Reducing methylation of histone 3.3 lysine 4 in the medial ganglionic eminence and hypothalamus recapitulates neurodevelopmental disorder phenotypes

Domain: chemistry
Published: 2026-02-20
Editorial outcome: major_revision

Abstract
Methylation of lysine 4 on histone H3 (H3K4) is enriched on active promoters and enhancers where it promotes gene activation. Disruption of H3K4 methylation is associated with numerous neurodevelopmental diseases (NDDs) that display intellectual disability and abnormal body growth. Here, we perturb H3K4 methylation in the medial ganglionic eminence (MGE) and hypothalamus, two brain regions associated with these disease phenotypes. These mutant mice have fewer forebrain interneurons, deficient network rhythmogenesis, and increased spontaneous seizures and seizure susceptibility. Mutant mice are significantly smaller than control littermates, but they eventually became obese due to striking changes in the genetic and cellular hypothalamus environment in these mice. Perturbation of H3K4 methylation in these cells produces deficits in numerous NDD-associated behaviors, with a bias for more severe phenotypes in female mice. Single nuclei sequencing reveals transcriptional changes in the embryonic and adult brain that underlie many of these phenotypes. In sum, our findings highlight the critical role of H3K4 methylation in regulating survival and cell-specific gene regulatory mechanisms in forebrain GABAergic and hypothalamic cells during neurodevelopment to control network excitability and body size homoeostasis.

Introduction
Chromatin remodelers and epigenetic-modifying enzymes regulate proper gene expression throughout development. There are ~300 human genes coding for proteins that drive epigenetic modifications, and these genes are highly intolerant to genetic variation1. Predictably, heterozygous loss-of-function variants in these genes leads to numerous neurodevelopmental diseases (NDDs)2, with many of these NDDs displaying a co-occurrence of intellectual disability (ID), epilepsy, and abnormal body growth3,4.

Many epigenetic machinery proteins target post-translational modifications of histones that are highly dynamic and required for proper transcriptional activation and repression5,6. One critical modification is methylation of histone H3 lysine 4 (H3K4), with trimethylation of H3K4 (H3K4me3) strongly correlating with promoter activation and gene expression. Dysregulation of H3K4me is associated with numerous NDDs such as autism spectrum disorder (ASD), schizophrenia and substance-related disorders7–9. Canonical histones H3.1 and H3.2 are enriched in fetal mouse and human brains and decrease over time, with histone H3 variant H3.3 becoming the predominant histone H3 in postmitotic neurons through adulthood10–12. H3.3 is encoded by 2 genes, H3f3a and H3f3b. Removing one or both genes in mice leads to variable survivability and phenotypes depending on genetic targeting strategy, compensation and onset of gene loss13. Notably, removal of both genes in the dorsal forebrain results in lethality several hours after birth12, complicating assessment of H3K4me function throughout neurodevelopment.

There are 6 members of the lysine-specific methyltransferase 2 (KMT2) family that methylate H3K4 in mammals (KMT2A-D & F-G), and 7 histone lysine demethylases that target H3K4me (KDM1A-B, KDM2B & KDM5A-D)9,14. Patients with variants in KMT2A (Wiedemann-Steiner syndrome), KMT2C (Kleefstra syndrome) and KMT2D (Kabuki syndrome) display ID, microcephaly, epilepsy and short stature6,15–18. Variants in demethylases KDM5A-C are associated with ID and ASD9, indicating a delicate balance H3K4 methylation is critical for normal brain development.

The comorbidities of epilepsy, ID and abnormal body growth in these H3K4me-associated diseases are indicative of altered excitatory-inhibitory balance and hypothalamic defects. Several studies have examined decreased H3K4me in excitatory cells19–23, but disruption of H3K4me in forebrain inhibitory interneurons and hypothalamic cells has not been explored. Interneurons are a highly diverse cell population that arise from two transient structures in the embryonic ventral forebrain, the medial and caudal ganglionic eminences (MGE and CGE, respectively)24–26. Abnormal development and function of interneurons has been linked to the pathobiology of NDDs such as schizophrenia, autism and epilepsy27–30, and many NDD-associated genes are enriched in interneuron progenitors31–37.

Here, we have reduced H3K4me specifically in the MGE and hypothalamus, two cell populations associated with common phenotypes of H3K4me dysregulation such as ID, epilepsy and control of body growth. Our results reveal significant genetic and cellular changes of MGE-derived interneurons and hypothalamic cells, leading to behavioral phenotypes associated with NDDs. In many assays, female mice display greater deficits compared to males. Electrophysiology recordings confirmed altered intrinsic properties of hippocampal interneurons and reduced network synchrony in mutant mice. In sum, our multimodal analyses reveal that reduction of H3K4 methylation leads to similar and distinct changes in forebrain interneurons and hypothalamic cells that, in part, underlie a variety of complex phenotypes related to NDDs.

Results
Disruption of H3K4 methylation in the brain alters viability and body size
To manipulate H3K4me, we used the conditional transgenic mouse line LSL-K4M with a floxed-stop cassette followed by an HA-tagged human H3.3 sequence where the lysine 4 residue is mutated to a methionine (H3.3K4M)38, thus disrupting normal H3K4 methylation (Fig. 1A). The transcription factor Nkx2.1 is a ‘master regulator’ for all MGE-derived interneurons39,40 and is also expressed in the developing hypothalamus41 where it is critical for nuclei involved in regulating appetite and energy homeostasis42–44. In addition to neurons, Nkx2.1+ progenitors give rise to astrocytes and oligodendrocytes in the MGE, and oligodendrocytes, astrocytes and tanycytes in the hypothalamus45. Thus, Nkx2.1-Cre mice allow us to target two brain regions critical for the most common H3K4 methylation disease-related phenotypes: epilepsy and altered body growth.

We generated Nkx2.1-Cre;H3.3K4M;Ai9 wild-type, heterozygous and homozygous mice (H3.3K4M WT, Het and Hom, respectively) to drive expression of H3.3K4M protein in the MGE and hypothalamus, with Nkx2.1-lineage cells expressing the red fluorescent reporter tdTomato. These H3.3K4M Hom mice contain both endogenous WT H3.3 genes (h3f3a and h3f3b) and the exogenous mutant H3.3K4M alleles, thus mimicking heterozygous disease-associated variants in humans with reduced H3K4 methylation (Fig. 1B). We confirmed strong expression of HA-tagged H3.3K4M protein throughout the ventricular zone (VZ), subventricular zone (SVZ) and mantle of the MGE and hypothalamus, and a corresponding reduction of H3K4me1, H3K4me2 and H3K4me3 in E13.5 H3.3K4M Hom MGE via immunohistochemistry (Fig. 1C, D). The strongest reduction of H3K4 methylation occurs in the mantle, where the H3.3 variant is actively replacing canonical histones H3.1 and H3.2 in postmitotic cells12. Western blots demonstrated a significant reduction of H3K4me3 in the E13.5 MGE and hypothalamus of H3.3K4M Hom mice, with a moderate reduction in H3.3K4M Het mice (Fig. 1E). Having H3.3K4M active prior to cell cycle exit is critical to substitute endogenous H3.3 with mutant H3.3K4M protein due to the prolonged half-life of histone proteins in the brain46,47. We do not observe any significant difference in total histone H3.3 levels in the E13.5 MGE or hypothalamus of mutant mice (Supplementary Fig. 1A).

Male and female H3.3K4M Hom mice were significantly smaller than WT mice during the first 6 postnatal weeks, with ~25% of Hom mice dying during this period (Fig. 2A–C and Supplementary Fig. 1B). H3.3K4M Het mice were similar size as WT littermates, but ~20% of Het mice also died by 6 weeks. H3.3K4M Hom mice brains weighed less, had larger lateral ventricles and had a moderate but significant reduction in cortical thickness, particularly layer II–IV (Fig. 2D–F and Supplementary Fig. 1C, D). While H3.3K4M Het and Hom mice continued to die over the next couple months (~55% Hom and ~30% Het die by 20 weeks), the survivors eventually surpassed the size of WT mice. From postnatal weeks 6–9, H3.3K4M Hom mice consumed more food and gained more weight compared to WT mice (Fig. 2G). By 20 weeks, male and female H3.3K4M Het and Hom mice weighed significantly more than WT mice with higher fat weight and serum leptin levels (Fig. 2H–J and Supplementary Fig. 1E). This trend continued up to 1 year old mice where H3.3K4M Hom mice weighed ~35% more, had >4-fold more fat weight, and ~2.5-fold higher serum leptin levels compared to WT (Fig. 2K–M). Thus, perturbation of H3K4 methylation in the MGE and/or hypothalamus increases postnatal lethality and causes significant changes in brain size and body growth, likely due in part to increased food consumption and leptin levels.

Decreased numbers and altered function of MGE-derived interneurons leading to increased seizure susceptibility
Some H3.3K4M Hom mice had spontaneous seizures suggesting a deficit in normal interneuron function. The MGE gives rise to nearly all parvalbumin- and somatostatin-expressing interneurons (PV+ and SST+, respectively) in the forebrain, as well as a population of neuronal nitric oxide synthase-expressing (nNos+) neurogliaform and ivy cells in the hippocampus24,25. We observed a significant decrease in the density of MGE-derived cortical interneurons in the P21 cortex of Het and Hom mice, with the strongest effect on PV+ cells (Fig. 3A, B). This decrease in PV+ cells was present in both the deep and superficial layers (Supplementary Fig. 2). Similar results were observed in the hippocampus, with a ~40% reduction in MGE-derived interneurons affecting PV+, SST+ and nNos+ subtypes (Fig. 3C, D). Similar changes in interneuron densities and percentages were observed in male and female mice. We also detected a population of MGE-derived Tom+/Olig2+ oligodendrocytes in CA3 as previously described48. These Olig2+ oligodendrocytes were almost completely lost in the P21 H3.3K4M Hom mice, and we observed an apparent corresponding decrease in myelin basic protein (MBP) in CA3 (Supplementary Fig. 3). Thus, disruption of H3K4 methylation in the MGE reduces interneurons in the cortex and hippocampus.

To specifically target H3.3K4M expression to MGE cells, we tried generating Lhx6-Cre;H3.3K4M Hom mice, as Lhx6 is expressed in postmitotic MGE-derived interneurons and is critical for their development49. We never obtained Lhx6-Cre;H3.3K4M Hom offspring, and while most Het mice also died during embryogenesis or shortly after birth, several survived for one month. These Lhx6-Cre;H3.3K4M Het mice were smaller than WT littermates, their brains weighed significantly less, and there was a trend towards decreased PV+ and SST+ hippocampal interneurons that is similar to Nkx2.1-Cre;H3.3K4M Het mice (Supplementary Fig. 4). The lethality and inefficiency of generating Lhx6-Cre;H3.3K4M mutant mice prohibited further analysis.

Some P10-P14 H3.3K4M Hom mice displayed robust spasm-like behaviors (e.g., rapid, full flexions and extensions of limbs, trunk flexion, trunk curling and back arching) (Fig. 4A). This seizure activity was also observed in 3-month-old H3.3K4M Hom mice, which exhibit head nodding, continuous whole-body myoclonus, rearing tonic seizure and tonic-clonic seizure with wild jumping (Fig. 4A and Supplementary Video 1). This behavior was strongly sex-biased, with 70% of H3.3K4M Hom female mice displaying spontaneous seizures while only ~14% of Hom males showed this behavior (Fig. 4B). We did not observe spontaneous seizure-like behavior in H3.3K4M Het mice.

To assess seizure susceptibility, we induced seizures with a single injection of Pentylenetetrazole (PTZ) at 5 months. We used a dose of 20 mg/kg, ~50% of the typical effective concentration50 because some Hom mice already display spontaneous seizures. While both male and female 5-month-old H3.3K4M Hom mice displayed increased seizure susceptibility compared to WT, female Hom mice had more severe seizures, with 60% not recovering, while no males died (Fig. 4B and Supplementary Fig. 5A). Since Hom mice are already obese at 5 months, we also performed the PTZ injections at 3 months when WT and Hom mice are equal size. Since female mice are more sensitive to PTZ-induced seizures51, we increased the concentration of PTZ (40 mg/kg) for male mice. Both male and female Hom mice displayed increased seizure susceptibility at 3 months, with the increased dose in males generating similar results to female mice (Fig. 4B, C).

The temporal lobe, particularly the hippocampus, is the most common epileptic locus, and that is where we observe the greatest interneuron loss (Fig. 3). To assess the function of MGE-derived interneurons, we performed whole-cell patch-clamp recordings on Tom+ cells in ex vivo hippocampal slices from 8-week-old female WT and H3.3K4M Hom mice. Quantifying six intrinsic properties (input resistance, Tau, capacitance, Sag ratio, maximum firing rate, AP half-width) was sufficient to define the three expected interneuron cell types via unbiased hierarchical clustering and K-means clustering (Supplementary Fig. 5B–D). Consistent with our immunohistochemical findings, putative PV+/fast-spiking (FS), SST+/non-FS (NFS), and nNos+/slow-spiking (SS) interneurons were observed in H3.3K4M Hom mice with properties largely similar to those in WT mice. However, at the population level, all three interneuron subtypes in H3.3K4M Hom mice showed greater variability in intrinsic properties compared to WT, leading to less refined clustering on the PCA maps (Supplementary Fig. 5D). This increased variance was statistically significant in some instances, such as the firing rate and capacitance in FS cells (Supplementary Fig. 5E).

Cortical circuit information coding requires precision in the timing, extent and synchrony of activity within glutamatergic principal cell assemblies that is largely orchestrated by local circuit interneurons. Brain oscillations in the gamma-frequency band are critical for higher cognitive function and critically rely on balanced phasic excitatory and inhibitory drive52–56. To examine if the interneuron deficits observed in H3.3K4M Hom mice disrupt network rhythmogenesis, we probed kainate (KA)-induced gamma oscillations in ex vivo hippocampal slices from 3-month-old male and female mice (Fig. 4D, E). Gamma oscillations were detected in 77% of slices from WT mice but only 54% of slices from H3.3K4M Hom mice (Fig. 4F), suggesting a reduced propensity for physiological network rhythmogenesis in H3.3K4M Hom mice. Slices from H3.3K4M Hom mice displayed significantly decreased gamma power and peak frequency compared to WT mice (Fig. 4E–H), indicating that H3.3K4M slices produce smaller and slower oscillations. In addition, multiple slices from H3.3K4M mice developed KA-induced interictal epileptiform (IE) events, which were not observed in any slices from WT mice (Fig. 4F, I, J). This finding suggests unrestrained network hyperexcitability in H3.3K4M Hom mice, consistent with the increased seizure susceptibility observed in vivo (Fig. 4C and Supplementary Fig. 5A).

In sum, reducing H3K4 methylation decreases the number of MGE-derived interneurons, alters their intrinsic properties, and disrupts normal network activity, leading to spontaneous seizures and increased seizure susceptibility; this is similar to temporal lobe epilepsy (TLE) patients and animal models of TLE57–60.

Disrupted tangential migration of interneurons in H3.3K4M Hom mice
To identify mechanisms underlying reduced interneuron numbers, we explored changes in cell cycle dynamics, interneuron migration and programmed cell death. At E13.5, we observed no differences in the M-phase marker phospho-histone 3 (PH3) or the proliferative marker Ki-67 in the MGE between H3.3K4M WT and mutant embryos (Fig. 5A, B). However, there was a significant reduction in the number MGE-derived Tom+ interneurons migrating into the cortex in H3.3K4M Het and Hom mice (Fig. 5A–C). To determine if migration was delayed or reduced, we examined the cortex of P4 mice, before the period when ~20–40% of cortical interneurons undergo programmed apoptosis from P5–P1261–63. A striking reduction of Tom+ cells is still present in the hippocampus of H3.3K4M Hom mice at P4 (Fig. 5D), indicating that tangential migration is not simply delayed but instead strongly reduced in mutant mice. We also examined apoptotic cells in the E13.5 MGE and P4 cortex. Very fewer TUNEL+ apoptotic cells were detected at both ages, and there was no obvious difference between genotypes (Fig. 5E). Thus, the significant reduction of forebrain interneurons in H3.3K4M mutant mice arises primarily from decreased tangential migration of MGE-derived interneurons.

Increased anxiety and impaired locomotor activity in H3.3K4M Hom mice
We performed a series of behavior tests on juvenile (5–11 weeks) or adult (3–5 months) H3.3K4M WT and mutant mice (Supplementary Fig. 6A) to determine if disruption of H3K4 methylation in the MGE and/or hypothalamus induces behavioral deficits associated with NDDs. To assess anxiety behaviors, we characterized mice in the open field and light-dark box tests. Both female and male H3.3K4M Hom mice spent significantly less time in the center zone of the open field compared WT mice (Fig. 6A, B). Female Hom mice displayed a trend of increased average speed in the open field test, which was not observed in male mice (Fig. 6B). In the light-dark box test, H3.3K4M Hom male and female mice spent more time in the dark zone compared to WT mice (Fig. 6C). These two tests confirmed increased anxiety-like behavior with reduced H3K4 methylation.

Since H3.3K4M Hom mice sometimes jumped out of the cage during normal handling, we assessed these mice for increased impulsivity, a primary clinical symptom of ADHD that is observed in other NDDs64,65. We used the cliff avoidance reaction (CAR) test to assess impulsivity in these mice. Female H3.3K4M Hom mice had significantly more jump off events from the platform compared with WT (Fig. 6D and Supplementary Fig. 6B), suggesting increased impulsive-like behaviors. There was no difference in male mice.

H3.3K4M Hom mice also displayed unsteady gait and altered locomotion. To assess general locomotor activity, we continuously recorded singly housed mice for 4 days using the Photobeam Activity System. Both H3.3K4M Hom female and male mice displayed decreased ambulatory movements compared to WT mice, with an increase in fine movements in the center zone (Fig. 6E and Supplementary Fig. 6C). No difference in rearing activity was observed (Supplementary Fig. 6C). We used the Free-Walk system to quantify gait parameters and motor coordination in 3-month-old mice (Fig. 6F). Female H3.3K4M Hom mice displayed significantly reduced size, pressure, stance of hind-right paw, decreased BaseFront, and an increased stride length compared to WT mice (Fig. 6G and Supplementary Fig. 6D). Gait properties of male H3.3K4M Hom mice were normal except for a slight increase in BaseFront. Thus, H3.3K4M Hom mice display increased anxiety, decreased general locomotion and altered gait activity, with females generally displaying more severe symptoms compared to males.

H3.3K4M Hom mice display impaired sensorimotor gating and memory
We then assessed higher-order cognition and behavior in H3.3K4M mice. Proper sensorimotor gating is a critical aspect of normal brain function and animal behavior, and deficits in prepulse inhibition (PPI) are observed in NDDs such as schizophrenia and OCD66. In the acoustic startle reflex test, female H3.3K4M Hom mice showed an increase in startle amplitude and a decrease percent of PPI compared to WT mice (Fig. 7A). No significant differences were observed in male H3.3K4M Hom mice.

We used the Barnes maze and 2-object novel recognition paradigms to explore memory deficits in H3.3K4M mice. In the Barnes maze, female H3.3K4M Hom mice exhibited a significantly shorter time around the target hole and trended to spend less time in the target quadrant compared to WT mice (Fig. 7B), indicating decreased memory performance in this task. Notably, H3.3K4M Hom mice traveled a greater total distance compared to WT mice, indicative of hyperactivity and increased anxiety (Supplementary Fig. 6E). In the 2-object novel recognition assay, WT female mice exhibited preference for novel versus familiar objects as expected, while female H3.3K4M Hom mice showed no preference for the novel object (Fig. 7C). No significant differences were observed in the male H3.3K4M Hom mice.

To evaluate sociability deficits, we performed the three-chamber test. We found no significant differences in sociability between H3.3K4M Hom and WT mice (Fig. 7D and Supplementary Fig. 6F). Curiously, H3.3K4M Hom female mice spent more time directly interacting with (sniffing) both the empty object and mice compared WT. We also observed nest-building defects in most singly housed H3.3K4M female and male Hom mice (Fig. 7E), which is observed in numerous mouse models of NDDs67,68.

Altogether, our behavioral data indicate that reduced H3K4 methylation in the MGE and/or hypothalamus produces a broad range of behavioral phenotypes, many of which are commonly found in NDDs and other models of altered H3K4 methylation8,9. H3.3K4M Hom mice display increased anxiety and impulsivity, hypoactivity and decreased ambulatory locomotion in the home cage, abnormal gait, and deficits in sensorimotor gating and memory. Notably, female Hom mice displayed more severe phenotypes compared to males in many assays, indicating a clear sex bias in these mutant mice.

Alterations in transcriptome associated with interneurons fate and seizure
We performed single-nucleus Multiome analysis (snRNA-seq + snATAC-seq) on E13.5 MGE, E13.5 hypothalamus, P60 cortical MGE-derived interneurons and P60 hypothalamus cells from male and female WT and H3.3K4M Hom mice. We generated Nkx2.1-Cre;H3.3K4M;Sun1-sfGFP mice to harvest Nkx2.1-lineage GFP+ nuclei via flow cytometry (Supplementary Fig. 7). We obtained 50,684 nuclei from embryonic mice and 22,116 nuclei from P60 mouse brains (Supplementary Data 1). There was clear segregation of both ages and brain regions when visualizing RNA-only, ATAC-only or integrated RNA and ATAC data using the weighted nearest neighbor (WNN) analysis (Fig. 8A and Supplementary Fig. 8A–D). Cells from the same age and brain region largely overlapped regardless of genotype or sex (Fig. 8B and Supplementary Fig. 8E, F).

We observed a smaller percentage of GFP+ nuclei in the MGE from H3.3K4M Hom mice (75.3% in WT vs. 62.1% in Hom) (Supplementary Fig. 7), which is consistent with fewer interneurons in the cortex and hippocampus in mutant mice. We identified 20 putative cell clusters that can be classified as apical progenitors (Nestin+), basal progenitors (Ccnd2+ and Nestin-) and postmitotic neurons (Dcx+ and Ccnd2-) following the expected developmental trajectory (Fig. 8C, D and Supplementary Fig. 9A, B). These cells can be further classified into 6 progenitor and 7 postmitotic cell types based on their molecular signatures (Fig. 8E and Supplementary Fig. 9C). The proportion of PV-fated (Maf+) and Sst-fated (Sst+) cells was decreased in H3.3K4M Hom MGE (Fig. 8E), matching reductions of SST+ and PV+ interneurons in postnatal mice.

We identified many differentially expressed genes (DEGs) in both H3.3K4M Hom female and male MGE, with the overwhelming majority of DEGs being downregulated in Hom mice (Fig. 8F and Supplementary Fig. 9D). Nearly all differentially accessible peaks were also less accessible in the MGE of H3.3K4M Hom mice (Supplementary Fig. 8G). Many genes critical for general interneuron development (Dlx1, Dlx6, Gad1) and specific for MGE-derived interneurons (Nkx2.1, Lhx6, Lhx8) (Fig. 8F and Supplementary Fig. 9D) were downregulated in Hom female and/or male mice. Of note, 2 genes predictive of PV+ interneurons, Maf and Mef2c69,70, are respectively reduced in H3.3K4M Hom female MGE and male MGE, which is consistent with the specific reduction of PV+ cells in the postnatal cortex (Fig. 3). Gene ontology (GO) analysis revealed that DEGs were enriched in categories relating to forebrain development and neuron migration (Fig. 8G and Supplementary Fig. 9E).

P60 MGE-derived interneurons were cleanly divided into SST+ and PV+ interneurons, with a small population of oligodendrocytes (Fig. 8H and Supplementary Fig. 10A). Based on previous PV+ classifications71, we identified six putative PV+ interneuron subtypes and noted a shift in the relative proportion of specific subtypes in H3.3K4M Hom mice: an increase of the PV-Reln population (from 40.3% to 53.0%) and a concomitant reduction in PV-Col25a1 cells (from 23.3% to 13.4%) (Fig. 8I and Supplementary Fig. 10B, C). We confirmed this predicted increase of PV/Reln+ cells in the cortex of H3.3K4M Hom mice (Supplementary Fig. 10D). Genes related to cell-cell adhesion (Dock4, Cntnap2, Cntn5) were strongly upregulated in PV+ cells in H3.3K4M Hom mice, and the top gene families were related to axonogenesis and cell adhesion in both female and male mice (Fig. 8J, K and Supplementary Fig. 10E, F). Several genes involved in potassium ion transport (Kcnq5, Kcnab1, Kcna1, Kcnc1, Kcnc3, etc.) were dysregulated in the either female or male mice (Fig. 8J and Supplementary Fig. 10E). Of note, Reln was upregulated in Hom mice, in agreement with the proportional increase of this PV+ subtype (Fig. 8I). Comparing DEGs in PV+ cells from male and female H3.3K4M Hom mice reveals both unique and shared features (Supplementary Fig. 10G).

We performed weighted gene co-expression network analysis (WGCNA)72,73 to explore changes in gene regulatory networks in PV+ cells upon reduction of H3K4 methylation. Eight co-expression modules were found, with 5 of these modules being differentially regulated between WT and H3.3K4M Hom mice (Supplementary Fig. 10H). Notably, the downregulated potassium transporter genes Kcna1, Kcnc1 and Kcnc3 were found in module PV-M8, which has the largest change between WT and H3.3K4M Hom mice (Fig. 8L). Col25a1 was found in another module downregulated in Hom mice (PV-M5), which matches the reduction of PV-Col25a1 subtype. Potassium ion channels, particularly Kv3 channels, are highly expressed in PV+ interneurons, and dysregulation of Kv3 channels alters their fast-spiking properties and increases seizure susceptibility74,75, which matches the behavioral and electrophysiological changes in H3.3K4M Hom mice.

We identified 11 putative SST+ interneuron subtypes based on previous SST+ classifications71,76 (Supplementary Fig. 11A, B). The most notable change was the proportional increase of the SST_Plpp4 subtype in H3.3K4M Hom mice, which we confirmed via FISH (Supplementary Fig. 11A, C). Genes related to neuron cell adhesion and regulation of synapse organization (Lama4, Spock1, Stxbp6) were downregulated in H3.3K4M Hom female and male SST+ cells (Supplementary Fig. 11D–F), which could indicate altered synaptic connectivity with pyramidal neurons. Several genes were increased in female Hom mice but decreased in males (Ubb, Atp6v0c, Stmn3), which could be related with biased sex-based behaviors.

In sum, perturbation of H3K4 methylation alters the transcriptome of embryonic MGE and adult MGE-derived interneurons that are highly correlative, and likely causative, to interneuron-related phenotypes in the mutant mice. This includes a decrease of subtype predictive genes in the MGE, changes in the proportion of PV+ and SST+ subtypes in the adult cortex, and dysregulation of gene families involved in potassium ion transport and cell adhesion that are associated with synaptic physiology and seizure susceptibility.

Dysregulation of hypothalamic gene expression, cellular composition and structure in H3.3K4M mutant mice
We subsetted the E13.5 hypothalamus datasets and used gene expression studies77–80 to identify Nes+ neural progenitor cells (NPCs) and 11 putative hypothalamic nuclei consisting of both glutamatergic (Slc17A6+) and GABAergic (Slc32a1+) cells (Fig. 9A, B and Supplementary Fig. 12A–C). While interneurons undergo extensive postnatal diversification and transcriptional changes, hypothalamic subtypes emerge early during embryogenesis with minimal postnatal diversification45,81, which allows us to identify putative hypothalamic cell types with high confidence.

Like the MGE, we observed a lower percentage of GFP+ nuclei in the H3.3K4M Hom hypothalamus compared to WT during sorting (41.5% GFP+ nuclei in WT vs 25.5% in Hom) (Supplementary Fig. 7). There was a more even distribution of upregulated and downregulated DEGs compared to MGE, with a notable increase in differentially accessible peaks being enriched in the mutant hypothalamus (Fig. 9D, E and Supplementary Fig. 12E-F). We confirmed increased Ki-67+ cells in the E13.5 hypothalamus of H3.3K4M Hom mice (Supplementary Fig. 12G). In addition, there is increased accessibility at the Nes promoter and intron enhancer in H3.3K4M Hom female and male mice compared to WT (Fig. 9F and Supplementary Fig. 12H).

The greatest proportional reduction in H3.3K4M Hom mice were the arcuate nuclei (ARC), which is critical for regulating energy metabolism and food intake82, and the ventromedial hypothalamus (VMH) nuclei that control neuroendocrine functions such as appetite and glucose regulation83 (Fig. 9C and Supplementary Fig. 12D). The ARC marker gene Pomc was decreased in the Hom hypothalamus (Fig. 9D), consistent with a decrease proportion of ARC-fated nuclei. We also identified both sex-specific and shared DEGs in E13.5 male and female hypothalamus (Supplementary Fig. 12I). Similar to the MGE, we did not identify any differences in apoptosis in the hypothalamus between WT and Hom mice (Supplementary Fig. 12J).

In the P60 hypothalamus, we identified 40 putative cell clusters with clear separation of neurons (Syt1), astrocytes (Agt), oligodendrocytes (Mog) and tanycytes (Rax) (Fig. 9G–I and Supplementary Fig. 12A, B). Specific hypothalamic nuclei could not be cleanly resolved in our dataset because we obtained too few cells relative to the heterogeneity of cell types and nuclei in the adult hypothalamus (Supplementary Data 1); a recent study compiled ~400,000 mouse hypothalamic cells and identified 185 distinct cell clusters84. DEGs in adult hypothalamic neurons were associated with axonogenesis, cell adhesion and NADH metabolic process (Supplementary Fig. 12C–E).

We found a striking increase in the proportion of astrocytes (8.2% in WT vs. 27.3% in Hom) and decrease in oligodendrocytes (12.3% in WT vs. 2.2% in Hom) (Fig. 9I) in H3.3K4M Hom mice. We confirmed an increase in hypothalamic GFAP+/Tom+ Nkx2.1-lineage astrocytes, and overall astrocyte numbers, in both male and female H3.3K4M Hom mice (Supplementary Fig. 14A–C). This increase in astrocytes is also found in diet-induced obese mice85. Astrocytes play critical roles in controlling glucose metabolism and energy balance44,86,87, inflammatory processes83, maintaining homeostasis of neurotransmitter and ions88, regulating synapse plasticity linked to behaviors89. Unlike other cell types, hypothalamic astrocytes from WT and Hom mice form distinct clusters in the UMAP plots (Fig. 9G). Re-clustering the astrocytes revealed sex and genotype-specific clusters; in particular, we observed cluster 5 was enriched in innate immune reactivity-related genes (Usp18, Oasl2, Ifit1) and was only present in H3.3K4M Hom female and male astrocytes (Fig. 9J–L and Supplementary Fig. 13F). DEGs in astrocytes include those involved in regulation of monoatomic ion transport (Asic2, Grm5, Gabrb1), synaptic transmission (Nrxn3, Nrxn1), glial cell differentiation and maturation (Npas3, Gpc5) and regulation of membrane potential (Kcnq3), which may indicate disruption of normal hypothalamic homeostasis in H3.3K4M Hom female and male mice (Fig. 9M, N and Supplementary Fig. 13G–I).

In sum, perturbation of H3K4 methylation alters the transcriptome of embryonic and adult hypothalamic cells, with a notable increase in the proportion of NPCs and reduction in the proportion of ARC and VMH nuclei, which both play critical roles in feeding behavior and growth hormone regulation. We also observed a large increase in hypothalamic astrocytes in H3.3K4M mice and distinct transcriptional astrocyte subtypes that likely alter hypothalamic function.

Abnormal cellular organization in the hypothalamus of H3.3K4M Hom mice
We explored the hypothalamic cellular organization in more detail. Tanycytes are specialized glial cells within the blood-brain barrier of the hypothalamus that can detect nutrients and metabolites in the blood and cerebrospinal fluid, sending this information to the ARC and VMH to regulate body weight and energy metabolism90,91. They also act as neural progenitor cells, giving rise to new neurons and glia cells in the adult hypothalamus90. In adult WT mice, Nkx2.1-lineage Tom+/Vimentin+ (Vim+) tanycytes line the third ventricle (α-tan) and median eminence (ME, β-tan), with their processes extending out in an organized manner as previously described92 (Fig. 10A). A layer of Tom-/ GFAP+ astrocytes lie just below these tanycytes in the ME. In male and female H3.3K4M Hom mice, this organization is completely disrupted, with Vim+ tanycytes scattered throughout the ME and GFAP+ astrocytes clumped at the lateral edges of the ME (Fig. 10A). α-tan processes along the wall of the third ventricle are more disorganized and fasciculated compared to WT mice. In addition, oligodendrocytes line the dorsal portion of the ME, where they are involved in the regulation of energy availability and leptin sensitivity93. This myelin band was present in the ME of WT mice but was absent in Hom mice, with myelin instead restricted to the lateral edges of the ME, similar to astrocytes (Fig. 10B). Some hypothalamic neurons project to the ME and release the neuropeptide SST that is involved in growth hormone release and food intake94. We observed significantly less Sst in the ME in both Hom female and male mice (Fig. 10C).

We subsetted out the Rax+/Col23a1+ tanycytes in the hypothalamus and identified the 2 tanycyte subtypes as previously described95: Vcan+ α-tans and Col25a1+ β-tans (Fig. 10D). We identified very few DEGs in tanycytes, in large part due to low cell numbers. Fgf14 was the most downregulated gene in H3.3K4M Hom tanycytes (Fig. 10D), and FGF signaling in tanycytes regulates their morphology and proliferation92,96, lipid homeostasis97 and numerous hypothalamic metabolic process98,99. While a reduction in Fgf14 in tanycytes was difficult to confirm via FISH, we observed slightly decreased Fgf14 levels in the ARC nucleus adjacent to tanycytes in H3.3K4M Hom mice (Fig. 10F) that may indicate altered FGF14 signaling, but this still needs further confirmation.

In sum, reduced H3K4 methylation alters astrocyte, oligodendrocyte and tanycyte organization in the hypothalamus, specifically in the ME. Disruption of normal cellular architecture at this critical blood-brain barrier junction will alter hypothalamic function and hormonal regulation that drives initial growth retardation, followed by obesity, increased leptin levels, and likely contributes to altered behavioral phenotypes in H3.3K4M Hom mice.

Methods
Animals
All experimental procedures were conducted in accordance with the National Institutes of Health guidelines and were approved by the NICHD Animal Care and Use Committee (protocol #23-047). The following mouse lines were used in this study: LSL-K4M (gift from Dr. Kai Ge, NIDDK)38; Nkx2.1-Cre (Jax #008661)41, Lhx6-Cre (Jax #026555)146, Ai9 (Jax #007909)147, Sun1-sfGFP (Jax #030952)148. In the LSL-K4M mouse, the transgene construct is CAG promoter-LoxP-Stop-LoxP-H3.3K4M-FLAG/HA-polyA, using the human H3.3b gene sequence (https://www.addgene.org/128743/). The insertion site of the transgene is unknown. LSL-K4M mice were genotyped with forward primer (5‘-GGGTCTGTTCGAAGATACCAACC-3‘) and reverse primer (5‘-GTAGTCGGGCACGTCGTA-3‘), with heterozygous and homozygous mice determined via qPCR. Mice were housed under standard conditions (12h light and 12h dark). The morning on which the vaginal plug was observed was denoted E0.5.

Brain harvesting
Embryonic brain dissections: Pregnant dams were anesthetized with an i.p. injection of Euthasol (270 mg/kg, 50 μL injection per 30g mouse), E13.5 embryos were removed and placed in ice-cold carbogenated artificial cerebral spinal fluid (ACSF, in mM: 87 NaCl, 26 NaHCO3, 2.5 KCl, 1.25 NaH2PO4, 0.5 CaCl2, 7 MgCl2, 10 glucose, 75 sucrose, saturated with 95% O2, 5% CO2, pH 7.4). Embryonic tails were cut for genotyping. MGE and hypothalamus were dissected from individual brains in ice-cold ACSF, transferred to individual 0.5mL Eppendorf tubes, then flash frozen on dry ice and stored at −80°C. For analyzing fixed tissue, E13.5 brains were removed and drop-fixed in 4% paraformaldehyde (PFA) in PBS overnight, washed in PBS, incubated in 30% sucrose in PBS at 4°C overnight, embedded in OCT and stored at −80°C.

Adult brain dissections: P21-P150 mice were anesthetized with Euthasol (270 mg/kg, 50 μL injection per 30g mouse). For harvesting brain regions, the somatosensory cortex and hypothalamus were dissected, transferred to 1.5 mL Eppendorf tubes, then flash frozen on dry ice and stored at −80°C. For analyzing fixed brains, mice were perfused with PBS followed by 4% PFA, brains were removed and post-fixed in 4% PFA overnight, washed in PBS, incubated in 30% sucrose in PBS at 4°C overnight, then embedded in OCT and stored at −80°C.

Tissue staining and microscopy
P21-P150 brains were cryosectioned at 30 μm, sections transferred to 96-well plates containing antifreeze solution (30% ethylene glycol, 30% glycerol, 40% PBS) and stored at −20°C. Prior to staining, brain sections were washed in PBS (3×10 min). Brain sections were incubated in block solution (10% Normal Donkey Serum in PBS + 0.3% Triton X-100) for 1 hour RT, then incubated for 24–48 hour in primary antibodies in block solution at 4°C. Then sections were washed in PBS (4×10 min) and incubated with secondary antibodies and DAPI for 2h at RT or overnight at 4°C. Brain sections were washed and mounted.

E13.5 embryonic brains were cryosectioned at 14 μm, mounted on slides, dried and stored at −20°C. Slides were incubated in block solution for 1h RT, then incubated overnight with primary antibodies in block solution and secondary antibodies for 1-2h RT.

The following primary antibodies were used in this study: rat-anti SST (1:300, Millipore MAB354), goat-anti PV (1:1000, Swant PVG213), rabbit-anti nNos (1:500, Millipore MAB5380), rabbit-anti H3K4me3 (1:500, Cell Signaling TECHNOLOGY 9751S), rat-anti HA (1:200, Roche 11867423001), mouse-anti GFAP (1:500, Sigma-Aldrich G3893), rabbit-anti NeuN (1:500, abcam ab177487), rabbit-anti Vimentin (1:500, Cell Signaling TECHNOLOGY 5741T), rabbit-anti Olig2 (1:500, Sigma-Aldrich AB9610), mouse-anti MBP (1:1000, Invitrogen MA1-10837), rat-anti Ki67 (1:100, Invitrogen 14-5698-82), rabbit-anti Phospho-Histone H3 (Ser10) (1:400, Invitrogen PA5-17869), rabbit-anti Cux1 (1:300, Proteintech 11733-1-AP). Species-specific fluorescent secondary antibodies (1:500) were used conjugated to Alexa Fluor 488, 647 and 790.

Nissl Staining: Slides were warmed to RT and then treated in the following solution sequence for 5 min each: 70% EtOH, 95% EtOH, 100% EtOH, 95% EtOH, 70% EtOH and 50% EtOH. Slides were then rinsed with ddH2O, incubated in 0.1% warm crystal violet stain for 10–20 min, then washed with ddH2O. Slides were then incubated in 70% EtOH and 95% EtOH for 5 min each, then 100% EtOH for 2 min twice. Slides were then incubated with a xylene for 5 min twice, air dried overnight and mounted with coverslips.

Fluorescent in situ Hybridization: Brains were cryosectioned at 14 μm. RNAscope Multiplex Fluorescent v2 in situ hybridization assays (Advanced Cell Diagnostics) were performed according to the manufacturer’s instructions. All probes and reagents from Advanced Cell Diagnostics. 1: probe-Mm-Reln-C3 (405981-C3), probe-Mm-Pvalb (421931) and tdtomato-C2 (317041-C2); 2: probe-Mm-Sst-C3 (404631-C3), probe-Mm-Plpp4 (471731) and tdtomato-C2 (317041-C2); 3: probe-Mm-Fgf14-C3 (462801-C3), probe-Mm-Vim (457961) and tdtomato-C2 (317041-C2).

All images were captured on an Olympus VS200 scanner (VS200 ASW) or Zeiss Axioimager.M2 (with Zen Blue software). Post-processing was performed with Adobe Photoshop or ImageJ.

Electrophysiology experiments
Adult mice were anesthetized with isoflurane and decapitated. Brains were quickly dissected and placed in ice-cold artificial cerebrospinal fluid (ACSF) modified for dissection containing (in mM): 80 NaCl, 25 NaHCO3, 10 glucose, 1.25 NaHPO4, 3.5 KCl, 4.5 MgCl2, 0.5 CaCl2, 90 sucrose (Sigma-Aldrich) and bubbled with carbogen (95% O2 and 5% CO2). Horizontal hippocampal sections were prepared from the two hemispheres using a Leica VT1200S vibratome (Leica Microsystems). Recordings were performed using a Multiclamp 700B amplifier and acquired using pCLAMP 10.4 software (Molecular Devices). Signals were acquired at 5 kHz, digitized, and stored using Digidata 1322A and pCLAMP 10.4 software (Molecular Devices, CA, USA).

Ex vivo gamma oscillation recordings and analysis
Nine 3-month-old mice were used for local field potential (LFP) recordings (WT n=4 and Hom n=5 with both male and female). 350 µm thick hippocampal slices were placed into an interface holding chamber containing ACSF modified for LFP: 124 mM NaCl, 30 mM NaHCO3, 10 mM glucose, 1.25 mM NaHPO4, 3.5 mM KCl, 1.5 mM MgCl2, 1.5 mM CaCl2 (Sigma-Aldrich) continuously supplied with humidified carbogen gas (95% O2 and 5% CO2). The chamber was held at 34°C during the slicing process and subsequently allowed to cool down to room temperature (~22°C) for at least 1h to let the slices recover. Hippocampal gamma oscillations experiments were performed as described previously149. Oscillations were recorded at 34°C using borosilicate microelectrodes (1.5–2.5 MΩ) filled with ACSF placed in the CA3 stratum pyramidale and elicited by adding kainic acid (400 nM; Tocris Bioscience) to the extracellular bath. The oscillations were allowed to stabilize for at least 20 min before recordings were performed. Signals were conditioned using a HumBug 50Hz noise eliminator (Quest Scientific).

Before analysis, signals were low pass filtered at 0.1 KHz and high pass filtered at 10 Hz. For oscillation power spectra, Fast Fourier Transformations were obtained from 60 s of LFP recording (segment length 8192 points). Frequency variance data was obtained from the power spectra described above using the Axograph software (Kagi, Berkeley, CA, USA). Gamma power was calculated by integrating the power spectral density from 20 to 80 Hz using Clampfit 11.2.

Whole-cell patch clamp recordings and analysis
Six 8-week-old mice (n=3 WT and Hom female brains) were used to perform whole cell patch clamp recordings of MGE derived hippocampal interneurons. 300 µm thick hippocampal slices were immediately placed into a submerged holding chamber containing standard ACSF: 130 mM NaCl, 24 mM NaHCO3, 10 mM glucose, 1.25 mM NaH2PO4, 3.5 mM KCl, 1.5 mM MgCl2, 2.5 mM CaCl2 (Sigma-Aldrich) continuously supplied with humidified carbogen gas (95% O2 and 5% CO2). Whole-cell patch clamp recordings were performed on slices in the submerged recording chamber at 34°C with borosilicate glass electrodes filled with intracellular recording solution containing 130 mM K-gluconate, 5 mM KCl, 10 mM HEPES, 3 mM MgCl2, 2 mM Na2ATP, 0.3 mM NaGTP, 0.6 mM EGTA, and 0.2% biocytin. Voltage clamp and current clamp configurations were used.

Analysis of whole cell patch clamp recordings was performed using the AutoAnt Software150. Principal Component Analysis (PCA) was used to classify 93 interneurons (WT n=49 and Hom n=44) based in their intrinsic properties (Input resistance, membrane time constant, membrane capacitance, sag ratio, maximum firing rate and action potential half-width). The PCA was performed in Python with the Sklearn package. The kmeans clustering algorithm was used to divide the standardized data using 3 principal components. The adequate number of clusters for the dataset was identified using the Silhouette Score.

Pentylenetetrazole (PTZ) injection and recording
PTZ was prepared on the day of use within sterile 0.9% (w/v) NaCl. PTZ injection and recording was performed on 3-month and 5-month-old mice. All 5-month animals will receive an IP injection of PTZ (Sigma-Aldrich, P6500) at 20 mg/kg and were recorded immediately via video tracking system ANY-maze (Stoelting Company) for 60 min. 3-month female animals will receive an IP injection of PTZ at 20 mg/kg, while 3-month male animals will receive an IP injection of PTZ at 40 mg/kg due to decreased PTZ sensitivity. Epileptic behaviors were scored based on the modified Racine scoring system151: stage 0 = normal behavior, stage 1 = immobility and rigidity; stage 2 = head bobbing, facial, forelimb, or hindlimb myoclonus; stage 3 = continuous whole-body myoclonus, myoclonic jerks or tail held up stiffly; stage 4 = tonic seizure, continuous rearing and falling, stage 5 = clonic-tonic seizure, stage 6 = death. Total seizure scores were calculated at every 5 min block in the first 20 min. The mortality was also noted during the recording process.

Behavioral tests
All behavioral tests were performed on WT, Het and Hom littermates according to approved NICHD and/or NIMH standard operating procedures (SOPs) and cleaning protocols. Animal behavioral tests were carried out during the daylight cycle in the NICHD animal behavioral room or at the NIMH Rodent Behavior Core. All mice were habituated to the testing environment for 30–60 min before behavior tests. All behavior assays were performed blind to genotype, and 1-3 independent cohorts were used for each behavioral test. The sequence of tests was as follows, with at least one week elapsed between any two behavioral tests. Juvenile and young adults (6–11 weeks old): Home-cage test (5-6 weeks old); Open-field test (7-8 weeks old); Three-chamber test (9-10 weeks old); Barnes maze test (10-11 weeks). Adults (3–5 months old): Free-walk test; Light-dark box test; Novel object recognition test; Cliff avoidance reaction; Prepulse inhibition startle test.

Home-cage test
Individually housed juvenile mice were observed in their home cages placed in the Photobeam Activity System-Home Cage (PAS-HC, SD Instruments) to assess activity (ambulation, fine movements and rearing) that can be quantified by beam breaks. Mice were kept in the cage for 4 days with appropriate food and water. During this testing, mice were housed in a ‘quiet’ room to minimize outside interactions. Mice were returned to group housing after the experiment. The total ambulatory movements, fine movements, rearing and general locomotor activity were analyzed and processed using the PAS-HC data collection transferred to Excel. n (female)=8 WT and 11 Hom; n (male)=7 WT and 4 Hom.

Open-field test
Mice were originally placed in the center of open field apparatus (40cm×40cmx30cm Plexiglas box) and recorded for 25 minutes. The average speed and time spent in the different open field areas were recorded and analyzed with the video tracking system ANY-maze (Stoelting Company). n (female)=13 WT, 8 Het and 11 Hom; n (male)=12 WT, 11 Het and 7 Hom.

Three-chamber test
Tests were performed in an opaque Plexiglas rectangular box (60cm×45cmx20cm) consisting of 3-chambers, with clear dividing walls and an open central chamber allowing free access between chambers. Wire cups were used to hold a single stimuli mouse (C57BL/6J mice that were sex and age-matched with test mice). The test consists of three 10-minute phases that were all recorded using the ANY-maze system. For phase 1, the test mouse was placed in the center chamber with closed walls. For phase 2, two empty wire cups were placed in each side chambers and the closed walls were opened, which let the mouse explore all three chambers. For phase 3, the test mouse was confined in the center chamber and a stimuli mouse was placed in a wire cup on one side of the chamber. Both walls were removed, and the test mouse explored chambers for 10 minutes. Sniffing (investigating) was defined as being within 3cm of either cup’s edge. The total times spent in each region and time investigating each stimulus (stimuli mouse or empty cup) was recorded and quantified. n (female)=13 WT, 6 Het and 11 Hom; n (male)=12 WT, 11 Het and 7 Hom.

Barnes maze test
The Barnes maze consists of a white elevated circular platform (diameter=122cm) with 80cm support frame containing 20 equally distributed holes (diameter=5cm), with 1 ‘escape’ hole leading to a hidden black drop box. A light source was affixed above the platform to brightly illuminate the maze, and 90 dB white noise generated from loudspeakers was used to motivate mice to enter the dark escape box. Three distal visual cues surrounded the platform.

The protocol used a 6-day paradigm that included a habituation, training and probe trial phase. During habituation (day 1), the mouse was placed in the escape tunnel for 1 min and then placed in the center of platform until it enters the escape hole or 3 minutes elapse. In the training days (days 2–5), the mouse is placed in a start chamber located in the center of one of the four quadrants, and the mouse explores the platform until it enters the escape hole or 3 min elapse. If a mouse fails to find the escape tunnel within 3 min, the mouse is guided to the dark escape tunnel and remains there for 15 s. Mice underwent 4 trials on each training day. For the probe trial (day 6), the escape tunnel is removed, and mice are placed in the center of the platform to explore the maze for 90 seconds. Time spent around the target hole was defined as being within 1 cm of the target hole edge. All trials were recorded using the ANY-maze system allowing quantification of the time each mouse spent in the target hole and target quadrant. n (female)=13 WT and 9 Hom; n (male)=8 WT and 4 Hom.

Free-walk test
FreeWalkScan 2.0 (CleverSys Inc.) was used to assess mice gait. 3-month-old mice with similar body weight move freely in a 40cm×40cm×30cm (length×width×height) chamber. A high-speed camera below a clear bottom plate captures mouse movement for 5 minutes with red light in a dark room. Videos are analyzed using FreewalkScanTM 2.0 software for various characteristics of gait, including BaseFront (distance between sequential footprints of front limbs), Stride Length (distance between two sequential footprints of the same paw), Contact Size, Average Pressure and Stance Time (time corresponding paw stays on ground) of each paw. n (female)=9 WT, 9 Het and 4 Hom; n (male)=6 WT, 9 Het and 8 Hom.

Light-dark box test
The apparatus (40cm×40cmx30cm) consists of dark chamber (40cm×15cmx15cm) and light chamber compartment. There is a door between two chambers which allow mice to freely move between chambers. Bright white lights illuminated the box from above as mice were placed in the box for 10 min. The test was recorded by an ANY-maze video trace camera to determine the amount of time each mouse spent in the light chamber. n (female)=14 WT, 12 Het and 7 Hom; n (male)=9 WT, 11 Het and 9 Hom.

2 Object Novel object recognition test
Novel object recognition test was conducted in an open field arena (40cm×40cm white Plexiglas box). The protocol used a 3-day paradigm that includes habituation, training, and a testing phase. During habituation (day 1), mice were placed into the center of an open field arena and allowed to explore for 10 minutes. For training (day 2), two identical objects were placed on either side of the center of the arena, and the mice explored the arena for 10 minutes. For testing (day 3), a familiar object and a novel object (dissimilar object) were placed in the same position as in the training day, and mice explored the arena for 10 minutes. ANY-maze was used to record each trial. The amount of time each mouse spent with their nose oriented toward the object within 2.5 cm of the object edge was considered ‘exploration time’. The recognition index was calculated as: Recognition index = (time spent exploring novel - time spent exploring familiar) / (total time spent exploring both objects). n (female)=9 WT, 10 Het and 7 Hom; n (male)=6 WT, 7 Het and 6 Hom.

Prepulse inhibition (PPI) of the acoustic startle reflex
Startle response and pre-pulse inhibition were performed via SR-LAB-Startle Response System (San Diego Instruments). Mice are placed in a startle apparatus with sound-attenuated chamber. Whole-body startle movements were detected by a piezoelectric accelerometer mounted beneath the cage, converted to electrical signals, and digitized and stored by a computer. A loudspeaker mounted to the side of the cage produced white noise acoustic stimuli. A continuous 64 dB background noise was present throughout the test. The test began with a 5-minute acclimation to the apparatus, followed by 12 pulse-alone trials. To measure PPI, 12 blocks containing 6 trials were presented: 1 pulse-alone trial, 4 prepulse-pulse trials, and 1 no-stimulus trial. Pulse-alone trials consisted of a 40 ms startle pulse at 120 dB; prepulse-pulse trials consisted of a 20 ms prepulse at 66, 68, 72, or 76 dB followed 100 ms later by the pulse; and no-stimulus trials consisted of background noise only. Trials will be presented in pseudorandom order, with an inter-trial interval of 10–20 s (mean of 15 s). Startle response will be determined as the peak amplitude within 100 ms of the startle pulse onset. The entire test lasted 30 minutes. n (female)=10 WT and 7 Hom; n (male)=6 WT and 5 Hom.

Cliff avoidance reaction (CAR) test
The CAR test was performed as described previously65 with slight modifications to assess impulsive-like behaviors. The apparatus consists of a square platform (20cm×20cm) and support by a rod (Height=40cm) that was placed in an open field box with soft padding. After habitation, the mouse was gently placed onto the platform, and they were also placed back onto the platform after falling. The latency to first jump off and numbers of jump off were recorded. The test was performed and analyzed for 15 min under white lights. n (female)=13 WT and 7 Hom; n (male)=5 WT and 5 Hom.

Body composition and Elisa analysis
Analysis of body composition in live mice using the EchoMRI 100 analyzer (Echo Medical Systems) in the Mouse Metabolism Core of NIDDK. This test revealed both the fat weight and lean weight for each mouse. Serum leptin levels were measured using a mouse leptin ELISA kit (ThermoFisher, #KMC2281) in accordance with their standard protocol.

Cell counting and analysis
All cell counts on Nkx2.1-Cre;K4M;Ai9 mice were performed manually using Photoshop and were blind to genotypes. Cortex: Cells were counted from 3 non-consecutive brain sections through the somatosensory cortex for each P21 brain, from 7 WT (4 male and 3 female), 4 Het (2 male and 2 female) and 8 Hom (4 male and 4 female) mice. DAPI staining was used to divide the cortex into superficial (I-III) and deep (IV-VI) layers. Tom+, PV+ and SST+ cells were counted, and the density was calculated by cells/mm2. Any PV+ and SST+ cells that were Tom- were excluded from the counts. Hippocampus: Cells were counted from 4 non-consecutive sections spanning the anterior-to-middle P21 dorsal hippocampus, from 7 WT (4 male and 3 female) and 8 Hom (5 male and 3 female) brains. Tom+, PV+, SST+ and nNos+ cells counted, and the density was calculated by cell numbers/mm2. Small Tom+ cell bodies in CA2/3 that are primarily Olig2+ oligodendrocytes were excluded from these interneuron counts and counted as a separate group. The thickness of the primary somatosensory cortex (S1) (bregma: from −1.67 to −1.79 mm) and the primary visual cortex (V1) (bregma: from −2.69 to −2.91 mm) from P21 mice were measured. Cux1 immunostaining was used to label layers II-IV. 3 mice per genotype, 5–8 sections measured per brain. Hypothalamus: Six coronal brain sections (bregma: from −1.58 to −1.94 mm) were analyzed per mouse. The mean fluorescent intensity of GFAP in the region of interest was measured using ImageJ software (v1.53; NIH) from 4 WT and 3 Hom of adult mice (3-5 months old). Median eminence (ME): The mean fluorescent intensity of MBP was measured using ImageJ software from 4 WT and 3 Hom of ~P60 adult mice, while the intensity of Sst was measured from 5 WT and 4 Hom of P21 mice. MGE: PH3+ cells in the E13.5 MGE (3 WT, 3 Het and 4 Hom, combined male and female samples, 3–6 brain sections per mouse) and Tom+ cells in the E13.5 cortex (4 WT, 3 Het and 4 Hom mice, combined male and female samples, 4 brain sections per mouse) were counted using ImageJ software. Cell counts from Lhx6-Cre;K4M hippocampus were performed from 3 WT and 3 Het of P30 mice (3-5 sections per mice).

TUNEL apoptosis assay
For detecting DNA fragmentation by fluorescence microscopy, we used the ApoBrdU-IHC DNA fragmentation assay kit (Abcam, ab66098). Apoptosis signals were detected in accordance with standard protocol for tissue sections, including sections preparation, DNA labeling and antibody detecting. Brain tissues with mixed sex from E13.5 and P4 WT and Hom mice were used.

Western blot
Total core histone proteins were extracted using the EpiQuik Total Histone Extraction Kit (EpigenTek). Total proteins were extracted using the RIPA buffer (Thermo Scientific). E13.5 MGE and hypothalamus samples were obtained as described above. Protein amount was qualified using Pierce BCA Protein Assay Kit (Thermo Scientific), typically ~35 μg protein was obtained from tissue from 1 embryo. Total 40 μL Loading samples were prepared with Sample Reducing Agent (ThermoFisher, B0009) and Bolt LDS Sample Buffer (ThermoFisher, B0007) and were load on a 4–12% Bolt Bis-Tris Plus Mini Protein Gel (ThermoFisher, NW04120BOX), running 20–30 min at 200 V with Blot MES SDS Running buffer (ThermoFisher, B0002). After that, gel was transferred to PVDF membranes using the iBlot 2 Gel Transfer Device (Invitrogen) and transferred for 7 min at 20 V. Membrane was blocked 30 min in block buffer (SuperBlock Dry Blend Blocking Buffer, ThermoFisher #37545). Membranes were incubated in primary antibody overnight at 4°C, secondary antibody for 1h at RT, washed 3 times with TBST washing buffer, and then blots were imaged on the ChemiDoC MP Imaging System (Bio-Rad). The following primary antibodies were used: mouse anti-H3 (1:1000, Cell Signaling Technology #3638), rabbit anti-H3K4me3 (1:500, Cell Signaling Technology #9751), rabbit anti-H3K4me1 (1:1000, Abcam ab8895), rabbit anti-H3K4me2 (1:500, Abcam ab7766), hFAB rhodamine anti-Tubulin (1:2000, Bio-rad #12004166), rabbit anti-H3.3 (1:1000, Invitrogen #PA5-22388). The following secondary antibodies were used: anti-mouse-Starbright Blue-520 (Bio-Rad # 64456855; 1:2000) and anti-Rabbit-Starbright Blue-700 (Bio-Rad #64484700; 1:2000).

Multiome sequencing and analysis
Pregnant dams and P60 mice were terminally anesthetized with an i.p. injection of Euthasol (270 mg/kg, 50 μl injection per 30g mouse). Tissue from male and female mice were processed separately for E13.5 and P60 samples. Frozen E13.5 and P60 tissue from Nkx2.1-Cre;H3.3K4M;sun1-GFP mice were mechanically lysed, washed and filtered to generate single nuclei suspensions for the 10x Genomics Multiome assay as previously described152. These suspensions were then sorted via flow cytometry (Sony SH800) to harvest ~100,000 GFP+ nuclei. The percent of GFP+ nuclei/Draq5+ nuclei was recorded for all sorts to determine the average percent of GFP+ nuclei for each condition. Nuclei solutions were centrifuged in a bucket centrifuge at 500×g for 5 min at 4oC. Supernatant was removed and nuclei constituted in 1x Nuclei buffer at a density of 3000 nuclei/ml. 5 μl of this solution was then used for the 10x Genomics Multiome Assay per manufacturer’s protocol. Our single-cell Multiome workflow ‘multiome-wf’ can be found at GitHub (https://github.com/NICHD-BSPC/multiome-wf) with documentation located here (https://nichd-bspc.github.io/multiome-wf/). Sample details and sequencing information are described in Supplementary Data 1.

Sequencing analysis: Gene expression and open chromatin state were profiled using 10x Chromium Multiome ATAC+Gene Expression kit (10x Genomics, 1000285). Joint libraries were simultaneously created by following the standard protocol. Sequencing was conducted with paired-end (50×50 bp) using an Illumina HiSeq 2500 or NovaSeq 6000. The raw sequencing data were processed using the Cell Ranger ARC (v2.0.0) pipeline. The cellranger-arc mkfastq command was used to generate the demultiplexed FASTQ files from BCL files. The sequencing reads were aligned to custom built mouse (GRCm38/mm10) reference genome using cellranger-arc count. The cellranger-arc count command was used to generate gene-by-barcode (snRNA-seq) and peak-by-barcode (snATAC-seq) matrices. The cellranger-arc aggr command, without depth normalization (--normalize=none) was used to aggregate sample datasets into a single feature-barcode matrix file.

snRNA-seq data analysis: The aggregated feature-barcode matrix was used as input to Seurat (v5.2.1)153 in R (v4.4.2, https://cran.r-project.org). Low-quality cells were removed based on the following QC metrics: RNA feature counts, UMI counts, mitochondrial counts and ribosomal counts. Cells that have RNA feature counts more than 100 and UMI counts less than three standard deviations (SD) of the mean were kept. Cells that have less than 5% mitochondrial counts and 10% ribosomal counts were kept. In addition, R package scDblFinder (v1.20.0)154 was used to remove the computationally likely doublets. After filtering out low-quality cells and doublets, we performed the standard Seurat workflow with default parameters unless otherwise noted: NormalizeData (LogNormalize), FindVariableFeatures (nfeatures=2000), ScaleData, RunPCA, FindNeighbors, FindClusters and RunUMAP steps. Dim was set to 30 based on inspection of variance across the principal components (PCs) using the ElbowPlot function.

snATAC-seq data analysis: The aggregated peak-by-barcode matrix was used as input to Signac (v1.14.0)155 in R. Low-quality cells were removed based on the following QC metrics: number of chromatin accessibility peaks, nucleosome signal and transcription start site enrichment score. Cells with more than 1000 chromatin accessibility peaks, a transcription start site enrichment score greater than 2, and a nucleosome signal less than 4 were kept. Doublets were also removed by using scDblFinder (v1.20.0)154. After filtering out the low-quality cells and doublets, we proceeded with normalization, identified highly variable peaks and reduced dimensions using the function of RunTFIDF, FindTopFeatures (min.cutoff=“q0”) and RunSVD in Signac with default parameters. The first Latent Semantic Indexing (LSI) component was not used to downstream analysis due to its strong correlation with the sequencing depth. Then, regular non-linear dimension reduction and clustering were performed by RunUMAP (dims=2:30), FindNeighbors (dims=2:30) and FindClusters in Seurat. The gene activity scores were calculated using the GeneActivity function and added to the Seurat object.

Multimodal analysis: The shared cells across the two modalities (snRNA-seq and snATAC-seq datasets) were merged, and the RNA and ATAC dimensionality reduction was recomputed using default parameters. Multimodal data integration was performed by finding the Weighted Nearest Neighbor (WNN)156 using Seurat::FindMultiModalNeighbors. The dimensionality reduction was separately set to 1:30 (snRNA assay) and 2:30 (snATAC assay). The joint UMAP and clustering was performed using the WNN graph with default parameters.

Subcluster and annotation analysis: To better analyze the different tissue, cells belonging to E13.5 MGE, E13.5 hypothalamus, P60 cortex and P60 hypothalamus from total Multiome data were separately subsetted. Shared cells across the two modalities were merged, and the RNA and ATAC dimensionality reduction was recomputed using default parameters. Multimodal data integration was recomputed by finding the Weighted Nearest Neighbor (WNN)156 using Seurat::FindMultiModalNeighbors. All cell types were defined using the RNA expression only. Cell types of E13.5 MGE were identified with the FindAllMarkers function within clusters and integrated with mature cell type biomarkers. Putative hypothalamic nuclei of E13.5 hypothalamus datasets were annotated via gene expression using studies77–79. In E13.5 hypothalamus datasets, we identified some cells corresponding to the adjacent prethalamus and telencephalon area via region-specific markers77, and the telencephalon cells were removed before performing other analysis. We mapped our E13.5 hypothalamus dataset to the Hypothalamic Development Database 280 with the TransferData function. P60 cortex datasets were annotated using the Allen Brain Altas scRNA dataset157 with the TransferData function, and subtypes with low cell numbers were removed. PV+ and SST+ interneuron subtypes were annotated based on previous publications71,76 and FindAllMarkers function. For P60 hypothalamus, Seurat clusters with low cell were removed and the remaining cells were annotated with mature cell type biomarkers. Astrocytes subclusters from P60 hypothalamus datasets were defined with default Seurat algorithm.

Differential analysis: Differentially expressed genes between genotypes and sexes were computed using the Wilcoxon rank sum test implemented in the Seurat FindMarkers function with Bonferroni correction (significance: log2FC > ±0.2, adjusted P-value < 10-6) for the E13.5 MGE, E13.5 hypothalamus and P60 cortex datasets. For P60 hypothalamus, significant differentially expressed genes were defined with log2FC > ±0.2, adjusted P-value < 0.05 due to low cell numbers. DEGs in tanycytes were analyzed with combined female and male samples due to low cell numbers. Differentially expressed peaks between different genotypes were computed using the Wilcoxon rank sum test implemented in the Seurat FindMarkers function with Bonferroni correction (significance: log2FC > ±0.2, adjusted P-value < 10−6) with combined sex. The EnhancedVolcano function from the EnhancedVolcano package (v1.24.0, https://github.com/kevinblighe/EnhancedVolcano) was used to visualize the DEGs between different conditions per population and parameters.

Enrichment analysis: Gene ontology pathway analysis was performed on each group’s (e.g., genotype and sex) DEGs with adjusted p-values < 0.05 using the enrichGO function from the clusterProfiler package (v4.14.0)158. The parameters were set as following: OrgDb=org.Mm.eg.db (v3.20.0), keyType=“SYMBOL”, ont=“BP”, universe=“All detected genes in specific population”. ClusterProfiler::simplify function was also used to reduce the redundancy among enriched terms.

Co-expression network analysis: The weighted gene co-expression network analysis in high-dimensional transcriptomics data (hdWGCNA) was performed by the R package hdWGCNA (v0.4.03)73. We set up Seurat objects for WGCNA using cells from P60 cortex interneurons with the SetupForWGCNA function and constructed metacells in each group with the MetacellsByGroups function. The expression matrix of the PV+ population from combined sex and genotypes was prepared with the SetDatExpr function. The co-expression relationships between genes were raised by the power to focus on strong connections. Thus, the soft power threshold was set to 6 by performing a parameter sweep with the TestSoftPowers function, and the co-expression in the PV+ population from combined sex and genotypes was constructed with the ConstructNetwork function. Module Eigengenes and connectivity were computed with default parameters in the PV population. Then, the top hub genes were determined with the GetHubGenes function. We ran the UMAP algorithm on the hdWGCNA topological overlap matrix with the RunModuleUMAP function and visualized the UMAP co-expression network with the ModuleUMAPPlot function in hdWGCNA. Differential Module Eigengene (DME) analysis was also performed between different genotypes in the PV population using the FindDMEs function (test.use=“wilcox”) and visualized with the PlotDMEsVolcano function in hdWGCNA with default parameters.

Pseudo-time analysis: Single-cell trajectory analysis was performed using Monocle3 (v1.3.7)159 workflow. MGE Seurat objects with combined sex and genotypes were converted to a CellDataSet object as.cell_data_set function. The reduced dimensionality of CellDataSet object was set to WNN. UMAP of MGE Seurat object. Learning trajectory graph using the learn_graph function. The root of the trajectory was set to Nes+ clusters and order cells using the order_cells function with default parameters. Plotting trajectory colored by pseudotime with the plot_cells function.

Visualization: Uniform Manifold Approximation and Projection (UMAP) coordinates and WNN clustering, computed by Seurat on multimodal integrated datasets, were visualized using the DimPlot function. The expression of genes of interest was visualized using FeaturePlot, VlnPlot or DoHeatmap function. Packages ggplot2 (v3.5.1) and patchwork (v1.3.0) were used to visualize during the Multiome datasets analysis process.

Statistics and reproducibility
Statistical tests were performed using GraphPad Prism and R. Unless otherwise stated, Standard Unpaired 2-tailed t test, 2-tailed Welch’s t tests and 2-tailed Mann-Whitney test were performed based on normality when comes to two groups; Ordinary one-way ANOVA followed by Tukey’s multiple comparison tests, Kruskal-Wallis followed by Dunn’s multiple comparisons based on normality when comes to three groups; Two-way ANOVA followed by Tukey’s multiple comparison tests or Šídák’s multiple comparisons test when comes to multiple groups. Unless otherwise stated, data are presented as mean values +/- SEM. 2-tailed Wilcoxon rank sum test with Bonferroni correction was performed in Multiome-seq data, and 1-tailed Fisher’s exact with BH correction was performed in GO enrichment data. In Figs. 1C, D, 2E, 5D, E, 10A, assays were repeated ≥ 3 biological replicates with technical replicates with similar results. In Fig. 10E, assays were repeated 2 biological replicates with technical replicates.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Li and colleagues present a very thorough and well written study on the role of histone 3.3 lysine 4 methylation depletion upon neurological development and functional changes due to targeted gene manipulation in either the MGE or hypothalamic regions. The authors employed a strategic approach to mimic humans with loss of function mutations in this gene, by expressing a version of histone 3.3 that was incapable of having lysine 4 methylated and in turn competed with endogenous histone 3.3 like humans with mutations in this gene. The authors also studied dosage of this effect by assessing both het and homozygous expression of this interfering mutant. The extent of approaches to characterize these mice and the relevance to the human condition are paramount and I think the authors did a superb job but just have some suggestions to help better establish some mechanisms underlying their findings and other minor things that I hope will improve the manuscript below:\n1) For Figure 1 text in the results section, I would like to see more detail in the text describing aspects of experiments detailed in panels D and E to help readers.\n2) Just to be safe, in line 142 where it first comes up, can you change \"MGE and hypothalamus\" to \"MGE and/or hypothalamus.\" Also make this edit later as this comes up a few times.\n3) In Figure 2D the Y-axis values seem off, can you check and make sure the values reflect the weight in grams of the mice or change?\n4) In Figure 2F, it is interesting that Nkx2.1-Cre manipulation of cortical interneurons would result in microcephaly, which is normally due to changes in pyramidal neuron morphology. Not asking for additional experiments here but this is an interesting and potentially novel phenomenon that should be mentioned/speculated in the discussion.\n5) Starting in Figure 5, some panels are hard to see, including the Tom and PV images. Can the authors make these brighter in the figures?\n6) In figure 4B, bottom table, the authors mention they used a higher dose of PTZ to induce seizures in males. Can the authors color-code or do something else to reflect this in the table? This would help readers with the presentation of the data.\n7) In Figure 4E, this is a minor thing but could the authors correlate the language using epileptiform bursts mentioned in the text to the interictal events presented in Figure 4F? Just wanted the language to match so readers had an easier time understanding.\n8) The loss of cortical interneurons is a major phenotype in these mice but how this occurs is not known. The authors have done a great job with their RNA-seq suggesting that there are less mutant MGE cells later in the paper but this is not yet tested. Can the authors look at embryonic WT and mutants to compare proliferation and apoptosis to better understand how this loss of cells is propagated? Maybe something as simple as either PH3 or SOX2 staining (proliferation) and cleaved caspase-3 (apoptosis) staining in the MGE, and potentially hypothalamus, to answer?\n9) In lines 391-392 of the results section, it is mentioned that the hypothalamus has a reduction in GFP+ cells. Are there any volumetric/regional assessments that could be done to assess how this impacts the hypothalamus and various nuclei? It would be very interesting to see if any changes occur in distinct regions of the hypothalamus already implicated herein.", "reviewer_2": "In this manuscript, Li and colleagues described experiments in which they perturbed H3K4 methylation in the developing medial ganglionic eminence (MGE) and the hypothalamus of mice. To this end, they express (at different doses in heterozygous and homozygous mice) a mutated form of H3.3 in which the lysine 4 residue is mutated to a methionine. These mutant mice have persistent body weight and growth problems (initial undergrowth, followed by obesity), fewer GABAergic interneurons, abnormal network dynamics, and increased seizure susceptibility. Mutant mice also exhibit behavioural abnormalities, which were more prevalent in females than males. The authors also performed single-cell sequencing experiments to identify transcriptional changes that could underlie the cellular phenotypes described in mutant mice.\nThis paper demonstrates a critical role for H3K4 methylation in the development of the MGE and the hypothalamus of mice. Disrupting H3K4 methylation impacts gene expression during embryonic development, causing multiple cellular alterations that, in turn, affect the neuronal populations generated in the brain regions studied. However, while the paper does a very nice job at identifying phenotypes caused by the likely insertion of mutated histones in the chromatin (although this is not directly shown in this manuscript), there is little mechanistic insight into the process that H3K4 methylation regulates to cause the abnormalities. Are the phenotypes caused by abnormal patterning, neurogenesis, migration, survival, or wiring? Are the phenotypes independent or causally linked? Are the transcriptional changes sufficient to explain the cellular abnormalities?\nMain issues\nThe authors report that disrupting H3K4 methylation in the MGE and hypothalamus has a massive impact on food consumption and, consequently, growth. With this in mind, it is difficult to rule out that some of the changes observed in the brain are not indirectly caused by abnormal metabolism and growth in these animals, rather than a direct effect on the methylation profile of H3.3 at specific genomic loci. In this context, while the single-cell analyses in embryos may hint at possible mechanisms underlying the phenotypes described by the authors, the data obtained from the adult mice are hard to interpret. Since the hypothalamic deficits are likely at the core of the metabolic alterations, one possible way to circumvent this problem is to express H3.3K4M only in the MGE and study what features this manipulation recapitulates from those observed in Nkx2.1-Cre mice.\nThe authors describe differences in seizure susceptibility between male and female mice. However, sex differences were not explored for GABAergic interneurons in the cortex, which the authors suggest are at the core of these problems. Are there differences in the density and relative proportion of interneurons in male and female mice? Are the analyses performed in Figure 3 powered to detect differences in SST interneurons? There is a clear trend in Figure 3B.\nThe characterization of hypothalamic deficits is primarily based on single-cell analyses. It would be appropriate to identify key cellular populations and examine cell densities, proportions, and other relevant characteristics using histology, which would help build a picture of the deficits more similar to what is described for cells derived from the MGE. This would allow the authors to explore common themes emerging from the analysis of the two brain regions. As it stands, it is unclear why studying the MGE and the hypothalamus simultaneously adds to our understanding of the role of H3K4 methylation in brain development and function.\nOther issues\nLSL-K4M mice are not described in any detail. What locus is it used to drive the expression of H3.3K4M? Does the exogenous expression of H3.3K4M influence the endogenous expression of H3.3K4? One would infer that the reduced methylation is caused by the availability of mutated H3.3 but, is it possible that levels of wild type H3.3 are changed in these mice? This could be explored through Western blots.\nThe authors should also carry out Western blots for me1 and me2 to quantify how the expression of H3.3K4M impact those levels in heterozygous and homozygous mice (as shown for me3 in Figure 1E).\nThe age of the embryos analyzed is Figure 1 is not included in the text or figure legends.\nThere are several statements at the end of some sections of the Results that belong to the Discussion. Some examples: (1) “Such increased variability in electrical properties is reminiscent of the developing hippocampus, suggesting interneuron maturation deficits in H3.3K4M Hom mice”. This is speculative and not a direct conclusion of the experiments; there are several alternative explanations. (2) “As widespread perisomatic inhibition is critical for both circuit gamma entrainment and seizure control, it is likely that the deficits in hippocampal FS/PV+ cells in H3.3K4M mice directly contribute to disrupted rhythmogenesis and epilepsy”. Again, this is speculative and not a direct conclusion of the experiments. The authors described defects in other interneuron populations that shape gamma rhythms. Indicating that PV interneurons cause the network dynamic changes and seizure susceptibility is a stretch, especially in the Results section. Please revise the Results to avoid these and similar statements.", "reviewer_3": "Summary of the Study \nThis manuscript investigates how reduced H3K4 methylation in the medial ganglionic eminence (MGE) and hypothalamus affects cortical interneuron (cIN) generation and function, as well as hypothalamic circuits controlling body growth. By using a conditional H3.3K4M mouse model driven by Nkx2.1-Cre, the authors describe reductions in interneuron numbers, altered network excitability and seizures, as well as notable changes in postnatal growth and obesity. The data include morphological, behavioral, electrophysiological, and single-cell analyses that point toward significant, cell-type-specific deficits in both MGE-derived interneurons and hypothalamic populations.\nMajor Points\nValidation of the mouse model:\nThe authors would need to verify or test the actual timing of Cre expression.\nThe reduction of H3K4me3 in MGE-derived cINs is not properly shown. First, I wonder why the tDTomato signal is so blurry. In numerous manuscripts, including Tanuguci et al., 2011 (DOI: 10.1016/j.neuron.2011.07.026), where Nkx2.1-Cre mediated reporter expression is shown in the embryonic MGE, discrete reporter positive cells are visible in the VZ and SVZ. However, the tdTom signal in Figure 1C and D is very blurry. This hampers proper visualization of analyzing H3K4me3 levels in the recombined cells. However, at adult stages (Fig. 3A) clear tDTom cells are visible. As Nkx2.1 is down regulated in cortical fated interneurons upon becoming postmitotic, while staying expressed in e.g. striatal fated cells, such discrete cellular signals would help to show downregulation in cortical fated cells, either by analyzing cells that have reached the cortex or by co-labeling e.g. with Calbindin antibodies. Also, the authors state in line 124: „Having H3.3K4M active prior to cell cycle exit is critical to substitute endogenous H3.3 with mutant H3.3K4M protein due to the prolonged half-life of histone proteins in the brain…“ However, there are no clear tdTom+ cells visible in the ventricular zone (VZ), where the progenitors reside. Also, the reduction of H4me2/3 level in the VZ is not properly visible (rather in the SVZ). I wonder, whether successful Cre-recombination could be shown in the sc-transcriptome analyses? This would also allow proper discrimination of progenitors versus postmitotic cINs (however, here a different mouse line was used, commented later).\nFor the hypothalamus, no immunostainings are shown in that regard, which should be provided as well. Here, I would similarly expect clear cellular tdTomato labeling, which allows determination and quantification of H3K4me3 fluorescence intensity in tdTom cells.\nIn addition, the authors provided Western Blot data, which showed a strong reduction for the hypothalamus in the HOM mice, but less clear reduction for the MGE. I assume from the methods, that bulk tissue was used here. Maybe FACS-mediated enrichment of the recombined cells would yield in clearer reductions in K4me3 levels.\nEmbryonic fate-mapping and characterization of potential defective processes is missing\nWhat I miss in general in this manuscript is fate mapping of the cells at embryonic stages. By providing embryonic sections with proper tdTomato cell labeling not only allows the determination of H3K4me3 reduction in e.g. cells that have reached the cortex, it would also allow to assess which developmental defects occur that lead to reduced cIN numbers in the adult mice. Is the migration altered (e.g. misrouted)? Or do HOM-cINs die? At which stage (embryonically or postnatally)? Same for the reduced numbers of nNos+ interneurons seen in the hippocampus.\nMore comprehensive embryonic phenotyping would also serve to better understand the findings the authors report in postnatal / adult mice, such as the reduced cortical thickness in postnatal brains of mutant mice (Fig. 2). It is known that invading cINs can influence cortical progenitors (DOI: 10.1016/j.cell.2018.01.031), and thus potentially cortical neurogenesis and layer thicknesses. Embryonic fate-mapping could serve to investigate potential migration defects that correlate with changes in cortical progenitor proliferation and lamination. This would strengthen the mechanistic link to cortical thinning.\nAdult phenotyping:\nIn matters of cortical thinning, it should also be clearly stated which Bregma and cortical area was analyzed. In Fig. 2F, S1 is stated. Is the effect only seen in S1? Analyzing whether the reduction affects all layers or distinct layers, could provide valuable information. In the magnifications provided in 2F, it seems that II-IV are similar, and that the reduction is mainly seen in layer I and layer VI. Here, a more detailed analysis would be favorable (and could help to correlate the effects with potential embryonic defects, such as changes in neurogenesis during e.g. the formation of layer VI).\nTo discriminate between the different layers, layer-specific markers would be needed, which is also required for the cell density measurements depicted in Extended Figure 1. Here, PV and SST+ cIN density was claimed to be analyzed in the superficial layers (I-III) and deep layers IV-VI, based on DAPI staining. However, using DAPI staining to discriminate layer IV form layer III is very imprecise…see also Figure 3A. Also I would not necessarily agree with the demarcation of the deep layer VI shown in 3A. Layer specific labeling needs to be included, such as RORB to stain layer IV or marker for VIb. For the analysis performed in 2B it needs to be mentioned in the legend or the graph axis, whether PV+ or PV/tdTom+ cells were analyzed? As not every PV cells is also positive for tdTom. Same for SST, and the analysis in Fig. 3D (Hippocampus).\nIn the hippocampus, the authors observed also PV+ cell reduction and a more severe reduction of nNOS⁺ interneurons. Mutant mice showed increased seizure susceptibility, for which PTZ injections and patch clamp-recordings in slices were performed to characterize electrophysiological properties. The authors revealed changes at the population level (greater variability) for three interneuron subtypes in H3.3K4M Hom mice, which they interpret as impaired maturation. Moreover, altered gamma oscillations were found.\nIn Figure 4 (Differences in Seizure-Related Mortality at 3 vs. 5 Months) the authors report different seizure susceptibilities and mortality rates at 3 vs. 5 months (e.g., female mortality is ~50% at 5 months, but at 3 months, males show higher seizure mortality). The text suggests that the higher PTZ dosage for males at 3 months may underlie this effect, yet it remains somewhat confusing why the absolute differences (between males and females, at each time point) shift so dramatically. It should be discussed whether the PTZ dose alone explains male–female differences or if age-related changes in brain maturity or H3K4 methylation might also be factors.\nFor PTZ experiments, did the authors used Racine scoring?\nDevelopmental vs. Adult-Stage Functional Changes It would be helpful to discuss to what extent the observed electrophysiological and behavioral phenotypes arise from early developmental disruption of H3K4 methylation versus potential ongoing dysfunction in adult Nkx2.1-lineage cells. Since the H3.3K4M mutation is constitutively expressed, both mechanisms are possible. Although inducible Cre lines or temporally restricted models may be beyond the scope of this study, a brief discussion of this limitation, and how future studies might approach developmental versus adult-stage contributions, would strengthen the interpretation of the results.\nSex Differences in Phenotypes\nCertain figures (e.g., Figures 4 and 5) present male and female data separately (e.g., PTZ-induced seizures and related behavioral outcomes), revealing sex-specific phenotypes. However, earlier figures (Figures 1–3), which assess H3K4 methylation levels, cell counts, cortical thickness, survival and body weight, do not consistently stratify the data by sex. This inconsistency limits the interpretation of sex-specific findings. The authors should clarify whether male vs. female comparisons were performed for these earlier datasets and either present the data accordingly or explicitly state if no sex differences were observed. As the behavioral phenotyping reveals intriguing sex-dependent effects, aligning these outcomes with corresponding cellular phenotypes would strengthen the study and provide a more integrated understanding of the observed differences.\nAlthough the manuscript highlights sex-specific behavioral and transcriptional effects in H3.3K4M Hom mice, the mechanistic link to H3K4 methylation remains somewhat speculative. The Discussion could be strengthened by elaborating on known epigenetic or neuroendocrine sex differences that may modulate H3K4 methylation patterns, such as hormonal control of chromatin modifiers or differences in X-inactivation escape genes. Furthermore, it would be helpful to clarify whether the authors observed comparable reductions in H3K4me3 or chromatin accessibility in male and female samples, or whether sex-specific effects may reflect differential sensitivity to epigenetic dysregulation.\nWeight Gain: Hypothalamic vs. cIN Contributions While the manuscript focuses on hypothalamic disruptions leading to overeating/obesity, it is worth considering the possibility that altered cIN functionality could contribute to weight changes indirectly through mood or motivational deficits (for instance, depression-like or other behavioral changes can also predispose to weight gain). The causal link between hypothalamic gene expression changes and increases in body weight could be more strengthened or discussed.\nSingle nuclear RNA seq experiments\nIn subsequent single-cell analyses (Figure 7, Extended Data 6), the manuscript transitions from using an Ai9/tdTomato reporter to a Sun1-GFP reporter system, and thus to another mouse line. Please justify why a different fluorescent reporter (Sun1-GFP) was used for the single-nucleus analysis. Clarify that the experiment is directly comparable to the earlier Ai9 lines.\nWhat about Sex-Specific Differences in the snRNA-Seq/ATAC-Seq analysis? The documentation of how sex was incorporated into the overall experimental design and analysis pipeline is inconsistent also for the seq-data. Specifically, the figure legends, methods and main results section do not clearly state which datasets (e.g., snRNA-seq, snATAC-seq, WNN-integrated data, pseudotime, and WGCNA analyses) were generated from male, female, or mixed-sex animals. It is also not evident whether sex was treated as a covariate during data integration and normalization, or whether sex-specific analyses were systematically performed across all comparisons. Given the increasing recognition of sex as a critical biological variable and its relevance to the phenotypes described (e.g., seizure susceptibility), I recommend that the authors provide a clearer account of the sex composition of each dataset and clarify in the figure legends and Methods whether data were pooled, stratified, or sex-balanced. This information is essential for interpreting the generalizability of the findings and for evaluating the extent to which observed effects are sex-dependent. Same accounts for Figure 8.Information on sex should also be mentioned in the results e.g. when describing altered expression in Mef2c.\nIn the provided sex-specific comparisons, where differences in gene expression (e.g., potassium channel genes in PV+ cells) are reported, refer to the H3.3K4M HOM samples. However, it remains unclear whether sex-specific differences were also present in the adult cortical control (WT) samples, or whether these only emerged in the mutant condition. This distinction is important for interpreting whether the observed differences reflect baseline sexual dimorphism or genotype–sex interactions. For PV+ cells sex specific divergent regulation was shown (Extended Data 6H). However, for SST cells, the analysis is not shown sex-specifically. Are there now sex-specific effects? Illustrating these differences more systematically and clearly could significantly strengthen the discussion about sexual dimorphism.\nIn Figure 7C and Extended Data 6A, more detail on cluster annotation (including specific marker genes) would help the reader interpret the results.\nDoes the color code presented in Figure 8C refers to the UMAP in 8A (right)? Should be mentioned in the legend.\nExtended Fig. 5G: why are the accessible peaks different/complementary in MGE versus Hypothalamus (Numerous decreased peaks in HOM-MGE but increased in hypothalamic samples)?\nHypothalamic findings:\nThe hypothalamic analysis reveals substantial changes in cellular composition and organization in H3.3K4M Hom mice, including increased NPCs, loss of ARC/PVN populations, and expansion of astrocytes. The disorganization of tanycytes and myelin in the median eminence is striking and well illustrated histologically. However, transcriptomic analysis of tanycytes was limited by low cell numbers, with few DEGs detected. The interpretation of specific transcriptional changes, such as the downregulation of Fgf14, would benefit from orthogonal validation (e.g., in situ hybridization or qPCR) to confirm this finding independently of the single-nucleus dataset. Strengthening the support for these changes would enhance the mechanistic link between H3K4me dysregulation and hypothalamic function.\nThe reduction in Olig2+ cells is an interesting finding and points to fate regulation by H3K4me3. The authors report a „corresponding decrease in myelin basic protein (MBP) in this region (Extended Data Fig. 2).“ However, I could not find a quantification here. This would need to be provided for that statement.\nWhile the expansion of hypothalamic astrocytes in H3.3K4M Hom mice is clearly documented, the manuscript would benefit from additional developmental context clarifying how Nkx2.1-expressing progenitors contribute to glial lineages, particularly in the hypothalamus. Since Nkx2.1-lineage cells are widely known for giving rise to cortical interneurons, it would be helpful to note that these progenitors can also generate astrocytes and tanycytes in ventral forebrain regions, as supported by lineage tracing studies, e.g. in the introduction. This background would help interpret whether the observed glial expansion reflects an expected regional fate or a fate shift resulting from altered H3K4 methylation.\nThe Discussion clearly integrates behavioral, transcriptomic, and cellular data to support a model of H3K4 methylation-dependent regulation of interneuron and hypothalamic development. However, the authors should consider including additional developmental context for the gliogenic potential of Nkx2.1+ progenitors, particularly in the hypothalamus, to better support the interpretation of astrocyte expansion.\nOverall, this manuscript presents an ambitious and multidimensional study of H3K4 methylation in forebrain development and function. The integration of single-cell epigenomics, electrophysiology, and behavior is commendable, and the focus on both MGE-derived interneurons and hypothalamic circuits is very comprehensive. Addressing the points raised here — particularly the developmental issues, glial fate interpretation, sex-stratified analyses, and figure clarity — will help to enhance the robustness and interpretability of the findings."}, "author_response": "Response To Reviewers\nWe thank the reviewers for their overall positive comments and feedback on our manuscript. We found\nmany of their suggestions to be helpful in strengthening the data presentation and conclusions of our\nstudy. Our point-by-point response to the each of the reviewers’ suggestions is below, with reviewers’\ncomments indented & italicized, and our response in blue font. All significant changes in the Revised\nManuscript are in red text.\nReviewer #1:\nLi and colleagues present a very thorough and well written study on the role of histone 3.3 lysine 4\nmethylation depletion upon neurological development and functional changes due to targeted gene\nmanipulation in either the MGE or hypothalamic regions. The authors employed a strategic\napproach to mimic humans with loss of function mutations in this gene, by expressing a version of\nhistone 3.3 that was incapable of having lysine 4 methylated and in turn competed with endogenous\nhistone 3.3 like humans with mutations in this gene. The authors also studied dosage of this effect\nby assessing both het and homozygous expression of this interfering mutant. The extent of\napproaches to characterize these mice and the relevance to the human condition are paramount\nand I think the authors did a superb job but just have some suggestions to help better establish\nsome mechanisms underlying their findings and other minor things that I hope will improve the\nmanuscript below:\nWe thank the reviewer for these positive comments.\n1. For Figure 1 text in the results section, I would like to see more detail in the text describing aspects\nof experiments detailed in panels D and E to help readers.\nThank you for this suggestion, we have added additional details in the Results to better explain the\nlogic for Figure 1D-E.\n2. Just to be safe, in line 142 where it first comes up, can you change \"MGE and hypothalamus\" to\n\"MGE and/or hypothalamus.\" Also make this edit later as this comes up a few times.\nWe changed ‘and’ to ‘and/or’ at line 142 and at several other locations where this edit was warranted.\n3. In Figure 2D the Y-axis values seem off, can you check and make sure the values reflect the\nweight in grams of the mice or change?\nThe graph in Figure 2D displays brain weight of WT, Het and Hom mice at 3 weeks. The Y-axis is\ncorrect, with the brains weighing between 0.3 and 0.4 grams. To clarify this point, we have relabeled\nthe Y-axis to read ‘Brain weight (g)’.\n4. In Figure 2F, it is interesting that Nkx2.1-Cre manipulation of cortical interneurons would result in\nmicrocephaly, which is normally due to changes in pyramidal neuron morphology. Not asking for\nadditional experiments here but this is an interesting and potentially novel phenomenon that should\nbe mentioned/speculated in the discussion.\nmice are microcephalic in the manuscript. Microcephaly is diagnosed when a baby/infant’s head\ncircumference is in the third percentile or lower. While we observe a statistically significant decrease in\nbrain weight between WT and H3.3K4M Hom mice, we do not know if this would fall into this medical\ndefinition of microcephaly (and we don’t know of a good way to define the 3rd percentile of mouse brain\nweight). While many mutations resulting in smaller brains do directly affect pyramidal cell development,\nthere are examples where mutations restricted to GABAergic progenitors results in smaller brains, such\nArx & Nkx2.1 (reviewed in PMID: 38094004). Thus, we don’t believe that smaller brains in our mutant\nmice are a novel phenomenon whereby mutations in GABAergic progenitors result in smaller brains.\n5. Starting in Figure 5, some panels are hard to see, including the Tom and PV images. Can the\nauthors make these brighter in the figures?\nThank you for this suggestion, we have increased the intensity of the Tom and PV channels and\nimproved the signal-to-background ratio on immunohistochemistry images in several figures.\n6. In figure 4B, bottom table, the authors mention they used a higher dose of PTZ to induce seizures\nin males. Can the authors color-code or do something else to reflect this in the table? This would\nhelp readers with the presentation of the data.\nWe have changed the 3-month male column heading to read ‘Male (40)’ to highlight this distinction, and\nwe added the following sentence in Figure 4B legend: “3-month-old males received a 40 mg/kg PTZ\ndose (40) whereas all other conditions received a 20 mg/kg dose.”\n7. In Figure 4E, this is a minor thing but could the authors correlate the language using epileptiform\nbursts mentioned in the text to the interictal events presented in Figure 4F? Just wanted the\nlanguage to match so readers had an easier time understanding.\nThank you for pointing this out, we now refer to these as interictal events in the Results & Figure legend.\n8. The loss of cortical interneurons is a major phenotype in these mice but how this occurs is not\nknown. The authors have done a great job with their RNA-seq suggesting that there are less mutant\nMGE cells later in the paper but this is not yet tested. Can the authors look at embryonic WT and\nmutants to compare proliferation and apoptosis to better understand how this loss of cells is\npropagated? Maybe something as simple as either PH3 or SOX2 staining (proliferation) and cleaved\ncaspase-3 (apoptosis) staining in the MGE, and potentially hypothalamus, to answer?\nWe thank the reviewer for this suggestion. To gain additional mechanistic insight, we have assessed\ncell proliferation (via PH3 & Ki67 staining), apoptotic cell death (via TUNEL staining) and interneuron\nmigration in WT, Het & Hom mice. These new data are summarized below:\n1. Cell cycle dynamics: We quantified the density of PH3+ cells in the MGE and did not identify any\ndifferences in PH3+ cells between any genotypes. We also did not observe any striking differences\nin Ki67 expression between genotypes, though not quantified. These findings demonstrate that\nthere are no changes in cell proliferation in H3.3K4M mutation mice. This result is not surprising\nsince histone variant H3.3 is weakly expressed in cycling VZ/SVZ cells and gets strongly\nupregulated in postmitotic neurons (Funk…Kwan PNAS 2022, see Reviewer #3, point #3 below).\n2\n2. Apoptosis: We performed TUNEL staining to detect apoptotic cells in the E13.5 MGE, E13.5\nhypothalamus and P4 cortex. We did not observe any obvious difference of TUNEL positive cells\neither at E13.5 MGE or P4 cortex. Since there were very few TUNEL+ cells in all genotypes, we did\nnot perform quantitative analysis on this tissue.\n3. Cell migration: We did observe clear differences in the number of tdTom+ cells migrating into the\ncortex, with significantly fewer interneurons entering the cortex in E13.5 H3.3K4M Hom mice\ncompared to WT. To determine if this migration was simply delayed in mutant mice or rather led to\nsignificant reductions in interneurons, we also examined P4 WT and Hom brains. This is prior to the\nintrinsic programmed interneuron apoptosis that removes 20-40% of interneurons from ~P6-P12.\nWe observe a striking decrease in Tom+ cells in the forebrain of Hom brains, most notably in the\nhippocampus. These findings indicate that reduced tangential migration into the cortex is likely the\nprimary cause for reduced cell numbers in these Hom mice.\nThese data are presented in New Fig 5 & Extended Data Figure 12, described in the Results and\nintegrated into the Discussion. This data provides significant mechanistic insight into how reduced\nH3.3K4 methylation reduces interneuron numbers in the forebrain, and we again thank the reviewer for\nthis suggestion.\n9. In lines 391-392 of the results section, it is mentioned that the hypothalamus has a reduction in\nGFP+ cells. Are there any volumetric/regional assessments that could be done to assess how this\nimpacts the hypothalamus and various nuclei? It would be very interesting to see if any changes\noccur in distinct regions of the hypothalamus already implicated herein.\nThe reviewer raises an interesting question about assessing cellular/volumetric changes in specific\nhypothalamic nuclei. The decrease in percent of GFP+ nuclei in the Hom mice mentioned here is based\non our flow cytometry analysis of E13.5 hypothalamus. While we can assign many of these cells to\nputative mature hypothalamic nuclei based on transcriptomes at this age (Figure 9 A-C & Extended\nData Figure 12A-D), that does not mean that the structure & organization of these nuclei are\nrepresentative of the mature brain. Additionally, hypothalamic neurogenesis will continue for several\ndays after this E13.5 timepoint, with postmitotic cells still migrating to their proper hypothalamic nuclei.\nAnd different hypothalamic nuclei may be generated across different timescales. Thus while we agree\nwith the reviewer’s point that distinct hypothalamic nuclei could be differentially affected in this mutation\n(and this is supported by Extended Data Figure 12D), it’s unclear that different regional and/or\nvolumetric analysis of GFP+ nuclei at E13.5 would be informative to address this question.\nWe now include representative examples of our flow cytometry plots in Extended Data Figure 7 and\nhave cited this figure in the relevant portions of the manuscript. We have also clarified in the Methods\nhow we calculated the average % GFP+ nuclei in these experiments.\nReviewer #2:\nIn this manuscript, Li and colleagues described experiments in which they perturbed H3K4\nmethylation in the developing medial ganglionic eminence (MGE) and the hypothalamus of mice.\nTo this end, they express (at different doses in heterozygous and homozygous mice) a mutated\nform of H3.3 in which the lysine 4 residue is mutated to a methionine. These mutant mice have\npersistent body weight and growth problems (initial undergrowth, followed by obesity), fewer\nmice also exhibit behavioural abnormalities, which were more prevalent in females than males. The\nauthors also performed single-cell sequencing experiments to identify transcriptional changes that\ncould underlie the cellular phenotypes described in mutant mice. This paper demonstrates a critical\nrole for H3K4 methylation in the development of the MGE and the hypothalamus of mice. Disrupting\nH3K4 methylation impacts gene expression during embryonic development, causing multiple\ncellular alterations that, in turn, affect the neuronal populations generated in the brain regions\nstudied. However, while the paper does a very nice job at identifying phenotypes caused by the\nlikely insertion of mutated histones in the chromatin (although this is not directly shown in this\nmanuscript), there is little mechanistic insight into the process that H3K4 methylation regulates to\ncause the abnormalities. Are the phenotypes caused by abnormal patterning, neurogenesis,\nmigration, survival, or wiring? Are the phenotypes independent or causally linked? Are the\ntranscriptional changes sufficient to explain the cellular abnormalities?\nWe thank the reviewer for this accurate summary of our manuscript and the positive feedback, and we\nhave addressed the questions raised here more specifically below.\nMain issues\n1. The authors report that disrupting H3K4 methylation in the MGE and hypothalamus has a massive\nimpact on food consumption and, consequently, growth. With this in mind, it is difficult to rule out\nthat some of the changes observed in the brain are not indirectly caused by abnormal metabolism\nand growth in these animals, rather than a direct effect on the methylation profile of H3.3 at specific\ngenomic loci. In this context, while the single-cell analyses in embryos may hint at possible\nmechanisms underlying the phenotypes described by the authors, the data obtained from the adult\nmice are hard to interpret. Since the hypothalamic deficits are likely at the core of the metabolic\nalterations, one possible way to circumvent this problem is to express H3.3K4M only in the MGE\nand study what features this manipulation recapitulates from those observed in Nkx2.1-Cre mice.\nOne strength of our study is that we have modified H3.3K4 methylation in 2 brain regions that are\nassociated with the most common phenotypes of perturbed H3K4 methylation in human disease:\nepilepsy, ID and abnormal body growth. However, the reviewer is correct that it is challenging to\ndefinitively link specific phenotypes to either interneuron or hypothalamic dysfunction, or possibly that\nchanges in both cell populations could underlie some phenotypes. And we cannot definitively rule out\nindirect effects of the H3.3K4M mutation. To highlight this issue, a recent study demonstrated that\ndisrupting activity of SST+ interneurons in the gustatory cortex can alter taste sensitivity and sucrose\nconsumption (Yevoo…Maffei, Current Biology 2025). Thus, it’s possible that altered interneuron\nfunction in the gustatory cortex could, in part, play a role in appetite drive and/or obesity. We have\nexpanded on these issues in the Discussion.\nAs the reviewer suggested, ideally we could express the H3.3K4M mutation only in MGE-derived cells\n(or hypothalamic cells) to circumvent this problem. There are 2 Cre-driver mouse lines that are widely\nused in the field to target the MGE: Nkx2.1-Cre (used here) and Lhx6-Cre (as Lhx6 is expressed in\npostmitotic MGE cells but not LGE or CGE cells). However, Lhx6 is also expressed in a subpopulation\nof hypothalamic nuclei during embryogenesis (Kim…Blackshaw Comm Bio 2021), so even this Cre-\ndriver is not clean to target MGE cells.\nsimilar results in a previous study where we could not generate Lhx6-Cre;Ezh2 mutant mice,\nRhodes…Petros Frontiers 2024). Lhx6 is expressed in the mesenchymal lineage and is critical for\npalate development (PMID: 26071365), so it’s possible that perturbation of H3K4 methylation in Lhx6-\nlineage mesenchymal cells leads to aberrant palate development and lethality.\nWhile most Lhx6-Cre;H3.3K4M Het mice also died during embryogenesis and early postnatal period,\na few of them survived to one month. These Lhx6-Cre;H3.3K4M Het mice appeared underweight\ncompared to WT littermates and their brains weighed significantly less. We detected H3K4M protein\nvia HA immunostaining in the postmitotic cells of MGE as expected. There was a trend towards\ndecreased PV+ and SST+ interneurons in the hippocampus, like the trends observed in Nkx2.1-\nCre;H3.3K4M Het mice. These data are presented in New Extended Data Figure 4 and in the Results.\nWhile this does not eliminate possible contribution from H3.3K4 methylation defects in the\nhypothalamus (since Lhx6 is also expressed in hypothalamus ID&TT nuclei), it does confirm some of\nthe basic brain weight & interneuron changes in a second driver line targeting the MGE.\nOther driver lines used to target MGE-derived interneurons are SST-Cre and PV-Cre, which label\nseparate populations of MGE-derived interneurons. But PV is not expressed until the second postnatal\nweek. SST begins to be expressed in postmitotic MGE cells, but SST expression (and thus Cre) slowly\nramps up in all SST+ cells over several days. As histone variant H3.3 begins to replace H3.1 and H3.2\nshortly after becoming postmitotic (see Reviewer #3, Point #3 below), we do not believe either of these\nCre-drivers is ideal to introduce the H3.3K4M mutation in MGE precursors. Additionally, neither SST\nnor PV are restricted to MGE-derived interneurons (PV is highly expressed by Purkinje cells for\nexample), so altered H3.3K4 methylation in other cell types in these lines could still be confounding.\nThus, while we agree with the reviewer’s suggestion, we do not know of a ‘clean’ Cre-driver to\nspecifically label only MGE cells. One could use viral injections or in utero electroporation to genetically\ntarget the MGE, but these are technically very challenging experiments, would be unilateral, and would\nonly target a subset of MGE cells, thus complicating data interpretation with these approaches.\n2. The authors describe differences in seizure susceptibility between male and female mice.\nHowever, sex differences were not explored for GABAergic interneurons in the cortex, which the\nauthors suggest are at the core of these problems. Are there differences in the density and relative\nproportion of interneurons in male and female mice? Are the analyses performed in Figure 3\npowered to detect differences in SST interneurons? There is a clear trend in Figure 3B.\nWe thank the reviewer for this suggestion. In Figure 3 and Extended Data Figure 2, we now indicate\nthe sex of the mice used for cell counts, with open circles = female and closed circles = male.\nWe increased the n to obtain at least 3 male & 3 female mice for WT and Hom cortex and hippocampus\ncounts (with 2 males & 2 females for Het mice). We do not observe any significant differences in either\nthe density or percentage of Tomato, PV+ or SST+ cells between male & female mice for each\ngenotype. Because splitting the animals by sex increased the variance and reduces the power of the\nstatistics, we still report the statistics from combining the sexes in the manuscript. However, we have\nperformed unpaired t-tests between males & female WT and Hom mice for each cell type & brain region,\nand the results are very consistent between sexes: i.e., for nearly every datapoint, (1) there is a\nstatistically significant difference between male WT vs. Hom AND female WT vs. Hom, or (2) there is\nNOT a statistically significant difference between male WT vs Hom and female WT vs. Hom.\nof cortical SST+ cells in the H3.3K4M Hom mouse, rather than just a clear trend. We have updated the\ntext in the Results, Methods and relevant figure legends to reflect these changes.\n3. The characterization of hypothalamic deficits is primarily based on single-cell analyses. It would\nbe appropriate to identify key cellular populations and examine cell densities, proportions, and other\nrelevant characteristics using histology, which would help build a picture of the deficits more similar\nto what is described for cells derived from the MGE. This would allow the authors to explore common\nthemes emerging from the analysis of the two brain regions.\nMGE-derived interneurons can be cleanly classified and labeled into two large subgroups (PV+ and\nSST+), and an additional nNos+ population in the hippocampus. This makes analysis relatively\nstraightforward to assess changes in the absolute number or percentage of these cell types. In the\nhypothalamus, Nkx2.1+ progenitors give rise to glutamatergic & GABAergic neurons cells as well as\nnon-neuron cells (astrocytes, oligodendrocytes and tanycytes) that populate >12 different hypothalamic\nnuclei (Extended Data Fig 12C and related references). While it would be ideal to characterize all these\ncell types, it is beyond the scope of this manuscript to comprehensively examine cell densities & other\ncharacteristics of cells in so many distinct hypothalamic nuclei.\nWe note that our immunostaining results in old Figure 8/New Figure 10 reveal striking cellular\norganization changes in the hypothalamus of H3.3K4M Hom mice, with complete disorganization of\ntanycytes and astrocytes in the median eminence and lateral wall of the third ventricle. Additionally, we\nquantify increased hypothalamic astrocytes in Hom mice (Extended Data Figure 14) and altered\noligodendrocyte organization and SST expression in the median eminence (Figure 10). We also\nobserved an increase signal of Ki67 in the E13.5 hypothalamus of Hom mice (Extended Data Figure\n12G). Some of these findings were predicted from our single cell sequencing data. We have now\nperformed TUNEL staining to detect apoptotic cells at E13.5 and P4 in the hypothalamus, and like the\nMGE we do not detect any differences between WT and Hom mice. Thus, we believe that our data\nhighlights important changes in cell populations and organization of the hypothalamus beyond our\nsingle cell analyses, which likely are (in part) causative of some phenotypes in the H3.3K4M Hom mice.\n4. As it stands, it is unclear why studying the MGE and the hypothalamus simultaneously adds to\nour understanding of the role of H3K4 methylation in brain development and function.\nThe reviewer questions why we explored H3K4 methylation in both the MGE and hypothalamus\nsimultaneously. The motivation behind this strategy comes from the observation that two of the most\ncommon phenotypes found in NDDs related to histone modifications are intellectual disability with\nepilepsy (interneuron related) and abnormal body growth (hypothalamus related) (see reviews PMID\n31595951 and 28475857 highlighting this point). Thus, we were curious if targeting the H3.3K4M\nmutation to these two brain regions was sufficient to induce phenotypes consistent with these human\ndiseases. And this was largely successful, as these H3.3K4M Hom mice display spontaneous seizures\nand/or increased seizure susceptibility, altered performance in numerous behavior assays, and\nabnormal body growth (initially underweight but eventually catch up and become obese). Additionally,\nas noted above in our response to point #1, we do not know of a genetic strategy to specifically target\nthe H3.3K4M mutation to MGE interneurons because the Nkx2.1-Cre line also targets the\nhypothalamus (which we view as benefit for this study). And we have now included new data from Lhx6-\nof hypothalamic nuclei. While our original submission noted the co-occurrence of epilepsy and\nabnormal body growth in NDDs associated with histone defects, we have re-emphasized this\nrelationship at several points in the revised manuscript.\nOther issues\n5. LSL-K4M mice are not described in any detail. What locus is it used to drive the expression of\nH3.3K4M?\nThe LSL-K4M mice were generated in Dr. Kei Ge’s lab and described in their original paper (Jang…Ge\nNuc Acid Res 2019). The transgene construct is CAG promoter-LoxP-Stop-LoxP-H3.3K4M-FLAG/HA-\npolyA, using the human H3.3b gene sequence (https://www.addgene.org/128743). We confirmed with\nDr. Ge that they never determined where the exogenous H3.3K4M cassette was integrated. A more\ndetailed description has been added to the Methods section.\n6. Does the exogenous expression of H3.3K4M influence the endogenous expression of H3.3K4?\nOne would infer that the reduced methylation is caused by the availability of mutated H3.3 but, is it\npossible that levels of wild type H3.3 are changed in these mice? This could be explored through\nWestern blots.\nThe reviewer raises a valid concern about expression of endogenous H3.3 levels being altered in the\npresence of the exogenous H3.3K4M variant. The Western blots in Figure 1E show protein levels for\nboth H3K4me3 levels and total histone H3 protein (bottom row) in the MGE and hypothalamus.\nFor our Western Blots in Figure 1E, we extracted histone proteins using the EpiQuik Total Histone Extraction\nKit (EpigenTek), as this was the only way we could obtain sufficient levels of histones to detect specific\nhistone modifications in Western blots. We tried several different tissue preparation conditions (Repo\nbuffer, nuclei harvesting, etc.), but we were unable to obtain strong H3K4me3 signal in these preps for\nquantification. Thus we don’t have a way to normalize for total histone levels in this prep, since we are\nonly extracting histone proteins.\nTo determine total H3.3 histone levels, we extracted total protein from E13.5 MGE and hypothalamus\nand normalized protein levels to tubulin. We detected no significant differences in H3.3 histone levels\nin the MGE and hypothalamus between H3.3K4M WT, Het and Hom mice (see image below). This\nagrees with the original LSL-K4M manuscript (Jang…Ge Nuc Acid Res 2019), which performed\nWestern blots on total H3 protein and did not identify any changes in total H3 histone protein levels\nbetween Cre+ and Cre-negative conditions (see point #7 below). We have not included this data in the\nmanuscript, but we can if the reviewers feel strongly about this data point.\n7\n7. The authors should also carry out Western blots for me1 and me2 to quantify how the expression\nof H3.3K4M impact those levels in heterozygous and homozygous mice (as shown for me3 in Figure\n1E).\nWe performed immunostaining of H3K4me1, me2 and me3 in the MGE, with a clear downregulation of\neach in the MGE (Fig 1D), but we only performed Western blots on H3K4me3 to quantify this reduction.\nThe original manuscript (Jang…Ge Nuc Acid Res 2019) performed H3K4me1, me2 and me3 Western\nblots (Figure 3A from this paper, relevant panel below). They observe a similar reduction of H3K4me1,\nme2 and me3 in cells from their LSL-K4M mice in their Western blots (albeit using a different Cre-driver\nline to target adipocytes & with no quantification). There are numerous publications with Western blots\nfrom H3K4M mutant mice or cell lines showing equivalent reduction in H3K4me1, me2 and me3 levels\n(image from recent Cell paper below), usually without quantification. This result is expected since the\nmutant H3.3K4M histone variant has no lysine to be methylated and thus should have an equal\nreduction in me1, me2 and me3 levels. Relevant to the point above, neither study observes any obvious\nchange in H3 levels between control and H3K4M+ conditions. Therefore, we believe that collecting\nseveral litters of embryos to gather enough tissue from WT, Het and Hom mice for multiple replicates\nof me1 & me2 Western Blots is not necessary because (1) our immunostaining results reveal a similar\ndecrease in me1, me2 and me3 levels in the MGE, (2) the requested Western blots were already\nperformed in the original manuscript that generated these LSL-K4M mice (and other manuscripts as\nwell), and (3) the loss of the H3K4 residue means that there should be no difference between me1,\nme2 and me3.\n8\n8. The age of the embryos analyzed is Figure 1 is not included in the text or figure legends.\nWe thank the reviewer for noting this omission, we have included the age E13.5 in the main text and\nthe Figure 1 legend.\n9. There are several statements at the end of some sections of the Results that belong to the\nDiscussion. Some examples: (1) “Such increased variability in electrical properties is reminiscent of\nthe developing hippocampus, suggesting interneuron maturation deficits in H3.3K4M Hom mice”.\nThis is speculative and not a direct conclusion of the experiments; there are several alternative\nexplanations. (2) “As widespread perisomatic inhibition is critical for both circuit gamma entrainment\nand seizure control, it is likely that the deficits in hippocampal FS/PV+ cells in H3.3K4M mice directly\ncontribute to disrupted rhythmogenesis and epilepsy”. Again, this is speculative and not a direct\nconclusion of the experiments. The authors described defects in other interneuron populations that\nshape gamma rhythms. Indicating that PV interneurons cause the network dynamic changes and\nseizure susceptibility is a stretch, especially in the Results section. Please revise the Results to\navoid these and similar statements.\nWe removed these 2 sentences from the Results and integrated them into the Discussion section.\nReviewer #3:\nSummary of the Study: This manuscript investigates how reduced H3K4 methylation in the medial\nganglionic eminence (MGE) and hypothalamus affects cortical interneuron (cIN) generation and\nfunction, as well as hypothalamic circuits controlling body growth. By using a conditional H3.3K4M\nmouse model driven by Nkx2.1-Cre, the authors describe reductions in interneuron numbers,\naltered network excitability and seizures, as well as notable changes in postnatal growth and\nobesity. The data include morphological, behavioral, electrophysiological, and single-cell analyses\nthat point toward significant, cell-type-specific deficits in both MGE-derived interneurons and\nhypothalamic populations.\nMajor Points\n9\n1. Validation of the mouse model: The authors would need to verify or test the actual timing of Cre\nexpression. The reduction of H3K4me3 in MGE-derived cINs is not properly shown. First, I wonder\nwhy the tDTomato signal is so blurry. In numerous manuscripts, including Tanuguchi et al., 2011\n(DOI: 10.1016/j.neuron.2011.07.026), where Nkx2.1-Cre mediated reporter expression is shown in\nthe embryonic MGE, discrete reporter positive cells are visible in the VZ and SVZ. However, the\ntdTom signal in Figure 1C and D is very blurry. This hampers proper visualization of analyzing\nH3K4me3 levels in the recombined cells. However, at adult stages (Fig. 3A) clear tDTom cells are\nvisible.\nThe Nkx2.1-Cre mouse was generated in Stewart Anderson’s lab in 2008. Since then, it has been used\nby dozens of labs in >100 studies as an efficient and reliable driver of Cre expression in Nkx2.1-\nexpressing cells in the MGE. The image below is from the original 2008 Xu…Anderson study showing\nexcellent overlap between Nkx2.1 protein and Cre protein (B-C), as well as Cre protein and a Cre-\ndependent GFP reporter (G-H). Essentially all MGE cells in Nkx2.1-Cre mice express Cre except for a\nvery small proportion of the dorsal MGE, and we mentioned this caveat in our manuscript. We believe\nthat Figure 1C-D in our manuscript clearly show that the tdTom is present throughout the VZ, SVZ and\nmantle as expected. To clarify this specific point, we have added ‘VZ’, ‘SVZ’ and ‘mantle’ labels to the\nTom panel in Figure 1C. Based on these points, we have no concern about the timing of Cre expression,\nand we are quite confident that basically all MGE cells in the images are Cre+/TdTom+ (and thus\nexpressing the H3.3K4M mutant histone). The tdTom+ reporter in the Ai9 mouse line is extremely bright\nand fills the entire cell (not just the nuclei like the Cre immunostained images below), and it can be\nsaturating in regions where all cells are TdTom+ like the MGE. That is likely why the tdTom panels\nappear blurrier in some panels, but this is indicative that all cells are expressing the bright tdTom\nfluorophore. As the reviewer notes, the Tom+ cells are more visible in the adult brains in Figure 3\nbecause the Tom+ cells are significantly less dense, and thus individual Tom+ cells are clearly visible.\nWe also note that the Taniguchi et al., 2011 Neuron paper that the reviewer sites use the Nkx2.1-CreER\nmouse (which was created in that paper), they do not use the Nkx2.1-Cre mouse in that study.\nTamoxifen induction in this Nkx2.1-CreER mouse will label only a subset of MGE cells (maybe 50%?),\nwhich is why individual cells are more clearly visible in the MGE of Figure 2 in that manuscript compared\nto our Nkx2.1-Cre mouse where nearly 100% of MGE cells are labeled. Thus directly comparing cell\nlabeling in our study vs. the Taniguchi study is not valid since different Cre-driver lines were used.\n2. As Nkx2.1 is down regulated in cortical fated interneurons upon becoming postmitotic, while\nstaying expressed in e.g. striatal fated cells, such discrete cellular signals would help to show\n10\ndownregulation in cortical fated cells, either by analyzing cells that have reached the cortex or by\nco-labeling e.g. with Calbindin antibodies.\nThe reviewer is correct that Nkx2.1 is downregulated postmitotically in cortical interneurons while it\nremains expressed in striatal interneurons. However, we are using an Nkx2.1-Cre;Ai9 mouse line in\nwhich all cells that express Nkx2.1 at any point in their development will express tdTomato throughout\ntheir lifespan, regardless of whether the Nkx2.1 gene/protein are expressed or downregulated. Thus,\ntdTom+ cells represent Nkx2.1-lineage cells (and more relevant, Cre-lineage cells that underwent\nH3.3K4M recombination), rather than being a reporter of Nkx2.1 expression. And it is these tdTom+\ncells that express(ed) Nkx2.1 and continually express Cre and H3.3K4M that we wish to analyze. We\ndo not understand the reviewer’s point about labeling with a calbindin antibody, as calbindin is\nexpressed by many glutamatergic cells in the cortex and hippocampus in addition to interneurons.\n3. Also, the authors state in line 124: “Having H3.3K4M active prior to cell cycle exit is critical to\nsubstitute endogenous H3.3 with mutant H3.3K4M protein due to the prolonged half-life of histone\nproteins in the brain…” However, there are no clear tdTom+ cells visible in the ventricular zone (VZ),\nwhere the progenitors reside. Also, the reduction of H4me2/3 level in the VZ is not properly visible\n(rather in the SVZ). I wonder, whether successful Cre-recombination could be shown in the sc-\ntranscriptome analyses? This would also allow proper discrimination of progenitors versus\npostmitotic cINs (however, here a different mouse line was used, commented later).\nAs stated in response to point #1 above, the Tom signal (indicative of Cre expression) in Figure 1C-D\nand the HA immunostaining in Figure 1C is clearly present in the VZ and SVZ. So we do not understand\nwhy the reviewer believes that “…there are no clear tdTom+ cells visible in the VZ”. As stated above,\nwe have no concerns about Cre-recombination since Tom signal (and HA protein) is visible throughout\nthe MGE VZ, SVZ and mantle as expected.\nThe reviewer is correct that the strongest decrease of H3K4 methylation levels (via immunostaining) in\nFigure 1D is in postmitotic cells in the mantle, while this reduction is less obvious and/or not present in\nthe VZ/SVZ. The H3K4me1, me2 and me3 antibodies used in this study do not distinguish between\nhistone 3 variants H3.1, H3.2 and H3.3; they recognize me1, me2 or me3 on all 3 H3 variants. As stated\nin response to Reviewer #2 point #5 comment above and clarified in the Methods section, the LSL-\nK4M mouse line used a human H3.3b gene sequence for the H3.3K4M mutant allele. Thus H3.1 and\nH3.2 histone variants should be unaffected in this mouse line. In the Introduction, we described an\nimportant study (ref 12, Funk…Kwan PNAS 2022) showing that H3.1 and H3.2 variants are enriched\nin cycling neural progenitors (VZ and SVZ cells), with the H3.3 variant replacing H3.1/2 in postmitotic\ncells in the mouse brain. See Figure 1B below from this paper showing very low H3.3 expression in the\nVZ/SVZ of the cortex, with strong upregulation of H3.3 in postmitotic neurons in the cortical plate.\neffect of H3.3K4 methylation to be in postmitotic cells where variant H3.3 is actively replacing H3.1/2\nvariants. The stronger H3K4 methylation signal in the VZ/SVZ regions in H3.3K4M Hom mice is likely\nfrom methylation on histone variants H3.1 and H3.2 that are not genetically altered in these mice. Thus,\nthe strong reduction of H3K4 methylation in postmitotic cells in the mantle with a weaker (or no)\nreduction in H3K4 methylation in cycling VZ/SVZ cells is consistent with this predicted transition of\nhistone variants. We apologize for not making this point clearer in the text, and we have attempted to\nclarify this logic in the Results.\n4. For the hypothalamus, no immunostainings are shown in that regard, which should be provided\nas well. Here, I would similarly expect clear cellular tdTomato labeling, which allows determination\nand quantification of H3K4me3 fluorescence intensity in tdTom cells.\nThe bottom panels of Figure 1C show Tom expression and HA immunostaining through the WT and\nH3.3K4M hypothalamus, clearly showing that tdTom is expressed in the VZ, SVZ and postmitotic cells\nas in the MGE.\nRegarding the reviewer's comment about “…quantification of H3K4me3 fluorescence intensity in tdTom\ncells.”, we did not perform any quantification of H3K4me3 fluorescence intensity in this manuscript, as\nthis approach is much less reliable than quantifying protein levels in Western Blots. We could generate\na region of interest (ROI) in the MGE and hypothalamus and then quantify the H3K4 methylation levels\nwithin this ROI. But we argue that this is far inferior to quantification using Western Blots because: (1)\neach immunostaining reaction has inherent variability in terms of signal intensity, microscopy\nconditions, etc., (2) it’s unclear how we could normalize these issues between samples, and (3)\nmatching MGE and hypothalamic sections along the anterior-posterior axes of the brain can be very\nchallenging, and different cellular densities along the axes of both tissues can lead to inaccurate spatial\ncomparisons between samples. Quantifying protein levels in Western Blots avoids many of these\ncomplications and is a more standard and accepted quantitative approach in the field.\n5. In addition, the authors provided Western Blot data, which showed a strong reduction for the\nhypothalamus in the HOM mice, but less clear reduction for the MGE. I assume from the methods,\n12\nthat bulk tissue was used here. Maybe FACS-mediated enrichment of the recombined cells would\nyield in clearer reductions in K4me3 levels.\nThe reviewer is correct that the Western blots were performed on bulk tissue from dissections of E13.5\nMGE and hypothalamus. In H3.3K4M Hom mice, the reduction in the hypothalamus (~80% reduction)\nis stronger than in the MGE (~50% reduction), but both are significantly reduced in comparison to their\nrespective WT samples. It is possible to sort the tissue to collect only Ai9+ cells from the MGE and\nhypothalamus, as our dissections certainly contain some Cre-negative cells that do not express\nH3.3K4M protein. But since we observe the expected decrease in H3K4 methylation levels via\nimmunostaining (unquantified) and Western Blots (quantified), we do not see a need to sort these cells.\n6. Embryonic fate-mapping and characterization of potential defective processes is missing. What I\nmiss in general in this manuscript is fate mapping of the cells at embryonic stages. By providing\nembryonic sections with proper tdTomato cell labeling not only allows the determination of H3K4me3\nreduction in e.g. cells that have reached the cortex, it would also allow to assess which\ndevelopmental defects occur that lead to reduced cIN numbers in the adult mice. Is the migration\naltered (e.g. misrouted)? Or do HOM-cINs die? At which stage (embryonically or postnatally)? Same\nfor the reduced numbers of nNos+ interneurons seen in the hippocampus. More comprehensive\nembryonic phenotyping would also serve to better understand the findings the authors report in\npostnatal/adult mice, such as the reduced cortical thickness in postnatal brains of mutant mice (Fig.\n2).\nThe reviewer raises an important question that all developmental studies struggle to comprehensively\nanswer: defining the age and mechanism that an embryonic mutation generates a phenotype in the\nadult mouse. We have performed several experiments to address these concerns addressed in our\nresponse to Reviewer #1 point # 8 above and have restated them below:\n1. Cell cycle dynamics: We quantified the density of PH3+ cells in the MGE and did not identify any\ndifferences in PH3+ cells between any genotypes. We also did not observe any striking differences\nin Ki67 expression between genotypes, though not quantified. These findings demonstrate that\nthere are no changes in cell proliferation in H3.3K4M mutation mice. This result is not surprising\nsince histone variant H3.3 is weakly expressed in cycling VZ/SVZ cells and gets strongly\nupregulated in postmitotic neurons (Funk…Kwan PNAS 2022, see Reviewer #3, point #3 below).\n2. Apoptosis: We performed TUNEL staining to detect apoptotic cells in the E13.5 MGE, E13.5\nhypothalamus and P4 cortex. We did not observe any obvious difference of TUNEL positive cells\neither at E13.5 MGE or P4 cortex. Since there were very few TUNEL+ cells in all genotypes, we did\nnot perform quantitative analysis on this tissue.\n3. Cell migration: We did observe clear differences in the number of tdTom+ cells migrating into the\ncortex, with significantly fewer interneurons entering the cortex in E13.5 H3.3K4M Hom mice\ncompared to WT. To determine if this migration was simply delayed in mutant mice or rather led to\nsignificant reductions in interneurons, we also examined P4 WT and Hom brains. This is prior to the\nintrinsic programmed interneuron apoptosis that removes 20-40% of interneurons from ~P6-P12.\nWe observe a striking decrease in Tom+ cells in the forebrain of Hom brains, most notably in the\nhippocampus. These findings indicate that reduced tangential migration into the cortex is likely the\nprimary cause for reduced cell numbers in these Hom mice.\nDiscussion.\nRegarding nNos+ interneurons in the hippocampus, we have previously shown that nNos protein is not\nexpressed in hippocampal interneurons at P1 (Figure S5A, Quattrocolo…Petros Cell Reports 2017).\nWe do not know when nNos gets upregulated in these cells postnatally. But since nNos is not expressed\nembryonically, we have no way to track these cells from an embryonic fate-mapping perspective.\n7. It is known that invading cINs can influence cortical progenitors (DOI: 10.1016/j.cell.2018.01.031),\nand thus potentially cortical neurogenesis and layer thicknesses. Embryonic fate-mapping could\nserve to investigate potential migration defects that correlate with changes in cortical progenitor\nproliferation and lamination. This would strengthen the mechanistic link to cortical thinning.\nThe reviewer is correct that there is crosstalk between interneuron progenitors and cortical excitatory\nneuron progenitors that is critical for proper maturation and cortical integration of both cell types. And\nperturbation of this bidirectional signaling interactions between interneurons and pyramidal cells can\ndisrupt normal cortical maturation. We feel that an extensive characterization of this interneuron-\npyramidal cell interaction is beyond the scope of this manuscript, as many of the genes and\nmechanisms directing this process are still being uncovered. And we are unclear exactly how\n“embryonic fate-mapping” would lead to a “…mechanistic link to cortical thinning”, especially\nconsidering our data where we do not see huge differences in interneuron changes between deep and\nsuperficial cortical layers. We have addressed these ideas in the Discussion.\n8. Adult phenotyping: In matters of cortical thinning, it should also be clearly stated which Bregma\nand cortical area was analyzed. In Fig. 2F, S1 is stated. Is the effect only seen in S1?\nWe thank the reviewer for this suggestion. We have performed additional analysis on V1 and found that\nV1 thickness is also significantly reduced in H3.3K4M mice, so this effect is not specific to S1. This data\nhas now been included in Extended Data Fig 1C. Bregma coordinates were added to the Methods.\n9. Analyzing whether the reduction affects all layers or distinct layers, could provide valuable\ninformation. In the magnifications provided in 2F, it seems that II-IV are similar, and that the reduction\nis mainly seen in layer I and layer VI. Here, a more detailed analysis would be favorable (and could\nhelp to correlate the effects with potential embryonic defects, such as changes in neurogenesis\nduring e.g. the formation of layer VI). To discriminate between the different layers, layer-specific\nmarkers would be needed, which is also required for the cell density measurements depicted in\nExtended Figure 1. Here, PV and SST+ cIN density was claimed to be analyzed in the superficial\nlayers (I-III) and deep layers IV-VI, based on DAPI staining. However, using DAPI staining to\ndiscriminate layer IV form layer III is very imprecise…see also Figure 3A. Also I would not\nnecessarily agree with the demarcation of the deep layer VI shown in 3A. Layer specific labeling\nneeds to be included, such as RORB to stain layer IV or marker for Vib.\nWe concede that using DAPI to demarcate general layer boundaries is less precise than performing\nstaining for layer specific cortical layer markers such as Rorb, Ctip2, Satb2, etc. However, we dispute\nthe reviewer’s claim that “…using DAPI staining to discriminate layer IV from layer III is very imprecise”.\nIn S1 where these counts were performed, the strong increase in DAPI+ cell density in layer IV is quite\n14\nvisible by eye (and is evident in Figure 3A), making it a reliable, albeit imperfect, marker to distinguish\nlayer III from layer IV in S1. The reviewer’s concern about using DAPI to define the layer VI/subplate\nboundary is more valid, but as you can see from Figure 3, there are very few Tom+ cells below the\nyellow dotted line anyway. So defining this lower layer VI boundary would have a minimal effect on the\nnumber of cells included in Layer VI.\nPerforming the cell counts as we have done requires four channels for Tom (594), PV (647),\nSST (488) and DAPI (450) (and in the hippocampus, a fifth channel for nNos (750)). This only leaves us the far-\nred channel (750) for one additional fluorophore. With only 1 layer labeled per brain section, we could\nonly count cells in 1 layer for each immunostained brain slice. This would increase the number of brain\nsections to be immunostained, imaged, processed and counted by 4-5 fold just to obtain roughly the\nsame data we already have. Thus, the reviewer’s suggestion to combine the PV and SST cell counts\nwith specific cortical layer markers would be ideal. But in practice, this would be incredibly burdensome\nand tedious to repeat the cell counts using this strategy, which would be an incremental increase in\ndetail to what we’ve already generated here.\nIn addition to our technical notes above, we want to emphasize that we performed these cell counts in\nsuperficial vs. deep layers because, as the reviewer implies, this could have important implications in\nrelation to neurogenesis. As shown in Extended Data Fig. 2, we do not observe any striking differences\nin shifts of PV+ or SST+ cell numbers between deep and superficial layers in the H3.3K4M Hom mice;\nthe general patterns hold true for both deep and superficial layers. And our new data in Figure 5 does\nnot detect any changes in cell cycle dynamics (via PH3 and Ki67 immunostaining) between WT and\nH3.3K4M Hom mice. Therefore, we don’t believe any further detailed investigation into layer-specific\nchanges is warranted. Our goal here was not to explore differential interactions between interneurons\nand excitatory cells in particular cortical layers, but rather to quantify general changes in PV+ and SST+\nin the H3.3K4M Hom mice.\n10. For the analysis performed in 2B it needs to be mentioned in the legend or the graph axis,\nwhether PV+ or PV/tdTom+ cells were analyzed? As not every PV cells is also positive for tdTom.\nSame for SST, and the analysis in Fig. 3D (Hippocampus).\nWe apologize for not being clearer on this point in the figure legend. In the methods, we stated “Any\nPV+ and SST+ cells that were Tom- were excluded from the counts.” This is because PV+ or SST+\ncells that are tdTom-negative did not express Cre due to either incomplete recombination or, more likely,\nbecause these cells arise from the most dorsal MGE where Cre is not expressed in this Nkx2.1-Cre\nmouse line. Thus they also would not express the mutant H3.3K4M histone. We have now included this\nsentence in the legend for Figure 3.\n11. In the hippocampus, the authors observed also PV+ cell reduction and a more severe reduction\nof nNOS⁺ interneurons. Mutant mice showed increased seizure susceptibility, for which PTZ\ninjections and patch clamp-recordings in slices were performed to characterize electrophysiological\nproperties. The authors revealed changes at the population level (greater variability) for three\ninterneuron subtypes in H3.3K4M Hom mice, which they interpret as impaired maturation. Moreover,\naltered gamma oscillations were found.\nsummary of our findings in this section. We apologize if we are misunderstanding the reviewer’s critique\nor suggestion here.\n12. In Figure 4 (Differences in Seizure-Related Mortality at 3 vs. 5 Months) the authors report\ndifferent seizure susceptibilities and mortality rates at 3 vs. 5 months (e.g., female mortality is ~50%\nat 5 months, but at 3 months, males show higher seizure mortality). The text suggests that the\nhigher PTZ dosage for males at 3 months may underlie this effect, yet it remains somewhat\nconfusing why the absolute differences (between males and females, at each time point) shift so\ndramatically. It should be discussed whether the PTZ dose alone explains male–female differences\nor if age-related changes in brain maturity or H3K4 methylation might also be factors.\nThe reviewer states “…it remains somewhat confusing why the absolute differences (between males\nand females, at each time point) shift so dramatically”. Both the mortality rate (50% vs. 60% for Hom\nmice) and the time course of seizure severity (compare Figure 4C & Extended data Fig 5A) are very\nsimilar for 3-month & 5-month-old females. And the time course for seizure activity is also quite similar\nbetween male and female mice at 3-months. The most significant differences are (1) seizure strength\nof 5-month-old male & female Hom mice (where PTZ concentration is the same) and (2) decreased\nmortality rate of male Hom & Het mice from 3-months to 5-months (where PTZ concentration is\ndifferent). Based on our statement, “with the increased dose in males generating similar results to\nfemale mice”, we understand how the reviewer concluded that we suggested “…the higher PTZ dosage\nfor males at 3 months may underlie this effect.” To clarify these findings and address the reviewer’s\nconcerns, we expanded upon these points in the Discussion.\n13. For PTZ experiments, did the authors used Racine scoring?\nThe seizure scoring system from 0-6 is detailed in our Methods section and is taken directly from the\ncited manuscript (Shimada & Yamagata, JoVE 2018). We refer to this as a modified Racine scoring\nsystem, and we have updated this terminology in the Methods.\n14. Developmental vs. Adult-Stage Functional Changes: It would be helpful to discuss to what\nextent the observed electrophysiological and behavioral phenotypes arise from early developmental\ndisruption of H3K4 methylation versus potential ongoing dysfunction in adult Nkx2.1-lineage cells.\nSince the H3.3K4M mutation is constitutively expressed, both mechanisms are possible. Although\ninducible Cre lines or temporally restricted models may be beyond the scope of this study, a brief\ndiscussion of this limitation, and how future studies might approach developmental versus adult-\nstage contributions, would strengthen the interpretation of the results.\nCells develop along a continuum, with different characteristics maturing at distinct developmental\ntimepoints, many of which are dependent on earlier processes. In a model like this, it is always\nchallenging to definitively assign causation of phenotypic changes to genetic perturbation during\nneurogenesis, cell maturation, or juvenile/adult stages. And the reviewer is correct that using inducible\nCre lines and/or viruses to activate H3K4M expression at later timepoints is beyond the scope of this\nstudy (and would be a complete study on its own). While single cell sequencing data does reveal that\nmany genes associated with interneuron maturation (Nkx2.1, Lhx6, Dlx1, Gad1), and specifically PV+\ncells (Maf, Mef2c), are significantly downregulated in the MGE from H3.3K4M Hom mice, this does not\n16\ndirectly answer the reviewer’s question about the timing of e-phys & behavior phenotypes. We now\nraise this issue in the Discussion.\n15. Sex Differences in Phenotypes: Certain figures (e.g., Figures 4 and 5) present male and female\ndata separately (e.g., PTZ-induced seizures and related behavioral outcomes), revealing sex-\nspecific phenotypes. However, earlier figures (Figures 1–3), which assess H3K4 methylation levels,\ncell counts, cortical thickness, survival and body weight, do not consistently stratify the data by sex.\nThis inconsistency limits the interpretation of sex-specific findings. The authors should clarify\nwhether male vs. female comparisons were performed for these earlier datasets and either present\nthe data accordingly or explicitly state if no sex differences were observed. As the behavioral\nphenotyping reveals intriguing sex-dependent effects, aligning these outcomes with corresponding\ncellular phenotypes would strengthen the study and provide a more integrated understanding of the\nobserved differences.\nWe thank the reviewer for this suggestion. As detailed in our response to Reviewer #2 point #2 above,\nwe’ve now included the sex of all animals used in P21 cortical and hippocampal interneuron cell counts,\nand we do not observe any differences between the density or percentage of interneuron subtypes\nbetween male and female mice.\nAdditionally, we have now separated male & female mice for their weight trajectory from 1-9 weeks\n(related to Fig. 2B) and for their body weight at 20 weeks (related to Fig. 2I). We see the same effects\nof decreased body weight at these ages in both male and female H3.3K4M Hom mice compared to WT\ncontrols. We had too few Hom mice survive to 12 months to separate by sex; this would be very\nunderpowered. This new analysis is now reported in Extended Data Figure 1A-B.\nSome of our data were generated prior to our knowledge of sex differences in the mice, and thus we\ndo not have detailed sex info for all experiments. Especially for embryonic tissue, as that would require\nPCR genotyping at E13.5. For example, we do not know the sexes of embryonic brains used for E13.5\nH3K4me immunostaining or western blots. But a visible reduction in H3K4me levels via immunostaining\nand H3K4me3 via WB was consistent in all brains examined, with all data points containing a mix of\nboth male and female mice. Additionally, we did not observe a strong bias for either sex to die\nprematurely, as we would have noticed this over many generations of breeding. And both sexes become\nobese over time. Many data points in Figure 2 measuring body weight, food intake, fat/lean weight,\nleptin levels etc., have an n > 7 mice with combined male & female datapoints (except for 12-month\nHom mice since so few of them live that long), which should minimize any sex bias in our datasets. We\nclarified in the Results and Figure legends when changes were observed in both sexes.\n16. Although the manuscript highlights sex-specific behavioral and transcriptional effects in\nH3.3K4M Hom mice, the mechanistic link to H3K4 methylation remains somewhat speculative. The\nDiscussion could be strengthened by elaborating on known epigenetic or neuroendocrine sex\ndifferences that may modulate H3K4 methylation patterns, such as hormonal control of chromatin\nmodifiers or differences in X-inactivation escape genes. Furthermore, it would be helpful to clarify\nwhether the authors observed comparable reductions in H3K4me3 or chromatin accessibility in\nmale and female samples, or whether sex-specific effects may reflect differential sensitivity to\nepigenetic dysregulation.\nH3K4me3 levels between males & females, including escape from X inactivation. In this revised\nmanuscript, we have added several additional studies about the epigenetic or neuroendocrine sex\ndifferences that may modulate H3K4 methylation patterns in the Discussion. As stated above, we do\nnot have any evidence for sex bias reduction of H3K4 methylation in our data.\n17. Weight Gain: Hypothalamic vs. cIN Contributions. While the manuscript focuses on\nhypothalamic disruptions leading to overeating/obesity, it is worth considering the possibility that\naltered cIN functionality could contribute to weight changes indirectly through mood or motivational\ndeficits (for instance, depression-like or other behavioral changes can also predispose to weight\ngain). The causal link between hypothalamic gene expression changes and increases in body\nweight could be more strengthened or discussed.\nThis concern about definitively assigning causation of obesity & changes in body weight to the\nhypothalamus was also raised by Reviewer #2 point #1 above. We have addressed this issue in a new\nsection of the Discussion.\n18. Single nuclear RNA seq experiments: In subsequent single-cell analyses (Figure 7, Extended\nData 6), the manuscript transitions from using an Ai9/tdTomato reporter to a Sun1-GFP reporter\nsystem, and thus to another mouse line. Please justify why a different fluorescent reporter (Sun1-\nGFP) was used for the single-nucleus analysis. Clarify that the experiment is directly comparable to\nthe earlier Ai9 lines.\nIn the Ai9 mouse line, Cre recombination results in untethered tdTomato expression that fills the cell\nbodies, axons and dendrites. However, it does not label the nuclei, which we have confirmed via flow\ncytometry. To harvest nuclei from Nkx2.1-lineage cells, we switched to the Sun1-sfGFP/INTACT mouse\nline in which GFP is tethered to the Sun1 protein that is localized to the inner nuclear membrane. In\nthis original paper (Mo et al. Neuron 2015), the authors demonstrate superb specificity (>98%) of the\nGFP marker in VIP-Cre and PV-Cre driver lines. We (Rhodes…Petros Frontiers 2024) and many other\nlabs have used this mouse line to collect fluorescent nuclei for numerous downstream assays; it is\nwidely accepted in the field. It is quite common to switch reporter mouse lines depending on the assay\nbeing performed. As both the Ai9 and Sun1-GFP cassettes are targeted to the Rosa26 locus, we are\nconfident that the Nkx2.1-Cre;Ai9 and Nkx2.1-Cre;Sun1-GFP lines target the same population of cells;\nthere is no evidence to the contrary.\n19. What about Sex-Specific Differences in the snRNA-Seq/ATAC-Seq analysis? The\ndocumentation of how sex was incorporated into the overall experimental design and analysis\npipeline is inconsistent also for the seq-data. Specifically, the figure legends, methods and main\nresults section do not clearly state which datasets (e.g., snRNA-seq, snATAC-seq, WNN-integrated\ndata, pseudotime, and WGCNA analyses) were generated from male, female, or mixed-sex animals.\nIt is also not evident whether sex was treated as a covariate during data integration and\nnormalization, or whether sex-specific analyses were systematically performed across all\ncomparisons. Given the increasing recognition of sex as a critical biological variable and its\nrelevance to the phenotypes described (e.g., seizure susceptibility), I recommend that the authors\nprovide a clearer account of the sex composition of each dataset and clarify in the figure legends\n18\nand Methods whether data were pooled, stratified, or sex-balanced. This information is essential for\ninterpreting the generalizability of the findings and for evaluating the extent to which observed\neffects are sex-dependent. Same accounts for Figure 8. Information on sex should also be\nmentioned in the results e.g. when describing altered expression in Mef2c.\nWe apologize for not being clearer in our descriptions about sex composition of our single cell datasets\nin the text. We clarified this issue by including the statement “Data is combined male & female samples\nunless stated otherwise” at the start of all relevant figures. During the data integration and\nnormalization, sex wasn’t treated as a covariate. We have added a sentence in the ‘Multiome\nsequencing and analysis’ section in the methods stating that male & female samples were processed\nseparately. And we have also clarified PV-predictive genes Maf and Mef2c were each decreased in one\nsex. A detailed breakdown of the number of cells, reads, UMI & genes (RNA) and fragments (ATAC) for\neach age, genotype and sex is included in Supplementary Table 1. We also apologize for not being\nclearer about the datasets used in each type of analyses; we have clarified this in the Figure legends,\nMethods and Results.\nSeparating male & female datasets for all downstream analyses significantly decreases overall analytic\npower. Therefore, we decided to only separate sexes for generating volcano plots & clusterProfiler Go\nplots to characterize sex-specific DEGs in E13.5 MGE, E13.5 hypothalamus, P60 PV+, P60 SST+,\nP60\nhypothalamus neuron and astrocytes. These sex specific volcano plots are presented in Fig. 8-9 and\nExtended Data Figs. 9-13. We also generated Venn diagrams displaying the percentage of common\nand sex-specific DEGs for PV+ and SST+ cells in Extended Figs. 10-11 and hypothalamus in Extended\nFigs. 12-13. We have updated the figure legends and relevant sections of the Results accordingly, and\nincluded these sex-stratified datasets in Supplementary Table 1. In sum, we believe that separating\nsexes for a portion of single cell analyses provided greater depth to our dataset, highlighting similarities\nand differences in sex-specific DEGs. We thank the reviewer for this suggestion.\n20. In the provided sex-specific comparisons, where differences in gene expression (e.g., potassium\nchannel genes in PV+ cells) are reported, refer to the H3.3K4M HOM samples. However, it remains\nunclear whether sex-specific differences were also present in the adult cortical control (WT)\nsamples, or whether these only emerged in the mutant condition. This distinction is important for\ninterpreting whether the observed differences reflect baseline sexual dimorphism or genotype–sex\ninteractions. For PV+ cells sex specific divergent regulation was shown (Extended Data 6H).\nHowever, for SST cells, the analysis is not shown sex-specifically. Are there now sex-specific\neffects? Illustrating these differences more systematically and clearly could significantly strengthen\nthe discussion about sexual dimorphism.\nWe thank the reviewer for raising this important point. We agree that the comparison of DEGs between\nHom male and Hom female is incomplete without the same comparison between WT male and female.\nSince we have now generated sex-specific volcano plots & clusterProfiler Go plots (see above), this\ncomparison is no longer necessary and we have removed these panels (Old Fig S6H-I) and relevant\ntext from the revised manuscript. We also annotated SST+ subtypes and revealed an increase of\nSST_Plpp4 subtype in Hom cortex (Extended Data Fig. 11A-C). The sex-specific volcano plots for SST\npopulation were also generated (Extended Data Fig. 11D-E).\n19\n21. In Figure 7C and Extended Data 6A, more detail on cluster annotation (including specific marker\ngenes) would help the reader interpret the results.\nThank you for this suggestion. In old Figure 7C, the UMAP is color coded based on genotype (WT\ngreen, Hom red) as stated in the figure panel & legend. For old Extended Data Figure 6A, UMAP is\ncolor coded based on Seurat clusters, with each cluster defined in an unbiased manner by the Seurat\nalgorithm. We added additional details on cluster annotation with Seurat::FindAllmarker function and\nrelevant citations (Fig. 8E). In the revised manuscript, we have classified MGE cells into 6 progenitors\nand 7 postmitotic cell populations. As expected, 2 major interneurons type (PV-fated and Sst-fated) and\na population of GABAergic projection neurons (Lhx8+) were identified. We also showed the expression\nof several specific marker genes in Extended Data Fig. 9C.\n22. Does the color code presented in Figure 8C refers to the UMAP in 8A (right)? Should be mentioned\nin the legend.\nThe reviewer is correct; we have added a line in the legend of Figure 8C noting that the color scheme\nmatches the putative hypothalamic nuclei in panel A. We also updated the hypothalamic cell type\nannotation based a recent publication (Kim…Blackshaw Cell Reports 2025), which provided additional\ndepth to hypothalamic nuclei classification (Extended Data Fig. 12C). We have updated the figure\nlegends and relevant sections of the Results and Methods accordingly.\n23. Extended Fig. 5G: why are the accessible peaks different/complementary in MGE versus\nHypothalamus (Numerous decreased peaks in HOM-MGE but increased in hypothalamic samples)?\nIn the MGE, there is good correlation between most differentially expressed genes (Fig. 8F and\nExtended Data Fig. 9D) and most differentially accessible peaks (Extended Data Fig. 8G) being\ndownregulated in H3.3K4M Hom mice. However, in the hypothalamus, this relationship is less\ncorrelated, with a relatively even mix of DEGs increased and decreased (Fig. 9D & Extended Data Fig.\n12E) while most differentially accessible peaks are increased (Extended Data Fig. 8G). Many genes\nassociated with mitotic cell cycle phase transition were upregulated in the hypothalamus of Hom mice\ncompared to WT, which correlated with the increased proportion of neural progenitors in the\nhypothalamus (Fig. 9D-E & Extended Data Fig. 12E-F). Additionally, our Western Blot data reveals that\nthe downregulation of H3K4me3 is stronger in the hypothalamus compared to the MGE (Fig. 1E), so\ndifferential expression of the mutant allele could in part explain some of these differences. The reasons\nfor these differences are not clear, but we did devote a paragraph in the discussion to this issue and\nhighlight that these different trends can still result in similar neurogenic phenotypes.\n24. Hypothalamic findings: The hypothalamic analysis reveals substantial changes in cellular\ncomposition and organization in H3.3K4M Hom mice, including increased NPCs, loss of ARC/PVN\npopulations, and expansion of astrocytes. The disorganization of tanycytes and myelin in the\nmedian eminence is striking and well-illustrated histologically. However, transcriptomic analysis of\ntanycytes was limited by low cell numbers, with few DEGs detected. The interpretation of specific\ntranscriptional changes, such as the downregulation of Fgf14, would benefit from orthogonal\nvalidation (e.g., in situ hybridization or qPCR) to confirm this finding independently of the single-\nnucleus dataset. Strengthening the support for these changes would enhance the mechanistic link\nbetween H3K4me dysregulation and hypothalamic function.\nHom samples) allowed us to define progenitors and postmitotic putative hypothalamic nuclei with a high\ndegree of confidence. We have included new heatmap analyses highlighting the proportional increased\nNPCs and decreased ARC & VMH nuclei in the H3.3K4M Hom hypothalamus (Extended Data Fig.\n12D). And we have performed new immunostaining experiments in the E13.5 hypothalamus\ndemonstrating strong upregulation of Ki67 in H3.3K4M Hom mice (Extended Data Fig. 12G), which is\nfurther evidence of increased hypothalamic NPCs in these mice.\nHowever, the reviewer is correct that the power of our single cell analysis of tanycytes is low due to\nrecovering very few tanycytes in these dissections. This is expected as tanycytes make up a very small\npercentage of Nkx2.1-lineage hypothalamic cells. In the revised manuscript, we include new FISH data\non Fgf14, Vimentin and tdTomato in the hypothalamus. While validating Fgf14 expression levels in\ntanycytes is challenging due to the low abundance of these cells along the 3rd ventricle, we did observe\nless expression of Fgf14 in ARC cells in Hom mice (Fig. 10F), as showed below.\nAdditionally, Sst peptide is expressed in the ME where it is involved in growth hormone release and\nfood intake. New immunostaining data reveals a significant decrease of SST protein in the ME of\nH3.3K4M mice (Fig. 10C-D). We also performed TUNEL staining at E13.5 hypothalamus and did not\ndetect any differences in hypothalamic apoptosis between WT and mutant mice (Extended Data Fig.\n12J). These new results extend our characterization on how reduction of H3K4me effects hypothalamic\ndevelopment.\n25. The reduction in Olig2+ cells is an interesting finding and points to fate regulation by H3K4me3.\nThe authors report a „corresponding decrease in myelin basic protein (MBP) in this region (Extended\nData Fig. 2).“ However, I could not find a quantification here. This would need to be provided for\nthat statement.\nThe reviewer is correct that our statement “…corresponding decrease in [MBP]…” implies some form\nof analysis and statistical quantification that we did not perform. We are confident in this MBP reduction\nin CA3 of H3.3K4M mice as we see this change in multiple mice, but we did not quantify this result.\nThus we have softened our claim in the text to “… an apparent decrease in [MBP]…”.\n26. While the expansion of hypothalamic astrocytes in H3.3K4M Hom mice is clearly documented,\nthe manuscript would benefit from additional developmental context clarifying how Nkx2.1-\nexpressing progenitors contribute to glial lineages, particularly in the hypothalamus. Since Nkx2.1-\nlineage cells are widely known for giving rise to cortical interneurons, it would be helpful to note that\nthese progenitors can also generate astrocytes and tanycytes in ventral forebrain regions, as\nsupported by lineage tracing studies, e.g. in the introduction. This background would help interpret\nwhether the observed glial expansion reflects an expected regional fate or a fate shift resulting from\naltered H3K4 methylation.\nWe have added a sentence in the first paragraph of the Results stating that glia cells and tanycytes are\nderived from Nkx2.1+ progenitor cells. Since we don’t mention Nkx2.1 in the Introduction, we felt it was\nbest to include this concept when Nkx2.1 is introduced in the Results.\n21\n27. The Discussion clearly integrates behavioral, transcriptomic, and cellular data to support a\nmodel of H3K4 methylation-dependent regulation of interneuron and hypothalamic development.\nHowever, the authors should consider including additional developmental context for the gliogenic\npotential of Nkx2.1+ progenitors, particularly in the hypothalamus, to better support the\ninterpretation of astrocyte expansion.\nAs noted above, we now introduce the concept of Nkx2.1-lineage cells giving rise to glia (and tanycytes)\nin the first paragraph of the Results section. And we have added a section to the Discussion raising the\npossibilities that glia changes in the H3.3K4M mice could arise from gliogenesis of Nkx2.1-lineage cells\nvs. compensatory changes due to altered H3K4 methylation in Nkx2.1-lineage neurons.\n28. Overall, this manuscript presents an ambitious and multidimensional study of H3K4 methylation\nin forebrain development and function. The integration of single-cell epigenomics, electrophysiology,\nand behavior is commendable, and the focus on both MGE-derived interneurons and hypothalamic\ncircuits is very comprehensive. Addressing the points raised here — particularly the developmental\nissues, glial fate interpretation, sex-stratified analyses, and figure clarity — will help to enhance the\nrobustness and interpretability of the findings.\nWe thank the reviewer for these positive comments. We have attempted to address the reviewer’s\nconcerns, which we feel have strengthened our manuscript.\nWe thank the reviewers for their positive response to our revised manuscript. We address the remaining\nspecific comments below, with reviewers’ comments indented & italicized, and our response in blue\nfont. All significant changes in the Revised Manuscript are in red text:\nReviewer #1:\nThe authors have answered all my questions.\nWe thank the reviewer for their previous suggestions, and we’re pleased to have adequately addressed\nthem in our revised manuscript.\nReviewer #2:\nThe authors have addressed all the concerns I raised in the previous version of the manuscript,\nproviding explanations for those that could not be resolved experimentally. I believe the\nexpanded characterization of the mutant mice, including the new western blots only included in\nthe rebuttal letter, should be incorporated into a revised version of the manuscript, as it will make\nthe paper clearer without requiring reference to the original paper describing the generation of\nthe mouse mutants. I have no further comments for the authors.\nWe thank the reviewer for their previous suggestions, and we’re pleased to have adequately addressed\nthem in our revised manuscript. We have added the Western blots showing no change in overall H3.3\nlevels to Extended Data Figure 1A in the revised manuscript & updated the the Methods accordingly.\nReviewer #3:\nThe authors have made a substantial effort to address the comments of all reviewers, and they\nconducted additional analyses (e.g., PH3, Ki67, and migration assays), as well as\ncorrections/additions of text throughout the manuscript. All of this indeed strengthen the\nmanuscripts´ conclusions. However, some issues remain that, in my view, should be clarified or\nfurther improved before acceptance.\nWe thank the reviewer for their previous suggestions, and we’re pleased to have adequately addressed\nmost of their concerns.\n1. Figure quality and interpretation of the VZ signal: In Figure 1c, the ventricular zone (VZ) still\nlacks a clearly discernible cellular signal (it has not been replaced), which I interpreted as\nbackground rather than specific staining. The authors state that this is a long-standing model\nand provided a staining form the original publication. The new Figure 5a (very left panel) actually\nseems to shows a more convincing VZ signal, but the resolution and magnification are too low\nto allow a definitive assessment. I still would recommend to replace Figure 1c with a higher-\nquality image that clearly shows tdTom cells in the VZ.\nIn Figure 1C, we have added new images showing HA staining and Tom signal in MGE sections from\nWT and Hom mice that we hope alleviates the reviewer’s concerns. We are confident that these images\ndepict real HA and Tomato signal in VZ, SVZ and mantle cells throughout the MGE. This is not\nbackground signal, as there is a lack of Tomato signal in portions of the DAPI+ non-MGE tissue in the\nmerged panel for both WT and Hom mice.\n2. In matters of cortical layer markers: relying on DAPI alone is not sufficient for defining laminar\nboundaries, albeit I do agree that layer 4 is nicely visible. However, the border of layer 6 is not\nclearly visible without a marker, and this might impair the density measurements. Please also\ncompare 3A DAPI with 2F DAPI. In 2F the cortex seems measured to more “ventral” levels. So\nthere is inconsistency in that.\nThis is basically the same point that the reviewer raises in point #5, so we address the reviewer’s\ngeneral concern about measuring cortical thickness below.\n3. I do agree with the authors, that interneuron–progenitor communication is not the primary\nfocus of the study, which already is very comprehensive. Yet, I felt that the observed adult\nphenotypes could be better contextualized by discussing potential developmental correlates.\nImmunolabeling for TBR1 (deep-layer neurons at E13.5) or progenitor markers such as\nPAX6 and EOMES (TBR2) at embryonic stages could have helped to link developmental alterations\n(reduced invasion of cINs into the cortex at embryonic stages) to changes in cortical progenitor\npopulations and to the later layer shifts. I wonder, whether the newly included KI67 staining and\nPH3 stainings, analyzed in the MGE, could also be analyzed in the cortex? Less numbers of\ncycling progenitors might already provide some insights. Even if such experiments are beyond\nthe current scope, an explicit acknowledgment of this limitation and of possible mechanistic links\nwould strengthen the discussion.\nTo address the reviewer’s concerns, we examined expression of Pax6 and Ki67 in the lateral cortex\n(since that’s where the largest reduction in migrating interneurons was observed) of E13.5 WT and\nHom mice. We did not detect any obvious differences in either marker between WT and Hom cortex.\nWe also quantified PH3+ cells in the E13.5 lateral cortex and found no significant difference between\nHom and WT cortex. See Pax6 staining and PH3 quantification below. Thus, we see no evidence that\nthe reduction of MGE-derived interneurons migrating into the cortex affects cell cycle dynamics and\nneurogenesis of dorsal cortical progenitors. We feel that additional analysis looking at the interaction\nbetween postmitotic interneurons and excitatory neurons is beyond the scope of this manuscript. We\ndo not include these new results in our manuscript, but we have expanded on the non-cell autonomous\neffects of reduced interneuron migration in the Discussion (lines 531-535).\n4. In that matters, the authors wrote in line 528: “The primary cause for reduced forebrain\ninterneurons was decreased cortical migration, leading to smaller brains and reduced cortical\nthickness.” This causality is not proven. As cINs make up rather a minority, I doubt that their\nreduced density makes up the reduced thickness. The possible effect on cortical progenitors\nand excitatory neurons needs to be at least discussed.\nThe reviewer is correct that we have overstated our findings here, we do not know if decreased\nmigration is the primary cause of smaller brains & reduced cortical thickness. We have modified this\nsentence from “The primary cause…” to “A likely cause for reduced forebrain interneurons was\ndecreased cortical migration.”, which is a more accurate interpretation of our data.\nIn our original Response to Reviewers, we noted that there is evidence in the literature that loss of\nMGE-derived interneurons is sufficient to reduce cortical thickness. Mutations in Nkx2.1 and Arx, two\ngenes restricted to the MGE, result in thinner cortices and smaller brains/microcephaly (reviewed in\nPMID 38094004). Whether this is solely from loss of interneurons, or due to additional changes and/or\nloss of excitatory neurons, is not clear. And based on the data above, we do not observe any change\nin cell cycle dynamics of dorsal cortical progenitors. We have expanded on this point in the Discussion\n(lines 531-535), and see the third point below.\n5. Speaking of the cortical thickness reduction: it is a strong reduction in Figure 2F, whereas this\nreduction is absolutely not visible in Figure 3A, although it is both S1 of 3 weeks old mice. As\nthere is only one scale bar shown for the WT in 3A, I assume it is the same scaling. The cortex\nthickness in WT and HOM is nearly the same…How do the authors explain that? Layer 1 in 2F\nWT seem very large. Are the regions chosen for measuring well controlled and matched between\nthe genotypes?\nFirst, the yellow dotted lines in Fig 3A were added so that readers could visualize the deep and\nsuperficial cortical boundaries. These are not the actual boundaries used in cell counts, which were\ncarefully drawn based on DAPI staining. We concede that the layer VI boundary in Fig 3A is not very\naccurate and is confusing in comparison to old Fig 2F. We apologize for this confusion. We have\nremoved the layer VI boundary in the DAPI image of Fig 3A since this boundary is not clearly visible in\nboth WT and Hom DAPI panels (but see below). We kept the layer III/IV boundary line which is accurate.\nSecond, the sections used to measure S1 and V1 cortical thickness for WT and Hom mice were well\nmatched based on brain makers. Conversely, the panels in Figure 3A were selected to highlight the\nchanges in PV and SST interneurons. While the Fig 3A images are both from S1, they are not\nnecessarily matched along this same axes. That being said, we note that the white matter is present in\nthe bottom of the Hom section in Fig 3A (note the altered DAPI staining at the bottom of the Hom DAPI\npanel indicative of white matter) and is not present in the WT panel. This indicates that in Fig 3A, the\nHom image is in fact thinner compared to the WT image, which is consistent with our quantification\nindicating a moderate but significant reduction in cortical thickness in Hom mice.\nThird, we note that the difference in S1 and V1 cortical thickness between WT and Hom mice, while\nstatistically significant, is actually a relatively small reduction (Average S1 thickness: WT 801µm, Hom\n722µm, difference of 79 µm or ~10% reduction. Average V1 thickness: WT 810µm, Hom 774µm,\ndifference of 36µm or ~4.5% reduction). Related to the reviewer’s comments in point #4 above, while\nwe cannot be certain if this decrease in cortical thickness is due solely to the reduction in cortical\ninterneurons (see Discussion lines 531-535), it is certainly possible that these small but significant\nreductions in cortical thickness could be directly due to a reduction of MGE-derived interneurons.\nFourth, to alleviate the reviewer’s concerns about labeling cortical layers, we have reanalyzed cortical\nthickness of S1 and V1 on brain sections stained with Cux1 to define layers II-IV. This allowed us to\nmeasure the total cortical thickness, as well as the thickness of layer I, layers II-IV and layers V-VI. We\nfind that the greatest change in cortical thickness is in layers II-IV, and we do not see any change in\nlayer I thickness in either brain region. This new Cux1+ image and cortical thickness counts are now\npresented in Figure 2F (lines 131-132) and Extended Data Figure 1C-D.\nResponse to Reviewers\nWe thank the reviewers for their positive response to our revised manuscript. Reviewer #3 had two\nadditional comments that we address below.\nReviewer #3:\nThe revised manuscript has improved in presentation and wording and is suitable for publication.\nFor completeness, I would like to note two points that, in my view, are not fully addressed and\nshould be kept in mind when interpreting the data.\nWe are pleased that our revised manuscript has addressed the reviewer’s concerns and their opinion\nthat our manuscript is now suitable for publication.\nFirst, the pronounced thinning of upper cortical layers is unlikely to be explained by the reported\nchanges in interneuron abundance, which appear rather modest. Given that later embryonic\nstages of corticogenesis were not examined, alterations in excitatory neuron development remain\na plausible alternative explanation. Excluding this possibility would have required analyses at\nlater developmental time points, when upper cortical layers are generated.\nIn the previous revision, we added the following sentences in the Discussion (lines 531-536) which we\nfeel adequately addresses this issue, and we do not feel a need to adjust this statement:\nThere is evidence that disruption of interneuron fate and migration can lead to non-cell autonomous\nchanges in excitatory cells, including altered neurogenesis and defects in cortical lamination and\nneuron activity101,102. Whether the reduced cortical thickness in H3.3K4M mutant mice is solely from\nloss of interneurons or also arises from altered excitatory neuron development is not clear.\nSecond, if the reported differences in cortical thickness are robust and systematic, they would be\nexpected to be detectable across sections. The discrepancy between the quantitative\nmeasurements shown in Figure 2F and the qualitative appearance of cortical sections in Figure\n3 therefore remains difficult to reconcile. The authors’ response clarifies their interpretation but\ndoes not fully resolve this issue. I note this point for completeness. These points do not detract\nfrom the overall quality or significance of the study.\nWe quantified a significant reduction of cortical thickness in 2 different cortical regions (S1 and V1)\npresented in Figure 1 and Supplementary Figure 1. In our previous response, we noted that the overall\nreduced thickness is ~10% in S1 and ~4.5% in V1. Whether this should be considered “…robust and\nsystemic…” is up to interpretation, but these differences are statistically significant. While we\nunderstand the reviewer’s point here, we do not believe that there is a significant discrepancy between\nFigure 2F and 3A as the reviewer describes for all the reasons mentioned in our previous Response to\nReviewers. As the reviewer notes that this issue does not “…detract from the overall quality or\nsignificant of the study.”, we have no further comment on this issue."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have answered all my questions.", "reviewer_2": "The authors have addressed all the concerns I raised in the previous version of the manuscript, providing explanations for those that could not be resolved experimentally. I believe the expanded characterisation of the mutant mice, including the new western blots only included in the rebuttal letter, should be incorporated into a revised version of the manuscript, as it will make the paper clearer without requiring reference to the original paper describing the generation of the mouse mutants. I have no further comments for the authors.", "reviewer_3": "The authors have made a substantial effort to address the comments of all reviewers, and they conducted additional analyses (e.g., PH3, Ki67, and migration assays), as well as corrections / additions of text throughout the manuscript. All of this indeed strengthen the manuscripts´conclusions . However, some issues remain that, in my view, should be clarified or further improved before acceptance.\n1. Figure quality and interpretation of the VZ signal:\nIn Figure 1c, the ventricular zone (VZ) still lacks a clearly discernible cellular signal (it has not been replaced), which I interpreted as background rather than specific staining. The authors Tate that this is a long-standing model and provided a staining form the original publication.\nThe new Figure 5a (very left panel) actually seems to shows a more convincing VZ signal, but the resolution and magnification are too low to allow a definitive assessment. I still would recommend to replaceFigure 1c with a higher-quality image that clearly shows tdTom cells in the VZ.\n2. in matters of cortical layer markers: relying on DAPI alone is not sufficient for defining laminar boundaries, albeit I do agree that layer 4 is nicely visible. .However, the border of layer 6 is not clearly visible without a marker, and this might impair the density measurements. Please also compare 3A DAPI with 2F DAPI. In 2F the cortex seems measured to more „ventral“ levels. So there is inconsistency in that.\n3. I do agree with the authors, that interneuron–progenitor communication is not the primary focus of the study, which already is very very comprehensive. Yet, I felt that the observed adult phenotypes could be better contextualized by discussing potential developmental correlates. Immunolabeling for TBR1 (deep-layer neurons at E13.5) or progenitor markers such as PAX6 and EOMES (TBR2) at embryonic stages could have helped to link developmental alterations (reduced invasion of cINs in to the cortex at embryonic stages) to changes in cortical progenitor populations and to the later layer shifts. I wonder, whether the newly included KI67 staining and PH3 stainings, analyzed in the MGE, could also be analyzed in the cortex? Less numbers of cycling progenitors might already provide some insights. Even if such experiments are beyond the current scope, an explicit acknowledgment of this limitation and of possible mechanistic links would strengthen the discussion. In that matters, the authors wrote in line 528:“The primary cause for reduced forebrain interneurons was decreased cortical migration, leading to smaller brains and reduced cortical thickness.“\nThis causality is not proven. As cINs make up rather a minority, I doubt that their reduced density makes up the reduced thickness. The possible effect on cortical progenitors and excitatory neurons needs to be at least discussed.\nSpeaking of the cortical thickness reduction: it is a strong reduction in Figure 2F, whereas this reduction is absolutely not visible in Figure 3 A, although it is both S1 of 3 weeks old mice. As there is only one scale bar shown for the WT in 3A, I assume it is the same scaling. The cortex thickness in WT and HOM is nearly the same…How do the authors explain that? Layer 1 in 2F WT seem very large. Are the regions chosen for measuring well controlled and matched between the genotypes?"}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_3": "The revised manuscript has improved in presentation and wording and is suitable for publication. For completeness, I would like to note two points that, in my view, are not fully addressed and should be kept in mind when interpreting the data.\nFirst, the pronounced thinning of upper cortical layers is unlikely to be explained by the reported changes in interneuron abundance, which appear rather modest. Given that later embryonic stages of corticogenesis were not examined, alterations in excitatory neuron development remain a plausible alternative explanation. Excluding this possibility would have required analyses at later developmental time points, when upper cortical layers are generated.\nSecond, if the reported differences in cortical thickness are robust and systematic, they would be expected to be detectable across sections. The discrepancy between the quantitative measurements shown in Figure 2F and the qualitative appearance of cortical sections in Figure 3 therefore remains difficult to reconcile. The authors’ response clarifies their interpretation but does not fully resolve this issue. I note this point for completeness.\nThese points do not detract from the overall quality or significance of the study."}, "author_response": ""}]

Decision letter