-mediated METTL16 knockdown led to decreases in m⁶A marks beyond its known substrates MAT2A and U6 snRNA.²¹ We noted that METTL16 depletion could reduce m⁶A levels due to direct methylation or it may reduce m⁶A indirectly through MAT2A regulation. Importantly, our previous studies were inconclusive due to nonstandard m⁶A peak-calling and consensus sequence analysis. Therefore, we re-analyzed the MeRIP-seq data and identified differentially methylated peaks by fitting to a statistical model to account for the overdispersion of the data, resulting in more reliable detection of altered peak expression. We identified 4,093 peaks that changed upon METTL16 knockdown, the majority of which were downregulated (Figure 5A). The distribution of these differentially methylated peaks were typical of m⁶A, with strong enhancement downstream of stop codons and a peak at the 5′ UTR, likely reflecting m⁶Am (Figure 5B). More importantly, the overwhelming majority of peaks lack the METTL16 consensus motif UACAGARAA but contain DRACH or RAC sites, as expected for METTL3 substrates (Figure 5C).^58–61 Furthermore, the DRACH/RAC peaks are enriched downstream of stop codons, further supporting the conclusion that they are canonical METTL3 sites (Figure 5D). Thus, the bulk of the downregulated m⁶A peaks are likely METTL3 substrates. We favor the hypothesis that decreased SAM levels after METTL16 knockdown impact METTL3 activity, but we cannot exclude that these changes reflect cellular responses to METTL16 loss. Nonetheless, these are unlikely to be METTL16 m⁶A substrates.

Figure 5. — (A) Pie chart of depleted or enriched differential m⁶A peaks after METTL16 knockdown.

(B) Metagene plot of the differential m⁶A peaks after METTL16 knockdown.

(C) Pie chart showing representation of sequence motifs in differential m⁶A peaks after METTL16 knockdown. Some m⁶A peaks contained multiple sequence motifs, so this pie chart represents over 100%.

(D) Metagene plot of the differential m⁶A peaks after METTL16 knockdown that contained DRACH/RAC motifs.

(E) Metagene plot of the differential m⁶A peaks after METTL16 knockdown that contained neither DRACH/RAC motifs nor UACAGARAA motifs.

We also observed 744 peaks that had neither DRACH/RAC nor UACAGARAA motifs. Metagene plots show that these peaks are enriched at the 5′ ends of transcripts, suggesting that these represent m⁶Am sites (Figure 5E). Again, we cannot distinguish whether these are lost due to SAM depletion or represent posttranscriptional changes in gene expression. Interestingly, the enzyme that deposits m⁶Am, PCIF1/CAPAM, has an ~10-fold higher K_m for SAM than METTL3, suggesting it may be more likely to be affected by SAM reductions.^27,62,63 Most importantly, as with the identified DRACH/RAC sites, these sites are unlikely to be METTL16 substrates.

We identified 15 peaks that contain UACAGARAA sites, 10 of which were not in U6 snRNA or MAT2A, but all 10 of these peaks also contain DRACH/RAC sites. We previously tested the peak on GNPTG, but the UACAGARAA was not methylated by METTL16 in vitro.²¹ Moreover, miCLIP analysis identified an m⁶A modification within a DRACH (GAACA) site within the peak, further supporting that it is a METTL3 substrate.⁶⁰ Using publicly available GLORI-seq data, we identified m⁶A sites within the peaks in each of the remaining nine genes.⁶⁴ None of the UACAGARAA sites were methylated, but seven of these genes had validated methylation of DRACH/RAC sites (Table S1). The remaining two genes had multiple methylation sites, but none overlapped the UACAGARAA-containing peak. These data do not identify any additional METTL16 substrates apart from MAT2A and U6 snRNA.

METTL16 methylates U6 snRNA pseudogenes

To determine METTL16’s effects on m⁶A at nucleotide resolution, we performed GLORI-seq using RNA from EV, WT, and K163A cells ±auxin for 72 h. In GLORI-seq, unmodified adenosines are chemically deaminated to inosine to cause A-to-G transitions upon sequencing, but methylated adenosines are protected from deamination. We examined m⁶A sites among five different comparison groups: each pooled line ±auxin (EV+/EV−, WT+/WT−, KA+/KA−) and EV plus auxin compared with WT or K163A plus auxin (EV+/WT+, EV+/KA+). We identified considerably fewer hypomethylated m⁶A sites using GLORI than by MeRIP-seq, and the number is similar to the hypermethylated sites (Figure 6A). This is likely due to the relatively low sequence coverage in our data, but false positives in the MeRIP-seq data or cell-type differences may contribute. Nonetheless, a large fraction of the hypomethylated sites in each comparison were found within DRACH/RAC motifs and were enriched near translation stop sites (Figures 6B–6D). These site-specific data further bolster the conclusion that the majority of m⁶A loss in protein coding genes that occurs after METTL16 depletion is due to altered METTL3 activity.

Figure 6. — (A) Number of hypomethylated or hypermethylated sites identified in the five comparison groups.

(B) Percent of m⁶A sites within each consensus motif for all or hypomethylated sites. Numbers above the bars represent number of sites.

(C) Metagene plot of all m⁶A sites identified in the EV+/WT+ samples.

(D) Metagene plot of the hypomethylated sites in the EV+/WT+ samples.

(E) The fold change (Log2FC) of the 61 U6 snRNA pseudogenes (RNU6) identified in any of the five comparison groups. One-way ANOVA with Welch’s correction was performed to determine p values.

(F) The fold change of each hypomethylated U6 snRNA pseudogene and all other hypomethylated transcripts (Other) within each comparison group. Unpaired t tests with Welch’s correction were performed between each pair.

(G) The m⁶A abundance in EV-auxin conditions is shown for all hypomethylated RNAs in the EV+/EV− comparison group. U6 snRNA pseudogenes (RNU6) were compared with all other transcripts (Other) ±auxin. MAT2A hp1 and hp3 sites are blue. Unpaired t tests with Welch’s correction were performed to determine significance of the differences between the −auxin samples.

(H) Alignment of the consensus TACAGAGAA sequence variants in 24 of 61 U6 snRNA pseudogenes. Blue background with white letters conforms to the consensus.

(I) The m⁶A abundance for each of the three −auxin samples for the 61 U6 snRNA pseudogenes according to the consensus variant. Variant bases are underlined. An unpaired t test with Welch’s correction was performed to determine the significance of the difference between the consensus and most common variant TATAGAGAA.

MAT2A was among the RNAs identified as hypomethylated after METTL16 depletion (Figure S4A). In addition, we identified a total of 61 U6 snRNA pseudogenes among the 5 comparison groups with reduced methylation after METTL16 depletion (e.g., RNU6–39P, Figure S4A). U6 pseudogene methylation decreased an average of 4.4-fold in EV cells, which was significantly less in WT or K163A cells (1.1- and 1.4-fold; Figure 6E, circles). Similarly, comparison of +auxin samples reflected loss of U6 snRNA pseudogene transcript methylation (7.6- and 4.4-fold; Figure 6E, squares). K163A did not complement as well as WT, presumably due to lower SAM levels, but it remains formally possible that the mutation modestly decreases U6 snRNA methylation. U6 snRNA itself is lost during mapping due to the presence of multiple U6 snRNA genes.

We next compared changes between U6 snRNA pseudogenes and other hypomethylated transcripts after METTL16 depletion and observed a significantly greater effect on U6 pseudogenes (Figure 6F, RNU6 vs. “Other”). As expected, the 26 U6 snRNA pseudogenes hypomethylated in the KA +/KA− samples were significantly less affected by METTL16 depletion than those in EV samples (Figure 6F, orange and red circles). In fact, the U6 snRNA pseudogene transcripts in the KA+/KA− comparison group showed a significantly lower fold-change than the corresponding other hypomethylated transcripts (Figure 6F, orange circles and triangles). Next, we compared m6A abundance as measured by GLORI in U6 snRNA pseudogene transcripts compared with other hypomethylated transcripts (EV+/EV−). In untreated cells, U6 snRNA pseudogenes were highly methylated compared with the other hypomethylated transcripts (Figure 6G, 95%–15%). Thus, U6 snRNA pseudogene methylation is higher at basal state and more responsive to METTL16 depletion than other METTL16-responsive hypomethylated transcripts.

Finally, we examined the sequences surrounding the methylation sites in the 61 U6 snRNA pseudogenes and found that 37 had the consensus T₁A₂C₃A₄G₅A₆G₇A₈A₉ nonamer (Figure S4B). Among the remaining 24 sites, the only invariant position was T_1, and RNU6–1328P had 4 purine transitions (Figures S4B and 6H). The most frequent variation was a C₃→T transition. To approximate the effects of these mutations on methylation, we compared the average methylation level of each site in all three −auxin samples (Figure 6I). Surprisingly, the TATAGAGAA-containing RNAs had a slightly higher m⁶A fraction compared with TACAGAGAA (98% vs. 95%). Moreover, the two genes that had a C₃→T variant combined with another base change (RNU6–151P and RNU6–201P) were also highly methylated (Figure 6I). Other variants displayed a range of methylation efficiencies. These data expand the repertoire of METTL16 substrates beyond the previously reported MAT2A and U6 snRNA TACAGARAA sequences.

METTL16 K163A mutant cells are hypersensitive to MTAP deletion

MTAP-deleted cells are sensitive to decreases in intracellular SAM abundance, so we hypothesized that the K163A cells would be hypersensitive to MTAP deletion.^14–20 To test this, we used three independent sgRNAs to knockout MTAP in WT and K163A cells (Figure S5A) and compared their growth with non-targeting control (sgNT) cells. We observed no differences in growth in cells expressing endogenous METTL16-AID (−auxin), but all three MTAP-deleted cells in the K163A mutant (+auxin) were severely compromised (Figure S5B), particularly when compared with matched sgNT cells (Figure 7A). Thus, the K163A mutation creates a vulnerability to MTAP deletion.

Figure 7. — (A) CellTiter Glo assays of WT or K163A cells with MTAP deletion or NT controls. Each point was quantified relative to the matched sgNT (n = 4 except 12 days n = 3). These values for K163A lines were compared with WT by two-tailed unpaired t test. For all three sgMTAP samples, **p < 0.01 at 9 and 12 days.

(B) Western blot from WT or K163A cells with MTAP-deleted or NT controls after 48 h of auxin. Protein was run in duplicate for MAT2A and SDMA, with actin loading controls shown for each blot.

(C) Quantification of western blots as in (B). MAT2A was normalized to actin, with sgNT in WT cells set to 1 (n = 3). Unpaired two-tailed t tests compared K163A plus auxin samples to their corresponding WT controls. p values for sgNT, sgMTAP1, sgMTAP2, and sgMTAP3 were ***0.00045, *0.017, **0.0057, and **0.0068, respectively.

(D) Quantification of western blots as in (B). SDMA protein levels from the 50–75 kDa cluster were normalized to actin, with sgNT for each cell line set to 1 (n = 3).

All error bars represent standard deviation from the mean.

Upon MTAP deletion, SAM levels are largely unchanged.¹⁴ Consistent with this, we observed no increases in MAT2A protein levels after MTAP deletion (Figures 7B and 7C; −aux samples). Instead, we observed an ~2-fold decrease in MAT2A protein in K163A cells after auxin treatment regardless of MTAP status. Similar results were observed with MAT2A RNA expression (Figures S5C and S5D). These data suggest that the synthetic growth defects between K163A and MTAP deletion are the consequence of effects on basal SAM levels, not a result of the inability to upregulate MAT2A in response to low SAM. Overall, these observations further support the idea that METTL16 functions in SAM homeostasis in normal growth conditions, and this activity becomes essential for cells after MTAP depletion.

To further explore the molecular basis for the synthetic growth defects, we examined bulk SDMA with protein extracted from cells ±auxin for 48 h. The strongest SDMA signal was observed as a doublet at ~26–28 kDa with an additional fainter cluster of ~3–5 proteins between 50 and 75 kDa (Figure 7B). As expected, both of these signals decreased significantly upon MTAP deletion regardless of METTL16 (Figures 7B, 7D, and S5E). Interestingly, the 50–75 kDa cluster tended to be lowest in K163A MTAP-deleted cells after auxin treatment (Figure 7D, orange patterns). Admittedly, low signal/noise and high variability do not support statistically significant differences from WT. Therefore, we cannot conclude that the reduced growth in MTAP-deleted K163A cells stems from PRMT5 inhibition, but there is a trend toward reduced PRMT5 activity. Nonetheless, whether this is due to PRMT5 inhibition, MTA interference with other methyltransferases, or due to MTAP’s functions in the Met salvage pathway, the observation that a hyperactive METTL16 mutant creates a sensitivity to MTAP deletion supports its role as a critical regulator of SAM homeostasis.

DISCUSSION

Previous studies established that METTL16 regulates MAT2A mRNA levels, but the question remained whether this regulation played a physiologically relevant role in SAM homeostasis. Supporting an important role in SAM maintenance, the K163A mutant that specifically abrogates METTL16’s ability to regulate MAT2A leads to ~5-fold decreases in intracellular SAM concentrations. This decrease in SAM production allowed growth in normal media but does not support growth under low SAM conditions. Moreover, we found that this mutant displayed synthetic growth defects in MTAP-deleted cells, which are known to be hypersensitive to reduced SAM. We also found that loss of METTL16 causes a decrease in translation due to its role in MAT2A induction, further supporting its physiological relevance. Finally, we found that decreased SAM levels from loss of METTL16 reduces methylation of H3K4me3, H3K36me3, H3K9me3, and H3K27me3, and correlates with reductions in RNA methylation, suggesting that the methyltransferases responsible for these methyl marks are sensitive to reduced SAM. Taken together, these data support the conclusion that METTL16 is a physiologically relevant regulator of SAM homeostasis with a global impact on cellular function.

Multiple mechanistic aspects of the model in Figure 1A have been established, while others are less well substantiated. The model proposes a feedback loop in which MAT2A expression is linked to SAM levels by METTL16’s catalytic efficiency. In this study, we find that the catalytically hyperactive K163A METTL16 is unable to induce MAT2A expression or maintain the typical SAM setpoint of cells, strongly substantiating this core principle of the feedback loop. Moreover, since K163A abrogates METTL16’s K-loop that blocks the SAM binding pocket, these results tie METTL16’s weak affinity for SAM to its role in regulating SAM homeostasis. In other words, we demonstrate that by altering METTL16’s SAM-sensing ability, we can alter the SAM threshold at which METTL16 induces or represses MAT2A expression and change the SAM setpoint of the cell. Here, we tested one mutation, but we can imagine that other mutations in METTL16 can affect SAM binding and/or catalysis to establish a range of intracellular SAM setpoints. Such a mutational series will provide a useful toolbox to study the effects of SAM concentrations on any number of SAM-dependent cellular pathways.

Several of the phenotypes examined here were modestly affected by METTL16 alone but were amplified in the presence of MAT2Ai. As such, we conclude that METTL16-dependent regulation of MAT2A mRNA is critical in SAM-limiting environments. Moreover, we expect that different methyltransferases may be impacted by SAM reductions differentially due in part to the variations of the K_m of methyltransferases for SAM. We found that loss of METTL16 combined with MAT2A inhibition had a synergistic effect on specific histone methylation marks. Our data suggest that H3K4me3 and H3K36me3 are affected at different SAM thresholds, because H3K36me3 was more sensitive to drops in SAM than H3K4me3. Interestingly, the K_m of MLL2 and SETD2 (the methyltransferases responsible for H3K4me3 and H3K36me3, respectively) for SAM are both ~4 μM, suggesting that there may be more factors involved in determining the SAM threshold of different histone methylation marks.^65–68 Methylation of histones is sequential, so the affinity of the upstream methyltransferases for SAM may also be a factor. Also, it is possible that proximity to demethylases, methyltransferases, and local SAM concentration at different sites of histone methylation may also play a role in which methylation sites are more sensitive to drops in SAM.^69–71 Regardless, we were intrigued that H3K36me3 was more sensitive to drops in SAM than H3K4me3, because this is consistent with their potential to serve as methyl sinks. The methyl sink hypothesis proposes that some histone methylation marks are not necessarily used for epigenetic regulation of gene expression but instead are used to maintain proper SAM levels by using up excess SAM.⁵² Interestingly, previous studies have suggested that both H3K36 and H3K4 may serve as sinks, but H3K36 is a preferred sink in yeast. Therefore, when there is less extra SAM in the cell, H3K36me3 will decrease while H3K4me3 may be unaffected. Only upon further drops in SAM concentration will H3K4me3 also decrease. Thus, our data showing considerably greater changes in H3K36me3 upon SAM depletion compared with H3K4me3 are consistent with the potential preferential role of H3K36 over H3K4 as a methyl sink (Figure 3). However, neither H3K9me3 nor H3K27me3 are suggested to be strong methyl sinks, yet H3K27me3 behaved similarly to H3K36me3. This may be due to the low abundance of H3K27me3, or differences between human and yeast cells.

Reanalysis of our MeRIP-seq data suggested that loss of METTL16 indirectly decreases m⁶A and likely m⁶Am as well. The simplest rationale for these losses is that decreases in SAM levels affect METTL3/14 and PCIF1/CAPAM activity. In fact, we were unable to confirm any direct m⁶A substrates of METTL16 of the over 4,000 differential peaks besides U6 snRNA and MAT2A mRNA in the MeRIP-seq data. An important caveat is that our analysis focused on UACAGARAA elements and, should METTL16 be able to methylate other sites, we would not identify these peaks. Indeed, the BCAT1 and BCAT2 transcripts were suggested to be METTL16 substrates based on cell-based and in vitro analyses.⁴² However, neither has a UCAGARAA sequence, and those authors suggested that METTL16 was responsible for methylating DRACH sequences. While we think it is unlikely that METTL16 has evolved to methylate a secondary consensus comprised of the same consensus sequence as METTL3, we do note that another group has reported that BCAT2 is methylated in its 5′ UTR within a CACAG site, resembling a METTL16 substrate.⁶⁴ Furthermore, our GLORI-seq data were also inconsistent with a large number of METTL16 substrates, although the coverage was relatively low. Even with these caveats, it seems probable that the METTL16 methylome is limited, consistent with previous conclusions,⁴⁰ and that reports of hundreds or thousands of direct substrates are likely overestimations of its activity.^41–43

The identification of U6 snRNA pseudogenes by GLORI-seq expands the narrow list of METTL16 substrates and informs the specificity of the enzyme (Figures 6 and S4). Previously, the TACAGARAA consensus reflected the seven METTL16 sites in MAT2A and U6 snRNA. However, if we include all U6 snRNA pseudogenes variants, the least stringent consensus sequence is TRYARDRRD as each base except the first has at least one variant. It is possible that the sequence requirements may differ between U6 snRNA-related substrates and MAT2A. Indeed, an A₂→G or a C₃→U mutation in MAT2A hp1 significantly reduces METTL16 activity in vitro,²⁹ but maintain a high average (>95%) methylation in U6 snRNA pseudogene transcripts (Figure 6I). Perhaps the methylation of U6 snRNA is more efficient than MAT2A hairpins, consistent with the regulatory role of the hairpins, but a constitutive function for U6 snRNA methylation. Interestingly, there are nearly 1,300 annotated U6 snRNA pseudogenes, many of which are derived from chimerization with LINE1 retrotransposons.^72–74 They are generally considered to be nonfunctional, but it is possible that expressed pseudogenes have cis- or trans-regulatory function or serve as U6 snRNA orthologs. If so, METTL16 methylation may contribute to those functions. Alternatively, these sites may simply be vestiges of their U6 snRNA origins. In any case, the findings presented here open doors for explorations of METTL16 substrate specificity and function.

We think the observations here have potentially important consequences for ongoing efforts to target MTAP-deleted cancer cells by exploiting their sensitivity to low SAM through MAT2A inhibition.^17–20 We show that METTL16 K163A has a synthetic growth defect with MTAP deletion (Figure 7) and that this phenotype is likely the result of the ~5-fold drops in SAM observed in K163A cells in nutrient-rich media (Figure 2C). We also observed that K163A exacerbated the effects of MAT2Ai (Figure 2E), and others reported that the effectiveness of some MAT2A inhibitors were blunted by the upregulation of MAT2A after inhibition.⁷⁵ Thus, inhibition of METTL16-dependent upregulation of MAT2A either alone or in combination with MAT2A inhibitors may provide therapeutic opportunities to target MTAP-deleted cancers. However, the mechanisms of any approaches would have to consider the complexities of the two-tiered regulatory pathway. For example, a METTL16 inhibitor that inhibits catalysis but allows RNA binding would be ineffective because it would increase MAT2A expression by driving more splicing and stabilizing the transcript. While inhibiting RNA binding instead could reduce MAT2A splicing, it would also cause hypomethylation and stabilization of MAT2A mRNA. Furthermore, complete inhibition of METTL16 RNA binding is likely to be toxic due to its requirement for U6 snRNA methylation. Ideally, we could identify interventions that enhance SAM binding of METTL16, perhaps by inhibiting the K-loop auto-regulatory activity like K163A. This would cause decreased splicing, decreased stability from hypermethylation, and would maintain U6 snRNA methylation. We can also imagine antisense strategies that target the DI, hp1, or the CFI_m binding site, all of which are necessary for MAT2A splicing induction in low SAM environments. Beyond MTAP-deleted cancers, there are other disease states proposed to be impacted by too high or too low of SAM levels. Therefore, adjusting the catalytic activity of METTL16 and therefore SAM setpoint of the cell has a wide range of potential therapeutic benefits.

Limitations of the study

It is worth noting limitations of our complementation experiments. First, WT METTL16 did not fully rescue most phenotypes, at times making it difficult to make strong conclusions about K163A METTL16 and SAM. Second, we are operating under the assumption that the phenotypes we observe in cells expressing K163A METTL16 are due to its inability to maintain SAM levels. While this is undoubtedly the simplest hypothesis, K163A METTL16 does not rescue U6 snRNA methylation quite as well as WT, which could also contribute to some of the phenotypes observed. Another limitation of this study is low coverage in the GLORI-seq, which likely leads to many m⁶A marks going undetected. The looser METTL16 consensus sequence was only inferred from observed substrates and was not observed in contexts outside of U6 snRNA or tested in vitro. Finally, this work used exclusively HCT116-derived lines, so appropriate caution should be taken when applying the conclusions to other cell lines or tissues.

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Nicholas Conrad (nicholas.conrad@utsouthwestern.edu).

Materials availability

All unique/stable reagents generated in this study are available from the lead contact.

Data and code availability

GLORI-seq data are available at GEO: GSE296342.
This study does not report original code.

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANTS DETAILS

Cell lines

HCT116-derived cell lines were grown at 37°C with 5% CO₂ in DMEM (Sigma D5796) supplemented with 10% tetracycline tested FBS (bio-techne R&D Systems S10350), penicillin-streptomycin (Sigma P0781), and 2mM L-glutamine (Fisher BP379–100). Fresh media was used, and we supplemented with an extra 200 μM L-methionine (Sigma M9625) and/or changed media when specified depending on the experiment so that methionine would not be unintentionally depleted. The original METTL16-AID cell line was maintained in 50 μg/mL hygromycin (hyg, Sigma H3274), and the complementation cell lines were maintained in 50 μg/mL hyg and 100 μg/mL G418 (Fisher BP673). To induce TIR1 and exogenous METTL16 expression, cells were treated overnight with 500–1000 ng/mL of doxycycline (dox, Fisher BP2653). Then, to degrade endogenous METTL16, cells were treated for the designated time with 1 mM auxin (Sigma I5148). Alternatively, cells were treated for the designated time with 1 μM MAT2Ai (MCE HY-112130) unless otherwise stated. The Tet-Off METTL16-AID cell lines (GFP and MAT2A) were maintained in 50 μg/mL hyg, 100 μg/mL G418, 10 μg/mL blasticidin (blast, cat# A1113903), and 5–100 ng/mL dox. To induce TIR1 and exogenous GFP or MAT2A expression, cells were washed twice with PBS +/+ and allowed to grow in dox-free media overnight. Then, to degrade endogenous METTL16, cells were once again treated for the designated time with 1 mM auxin.

HCT116 cells are male, 293T cells are female, and both lines have been authenticated using ATCC STR analysis. All lines are checked periodically for mycoplasma.

METHOD DETAILS

Methionine depletion

Prior to methionine depletion, cells were treated with 1 mM auxin and the media was supplemented with an additional 200 μM methionine. Two hours later, cells were washed twice with Dulbecco’s Phosphate Buffer Saline (PBS) with calcium chloride and magnesium chloride (Sigma D8662). Then, methionine-free DMEM (Sigma D0422) supplemented with 10% tetracycline-tested FBS, penicillin-streptomycin, 2 mM L-glutamine, 1mM sodium pyruvate (Thermo 11360070) and 0.4mM L-cysteine (Sigma C7352) was added.

Transfection

Cells were transfected using FuGENE HD transfection reagent (Promega E2311) according to the manufacturer’s protocol. For 6-well plates, 2 μg of DNA was combined with 6 μL of FuGENE and 100 μL of Opti-MEM then incubated for 15 min at room temperature before applying dropwise to the media.

Plasmid construction

To generate MTAP knockout lines, three MTAP sequences were inserted into sgRNA expression plasmid lentiCRISPRv2, a gift from Dr. Feng Zhang.⁷⁶ Oligonucleotides were annealed, phosphorylated, and inserted into BsmBI digested lentiCRISPRv2 as previously described.⁷⁶ DNA oligonucleotides for sgMTAP1, sgMTAP2, and sgMTAP3 were NC2878 and NC2879, NC2880 and NC2881, and NC2882 and NC2883, respectively (see Table S2 for all primer sequences). The sgNT plasmid was a generous gift of Dr. John Schoggins. These plasmids are named pNC1151–1154.

To generate METTL16-AID lines, the CRISPR-targeting plasmid pNC1410 was derived from pX459, a gift from Dr. Feng Zhang.⁷⁷ Oligonucleotides NC3639 and NC3640 were annealed, phosphorylated, and inserted into BbsI-digested pX459 as described.⁷⁷ The homologous repair plasmid, pNC1486 was derived from plasmids that were used in a selection strategy that proved to be ineffective. First, we made pNC1393 by Gibson assembly of a pBluescript SK + digested with HindIII and BamHI with a synthesized insert (Genewiz) that encoded Flag-mAID followed by a T2A “self-cleaving” peptide and a hyg resistance marker (Table S3); unfortunately, an incorrect hyg resistance sequence was included. Next, we generated pNC1408 using 4-piece Gibson assembly: two fragments were the products of pNC1393 digested with HindIII and BamHI. In addition, the METTL16 left and right homology arms were amplified from genomic HCT116 DNA using primers NC3633 with NC3634 and NC3636 with NC3637, respectively. Finally, pNC1408 was used to generate pNC1486. First, the vector was digested with EcoRV and BamHI to generate the vector sequence and the insert was made by amplification using NC3868 and NC3869 with pNC1408 as template. The vector and insert were used for Gibson assembly to generate pNC1486 which has the METTL16 left and right homology arms flanking Flag-mAID.

The plasmid pNC1549, used for inducible expression of osTIR1 from the AAVS1 locus, was generated as follows. First, we ordered a synthesized plasmid pNC1537 (Genscript, Table S3). This plasmid has a pBluescript II SK + parental vector and an insert that includes the AAVS1 homology arms flanking Tet-responsive promoter cloning site and BGH poly(A) site, followed by a PGK promoter driving a hyg resistance gene separated from the rTetR gene with a T2A element. Next, NLS-ARF16-PB1-HA-P2A-OsTIR1 was amplified using NC4008 and NC4009 with pMGS46 as a template; pMGS46 (ARF16-PB1-HA-P2A-OsTIR1) was a gift from Dr. Michael Guertin.⁷⁸ The resulting PCR product was inserted into pNC1537 digested with SnaBI and AflII using Gibson assembly.

Dox-inducible, myc-tagged METTL16 plasmids for complementation were derived from CMV-driven, FLAG-tagged METTL16 plasmids, or CMV-driven FLAG alone for EV. pNC1680 (WT myc-METTL16) was derived from pNC1099 (WT flag-METTL16²¹). pNC1099 was digested with NruI and HindIII to remove the CMV promoter and FLAG tag. The TetRP dox-inducible promoter was amplified from pNC1549, using the forward primer NC4450 to also add a minimal FRT site, and using the reverse primer NC4456 to add a myc tag. Gibson cloning was performed to insert the FRT-TetRP-myc PCR product into the digested pNC1099 to make pNC1680. For the other complementation constructs, pNC226 (pcDNA-Flag⁷⁹), pNC1145 (catalytic dead N184A flag-METTL16²⁹), and pNC1298 (hyperactive K163A flag-METTL16²⁹) were digested with NruI and BamHI to remove the CMV promoter and FLAG tag. Then FRT-TetRP-myc was amplified from pNC1680 using forward primer NC4499 and either reverse primer NC4500 (for making pNC1692 and pNC1693) or reverse primer NC4503 (for making pNC1688) which adds a stop codon immediately following the myc tag. Gibson cloning was performed to insert the FRT-TetRP-myc PCR product into the digested pNC226, pNC1145, and pNC1298 to make pNC1688 (EV, aka myc-STOP), pNC1692 (catalytic dead N184A myc-METTL16), and pNC1693 (hyperactive K163A myc-METTL16).

Plasmid pNC1746 for Tet-Off TIR1 expression was generated by replacing the ARF16-BP1-HA-P2A-OsTIR1 module in pNC1549 with NLS-HA-OsTIR1. The insert was generated using primers NC4339 with NC4009 followed by primers NC4341 with NC4009. This was cloned into pNC1549 digested with SnaBI and AflII. The rTetR gene in the resulting plasmid was then replaced by digestion with AfeI and SalI and insertion of the TetR which was amplified with NC4410 and NC4409.

Plasmid pLenti CMVtight eGFP Blast (w782–1) was a gift from Eric Campeau (pNC1745). To make Tet-Off MAT2A (pNC1761), we digested this plasmid with ApaI and inserted V5-MAT2A amplified using primers NC4685 and NC4730 using Gibson assembly.

Generation of METTL16-AID lines

To make the METTL16-AID lines, we first integrated the mini-AID into the endogenous METTL16 locus using CRISPR-directed dsDNA break and homologous repair.⁸⁸ Three mg of pNC1410 (Cas9 and guide) and 13 mg of pNC1486 (homologous insert cassette) were transfected into HCT116 cells on a 10-cm plate. Eight hr post-transfection, media was changed, and the freshly added media was supplemented with 1 mg/mL puromycin and 1mM SCR7 to inhibit non-homologous end-joining.⁸⁹ Cells were maintained in SCR7/puromycin for 3 days then both compounds were removed as the puromycin resistance is only transiently expressed. In this scheme, mAID integration is not stably selectable. Single cells were sorted and plated onto 96-well plates and expanded for several weeks. Mini-AID insertion and homozygosity were screened by PCR amplification using primers NC3845 and NC3847 following DNA extraction with DNAzol (ThermoFisher 10503027). Subsequent lines were derived from homozygous METTL16-Flag-AID integrated clonal line D1–15.

We next inserted a dox-inducible (Tet-On) osTIR1 into the AAVS1 safe harbor locus of the D1–15 homozygous line. 1.6 μg of pNC1549 (osTIR1 and hyg resistance), 0.2 μg of pNC1050 (Talen L), and 0.2 μg of pNC1051 (Talen R) were transfected into D1–15 in a 6-well plate. Talen L and Talen R were gifts from Dr. Feng Zheng.⁸⁰ The day after transfection, cells were seeded into a 10-cm plate. The following day, media was changed and supplemented with 250 μg/mL of hyg. After several weeks of hyg selection, single cells were sorted and plated onto 96-well plates and expanded for several weeks. Clones were screened for auxin-dependent METTL16 degradation by western blot, and subsequent lines were derived from clone H3. Tet-Off lines were generated in the same fashion, except pNC1746 was used instead of pNC1549; the resulting clone was B2–5.

We next inserted different METTL16 mutants or empty vector (EV) into the H3 clone. 1.6 μg of pNC450 (pOG44, Flp-recombinase) and 0.4 μg of pNC1680, pNC1688, pNC1692, or pNC1693 (WT METTL16, EV, catalytic dead METTL16, or hyperactive METTL16) were transfected into the H3 clonal cell line in a 6-well plate. The intention was to insert these mutants into an FRT site that was included in the TIR1 construct (pNC1549), but we later discovered that the minimal FRT site we used was not sufficient for recombination in cells. The day after transfection, cells were seeded into a 10-cm plate in media supplemented with 600 μg/mL G418 for selection along with 50 μg/mL hyg for maintenance. After ~3 weeks of selection, these pools were frozen down. A similar procedure was followed to generate an EV Tet-Off METTL16-AID line from B2–5 for subsequent MAT2A overexpression.

We next used lentivirus to complement the EV Tet-Off METTL16-AID cell line with either GFP or MAT2A. Lentiviruses were produced in 293T cells by co-transfection of 1 μg Tet-Off GFP or MAT2A (pNC1745 or pNC1761), 0.6 μg of psPAX2 and 0.4 μg of pMD2.G on a 6-well plate. Both packaging plasmids were gifts from Dr. Didier Trono. After overnight transfection, media was changed to 3% Tet-system approved FBS media. Twenty-four hours later, lentivirus containing media was collected and replaced for another 24 h. The resulting 48 h supernatants were pooled with the 24-h supernatants, HEPES buffer (pH 7.2) was added to 20mM, and the virus stocks were filtered through a 0.45 μm filter. The stocks were diluted 1:1 in media with 3% Tet-system approved FBS containing media and polybrene was added to a final concentration of 8 mg/mL. One mL of this media solution was added to the EV Tet-Off METTL16-AID line on a 6-well plate and viral transduction proceeded overnight. The next day, the media was changed to normal 10% Tet-approved FBS DMEM media and the following day cells were split and G418, hyg, dox, and 5 μg/mL blast were added to the media. Cells were selected in blast for 2 weeks, and GFP and MAT2A expression were validated by microscopy and western blot. We saw efficient expression in these pools, so we did not make single-cell clones of these lines.

Generation of MTAP knockout cell lines

Lentiviruses were produced in 293T cells by co-transfection of 1 μg lentiCRISPRv2 (sgMTAP1–3 or sgNT; pNC1151–1154) plasmid, 0.6 μg of psPAX2 and 0.4 μg of pMD2.G on a 6-well plate, following the protocol described above. One mL of the lentivirus-media solution was added to the WT or K163A METTL16-AID lines on a 6-well plate and viral transduction proceeded overnight. The next day, the media was changed to normal 10% Tet-approved FBS DMEM media and the following day cells were split and G418, hyg, and 1 mg/mL puromycin were added to the media. Cells were selected in puromycin for 5–7 days and MTAP deletion was validated by western blot. We saw efficient MTAP knockout in these pools, so we did not make single-cell clones of these lines.

Western blotting

In most cases, cells were harvested using trypsin and centrifuged before being lysed in RSB100T (10 mM Tris-HCl pH 7.5, 100 mM NaCl, 2.5 mM MgCl₂, 0.5% Triton X-100) with 1X Protease Inhibitor Cocktail Set V (Millipore 539137) and 1 mM PMSF. SDS loading buffer was then added to the lysates (1X is 50 mM Tris-HCl pH 6.8, 10% glycerol, 2% SDS, 1% Beta-mercaptoethanol, and 0.05% bromophenol blue) followed by sonication, and nanodrop was used to estimate protein concentration. Protein was denatured at 100C for 5min just prior to loading on the gel. Alternatively, when examining histone methylation, cells were lysed in MNase Buffer (50 mM Tris-HCl pH 7.5, 1 mM CaCl₂, 0.2% Triton X-100) with 1X protease inhibitor cocktail (PIC) and 1 μL of MNase per 50 μL of buffer and incubated at 37C for 5 min before SDS loading buffer was added. In this case, protein was denatured at 70C for 10min. Based on nanodrop concentration, 25–50 μg of denatured protein was loaded and resolved by SDS-PAGE by standard western blotting protocols. Imaging of western blots was done with infrared detection with Odyssey Fc and signal was quantified by ImageStudio (v5.2) software (Li-Cor Biosciences).

Northern blotting

Northern blots were performed using standard techniques with radiolabeled probes. RNA probes were made using PCR products with a T7 RNA Polymerase promoter and oligoprobes were made with primers that were end labeled using T4 PNK. Primers for making probes can be found in Table S2. Imaging was perfomred with a Typhoon FLA 9500 phosphorimager and quantified using ImageQuant.

CellTiter-Glo

The CellTiter-Glo Luminescent Cell Viability assays were performed per manufacturer’s instructions (ProMega). Briefly, equal amounts of cells were seeded into 6-well plates and grown with the indicated treatments. At the indicated time, cells were harvested using trypsin and quenched with cell growth media. 100 μL of room temperature cell mix was mixed with 100 μL of room temperature CellTiter-Glo Reagent in an opaque-walled 96-well plate. The mixture was incubated for 10 min before luminescence was measured using a FLUOstar OPTIMA plate reader.

SUnSET assay

Negative controls were treated with 200 μg/mL cycloheximide (CHX) for 15 min to inhibit translation, then all cells were treated with 5–10 μg/mL puromycin (10 μg/mL for Figures 4A and 5 μg/mL for Figure 4B) for 15 min immediately prior to harvesting. Care was taken to ensure that cells were ~50–90% confluent at time of puromycin treatment. For Figure 4A, fresh media was added 6 h before harvesting each time point. For Figure 4B, fresh media was added ~24 h before harvesting.

m⁶A IP

Cells were grown in 10-cm plates and RNA was extracted using TRI-zol. 10 μg of RNA was incubated with 1 μM NC2213a (Table S2) for 5 min at 65C, then slowly cooled to 37C for ~20min. Annealed RNA and primer were then treated with RNaseH, then RNA was treated with RQ1 DNase followed by phenol:chloroform:iso-amyl alcohol 25:24:1 (PCA) extraction and EtOH precipitation. RNA was subsequently added to IP Buffer (10 mM Tris-HCl pH7.5, 150 mM NaCl, and 0.1% Igepal) with RNasin and 1–2 μg of m⁶A or IgG antibody, and 1% was taken as input samples. RNA was allowed to bind the m⁶A antibody on a rocker for 1 h at 4C. Meanwhile, 15μL of DynaBeads Protein A slurry were washed 3 times with IP buffer, then incubated with blocker (IP buffer +0.1 mg/mL cRNA) at 4C for 1 h. Bound RNA/antibody mixture was then added to blocked beads and incubated on rocker at 4C for 1 h. Beads were then washed 3 more times with IP buffer and then twice with low salt buffer (10 mM Tris-HCl pH7.5, 10 mM NaCl, and 0.1% Igepal). RNA was eluted in G-50 buffer (20mM Tris-HCl pH 6.8, 300mM sodium acetate, 2mM EDTA, and 0.25% SDS) with 0.1 mg/mL Proteinase K for 30 min at 37C. Then, PCA extraction was performed followed by EtOH precipitation. RNA was resuspended in urea dye (8 M Urea, 2.5 mM EDTA, 0.15% Bromophenol Blue, 0.15% Xylene cyanol, and 1X TBE) and half was denatured at 90C for 5 min then run on an 8% urea gel at 25 W with 1X TBE. RNAs were detected using standard northern blotting protocol for U6 and U2 snRNA.

4SU-m⁶A IP

Cells were grown overnight in 15-cm plates with 100 μM 4-Thiouridine (4SU), and then RNA was extracted using TRIzol. RNA was treated with RNaseH and DNase, as in the m⁶A IP outlined above, then biotinylated for 3 h in a 25C water bath with 20 mM NaOAc, 1 mM EDTA, 0.1% SDS, and 0.2 mg/mL of Biotin-HPDP in DMF. 3 chloroform extractions were then performed followed by an EtOH precipitation with 1M NH4OAc. Next, streptavidin pulldown was performed to select for biotinylated RNA. 40 μL of DynaI MyOne Streptavidin T1 bead slurry per sample were washed 3 times in MPG 1:10-I (100 mM NaCl, 1 mM EDTA, 10 mM Tris-HCl Ph 7.5, 0.1% Igepal). Then, beads were blocked in MPG 1:10-I supplemented with 0.1 μg/μL poly(A) RNA (Sigma), 0.1 μg/μL cRNA, 0.1 μg/μL sheared salmon sperm DNA (Sigma) and 0.1% SDS for 1 h at room temperature on a rocker. ~250 μg of biotinylated RNA was denatured at 65C for 5 min before being mixed with the blocker/beads mixture and incubated for 1 h at room temperature on a rocker. Beads were washed 10 times: MPG1:10-I, MPG1:10 (no Igepal) at 55°C, three times with MPG-I (1 M NaCl, 10 mM EDTA, 100 mM Tris-HCl pH 7.5, 0.1% Igepal), MPG1:10-I, twice with MPG-I no salt (10 mM EDTA, 100 mM Tris-HCl pH 7.5, 0.1% Igepal), and MPG1:10-I. Then, RNA was eluted from the beads twice with MPG 1:10-I with 5% beta-mercaptoethanol for 5 min at room temperature. After elution, PCA extraction and EtOH precipitation were performed. Finally, m⁶A IP was performed as described above but with 20 μL of DynaBeads Protein A slurry.

Anti-myc IP

To verify expression of myc-METTL16 transgenes, we had to immunoprecipitate myc-METTL16 from the pooled cells because the single myc tag was not sufficient for detectable signal in whole cell lysates. Cells were grown on a 60-mm plate and harvested at ~90% confluency. After washing the cells in ice-cold 1X PBS and collecting by centrifugation at 750g for 3 min at 4°, cells were lysed on ice for 5 min in RSB100T (100 mM NaCl, 10 mM Tris [pH 7.5], 2.5 mM MgCl₂, 0.5% Triton X-100) supplemented with 1 mM CaCl₂, 1 mM PMSF, and 1X Protease Inhibitor Cocktail Set V (Millipore 539137). The samples were sonicated once for 10 s at 20% amplitude on a Sonics Vibra cell model VCX130PB with model CV188 probe and then 10 U of RQ1 DNase were added (Promega M610A) for 15 min at room temperature while nutating. Lysates were then clarified at 20,000 x g for 10 min at 4°. Twenty μL of Myc-trap magnetic agarose beads (ChromoTek ytma-20) bead slurry per sample was washed in RSB100T and then the lysate was added to the beads and incubated for 2 h at 4° with nutating. Samples were then washed in RSB100T five times and bound proteins were eluted in 1X SDS loading buffer at 95° for 5 min.

Mass spectrometry for SAM quantification

Cells were grown in 60-mm plates, and fresh media with an extra 200 μM Met was added the day before harvesting. We harvested metabolites one plate at a time. First, plates were washed twice with PBS plus calcium chloride and magnesium chloride. Then, 600 μL of ice-cold 80% methanol was added, and plates were immediately snap frozen on liquid nitrogen. Samples were then scraped from plates and mixed by pipetting on ice before being transferred to ice-cold tubes and frozen in liquid nitrogen. Once all plates had been harvested, we thawed samples in a room temperature water bath while pipetting to mix before centrifugation at 6000g at 4C for 10 min to pellet cell debris. Methanol supernatants containing metabolites were snap frozen again in liquid nitrogen and stored at −80C. Then, RSB100T was added to the cell pellets before sonication. Relative protein concentration was then measured by nanodrop and used to adjust loading volume of the methanol supernatants. Methanol supernatants were then aliquoted and dried using a speed vacuum.

Samples were analyzed using reversed-phase HPLC coupled to tandem mass spectrometry as described previously.⁹⁰ Extracted metabolites were resuspended with 5 mM ammonium acetate (pH 5.5) and filtered using 0.2 μM PVDF filters to eliminate insoluble materials. Metabolites were separated on a Synergi Fusion-RP column (4 μm particle size, 80 A° pore size, 150 × 2 mm, Phenomenex, 00F-4424-B0) using a Shimadzu HPLC (LC-40D x3) and detected by mass spectrometer (Sciex Triple Quad 5500+) with Q1 and Q3 mass as follows: SAM (399/250), SAH (385/136). The column temperature, sample injection volume, the flow rate was set to 25°C, 30 μL, and 0.5 mL/min respectively. The HPLC conditions were as follows: Solvent A: Water with 5 mM ammonium acetate LC/MS grade, pH 5.5 and Solvent B: Methanol with 5 mM ammonium acetate, LC/MS grade. Gradient condition was: 0–5 min, 0% B; 5–6 min, 0.2% B; 6–7 min, 1% B; 7–8 min, 3% B; 8–14 min, 5% B; 14–16 min, 25% B; 16–18 min, 50% B; 18–22 min, 100% B; 23–28 min, 0% B. Total run time: 28 min. Metabolites were quantified using SCIEX OS by calculating total peak area and standard curves generated by injecting SAM and SAH standards. Data was also normalized using TIC.

GLORI-seq

For the 72 h GLORI-Seq experiment, EV, K163A, and WT complemented METTL16-AID cells were seeded into 6-well plates +/− 1 μg/mL dox. The next day, fresh media +/− 1 μg/mL dox and 1 mM auxin was added, and cells were treated for 72 h, with daily media/drug changes. After 72 h, RNA was harvested using TRIzol, treated with RQ1 DNase, and then rRNA was removed using the RiboMinus Eukaryote System V2. After this, GLORI-Seq was performed as described previously.⁸⁵ In brief, RNA was fragmented at 94°C for 4 min using the NEBNext Magnesium RNA Fragmentation Module to ~100–200 nt. Then, guanosines were protected, non-methylated adenosines were deaminated to inosines, and guanosines were deprotected. End repair was performed using Antarctic phosphatase and T4 PNK. Then, libraries were made using the NEBNext Small RNA Library Prep Set for Illumina with the NEBNext Multiplex Oligos for Illumina, Index primers set 1 and 2. Libraries were purified using the Monarch Spin PCR & DNA Cleanup Kit and a 1.5X KAPA Pure Beads cleanup. Finally, 150×150 paired-end sequencing was done on a NovaSeq X 1.5B flow cell with 10% PhiX added to diversify. Note 3 biological replicates were performed for each experiment.

QUANTIFICATION AND STATISTICAL ANALYSIS

All of the statistical details of experiments can be found in the figure legends. Basic analysis was done using GraphPad Prism.

MeRIP-seq analysis

Differential methylation analysis

Raw fastq files from input and m⁶A MeRIP-seq performed in siControl and siMETTL16 knockdown HEK293A-TOA cells²¹ were downloaded from the GEO database (GEO: GSE90914). Reads were trimmed for remaining adapter sequences using Trim Galore (0.6.10) and aligned to the hg38 genome using STAR (2.7.11b).⁸¹ The resulting bamfiles were used for differential methylation analysis using exomePeak2 (1.16.2) in R (4.4.1). The function with options used was exomePeak2(bam_ip = [list of siControl bamfiles], bam_input = [list of siControl bamfiles], bam_input_treated = [list of siMETTL16 bamfiles], bam_ip_treated = [list of siMETTL16 bamfiles], gff = [hg38 gtf file]). Differential peaks were considered significant if the adjusted p-value was <0.05, absolute value of the log2 fold change was >0.58 (corresponds to 1.5-fold change), and the RPKM in both siControl and siMETTL16 inputs was ≥0.5.

Filtering differentially methylated peaks by contained motif

A bedfile containing genomic locations of all annotated exons was generated from an hg38 gtf file. BEDTools (2.30.0)⁸² intersect was then used to find exonic sequences overlapping differentially methylated peaks (all transcripts considered). BEDTools getfasta was then used to acquire the sequence of all exonic regions overlapping peaks. In R, all exonic sequences were then concatenated in order for each peak to get a list of all the exonic sequences contained within the differentially methylated peaks. These exonic peak sequences were then filtered for DRACH, (non-DRACH) RAC, and UACAGARAA sequences.

Metagene analysis

metaplotR⁸³ was used to generate all metagenes used in the differential methylation analysis.

Statistical analysis and image generation

All statistical analysis was performed using R (4.4.1) and all image generation resulting from the differential methylation analysis was done using the ggplot2 package.

GLORI-seq analysis

Adapter sequences were trimmed from reads using Flexbar (v. 3.5.0),⁸⁴ and the sequences were aligned to the hg38 genome using STAR (v.2.7.5c).⁸¹ m⁶A sites were identified with GLORI-tools (https://github.com/liucongcas/GLORI-tools)⁸⁵ by reporting the A-to-G conversion rate. Incomplete conversions and false positives were eliminated with the following filters: ≥5 variant nucleotides, ≥15 coverage of A + G, and ≥0.1 A rate. Differentially methylated RNAs and sites were obtained using Bullseye (https://github.com/mflamand/Bullseye). m⁶A sites in all three biological replicates were analyzed using a quasi-binomial generalized linear model. Sites with adjusted p < 0.05 and, a fold-change cut-off ≥0.58 and a minimum read count of 20 reads were considered differentially edited. Differentially methylated RNAs were determined by taking the average of the A-to-G conversion rate of individual m⁶A sites across the transcript and using the same cut-off thresholds as described above for individual sites. Sequence motifs DRACH, (Non-DRACH) RAC and UACAGARAA, were identified from BED files containing m⁶A site coordinates generated from an hg38 GTF file. Jalview Version: 2.11.4.1 was used for the alignments and consensus figures.⁸⁶ Sequence logos were created using Weblogo.⁸⁷

ADDITIONAL RESOURCES

No new resources or clinical trials were generated in this study.

Supplementary Material

NIHMS2099528-supplement-1.pdf^{(5.1MB, pdf)}

NIHMS2099528-supplement-2.xlsx^{(17.1MB, xlsx)}

NIHMS2099528-supplement-3.xlsx^{(226.3KB, xlsx)}

NIHMS2099528-supplement-4.xlsx^{(262.9KB, xlsx)}

NIHMS2099528-supplement-5.xlsx^{(11.3KB, xlsx)}

NIHMS2099528-supplement-6.xlsx^{(12KB, xlsx)}

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2025.115966.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER

Antibodies

m⁶A	Synaptic Systems	Cat#202 003; RRID: AB_2279214
IgG	Millipore	Cat#12–370; AB_145841
METTL16	Sigma-Aldrich	Cat#hpa020352; RRID: AB_1853828
METTL16	Bethyl	Cat#A304-192A; RRID: AB_2620389
MAT2A	Novus	Cat#NB110-94158; RRID: AB_1237164
ACTIN	Abcam	Cat#ab6276; RRID: AB_2223210
ACTIN	Proteintech	Cat#66009-1; RRID: AB_2687938
PUROMYCIN	Millipore	Cat#MABE343; RRID: AB_2566826
PABPN1	Abcam	Cat#ab75855; RRID: AB_1310538
MTAP	Abcam	Cat#ab55517; RRID: AB_944282
SDMA	Cell Signaling Technology	Cat#13222; RRID: AB_2714013
H3K4me3	Abcam	Cat#ab8580; RRID: AB_306649
H3K36me3	Abcam	Cat#ab9050; RRID: AB_306966
H3K9me3	Abcam	Cat#ab8898; RRID: AB_306848
H3K27me3	Abcam	Cat#ab6002; RRID: AB_305237
H3	Santa Cruz	Cat#sc-517576; RRID:AB_2848194
MYC	Bethyl	Cat#A190-105A; RRID:AB_67390
IRDye 800CW Goat anti-Rabbit IgG	LICORbio	Cat#926–32211; RRID: AB_621843
IRDye 680LT Goat anti-Rabbit IgG	LICORbio	Cat#926–68021; RRID: AB_10706309
IRDye 800CW Goat anti-Mouse IgG	LICORbio	Cat#926–32210; RRID: AB_2687825
IRDye 680LT Goat anti-Mouse IgG	LICORbio	Cat#926–68020; RRID: AB_10706161

Chemicals, peptides, and recombinant proteins

tet-tested FBS	Bio-techne R&D Systems	Cat#S10350
DMEM	Sigma	Cat#D5796
G418	Fisher	Cat#BP673-5
Hygromycin	Sigma	Cat#H3274
Blasticidin	ThermoFisher	Cat#A1113903
Puromycin	Sigma	Cat#P8833
PMSF	Sigma	Cat#P7626
Protease Inhibitor Cocktail Set V	Millipore	Cat#539137
RQ1 Dnase	Promega	Cat#M610A
RNaseH	Promega	Cat#M428C
Mnase	NEB	Cat#M0247S
T4 PNK	NEB	Cat#M0201S
RNasin	Promega	Cat#N261B
Proteinase K	Fisher	Cat#BP1700
SCR7	Fisher	Cat#M60082-2
Dnazol	ThermoFisher	Cat#10503027
FuGENE HD transfection reagent	Promega	Cat#E2311
Opti-MEM	ThermoFisher	Cat#31985
TRIzol	MRC	Cat#TR 118
Penicillin-Streptomycin	Sigma	Cat#P0781
L-glutamine	Fisher	Cat#BP379-100
Methionine-free DMEM	Sigma	Cat#D0422
Sodium Pyruvate	ThermoFisher	Cat#11360070
L-cysteine	Sigma	Cat#C7352
L-methionine	Sigma	Cat#M9625
Doxycycline	Fisher	Cat#BP2653
Auxin	Sigma	Cat#I5148
AGI-24512 (MAT2Ai)	MCE	Cat#HY-112130
PBS + CaCl2 + MgCl2	Sigma	Cat#D8662
Gibson Assembly Master Mix	NEB	Cat#M5510A
Trypsin	Sigma	Cat#T4174
Cycloheximide	Sigma	Cat#01810-1G
cRNA	Sigma	Cat#R6625
4SU	ThermoFisher	Cat#J60679.MD
Biotin-HPDP	ThermoFisher	Cat#21341
poly(A) RNA	Sigma	Cat#P9403
ssDNA	Sigma	Cat#S3126
Polybrene	Sigma	Cat#H9268
Antarctic phosphatase	NEB	Cat#M0289S

Critical commercial assays

CellTiter-Glo Reagent	ProMega	Cat#G7570
NEBNext Magnesium RNA Fragmentation Module	NEB	Cat#E6150S
RiboMinus Eukaryote System V2	ThermoFisher	A15026
NEBNext Small RNA Library Prep Set for Illumina	NEB	E7330S
NEBNext Multiplex Oligos for Illumina, Index primers set 1	NEB	E7335S
NEBNext Multiplex Oligos for Illumina, Index primers set 2	NEB	E7500S
Monarch Spin PCR & DNA Cleanup Kit	NEB	T1130S

Deposited data

Raw and analyzed GLORI-Seq data	This paper	GEO: GSE296342

Experimental models: Cell lines

HEK 293T	Dr. Joshua Mendell	RRID: CVCL_0063
HCT116	ATCC	Cat#CCL-247; RRID: CVCL_0291

Oligonucleotides

See Table S5	This paper, ordered from Millipore	N/A

Recombinant DNA

pMD2.G	Didier Trono	Addgene plasmid # 12259
psPAX2	Didier Trono	Addgene plasmid # 12260
lentiCRISPRv2	Sanjana et al.⁷⁶	Addgene plasmid # 52961
pX459	Ran et al.⁷⁷	Addgene plasmid # 62988
pMGS46	Sathyan et al.⁷⁸	Addgene plasmid # 126580
pBluescript SK+; pNC65	Stratagene, La Jolla, CA	Cat#212205
pcDNA-flag; pNC226	Sahin et al.⁷⁹	N/A
pOG44; pNC450	ThermoFisher	Cat#V600520
Talen L; pNC1050	Sajana et al.⁸⁰	Addgene plasmid # 35431
Talen R; pNC1051	Sajana et al.⁸⁰	Addgene plasmid # 35432
WT flag-METTL16; pNC1099	Pendleton et al.²¹	N/A
catalytic dead N184A flag-METTL16; pNC1145	Doxtader et al.²⁹	N/A
pLenti-sgMTAP #1; pNC1151	This paper	N/A
pLenti-sgMTAP #2; pNC1152	This paper	N/A
pLenti-sgMTAP #3; pNC1153	This paper	N/A
pLenti-CRISPR-vs-NT; pNC1154	Dr. John Schoggins	N/A
hyperactive K163A flag-mettl16; pNC1298	Doxtader et al.²⁹	N/A
pBS-Flag-mAID-T2A-Hyg; pNC1393	This paper	N/A
M16-C-sg1/2-Hyg; pNC1408	This paper	N/A
Cas9 and guide; pNC1410	This paper	N/A
M16-C-sg2-Flag-AID-Donor; pNC1486	This paper	N/A
pBS-AAVS1-TetOn-Hyg-FRT; pNC1537	Genscript, see Table S6 for sequence	N/A
pBS-AAVS1-TetON-ARF16 PB1 - TIR1; pNC1549	This paper	N/A
WT myc-METTL16; pNC1680	This paper	N/A
EV, aka myc-STOP; pNC1688	This paper	N/A
catalytic dead N184A myc-METTL16; pNC1692	This paper	N/A
hyperactive K163A myc-METTL16; pNC1693	This paper	N/A
Flag-mAID-T2A-Hyg Insert	Genewiz, see Table S6 for sequence	N/A
pBS-hAAVS1-TetOFF-HA-OsTIR1-HYG-FRT48; pNC1746	This paper	N/A
pLenti CMVtight eGFP Blast (w782-1); pNC1745	Eric Campeau	Addgene plasmid # 26584
pLenti Tet-Off V5-MAT2A-T2A-eGFP; pNC1761	This paper	N/A

Software and algorithms

ImageQuantTL Ver 8.2.0	GE Healthcare Life Sciences	N/A
Image Studio Ver 5.2	Li-Cor	N/A
Prism 10	GraphPad	N/A
OptimaApp Ver 2.20R2	BMG LabTech	N/A
Trim Galore (0.6.10)	Dr. Felix Krueger	RRID:SCR_011847
STAR (2.7.11b)	Dobin et al.⁸¹	RRID:SCR_004463
exomePeak2 (1.16.2)	W.Zhen	https://github.com/ZW-xjtlu/exomePeak2
R (4.4.1)	R Development Core Team	https://www.r-project.org/
BEDTools (2.30.0)	Quinlan and Hall⁸²	RRID:SCR_006646
metaplotR	Olarerin-George and Jaffrey⁸³	N/A
Flexbar (v. 3.5.0)	Dodt et al.⁸⁴	N/A
STAR (v.2.7.5c)	Dobin et al.⁸¹	RRID:SCR_004463
GLORI-tools	Shen et al.⁸⁵	https://github.com/liucongcas/GLORI-tools
Bullseye	R-Universe	https://github.com/mflamand/Bullseye
Jalview Version: 2.11.4.1	Waterhouse et al.⁸⁶	N/A
Weblogo	Crooks et al.⁸⁷	N/A
Biorender	https://BioRender.com	N/A

Other

0.45 μm filter	Millipore	Cat#SLHPR33RS
Dynabeads Protein A	ThermoFisher	Cat#10002D
Dynabeads^™ MyOne^™ Streptavidin T1	ThermoFisher	Cat#65602
Myc-trap magnetic agarose beads	ChromoTek	Cat#ytma-20
KAPA Pure Beads	Roche	Cat# KK8000
Sonicator	Sonics	Vibra cell model VCX130PB
Sonicator probe	Sonics	model CV188
Plate reader	BMG LabTech	FLUOstar OPTIMA
Li-Cor	Li-Cor	Odyssey FC
Phosphorimager	GE Healthcare Life Sciences	Typhoon FLA 9500
Synergi Fusion-RP column (4 μm particle size, 80 A° pore size, 150 × 2 mm)	Phenomenex	Cat#00F-4424-B0

Open in a new tab

Highlights.

METTL16’s catalytic efficiency helps establish the intracellular SAM setpoint
METTL16’s regulation of SAM levels affects translation and histone methylation
Hyperactive METTL16 is synthetically lethal with MTAP deletion
METTL16 methylates U6 snRNA pseudogenes and indirectly impacts other m⁶A marks

ACKNOWLEDGMENTS

We thank Drs. Julio Ruiz, Minseon Kim, and Kathryn Pendleton for generating plasmids that were used in this study. We also thank Drs. Feng Zhang, Michael Guertin, Didier Trono, Eric Campeau, and John Schoggins for sharing plasmids. This research was supported by the National Institutes of Health (R01GM127311, 3R01GM127311-01A1S1, R01AI153175, R01AI123165, RM1HG011563-01A1, and R35GM136370) and by the American Cancer Society (DBG-23–1034956-01-RMC).

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

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Associated Data

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

Supplementary Materials

NIHMS2099528-supplement-1.pdf^{(5.1MB, pdf)}

NIHMS2099528-supplement-2.xlsx^{(17.1MB, xlsx)}

NIHMS2099528-supplement-3.xlsx^{(226.3KB, xlsx)}

NIHMS2099528-supplement-4.xlsx^{(262.9KB, xlsx)}

NIHMS2099528-supplement-5.xlsx^{(11.3KB, xlsx)}

NIHMS2099528-supplement-6.xlsx^{(12KB, xlsx)}

Data Availability Statement

GLORI-seq data are available at GEO: GSE296342.
This study does not report original code.

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PERMALINK

The catalytic efficiency of METTL16 affects cellular processes by governing the intracellular S-adenosylmethionine setpoint

Juliana N Flaherty

Enakshi Sivasudhan

Matthew Tegowski

Zheng Xing

Madeline M McGinnis

Olga V Hunter

Kyah M Featherston

Komal Sethia

Benjamin P Tu

Kate D Meyer

Nicholas K Conrad

SUMMARY

In brief

Graphical Abstract

INTRODUCTION

Figure 1. A METTL16 rapid degron system confirms METTL16 is essential for viability, MAT2A splicing induction, and SAM maintenance.

RESULTS

A METTL16 rapid degron system confirms METTL16 is essential for viability, MAT2A splicing induction, and SAM maintenance

Hyperactive METTL16 supports viability but changes MAT2A and SAM levels in cells

Figure 2. METTL16 K163A cells are viable but have decreased SAM levels and are compromised in their ability to respond to SAM reductions.

METTL16 K163A mutant cells are hypersensitive to MAT2A inhibition

METTL16 K163A rescues U6 snRNA methylation

MAT2A inhibition and METTL16 depletion synergistically decrease histone methylation due to decreased SAM

Figure 3. MAT2A inhibition and METTL16 depletion synergistically decrease histone methylation due to decreased SAM.

METTL16 decreases translation by impacting SAM levels

Figure 4. METTL16 indirectly decreases translation by impacting SAM levels.

METTL16 knockdown reduces m6A levels at sites enriched for METTL3 motifs

Figure 5. METTL16 knockdown reduces m6A levels at sites enriched for METTL3 motifs.

METTL16 methylates U6 snRNA pseudogenes

Figure 6. GLORI-seq reveals U6 snRNA pseudogenes are methylated by METTL16.

METTL16 K163A mutant cells are hypersensitive to MTAP deletion

Figure 7. METTL16 K163A mutant cells are hypersensitive to MTAP deletion.

DISCUSSION

Limitations of the study

RESOURCE AVAILABILITY

Lead contact

Materials availability

Data and code availability

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANTS DETAILS

Cell lines

METHOD DETAILS

Methionine depletion

Transfection

Plasmid construction

Generation of METTL16-AID lines

Generation of MTAP knockout cell lines

Western blotting

Northern blotting

CellTiter-Glo

SUnSET assay

m6A IP

4SU-m6A IP

Anti-myc IP

Mass spectrometry for SAM quantification

GLORI-seq

QUANTIFICATION AND STATISTICAL ANALYSIS

MeRIP-seq analysis

Differential methylation analysis

Filtering differentially methylated peaks by contained motif

Metagene analysis

Statistical analysis and image generation

GLORI-seq analysis

ADDITIONAL RESOURCES

Supplementary Material

KEY RESOURCES TABLE.

Highlights.

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Figure 5. METTL16 knockdown reduces m⁶A levels at sites enriched for METTL3 motifs.

m⁶A IP

4SU-m⁶A IP