RNA stability controlled by m6A methylation contributes to X-to-autosome dosage compensation in mammals

In mammals, X-chromosomal genes are expressed from a single copy since males (XY) possess a single X chromosome, while females (XX) undergo X inactivation. To compensate for this reduction in dosage compared with two active copies of autosomes, it has been proposed that genes from the active X chromosome exhibit dosage compensation. However, the existence and mechanisms of X-to-autosome dosage compensation are still under debate. Here we show that X-chromosomal transcripts have fewer m6A modifications and are more stable than their autosomal counterparts. Acute depletion of m6A selectively stabilizes autosomal transcripts, resulting in perturbed dosage compensation in mouse embryonic stem cells. We propose that higher stability of X-chromosomal transcripts is directed by lower levels of m6A, indicating that mammalian dosage compensation is partly regulated by epitranscriptomic RNA modifications.

In mammals, X-chromosomal genes are expressed from a single copy since males (XY) possess a single X chromosome, while females (XX) undergo X inactivation. To compensate for this reduction in dosage compared with two active copies of autosomes, it has been proposed that genes from the active X chromosome exhibit dosage compensation. However, the existence and mechanisms of X-to-autosome dosage compensation are still under debate. Here we show that X-chromosomal transcripts have fewer m 6 A modifications and are more stable than their autosomal counterparts. Acute depletion of m 6 A selectively stabilizes autosomal transcripts, resulting in perturbed dosage compensation in mouse embryonic stem cells. We propose that higher stability of X-chromosomal transcripts is directed by lower levels of m 6 A, indicating that mammalian dosage compensation is partly regulated by epitranscriptomic RNA modifications.
Sex chromosomes evolved from a pair of autosomes. During this process, the chromosome present in only the heterogametic sex (that is, the Y chromosome in male mammals) acquires mutations, undergoes recurrent chromosomal rearrangements, and eventually becomes highly degenerated, gene-poor, and heterochromatic 1 . Consequently, the X chromosome and most of its genes are present in a single copy in males, whereas two X chromosomes are present in females. To equalize expression between sexes in eutherian female mammals, one randomly chosen X chromosome is inactivated (X i ) early in development at around the implantation stage, while the other X chromosome remains active (X a ). Therefore, XY males and X i X a females exhibit an imbalance of gene dosage between sex chromosomes and autosomes, which are present in one and two active copies, respectively 2 . To restore the balance between X chromosomes and autosomes, Susumu Ohno hypothesized that the expression of X-chromosomal genes is upregulated by twofold 3 . Indeed, there are several mechanisms for how this could be achieved. For instance, previous studies have proposed that higher RNA polymerase II occupancy, as well as more activating epigenetic marks and gains in chromatin accessibility on the X chromosome, plays a role in dosage compensation [4][5][6][7] . Additionally, higher RNA stability of X-chromosomal transcripts has been observed 6,8 . There is evidence that nonsense-mediated mRNA decay (NMD) targets are enriched for autosomal transcripts 9 , which could partially explain the higher RNA stability of X-chromosomal transcripts. Another recent study has proposed that dosage compensation could be mediated by elevated translation of X-chromosomal transcripts 10 . Eventually, dosage compensation may be required for only a certain subset of transcripts that are dosage-sensitive, for instance, if stoichiometry with Article https://doi.org/10.1038/s41594-023-00997-7 m 6 A depletion (Fig. 1b,c). Using high-confidence m 6 A sites, which we had previously mapped in the same cell line using miCLIP2 (m 6 A individual-nucleotide resolution ultraviolet (UV) crosslinking and immunoprecipitation) and m 6 Aboost 33 , we confirmed that, in control conditions, transcripts with m 6 A sites had significantly shorter half-lives than did unmethylated transcripts 28 (Fig. 1d). Furthermore, the transcripts with m 6 A sites were significantly stabilized upon acute m 6 A depletion (8% median increase), whereas unmethylated transcripts were largely unaffected (0.3% median decrease, Fig. 1e).
Having ensured the high quality of our dataset, we turned to chromosomal differences in mRNA stability. X-chromosomal transcripts had significantly longer half-lives than autosomal transcripts under control conditions (Extended Data Fig. 2g, left). Importantly, the half-lives of autosomal transcripts significantly increased after acute m 6 A depletion (5% median increase), whereas the stability of X-chromosomal transcripts remained unchanged (0.2% median decrease, Fig. 1f). Transcripts on all autosomes responded similarly, while the X chromosome was the only chromosome that seemed to be excluded from this increase ( Fig. 1g and Extended Data Fig. 2g). These results indicated that m 6 A-mediated RNA stability could play a direct role in X-to-autosome dosage compensation in mESCs. To further support this, we reanalyzed published mRNA half-lives for wild-type (WT) and Mettl3 KO mESCs 34 and observed the same difference in RNA stabilization between X-chromosomal and autosomal transcripts (Fig. 1h). The deviation in absolute values between the two experiments may result from chromosomal differences or from compensatory mechanisms after KO generation, such as induced expression of alternatively spliced Mettl3 isoforms 30 . Collectively, the intersection between our experiments and published data conclusively shows that m 6 A modifications destabilize autosomal transcripts, while X-chromosomal transcripts are largely excluded from such regulation.

X-chromosomal transcripts are less affected by m 6 A depletion
To test whether the chromosomal differences in RNA stability contribute to balancing expression levels between the X chromosome and autosomes, we performed RNA sequencing (RNA-seq) experiments to measure the transcript expression levels after m 6 Table 2). The degree of upregulation correlated with the number of m 6 A sites, such that the most heavily methylated transcripts showed the strongest upregulation (Extended Data Fig. 3c). Strikingly, we observed a marked difference in the response to m 6 A depletion between X-chromosomal and autosomal transcripts. On autosomes, we found more upregulated genes relative to the X chromosome, whereas the X-chromosomal transcripts showed by far the lowest median fold change of all chromosomes (Fig. 2a). Between autosomes, observed changes were very similar, suggesting that transcripts on all autosomes were equally affected by acute m 6 A depletion.
To directly assess the balance between X-chromosomal and autosomal transcript levels, we determined the X-chromosomal-to-autosomal (X:A) expression ratio 5,35 . In DMSO-treated cells, the median X:A ratio was approximately one when excluding silent genes or those with low expression, illustrating that X-to-autosome dosage compensation is functional in male mESCs (Extended Data Fig. 3d,e). Importantly, the X:A ratio significantly decreased in the m 6 A-depleted conditions, indicating that m 6 A depletion leads to an imbalance in X-to-autosome dosage compensation (Fig. 2b). We note that the X:A ratio does not reach 0.5, suggesting that m 6 A acts in addition to other regulatory mechanisms in X-to-autosome dosage compensation.
The differential effects of m 6 A depletion on X-chromosomal and autosomal genes were further supported in a time-course RNA-seq experiment with 3-12 h STM2457 treatment (Extended Data Fig. 1b,c  and Supplementary Table 2). Of note, autosomal transcripts showed a distinct response from X-chromosomal transcripts already after 6 h of m 6 A depletion, which persisted throughout 9 h and 12 h of treatment transcripts from other chromosomes is necessary for proper complex formation 11 . Some dosage-sensitive transcripts may also be protected from the degeneration process occurring on the Y chromosome and thus be retained in two copies 12 . However, Ohno's hypothesis is still under investigation, and both transcriptional and post-transcriptional mechanisms could play a role or act together 10,[13][14][15][16][17] . If the latter is the case, this creates the conundrum of how the chromosomal origin of a transcript is 'remembered' in downstream steps of gene expression that occur at the RNA level.
RNA modifications are increasingly being recognized for their role in post-transcriptional gene regulation. By their 'epitranscriptomic' nature, they have the potential to bridge DNA context to mRNA fate. N 6 -methyladenosine (m 6 A) is the most abundant internal mRNA modification, with estimates ranging from 1 to 13 modifications present per transcript [18][19][20][21] . Conserved adenine methyltransferases, such as METTL3, co-transcriptionally modify nascent mRNAs in the nucleus. The majority of m 6 A sites occur within a DRACH motif (that is, [G/A/U] [G>A]m 6 AC[U>A>C]), with GGACH as the predominantly methylated sequence [22][23][24] . m 6 A-methylated transcripts recruit different reader proteins. Most prominently, YTHDF proteins (YTHDF1, YTHDF2, and YTHDF3) reduce the stability of m 6 A-modified transcripts in the cytoplasm by promoting their degradation [25][26][27] . Hence, m 6 A modifications affect mRNA fate in the cytoplasm upon their deposition in the nucleus.
In this Article, we show that m 6 A RNA modifications play a key role in X-to-autosome dosage compensation. We find that the m 6 A content is reduced in transcripts from the X chromosome, leading to more stable transcripts and longer half-lives. This is crucial to equalize the imbalance in gene dosage between autosomes and the X chromosome.

Autosomal transcripts are stabilized by m 6 A depletion
One of the most prominent functions of m 6 A is regulating mRNA levels by promoting RNA decay 25 . It has been proposed that X-chromosomal transcripts are more stable than autosomal transcripts 6,8 , so we hypothesized that m 6 A-mediated RNA stability may be involved in X-to-autosome dosage compensation. To investigate this, we first confirmed the chromosomal differences in RNA stability in published mRNA half-lives from mouse embryonic stem cells (mESCs), measured by thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq) 28 . Indeed, transcripts originating from the X chromosome had significantly longer half-lives than autosomal transcripts (Extended Data Fig. 1a).
To investigate the direct impact of m 6 A depletion, we employed the small-molecule inhibitor STM2457, which specifically targets the major mRNA m 6 A methyltransferase Mettl3 (ref. 29). We corroborated in a time-course experiment that the m 6 A levels already showed a strong reduction after 3 h and reached a low point after 6 h of inhibitor treatment (Extended Data Fig. 1b). Compared with a Mettl3 knockout (KO), this acute m 6 A depletion enabled us to investigate the immediate response to m 6 A depletion while minimizing secondary effects 30 . Expression analysis of marker genes 31 and quantitative polymerase chain reaction (qPCR) validations showed that the pluripotent state of the mESC remained unimpaired throughout the treatment (Extended Data Fig. 1c,d).
To determine the effect of m 6 A depletion on mRNA half-lives, we performed SLAM-seq in m 6 A-depleted and control conditions (6 h STM2457-treated or DMSO-treated as control, Fig. 1a and Extended Data Fig. 2a,b). We achieved a stable 4-thiouridine (s 4 U) incorporation rate of 1.36% after 24 h of labeling, which gradually decreased upon washout (Extended Data Fig. 2c). By fitting the SLAM-seq data using an exponential decay model and filtering for expression and a sufficient goodness of fit (see Methods) 28 , we obtained half-life estimates for 7,310 transcripts (Supplementary Table 1, Fig. 1b,c, and Extended Data Fig. 2d,e). The estimated half-lives in the control condition correlated well with previously published mRNA half-lives 28 (Extended Data Fig. 2f).
Consistent with a role for m 6 A in destabilizing transcripts 25,32 , the median half-life of mRNAs significantly increased upon acute Article https://doi.org/10.1038/s41594-023-00997-7 ( Fig. 2c and Extended Data Fig. 4a, b). This was validated by qPCR for five autosomal and five X-chromosomal transcripts after 9 h of m 6 A depletion (Extended Data Fig. 4c). The clear separation of X-chromosomal and autosomal transcripts at around 6 h was in line with the observed mRNA stability changes after the same treatment duration (Fig. 1g) and supported a direct effect of m 6 A in transcript destabilization.
Next, we investigated whether m 6 A similarly regulates X-chromosomal transcripts in humans. To this end, we performed RNA-seq of primary human fibroblasts (male) after 9 h of m 6 A depletion ( Fig. 2d and Extended Data Fig. 5a). As in mESCs, we observed a clear separation of the X chromosome and autosomes, such that X-chromosomal transcripts displayed significantly lower expression changes in response to m 6 A depletion (Fig. 2d). This was further corroborated by RNA-seq data colleted following m 6 A depletion in human HEK293T (female), C643 (male), and RPE1 (female) cells, which consistently demonstrated the same effect across all cell types (Extended Data Fig. 5a,b). Similar to our findings in mESCs, X:A expression ratios were close to one for human fibroblasts and RPE1 cells, whereas higher median X:A ratios were obtained for HEK293T and C643 cells, possibly owing to aneuploidies (Fig. 2e). Importantly, the X:A ratio was significantly lowered in all cases in response to m 6 A depletion, indicating that m 6 A depletion results in an imbalance of X-chromosomal to autosomal transcript expression. We conclude that the same mechanism we observe in mice is also active in humans, whereby autosomal and X-chromosomal transcripts are differentially affected by m 6 A depletion. Our data thus support a conserved role for m 6 A in X-to-autosome dosage compensation in mammals.

m 6 A is reduced on transcripts from the X chromosome
Our RNA-seq data showed that autosomal transcripts are more susceptible to m 6 A depletion than are X-chromosomal transcripts. To test whether these differences are driven by different methylation levels, we analyzed the distribution of m 6 A sites across chromosomes in male mESCs using miCLIP2 data 33 . Because m 6 A detection in miCLIP2 experiments partially depends on the underlying RNA abundance 33 , we quantified m 6 A sites within expression bins (Extended Data Fig. 6a). Remarkably, 74.5% of all transcripts with intermediate expression (bins 4-8) harbored at least one m 6 A site, with an average of one to five m 6 A sites per transcript. By contrast, on transcripts with low expression (bins 1-3), we found no m 6 A sites in most cases, most likely owing to detection limits ( Fig. 3a and Extended Data Fig. 6b).
Intriguingly, separation by chromosomes revealed a significantly lower level of m 6 A modifications on X-chromosomal transcripts, which were reduced by almost half compared with the genomic average (56% remaining, Fig. 3b). By contrast, transcripts on all autosomes showed similar numbers of m 6 A sites ( Fig. 3c and Extended Data Fig.  6c). For further quantification, we calculated the average fold change in m 6 A numbers on a given chromosome relative to all chromosomes. Importantly, this confirmed that all autosomes showed a similar level of m 6 A modifications and that X-chromosomal transcripts were unique in carrying fewer m 6 A sites ( Fig. 3d and Extended Data Fig. 6d). As a control, we ensured that this observation was independent of differences in the numbers or lengths of transcripts between chromosomes (see Methods and Extended Data Fig. 6e,f). We observed the same reduction in m 6 A levels on X-chromosomal transcripts in recently published mESC m 6 A-seq2 data 36 (Fig. 3e). This phenomenon was not restricted to mESCs: we found a similar reduction in m 6 A levels on X-chromosomal transcripts in high-confidence m 6 A sites from mouse heart (female) samples and mouse macrophages (male) 33 (Fig. 3f). The distinct m 6 A patterns also extend to human cells, because human HEK293T (female) and C643 (male) cells displayed a consistent reduction of X-chromosomal m 6 A sites (Fig. 3g). The strength of the reduction was, to some degree, tissue-and species-dependent. Collectively, our results show that X-chromosomal transcripts have fewer m 6 A modifications than do autosomal transcripts across different tissues and cell lines from mice and humans, further supporting that m 6 A-mediated dosage compensation is a conserved mechanism.

Reduced m 6 A levels are due to GGACH motif depletion
In mammals, m 6 A occurs mainly in a DRACH consensus sequence, with GGACH being the most frequently methylated DRACH motif 23,24 . To test whether sequence composition has a role in the observed differences in m 6 A levels between chromosomes, we counted the occurrence of GGACH motifs for transcripts on all chromosomes. Remarkably, transcripts on the X chromosome harbored significantly fewer GGACH motifs in their coding sequence (CDS) and 3′ untranslated region (3′ UTR) than did autosomal transcripts ( Fig. 4a and Extended Data Fig. 7a). Within 3′ UTRs, autosomal transcripts contained, on average, 3.1 GGACH per kilobase of sequence; this value dropped to 1.7 in X-chromosomal transcripts. This suggests that the lower levels of m 6 A modifications in X-chromosomal transcripts are intrinsically encoded by fewer GGACH motifs. To further investigate this, we compared strongly and weakly methylated DRACH motifs (Extended Data Fig. 7b). Although the strong DRACH motifs were depleted on X-chromosomal transcripts, the weak DRACH motifs were equally abundant on X-chromosomal and autosomal transcripts (Extended Data Fig. 7c,d). This supports the idea that the lower m 6 A levels on X-chromosomal transcripts are a consequence of a reduced number of strongly methylated DRACH motifs. In addition, we observed that, among the GGACH motifs that are present, the fraction that was methylated in mESCs was slightly lower in X-chromosomal than in autosomal transcripts ( data from mESCs 37 . We observed slightly fewer Mettl3 peaks on the X chromosome, indicating that the co-transcriptional recruitment of Mettl3 to X-chromosomal genes may be reduced (Extended Data Fig. 8a). Previous reports have suggested that X-to-autosome dosage compensation may be more relevant for certain gene sets than others. For instance, housekeeping genes have been suggested to rely more heavily on upregulation than do tissue-specific genes or recently and independently evolved genes on the X chromosome 5,38,39 . However, we did not observe significant differences in GGACH motifs for different gene sets suggested in the literature (Extended Data Fig. 8b). Furthermore, X-chromosomal genes that have been reported to escape X chromosome inactivation (escaper genes) did not show a significant difference in the number of GGACH motifs compared to other X-chromosomal genes, suggesting that they are equally depleted in m 6 A sites as other X-chromosomal genes 40 . Nonetheless, judging from general variability in GGACH motif content, not all X-chromosomal genes appeared to be equally dependent on dosage compensation. To further investigate this, we performed Gene Ontology (GO) analyses on the 200 genes with the smallest number of GGACH motifs, revealing functionalities related to nucleosomes/DNA packaging and ribosomes being the most significantly enriched (Extended Data Fig. 8c). Indeed, X-chromosomal genes encoding ribosomal proteins and histones harbored almost no GGACH motifs and thereby clearly differed from their autosomal counterparts (Extended Data Fig. 8d), suggesting that proteostasis of these important cellular complexes may be controlled by differential X-to-autosomal m 6 A methylation. This fits with previous reports showing that the majority of the Minute phenotypes in Drosophila are caused by haploinsufficiency of ribosomal proteins 41 and that ribosomal protein stoichiometry is tightly controlled in the mouse brain 42 .
Next, we wanted to investigate whether GGACH motifs evolved in a sex-chromosome-specific manner. Sex chromosomes are derived from ancestral autosomes. If the selective upregulation of X-chromosomal genes occurs by the reduction of GGACH motifs, outgroup species in which these genes are located on autosomes should not display such a motif disparity. For mammals, the chicken (Gallus gallus) is an informative outgroup to investigate the evolution of sex-chromosome expression patterns, because the ancestral eutherian X chromosome corresponds to chromosomes 1 and 4 in chicken 43 . Consequently, the orthologs of X-chromosomal mouse genes are located on autosomes in chicken and are not subjected to sex-chromosome-linked evolutionary changes 17 (Fig. 4c,d). It will be interesting to generate m 6 A maps in different mammalian species to disentangle the contribution of m 6 A to the evolution of mammalian dosage compensation. This will also enable the investigation of X-chromosomal regions of different evolutionary origins, such as X-added region (XAR), X-conserved region (XCR), and pseudoautosomal region (PAR).
To investigate whether the reduction of GGACH motifs on the X chromosome in mouse is a sex-chromosome-linked feature, we compared the GGACH motif content in chicken genes that are orthologous to mouse X-chromosomal or autosomal genes. Of note, given that almost all of these genes reside on autosomes in chicken (Fig. 4d), we observed no difference in GGACH content regardless of whether the orthologs in mouse were on autosomes or the X chromosome (Fig. 4e). This parity of GGACH motifs in the chicken orthologs indicated that Article https://doi.org/10.1038/s41594-023-00997-7 the reduced number of GGACH motifs on the mouse X chromosome has evolved specifically as a characteristic of a sex chromosome, in line with the resulting need for X-to-autosome dosage compensation.

m 6 A contributes to dosage compensation in both sexes
The finding that GGACH motifs are less abundant on the X chromosome suggests that reduced m 6 A levels are an intrinsic feature of X-chromosomal transcripts, which occurs in both sexes independently of X chromosome dosage. To analyze this, we performed RNA-seq experiments in female mESCs in which both X chromosomes are still active and hence dosage compensation is not required. Female mESCs were cultured under standard conditions to ensure that their naive state of pluripotency was maintained 32 . Since female mESCs in cell culture are prone to lose one X chromosome, clonal populations of XX and X0 cells were derived from a given culture plate as matched controls 44-46 . We performed m 6 A depletion (9 h) on 20 colonies and then determined their chromosome content by DNA-seq to choose three XX and three X0 colonies for RNA-seq analyses (Extended Data Fig. 9a-c). Expression analysis revealed that, in female mESCs with two X chromosomes, the median X:A ratio rose above one, indicating that, with two active X chromosomes, genes reach higher levels of expression than autosomes (Fig. 4g). This supports that one X chromosome is sufficient to obtain a median X:A ratio of 1, whereas two active X chromosomes lead to an excess of X-chromosomal gene expression. Again, the X:A ratio significantly decreased upon m 6 A depletion, further supporting the idea that the depletion of m 6 A impairs X-to-autosome dosage compensation. We found that, in both XX and X0 colonies, X-chromosomal transcripts significantly differed in their response to m 6 A depletion compared with autosomal transcripts (Fig. 4f and Extended Data Fig. 9d). Subsequently, we identified m 6 A sites in female bulk mESCs using miCLIP2 (ref. 33). In line with our RNA-seq results, and similar to male mESCs, female mESCs had a less m 6 A content on X-chromosomal transcripts ( Fig. 4h and Supplementary Table 3). This indicated that, although both X chromosomes are still active in female mESCs, the cells may be able to tolerate higher levels of X-chromosomal transcripts during very early development. The reduced X-chromosomal m 6 A content in female mESCs further supported our finding that the reduced m 6 A levels are intrinsically encoded in the GGACH motif content. Altogether, our results indicate that m 6 A-dependent destabilization of autosomal transcripts also occurs in female mESCs prior to X chromosome inactivation. Wilcoxon rank-sum test). b, Methylation levels of GGACH motifs are slightly reduced on X-chromosomal transcripts. Fraction of m 6 A sites per chromosome with methylation in miCLIP2 data from male mESCs. Boxes represent quartiles, center lines denote medians, and whiskers extend to most extreme values within 1.5 × interquartile range. c, Location of mouse X-chromosomal orthologs in human, opossum (Monodelphis domestica), and chicken. d, Percentage of orthologs of X-chromosomal or autosomal genes in mouse that are located on autosomes or sex chromosomes in human, opossum, and chicken. e, GGACH motifs in transcripts (exons) from mouse genes and corresponding orthologs in chicken, opossum, and human (n = 6,520). Orthologs to mouse X-chromosomal and autosomal genes are indicated in orange and gray, respectively (two-tailed Wilcoxon rank-sum test, *P value < 0.05, **P value < 0.01, ***P value < 0.001, P value = 1.2 × 10 -18 (mouse), 2.7 × 10 -6 (human), 0.001227 (opossum), 0.8602 (chicken)). Boxes are as in a. f, Effects of m 6 A depletion on expression of autosomal and X-chromosomal transcripts in XX and X0 clones of female mESCs (P value = 1.64 × 10 -12 and 3.5 × 10 -11 , respectively, two-tailed Wilcoxon rank-sum test, Extended Data Fig. 9a-c). Median fold changes (log 2 ) of transcripts from autosomes (n = 19, gray) and the X chromosome (n = 1, orange), estimated by RNA-seq after m 6 A depletion (STM2457, 9 h). Boxes are as in a. g, X:A expression ratios are significantly reduced upon m 6 A depletion (P value = 4.12 × 10 -15 (mESC), P value = 2.06 × 10 -11 (female mESC XX), P value = 1.08 × 10 -10 (female mESC X0). P values are as in Figure 2b, multiple testing correction). h, Median fold change (log 2 ) of m 6 A sites per transcript on each chromosome relative to all chromosomes (P = 0.0018, autosomal (gray) versus X-chromosomal (orange) transcripts, twotailed Wald test in generalized linear mixed model for negative binomial data).

Discussion
X-chromosomal genes are expressed from only one active chromosome copy in mice and humans. To balance the genetic input between dual-copy autosomal and single-copy X-chromosomal transcripts, Susumo Ohno hypothesized more than 50 years ago that compensating mechanisms are required for balancing gene expression 3 . Here, we uncover that differential m 6 A methylation adds a layer of complexity to X-to-autosomal dosage compensation in eutherian mammals. This causes a global destabilization of m 6 A-containing autosomal transcripts, while X-chromosomal transcripts bypass this regulatory mechanism (Fig. 5). Importantly, we show that the inhibition of m 6 A methylation predominantly stabilizes autosomal transcripts and thereby affects the X-to-autosome balance of gene expression.
Several sex-chromosome-compensating mechanisms identified so far, including X inactivation in mammals, XX dampening in Caenorhabditis elegans, and X-chromosomal upregulation in Drosophila melanogaster, act on the chromatin environment of the sex chromosomes and have been shown to influence RNA polymerase II occupancy and transcription of X-chromosomal genes 7,16,47-52 . In addition, RNA-regulatory mechanisms, including higher RNA stability and translational efficiency of X-chromosomal transcripts, as well as an enrichment of NMD targets and microRNA-targeting sites among autosomal transcripts, have been described as X-to-autosome dosage-compensation pathways 4,6,[8][9][10]53,54 .
In contrast to the previously described regulatory mechanisms, m 6 A-mediated dosage compensation acts globally at the epitranscriptomic level and adds an additional layer of regulation to X-to-autosome dosage compensation. Importantly, by inhibiting m 6 A methylation, we can interfere experimentally with this process, thereby partly disrupting X-to-autosomal dosage compensation. We propose that m 6 A-mediated dosage compensation is co-transcriptionally initiated in the nucleus, where m 6 A deposition is catalyzed 22 , and then executed in the cytoplasm, where m 6 A-modified transcripts are presumably degraded [25][26][27] . Several reasons as to why mammals evolved an epitranscriptomic mechanism for dosage compensation are conceivable. For instance, such a mechanism might be most compatible with the epigenetically installed X-chromosome inactivation in females. By contrast, installing two epigenetic pathways that antagonistically affect the two X chromosomes at the same time might be more difficult to evolve. Interestingly, X chromosome inactivation has also been shown to depend on m 6 A methylation of the non-coding RNA XIST 55 , suggesting that dosage compensation and X chromosome inactivation might be coordinated. Furthermore, RNA-based gene regulation is often used to fine-tune gene expression 56 . This meets the needs of dosage compensation, which requires a maximum of a twofold expression regulation. Hence, m 6 A regulation might be ideally suited to establish and maintain small changes. Finally, RNA-based mechanisms offer an elegant means to uncouple X-to-autosome dosage compensation from other levels of gene expression regulation. Because RNA-based mechanisms globally affect all X-chromosomal and autosomal transcripts that are expressed at a given moment, it facilitates genetic equilibrium between chromosomes without interfering with transcriptional regulation per se; for instance, cell-type-specific regulation would remain unaffected.
Our data suggest that differential m 6 A methylation evolved through a loss and/or gain of m 6 A consensus motifs (GGACH) on X-chromosomal and autosomal transcripts during mammalian sex-chromosome evolution, respectively. This means that m 6 A dosage compensation is hardcoded in the individual transcripts and consistently acts on both male and female cells. On top of this, there could be mechanisms that globally modulate m 6 A methylation on X-chromosomal or autosomal transcripts, such as Mettl3 recruitment through the chromatin mark trimethylated histone H3 K36 (ref. 57) or a local sequestration of Mettl3 through LINE-1 transposons that are heavily m 6 A-methylated and enriched on the X chromosome 58,59 . Moreover, the m 6 A-mediated effects may be linked to the previously suggested role of NMD in X-to-autosome dosage compensation 9 , since the NMD key factor UPF1 has been found to be associated with YTHDF2 (ref. 60).
An exciting question for future research is how the hardcoding of m 6 A-mediated dosage compensation evolved. Here, the short and redundant m 6 A consensus sequence could enable easy generation or removal of consensus sequences. However, why would evolution globally select for m 6 A sites to differentially affect transcripts from different chromosomes? We think that using predominantly hardcoded m 6 A sites allows global modulation of dosage compensation, for instance through the overall methylation levels or the expression of the m 6 A reader proteins that control RNA decay under certain conditions. Although m 6 A levels appear to be relatively stable between tissues in mice and humans 61 , it will be interesting to decipher how dosage compensation is globally modulated in different tissues, developmental stages, and pathological conditions.

Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41594-023-00997-7.
Human primary dermal fibroblasts were provided by S. Schweiger (University Medicine Mainz, Germany). Cells were grown in IMDM medium (Thermo Fisher Scientific, 12440053) supplemented with 15% FBS and 1% P/S.

Primary human dermal fibroblasts derivation
Primary human dermal fibroblasts were isolated from skin punch biopsies obtained at the Children's Hospital of the University Medical Center in Mainz, Germany, as previously described with small adjustments 62 . Briefly, 4-mm skin biopsies were processed in small pieces and transferred into a 6-well plate coated with 0.1% gelatine. DMEM (Thermo Fisher Scientific, 21969035) supplemented with 20% FBS (Pan Biotech, P40-47500) and 1% P/S (Thermo Fisher Scientific, 10378016) was used for culturing the skin biopsies, and medium was exchanged every other day. After 3-4 weeks, when the 6-well plate was full of dermal fibroblasts that had migrated out of the skin biopsies, cells were transferred to T75 flasks and cultured in standard conditions. Human dermal fibroblasts were further expanded or frozen in liquid nitrogen for long-term storage. Ethical approval by the local ethical committee was obtained (no. 4485), and consent for research use was obtained in an anonymized way.

Mettl3 inhibitor treatment
For acute m 6 A depletion in mESCs, the Mettl3 inhibitor STM2457 (STORM Therapeutics) was used. Cells were treated with medium supplemented with 20 µM STM2457 in DMSO 0.2% (vol/vol) or with DMSO 0.2% (vol/vol) alone as control. m 6 A depletion was monitored by liquid chromatography with tandem mass spectrometry (LC-MS/ MS). After 3-24 h of treatment, cells were washed twice with ice-cold 1× PBS and collected on ice for further analysis

RNA isolation and poly(A) selection
Cells were washed twice with ice-cold 1× PBS and collected on ice. For total RNA isolation, the RNeasy Plus Mini Kit (Qiagen, 74136) was used, following the manufacturer's instructions. For poly(A) selection, Oligo d(T)25 Magnetic Beads (Thermo Fisher Scientific, 61002) were used, following the manufacturer's instructions.

qPCR
For quantification of mRNA levels, 500 ng total RNA was reverse transcribed into complementary DNA (cDNA) using the RevertAid Reverse Transcriptase (Thermo Fisher Scientific, 10161310) using oligo(dT)18 primer (Thermo Fisher Scientific, SO131), following the manufacturer's instructions. In accordance with the manufacturer's instructions, qPCR reactions were performed in technical triplicates using the Luminaris HiGreen qPCR Master Mix, low ROX (Thermo Fisher Scientific, K0971), with forward and reverse primer (0.3 µM each) and 2 µl of 1:10 diluted cDNA as template. All qPCR reactions were run on a ViiA 7 Real-Time PCR System (Applied Biosystems). All qPCR primers are listed in Supplementary Table 4.

SLAM-seq
Cell viability for optimization. To determine the 10% maximal inhibitory concentration in a determined time window (IC 10,ti ), the Cell Viability Titration Module from LeXogen (059.24) was used, following the manufacturer's recommended protocol. In brief, 5,000 cells were plated in a 96-well plate 1 d prior to the experiment. Cells were incubated for 24 h in media supplemented with varying s 4 U concentrations. For optimal incorporation, the s 4 U-supplemented media were exchanged every 3 h. Cell viability was assessed using the CellTiter-Glo Luminescent Cell Viability Assay Kit from Promega (G7570), following the manufacturer's recommended protocol. The luminescence was measured using Tecan Infinite M200 Pro plate reader. The cell doubling time of male mESCs in the presence of 100 µM s 4 U was 13.3 h, as determined by cell counting.
SLAM-seq experiment. mRNA half-lives were determined by SLAM-seq using the Catabolic Kinetics LeXogen Kit (062.24). In brief, mESCs were seeded 1 d before the experiment in a 24-well plate to reach full confluency, according to the doubling time, at the time of sample collection. The metabolic labeling was performed by addition of 100 µM s 4 U to the mESC medium for 24 h. The medium was exchanged every 3 h. After the metabolic labeling, cells were washed twice with 1× PBS, and fresh medium was supplemented with a 100× excess of uridine. At time points increasing at a 1.5× rate, medium was removed and cells were directly lysed in TRIzol (Thermo Fisher Scientific,15596026) reagent in reducing conditions. Total RNA was resuspended in the elution buffer in the Lexogen catabolic kit. The iodoacetamide treatment was performed using 5 µg of RNA. The library preparation for sequencing was performed using the QuantSeq 3′ mRNA-Seq Library Prep Kit for Illumina (FWD) from Lexogen, following the recommended protocol.
For stable m 6 A depletion, STM2457 or DMSO was supplemented 6 h prior to the uridine chase. The media for the uridine chase were supplemented with STM2457 and DMSO for continuous m 6 A depletion.
SLAM-seq library preparation. Library preparation for next-generation sequencing was performed with QuantSeq 3′ mRNA-Seq Library Prep Kit FWD (Lexogen, 015), following the manufacturer's standard protocol (015UG009V0252). Prepared libraries were profiled on a 2100 Bioanalyzer (Agilent Technologies) and quantified using the Qubit dsDNA HS Assay Kit, in a Qubit 2.0 Fluorometer (Life Technologies). All samples were pooled together in an equimolar ratio and sequenced on an Illumina NextSeq 500 sequencing device using three High Output flow cells as 84-nt single-end reads.
Raw data were quality checked using FastQC (v0.11.8) (https:// www.bioinformatics.babraham.ac.uk/projects/fastqc/). Sequencing data were processed using SLAM-DUNK (v0.4.3) 64 with the following parameters: mapping was performed allowing multiple mapping to up to 100 genomic positions for a given read (-n 100). Reads were filtered using SLAM-DUNK -filter with default parameters. For annotation of single-nucleotide polymorphisms (SNPs), all unlabeled samples were merged and SNPs were called using SLAM-DUNK snp with default parameters and -f 0.2. Transition rates were calculated using SLAM-DUNK count with default parameters, providing the SNP annotation of unlabeled samples (-v). If more than one 3′ UTR per gene remained, they were collapsed using SLAM-DUNK collapse 64 . Only genes on canonical chromosomes 1-19 and X were considered. Incorporation rate. s 4 U incorporation rates were calculated by dividing the number of T-to-C conversions on Ts for each 3′ UTR by the overall T coverage.
Half-life calculation. To calculate mRNA half-lives, T-to-C background conversion rates (no s 4 U labeling) were subtracted from T-to-C conversion rates of s 4 U-labeled data. Only 3′ UTRs with reads covering over 100 Ts (T-coverage > 100) were kept (Extended Data Fig. 2d). For each time point, T-to-C conversion rates were normalized to the time point after 24 h of s 4 U labelling (that is, the onset of the uridine chase), which corresponds to the highest amount of s 4 U incorporation in the RNA (24 h s 4 U labelling, T0) and fitted using an exponential decay model for a first-order reaction using the lm.

RNA-seq library preparation and data processing
RNA-seq library preparation. RNA-seq library preparation was performed with Illumina's Stranded mRNA Prep Ligation Kit following the Stranded mRNA Prep Ligation Reference Guide ( June 2020) (document no. 1000000124518 v00). Libraries were profiled on a 2100 Bioanalyzer (Agilent Technologies) and quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Q32851) in a Qubit 2.0 Fluorometer (Life Technologies), following the manufacturer's recommended protocols. Samples were pooled in equimolar ratios and sequenced on an Illumina NextSeq 500 sequencing device with one or two dark cycles upfront as 79-, 80-or 155-nt single-end reads.
Data processing. Basic quality controls were done for all RNA-seq samples using FastQC (v0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Prior to mapping, possible remaining adapter sequences were trimmed using Cutadapt 67 (v1.18). A minimal overlap of 3 nt between read and adapter was required, and only reads with a length of at least 50 nt after trimming (--minimum-length 50) were kept for further analysis. For samples sequenced with only one dark cycle at the start of the reads, an additional 1 nt was trimmed at their 5′ ends (--cut 1).
Differential gene expression analysis. Differential gene expression between conditions was performed using the R/Bioconductor package DESeq2 (v1.34.0) 65 in the R environment (v4.1.2; https://www.R-project. org/). DESeq2 was used with a significance threshold of adjusted P value < 0.01 (which was also used to optimize independent filtering). Because normalization to total transcript abundance can introduce biases, especially when the majority of genes are affected by the treatment, we included spike-ins in our initial RNA-seq dataset. As an alternative normalization strategy to spike-ins, we tested 100 randomly chosen genes without any m 6 A sites but noticeable expression (reads per kilobase per million fragments mapped (RPKM) > 10) for normalization. To validate this normalization approach, the calculated fold changes were compared with spike-in-normalized data. Because the correlation between both normalization strategies was very high, we used the 100 genes for normalization in all further analyses (Extended Data Fig. 3b). For RNA-seq expression change analysis, see Supplementary Methods and Supplementary Table 5.

Quantification of m 6 A sites in transcripts
m 6 A sites from miCLIP2 for male mESCs, mouse heart samples, mouse macrophages, and human HEK293T and C643 cells were taken from ref. 33 (Gene Expression Omnibus (GEO) accession number GSE163500). m 6 A sites were predicted using m 6 Aboost as described in ref. 33. For miCLIP2 mouse heart data, only m 6 A sites that were predicted by m 6 Aboost in both datasets (1 µg and 300 ng) were considered for the analysis.
Comparison of m 6 A sites per transcripts. Numbers of m 6 A sites were counted for each protein-coding transcript. Only transcripts on canonical chromosomes 1-19 and X were considered. To account for expression differences, transcripts were stratified according to their expression levels on the basis of the respective miCLIP2 data. Expression levels were estimated using htseq-count 71 (v0.11.1) and genome annotation of GENCODE 63 release M23 on the truncation reads from miCLIP2 data (noC2T reads) 33 . The derived transcript per million (TPM) values for all replicates (n = 3) were averaged, log 10 -transformed, and used to stratify all transcripts into 12 equal-width bins (step size of log 10 (TPM) = 0.25), collecting all transcripts with log 10 (TPM) < 0.5 or > 3 into the outer bins (Extended Data Fig. 6a). A minimum of TPM > 1 was set. For each expression bin, the mean and 95% confidence interval of the number of m 6 A sites per transcript were calculated (Fig. 3a-c and Extended Data Fig. 6c). To estimate the fold change of m 6 A sites per chromosome compared with all other chromosomes (Fig. 3d,f,g), only transcripts with intermediate expression (bins 3-8) were taken into account (mouse). For HEK293T data, bins 4-9 were used, and for C643 data, bins 5-10 were used. For each bin, the difference of m 6 A levels of a chromosome relative to all chromosomes was calculated. For this, Article https://doi.org/10.1038/s41594-023-00997-7 the mean number of m 6 A sites on transcripts of the chromosome was divided by the mean number of m 6 A sites on transcripts of all chromosomes in the given bin (for example, orange dots (X chromosome) over gray dots (all transcripts) in Figure 3b). This resulted in a fold change of m 6 A sites of each chromosome over all chromosomes for each of the six considered bins (Extended Data Fig. 6d). For comparison with other chromosomes (Fig. 3d,f,g), the mean fold change per chromosome over all expression bins was calculated (Extended Data Fig. 6d, red dot).

Control for transcript-length biases.
To exclude biases from different transcript lengths, we repeated the analysis using only m 6 A sites within a 201-nt window (−50 nt to +150 nt) around the stop codon, in which a large fraction of m 6 A sites accumulate 23 . To obtain stop codon positions, transcript annotations from GENCODE 63 release M23 were filtered for the following parameters: transcript support level ≤ 3, level ≤ 2, and the presence of a Consensus Coding Sequence (CCDS) ID (ccdsid). If more than one transcript per gene remained, the longer isoform was chosen. Repeating the analyses with this subset, as described above, supported our observation that X-chromosomal transcripts harbor fewer m 6 A sites without being influenced by differences in transcript lengths (Extended Data Fig. 6e).

Subsampling of transcripts in expression bins.
To account for potential biases from different numbers of transcripts in the expression bins for each chromosome, we randomly picked 30 genes for each expression bin (using bins 3-5, 90 genes in total) and calculated the fold change of m 6 A content on transcripts for each chromosome compared with all other chromosomes, as described above. The procedure was repeated 100 times. The distribution of resulting fold change values supports that X-chromosomal transcripts harbor fewer m 6 A sites, regardless of the number of transcripts considered (Extended Data Fig. 6f). Table 6.

Analysis of published m 6 A-seq2 data
Published m 6 A-seq2 data for wild-type and Mettl3 KO mESCs were retrieved from ref. 36. We used the 'gene index,' that is, the ratio of m 6 A IP values over IP for whole genes, as a measure of the transcripts methylation level, as described in ref. 36 (Fig. 3e). Chromosome locations of the genes (n = 6,278) were assigned using the provided gene name in the R/Bioconductor package biomaRt in the R environment 72,73 .

DRACH motif analyses
GGACH motifs in mouse transcripts. Mouse transcript annotations from GENCODE 63 release M23 were filtered for the following parameters: transcript support level ≤ 3, level ≤ 2, and the presence of a CCDS ID. If more than one transcript annotation remained for a gene, the longest transcript was chosen. Different transcript regions (3′ UTR, 5′ UTR, CDS) were grouped per gene, and GGACH motifs were counted per base pair in different transcript regions, for example, the sum of GGACH motifs in CDS fragments of a given gene was divided by sum of CDS fragment lengths.
GGACH motifs in chicken, opossum, and human orthologs. Orthologs of mouse genes in chicken (Gallus gallus), human (Homo sapiens), and opossum (Monodelphis domestica) were retrieved from the orthologous matrix (OMA) browser 74 (accessed on 21 March 2022, for opossum 28 July 22). Only one-to-one orthologs were kept. Genes were filtered to have orthologs in all three species (n = 6,520). Then, numbers of GGACH motifs per base pair of all protein-coding exons were quantified on the basis of GENCODE annotation (release 31) 63 for human and ENSEMBL annotation (release 107, genome assembly GRCg6a) 75 for chicken and opossum annotation (ASM229v1). GGACH motifs per base pair were quantified and visualized as described above.

Estimation of methylation levels. See Supplementary Methods.
GGACH in gene sets from literature. Independently evolved gene sets and genes with or without an ortholog on the human X chromosome were taken from ref. 39. Escaper genes were taken from ref. 16. Testis-specific genes were taken from ref. 5. Genes from the X-addedXAR and XCR were annotated by identifying X-chromosomal genes in mouse with the location of chicken orthologs on chromosome 1 (XAR) and chromosome 4 (XCR).

ChIP-seq analysis
ChIP-seq peaks were obtained from ref. 37. The numbers of peaks per chromosome were divided by chromosome lengths. To calculate the peak ratio per chromosome compared with all other chromosomes, the normalized peak number per chromosome was divided by the median peak number of all chromosomes.

GO analysis
GO term enrichment was performed using the enrichGO function of clusterProfiler 76 (v.4.2.2). Cellular components (ont = 'CC') were enriched using a P value cut-off of 0.01 and a q value cut-off of 0.05, and P values were corrected using Benjamini-Hochberg correction (pAdjustMethod = 'BH').

DNA-seq to determine copy number variation
See Supplementary Methods.

Statistics and reproducibility
All statistical analyses were performed using R. All boxplots in this study are defined as follows: boxes represent quartiles, center lines denote medians, and whiskers extend to most extreme values within 1.5 × interquartile range. All statistical tests performed in this study were two-tailed. All indicated replicate numbers refer to independent biological replicates. No statistical method was used to predetermine sample size. The experiments were not randomized. No data were excluded from the analysis, unless stated otherwise. The investigators were not blinded during allocation in experiments or to outcome assessment.

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

Data availability
All high-throughput sequencing datasets generated in this study were submitted to the Gene Expression Omnibus (GEO) under the SuperSeries accession GSE203653. RNA-seq data for human primary fibroblasts are available via the EGA European Genome-Phenome Archive under the accession number EGAS00001007112. Source data are provided with this paper. control conditions. Quantification of m 6 A as percent of A in poly(A) + RNA. n = 2 independent biological replicates. c. Expression levels of marker genes confirm the pluripotent state of the male mESC throughout the time course experiment. Gene expression levels (RNA-seq) are shown as reads per kilobase of transcript per million mapped reads (RPKM, mean over all replicates, log 10 ) in m 6 A-depleted (STM2457, 3-24 h) and control conditions. d. Quantitative real-time PCR (qPCR) to quantify expression changes of stem cell marker genes in m 6 A-depleted (STM2457, 9 h) and control conditions. Normalised C T values (∆C T , normalised to Gapdh expression) are compared between conditions. Fold changes are displayed as mean s.d.m., two-sided Student's t-test on log 2 -transformed data, n = 4 independent biological samples, ns, not significant. P value = 0. 8  Article https://doi.org/10.1038/s41594-023-00997-7 Extended Data Fig. 6 | X-chromosomal transcripts harbour less m 6 A sites than autosomal transcripts in male mESC. a. Transcripts were stratified into 12 bins (#1-12) according to their expression in male mESC (transcripts per million [TPM, log 10 ], see Methods). x-axis depicts boundaries between bins (in TPM). Bin number (#) and number of transcripts therein are given below and above each bar, respectively. Bins #3-8 that were used for quantifications of m 6 A sites per transcripts are highlighted in black. b. Quantification of m 6 A for each transcript in the different expression bins of autosomal (grey) and X-chromosomal (orange) transcripts. Boxes represent quartiles, centre lines denote medians, and whiskers extend to most extreme values within 1.5× interquartile range. c. Quantification of m 6 A sites per transcript for all mouse chromosomes. Data points indicate mean number of m 6 A sites per transcript and 95% confidence interval (left y-axis) in each expression bin (x-axis, bins as defined in a) for all chromosomes (chromosome name and total number of expressed transcripts given above).
Grey bars indicate the percentage of transcripts in each expression bin (right y-axis) relative to all expressed transcripts on the chromosome. Absolute numbers of transcripts in each bin are given above the bars. Only genes with a mean TPM > 1 over all samples were considered. d. Fold change (log 2 , grey dots) in mean m 6 A sites per transcripts for expression bins #3-8 (n of mean of expression bins = 6) on an individual chromosome over the mean m 6 A sites per transcripts across all chromosomes. Red dots indicate mean fold change of the six bins on the given chromosome. Boxes as in b. e. Same as d. using only m 6 A sites in a fixed window around stop codons (−50 nt to +150 nt) to exclude confounding effects of transcript length differences. Boxes as in b. f. Same as c. after randomly subsampling n = 30 genes from expression bins #3-5 to exclude potential biases from different numbers of transcripts in the expression bins for each chromosome. Shown is the distribution of mean m 6 A sites per transcript for each chromosome from 100 repeats of subsampling. Boxes as in b.