Antecedent enhancer activity predicts future susceptibility to seizures in mice

Domain: biology
Published: 2026-01-08
Editorial outcome: major_revision

Abstract
Wide variation of responses to identical stimuli presented to genetically inbred mice suggests the hypothesis that stochastic non-genetic variation, such as in chromatin state or enhancer activity during neurodevelopment, can mediate such phenotypic differences. However, this hypothesis is largely untested since capturing pre-existing molecular states requires non-destructive, longitudinal recording. Therefore, we tested the potential of Calling Cards (CC) to record transient neuronal enhancer activity during postnatal development in mice, and thereby associate such non-genetic variation with a subsequent phenotypic presentation – degree of seizure response to the pro-convulsant pentylenetetrazol. We show that recorded differences in enhancer activity at 243 loci predict a severe vs. mild response, and that these are enriched near genes associated with human epilepsy. We also validated pharmacologically a seizure-modifying role for two previously unassociated genes, Htr1f and Let7c. This proof-of-principle supports using CC broadly to discover predisposition loci for other neuropsychiatric traits and behaviors. Finally, as human disease is also influenced by non-genetic factors, similar epigenetic predispositions are possible in humans.

Introduction
Neuropsychiatric disease risk, while heavily impacted by genetic makeup, is also influenced by non-genetic factors during development. For example, unique epigenetic states may be established early in life in response to factors such as environmental exposures (e.g., infections, head traumas) or psychosocial background, causing some cells in the body to activate specific sets of enhancers. Thus, inter-individual differences could be attributed partially to naturally-occurring variation in enhancer states across the population. This variation could contribute to brain disorders like epilepsy, which can have variable presentation even between identical twins1–3. However, a substantial challenge in studying any kind of epigenetic risk factors is disentangling pre-existing molecular states from those occurring as a consequence of an experimental manipulation or the disease itself. Overcoming this challenge would represent an important milestone, as connecting non-genetic variation to stochastic differences in pre-existing enhancer states could help identify novel mediators of seizure propensity or other naturally variable neuropsychiatric disease traits.

Epilepsy affects 46 million individuals worldwide and is characterized by unprovoked, recurrent seizures that can be highly variable in onset, frequency, duration, or intensity4,5. Human genome-wide association studies (GWAS) have identified many inherited risk factors for epilepsy. Many monogenic forms are causally linked to loss- or gain-of-function mutations in proteins influencing membrane excitability, such as SCN1A or GABRA16. Furthermore, other GWAS implicated genes like BCL11A or RORB are transcription factors that influence neuron development and identity, rather than membrane excitability7,8, and many gene variants that cause neurodevelopmental disorders or intellectual disability (ID) broadly also exhibit comorbid epilepsy9. Nevertheless, even among individuals with the same mutation, seizure frequency and intensity can vary substantially, suggesting other processes influence risk. Indeed, it is well-established that non-genetic factors such as traumatic brain injury or stroke also increase risk for developing seizures, and in some at-risk individuals, stress, alcohol consumption, or sleep deprivation can trigger an episode10. These lines of evidence argue that non-genetic risk factors arising stochastically during typical neurodevelopment, or that are uniquely acquired, may also influence seizure risk.

Variability in neuropsychiatric phenotypes influenced by non-genetic factors also exists in animals. For example, mouse models of acute seizure (e.g., induction with GABA-A receptor antagonist pentylenetetrazol; PTZ), result in a range of seizure responses even in effectively isogenic mice11. Specifically, while most mice with identical genetic backgrounds experience generalized tonic-clonic activity, some only exhibit mild freezing while others proceed to death. We hypothesize that this natural variability in presentation might be due to stochastic variation in pre-existing epigenetic states established during brain development. We therefore posited that PTZ induction in naive mice might represent a model system to examine 1) whether such pre-existing molecular states can influence subsequent phenotype or presentation, and 2) if so, to identify naturally-occurring susceptibility factors from such molecular states.

However, most methods of assessing molecular state, such as RNA-seq or ChIP-seq, are destructive, and can therefore only provide a snapshot of the molecular state at the time of sacrifice. They cannot, therefore, be used to connect pre-existing cellular states to behavioral outcomes12. Indeed, several studies using such methods have investigated global epigenetic changes such as genome-wide DNA methylation13–18, transcription factor19,20 and modified histone21 binding, chromatin accessibility and conformation19, following seizure induction. However, the prior states that influenced these changes remain unknown. Calling Cards (CC) is a transposon-transposase based technology that can create a permanent record of one such molecular state: transient transcription factor-DNA interactions22–24. The crux of CC is a self-reporting transposon that is directed to a genomic locus by the transposase hyperPiggyBac, which has a natural affinity for BRD4 binding sites in the genome. BRD4 is a histone acetylation reader that preferentially binds at active enhancers and promoters; increased levels of BRD4 binding often predict increased gene expression25,26. The transposase-DNA interaction results in a donor transposon insertion at a TTAA site at that locus, serving as a permanent record (a “calling card”) of nearby BRD4 binding that can be read out through targeted sequencing after sacrifice. Unlike epigenetic profiling tools like ChIP-seq or ATAC-seq, CC can record cumulative enhancer activity across extended time windows (i.e., across development prior to PTZ injection). Because CC insertion and subsequent transposon reporter expression requires relatively long time windows (24–48h), minimal recording of molecular state occurs in the few minutes between PTZ injection and seizure (in contrast to rapid RNA induction of immediate early genes like c-fos). Therefore, CC could circumvent the chief challenge of disentangling pre-existing molecular states from those occurring as a consequence of seizure induction. If so, then CC could be applied broadly to understanding variability in many other neurological diseases or behaviors beyond acute seizure induction (Fig. 1).

Here, we examined whether such a pre-recorded epigenetic status can predict future seizure susceptibility. We implemented neuron-specific CC in inbred, isogenic mice immediately after birth to record cumulative BRD4 binding events through one month of age. We then induced acute seizures and sacrificed animals after 15min of treatment. Next, we stratified mice based on seizure responses and tested for differential CC transposon insertions between severe vs. mild seizure responders. We identified numerous loci with pre-existing enhancer activity differences between these groups. Further, these putative enhancer sites mapped near known epilepsy risk genes, and susceptibility loci were enriched for genes influencing neurodevelopmental maturity. Finally, we implemented pharmacological methods to support the identification of two previously understudied seizure-modifying genes from our dataset. This work supports the hypothesis that pre-existing molecular states that affect novel or established epilepsy pathways can influence seizure susceptibility. Our study thus provides a template for examining antecedent epigenetic and molecular states for various other neuropsychiatric diseases and traits.

Results
Recording pre-seizure Calling Cards profiles in a pharmacological model of acute seizures
Studies in mice have shown that there is substantial variability in seizure susceptibility across genetically identical individuals27–30. Therefore, we sought to record pre-existing variation in enhancer activity and correlate inter-individual difference in activity with seizure severity in a PTZ-induced acute seizure model in isogenic mice. We delivered conditionally expressed (FLEX, Front Flip), AAV Calling Cards viral vectors (Fig. 1A) to the cortices of a cohort of P0-1 mouse pups of the Syn1::Cre genotype. These CC reagents record the binding of BRD4, an epigenetic factor which marks active enhancer regions22,31,32. The FLEX system with neuronal (Syn1) Cre ensures recording occurs in neurons and limit the background contribution from other cells. At P28, after recording pre-seizure CC occupancy profiles in each animal, mice were administered acute seizures through intraperitoneal injection of the GABA-A receptor antagonist PTZ at 65mg/kg. Mice were observed for 15min and were then immediately euthanized, reasoning 15min would allow insufficient time for any seizure-related CC insertions and expression (especially relative to the 4 weeks of recording that have already occurred), while allowing long enough to stratify seizure responses. Cortical tissues were then collected for CC analysis (Fig. 2A). Seizures were scored based on a modified Racine scale, ranging from 1 (immobility) to 5 (generalized tonic-clonic seizure) to 8 (death). We therefore trichotomized animals by seizure scores into mild (score 1–4), moderate (score 5), or severe (score 6–8) (Table 1). Of the 32 mice tested, 12 were mild responders, 9 were moderate, and 11 were severe (Fig. 2B, Supplementary Data 1). Multiple regression analysis revealed that seizure scores were not related to factors such as sex or weight, likely due to our controlling for weight with dosing (Supplementary Fig. S1A, B, Supplementary Data 1).

To confirm that our CC recordings were not influenced by the 15-min seizure, we investigated the dynamics of transposon hopping (transposition) in a simplified in vitro system. For this, we used the Broken Heart (BH) transposon construct, in which the DsRed coding sequence is interrupted by a donor transposon. PiggyBac (PB) transposase activity removes the donor transposon from the construct and inserts it into the genome, leaving an intact DsRed sequence (Supplementary Fig. S2A). An N2A cell line, engineered to be constitutively expressing PB, was electroporated with BH plasmid and collected for lysis on a time course starting at 15min following electroporation (Supplementary Fig. S2B). Transposition was detectable beginning 2–5h following electroporation with BH (Supplementary Fig. S2C, D). Therefore, a 15min seizure would not allow sufficient time for hopping. Furthermore, this simplified system represents the fastest scenario for transposition, because the PB enzyme is already present. Furthermore, unlike with SRTs, new transcription is not required for measurement of transposition in this system, as we are only measuring excision of the transposon from the donor construct. For the in vivo CC using self-reporting transposons, this step is followed by insertion of the transposon into the genome and transcription of the transposon. The additional time required for these steps would further preclude the detection of new CC insertions deposited during seizure that could influence the recording profiles. Therefore, we are confident that our in vivo CC will measure only pre-existing enhancer profiles.

Calling Cards identifies enhancer loci related to later seizure responses
To test whether epigenetic enhancer activity (indicated by BRD4-directed CC insertions) might predict seizure severity, we sought to compare CC profiles between mice that experienced mild or severe responses. We hypothesized that the stochastic distribution in seizure presentation would be matched by stochastic differences at the molecular level. Therefore, we expected that mice with moderate responses would appear as an intermediate state that incorporates both susceptibility and resistance molecular phenotypes. Therefore, a primary comparison between the mild (~1/3) and the severe (~1/3) groups would optimize our chance of detecting susceptibility/resistance states. To do this, we prepared and sequenced CC libraries from all mild (12) and severe (11) responders to recover CC insertions. To increase our sensitivity to detect unique insertions, libraries were prepared from separate tissue fragments of each cortex (up to six per animal). We selected the cortex for analysis because the cortex is quickly activated following intraperitoneal PTZ administration in rodents33. CC enhancer profiles were generally very similar across animals (Supplementary Fig. S3A, B). Animals passing QC metrics (methods) were included for downstream analysis, leaving 10 mice per group. As expected, we found that insertions were distributed across the genome (Supplementary Fig. S3C) and could not be attributed to any one mouse (Supplementary Fig. S3D). We used peak calling to define regions of enriched insertion density, for a total of 16,872 peaks (Supplementary Data 2). We expected that these peaks should include known enhancer sites. Indeed, peaks were located predominantly within intergenic regions and introns, rather than promoters, as would be expected (Supplementary Fig. S3E). Furthermore, overlaying CC peaks with ChIP-seq datasets of histone modifications from developing mouse forebrain34 revealed strong enrichment of active enhancer-associated marks H3K27ac (Fig. 3A, B, Supplementary Fig. S3F, I) and H3K4me1 (Supplementary Fig. S3G, I), but not of repressive mark H3K27me3 (Supplementary Fig. S3H, I), as established from prior work establishing the AAV CC system22. CC peak centers had 2.6-fold enrichment over flanking regions for H3K27ac (p=0), 1.5-fold enrichment for H3K4me1 (p=0), and no enrichment for H3K27me3 (p=0.13) (Supplementary Fig. S3J). This enrichment was specific to CC peaks, as shifting the peak coordinates by 20 kb resulted in loss of this enrichment (Supplementary Fig. S3F–H).

To test the hypothesis that there might be loci whose developmental epigenetic activity predicts later seizure presentation, we compared the number of insertions falling within each peak between mild and severe responders using Fisher’s exact test. We expected this categorical distinction between mild and severe to provide high sensitivity to detect differences related to seizure score. After Benjamini-Hocherberg correction, we observed 110 regions enriched for CC binding in mild-responding animals and 133 for severe responders (Fig. 3C and Supplementary Data 2).

We next annotated peaks with nearby genes and plotted the directionality of peaks toward mild or severe responders. As an example, we found relative enrichment in CC insertions around the Ramac/Homer2 gene in mild animals (Fig. 3D). Similarly, we observed heavy enrichment of CC insertions in peaks around Zic1/Zic4 in the severe-response mice, with seven peaks reaching significance (Fig. 3E). Among the strongest enrichment in mild responders occurred around Htr1f and Heatr3, and Sall3 and Bche for severe. We found enrichment near both coding and non-coding genes, such as the microRNA MirLet7c-1. Next, we conducted a posthoc analysis on these peaks showing categorical differences to examine whether the magnitude of change in enhancer utilization is also sometimes linearly correlated with seizure severity across animals. This complementary analysis would allow us to explore the quantitative relationship between seizure score and enhancer usage. Overall, within the set of mild or severe-related peaks, we found 15 sites that were significantly correlated with seizure score (Supplementary Data 2). Insertions near Gabrb1/Gabra4, Ahcyl1, Ramac/Homer2, Ptprk/Themis, Ano10, Kctd16/Pabpc2, and Mfap5/Aicda were negatively correlated with seizure score (Gabrb1/Gabra4 and Ramac/Homer2 shown in Fig. 3F, G). Insertions near Kcnc1, Man1a, Ifitm7, Nr2f2, Nyap2/Dock10, Sncaip/Zfp474, Lnp1/Tmem45a, and Snhg14/Snrpn were positively correlated with seizure score (Kcnc1 and Man1a shown in Fig. 3H, I). Notably, insertion numbers from strongly enriched genes like Ramac/Homer2 negatively correlated with numerical seizure score (ρ=−0.71, p=0.02), while others like Zic1/Zic4 did not show strong correlation (ρ=0.14, p>0.3). These results provide evidence for a subset of peaks having a dose-dependent relationship between enhancer occupancy and seizure outcomes.

To ensure that the differential peaks we discovered represented robust epigenetic differences related to seizure vulnerability and not mere individual differences, we shuffled the comparison groups 50 times to comprise animals with mixed seizure severity scores and performed the same peak calling and differential analysis. Indeed, the number and proportion of differential peaks we observed in our dataset (1.44%) were significantly higher than those observed in 49/50 shuffled arrangements (mean 0.44%, st. dev. 0.35%, p=0.02), indicating there is a biological coherence to these group assignments that was better than chance (Supplementary Fig. S4A). This suggests a substantial proportion of the 243 differentially bound regions we identified represented pre-existing variation in BRD4 binding that was predictive of later seizure severity. Furthermore, to determine the sensitivity of our CC approach to discovering differential peaks, we downsampled our dataset of insertion profiles to various percentages to reflect reduced sample sizes and quantified the number of differential peaks retained from the full dataset. Interestingly, while the number of differential peaks showed a relatively linear relationship with downsampling percentage, we did observe a plateau in the number of differential peaks significantly correlated to seizure score when downsampled to 80% (Supplementary Fig. S4B). Taken together, these results suggest that our analysis has high specificity (so that most of the differentially bound peaks we identified are likely bona fide) and reasonable sensitivity at this power.

Genes associated with seizure responses are associated with neurodevelopment and epilepsy
If pre-existing variation in neuronal enhancer activity predicts seizure severity, then genes near differentially active enhancers should be enriched for neurodevelopmental pathways and genes previously implicated in seizure disorders. We performed Gene Ontology (GO) for mild-response genes or severe-response genes against a background list consisting of all genes annotated near CC peaks. (We note that because only Syn1-Cre positive cells (neurons) had transposase activity, all loci should be from neurons). Surprisingly, even against this background of neuron loci, we found enrichment of multiple neurodevelopmental pathways in the severe-response genes, but not in mild-response genes. For example, enriched processes associated with severe genes included “generation of neurons” and “neuron differentiation” (Fig. 4A). Interestingly, severe-associated genes were also enriched for “L-glutamate transmembrane transporter activity,” such as transporters (Slc1a7, Slc7a11) and receptors (Grm1) (Fig. 4A). These suggest severe responders might carry a greater proportion of neurons in a more neurodevelopmentally immature state and/or might have cell populations more polarized toward excitatory neurons.

We were specifically interested in whether genes from either list were enriched with established epilepsy genes, either related to monogenic forms of epilepsy or identified as a risk factor in common variant genome-wide association studies. This would be consistent with a model where slight epigenetic variations in activities across numerous established epilepsy genes could summate to alter risk. To measure enrichment for known epilepsy genes, we tested our significant mild and severe gene sets for the number of overlaps with known epilepsy gene lists, and compared this to an expected distribution of overlaps. We used permutation testing to generate the expected distributions of overlap using sets of all genes identified by CC peaks to control for the neuronal bias inherent in genes drawn from neuronal CC peaks. As a negative control, we also included lists of genes implicated in cancer, hypertension, and rheumatoid arthritis. Prevention Genetics clinical panels include a list of seizure and epilepsy genes for which seizures are a major clinical feature (Supplementary Fig. S5F–H), as well as a comprehensive list of seizure- and epilepsy-associated genes (Fig. 4B–D). We found that of the 326 mild genes, 8 were associated with monogenic epilepsy (z-score 0.42, p=0.3888) (Supplementary Fig. S5F), and 37 with broader epilepsy associations (z-score 2.26, p=0.0187) (Fig. 4B). Among the 381 severe genes, 15 were associated with monogenic epilepsy (z-score 2.48, p=0.0152) (Supplementary Fig. S5G), and 47 with broader epilepsy associations (z-score 3.17, p=0.0023) (Fig. 4C). A recently updated list of seizure genes6 includes and categorizes genes related to epilepsy with varying levels of clinical evidence, ranging from monogenic forms of epilepsy to potentially-associated genes (Supplementary Fig. S5A–E, Fig. 4E). Of the mild genes, 5 overlapped with monogenic epilepsy genes (z-score 1.17, p=0.1828) (Supplementary Fig. S5C). Of the severe genes, 6 overlapped with monogenic epilepsy genes (z-score 1.35, p=0.1404) (Supplementary Fig. S5D). In total, 129 genes from both mild and severe groups (686) overlapped with the list of all epilepsy genes (z-score 2.85, p=0.003) (Fig. 4E). Owing to a similar etiology, we expected to observe overlap with other neurodevelopmental disorders. Indeed, mild and severe genes each showed strong overlap with autism genes35 (35 for mild, z-score 3.2, p=0.0017; 44 for severe, z-score 4.08, p=0.0001) (Supplementary Fig. S5I–K) and intellectual disability genes36 (44 for mild, z-score 2.71, p=0.0057; 51 for severe, z-score 2.89, p=0.003) (Supplementary Fig. S5L–N). As expected, the overlap with genes for cancer37,38 (22 mild, z-score 0.62, p=0.2958; 20 severe, z-score −0.59, p=0.754) and other less related diseases (hypertension39, rheumatoid arthritis40) across both response groups was not significantly different from other genes nearby to CC peaks (Fig. 4F, Supplementary Fig. S6). Even the overlap with Parkinson’s disease genes41 (5 mild, z-score 0.97, p=0.2329; 6 severe, z-score 1.13, p=0.1899) did not significantly differ from that of other CC genes (Supplementary Fig. S5O–Q), despite its being a neurological disease, potentially due to its neurodegenerative rather than neurodevelopmental etiology. Overall, our analysis indicates there is significant overlap between the epigenetic risk loci defined here and previously defined genetic risk loci.

Manipulation of seizure-response genes modifies seizure severity or latency
While there was significant overlap with known epilepsy genes, most CC implicated loci did not contain known epilepsy genes. Therefore, we predicted that these seizure response-related genes might represent previously uncharacterized pathways influencing seizure activity, and further, targetable pathways for future therapeutic leads. We screened the list of target genes for those that had small molecule antagonists or agonists using the Gene Drug Interactive Database. We found 56 such genes that had an interaction score of at least 10, representing likely target engagement (Supplementary Data 3). Owing to the availability of highly-selective, blood-brain barrier penetrant agonists, we selected Htr1f, which encodes the serotonin receptor 1f, for further study. Additionally, we had previously developed an antisense oligonucleotide (ASO) inhibitor for another potential target non-coding microRNA Mirlet7c-1 that can be delivered safely to the central nervous system. A peak at Htr1f was enriched in mild responders (mild/severe log2FC of 1.96), and two peaks at Mirlet7c-1 were modestly enriched in severe responders (log2FC 0.59 and 0.16; Fig. 3C, Supplementary Fig. S7, and Supplementary Data 2). Notably, neither HTR1F nor MIRLET7C has any existing ties to epilepsy disorders.

Htr1f encodes the serotonin receptor 1F, which is an inhibitory G-protein coupled receptor, and its most abundant expression in the central nervous system is in the retina and cortex42. To evaluate whether HTR1F activity can influence seizure responses, we delivered to P28 wildtype mice either vehicle or 8mg/kg HTR1F agonist LY344864 by intraperitoneal injection for 3 days, then administered PTZ (Fig. 5A). LY344864 is a highly selective serotonin receptor 1F agonist that can cross the blood-brain barrier43,44. We evaluated both the maximal seizure score and the latencies for each mouse to achieve a given score. LY344864 did not influence seizure score, but significantly extended time until mice experienced seizure responses (Fig. 5B). The first response, usually represented by freezing or immobility behavior, was delayed by 25s (median) (Fig. 5C). Score 3, which is marked by a “tail pop”, trended to a delay of 30s (Fig. 5D). Score 5, which is the onset of a generalized tonic-clonic seizure, was delayed by 180s (Fig. 5E).

Let-7c is a member of the Let-7 miRNA family, which share an identical seed sequence for target binding. Let-7 miRNAs are widely-expressed throughout the brain and suppress hundreds of target mRNAs, including those influencing neuronal synaptic development45. To evaluate whether Let-7 inhibition could influence seizures, we delivered an ASO to neutralize Let-7 miRNAs by intracerebroventricular injection to 3 month old mice, an age at which ASOs can be administered ICV easily (Fig. 5A). After one month, we administered PTZ and recorded seizure responses. Let-7 inhibition worsened seizure score and hastened the first response by 22.5s with a trend to delaying score 3 by 17.5s (Fig. 5F–I).

Methods
In vitro Calling Cards
To investigate the dynamics of transposon hopping, we engineered a cell line that could constitutively express the PB transposase. To generate the transposase construct, PB-ERT2 and T2A-GFP inserts were cloned into the pSMAL backbone. After confirmation of the construct by sequencing, HEK293T cells were transfected with the PB-ERT2-T2A-GFP construct (available upon request) to produce lentivirus. Neuro-2a (N2A) cells were transduced with PB-ERT2-T2A-GFP lentivirus. After visual confirmation of GFP expression, GFP-positive cells were bulk sorted to establish a polyclonal stable line.

The N2A parental cell line was obtained from ATCC (cat. no. CCL-131) and authenticated using ATCC standard procedures including karyotyping and testing for fungal, bacterial, ectromelia viral, and mycoplasma contamination. The N2A parental line tested negative for mycoplasma contamination. The engineered N2A-PB-ERT2-GFP line was not tested for mycoplasma contamination.

The N2A-PB-ERT2-T2A-GFP cells were cultured in EMEM (Corning #10-009-CV) + 10% fetal bovine serum (Gibco #A56708-01) + 1 uM 4-hydroxytamoxifen (4-OHT) (Sigma catalog: H6278-50MG) before and during the experiment for constitutive expression of PiggyBac transposase. Three replicate electroporations were performed. For each, a T-75 flask (6–7×106 cells) was split and prepared for electroporation using the Neon NxT Electroporation System 100uL Kit (ThermoFisher) following the manufacturer’s instructions. Six μg of Broken Heart (BH) transposon plasmid (Addgene: 203394) was electroporated using the following parameters: 1 pulse at 1400 V for 20 ms. The cells were immediately plated in culture medium into 24-well plates and collected in technical triplicate at the following time points after electroporation: 15min, 1h, 2h, 5h, 9h, 24h, and 48h. Cell collection occurred by aspirating the culture medium into a 1.5 ml tube, lifting cells remaining in the well with 0.25% Trypsin-EDTA (Gibco #25200056), and centrifuging the mixture for 3 min at 500×g. This process ensured capture of both adhered and not yet adhered cells to account for the adherence status at a wide range of time points. Following aspiration of the supernatant, cell pellets were frozen until completion of the time course. Thawed cell pellets were resuspended and lysed in 25mM NaOH with 0.2mM EDTA, pH=12, at 99°C for 30min. Following lysis, the lysate was neutralized with 40mM Tris-HCl, pH=5.2. Lysates were used as input for PCR amplification of BH using NEBNext Ultra II Q5 Master Mix (New England Biolabs #M0544X) with the following thermal cycling conditions: 98°C for 30sec; 35 cycles of 98°C for 10sec, 67°C for 15sec, and 72°C for 30sec; 72°C for 2min. Primer sequences were as follows: DsRed Fwd: 5′-CGACTACAAGAAGCTGTCCTTCC-3′, DsRed Rev: 5′-GCCCAGAGTCTTCTTCTGCAT-3′. PCR products were run on a 1.5% agarose gel stained with ethidium bromide and imaged on a UVP Gel Solo (Analytik Jena). Fiji was used to quantify band intensities following background subtraction.

Animals
All practices and procedures using mice were approved by the Washington University in St. Louis Institutional Animal Care and Use Committee (protocol numbers 19-0875 and 22-0253) in accordance with National Institutes of Health (NIH) guidelines. All mice used in this study were bred and maintained in the Washington University in St. Louis vivarium, where they were kept on a 12-h light/dark cycle, constant temperature and humidity, and had unlimited access to food and water. All mice were on a C57BL/6J background from Jackson Laboratory (catalog: 000664), and experiments were balanced for sex, except the Let7 ASO study (see Drug treatment and preparation). Syn1-Cre mice used for implementation of Calling Cards were ordered from Jackson Laboratory: B6.Cg-Tg(Syn1-cre)671Jxm/J65 (catalog: 003966) and were inbred with C57BL/6J. Non-transgenic mice were used in drug studies and were euthanized in their home cage using carbon dioxide.

In vivo Calling Cards constructs, virus production, and injections
Transposase and donor transposon plasmids were packaged into AAV9 capsids by the Hope Center Viral Vectors Core at Washington University School of Medicine. For the donor transposons, we used a barcoded pool of Self-Reporting Transposons (SRTs) (Addgene: 1000000213). SRTs report their location via RNA transcription and enable about an order of magnitude more insertions to be recovered per animal, compared to non-SRT transposons22. Insertions are recovered following reverse transcription and PCR amplification of the SRTs in the RNA- and each inserted locus can produce multiple RNA copies, whereas non-SRTs begin with fewer copies as they are DNA-based transposons66. The barcoded SRT is a further innovation that allows us to distinguish insertions at identical genomic locations by their different barcodes, increasing resolution by a factor of the number of unique barcodes67. SRTs were co-injected with the ‘Front Flip’ Cre-dependent Transposase, which cleanly drives activity only in Cre-expressing cells22. Titers for each virus ranged between 6.8×1013 viral genomes/ml. A 1:1 mixture by volume of transposase and donor transposon viruses was intracranially injected into the cortex of P0-1 Syn1-Cre male and female mice (two sites per hemisphere; 1 μL of viral mix per site). This injection paradigm results in widespread delivery throughout the brain22,24, though not necessarily evenly within and between animals. Therefore, QC steps described in the analysis sections below are performed to ensure sufficient expression of CC reagents for downstream analysis. Prior to injections, pregnant females were monitored daily, and newborn pups were injected within 24h. CC injected pups were not weaned prior to sacrifice at P28.

Seizure induction for Calling Cards and evaluation
Intraperitoneal (i.p.) injection of pentylenetetrazol (PTZ) is the most well-established method for PTZ-induced seizure induction30. PTZ delivered by i.p. rapidly enters the brain, typically resulting in epileptiform signs within 1–5min. Dosing for i.p. PTZ delivery was selected because a literature-wide review of this approach found that 50% of mice exhibited generalized tonic-clonic activity (score 5) with a 65mg/kg injection30. This dose was selected to achieve sizable groups for mild (score <5) and severe (score >5).

Mice were injected i.p. with 65mg/kg PTZ (Sigma catalog: P6500-25G) and placed in a standard home cage, where they were immediately evaluated for seizure activity for 15min while also being recorded with a video camera. Immediately afterwards, mice were anesthetized with isoflurane and decapitated. Cortices were extracted, and each hemi-cortex was dissected into three parts and flash frozen in liquid nitrogen. For CC libraries, Syn1-Cre animals were used at P28; for validation drug studies, non-transgenic mice were used at either P28 or at 3 month of age.

A previously defined ‘modified Racine scale’ was used to score seizure responses during the 15min period of observation11,29,68 (Table 1.) For the purposes of this study, we created pre-determined severity categories to assign scores of 1–4 as “mild” responses; score 5 as “moderate”; and scores 6–8 as “severe.” The time at which each mouse achieved a score threshold was marked, and the latency from PTZ injection was calculated for time to “first response”, “score 3” (tail pop), or “score 5” (generalized tonic clonic activity). Comparisons of seizure latencies between groups were performed using only mice that achieved at least that score. Seizures were observed and recorded. Seizure scoring was performed by a blinded examiner based exclusively on the video recordings.

Calling Cards library preparation and sequencing
Calling Cards protocol proceeded largely as described24. Briefly, RNA was extracted from cortical tissues according to the manufacturer’s instructions (Zymo RNA Clean & Concentrator-5 Kit and QIAGEN RNEasy Plus Mini Kit). SRT sequencing libraries were prepared from these RNA samples as previously described24 and sequenced on the NovaSeq X Plus platform (Illumina). Multiple cortical pieces (up to six per animal) were processed separately to further increase our ability to detect unique insertions even beyond the number of available barcodes. Data were then pooled by animals for subsequent analysis. Libraries were prepared from all mild and severe animals, and successfully sequenced for 11 mild and 11 severe animals.

Raw reads were processed into QBED files containing transposon insertion sites (at TTAA sites) using the nf-core/callingcards69 Nextflow70,71 pipeline with the default mammalian parameters for mouse as described24. Briefly, read quality control (QC) was performed with FastQC72 and reads were split for parallel processing with SeqKit73. Barcodes were extracted by UMI-tools74 and reads were trimmed with Trimmomatic75. Reads were aligned to mm10 using bwa-mem276. Insertions were counted using callingCardsTools77 and alignment QC was performed using samtools78, Picard Tools79, and RSeQC80. QC data was presented with MultiQC81. QBEDs from each cortical piece were merged per animal with the ‘cat’ shell command and sorted using BEDOPS82. Reads aligning to the same TTAA site with separate barcodes, or from spatially separate cortical pieces, were considered unique insertions. After filtering for insertions with read depth >1, all unique insertions were considered equally, regardless of read depth.

Software and package versions are listed in Supplementary Data 4.

Calling Cards peak calling and annotation
Significantly enriched insertion peak calling was performed with the pycallingcards module83. Insertion profiles for all animals within each severity category were merged to create mild and severe profiles. Peaks were called on the mild and severe profiles separately using the MACCs method, then merged using pybedtools for a final set of peaks. The following parameters were used for the peak calling: reference=“mm10”, pvalue_adj_cutoff=0, window_size=2000, step_size=800, lam_win_size=1000000, pseudocounts=1.

For quality control, pairwise Pearson correlation coefficients were calculated on insertion counts per million within each peak between animals. The average pairwise correlation coefficient was then calculated for each animal. Animals with average correlation coefficients above >0.91 were used for downstream analysis, including re-calling the peaks, resulting in 10 mild and 10 severe animals. Principal components analysis was also performed on the insertion counts per million within each peak between animals to assess similarity among samples. The two excluded animals with the lowest correlation coefficients, which were also outliers on the PCA, reflected the animals with the lowest total insertion counts.

Peak annotation was performed using bedtools84 and HOMER85. Because enhancers usually exert their effects on nearby genes86,87, genes were assigned to peaks based on proximity. We used bedtools to find up to five of the closest genes within one megabase of the peak, filtering out “Gm” and “Rik” genes that have not been assigned names. Genomic features of the peaks were identified by HOMER.

Histone modification enrichment analysis
Qualitative enrichment analysis of histone modifications at CC peaks and non-peak loci was performed using deepTools88. The following P0 mouse forebrain ChIP-Seq34 datasets from ENCODE89 were used: H3K27ac (ENCSR094TTT), H3K4me1 (ENCSR465PLB), and H3K27me3 (ENCSR070MOK). Enrichment was quantified by comparing ChIP-Seq signal at CC peak centers, defined as ±0.5 kb from center coordinates, versus at flanking regions, defined as two standard deviations of peak length away from peak centers (±6.4–7.4 kb), using paired two-tailed t-tests.

Differential peak analysis
For the main differential peak analysis, we compared the number of insertions falling within each peak between mild and severe groups (or between shuffled groups) using Fisher’s exact test implemented in pycallingcards83. Peaks with adjusted p-values <0.05 after Benjamini–Hochberg FDR correction were considered differential peaks. In addition, as a post-hoc we confirmed the correlation between insertion counts per million at each of the significant peaks and seizure score using the Spearman test, with Benjamini–Hochberg correction.

For the label shuffling analysis, animal profiles were shuffled into two groups in 50 random arrangements such that each group had 4–6 mild and 4–6 severe animals and roughly equal numbers of insertions. Downstream peak calling and differential analysis proceeded as described here. An empirical p-value was determined by dividing the number of arrangements with equal or greater significant peaks than the observed value by the total number of arrangements.

For the downsampling analysis, insertion profiles for the mild and severe groups were randomly downsampled three times per percentage to 20–90% of the full dataset using pycallingcards83. Differential peak analysis was performed as described above using the peaks called on the full dataset.

Gene ontology enrichment analysis
Gene ontology was performed using Panther classification system to identify enrichment in “biological” processes. Genes associated with mild responders or with severe responders were evaluated against a background of all genes annotated with CC peaks (16,240 genes). Processes achieving p<0.05 after Benjamini–Hochberg FDR correction are displayed.

Gene overlap significance testing
The significance of overlap between gene sets was statistically evaluated. The observed overlap between significant gene sets (mild, severe, both) and disease-related genes was compared to an expected distribution. This distribution was generated through 10,000 random permutations. In each permutation, genes were randomly sampled without replacement from the list of all genes annotated to CC peaks, with the sample size matched to each significant gene set being tested. The overlap between these random gene sets and the disease gene list was calculated. An empirical p-value was determined by dividing the number of permutations with equal or greater overlap than the observed value by the total number of permutations. Note the random gene lists and the significant sets are both drawn from peaks found in neurons, and thus this controls for any general neuron-specific bias to any disease.

The following disease gene datasets were used for the overlap analysis (Supplementary Data 5): monogenic epilepsy (Prevention Genetics Epilepsy and Seizure Panel, test code 7347; https://www.preventiongenetics.com/testInfo?val=Epilepsy-and-Seizure-Panel), epilepsy-related (Prevention Genetics Comprehensive Epilepsy and Seizure Panel, test code 16005; https://www.preventiongenetics.com/tests/pgmax-comprehensive-epilepsy-and-seizure-panel), monogenic and all epilepsy-related6, autism (SFARI genes; https://gene.sfari.org/database/human-gene/)35, ID (SysNDD phenotype search “intellectual disability”, category: definitive; https://sysndd.dbmr.unibe.ch/Phenotypes)36, Parkinson’s disease (Gene4PD, rare genes; http://genemed.tech/gene4pd/download)41, cancer (OncoKB; https://www.oncokb.org/cancer-genes)37,38, hypertension (CVD Atlas, PedAM Disease-variant association; https://ngdc.cncb.ac.cn/cvd/disease/CVDD000096)39, rheumatoid arthritis (RADB: http://www.bioapp.org/RADB/index.php/Index/index)40. Human disease gene IDs were converted to mouse gene IDs prior to overlap testing based on the Mouse Genome Informatics nomenclature.

Drug treatment and preparation
Experimental drug testing was performed in non-transgenic mice. Mice were randomly allocated into each group with balancing for sex, except for the Let-7 ASO study. This experiment only used female mice because there were differences in (expected) weight gain between male/female mice when using this ASO, which would interfere with PTZ dosing. Investigators were blinded to treatment groups in drug studies and during seizure scoring.

The HTR1F agonist LY344864 was ordered from Med Chem Express (Cat. No.: HY-13788). LY344864 was reconstituted in DMSO to stock concentration of 60mg/mL, aliquoted, and kept at −80°C. On the day of use, LY344864 was resuspended as 0.5% DMSO/drug in corn oil. LY344864 was delivered by intraperitoneal injection at 8mg/kg. Corn oil containing 0.5% DMSO was used as vehicle control. Non-transgenic mice were injected once-daily for 3 days from P28-P30. One hour after the final injection, PTZ was administered, and seizure responses were recorded.

The Let-7 inhibitor was ordered from Integrated DNA Technologies with the sequence AACTATACAACCTACTACCTCA. A non-targeting scrambled sequence ATAACACTCTAACCACTATCAC was used as a control. Each oligonucleotide was fully modified with 2-methoxyethyl bases and a phosphorothioate backbone. 20 nanomoles of Let7 inhibitor, corresponding to 173.4 micrograms, was delivered by intracerebroventricular injection at age 3 months as previously described90. A total volume of 10 microliters was injected at a rate of 1 microliter per second, with the needle left in place for 2 min both before and after injection. Coordinates from bregma were the following: X=−1.0mm lateral (right), Y=+0.3mm anterior, Z=−3.0mm ventral. Let7 inhibition in the CNS using the indicated sequence has previously been validated91,92. Four weeks after injection, mice were administered PTZ, and seizure responses were recorded.

Behavioral statistics
Multiple linear regressions were used to examine association between seizure score with mouse sex or weight. Seizure scores were non-normally distributed ordinal values, therefore non-parametric two-tailed Mann–Whitney tests were used to compare between treatment groups. Seizure latencies were compared between treatment groups using log-rank (Mantel–Cox) test.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study employed the \"Calling Cards\" technology to record epigenetic differences during mouse development and studied its association with seizure severity. Subsequent studies suggested HTR1F and Let-7c as potential therapeutic targets. The study design looks innovatively, and the data presentation is comprehensive. However, several critical issues need to be addressed to enhance scientific rigor and clinical relevance.\n\nMajor Concerns\n1. The sample size is relatively small (n=10 per group), which may limit statistical power.\n2. The manuscript does not clearly specify whether multiple comparison corrections (e.g., Benjamini-Hochberg) were applied.\n3. The study mainly focuses on a single-time-point comparison after seizure induction. A more in-depth longitudinal analysis throughout the entire postnatal development period, including multiple time points before and after PTZ injection, would be beneficial. This could help to better understand the dynamic changes of epigenetic states and how they relate to the development of seizure susceptibility over time. For example, it is unclear whether the epigenetic differences observed at P28 are stable or change over a longer time frame.\n4. The study does not validate whether the recorded enhancer activity strictly reflects developmental processes (P0 - P28) or might also include acute post-seizure changes. Since seizures can trigger a series of molecular changes, failure to distinguish between changes originating from the developmental process and those from post-seizure events could confound the accurate determination of the relationship between epigenetics and seizure susceptibility.\n5. The study does not clarify whether differential enhancer activity reflects changes in cell composition (e.g., excitatory/inhibitory neuron ratios) or cellular states, making it difficult to identify the specific pathways and key factors involved.\n6. The enrichment of ZIC family genes is not thoroughly discussed in the context of epilepsy mechanisms. Although the ZIC family genes show significant enrichment in the study, their specific roles in the development and progression of epilepsy, including upstream and downstream regulatory relationships, as well as synergistic or antagonistic effects with other epilepsy-related genes, have not been fully explored.\n7. The validation of HTR1F and Let-7c is limited to an acute seizure model in mice, with no assessment in chronic epilepsy models or human cell systems. There are differences in pathogenesis and disease courses between acute and chronic epilepsy models, and there are also species differences between mice and humans. This calls into question the reliability of translating the research results into clinical applications.\n8. Although the study uses CC to record BRD4-directed enhancer activity, it only scratches the surface of the complex epigenetic landscape. Other important epigenetic marks such as DNA methylation and histone modifications beyond those analyzed (e.g., H3K9me3, H3K36me3) are not investigated. These additional epigenetic factors could play crucial roles in modulating seizure susceptibility.\n9. The causal relationship between epigenetic marks and seizure susceptibility remains unvalidated. Since the genomic background of the mice used for experiments is not identical, it is possible that the differences in gene expression were associated with genetic factors. This is the most important question.", "reviewer_2": "This study is highly significant given the public health impact of epilepsy. The authors focus on the highly relevant, yet understudied, role of non-genetic, environmental factors in risk for developing seizures, The authors’ application of a mouse PTZ seizure model is well suited to this study of phenotypic variation in gene x environment regulation of neuronal physiology. The authors use a highly innovative approach - Calling Cards – to identify gene regulatory correlates of PTZ seizure susceptibility. The outcomes are identification of novel relationships between basal gene state and seizure susceptibility for Htr1f and Let7c.\n\nThis manuscript is significant, novel and a major contribution to the field of epilepsy and neuroepigenetics. The figures are excellent – well designed, labeled and clear. The data are well described and the conclusions are supported by the results. Importantly, the approach applies rigorous computational controls that test the null hypotheses, using appropriate methods (such as comparison to shuffled datasets and comparison to unrelated gene ontologies). The manuscript goes beyond the computational approach to functionally validate identified target genes that may promote seizure susceptibility.\n\nI recommend publication following a major textual revision without the addition of new experimental data.\n\nPlease revise the manuscript to address each comment and/or question listed below. These are intended to improve clarity of the text and ensure that the conclusions drawn are fully supported by the data.\n\nMAJOR\n1. I recommend revising the word ‘epigenetic’, as it does not accurately reflect the outcome measure of Calling Cards. I realize this may not be the convention of the prior literature, but I contend that this usage obscures the nuance and value of the CC approach.\nCC’s quantify transcription factor engagement, not alteration to the epigenome. Although BRD4 is a histone acetylation reader, CCs do not quantify histone acetylation, the relationship of BRD4 to dynamic histone acetylation, nor any other epigenetic modification on chromatin or DNA.\nLanguage in the results section is already excellent for his purpose: “…record pre-existing variation in enhancer activity and correlate inter-individual difference in activity with seizure severity in a PTZ-induced acute seizure model.… These CC reagents record the binding of BRD4, an epigenetic factor which marks active enhancer regions” etc.\n2. Please clarify that CC cannot record dynamic BRD4 engagement over development. The authors state, “We implemented neuron-specific CCs immediately after birth to record epigenetic activity through one month of age.” A revision would clarify that ”through…age” refers to new/additional BRD4 binding across time, but not dynamic loss/gain of BRD4 at individual loci across time.\n3. Re: Methods - Calling Cards Peak Calling and Annotation: Please clarify what data are applied to calculate the correlation coefficient – does this refer to data collected across the six pieces of cortex?\n4. Please add a table of the Racine score and correlation coefficient of each mouse used in the study.\na. Please revise inconsistencies in the methods and results sections in which the authors state “Animals with average correlation coefficients above > 0.91 were used for downstream analysis, resulting in 10 animals per group” and “Of the 32 mice tested, 12 were mild responders, 9 were moderate, and 11 were severe,” respectively.\nb. Were the racine scores of individual mice evenly distributed across all 8 categories? Or did authors design categories to assign equal numbers of mice to each?\n5. The data reported to validate CC peak overlap with enhancer-associated PTMs appear to be descriptive, which limits interpretability.\na. Fig 3AB – Can the authors quantify the extent of overlap between the CC peaks and P0 peaks of enhancer-associated PTMs (K27Ac, K4me1) and gene associated PTMs (K27me3)?\nb. Fig 3B – can the authors add a density plot of non-CC-peak loci to establish specific enrichment of K27Ac at CC peaks? The loci can be selected as proximal to the peak in the intergenic regions.\n6. Fig 3D-E vs 3F-I: Both approaches are valuable but lack details on rationale that would improve clarity.\na. Please compare and offer rationale for the two approaches applied to identify relevant genes (ie plotted directionality vs posthoc correlation).\nb. As presented, the results suggest that the CC insertion rate of genes shown in FIG D-E do not correlate with seizure score. An explicit statement will improve clarity.\nc. Please list the 13 that show such a correlation between CC insertion rate and Racine score, in addition to those shown in 3E.\n7. Is the overlap with CC genes and Prevention Genetics clinical panel statistically significant? The authors use a z-score to present these data, rather than a Fischer’s exact test, which would include a p-value. Can they provide additional explanation of how to interpret the significance of these data? How large is the Prevention Genetics clinical panels?\n8. The functional validation of novel epilepsy relevant targets, Htr1f and Let7c, would be meaningfully strengthened by the inclusion of positive and negative control experiments, using established modulators of any of the following: PTZ-induced seizure activity, CC-called genes that do overlap with epilepsy-related genes, CC-called genes that overlap with non-epilepsy related genes, etc. While I do not expect authors to conduct additional experiments for this manuscript, I recommend that authors revise their conclusions to reflect the absence of such control experiments and suggestions therein.\n\nMINOR\n1. How does the mechanism of SRTs enhance sensitivity and recovery, as described in the methods? Enhanced relative to non-self-reporting transposons? How does that strengthen the current work?\n2. Is a specific brain region is targeted by CCs “intracranially injected into the cortex” of neonates? Do authors expect AAV to spread to all neurons during development? Did the authors validate (equal) expression of the AAV in each cortical piece and/or each mouse used in the downstream analysis?\n3. What is the rationale for the dose and roa of PTZ?\n4. Time course: Why was 15 minutes selected for observation of seizure activity? Did observation begin immediately following PTZ injection (ie in the ‘first response’ score)?\n5. Was seizure activity recorded from the video recordings and/or live?\n6. I request that a reviewer with relevant expertise report on the rigor of the “modified Racine scale” applied by the authors.\n7. Please provide details regarding the use of TTAA site insertions in the analysis pipeline.\n8. Why are mice in the moderate seizure category (n=9) excluded from CC analyses?\n9. Fig 3C – why are CC insertions for mile and severe group plotted as negative and positive fold change when these are the insertions relatively enriched in each group? Please revise the methods to clarify the analysis and plotting.\n10. Please strike the following statement as it is not supported by the data (ie a correlation does not necessarily support predictive power): “These results indicate that CC occupancy at these peaks (BRD4 sites) is predictive of seizure outcomes.”\n11. Revise results/discussion to include published literature regarding Let-7 targets implicated in epilepsy and seizure."}, "author_response": "Dear editor and reviewers,\nThank you for the helpful comments. We have now responded to all critiques, developed new reagents\nand conducted key experiments regarding the minimum time required for the Calling Cards system to\nwork. We have also added more extensive analyses examining the sensitivity and specificity of our\nassay. All of this has led to substantially revising the manuscript. We have also clarified our usage of\nthe term ‘epigenetics’ and spent more effort delineating the unique features of the Calling Cards system\nrelative to more traditional epigenetic measures such as Chip-seq, ATAC, etc. We believe this clarifies\nthe goals of this paper and addresses the reviewer concerns.\nThank you for your time and attention, and we look forward to a constructive review process. Our\nresponses are in italics below.\nThe authors.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis study employed the \"Calling Cards\" technology to record epigenetic differences during mouse\ndevelopment and studied its association with seizure severity. Subsequent studies suggested HTR1F\nand Let-7c as potential therapeutic targets. The study design looks innovatively, and the data\npresentation is comprehensive. However, several critical issues need to be addressed to enhance\nscientific rigor and clinical relevance.\nWe thank the reviewer for their advice and critical feedback. We have carefully addressed rigor issues\nas described below, and clarified a variety of additional points with additional experiments, analyses, and\nsubsequent edits and writing. On the whole we believe this has produced a substantially improved\nmanuscript.\nMajor Concerns\n1. The sample size is relatively small (n=10 per group), which may limit statistical power.\nWe understand the reviewer’s concern about statistical power. As an aside, please note, this was 10 per\ngroup biological replicates, but often multiple brain samples per mouse, so in terms of sample handling\nwas closer to 100 sequenced libraries. However, we conservatively collapsed by individual animals.\nNonetheless, ipso facto, as we discovered some regions showing differences in BRD4 binding, there\nmust have been sufficient power. Nonetheless, we agree as a relatively new method, future readers might\nbenefit from a deeper exploration of power issues. Therefore, we conducted a series of analyses that\nexamined the specificity and sensitivity to detect our differentially bound regions.\nTo determine how specific the differences we found were to seizure severity, we performed the differential\npeak analysis using 50 permutations of shuffled animal IDs in the two groups of insertion profiles. In\n49/50 permutations, the number of significant peaks (as both an integer and as a proportion of the total\npeaks) we detected was significantly lower than the number we detected in our real dataset, which\nsuggests that our differential peaks genuinely reflect differences related to later seizure severity (new Fig\nS4A). This is now included in results and supplement.\nFurthermore, to determine the sensitivity of our paradigm to discovering differential peaks, we\ndownsampled our dataset of insertion profiles to various percentages to reflect reduced sample sizes,\nand quantified the number of differential peaks retained from the full dataset. While the number of\ndifferential peaks showed a relatively linear relationship with downsampling percentage, we did observe\na plateau in the number of differential peaks significantly correlated to seizure score when downsampled\nto 80% (new Fig S4B). Thus, the reviewer is correct that there may have been even more peaks available\nif we had added even more samples, but our hits with strong quantitative relationships with seizure\nseverity would have remained detectable even with n=8 per group. This is now included in results and\nsupplement.\nTaken together, these results suggest that our analysis has high specificity (so that most of the\ndifferentially bound peaks we identified are likely bona fide, including the ones not experimentally\nvalidated) and reasonable sensitivity at this power (so that analyzing more animals could identify\nadditional differentially bound peaks).\n2. The manuscript does not clearly specify whether multiple comparison corrections (e.g., Benjamini-\nHochberg) were applied.\nThank you for pointing this out. We have now highlighted that we are indeed using Benjamini-Hochberg\nFDR correction in results, in addition to the methods (Differential Peak Analysis). Column AB in\nSupplemental Table 2 shows the FDR corrected p-value.\n3. The study mainly focuses on a single-time-point comparison after seizure induction. A more in-depth\nlongitudinal analysis throughout the entire postnatal development period, including multiple time points\nbefore and after PTZ injection, would be beneficial. This could help to better understand the dynamic\nchanges of epigenetic states and how they relate to the development of seizure susceptibility over time.\nFor example, it is unclear whether the epigenetic differences observed at P28 are stable or change\nover a longer time frame.\nWe believe this comment highlights that our first submission needed some improvements in writing and\nclarity to better describe the unique contributions of Calling Cards (CC). Overall, the rationale for the\nproposed experiment here is a bit unclear to us, but we believe this is because we need to clarify (a) the\npurpose of the new Calling Cards method and (b) what is currently feasible with it:\nFeasibility: We currently do not have a method that allows us to start and stop recording with Calling\nCards, which precludes doing multiple time points before PTZ injection. As we discuss in the discussion,\nsuch ‘inducible Calling Cards’ is a dream and something we have worked out somewhat in cell culture1,\nbut our multiple years of attempts to take this induction toolkit in vivo have not yet succeeded robustly.\nTherefore, with the current technology, recording starts with the injection of the AAV reagents, and ends\nwhen the animal is sacked. Indeed, this is what led to the choice of this PTZ injection as our test case\nexperiment: Because we would be recording for weeks, but sacking within minutes of PTZ injection (too\nfast for new Calling Cards to be inserted and transcribed, see next comment), this lets us ascribe any\nepigenetic differences as being pre-existent to PTZ exposure. While future experiments with an inducible\nCalling Cards system might allow us to measure when these epigenetic changes emerge (example, by\nrecording different cohorts during different weeks across development, then sacking all mice after a PTZ\ntest at P28), currently this is not possible. Nonetheless, the current paper represents a major advance of\nwhat is possible, and opens up doors for numerous additional experiments while we continue to work out\ninducible Calling Cards in vivo.\nPurpose of Calling Cards: The reviewer was also suggesting including time points after PTZ injection.\nThis indicates we may have done a poor job explaining the unique advance of the Calling Cards platform.\nThe unique feature of Calling Cards is that it has the ability to record an epigenetic state before an\nexposure. Other existing methods (Cut N Run, ChIPseq, ATACseq) would all be fine for measuring what\nhappens after a seizure by a variety of models, and indeed such work has been done2–10 and thus we\nwould not contribute anything new by repeating those experiments here. But those existing methods\ncannot tell you anything about what the state was before the PTZ exposure. Indeed, the PTZ or other\nseizure-causing exposures themselves would potentially alter any pre-existing epigenetic state,\nobscuring it. Thus, the main purpose of the current study was testing the ability of Calling Cards to record\npre-existing states and test the hypothesis that pre-existing states predict outcomes. PTZ was just\nselected as a test case for this new approach idea. To address this, we have now added discussion of\nthe referenced seizure work and the advance of Calling Cards to the introduction (fourth paragraph).\nOverall, we believe we have addressed the ambiguity here by better writing on our part. Indeed, Reviewer\n2 (their Major Comment 1) proposed some big picture language changes that might help further clarify\nthese points for other readers. Please see our response to them below, but briefly we have made\nnumerous small changes throughout the paper to make it clear how CC differs from other epigenetics,\nand explained the rationale for recording before the PTZ-induction.\n4. The study does not validate whether the recorded enhancer activity strictly reflects developmental\nprocesses (P0 - P28) or might also include acute post-seizure changes. Since seizures can trigger a\nseries of molecular changes, failure to distinguish between changes originating from the developmental\nprocess and those from post-seizure events could confound the accurate determination of the\nrelationship between epigenetics and seizure susceptibility.\nThank you, this is a good point. We selected this manipulation (PTZ) as our test case because we could\nassess it so quickly that we thought new Calling Cards insertions would not have time to occur. Thus,\neven though some molecular changes might be that fast (e.g., Fos expression, which takes 5-10\nminutes), we assumed the insertion of a transposon into the genome, followed by transcription of the new\nlocus would take far longer than 15 minutes. However, as the reviewer points out, it would be ideal to\nrigorously test this assumption. Therefore, we have added a cell culture experiment to make sure new\nCalling Cards signals cannot occur in 15 minutes (new Fig S2). To do this, we developed a stable cell\nline where the transposase protein was always present in 100% of the cells. We could then use these to\ntest the rate of hopping (transposition) onset by using an acutely delivered ‘BrokenHeart’ transposon,\nwhere the ‘donor’ transposon interrupts an RFP fluorophore in a plasmid, and measured the fastest\npossible rate such transpositions could occur by measuring how early transposon excision from the\nBrokenHeart plasmid could be detected. Indeed, our results show that it takes at least 2-5 hours for such\nsignals to become detectable using the most simplified transposon system and a highly sensitive PCR\nassay. This timeline indicates it would be impossible for our recordings to show post-seizure events in 15\nminutes.\nNote that here we also just measured the first step (the hopping of the transposon) of what would be a\nmulti-step process required to detect a self-reporting transposon (SRT) insertion. For the SRTs, they\nwould also need to subsequently recruit the RNA Pol II complex to the insertion, initiate transcription and\nproduce RNA to be measurable by SRT sequencing. Thus, if the first step takes hours, production of new\nRNA from new sites in the genome in 15 minutes would be impossible. We have now added a new\nparagraph in the results section that describes this experiment and highlights this logic for the readers.\n5. The study does not clarify whether differential enhancer activity reflects changes in cell composition\n(e.g., excitatory/inhibitory neuron ratios) or cellular states, making it difficult to identify the specific\npathways and key factors involved.\nWe agree that it is difficult to disentangle changes in cell composition from cellular state. However, we\ndid manage to find some specific pathways and factors despite this limitation, such as “glutamate\nsignaling” in the severe group and GABA receptor subunits and transporters in the mild group. We\ncurrently devote a paragraph discussing these issues. However, distinguishing between cell composition\nor cellular state changes is out of the scope of the current paper: the brains from these mice have been\nhomogenized, so no cell counts are possible. More importantly, our goal with this first paper was to\nestablish that the Calling Cards method could record pre-existing molecular states in vivo, and to test the\nhypothesis that antecedent effects can predict later outcomes. Determining whether this reflects states\nor composition differences in these animals will require future experiments and some more technology\ndevelopment. Such things might, for example, be possible with single cell Calling Cards. However, we\nhave not yet been able to get this experiment to scale to the numbers of animals required to test the PTZ\nhypothesis. We have now added more discussion of this in the same paragraph.\n6. The enrichment of ZIC family genes is not thoroughly discussed in the context of epilepsy\nmechanisms. Although the ZIC family genes show significant enrichment in the study, their specific\nroles in the development and progression of epilepsy, including upstream and downstream regulatory\nrelationships, as well as synergistic or antagonistic effects with other epilepsy-related genes, have not\nbeen fully explored.\nThank you for this comment. We have expanded our discussion of the ZIC family of proteins in our\nmanuscript (discussion, sixth paragraph).\n7. The validation of HTR1F and Let-7c is limited to an acute seizure model in mice, with no assessment\nin chronic epilepsy models or human cell systems. There are differences in pathogenesis and disease\ncourses between acute and chronic epilepsy models, and there are also species differences between\nmice and humans. This calls into question the reliability of translating the research results into clinical\napplications.\nWe agree the validation was limited to acute seizures. This was deliberate because the ‘discovery’ Calling\nCards experiments were only done on acute seizures, and we thought it best to align the validation as\nclosely as possible with the discovery experiments.\nIndeed, as all epilepsy models require or exhibit multiple seizures over time (by definition), without an\n‘inducible Calling Cards’ system that could be used to stop recording before any seizure onset, there is\nnot a feasible route to doing an informative Calling Cards-based discovery study for epilepsy rather than\nacute seizure.\nWe also would agree that it would be premature to translate our top candidates to clinical studies based\non our data alone, and we tried to be very moderate in any suggestion of clinical claims. We have\nexpanded on this limitation in the discussion (e.g., “Overall, epigenetic risk factors such as those\nsuggested here might yield novel drug targets or adjuvants for existing therapies providing untapped\nstrategies to affect disease. However, these need to be first thoroughly tested in preclinical models of\nchronic epilepsy before advancing towards the clinic.”)\n8. Although the study uses CC to record BRD4-directed enhancer activity, it only scratches the surface\nof the complex epigenetic landscape. Other important epigenetic marks such as DNA methylation and\nhistone modifications beyond those analyzed (e.g., H3K9me3, H3K36me3) are not investigated. These\nadditional epigenetic factors could play crucial roles in modulating seizure susceptibility.\nThank you, this is a good point. We agree that correlating pre-existing differences in epigenetic marks\nlike H3K9me3 and H3K36me3 with behavioral outcomes would be extremely interesting. Unfortunately\nthere is no current way to record specific histone modifications and then read them out later. We plan to\nexplore other epigenetic readers and writers in the future. Other studies on epigenetic responses to\nseizure have been done using some of the marks suggested by the reviewer2–10, which we have now\nreferenced in the introduction. Furthermore, the reviewer’s point is taken that we did not investigate these\nother epigenetic measures, thus we have noted this and added a sentence on the capabilities of the CC\nmethod in the discussion, at the end of the first paragraph.\n9. The causal relationship between epigenetic marks and seizure susceptibility remains unvalidated.\nSince the genomic background of the mice used for experiments is not identical, it is possible that the\ndifferences in gene expression were associated with genetic factors. This is the most important\nquestion.\nWe apologize - we did not make it sufficiently clear that actually the genomic background of all these\nmice was genetically identical. These were all inbred, isogenic, C57BL/6J mice. The mice used for CC\nwere of the Syn1::Cre genotype, but these were also inbred on the C57BL/6J background. We think the\nambiguity came from this sentence in the intro: “For example, mouse models of acute seizure (e.g.,\ninduction with GABA-A receptor antagonist pentylenetetrazol; PTZ), result in a range of seizure\nresponses even in effectively isogenic mouse strains”: we meant there were a range of seizures in\nisogenic mice, but we see how this could be taken as meaning ‘across isogenic strains’. We have\nrephrased this sentence and added text in several places to make it clear we used genetically identical\nindividuals, thus removing any genetic explanation for the variance between the mice.\nReviewer #2 (Remarks to the Author):\nThis study is highly significant given the public health impact of epilepsy. The authors focus on the\nhighly relevant, yet understudied, role of non-genetic, environmental factors in risk for developing\nseizures, The authors’ application of a mouse PTZ seizure model is well suited to this study of\nphenotypic variation in gene x environment regulation of neuronal physiology. The authors use a\nhighly innovative approach - Calling Cards – to identify gene regulatory correlates of PTZ seizure\nsusceptibility. The outcomes are identification of novel relationships between basal gene state and\nseizure susceptibility for Htr1f and Let7c.\nThis manuscript is significant, novel and a major contribution to the field of epilepsy and\nneuroepigenetics. The figures are excellent – well designed, labeled and clear. The data are well\ndescribed and the conclusions are supported by the results. Importantly, the approach applies rigorous\ncomputational controls that test the null hypotheses, using appropriate methods (such as comparison\nto shuffled datasets and comparison to unrelated gene ontologies). The manuscript goes beyond the\ncomputational approach to functionally validate identified target genes that may promote seizure\nsusceptibility.\nI recommend publication following a major textual revision without the addition of new experimental\ndata.\nPlease revise the manuscript to address each comment and/or question listed below. These are\nintended to improve clarity of the text and ensure that the conclusions drawn are fully supported by the\ndata.\nWe thank the reviewer for their advice, enthusiasm, and critical feedback. We have carefully addressed\nthe concerns as described below.\nMAJOR\n1. I recommend revising the word ‘epigenetic’, as it does not accurately reflect the outcome measure\nof Calling Cards. I realize this may not be the convention of the prior literature, but I contend that this\nusage obscures the nuance and value of the CC approach. CC’s quantify transcription factor\nengagement, not alteration to the epigenome. Although BRD4 is a histone acetylation reader, CCs do\nnot quantify histone acetylation, the relationship of BRD4 to dynamic histone acetylation, nor any other\nepigenetic modification on chromatin or DNA.\nLanguage in the results section is already excellent for his purpose: “…record pre-existing variation in\nenhancer activity and correlate inter-individual difference in activity with seizure severity in a PTZ-\ninduced acute seizure model.… These CC reagents record the binding of BRD4, an epigenetic factor\nwhich marks active enhancer regions” etc.\nThank you. This is very good advice. We have carefully rephrased throughout the paper, removing the\nterm ‘epigenetics’ from the vast majority of spots (including title), and making sure the context was clear\nwhere it remained. We also added some referencing to discussion for our description of the challenge\nwith the term ’epigenetics’ which sometimes refers simply to ‘chromatin modifications’ and sometimes is\nmore broad.\n2. Please clarify that CC cannot record dynamic BRD4 engagement over development. The authors\nstate, “We implemented neuron-specific CCs immediately after birth to record epigenetic activity\nthrough one month of age.” A revision would clarify that ”through…age” refers to new/additional BRD4\nbinding across time, but not dynamic loss/gain of BRD4 at individual loci across time.\nThank you, this is good advice. We have revised this sentence to “...record cumulative BRD4 binding\nevents through one month of age.” For further clarification, we have also added a panel to Fig 1 illustrating\nthe accumulation of BR4-bound sites over time.\n3. Re: Methods - Calling Cards Peak Calling and Annotation: Please clarify what data are applied to\ncalculate the correlation coefficient – does this refer to data collected across the six pieces of cortex?\nWe have clarified this as follows. “For quality control, pairwise Pearson correlation coefficients were\ncalculated on insertion counts per million within each peak between animals. The average pairwise\ncorrelation coefficient was then calculated for each animal. Animals with average correlation coefficients\nabove > 0.91 were used for downstream analysis, including re-calling the peaks, resulting in 10 mild and\n10 severe animals. Principal components analysis was also performed on the insertion counts per million\nwithin each peak between animals to assess similarity among samples. The two excluded animals with\nthe lowest correlation coefficients, which were also outliers on the PCA, reflected the animals with the\nlowest total insertion counts.”\nThis QC step is based on the assumption that broadly the epigenetic history of most animals across the\nentire genome should be similar to one another, with only a fraction of loci predicting seizure. Thus, most\nshould correlate well, and this is what we have historically seen. We have now combined the mild and\nsevere animals for this analysis to avoid artificially exaggerating the differences between mild and severe.\nHowever, the result did not change. The two animals that did not correlate as well (between 0.8 and 0.9,\ntypically) also were outliers on PCA, and furthermore had the fewest measured insertions. Therefore we\ninterpret these as simply being animals that received less AAV due to technical variation in delivery. We\nhave now better described these QC steps to guide the readers through these exclusions.\n4. Please add a table of the Racine score and correlation coefficient of each mouse used in the study.\nThank you. We have added this (Supplemental Table 1).\na. Please revise inconsistencies in the methods and results sections in which the authors state “Animals\nwith average correlation coefficients above > 0.91 were used for downstream analysis, resulting in 10\nanimals per group” and “Of the 32 mice tested, 12 were mild responders, 9 were moderate, and 11\nwere severe,” respectively.\nThank you, we have clarified this in the text. Three animals failed sequencing analysis QC steps (failed\nsequencing, low insertion counts, lower correlations & outliers on PCA), thus were excluded prior to\nstatistical analysis. We have added this sentence to methods: “Libraries were prepared from all mild and\nsevere animals, and successfully sequenced for 11 mild and 11 severe animals,” as well as the text in\nthe comment above.\nb. Were the racine scores of individual mice evenly distributed across all 8 categories? Or did authors\ndesign categories to assign equal numbers of mice to each?\nThank you. We have clarified that the categories were pre-determined in the methods. The scores were\nnot evenly distributed across all 8 categories, as shown in Figure 2B. There was, however, roughly equal\ndistribution across the pre-determined categories: mild (0-4), moderate (5), and severe (6-8). The 8\ncategories were determined based on the established Racine scoring system.\n5. The data reported to validate CC peak overlap with enhancer-associated PTMs appear to be\ndescriptive, which limits interpretability.\na. Fig 3AB – Can the authors quantify the extent of overlap between the CC peaks and P0 peaks of\nenhancer-associated PTMs (K27Ac, K4me1) and gene associated PTMs (K27me3)?\nThank you, we conducted the additional suggested analyses and have added this quantification in Fig\nS3I-J.\nb. Fig 3B – can the authors add a density plot of non-CC-peak loci to establish specific enrichment of\nK27Ac at CC peaks? The loci can be selected as proximal to the peak in the intergenic regions.\nThank you, we have conducted the additional analytical controls suggested and added these plots in\nFigure S3F-H.\n6. Fig 3D-E vs 3F-I: Both approaches are valuable but lack details on rationale that would improve\nclarity.\na. Please compare and offer rationale for the two approaches applied to identify relevant genes (ie\nplotted directionality vs posthoc correlation).\nThank you, we have now added a couple of sentences on rationale, briefly: the binary distinction between\nmild and severe was expected to provide greater sensitivity to detect differences related to seizure score.\nAs such, the categorical distinction between mild (scores 0-4) and severe (scores 6-8) was established\na priori, and identified 243 peaks. To explore whether any of these peaks showed a quantitative linear\nrelationship between insertions and seizure score, we added a complementary analysis. Specifically, we\nexamined numerical correlation between insertion counts and seizure score for peaks meeting\nsignificance for the categorical comparison. We now added more language specifically describing this\nlogic to the results and discussion sections.\nb. As presented, the results suggest that the CC insertion rate of genes shown in FIG D-E do not\ncorrelate with seizure score. An explicit statement will improve clarity.\nThank you for pointing this out. We have replaced the example peak shown in 3G with Ramac/Homer2\n(3D), which does correlate with seizure score, and clarified in the text which peaks correlate. We have\nalso added text to clarify the distinction between numerical correlation with seizure score and categorical\npeak differences between seizure severity categories.\nc. Please list the 13 that show such a correlation between CC insertion rate and Racine score, in\naddition to those shown in 3E.\nThank you, these are now listed in the text of the results as well. Insertions near Gabrb1/Gabra4, Ahcyl1,\nRamac/Homer2, Ptprk/Themis, Ano10, Kctd16/Pabpc2, and Mfap5/Aicda were negatively correlated with\nseizure score (Gabrb1/Gabra4 and Ramac/Homer2 shown in Fig 3F,G). Insertions near Kcnc1, Man1A,\nIfitm7, Nr2f2, Nyap2/Dock10, Sncaip/Zfp474, Lnp1/Tmem45a, and Snhg14/Snrpn were positively\ncorrelated with score (Kcnc1 and Man1a shown in Fig 3H,I).\n7. Is the overlap with CC genes and Prevention Genetics clinical panel statistically significant? The\nauthors use a z-score to present these data, rather than a Fischer’s exact test, which would include a\np-value. Can they provide additional explanation of how to interpret the significance of these data? How\nlarge is the Prevention Genetics clinical panels?\nGood questions. The comprehensive epilepsy panel is 1481 genes and the monogenic epilepsy panel is\n392 genes; this information has been added to the figure legends for all gene lists. We have added more\nexplanation of the permutation tests to the results. The p-values were determined by dividing the number\nof permutations with equal or greater overlap than the observed value by the total number of permutations\n(methods, Gene Overlap Significance Testing). The p-values are provided on the individual graphs and\nthe heatmap with the summarized p-value panels has been moved from supplement to Figure 4G, right\nnext to the Z-score. So the Z-score shows the magnitude of the effect and the significance is right next\nto it. We have also added the p-values to the text in the results.\n8. The functional validation of novel epilepsy relevant targets, Htr1f and Let7c, would be meaningfully\nstrengthened by the inclusion of positive and negative control experiments, using established\nmodulators of any of the following: PTZ-induced seizure activity, CC-called genes that do overlap with\nepilepsy-related genes, CC-called genes that overlap with non-epilepsy related genes, etc. While I do\nnot expect authors to conduct additional experiments for this manuscript, I recommend that authors\nrevise their conclusions to reflect the absence of such control experiments and suggestions therein.\nThank you. While we included a negative control in each panel (vehicle injection or scramble ASO),\nwe\ndid not sufficiently highlight this in the figure legend. We have now edited this to make it more clear. We\nalso agree that a better discussion of prior work with other drugs would help put our work in context,\nincluding those with known epilepsy genes from our CC-discovered targets. The most notable of these\nis the GABA receptor cluster around Gabrb1/Gabra4. Therefore, we have added discussion of work with\ndiazepam + PTZ11 to compare the magnitudes of effect there as compared to what we see here, as\ndiazepam targets these receptors. This was added to the seventh paragraph of the discussion.\nMINOR\n1. How does the mechanism of SRTs enhance sensitivity and recovery, as described in the methods?\nEnhanced relative to non-self-reporting transposons? How does that strengthen the current work?\nThank you, we have clarified this in the methods: “SRTs report their location via RNA transcription and\nenable about an order of magnitude more insertions to be recovered per animal, compared to non-SRT\ntransposons12. Insertions are recovered following reverse transcription and PCR amplification of the\nSRTs in the RNA - and each inserted locus can produce multiple RNA copies, whereas non-SRTs begin\nwith fewer copies as they are DNA-based transposons13. The barcoded SRT is a further innovation that\nallows us to distinguish insertions at identical genomic locations by their different barcodes, increasing\nresolution by a factor of the number of unique barcodes14.”\n2. Is a specific brain region is targeted by CCs “intracranially injected into the cortex” of neonates?\na. Do authors expect AAV to spread to all neurons during development?\nGreat point, we have examined this in detail previously - to address the reviewer’s point, we have included\na more thorough description and referencing of this prior work and we have added a brief description in\nmethods. Briefly, we see widespread delivery via our intracranial injection, though not necessarily evenly\nwithin and between animals. Images are from our previous work below 12,15. We have now better clarified\nthis in the text.\n[REDACTED]\nb. Did the authors validate (equal) expression of the AAV in each cortical piece and/or each mouse\nused in the downstream analysis?\nWe do not generally observe perfectly equal expression across each cortical piece, likely due to\ndifferences in proximity to injection sites (see above). Indeed, this is part of the reason we include multiple\npieces per animal so we can sum across all of them to increase the number of integrations we assess\nper animal. In addition, our pipeline also includes QC steps where we check that each animal, after\nsumming all the pieces, has a sufficient number of insertions to warrant including. This is because, we\nnoted that animals with the lowest number of insertions also showed the poorest correlation of insertion\nprofiles (major comment #3, above) generally. We have described these steps better in the methods.\n3. What is the rationale for the dose and roa of PTZ?\nThank you, we have added these details to methods: “Intraperitoneal (i.p.) injection of PTZ is the most\nwell-established method for PTZ-induced seizure induction. PTZ delivered by i.p. rapidly enters the brain,\ntypically resulting in epileptiform signs within 1-5 minutes.” An alternative, intravenous delivery would be\nchallenging owing to difficulty with recording and is less commonly used pre-clinically. Regarding dosage:\na meta-analysis of studies using i.p. PTZ found that 50% of mice exhibited generalized tonic-clonic activity\n(score 5) with a 65mg/kg injection16. Thus, this dose was selected to achieve sizable groups for mild\n(score <5) and severe (score >5).\n4.Time course: Why was 15 minutes selected for observation of seizure activity? Did observation begin\nimmediately following PTZ injection (ie in the ‘first response’ score)?\nThank you for this comment. We selected 15 minutes for observation because we expected this would\nprovide enough time for mice to exhibit seizure responses and to harvest brain tissue while minimizing\ntime for transcriptional or epigenetic changes to occur, which may occur within 20-30 minutes. Indeed, in\nFig S2 we now show that it takes at least 2-5 hours for CC changes to become detectable. We have\nadded this rationale and experiment to the results. The PTZ paradigm typically results in rapid epileptiform\nactivity within 1-5 minutes, and indeed, we observed that PTZ induced the “first response” in our cohorts\nconsistently within 5 minutes.\nObservation began immediately following PTZ injection - we have added this detail to the methods.\n5. Was seizure activity recorded from the video recordings and/or live?\nSeizures were observed and recorded. Seizure scoring was performed by a blinded examiner based\nexclusively on the video recordings. These details were added to the methods.\n6. I request that a reviewer with relevant expertise report on the rigor of the “modified Racine scale”\napplied by the authors.\nThank you for this comment. The “Modified Racine scale” we used here is a well-established tool for\nscoring seizures17,18. We have previously published using this scale with fine modifications19. We now\nclarified in methods that these modifications were already peer reviewed and published. The\nmodifications are relatively minor. They address the limitations of scoring seizures from video recordings,\nwhich made it challenging to distinguish some individual stages on the traditional scale, so we collapsed\nsome of these behaviors. For example, a score of 3 is marked with “neck jerks” which are hard to\ndistinguish on video recording. Instead, the “tail pop” is an easily discerned, highly stereotyped behavior\nthat nearly all mice perform (see scoring).\n7. Please provide details regarding the use of TTAA site insertions in the analysis pipeline.\nThank you, we have now clarified in the introduction that the hyPB transposase inserts donor transposons\ninto TTAA sites at genomic loci directed by nearby BRD4 binding. We have also clarified in the methods\nthat the QBED files contain the sites of these transposon insertions at TTAA sites.\n8. Why are mice in the moderate seizure category (n=9) excluded from CC analyses?\nThank you for this comment. We made a practical decision given the sheer amount of sample handling\nand sequencing costs that we would focus on the most informative groups. We have clarified our rationale\nin results: “We hypothesized that the stochastic distribution in seizure presentation would be matched by\nstochastic differences at the molecular level. Therefore, we expected that mice with moderate responses\nwould appear as an intermediate state that incorporates both susceptibility and resistance molecular\nphenotypes. Therefore, a primary comparison between the mild (~1/3) and the severe (~1/3) groups\nwould optimize our chance of detecting susceptibility/resistance states.”\n9. Fig 3C – why are CC insertions for mile and severe group plotted as negative and positive fold\nchange when these are the insertions relatively enriched in each group? Please revise the methods to\nclarify the analysis and plotting.\nThank you. We had adopted the conventions used in RNAseq analysis when plotting the volcano plot,\nbut we agree that it is not perfectly suited to CC. We have changed the labels of 3C accordingly.\n10. Please strike the following statement as it is not supported by the data (ie a correlation does not\nnecessarily support predictive power): “These results indicate that CC occupancy at these peaks\n(BRD4 sites) is predictive of seizure outcomes.”\nWe have replaced this sentence with the following: “These results provide evidence for a subset of peaks\nhaving a dose-dependent relationship between enhancer occupancy and seizure outcomes.”\n11. Revise results/discussion to include published literature regarding Let-7 targets implicated in\nepilepsy and seizure.\nThank you, we have added this (discussion, seventh paragraph).\nReferences\n1. Qi, Z. et al. An optimized, broadly applicable piggyBac transposon induction system. Nucleic Acids Res 45,\ne55 (2017).\n2. Fernandez-Albert, J. et al. Immediate and deferred epigenomic signatures of in vivo neuronal activation in\nmouse hippocampus. Nat Neurosci 22, 1718–1730 (2019).\n3. Liu, Z. et al. Epigenomic tomography for probing spatially defined chromatin state in the brain. Cell Reports\nMethods 4, (2024).\n4. Stephens, G. S. et al. Genes Bound by ΔFosB in Different Conditions With Recurrent Seizures Regulate\nSimilar Neuronal Functions. Front. Neurosci. 14, (2020).\n5. Bahabry, R. et al. Alterations in DNA 5-hydroxymethylation patterns in the hippocampus of an experimental\nmodel of chronic epilepsy. Neurobiology of Disease 200, 106638 (2024).\n6. Berger, T. C. et al. Neuronal and glial DNA methylation and gene expression changes in early\nepileptogenesis. PLoS ONE 14, e0226575 (2019).\n7. Dębski, K. J. et al. Etiology matters – Genomic DNA Methylation Patterns in Three Rat Models of Acquired\nEpilepsy. Sci Rep 6, 25668 (2016).\n8. Kobow, K. et al. Deep sequencing reveals increased DNA methylation in chronic rat epilepsy. Acta\nNeuropathol 126, 741–756 (2013).\n9. Williams-Karnesky, R. L. et al. Epigenetic changes induced by adenosine augmentation therapy prevent\nepileptogenesis. J Clin Invest 123, 3552–3563 (2013).\n10. Miller-Delaney, S. F. C. et al. Differential DNA Methylation Patterns Define Status Epilepticus and\nEpileptic Tolerance. J. Neurosci. 32, 1577–1588 (2012).\n11. File, S. E. Tolerance to the anti-pentylenetetrazole effects of diazepam in the mouse.\nPsychopharmacology (Berl) 79, 284–286 (1983).\n12. Cammack, A. J. et al. A viral toolkit for recording transcription factor–DNA interactions in live mouse\ntissues. Proceedings of the National Academy of Sciences 117, 10003–10014 (2020).\n13. Moudgil, A. et al. Self-Reporting Transposons Enable Simultaneous Readout of Gene Expression and\nTranscription Factor Binding in Single Cells. Cell 182, 992-1008.e21 (2020).\n14. Lalli, M. et al. Measuring transcription factor binding and gene expression using barcoded self-reporting\ntransposon calling cards and transcriptomes. NAR Genomics and Bioinformatics 4, lqac061 (2022).\n15. Yen, A. et al. Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA\nInteractions Over Time in Cells and Tissues. Current Protocols 3, e883 (2023).\n16. Yuskaitis, C. J. et al. Factors influencing the acute pentylenetetrazole-induced seizure paradigm and a\nliterature review. Annals of Clinical and Translational Neurology 8, 1388–1397 (2021).\n17. Lüttjohann, A., Fabene, P. F. & van Luijtelaar, G. A revised Racine’s scale for PTZ-induced seizures in\nrats. Physiology & Behavior 98, 579–586 (2009).\n18. Van Erum, J., Van Dam, D. & De Deyn, P. P. PTZ-induced seizures in mice require a revised Racine\nscale. Epilepsy & Behavior 95, 51–55 (2019).\n19. DeVos, S. L. et al. Antisense Reduction of Tau in Adult Mice Protects against Seizures. J. Neurosci. 33,\n12887–12897 (2013)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The author has addressed most of my concerns. I have no further suggestions.", "reviewer_2": "The Authors have substantially revised the manuscript and addressed all weaknesses. The additional data in Figure S2 on the minimum time required for CC insertion is very valuable, along with the textual revisions and additions to the methods. Excited to see this out!"}, "author_response": ""}]

Decision letter