N⁶-methyladenosine (m⁶A) is considered a major covalent modification of the adenosine (A) base in coding and noncoding (nc) RNAs (Desrosiers et al., 1974; Bokar et al., 1994; Rottman et al., 1994; Dominissini et al., 2012; Meyer et al., 2012). It is linked to diverse physiological processes, including – but not limited to – RNA turnover (Ke et al., 2017; Knuckles et al., 2017; Liu et al., 2020), stem cell differentiation (Batista et al., 2014; Geula et al., 2015,) oncogenic translation (Lin et al., 2016; Barbieri et al., 2017; Vu et al., 2017; Choe et al., 2018), and DNA damage repair (Xiang et al., 2017; Zhang et al., 2020). In human cells, most m⁶A in cellular RNAs is installed by methyltransferase like-3 (METTL3) and METTL14 methyltransferases (Bokar et al., 1994; Dominissini et al., 2012; Liu et al., 2014), both of which belong to the β-class of S-adenosyl methionine (SAM)-dependent methyltransferases (N⁶-MTases) (Bujnicki et al., 2002; Woodcock et al., 2020) that also include N⁶-deoxyadenosine DNA methyltransferases (m⁶dA MTases), especially those from Type III restriction-modification (R-M) systems in bacteria (e.g., Mod_A/B dimer of EcoP15I) (Gupta et al., 2015). The first structures of METTL3-METTL14 complexes suggested common features in EcoP15I and METTL3-METTL14 (Wang et al., 2016b; Sledz and Jinek, 2016). We analyzed these MTases and found high structural similarity of the cores of human METTL3, METTL14, and EcoP15I, suggesting their evolutionary origin from a common ancestor (Figure 1a–e and Figure 1—figure supplement 1).

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While m⁶dA in bacterial DNA is deposited in a strictly sequence-dependent manner in double-stranded DNA (dsDNA) (Gupta et al., 2015; Malone et al., 1995), m⁶A in RNA by human METTL3-METTL14 shows less stringent sequence dependency for sequences flanking the target A within the recognition motif DRACH (D = A/G/U, R = A/G, H = A/C/U) (Bokar et al., 1994; Liu et al., 2014; Linder et al., 2015). Such relaxed specificity maybe required to expand the repertoire of target transcripts of METTL3-METTL14. However, this does not correlate with low levels of m⁶A in cellular RNA, which remains at ~0.1% of total pool of ribonucleotides in cultured mammalian cells (Dubin and Taylor, 1975). Of note, recent evidence suggests that all m⁶A is deposited co-transcriptionally, with most occurring on chromatin-associated RNAs, for example, nascent pre-mRNA (CA-RNA), promoter-associated RNA, enhancer RNA, and repeat RNA elements (Ke et al., 2017; Knuckles et al., 2017; Liu et al., 2020; Slobodin et al., 2017). Consistently, the METTL3-METTL14 m⁶A writers can be recruited to chromatin by modified histone tails (e.g., H3K36me3) (Huang et al., 2019b) or the transcription machinery (Barbieri et al., 2017; Slobodin et al., 2017). Certain long noncoding (lnc) RNAs that are essential for regulated transcription, such as chromatin-associated XIST (Patil et al., 2016) and NEAT2 (MALAT1) (Liu et al., 2015), also harbor m⁶A marks. A role of m⁶A in DNA damage response and repair has also been suggested (Zhang et al., 2020; Xiang et al., 2017). In those contexts, METTL3 localizes at sites of dsDNA breaks and installs the m⁶A on DNA-damage-associated RNAs (Zhang et al., 2020). METTL3 is also emerged as a pro-viral host factor in SARS-CoV-2 pathogenesis. Consistently, genetic depletion or inhibition of METTL3 by small molecules triggers innate immune response and limits viral growth (Burgess et al., 2021; Li et al., 2021).

The increasingly appreciated roles of m⁶A in nucleic acid metabolism thus seem to converge to a common point: the occurrence of m⁶A at chromatin, a predominantly DNA- and RNA-rich environment inside the nucleus. A recent study showed that METTL3-METTL14 can convert dA to m⁶dA within equivalent DRACH (D = A/G/U, R = A/G, H = A/C/U) motif in a single-stranded DNA (ssDNA) with a 2.5-fold preference over the equivalent RNA in vitro (Woodcock et al., 2019). Since m⁶dA levels in mammalian DNA are extremely low (0.006–0.01%) and thought to originate from RNA catabolism rather than from SAM-dependent MTase reactions (Musheev et al., 2020), fundamental questions about substrate(s) specificity and mechanisms of action of METTL3-METTL14 have emerged. By using classical biochemical and mass spectrometry assays, we show that METTL3-METTL14 preferentially methylates N⁶dA on ssDNAs to m⁶dA, and this activity is largely regulated by structured RNA elements prevalent in lnc RNAs, that are also found in other cellular RNAs. Thus, our work provides a framework to explore a new regulatory axis of RNA-mediated restriction of N⁶-deoxyadenosine (m⁶dA) methylation in mammalian genomes.

We compared the MTase cores of (Mod_B) of EcoP15I (aa 90–132, 169–261, 385–511, PDB: 4ZCF Gupta et al., 2015) and METTL14 (aa 116–402, PDB: 5IL0 Wang et al., 2016b). A secondary structure-based superposition (Krissinel and Henrick, 2004) of these two structures revealed common features and similar arrangement of canonical MTase motifs (motifs I and IV-X), including the motif IV (D/EPPY/W/L) that surrounds the Watson-Crick edge of the flipped target adenine base (Figure 1a–e and Figure 1—figure supplement 1). Previous studies identified three loops encompassing the sequence intervening MTase motifs IV and V (loop 1), VIII and VIII’ (loop 2), and motifs IX and X (loop 3) contribute to substrate binding (Wang et al., 2016b; Sledz and Jinek, 2016; Wang et al., 2016a). Due to lack of experimental structures of METTL3 or Mod_A (in complex with a flipped adenine base), we chose Mod_B and METTL14 for this comparison. While the MTase domain of METTL14 is enzymatically inactive, it harbors features that contribute to substrate binding (Wang et al., 2016b; Sledz and Jinek, 2016; Wang et al., 2016a). These observations prompted us to address the prevailing question about substrate specificity of the human full-length METTL3-METTL14 enzyme complex. We co-purified the full-length human METTL3-METTL14 complex and its truncated form devoid of the RGG motif of METTL14 (METL3-METL14_-RGG) from insect cells (see details in Materials and methods section) (Figure 1f). The RGG motifs are clustered sequences of arginines and glycines. These motifs are commonly present in diverse set of RNA-binding proteins that play important roles in physiological processes, such as RNA synthesis and processing (Thandapani et al., 2013). In human METTL14, a total of seven RGG triplets and two RG motifs are present at its C-terminus tail (aa 408–457). The region also harbors a number of aromatic amino acids, which can further stabilize the bound nucleic acids via hydrophobic or stacking interactions. Consistently, this region contributes to substrate binding and m⁶A activity of the METTL3-METTL14 enzyme complex (Schöller et al., 2018). The sequence encompassing the RGG motifs in METTL14 is well conserved in higher vertebrates (Liu et al., 2014).

Next, we designed RNA substrates of varied lengths (5–30 nucleotides) with at least one canonical m⁶A signature motif (RRACH), including an RNA oligo (rPal-Top+ bat) in which two RRACH sites were arranged in a palindromic fashion (Figure 1g). We included a 14-mer DNA oligo (d6T) according to Woodcock et al., 2019, that reported that METTL3-METTL14 exhibited highest methyltransferase (MTase) activity on d6T. We also included two structured RNAs with propensity to form a perfect stem-loop (23-mer rTCE23 or also known as SRE; Oberstrass et al., 2006) without a DRACH motif, and a 30-mer rNEAT2 (or MALAT1) encompassing one DRACH motif and predicted to form a stem-loop with a bulged stem (Figure 1h–i).

The m⁶A mark in mammalian RNAs is widespread and expands beyond internal adenines in mRNAs to lncRNAs (XIST Patil et al., 2016, NEAT2 or MALAT1 Liu et al., 2015), pri-miRNAs (Alarcón et al., 2015), and snoRNAs (Linder et al., 2015) – all of which are crucial to gene regulation and maintenance of cellular homeostasis. Of note, NEAT2 lncRNA can relocate a large subset of transcription units across cellular compartments from polycomb bodies to interchromatin granules (Yang et al., 2011). According to this model, a 49-nt long fragment of NEAT2 (nt 4917–4966) facilitates the recruitment of a mega transcription complex (Yang et al., 2011). We observed that this NEAT2 fragment harbors one RRACH motif (AAACA) in its loop region and fulfils the criteria of m⁶A peak signature (RRACH Bokar et al., 1994; Liu et al., 2014 or DRACH Linder et al., 2015 or ACA Garcia-Campos et al., 2019; Figure 1g). Thus, we decided to test the activity of METTL3-METTL14 on this regulatory module of the lncRNA. For simplicity of synthesis and purification, we focused on a slightly shorter version (30-nt; rNEAT2-30) of NEAT2-49 in which both the m⁶A signature motif and bulged stem regions were well preserved (Figure 1g–h). The rTCE23 could serve as a control – it forms a perfect stem-loop structure in solution – confirmed by solution NMR previously (Oberstrass et al., 2006) – but lacks an m⁶A signature motif (Figure 1g and i).

We observed higher enzymatic activity of METTL3-METTL14 on the d6T DNA oligo compared with its RNA counterpart (r6T) (Figure 2a and c). By closely examining the sequence of the 14-mer d6T/r6T, we found a propensity of these oligos to form duplex DNA (for d6T) or RNA (for r6T): 6 of the 14 nucleotides at 5’-end will pair with a complementary sequence to create a duplex with an 8-nt overhang at the 3’-end of each strand (Figures 1g and 2d). As a result of this self-annealing, two adenine bases are available for methylation (m⁶dA) at the end of the short palindromic sequence (5’-CCG•CGG-3’). We thus hypothesized that d6T (or 6T, as referred to by Woodcock et al., 2019) may not truly represent an ssDNA substrate. Thus, we designed a new DNA oligo (d6T*) in which two nucleotides, a deoxycytidine and a deoxyguanosine at –3 and –2 positions (relative to the signature motif RRACH), respectively, were replaced by two thymidines to disrupt base pairing and palindrome formation without affecting the core RRACH motif required for m⁶dA installation (Figure 1g). We reasoned that if METTL3-METTL14 indeed prefers a ssDNA for methylation, it should show higher activity for d6T*, a true ssDNA substrate, over d6T. Indeed, we found that the methyltransferase activity of METTL3-METTL14 toward d6T* increased by 2-fold compared with d6T (and >12-fold compared with r6T) (Figure 2a). Consistent with previous observations (Woodcock et al., 2019), METTL3-METTL14 showed no activity in the presence of a perfect duplex version of d6T DNA (d6T-ds). These results confirm a robust activity of METTL3-METTL14 on ssDNA substrates.

Figure 2

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MTase activity on individual RNA/DNA substrates.

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Binding isotherms of METTL3-METTL14.

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Comparison of MTase activity on different RNA/DNA substrates.

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Dose-dependent inhibition of MTase activity by RNA.

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RNA-mediated attenuation of MTase activity by intact mass analysis.

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We observed no methyltransferase activity on rTCE23 RNA, as expected; but surprisingly, some low (but consistently detectable) methyltransferase activity on rNEAT2 (Figure 2a). We then asked what attributes (sequence, length, and shape) in an RNA substrate are critical for METTL3-METTL14 to yield a high methyltransferase activity. To answer this question, we designed eight RNA oligos with varied length (5- to 22-nt) and sequences (Figure 1g). We also varied the position of the RRACH motif and the flanking sequences in these oligos. All oligos comprised one RRACH motif, except the RNA duplex Pal-Top+ bot, where two RRACH sites were arranged in a palindromic fashion within each 10-mer strand. We designed another variant of probe rC2-flip (22-mer, RRACH in the center), rC-flip wherein the RRACH motif resided at the 3’-end. The other three RNA oligos, FoxM1-p1 (13-nt, RRACH at 3’-end), c-Myc-p5 (8-nt), and c-Myc-p6 (11-nt, RRACH at 5’-end), were derived from the FOXM1 and MYC genes because of their involvement in m⁶A-mediated processes in glioblastoma and acute myeloid leukemia (Vu et al., 2017; Zhang et al., 2017). We covalently attached a fluorescein moiety at the 5’-end of all these oligos to facilitate the quantitative measurement of their binding affinities (equilibrium dissociation constant or Kd) to METTL3-METTL14 (see Materials and methods section for details).

We used a radiometric assay and fluorescence polarization (FP) to determine methylation activity and RNA binding, respectively. As shown in Figure 2a and c, RNA oligos with high propensity toward forming secondary structures (rNEAT2, rTCE23, Pal-Top+ bot) showed low to negligible levels of methylation by METTL3-METTL14. The other RNA oligos showed some levels of methylation, though they could not be methylated with as much efficiency as the ssDNA substrate, d6T*. The results of binding experiments are shown in Figure 2b and Table 1. The full-length METTL3-METTL14 binds to rNEAT2 with the highest affinity (Kd = 2 nM), followed by the hairpin rTCE23 (Kd = 25 nM), the 3’-overhang r6T (Kd = 114 nM), the palindromic Pal-Top+ bot (Kd = 127 nM), and the ssRNA FoxM1-p1 (Kd = 294 nM). Surprisingly, the DNA substrates (d6T and d6T*) that showed highest methyltransferase activity were the poorest binders, with Kd values of 370 and 509 nM, respectively.

If METTL3-METTL14 were to methylate ssDNA in vivo, and the evidence to date does not support its direct enzymatic origin (Musheev et al., 2020), then what could be the significance of high affinity RNA binding to structured RNA oligos (rNEAT2 and rTCE23)? One explanation is that structural motifs in ncRNA or mRNAs can serve as a recruitment platform for METTL3-METTL14 in a specific context. But how would the RNA-binding activity then affect DNA methylation activity? To answer this question, we measured the methyltransferase activity of METTL3-METTL14 in the presence of roughly equimolar mixtures of d6T* DNA with each RNA oligonucleotide. The methyltransferase activity of METTL3-METTL14 was significantly reduced in the presence of RNA (Figure 2c). Of note, RNAs with propensity to form secondary structures and highest binding affinity to METTL3-METTL14 (rNEAT2, rTCE23) attenuated methyltransferase activity to the most negligible levels (Figure 2c). To further explore the relationship between methyltransferase activity and substrate-binding affinity, we synthesized a variant of the stem-loop oligo (rNEAT2*) with the DRACH motif (5’-AAACA-3’) changed to an ideal m⁶A target sequence, that is, 5’-GGACU-3’ (Figure 1g). Although higher methylation levels were observed for rNEAT2* compared to rNEAT2, most likely attributed to a perfect GGACU motif in rNEAT2*, yet the methylation was significantly lower than that of d6T* DNA (last panel of Figure 2c). Moreover, rNEAT2* showed ~6 fold reduction in affinity compared to rNEAT2 (Figure 2b). Importantly, rNEAT2* also attenuated methylation of the single-stranded d6T* DNA by METTL3-METL14 but to a lesser extent than the rNEAT2 (Figure 2c). These results suggests that: (a) the sequence and shape of the target RNAs dictate the m⁶A activity of METTL3-METTL14, and (b) that higher affinity substrates tend to show lower methylation levels, possibly due to slow off rates of m⁶A RNA.

To further investigate RNA-mediated inhibition, we showed that the two structured RNAs (rTCE23 and rNEAT2) inhibited the DNA methylation activity of METTL3-METTL14 in a dose-dependent manner, with rNEAT2 (IC₅₀ = 216 nM) showing 4-fold stronger inhibition than rTCE23 (IC₅₀ = 887 nM) (Figure 2e). Next, we employed an LC/MS-based oligonucleotide intact mass analysis to further validate the results of the radiometric assay. As shown in Figure 2f, an equimolar (with respect to d6T* DNA) addition of rNEAT2 or rTCE23 significantly reduced the methyltransferase activity from 80.5–14.5% to 7.3%, respectively. We then performed nucleoside analysis of the METTL3-METTL14 reactions to unambiguously determine the identity of methylated base and efficiency of methylation (Figure 2g). We confirmed that deoxyadenosine on d6T* and adenosine on rNEAT2 were the only modified bases. In the absence of rNEAT2 or rTCE23, 86.3% of dAs on d6T* were modified. When rNEAT2 or rTCE23 were present, only 14.4% and 7.4%, respectively, of dAs on d6T* were modified. Additionally, 5.8% of the rAs on rNEAT2 were modified. As expected, no methylation on rTCE23 was detected (no RRACH motif). These results are consistent with those obtained by oligonucleotide intact mass analysis. Altogether, the methyltransferase and binding assays confirm that the binding to structured RNAs, especially those lacking the GGACU sequence, almost completely abolishes the methyltransferase activity METTL3-METTL14.

The C-terminal RGG repeat motif of METTL14 contributes to RNA binding and activity of METTL3-METTL14 (Schöller et al., 2018; Figure 3a). Thus, we hypothesized that the absence of RGG motif in METTL3-METTL14 should diminish its ability to bind RNA and consequently attenuate the effect of RNA on DNA methylation. In fact, we observed a 2- to 10-fold decrease in binding affinity by METTL3-METTL14_-RGG (Figure 3b and Table 1). This suggests that the RGG motifs play a major role in RNA and DNA substrates, while other parts of the enzyme (e.g., CCCH-type zinc finger domains in METTL3 Huang et al., 2019a and MTase core of METTL14 Wang et al., 2016a) may also contribute to the overall binding (Figure 3b). As expected, the deletion of RGG caused 60% reduction in DNA methylation activity. In the presence of rNEAT2, the activity of METTL3-METTL14_-RGG on d6T* DNA was reduced to about 75% of its activity in absence of the RNA; comparatively, a >90% activity reduction was observed for the full-length METTL3-METTL14 in presence of the same RNA (Figure 3c). These results suggest that the RGG motif, as a general RNA-binding domain, dictates the shape-specific RNA recognition and promotes an RNA-mediated restriction of the DNA methylation activity of METTL3-METTL14 (Table 1).

Figure 3

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Binding isotherms of METTL3-METTL14 (-RGG).

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Relative MTase activity of METTL3-METTL14 and METL3-METL14 (-RGG).

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Our results are noteworthy given the relevance of METTL3-METTL14 in regions where RNA and DNA are held in closed proximity, as supported by recent studies suggesting: (a) localization of METTL3 to sites of dsDNA breaks, and its role in DNA/RNA hybrid accumulation (Zhang et al., 2020), (b) recruitment of METTL3 to chromatin by promoter-bound transcription factors (Barbieri et al., 2017), (c) UV-induced DNA damage signaling (Xiang et al., 2017), and (d) metabolism of R-loops (Abakir et al., 2020). Earlier studies suggest the existence of a large complex of multiple subunits of ~1 MDa size, possessing RNA N⁶-adenosine methylation activity distributed over two sub-complexes of approximate molecular mass of ~200 and ~800 kDa (Bokar et al., 1994Bokar et al., 1997). One such subunit is WTAP, which alters both the pattern and location of the m⁶A transcriptome (Schwartz et al., 2014). Future studies could examine the role of WTAP and other partners of METTL3-METTL14 in substrate recognition and specificity, especially within the context of the regulatory role of RNA in DNA methylation.

In summary, we unveiled a new N⁶-deoxyadenosine methylation activity on ssDNA, which is regulated by binding of METTL3-METTL14 to shape-dependent structured regions of ncRNAs and chromatin-associated nascent pre-mRNAs (CA-RNA) (Figure 3d and e). The structured elements in RNA could facilitate the recruitment of METTL3-METTL14 to chromatin and/or keep the writer complex engaged during co-transcriptional occurrence of m⁶A. Such a topological arrangement of METTL3-METTL14/RNA interaction could also restrict m⁶dA deposition, especially to the single-stranded regions of DNA exposed during transcription (Slobodin et al., 2017), DNA recombination (Zhang et al., 2020), damage and repair (Xiang et al., 2017), and R-loop metabolism (Abakir et al., 2020).

A major difference in prokaryotic and eukaryotic genomes is the contrasting occurrence of the base methylation. The m⁶dA is a predominant modification mark in bacterial DNA, whereas the m⁵dC is more prevalent in eukaryotic genomes. How is the eukaryotic genome then protected from m⁶dA deposition despite having an efficient enzyme machinery (e.g., METTL3-METTL14) to accomplish such task? A comparative sequence/structural analysis of the bacterial EcoP15I and human METTL3 and METTL14 MTases provides some hints (Figures 1a–c–3a). Of note, there are two major differences that exist in the sequences of METTL3 and METTL14: (a) loss of the target recognition domain (TRD) and (b) acquisition of the RGG motif in METTL14. The TRD motif (in EcoP15I MTase) is a major contributor to binding and specificity for a dsDNA (Gupta et al., 2015), whereas the RGG motif is known for RNA binding (Thandapani et al., 2013). It is likely that gene fusion/genome arrangement event(s) could have occurred during evolution (Bujnicki et al., 2002; Woodcock et al., 2020) to ensure very low/no occurrence (Douvlataniotis et al., 2020) of m⁶dA in mammalian genome. The high affinity to structured RNAs as demonstrated here could also ensure efficient recruitment of METTL3-METTL14 to specific nuclear compartment(s) in human cells.

Interestingly, a recent study showed that a comprehensive network of RNA-RNA and RNA-DNA interactions mediated by ncRNAs (including MALAT1) creates distinct nuclear compartments and facilitates recruitment of proteins for RNA processing, heterochromatin assembly, and gene regulation (Quinodoz et al., 2020). Thus, the secondary structure and 3D shape of the RNAs appears to be crucial for these important biological processes. In line with these observations, our results suggest that structured elements in RNAs may play an important role in regulating the ssDNA and ssRNA methylation activity of METTL3-METTL14.

The information about coding sequences of human METTL3 (NCBI reference sequence GI: 33301371) and METTL14 (NCBI reference sequence GI: 172045930) used in this study is available at NCBI. Source data are provided as a separate Source Data file. Correspondence and requests for material should be addressed to Y.K.G. (guptay@uthscsa.edu).

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Author details

1. Shan Qi

   1. Greehey Children’s Cancer Research Institute, University of Texas Health at San Antonio, San Antonio, United States
   2. Department of Biochemistry and Structural Biology, University of Texas Health at San Antonio, San Antonio, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization

   Contributed equally with

   Javier Mota

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0175-6267

2. Javier Mota

   Greehey Children’s Cancer Research Institute, University of Texas Health at San Antonio, San Antonio, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Visualization

   Contributed equally with

   Shan Qi

   Competing interests

   No competing interests declared

3. Siu-Hong Chan

   New England Biolabs, Ipswich, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   is an employee of New England Biolabs, a manufacturer and vendor of molecular biology reagents

4. Johanna Villarreal

   Greehey Children’s Cancer Research Institute, University of Texas Health at San Antonio, San Antonio, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

5. Nan Dai

   New England Biolabs, Ipswich, United States

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   is an employee of New England Biolabs, a manufacturer and vendor of molecular biology reagents

6. Shailee Arya

   Greehey Children’s Cancer Research Institute, University of Texas Health at San Antonio, San Antonio, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

7. Robert A Hromas

   Division of Hematology and Medical Oncology, Department of Medicine, University of Texas Health at San Antonio, San Antonio, United States

   Contribution

   Resources

   Competing interests

   owns equity in Dialectic Therapeutics and Abfero

8. Manjeet K Rao

   Greehey Children’s Cancer Research Institute, University of Texas Health at San Antonio, San Antonio, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

9. Ivan R Corrêa Jr

   New England Biolabs, Ipswich, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   is an employee of New England Biolabs, a manufacturer and vendor of molecular biology reagents

   "This ORCID iD identifies the author of this article:" 0000-0002-3169-6878

10. Yogesh K Gupta

    1. Greehey Children’s Cancer Research Institute, University of Texas Health at San Antonio, San Antonio, United States
    2. Department of Biochemistry and Structural Biology, University of Texas Health at San Antonio, San Antonio, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

    For correspondence

    guptay@uthscsa.edu

    Competing interests

    is founder of Atomic Therapeutics. None of these affiliations affect the authors' impartiality, adherence to journal standards and policies, or availability of data

    "This ORCID iD identifies the author of this article:" 0000-0001-6372-5007

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