Abstract

   Analogous to DNA methylation and protein phosphorylation, it is now
   well understood that RNA is also subject to extensive processing and
   modification. N^6-methyladenosine (m^6A) is the most abundant internal
   RNA modification and regulates RNA fate in several ways, including
   stability and translational efficiency. The role of m^6A in both
   experimental and human epilepsy remains unknown. Here, we used
   transcriptome-wide m^6A arrays to obtain a detailed analysis of the
   hippocampal m^6A-ome from both mouse and human epilepsy samples. We
   combined this with human proteomic analyses and show that epileptic
   tissue displays disrupted metabolic and autophagic pathways that may be
   directly linked to m^6A processing. Specifically, our results suggest
   that m^6A levels inversely correlate with protein pathway activation.
   Finally, we show that elevated levels of m^6A decrease seizure
   susceptibility and severity in mice. Together, our findings indicate
   that m^6A represents an additional layer of gene regulation complexity
   in epilepsy and may contribute to the pathomechanisms that drive the
   development and maintenance of hyperexcitable brain networks.

   Keywords: Cell biology, Neuroscience

   Keywords: Epigenetics, Epilepsy, Mouse models
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   [74]graphic file with name jciinsight-10-188612-g071.jpg
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   This study reveals dysregulated m6A patterning in temporal lobe
   epilepsy, and links altered m6A to changes in protein expression,
   neuronal structure and seizure susceptibility across models.

Introduction

   Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome in
   adults. Often caused by direct brain insults such as traumatic brain
   injury (TBI) ([75]1), stroke ([76]2–[77]4), central nervous system
   (CNS) infections, and status epilepticus (SE) ([78]5), it is associated
   with high rates of drug refractoriness and comorbid symptoms such as
   persistent learning and memory impairment, depression, and anxiety
   ([79]6). Injurious brain insults trigger widespread and persistent
   changes in gene expression and gene expression regulation that govern
   cellular and network activity, representing an important mechanism of
   hyperexcitable network formation and maintenance. Indeed, to date,
   researchers have uncovered extensive dysregulation of the epigenome
   ([80]7), the transcriptome ([81]8, [82]9), the miRnome ([83]10), and
   the translatome ([84]11) in epilepsy. Identifying how gene readout and
   regulation are affected in epilepsy is critical, not only to improve
   our understanding of the molecular mechanisms that give rise to the
   disease but also to identify novel druggable targets to develop
   mechanism-based therapies that impact the underlying causes and
   progression of the disease. It is likely that additional, unexplored
   layers of gene regulation are also affected by epilepsy-provoking
   insults and contribute to the pathogenic gene expression changes that
   characterize and drive TLE development.

   RNA is subject to substantial covalent modifications, many of which are
   highly abundant in brain tissues ([85]12–[86]15). These modifications
   increase functional diversity and the information-carrying capacity of
   RNA moieties ([87]16). Methylation of adenosine residues at the N^6
   position (N^6-methyladenosine, m^6A) is the most abundant internal
   modification of messenger RNA (mRNA) ([88]17, [89]18) and has been
   reported to regulate RNA subcellular localization ([90]19), RNA
   stability ([91]20), and translational efficiency ([92]21, [93]22).
   Originally described decades ago as a structural modification on
   ribosomal RNA (rRNA) ([94]23), it has recently gained much attention
   following technological advancements that allowed transcriptome-wide
   profiling of m^6A in cells and tissues. Detection of m^6A on mRNA and
   characterization of enzymes that regulate both the catalytic deposition
   (writers: METTL3, METTL14, WTAP) and removal (erasers: FTO, ALKBH5) of
   m^6A uncovered a dynamic modification influenced by cellular needs and
   stimulus ([95]24). A large group of proteins have now also been
   identified that recognize and process m^6A-tagged RNA (readers:
   YTHDF1–3, ELAVL1, FXRP, and others) ([96]25–[97]27). m^6A is well
   conserved and abundant in brain tissue from both humans and mice
   ([98]17). Although little is known about m^6A in epilepsy, it has been
   extensively linked with brain function, having been shown to regulate
   neuronal development ([99]28), learning and memory ([100]15, [101]29),
   and the stress response ([102]30). Given its importance for normal
   brain function, it is therefore unsurprising that m^6A regulation is
   seemingly altered in many neurological conditions, including Alzheimer
   disease ([103]31, [104]32), Parkinson disease ([105]33, [106]34), and
   TBI ([107]35).

   Here, we performed what we believe to be the first comprehensive
   profiling of m^6A in epilepsy, using both a preclinical mouse model of
   the disease — intra-amygdala kainic acid–induced (IAKA-induced) TLE,
   and hippocampal tissue samples from individuals with drug-refractory
   TLE. We identify pathways that display common m^6A dysregulation in
   both mouse and human, suggesting conserved mechanisms in epilepsy. In
   addition, we explored the functional significance of m^6A dysregulation
   by performing proteomic analyses using the same human tissue and then
   performing functional assays in vitro using METTL3-overexpressing
   neurons. Finally, we established a clear link between m^6A and network
   excitability by pharmacologically elevating m^6A levels and assessing
   seizure resilience in mice. Together, our data suggest that m^6A is
   profoundly altered in TLE and directly changes network excitability,
   potentially via the regulation of metabolic and autophagic pathways.

Results

Dysregulation of m^6A-regulating enzymes in epilepsy.

   We first examined the expression patterns of m^6A-associated enzymes to
   gain broad insight into how epilepsy may alter the regulation of this
   pathway ([108]Figure 1A). Initial quantification of m^6A levels in
   total ([109]Figure 1B) and polyA-RNA ([110]Figure 1C) from the
   hippocampi of acute post-SE (24 hours after KA) and chronic mice (2
   weeks after KA) revealed no detectable changes in gross m^6A abundance
   at either disease stage. Using quantitative PCR (qPCR) and Western
   blotting, we next probed the expression of m^6A writers (METTL3 and
   METTL14) ([111]Supplemental Figure 1A and [112]Figure 1, D and G;
   supplemental material available online with this article;
   [113]https://doi.org/10.1172/jci.insight.188612DS1), readers (YTHDF1
   and YTHDF2) ([114]Supplemental Figure 1B and [115]Figure 1, E and H),
   and erasers (FTO and ALKBH5) ([116]Supplemental Figure 1C and
   [117]Figure 1, F and I) in acute or chronic mice versus time-matched
   controls. The abundance of most m^6A-associated enzymes was relatively
   stable across both stages of the disease in mice. However, a small but
   significant increase in METTL3 protein levels was detected in the early
   acute post-SE time point (24 hours after SE) ([118]Figure 1, D and G).
   We did not see any differences in mRNA levels for any m^6A writer,
   reader, or eraser at any stage of the disease ([119]Supplemental Figure
   1, A–C). A second KA model in rats confirmed the findings in mice
   ([120]Supplemental Figure 1D).

Figure 1. Subtle and transient changes in methyltransferase expression during
experimental epileptogenesis.

   [121]Figure 1
   [122]Open in a new tab

   (A) Schematic depicting the experimental approach. (B) Colorimetric
   quantification of m^6A from total RNA isolated from mouse hippocampus
   at acute (24 hours) and chronic time points after SE (2 weeks) compared
   to time-matched PBS controls (n = 3/group). Two-way ANOVA: f = 0.27, df
   = (3, 8), P = 0.27. (C) Colorimetric quantification of m^6A on
   polyA-tailed RNA isolated from acute and chronic hippocampal tissue
   from mice as well as time-matched PBS controls (n = 3/group). Two-way
   ANOVA: f = 2.49, df = (3, 8), P = 0.134. (D–F) Representative Western
   blot analysis of m^6A writers (D), readers (E) and erasers (F) at
   indicated time points (acute: 8 hours, 24 hours, chronic: 2 weeks) (n =
   3/group). (G–I) Densitometric quantification of Western blot data for
   each protein. Pairwise comparison of each time point to the control.
   METTL3: 8 hours vs. Ctrl t = 5.24, Bonferroni-adj. P = 0.104; 24 hours
   vs. Ctrl t = 10.14, Bonferroni-adj. P = 0.029; 2 weeks vs. Ctrl t =
   –1.51, Bonferroni-adj. P = 0.810. No other significant comparisons. *P
   < 0.05.

   We next performed the same analyses using human TLE samples compared to
   autopsy control tissue. Quantification of m^6A abundance in total RNA
   was unchanged in both nonsclerotic (NS-TLE) and sclerotic (Scl-TLE)
   hippocampal samples from patients with TLE compared to control samples
   ([123]Figure 2, A and B). We then quantified the expression of the same
   m^6A-associated enzymes examined previously in the human TLE samples
   and autopsy controls. Similarly to mice, we detected no significant
   changes in mRNA levels of any of the m^6A-associated enzymes in
   epilepsy ([124]Supplemental Figure 2, A–C). We did, however, detect
   significant changes at the protein level. These included a significant
   increase in METTL3 and METTL14 protein abundance in TLE compared with
   control tissue ([125]Figure 2C) as well as elevated levels of both FTO
   and ALKBH5 ([126]Figure 2E) in TLE samples. There were no overt
   differences in the reader proteins YTHDF1 and YTHDF2 in either group
   ([127]Figure 2D). Overall, in both mouse and human epilepsy, there are
   changes in the abundance of enzymes regulating m^6A (notably METTL3 in
   both mouse and human and FTO and ALKBH5 in human TLE), although no
   gross changes in m^6A were detectable using colorimetric assays.

Figure 2. m^6A regulators are disrupted in resected human TLE tissue compared
with controls.

   [128]Figure 2
   [129]Open in a new tab

   (A) Colorimetric quantification of m^6A from total RNA isolated from
   hippocampal tissue from controls (n = 10), nonsclerotic TLE samples
   (NS-TLE, n = 7) and TLE samples with sclerosis (Scl-TLE, n = 7).
   One-way ANOVA, F = 0.65, P = 0.5321. (B) m^6A quantification from
   control and combined epilepsy samples. Unpaired 2-tailed Student’s t
   test: t = 0.8223; df = 22; P = 0.4197. (C) Western blot analysis of
   m^6A writer proteins METTL3, METTL14, and WTAP in human TLE tissue and
   non-epileptic controls as well as densitometric quantification of blots
   (n = 11–12/group, epilepsy samples combination of Scl and NS). METTL3:
   Shapiro-Wilk: Ctrl: W = 0.873, P = 0.072; Epilepsy: W = 0.899, P =
   0.179; F test P = 0.046; Welch’s t test: P = 0.0001, t = –5.43.
   METTL14: Shapiro-Wilk Ctrl: P = 0.00005; Epilepsy: P = 0.00089;
   Mann-Whitney test: U = 28; P = 0.0188. (D) Western blot and
   densitometric quantification of m^6A readers YTHDF1 and 2 from epilepsy
   samples and matched controls (n = 11–12/group). YTHDF1: Shapiro-Wilk
   test: Ctrl P = 0.817; Epilepsy P = 0.0002. Mann-Whitney test: U = 40; P
   = 1.0. YTHDF2: unpaired 2-tailed t test; t = 0.5412, df = 21, P =
   0.5941. (E) Quantification by Western blot of m^6A erasers FTO and
   ALKBH5 from human TLE hippocampal tissue and autopsy matched controls
   (n = 11/12 per group). FTO: Shapiro-Wilk Ctrl: W = 0.8, P = 0.02;
   Epilepsy: W = 0.8, P = 0.01; Mann-Whitney test: U = 27, P = 0.0156.
   ALKBH5: Shapiro-Wilk: Ctrl: W = 0.743, P = 0.0023; Epilepsy: W = 0.487,
   P < 0.0001; Mann-Whitney test: U = 6, P < 0.0001. *P < 0.05, ****P <
   0.0001.

Temporal and dynamic m^6A patterning occurs in the hippocampus following SE.

   To examine m^6A patterning in the hippocampal transcriptome in the IAKA
   model of TLE, we performed an m^6A array on RNA isolated from mice at
   acute (24 hours) and chronic (2 weeks) time points after IAKA as well
   as time-matched control animals. We first measured gene expression
   irrespective of m^6A status by RNA sequencing (RNA-seq) and identified
   a large number of genes both up- and downregulated at both the acute
   and chronic time points ([130]Figure 3A, green/yellow bars). As
   expected, and in line with previous studies, we observed changes in
   expression patterns of epilepsy-associated genes, including those
   involved in synaptic transmission, inflammation, and cell death
   ([131]Supplemental Figure 3, A–D). Profiling of the m^6A-ome using m^6A
   arrays provides transcriptome-wide coverage with single-base
   resolution. Overall, we identified more extensive m^6A dysregulation in
   the acute mice compared with time-matched controls, although
   dysregulation was also detected in chronic mice ([132]Figure 3A,
   red/blue bars). There was little overlap in differential m^6A patterns
   in acute mice compared to chronic mice, with only 5 hypermethylated
   transcripts retaining their hypermethylation status into the chronic
   phase of the disease ([133]Figure 3B). Hypermethylation was more
   prominent than hypomethylation at both time points ([134]Figure 3C).
   Overall, in acute mice there were 133 hypermethylated sites and only 31
   hypomethylated sites compared with controls, while in chronic mice
   there were 67 hyper- and 11 hypomethylated sites compared with controls
   ([135]Figure 3, D and E). Some transcripts contained multiple
   differentially methylated sites. The majority of differential
   methylation events were transient, with m^6A status mostly returning to
   baseline levels in chronic mice, suggesting potential activity and
   disease-stage-dependent m^6A patterning ([136]Figure 3F).

Figure 3. Epilepsy development and progression is associated with altered
m^6A deposition.

   [137]Figure 3
   [138]Open in a new tab

   (A) Comparison of the extent of differential gene expression compared
   to the number of differentially methylated transcripts at acute (24
   hours) and chronic (2 weeks) time points in the IAKA model of TLE (n =
   3–4/group). (B) Upset plot demonstrating the dynamic reorganization of
   the m^6A epitranscriptome across the various stages of disease
   development in mice. (C) Percentage of differentially methylated
   transcripts at acute and chronic time points in IAKA mice exhibiting
   either hyper- or hypomethylation. (D–F) Volcano plots representations
   of differentially methylated transcripts from 24-hour (acute) (D) and
   chronic (2 weeks) (E) mice compared to controls and (F) comparison of
   differentially methylated transcripts between acute and chronic mouse
   hippocampus. The top 20 most differentially methylated transcripts are
   included on each plot.

   Analysis of m^6A stoichiometry on mRNA transcripts revealed asymmetric
   distribution of m^6A across transcripts, with several-fold higher
   frequency in the coding sequence (CDS) followed by the 3′UTR than in
   the 5′UTR in control animals ([139]Figure 4A). Acute (24 hours) and
   chronic (2 weeks) mice displayed a notable loss of m^6A in the 5′UTR,
   with comparable methylation observed in the 3′UTR and CDS at each
   disease stage ([140]Figure 4, B and C). To validate the array datasets,
   we performed methylated-RNA immunoprecipitation–qPCR (meRIP-qPCR) on
   several hyper- and hypomethylated transcripts. We quantified each
   transcript in the total RNA pool (input sample) and used this to
   normalize the corresponding m^6A-modified transcript levels in the
   immunoprecipitated (IP) fraction, allowing us to assess the accuracy of
   the array. We observed almost complete concordance of the array data
   and meRIP-qPCR, which was performed from brain tissue originating from
   an independent cohort of animals ([141]Figure 4, D and E). Analysis of
   genomic location of differentially methylated transcripts did not
   reveal any obvious methylation hotspots, although chromosomes 2 and 11
   (Chr2 and -11) displayed more extensive differential methylation
   patterns ([142]Figure 4F). Some chromosomes displayed only
   hypermethylation (Chr15), while others showed more extensive
   hypomethylation (Chr10), suggesting region-specific regulation.
   Ingenuity Pathway Analysis (IPA) demonstrated that differentially
   methylated transcripts in acute mice (24 hours) were associated with
   pathways such as inflammation and inflammation-related cell death and
   autophagy ([143]Figure 4G), while those in chronic mice (2 weeks) were
   associated with IL-6 signalling and cellular communication ([144]Figure
   4H).

Figure 4. Disrupted m^6A deposition is associated with epilepsy-associated
pathways in mice.

   [145]Figure 4
   [146]Open in a new tab

   (A–C) Distribution and stoichiometry of m^6A across transcripts in
   control (G), acute (24 hours) (H), and chronic (2 weeks) (I)
   hippocampus across the 3′UTR, coding sequence (CDS), and 5′UTR. (D and
   E) meRIP-qPCR validations of selected transcripts identified from m^6A
   array to confirm differential methylation in acute (D) and chronic mice
   (E) (n = 4–5/group). Unpaired 2-tailed t test or Mann-Whitney U test
   applied, see [147]Supplemental Table 3 for full statistical summary. *P
   < 0.05. (F) Visual mapping of genomic origin of differentially
   methylated transcripts from both acute and chronic mice. (G and H)
   Ingenuity pathway analysis (IPA) of pathway enrichment associated with
   differentially methylated transcripts at 24 hours (G) and 2 weeks after
   KA (H).

Human TLE is defined by altered m^6A patterns regardless of hippocampal
sclerosis and sex.

   Next, we profiled the m^6A patterning in human TLE using surgically
   resected hippocampal tissue from individuals with drug-refractory TLE
   and compared it to age- and sex-matched autopsy control tissue
   ([148]Supplemental Table 1). m^6A array profiling revealed significant
   differential methylation across transcripts in TLE compared with
   autopsy control. We separated epilepsy samples into those with
   hippocampal sclerosis (Scl) and those without (NS) based on the
   neuropathology reports. Both epilepsy groups displayed significantly
   altered m^6A patterns compared with controls ([149]Figure 5, A and B)
   but we found minimal difference in m^6A patterns between patients with
   Scl-TLE and those without (NS-TLE) ([150]Figure 5C). Interestingly,
   m^6A patterns were not influenced by sex ([151]Supplemental Figure 4A).
   For both TLE sample types there were over 800 hyper- and more than 300
   hypomethylated sites compared with controls. Hypermethylation by
   abundance was dominant in both the Scl and NS groups compared with
   control, with roughly 70% of all differential methylation events
   associated with an increase in m^6A abundance ([152]Figure 5D). There
   was significant overlap in the hypermethylated transcripts detected
   between the NS and Scl groups, suggesting differential m^6A is a
   broadly conserved feature of TLE independent of pathology ([153]Figure
   5, E and F).

Figure 5. The m^6A epitranscriptome is extensively disrupted in human TLE.

   [154]Figure 5
   [155]Open in a new tab

   (A–C) Volcano plot depictions of differentially methylated transcripts
   in NS human TLE versus controls (A), Scl-TLE versus controls (B), and
   NS- versus Scl-TLE (C) (n = 10 Ctrls, 7 NS-TLE and 7 Scl-TLE). (D)
   Percentages of differentially methylated transcripts in NS- and Scl-TLE
   that harbor hyper- or hypomethylation patterns compared to controls.
   (E) The overlap of differential methylation between both TLE groups
   compared to control tissue. (F) Upset plot showing overlap of
   differentially methylated transcripts and those commonly differentially
   methylated in NS- and Scl-TLE. (G–I) Distribution and stoichiometry of
   m^6A across transcripts in control (G), NS-TLE (H), and Scl-TLE (I)
   across the 3′UTR, coding sequence (CDS), and 5′UTR. (J) Genomic origin
   of differentially methylated transcripts regardless of detection in Scl
   or NS tissue samples. (K) IPA showing significantly differentially
   enriched biological processes associated with m^6A in NS and Scl tissue
   compared to controls.

   Analysis of m^6A stoichiometry showed a shift in deposition patterning,
   with an increase in the number of genes with more than 20% occupancy
   ([156]Figure 5, G–I). The distribution of m^6A across transcripts
   (i.e., 3′UTR, CDS, or 5′UTR) did not change in epilepsy regardless of
   Scl status ([157]Figure 5, G–I; insets). Genomic location of encoding
   genes did not reveal any chromosomal hotspots of targeted methylation
   or demethylation, although Chr17 (which shares evolutionary roots with
   mouse Chr11) ([158]36) and Chr19 contain regions of genes whose
   transcripts display enriched m^6A activity ([159]Figure 5J). IPA of
   differentially methylated transcripts from the combined epilepsy sample
   group highlighted autophagic pathways, mTOR signalling, and metabolism
   as pathways likely to be affected by disrupted methylation patterns in
   epilepsy ([160]Figure 5K). The current results overall identify
   extensive m^6A disruption in human TLE, which potentially affects
   several epilepsy-associated pathways.

Commonalities in m^6A disturbances in experimental and human TLE.

   We next determined whether m^6A dysregulation seen in experimental TLE
   is similar to that seen in the human condition by comparing our
   experimental TLE and human TLE datasets. Our initial analysis
   investigated whether m^6A sites profiled in mice possess human
   orthologs. Between 35% and 45% of the differentially methylated m^6A
   sites in experimental mice are orthologous in humans ([161]Figure 6A);
   however, of those, only a handful were also differentially methylated
   in human TLE (34 gene transcripts) ([162]Figure 6B). While specific
   sites were not commonly differentially methylated in both mouse and
   human epilepsy, there was some overlap in the functionality of
   transcripts that displayed differential methylation patterns such as
   inflammatory signalling and cell death pathways ([163]Figure 6, C and
   D).

Figure 6. Commonly differentially methylated transcripts in mouse and human
are linked to inflammatory and metabolic pathways.

   [164]Figure 6
   [165]Open in a new tab

   (A) Donut plot visualization of the presence (Yes)/absence (No) of
   corresponding human m^6A orthologous sites in differentially methylated
   (hyper and hypo) transcripts. (B) Overlap of differentially methylated
   transcripts in mouse and human with directionality and concordance. (C
   and D) EnrichR pathway analysis of commonly differentially methylated
   transcripts from mouse and human TLE.

Metabolic pathways are also perturbed at the proteomic level in human TLE.

   m^6A may impact RNA fate in a multitude of ways, including subcellular
   localization, stability, and/or translational efficiency. To determine
   whether pathways most likely impacted by differential m^6A patterning
   in epilepsy are functionally altered, we performed mass spectrometry
   (MS) to analyze the human TLE proteome using the same tissue samples
   used in the m^6A-profiling experiment. Hierarchical clustering and
   principal component analysis (PCA) showed good clustering of TLE
   samples; while control samples also clustered together, it was evident
   that samples did not separate by sex regardless of condition
   ([166]Supplemental Figure 4B). A large number of differentially
   expressed proteins were detected in the TLE groups (NS and Scl) when
   compared with control tissue ([167]Figure 7, A and B). However, similar
   to our findings in the m^6A array, the proteomes of both the NS- and
   Scl-TLE samples were remarkably similar ([168]Figure 7C). IPA of
   differentially expressed proteins in the combined epilepsy group
   ([169]Figure 7D) revealed overall disruption of many metabolism
   (mitochondrial dysfunction, glucose metabolism) and autophagy-related
   pathways. We then integrated the human m^6A data with the human
   proteomic dataset and found an inverse correlation between m^6A levels
   and activation or repression of the corresponding pathways ([170]Figure
   7E). These results may suggest that elevation of m^6A may be a
   molecular brake to silence pathways.

Figure 7. Proteomic analysis of human TLE reveals disruptions in metabolism
and autophagic signalling.

   [171]Figure 7
   [172]Open in a new tab

   (A–C) Volcano plot overview of differentially expressed proteins in
   NS-TLE (A) and Scl-TLE (B) compared to controls and NS- versus Scl-TLE
   (C) (n = 10 Ctrls, 7 NS-TLE, 7 Scl-TLE). (D) IPA of differentially
   expressed proteins in combined TLE groups and pathway enrichment. (E)
   Representation of directional association between differential
   methylation and subsequent protein translation.

Bidirectional modulation of m^6A alters neuronal metabolism, structure, and
seizure susceptibility.

   The metabolic phenotype common to both mouse and human TLE prompted us
   to explore the effect of elevated m^6A on neuronal function. First,
   using matured human induced pluripotent stem cell–derived
   (hIPSC-derived) neurons ([173]Figure 8, A–D), we overexpressed METTL3
   by transducing cells with a METTL3-overexpressing adeno-associated
   virus (AAV) vector ([174]Figure 8, E and F). RNA-seq and MS analyses of
   lysates from these cells detected only subtle changes in both
   transcription and protein profiles ([175]Figure 8, G and H). However,
   the pathways most perturbed by METTL3 overexpression were associated
   with cellular metabolism ([176]Figure 8I). Interestingly, in
   METTL3-overexpressing cells many downregulated transcripts were
   noncoding RNAs and pseudogenes ([177]Supplemental Figure 5). Finally,
   Seahorse analysis demonstrated that human neurons overexpressing METTL3
   had elevated rates of glycolysis compared with nontransduced or empty
   AAV–transduced neurons and this was corrected by pharmacological
   inhibition of METTL3 ([178]Figure 8, J and K). Overall, our data
   demonstrate that overexpression of METTL3 elevates the basal metabolic
   capabilities of even healthy neurons, making them more glycolytically
   active.

Figure 8. m^6A regulates hIPSC-derived neuronal metabolism.

   [179]Figure 8
   [180]Open in a new tab

   (A) Representative bright-field image of 30-day postdifferentiation
   mixed hIPSC-derived cortical neurons from healthy control donor.
   Original magnification, ×400. (B) Expression of mature neuronal markers
   (vGLUT2 and TUJ1) costained with the neuronal marker Hoechst (blue)
   following 30 days of maturation. (C and D) Representative mature
   neuronal activity and firing patterns confirmed by calcium imaging (C)
   and patch-clamp electrophysiology (D). (E and F) AAV transduction of
   mature neurons was confirmed by imaging for GFP (E) and probing METTL3
   overexpression by Western blot analysis and densitometric
   quantification (F). Scale bars: 50 μm (A and B), 50 μm (C), and 20 μm
   (D and E). METTL3: Mann-Whitney test: U = 3, P = 0.015 (n = 6/group. (G
   and H) Volcano plot depiction of differential gene expression (RNA-seq)
   (G), and protein levels (H) from neurons overexpressing METTL3 compared
   to control transduced neurons. (I) Gene ontology of differentially
   expressed genes and proteins by biological process. (J) Oxygen
   consumption rate of neurons overexpressing METTL3 compared to those
   that do not and those in which METTL3 is specifically inhibited.
   Comparison of means of normalized group data by 1-way ANOVA followed by
   Tukey’s multiple comparison testing: F = 0.414, P = 0.7436, n = 3
   independent experiments/plates 8 wells/treatment group per plate. (K)
   Extracellular acidification rate of the same neurons. Comparisons of
   means of normalized group data by 1-way ANOVA followed by Tukey’s
   multiple comparison testing: F = 5.794, P = 0.0017, n = 3 independent
   experiments/plates with 8 wells/ treatment group per plate.

   Next, to assess the effect of m^6A elevation on neuronal structure we
   first treated mouse primary hippocampal neurons with a commercially
   available METTL3 inhibitor (STM2457), and then assessed the effect of
   elevated m^6A on dendritic spine density ([181]Figure 9A). METTL3
   inhibition led to a detectable increase in m^6A levels for up to 24
   hours in a dose-dependent manner ([182]Figure 9, B and C). We first
   measured the effect of METTL3 inhibition on spine density as a function
   of distance from the soma. METTL3 inhibition increased the density of
   spines, with no clear effect on particular branch order ([183]Figure 9,
   D and E, and [184]Supplemental Figure 6, A–D). Immunocytochemistry
   (ICC) for the postsynaptic density protein PSD95 uncovered increased
   PSD95 immunoreactivity in cultures treated with the METTL3 inhibitor
   along dendrites ([185]Figure 9, D and F). This increase was apparent in
   branch orders 1 and 4 ([186]Figure 9F).

Figure 9. m^6A regulates neuronal structure and network activity.

   [187]Figure 9
   [188]Open in a new tab

   (A) Schematic of experimental approach using primary hippocampal
   neurons from E18 mouse pups to quantify dendritic spine density in
   cultures treated with either vehicle or METTL3 inhibitor. (B)
   Representative immunostaining (original magnification, ×10) of neuronal
   marker BIII_Tubulin and m6A levels following either control or METTL3
   inhibitor treatment. (C) Colorimetric assay quantification of neuronal
   m^6A levels over time following treatment with various concentrations
   of METTL3 inhibitor. Multiple unpaired 2-tailed t tests for each time
   point, 10 μM 24 hours t = 7.049, df = 8, P = 0.00032; 48 hours t = 1.7,
   df = 7, P = 0.25; 72 hours t = 0.55, df = 7, P = 0.59. (D)
   Representative confocal ICC images of primary hippocampal neurons
   stained for βIII-tubulin and with phalloidin to visualize dendritic
   spines (upper panels) and βIII-tubulin and PSD95 in the bottom panels
   as a postsynaptic density marker. Inset shows zoomed-in image with
   puncta staining. Scale bars: 20 μm. (E) Dendritic spine quantification
   characterized by distance from soma binned into 20-μm distances and
   according to branch order. Distance: 2-way repeated-measures (RM) ANOVA
   followed by Tukey’s post hoc multiple comparisons 100 μm t = 2.174, df
   = (62, 55), P = 0.0335; Branch order: 2-way ANOVA no significance. n >
   32 dendrites from 22 neurons per group. (F) PSD95 puncta quantification
   according to distance from soma and by branch order. Distance: 2-way
   ANOVA 20 μm t = 4.367, df = (53, 91), P = < 0.0001; 100 μm t = 2.269,
   df = (61, 21), P = 0.027. Branch order: 2-way ANOVA followed by
   multiple comparison t tests Order 1 t = 2.747, df = (63, 00), P = 0.03;
   Order 4 t = 3.283, df = (13, 00), P = 0.03. n > 32 dendrites from 22
   neurons per group. (G) Experimental design to test whether elevated
   m^6A levels alter seizure threshold in mice. (H) Kaplan-Meier curve
   depicting survival rates among vehicle- and FB23-2–treated animals
   following high-dose PTZ seizure challenge. Kaplan-Meier χ^2 = 3.4, df =
   1, P = 0.063. (I and J) Quantification of the maximum seizure stage
   reached (I) and total seizure numbers (J) in vehicle- or FB23-2–treated
   mice following PTZ administration over a 25-minute recording period, as
   measured using an adapted Racine scale. Seizure stage/number χ^2 df =
   (79.37, 5), P < 0.0001. *P < 0.05; ***P < 0.01; ***P < 0.001; ****P <
   0.0001.

   We next tested whether recreation of the hypermethylation phenotype
   seen following SE alters brain excitability. To do this, we treated
   naive mice with an FTO inhibitor, FB23-2 ([189]37), via
   intracerebroventricular (ICV) administration prior to a
   pentylenetetrazol-induced-seizure (PTZ-induced-seizure) challenge
   ([190]Figure 9G). FB23-2 treatment reduced overall hippocampal FTO
   levels ([191]Supplemental Figure 7, A and B) and elevated m^6A levels
   compared with vehicle-treated animals ([192]Supplemental Figure 7C and
   [193]Supplemental Figure 8, A–C). A single high dose of PTZ was then
   administered to both groups of animals and resulting seizure dynamics
   were quantified using an adapted Racine scale ([194]Supplemental Figure
   8F). Overall, we found that FB23-2–treated animals were more likely to
   survive the PTZ challenge and recording period ([195]Figure 9H).
   Although FB23-2 had no significant effect on latency to seizure onset
   ([196]Supplemental Figure 8D), it did reduce the overall severity of
   seizures experienced by mice compared with vehicle-treated controls
   ([197]Figure 9I) and the overall number of seizures mice experienced
   over the recording period ([198]Figure 9J and [199]Supplemental Figure
   8E). Together, these in vitro and in vivo data demonstrate a
   mechanistic link between m^6A levels and neuronal structure and
   function.

Discussion

   The present study is, to the best of our knowledge, the first
   transcriptome-wide and functional characterization of m^6A in epilepsy.
   Our study reveals disruption of m^6A patterning across different
   disease stages in mouse models of epileptogenesis and chronic epilepsy
   and significant dysregulation of m^6A in human TLE. Differentially
   methylated transcripts in both mouse and human TLE are associated with
   metabolic, autophagic, and RNA processing pathways. We combined our
   m^6A analyses with proteomics in human TLE and discovered that m^6A
   patterns are negatively correlated with protein pathway activation or
   repression. Finally, we demonstrate a clear link between m^6A abundance
   and network excitability, with elevated levels of m^6A increasing the
   seizure threshold of mice. Taken together, the findings suggest
   targeting RNA methylation may represent a novel approach to correct
   gene expression and treat or prevent TLE.

   Gene disruption is likely a driver of many of the underlying
   pathomechanisms that cause the formation and maintenance of
   hyperexcitable brain networks. Until now, the extent of RNA
   modification disruption in epilepsy has not been reported; however,
   several studies have recently identified involvement of m^6A in the
   regulation of epilepsy-associated RNA transcripts. These include
   demonstration that m^6A regulates NRF2, a transcription factor
   associated with cellular defence and oxidative stress ([200]38), a
   result supported by bioinformatic analysis of available sequencing
   datasets predicting m^6A-mediated regulation of cell death and
   metabolism in epilepsy ([201]39). Recently, the m^6A reader YTHDC2 was
   also reported to promote the development of TLE via regulation of the
   astrocytic glutamate-cystine antiporter xCT ([202]40).

   Here, our initial characterization of the main enzymes involved in
   regulation of m^6A demonstrated that METTL3, the primary catalytic
   agent of the pathway, was upregulated in both mouse and human tissue.
   We also detected elevated levels of both FTO and ALKBH5 in human TLE,
   both of which have been reported to remove m^6A, suggesting a more
   complex and nuanced regulation of m^6A in human epilepsy than in mouse.
   Additionally, it is conceivable that antiseizure medications may also
   influence m^6A patterning, considering some drugs such as valproic acid
   have been shown to regulate the epigenome. METTL3 and other regulators
   of m^6A are dysregulated in a range of other neurological conditions,
   including stroke, TBI, Huntington disease, and Alzheimer disease, and
   many studies have shown direct reorganization of the m^6A
   epitranscriptome following deliberate or disease-related changes in
   m^6A regulator proteins ([203]24, [204]30, [205]41–[206]44). Dynamic
   changes in m^6A can alter neuronal network properties and
   communication, thus impacting brain functions like learning and memory
   and disease-related pathways and possibly seizures ([207]29, [208]40,
   [209]45, [210]46).

   A major finding in the present study was the identification of
   differentially methylated transcripts and pathways in epilepsy (in both
   mouse and human). Overall, the number of differentially methylated
   transcripts in the mouse model were lower than in human TLE. Only 5
   gene transcripts were differentially methylated in acute and chronic
   mice, indicating dynamic and context-dependent methylation events. This
   may reflect the activation of disease-related pathways during
   epileptogenesis that may recover in chronic epilepsy when seizures
   become sporadic. Several studies have found that neuronal activity
   drives changes in m^6A activity, so it is likely that the m^6A profile
   before and after spontaneous seizure will differ. It is conceivable,
   therefore, that dynamic m^6A may contribute to the evolution of the
   disease over time. m^6A is usually asymmetrically distributed across
   mRNAs, with CDS and 3′UTR harboring more m^6A than the 5′UTR.
   Interestingly, the stoichiometry of the m^6A modification in mice
   revealed extensive loss of m^6A in the 5′UTR in particular in epilepsy,
   which may reflect targeted activity of an m^6A demethylase enzyme or
   increased processing/turnover of RNA transcripts with 5′UTR m^6A tags.
   In the present study, we found no overt differences in the protein
   levels of YTHDF1 or -2, although it is likely that redundancy exists
   among readers particularly from the YTH family ([211]27). Dysregulation
   of various m^6A readers has been reported in TLE, such as YTHDC2
   ([212]40), and in other neurological conditions including genetic
   variants in the Fxrp gene being a direct cause of the
   neurodevelopmental condition Fragile X syndrome ([213]47). Roughly half
   of the m^6A sites profiled in the mouse model have corresponding
   orthologs in human, making direct comparison of m^6A patterns between
   mice and humans difficult. Additionally, we were unable to directly
   compare identical tissue regions between mouse and human. Despite this,
   overlapping functions of differentially methylated transcripts were
   observed, suggesting conserved function of m^6A in epilepsy across
   species.

   We found that both Scl and NS sample types from the patients with TLE
   displayed more extensive m^6A dysregulation compared with controls than
   was seen in mice. Hypermethylation was prominent and there was large
   overlap between differentially methylated transcripts in both NS and
   Scl tissue compared with controls. It is unclear whether this is
   because m^6A is not involved in the cell death that defines sclerotic
   tissue or whether the patterns we observe reflect a bias inherent to
   profiling surviving tissue. The latter may be more plausible, as
   several studies have now demonstrated a clear involvement of m^6A in
   regulating neurodegenerative pathways and cell cycle events in
   Alzheimer disease ([214]48, [215]49) as well as in the pathogenesis of
   other neurodegenerative diseases like ALS ([216]50), Huntington disease
   ([217]44), and stroke ([218]51, [219]52). Furthermore, in our mouse
   model we found that differentially m^6A-tagged transcripts were
   associated with cell death processes in acute mice but not chronically
   epileptic mice, revealing a stage-specific role for m^6A in regulating
   cell death processes. Differential m^6A tagging was more likely to
   occur in the CDS in human TLE; however, the overall distribution of
   m^6A along transcripts did not change greatly compared to control. IPA
   of early-stage epileptogenesis in mice implicated disrupted m^6A
   deposition on transcripts associated with epileptogenic mechanisms such
   as cell death and inflammation, while analysis in chronic mouse and
   human TLE linked differential m^6A with metabolic processes, autophagy,
   and RNA processing in line with its role as a homeostatic mechanism.
   m^6A has previously been shown to regulate mitochondrial function and
   other metabolic activities ([220]53) and computational prediction of
   m^6A involvement in epilepsy suggested metabolism was intricately
   linked to m^6A ([221]39).

   Integration of the m^6A and proteomic datasets interestingly revealed
   an inverse correlation, i.e., hypomethylation of transcripts resulting
   in upregulation of protein and vice versa. This supports recent data
   demonstrating a clear role for m^6A as a negative regulator of
   transcript stability ([222]27). The m^6A array combined with the
   proteomic analysis made apparent that m^6A may influence metabolic
   pathways and processes as well as autophagy and mTOR signalling, which
   is in line with other reports from other tissues ([223]54).
   Overexpression of METTL3 in hIPSC-derived neurons did change the
   expression of metabolism-related transcripts and proteins and increased
   the rate of glycolysis, supporting previous computational predictions
   ([224]39). Only subtle changes in the transcriptome, proteome, and
   metabolism of hIPSC-derived neurons were detected when METTL3 was
   overexpressed, suggesting m^6A may couple gene output to translational
   processes to enable cellular adaptation to environment. This may
   explain the much greater changes in m^6A and the resulting proteome
   seen in epilepsy when cellular activity levels are elevated and disease
   processes activated.

   The current study focusses on the regulatory role of m^6A modifications
   and how they may shape the epileptic proteome; however, other
   posttranscriptional forms of regulation must also be considered. Among
   these, microRNAs (miRNAs) are well established as key regulators of the
   proteome and have previously been shown to play a crucial role in
   epilepsy, including ion channel regulation, metabolism, and autophagy,
   all of which are implicated in the proteomic changes observed in our
   study ([225]55, [226]56). Although miRNAs were not directly profiled
   here, their potential contributions to the observed proteome must be
   considered. Further studies are required to elucidate interplay between
   miRNAs and m^6A modifications in TLE, as these mechanisms may function
   synergistically or independently to shape disease-associated proteomic
   changes.

   Finally we found that m^6A levels are intricately linked with
   excitability. Reduced m^6A levels increased dendritic spine density and
   postsynaptic markers, while elevated m^6A levels decreased seizure
   susceptibility and severity in mice. While the precise mechanisms
   underlying this are still unclear, recent reports have demonstrated a
   synaptosome-specific m^6A-ome that may regulate protein translation at
   the synapse and govern excitability ([227]15). There are certain
   limitations associated with the current study, including the use of
   nonadjusted P values for the analysis of the mouse m^6A array. We
   therefore validated several differentially methylated transcripts using
   meRIP-qPCR and found good concordance; however, false positives cannot
   be fully excluded and additional sites are likely being missed.
   Furthermore, m^6A changes may be diluted due to the cellular
   heterogeneity of the input material masking potential cell-specific
   m^6A changes. Recent advances may now enable m^6A profiling at the
   single-cell level, which could also be explored in epilepsy and other
   diseases ([228]57). Although efforts are made to compare mouse and
   human m^6A data, we used whole mouse hippocampal tissue but only a
   subsection of resected human tissue due to limitations of availability.
   The use of behavior as a readout of seizure activity only permits
   quantification of convulsive seizures and may therefore miss
   subconvulsive seizure activity. Although we report no differences in
   m^6A and proteomic profiles based on sex, the sample size is small and
   so we are cautious to interpret that sex does not influence m^6A;
   instead, we conclude in the context of epilepsy that there is no clear
   m^6A signature based on sex. Finally, we have only profiled one RNA
   modification and it is likely that crosstalk and coordination of
   diverse RNA modifications exist ([229]16).

   Overall, our data suggest that m^6A represents an additional layer of
   gene regulation that contributes to the pathomechanisms of epilepsy by
   regulating RNA stability and protein translation. The elevation in
   METTL3 in epilepsy may represent an adaptive response, as elevated m^6A
   protected mice from seizures. Future preclinical work may now explore
   whether targeting m^6A-modifying enzymes may prove beneficial for the
   treatment of epilepsy. It may also be useful to explore m^6A
   dysregulation as a potential novel biomarker for epilepsy and
   associated comorbidities.

Methods

Sex as a biological variable

   This study involved both human tissue and animal models. Human
   hippocampal samples were obtained from individuals with TLE and
   included both male and female donors. PCA using sex as a variable did
   not reveal sex-specific clustering or patterns in m^6A methylation,
   suggesting minimal influence of sex on the epitranscriptomic landscape
   in this cohort. All animal experiments were conducted using adult male
   mice to maintain consistency with previous studies using the IAKA
   model.

Mouse models of TLE and acute seizures

IAKA in mice.

   For all mouse studies, we used 7- to 9-week-old-male C57BL/6JOlaHsd
   mice, originally from Harlan Laboratories and inbred in house at RCSI.
   Animals used (matching strains) at University College Dublin (UCD) were
   sourced from Envigo UK. Prior to IAKA administration, mice underwent a
   surgical procedure under general anesthesia (isoflurane; 5% induction,
   1%–2% maintenance) to implant a surface injection cannula (Bilaney
   Consultants, custom cut 7.65 mm) over the amygdala (coordinates from
   Bregma: IA: antero-posterior (AP) = –0.95 mm, lateral (L) = +2.85 mm,
   ventral (V) = 3.1 mm).

   Two to 3 days following this preparatory surgery, mice underwent IAKA
   microinjection (0.3 μg KA in 0.2 μL PBS; Sigma-Aldrich, K0250) to
   induce SE. After 40 minutes, mice then received an intraperitoneal
   (i.p.) injection of lorazepam (Macure, L0227; 8 mg/kg) to reduce
   seizure severity and reduce mortality. Mice were then allowed to
   recover in a small animal recovery chamber (Vet-Tech, HE010) at 26°C.
   At selected time points, animals were euthanized by administering
   0.1–0.2 mL 200 mg/mL pentobarbital (Dolethal) via i.p. administration
   followed by transcardial perfusion with ice-cold 1× RNase-free PBS
   (Thermo Fisher Scientific, AM9625).

   Following IAKA administration, mice generally develop spontaneous
   seizures after 3 to 5 days, with all animals displaying spontaneous
   seizures by 2 weeks after SE ([230]58).

PTZ in mice.

   A subcutaneous dose of PTZ (Sigma-Aldrich, P6500; 80 mg/kg prepared in
   PBS) was administered 24 hours after 20 μM FB23-2 (VWR International)
   ([231]37) or vehicle (0.5% DMSO, 40% PEG300, 5% Tween 80, 54.5% H[2]O)
   treatment. FB23-2 or vehicle was administered via a unilateral ICV
   administration in 0.4 μL volume. For ICV administration, the
   stereotaxic coordinates were determined using the Mouse Brain Atlas and
   verified using methylene blue in preliminary experiments (AP = –0.5 mm,
   L = –1.1 mm, V = –2.3 mm from Bregma). A 1-mm burr hole was prepared in
   the exposed skull and a 30-gauge stainless steel cannula connected to a
   Hamilton syringe was lowered to the correct depth and drug
   administered. The cannula was held in place for 8 minutes to prevent
   backflow. The incision was closed with tissue adhesive (Vetbond,
   IM1469SB). Seizures were recorded for 25 minutes following PTZ
   administration and scored using an adapted Racine scale
   ([232]Supplemental Figure 7). Scorers were blinded to treatment. All
   mice were euthanized at the end of the PTZ protocol.

Human brain tissue

   Hippocampi were collected directly from the surgical theater, frozen
   immediately in liquid nitrogen, and stored at –80°C. A pathologist
   confirmed the presence or absence of sclerosis for each tissue sample
   obtained ([233]59). Hippocampal sclerosis was defined as neuronal loss
   and gliosis primarily involving the CA1 and CA4/3 subfields with
   relative resistance of the CA2 region and sparing of the subiculum (if
   the latter were included in the resected specimen). NeuN immunostaining
   was employed to determine residual neuronal density and gliosis was
   determined using GFAP immunoreactivity. Autopsy control samples were
   sourced from the NIH NeuroBiobank, University of Maryland. Patient and
   control information is listed in [234]Supplemental Table 1.

m6A array

   RNA was isolated from both mouse and human hippocampal tissue samples
   as described in the [235]Supplemental Methods. Between 1 and 5 μg of
   RNA from each sample was sent to Arraystar for single-nucleotide
   resolution microarray analysis. The array (specific for mouse or human)
   enables quantification of m^6A abundance and stoichiometry at
   approximately 10,000 predefined m^6A sites across the transcriptome.
   Briefly, the total RNA was divided into 2 fractions. One fraction was
   treated with the RNA endoribonuclease MazF to cleave unmodified m^6A
   sites, while the other fraction was left untreated. MazF specifically
   cleaves RNA at unmethylated adenosine residues, allowing for the
   selective removal of unmodified RNA and preservation of m^6A-modified
   sites. The treated and untreated RNA fractions were separately labelled
   with Cy5 (for MazF-digested RNA) and Cy3 (for the undigested RNA) as
   complementary RNAs (cRNA) using the Arraystar Super RNA Labelling Kit.
   These labelled cRNAs were then hybridized onto the Arraystar Human or
   Mouse m^6A Single Nucleotide Array (8 × 15K, Arraystar). After
   hybridization, the arrays were washed and scanned in 2 color channels
   using an Agilent Scanner (G2505C).

   The acquired array images were analyzed using Agilent Feature
   Extraction software (version 11.01.1). The raw intensity values for the
   Cy5-labelled (MazF digested) and Cy3-labelled (undigested) cRNAs were
   normalized using the average log[2]-scaled Spike-in RNA intensities.
   The Spike-in RNAs, which are synthetic control RNA sequences added at
   known concentrations, allow for normalization by accounting for
   technical variations in RNA labelling and hybridization. Log[2] scaling
   was used to transform the intensity values into logarithmic scale to
   enable comparison of fold changes in signal intensities across samples.
   After Spike-in normalization, we filtered and retained probes with
   “Present” (P) or “marginal” (M) quality flags (which indicate the
   reliability of the probe signal compared to background) in at least 7
   of the 24 human samples and 6 out of 8 mouse samples for further m^6A
   site methylation abundance analysis. Differentially methylated sites
   between 2 comparison groups were compiled by fold change and
   statistical significance threshold (P < 0.05 [standard for this type of
   analysis]). For the human array, false discovery rate (FDR) was applied
   using the Benjamini-Hochberg procedure and adjusted P values of less
   than 0.05 were set as meaningfully significant. Data visualization was
   performed in RStudio (version 4.3.0 and 4.3.1;
   [236]https://cran.r-project.org/) (volcano plots [ggplot], chromosome
   plotting [Chromomap], Upset plots [UpsetR]) and GraphPad Prism (version
   9.2.0). Stoichiometric data were plotted using GraphPad Prism.
   Differentially methylated transcripts were analyzed using Qiagen IPA to
   identify associated pathways. To analyze the effects of sex on the m^6A
   profiles within and between human sample groups, a 3-column (factor)
   metadata table was created manually by merging a sample ID/condition
   metadata table, and a sample ID/sex metadata table. PCA was then
   performed on the processed quantification table and methylation
   quantification tables using the FactoMineR R library. The PCA plot was
   then generated using ggplot2, specifically labelling data points
   indicating condition and sex. ANOVA was then carried out to determine
   the effect of each factor and a permutational analysis of variance
   (PermANOVA) using the vegan R library was carried out to determine
   effects without the assumption of normality.

meRIP-qPCR

   Total RNA was isolated as described in the [237]Supplemental Methods
   using TRIzol (Invitrogen, 15596026) and quality and concentration
   assessed by Nanodrop (Thermo Fisher Scientific). Eighteen micrograms of
   RNA was separated based on Nanodrop quantification from each sample and
   subjected to fragmentation using an RNA fragmentation kit (Ambion,
   AM8740) according to the manufacturer’s instructions. This produced RNA
   fragments between 100 and 200 nucleotides in length (unfragmented RNA,
   i.e., leftover RNA not used for fragmentation, was kept as input
   comparison). The fragmented RNA was then incubated overnight at 4°C in
   IP buffer (10 mM Tris-HCl pH 7.4; Thermo Fisher Scientific, J62848.AK),
   150 mM NaCl (Sigma-Aldrich, 71386), and 0.1% IGEPAL CA-630
   (Sigma-Aldrich, l8896) containing either anti-m^6A antibody (Synaptic
   Systems, 202 003) ([238]17) or IgG (Abcam, ab171870) on a carousel. The
   antibody–RNA complexes were captured by incubation with washed Protein
   A/G magnetic Dynabeads (Thermo Fisher Scientific, 10015D) for 2 hours
   at 4°C with gentle rotation. After several washes to remove loosely
   bound complexes, m^6A-tagged RNAs were separated from the beads by
   elution by competition, which involved incubating the beads with
   elution buffer (IP buffer, 20 mM m^6A 5′-monophosphate sodium salt
   [Sigma-Aldrich, M2780], RNasin [Promega, N2611], nuclease-free water).
   This was repeated twice and the supernatants pooled and transferred to
   an RNeasy MiniElute Spin Column (Qiagen, 74204). The samples were then
   analyzed on a bioanalyzer using the RNA Pico Kit (Agilent, 5067-1511)
   according to the kit instructions. The purified RNA was then reverse
   transcribed and qPCR was performed using m^6A-site-flanking primers
   ([239]Supplemental Table 2). Input sample was run alongside the IP
   samples and used as comparison.

MS-based proteomics

   Total protein was extracted as described above and then lysed into
   smaller peptide fragments using the iST Sample Preparation Kit
   (PreOmics, P.O.00001) as per the manufacturer’s instructions. Briefly,
   lysis reagent was added to 1–100 μg of protein from each sample and
   heated on a dry block incubator (Thermo Fisher Scientific, 15392185) to
   95°C with rigorous mixing. Contaminating DNA in the samples was sheared
   by water bath sonication and samples were then transferred to
   cartridges and allowed to cool to room temperature (RT). The samples
   then underwent a digestion step using specific Digest buffer at 37°C
   for between 1 and 3 hours. The reaction was terminated using a
   kit-supplied stop solution and samples were then centrifuged at 3,800g
   for 3 minutes and the flow through was collected and discarded. The
   fraction of the sample bound to the cartridge were then washed twice
   and eluted with elution buffer twice using centrifugation. The samples
   were dried completely using a speed vac at 45°C. LC-Load buffer was
   added to each sample to obtain a 1 g/L concentration. The samples were
   finally sonicated at RT for 5 minutes and stored at –80°C until needed.
   Peptide samples were analyzed via liquid chromatography–tandem MS
   (LC-MS/MS) at the Conway Institute Proteomics core facility, UCD, on a
   Thermo Fisher Scientific Q-Exactive Mass Spectrometer connected to a
   Dionex Ultimate 3000 (RSLCnano) chromatography system. Peptides were
   separated on a C18 home-made column (C18-AQ Dr Maisch Reprosil-Pur, 100
   × 0.075 mm × 3 μm) over 60 minutes at a flow rate of 250 nL/min with a
   linear gradient of increasing acetonitrile from 1% to 27%. The MS was
   operated in data-independent mode; a high-resolution (70,000) MS scan
   (300–1600 m/z) was performed to select the 12 most intense ions and
   fragmented using high-energy C-trap dissociation for MS/MS analysis.

   For proteomic analysis, raw data from the Q-Exactive were processed
   using MaxQuant (version 2.0.3.0) ([240]60, [241]61). To identify
   peptides and proteins, MS/MS spectra were matched against the Homo
   sapiens database ([242]https://www.uniprot.org/proteomes/UP000005640;
   downloaded 2022_01 containing 75,777 entries). Database searches were
   performed with carbamidomethyl (CAM) as a fixed modification and
   acetylation (protein N-terminus) and oxidation (M) as variable
   modifications. Data were then filtered in Perseus (version 1.16.5.0;
   [243]https://maxquant.net/perseus/). All function calls/commands were
   carried out using the Perseus interface. Initially a matrix of
   label-free quantification (LFQ) intensity values were generated for
   each sample. Filters were then applied to remove values likely to be
   contaminants, proteins identified in reverse amino acid sequences, and
   proteins only identified by site. The LFQ values were then log
   transformed and histograms of the data were generated and checked for
   normality of data spread. The samples were then grouped by condition
   (control, Scl-TLE, and NS-TLE). Further filtering excluded proteins
   that were detected in less than 7 samples per group. Missing values
   were imputed using the “replace missing values from normal
   distribution” command. Pearson’s correlation was performed to
   scrutinize correlation between groups. Preliminary hierarchical
   clustering was performed to determine clustering and heatmaps were
   subsequently generated based on this clustering. Further processing and
   data visualization was carried out in RStudio using the limma R package
   for differential (protein) expression analysis, which incorporates FDR
   correction using the Benjamini-Hochberg method, ClusterProfile for
   enrichment analysis, and ggplot2 for graph generation. The resulting
   differentially expressed proteins were compared with various functional
   gene set databases in functional analysis/pathway enrichment analysis
   using IPA (Qiagen) or EnrichR. Sex differences were analyzed in the
   same way as described above (see m^6A array).

ICC

hIPSC ICC.

   Glass coverslips with cells were transferred to prewarmed DMEM/F-12
   (Gibco, A4192101) in a new plate. The cells were then fixed by covering
   wells with 4% paraformaldehyde (PFA) (Merck, 1004968350) in PBS and
   left to be incubated for 20 minutes at RT. The coverslips were then
   washed 3 times with PBS, blocked with 1% BSA (Merck, A9647) in PBS, and
   then permeabilized with 0.2% Triton X-100 (Merck) in 1% BSA for 30
   minutes. Coverslips were then incubated with primary antibodies in 1%
   BSA overnight at 4°C. The next day they were washed and treated with
   secondary antibodies (488 nm and 568 nm) for 75 minutes in the dark at
   RT. After washing and staining with Hoechst, coverslips were mounted on
   slides.

   For neural stem cell confirmation from hIPSCs, cultures were stained
   with antibodies against neural stem cell markers Nestin (Abcam,
   ab22035), Pax6 (BioLegend, 862001), and Sox2 (Abcam, ab97959). Neurons
   were stained with anti–βIII-tubulin (TUJ1) (Synaptic Systems, 302 306),
   anti-vGLUT2 (Proteintech, 25261-1-AP), anti–tyrosine hydroxylase
   (Sigma-Aldrich, ZRB2381), and anti-NeuN (Sigma-Aldrich, MAB377).
   Imaging was performed using a Leica DM400b epifluorescence microscope,
   capturing fluorescent signals in FITC, TRITC, and BFP channels. Images
   were processed with ImageJ version 2.14.0/1.54f (NIH).

Primary mouse neuron ICC.

   Postsynaptic density was visualized using PSD95 as a marker. To
   visualize dendritic spines, anti–βIII-tubulin complemented with Alexa
   Fluor 568 phalloidin to enable visualization of small spines
   (Invitrogen, A12380) were used. Cultured neurons were first fixed with
   4% PFA and permeabilized in blocking buffer (0.1% Triton X-100 in 2.5%
   BSA-PBS) for 1 hour at RT. Subsequently, neurons were incubated
   overnight at 4°C with primary antibodies against βIII-tubulin
   (BioLegend, 801201; 1:500), PSD95 (Abcam, ab18258; 1:200), and m^6A
   (Synaptic Systems, 202 003; 1:200). After overnight incubation, neurons
   were washed in PBS to remove loosely bound antibody and then incubated
   in species-specific secondary antibodies at RT for 1 hour. Anti–mouse
   IgG-Cy5 (Invitrogen, A10524; 1:500) and anti–rabbit IgG-FITC
   (Invitrogen, F-2765; 1:500) were used to bind and visualize primary
   antibodies. Alexa Fluor 568 phalloidin (Invitrogen, A12380; 1 U/slide)
   was added to the blocking buffer along with secondary antibodies.
   Coverslips were mounted using mounting medium with DAPI (Abcam,
   ab228549) and imaged using a Zeiss LSM 800 confocal microscope.

   For PSD95 and dendritic spine quantification, a total of at least 32
   dendrites from 22 neurons per treatment group were included in the
   analysis across a total of 4 experiments. Multiple dendrites sampled
   from an individual neuron were averaged for statistical analysis to
   avoid pseudoreplication. Each experiment used embryos from an
   individual dam (4 in total), the average litter size was 7.75. All
   imaging and quantifications were performed manually, in a blinded
   manner with respect to the treatment groups. Images were calibrated for
   distance per pixel length, and the distance from the soma was measured
   and divided into 20-μm segments using ImageJ. A total dendritic length
   of 4440–4500 μm, derived from at least 32 dendrites per treatment
   group, was analyzed. Images were generated to show the entire neuron
   using a 40× objective lens. Dendrites, distinct from other dendrites
   and crossings and extending at least 100 μm from the soma, were
   captured at ×63 magnification. Utilizing a 2-way repeated-measures (RM)
   ANOVA, following Tukey’s post hoc multiple-comparison test, all
   treatment groups along the dendritic distance were analyzed. PSD95 and
   spine density was also quantified by branch order, using multiple t
   test analysis. A significance level of 0.05 was chosen, and data are
   expressed as the mean ± SEM. Statistical analysis was performed using
   GraphPad Prism 8.0 software.

METTL3 overexpression

   hIPSC-derived neurons were transduced with AAV vectors (VectorBuilder,
   custom-designed) to overexpress human METTL3. Three distinct AAV
   vectors were used: AAV9-SYN1-EGFP-hMETTL3, which encodes the human
   METTL3 gene; AAV9-SYN1-EGFP-Empty, a control vector containing only the
   GFP reporter driven by the SYN1 promoter; and a GFP-only virus without
   the SYN1 promoter (negative control). The SYN1 promoter was used to
   drive neuron-specific expression of both the transgene (hMETTL3) and
   the GFP reporter, ensuring that the transduction selectively targeted
   neuronal cells. The GFP reporter enabled the monitoring of successful
   transduction by providing a fluorescent signal visible under
   appropriate microscopy conditions.

   For the transduction process, hIPSC-derived neurons were cultured as
   described above for 21 days. The AAV vectors were then added to the
   culture medium at a concentration of 1 × 10^4 genomic copies per cell
   and the neurons were incubated with the vectors for 48 hours, to allow
   efficient uptake and expression of the transgenes. After incubation,
   the cells were maintained in fresh culture medium for an additional
   period to ensure stable transgene expression.

   To confirm successful overexpression of METTL3, Western blotting was
   performed as described above to detect the METTL3 protein in the
   transduced neurons. The expression of GFP was also evaluated to verify
   the success of the transduction using GFP immunofluorescence
   microscopy, as described above. This dual approach allowed for both
   qualitative and quantitative confirmation of transgene expression.
   Control cells were similarly transduced with either the empty vector
   (AAV9-SYN1-EGFP-Empty) or GFP-only vector, and the same analyses were
   performed to compare the level of GFP expression and METTL3
   overexpression across conditions.

Reagents

   All reagents were purchased from commercial sources that are listed
   throughout. Antibodies used in this study were all purchased from
   commercial sources. Full Western blot images are provided in
   supplemental material. Catalog numbers for all antibodies are provided
   along with vendor information. The detected bands in Western blot
   analyses correspond to the expected molecular weights of the target
   proteins, consistent with the manufacturer’s specifications and
   previous literature where available. Primers were designed in-house
   using UCSC Genome Browser alignments (mouse, mm39; human, hg38) and
   NCBI Primer Blast.

Statistics

   Power analyses were performed during the experimental design stage.
   Effect size was determined depending on the type of downstream analysis
   based on previous studies along with a significance level of 0.05 and a
   1 – β value of 0.8, with equal allocation ratios used throughout.

   Unless otherwise stated in specific sections, statistical analyses were
   performed using appropriate parametric or nonparametric tests based on
   data distribution. Normality of the data was assessed using the
   Shapiro-Wilk test. For comparisons between 2 groups, unpaired t tests
   were used when both groups followed a normal distribution with equal
   variances, while Welch’s t test was applied when variances were
   unequal. If data were not normally distributed, the Mann-Whitney U test
   was used as a nonparametric alternative. For comparisons involving more
   than 2 groups, 1-way ANOVA was performed for normally distributed data,
   followed by Tukey’s post hoc test for multiple comparisons, while the
   Kruskal-Wallis test was used for nonparametric data, followed by Dunn’s
   test for pairwise comparisons. Statistical significance was set at P
   less than 0.05, and multiple comparisons were corrected using the
   Benjamini-Hochberg FDR method where applicable. All analyses were
   conducted using GraphPad Prism or RStudio.

Study approval

   All animal procedures were performed in accordance with the principles
   of the European Union Directive (2010/63/EU) and were reviewed and
   approved by the Research Ethics Committee of the Royal College of
   Surgeons Ireland (REC842) under license from the Health Products
   Regulatory Authority (HPRA) (AE19127/IO84, AE19127/I152, AE19127/I089)
   and the UCD Animal Research Ethics Committee (AREC) under license from
   the HPRA (AE18982/P194). The systemic KA model in rats was performed at
   Monash University following approval by the Alfred Research Alliance
   Animal Ethics Committee (E/2058/2020/M). All efforts were made to
   minimize animal suffering and to use the minimum number of animals
   required for robust statistical analysis.

   The human study was approved by the Ethics Committee at Beaumont
   Hospital, Dublin (05/18). Written, informed consent was obtained from
   all individuals participating in the study. All individuals were
   diagnosed with TLE and underwent surgical tissue resection as part of
   their treatment.

Data availability

   RNA-seq and m^6A array datasets are available from the NCBI Gene
   Expression Omnibus (GEO) under accession numbers [244]GSE295244
   (RNA-seq) and GEO [245]GSE297566 (m^6A array). The MS proteomics data
   have been deposited to the ProteomeXchange Consortium via the PRIDE
   ([246]62) partner repository with the dataset identifier PXD064519.

   Values for all data points in graphs are reported in the
   [247]Supporting Data Values file. All other datasets are available upon
   reasonable request to the corresponding author.

Author contributions

   JM, MMW, SS, LVB, PMCE, MC, DCH, and GPB designed the research studies.
   JM, MMW, SS, LVB, CT, JK, AH, TA, MKAEA, and MC conducted experiments.
   JM, MMW, SS, LVB, CT, JK, and MC acquired data. JM, MMW, SS, GL, LVB,
   CT, JK, MS, AL, MC, and GPB analyzed data. ASR, ZL, YH, AL, ND, JC,
   FMB, MAF, DFOB, and DCH provided resources. PMCE, EMJM, JCG, DCH, and
   GPB supervised the study. JM, MMW, SS, and GPB wrote the manuscript.
   DCH and GPB acquired funding. Co–first authors’ names are listed
   alphabetically according to their first name.

Supplementary Material

   Supplemental data
   [248]jciinsight-10-188612-s067.pdf^ (1.7MB, pdf)
   Unedited blot and gel images
   [249]jciinsight-10-188612-s068.pdf^ (2MB, pdf)
   Supporting data values
   [250]jciinsight-10-188612-s069.xlsx^ (815.4KB, xlsx)

Acknowledgments