KMT2D temporally activates neuronal transcriptional factor genes to mediate cerebellar granule cell differentiation

Shilpa S Dhar; Kyung-Pil Ko; Jinho Jang; Calena Brown-Abel; Tao Lin; Sharad Awasthi; Kaifu Chen; Roy V Sillitoe; Jae-Il Park; Min Gyu Lee

doi:10.1126/sciadv.adu7174

. 2025 Sep 26;11(39):eadu7174. doi: 10.1126/sciadv.adu7174

KMT2D temporally activates neuronal transcriptional factor genes to mediate cerebellar granule cell differentiation

Shilpa S Dhar ^1,^†, Kyung-Pil Ko ², Jinho Jang ², Calena Brown-Abel ^1,^†, Tao Lin ³, Sharad Awasthi ¹, Kaifu Chen ^4,⁵, Roy V Sillitoe ³, Jae-Il Park ^2,^*, Min Gyu Lee ^1,^*

^¹Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.

^²Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.

^³Department of Pathology and Immunology, Department of Neuroscience, Development, Disease Models and Therapeutics Graduate Program, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute of Texas Children’s Hospital, Houston, TX 77030, USA.

^⁴Basic and Translational Research Division, Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA.

^⁵Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.

Corresponding author. Email: mglee@mdanderson.org (M.G.L.); jaeil@mdanderson.org (J.-I.P.)

^†

Present address: GI Medical Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.

Roles

Shilpa S Dhar: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing

Kyung-Pil Ko: Data curation, Formal analysis, Investigation, Visualization

Jinho Jang: Data curation, Formal analysis, Software, Visualization

Calena Brown-Abel: Data curation, Formal analysis, Investigation, Project administration, Resources, Supervision, Validation

Tao Lin: Investigation, Resources

Sharad Awasthi: Investigation, Project administration, Resources

Kaifu Chen: Funding acquisition, Investigation, Writing - review & editing

Roy V Sillitoe: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Writing - review & editing

Jae-Il Park: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing

Min Gyu Lee: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing

PMCID: PMC12467062 PMID: 41004595

Abstract

Spatiotemporal gene expression is the fundamental feature of cellular differentiation, including neuron differentiation. The epigenetic mechanism underlying spatiotemporal gene regulation during in vivo neuron differentiation remains largely unknown. Granule cells (GCs) constitute the vast majority of neurons in the cerebellum, which contains most of neurons in the brain. Here, we show that Atoh1-Cre–mediated knockout (ACKO) of Kmt2d encoding the lysine methyltransferase KMT2D (MLL4) in cerebellar GC lineage inhibits the transition of GC progenitors to GCs while cell non-autonomously affecting other cerebellar cells. Kmt2d ACKO impaired cerebellum-associated behaviors and caused facial peculiarity, microcephaly, and reduced body size in mice. KMT2D temporally activated neuronal differentiation programs in cerebellar GCs. KMT2D-mediated activation of the key neuronal transcription factor genes En2, Pax6, and Myt1l via super-enhancer/enhancer programming was critical for GC differentiation. These findings reveal a unique epigenetic mechanism in which KMT2D temporally orchestrates gene expression required for cerebellar GC differentiation by programming neuronal enhancers.

KMT2D temporally orchestrates differentiation of cerebellar granule cells, which are a vast majority of neurons in the cerebellum.

INTRODUCTION

The fundamental feature of cellular differentiation, such as neuron differentiation in the brain, is spatiotemporal gene expression in which different genes are expressed at a specific time and in a specific tissue during development. The epigenetic mechanism underlying spatiotemporal regulation of gene expression during in vivo neuron differentiation remains poorly characterized. Notably, the cerebellum, often referred to as the “little brain,” contains about 80% of all of the neurons in the human brain (62 to 73% in the rodent brains), although it accounts for only about 10% of the brain’s volume in human (14 to 15% in rodents) (1). The cerebellum coordinates movement (2) and cognition (3). Cerebellar granule cells (GCs) account for about 80 to 92% of all cerebellar cells in rodents (4–7). Cerebellar GCs are glutamatergic excitatory neurons that differentiate and mature during early postnatal life (2). These cells are known to mediate sensory and motor functions (2) and reward responses (3). Cerebellar GC differentiation is spatiotemporally regulated (2).

The signaling proteins and transcription factors that regulate gene expression during neuron differentiation in the cerebellum have been extensively studied [reviewed by Sathyanesan et al. (8)]. A key example is the sonic hedgehog signaling pathway, which plays a major role in regulating prenatal and postnatal stages of cerebellum development, including GC development. In contrast with the great advances in research of signaling proteins and transcription factors, there are only few reports regarding the roles of epigenetic modifiers (e.g., H3K27 methyltransferase EZH2 and H3K79 methyltransferase DOT1L) in regulating cerebellar function (9, 10). Thus, the roles of epigenetic modifiers in cerebellar neuron development have not been well characterized. Moreover, little is known about how temporal gene expression is regulated in the cerebellum.

A critical gene regulatory signature that spatiotemporally activates gene expression is enhancer (11). Enhancers play important roles in regulating gene expression in young and old neurons (12). Monomethyl histone H3 lysine 4 (H3K4me1) and acetyl H3K27 (H3K27ac) are the two main histone marks of enhancers, which are co-occupied by transcriptional activators and coactivators [e.g., histone acetyltransferases p300 and CREB-binding protein (CBP)] (13, 14). H3K4me1 is important for spatiotemporal and epigenetic activation of gene expression (15–19). H3K27ac signifies active states of the enhancer and can be generated by p300 and CBP (20), although it also occupies active promoter regions and transcription start sites (14). Typical enhancers are characterized by a median size of 0.7 to 1.3 kb (21). Large clusters of enhancers called super-enhancers highly activate gene expression and are associated with regulation of cell identity (21–23). Enhancers can activate genes by interacting with their cognate promoters (11). As much as 75% of gene regulatory regions spanning promoters and transcription start sites are occupied by H3K4me3 peaks in human cells (24–26). H3K4me3 peaks spanning at least −500 to +3500 bp are known as broad H3K4me3 (also called H3K4me3 breadth), which often occupies tumor suppressor genes and cell identity genes and represents highly active states of genes (27, 28). This signature is positively associated with the transcription frequency of active genes (16, 17).

KMT2D (a COMPASS-like enzyme; also called MLL4) is an H3K4 methyltransferase capable of enzymatically generating H3K4me1, H3K4me2, and H3K4me3 in vitro (29–32). KMT2D can predominantly produce H3K4me1 rather than H3K4me3 in several other types of cells (33–36) and can help generate another enhancer mark, H3K27ac (35, 37, 38). We have demonstrated that KMT2D is required for maintaining both the enhancer mark H3K4me1 and H3K4me3 peaks at tumor suppressor genes in medulloblastoma cells (39). Notably, the KMT2D gene harbors truncations and missense mutations in up to 74% of patients with Kabuki syndrome (40, 41). This syndrome is a rare developmental disorder with multiple malformation that is characterized by distinctive facial features (e.g., depressed nasal tip, long palpebral fissures, arched eyebrows, and lowered lateral eyelid eversion), postnatal growth retardation (e.g., short stature and microcephaly), and intellectual disability (42). Patients with Kabuki syndrome often have cerebellar anomalies (43–45). Moreover, KMT2D is one of the most frequently mutated histone modifiers (8 to 10%) in medulloblastoma, which is one of the most common primary brain tumors in children younger than 15 years (46–50) and is usually located at the cerebellum. We and others have shown that KMT2D plays a role in suppressing medulloblastoma development in mice (39, 51). Notably, cerebellar GCs are the cells of origin for medulloblastoma (52, 53). However, the role that KMT2D plays during in vivo cerebellar neuron differentiation is unclear. In the present study, we sought to define the function of KMT2D in the differentiation of cerebellar neurons (especially cerebellar GCs) using genetically engineered mouse models and single-cell RNA sequencing (scRNA-seq).

RESULTS

Atoh1-Cre–mediated knockout of Kmt2d causes facial peculiarities, microcephaly, reduced body size, and defective cerebellum-dependent behaviors in mice

In our effort to determine the role of KMT2D in regulating neurons in the brain, we assessed KMT2D levels in the cerebellum and other brain regions by performing immunohistochemistry (IHC). Our results demonstrated that KMT2D levels were higher in the cerebellum (largely in the GC layers) than in other regions in the mouse brain (Fig. 1, A and B, and fig. S1, A and B). This prompted us to use the Atoh1-Cre allele to delete Kmt2d in the cerebellar GC lineage because cerebellar GCs account for up to 92% of all cerebellar cells and because, in this allele, Atoh1 directs expression of Cre primarily in the GC lineage in the cerebellum (although it also induces Cre expression in extracerebellar regions, such as the dorsal hindbrain and spinal cord neurons) (54, 55). Thus, we generated Atoh1-Cre Kmt2d^fl/fl mice and found that Kmt2d Atoh1–Cre–mediated knockout (ACKO) caused facial anomalies (e.g., increased facial angle and reduced facial length), microcephaly (i.e., reduced head circumference), and decreased body weight (i.e., short body size) (Fig. 1, C and D). Notably, it has been reported that increased facial angle, reduced facial length, and decreased body weight in mice are Kabuki syndrome–like characteristics (56). Although the overall morphology of the cerebellum and its lobules was comparable between Kmt2d^fl/fl mice and Atoh1-Cre Kmt2d^fl/fl mice for 1.0 to 1.5 years (fig. S1C), Kmt2d ACKO reduced the survival times of mice regardless of their sex (Fig. 1E and fig. S1, D and E).

Fig. 1. — (A and B) IHC analysis showing higher KMT2D levels in the cerebellum than in other brain regions. P4 (A) and 1-month-old (B) mouse brains were analyzed. Black bar, 500 μm; yellow bars, 50 μm. (C) An illustration showing the facial angle (FA) and facial length (FL) of a mouse. (D) The effect of *Kmt2d* ACKO on facial angle, facial length, head circumference, and body weight. (E) The effect of *Kmt2d* ACKO on overall survival of mice. NS, no significance. (F) The effect of *Kmt2d* ACKO on forelimb strides, hindlimb strides, and asymmetry in hindlimb strides (centimeters) according to a footprint test (n = 7 per genotype). (G) The effect of *Kmt2d* ACKO on latency to fall and traversal time in the raised beam task (n = 7 per genotype). Data are presented as the mean ± SEM (error bars) (n ≥ 3). *P < 0.05, **P < 0.01, and ***P < 0.001.

The cerebellum is important for motor coordination and balance (2). To investigate whether Kmt2d ACKO causes defects in motor coordination and balance in mice, we performed the footprint test using Kmt2d^fl/fl and Atoh1-Cre Kmt2d^fl/fl mice. In this test, we monitored forelimb and hindlimb stride and looked for asymmetry in the hindlimb stride. Kmt2d ACKO significantly reduced these stride parameters (Fig. 1F). In addition, we performed the balance beam test (a cerebellum-relevant behavior test) in which we placed mice on an elevated beam and stimulated them to walk across the beam and to enter a box at the opposite end of the beam (57). Kmt2d ACKO greatly increased the traversal times of mice and augmented the incidence of falls from the beam (Fig. 1G). These results suggest that Kmt2d ACKO impairs cerebellum-dependent motor behaviors.

Kmt2d ACKO has a negative impact on cerebellar GCs and Purkinje cells and reduces cellular levels of H3K4me1, H3K27ac, and H3K4me3 marks in cerebellar GCs

To test the effect of Kmt2d ACKO on cerebellar GC development, we performed immunofluorescence (IF) or IHC staining for NeuN, which labels GCs in the cerebellum. Although Atoh1-Cre targets GCs but not Purkinje cells (58, 59), the cross-talk between GCs and Purkinje cells is important for development of the latter. Thus, we also performed IF or IHC staining for the Purkinje cell marker calbindin. Purkinje cells are GABAergic inhibitory neurons that provide the main output of the cerebellar cortex (2). Our results demonstrated that Kmt2d ACKO greatly reduced NeuN and calbindin signals on postnatal day 5 (P5) and 1- and 4-month-old cerebella (Fig. 2, A to D) but not P1 cerebella (fig. S2, A to C). These results suggest that after P1, Kmt2d ACKO negatively affects GCs while also having a cell non-autonomous negative impact on Purkinje cells.

Fig. 2. — (A to D) Immunofluorescent images of *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* cerebella stained using antibodies against KMT2D, NeuN (a GC marker), and calbindin (a Purkinje cell marker). P5, 1-month-old, and 4-month-old cerebella were analyzed. Yellow bars, 50 μm; white bars, 350 μm; DAPI, 4′,6-diamidino-2-phenylindole.

As aforementioned, KMT2D up-regulates the enhancer marks H3K4me1 and H3K27ac in several types of cells, including but not limited to adipocytes, mouse embryonic stem cells, growth hormone-releasing hormone-producing neurons, medulloblastoma cells, and lung tumor cells (33–35, 39, 60). In addition, KMT2D can positively regulate H3K4me3 at gene promoters and the transcription start sites in medulloblastoma cells and other types of cells (31, 39, 61). To determine whether KMT2D controls H3K4me1, H3K27ac, and H3K4me3 at the cellular levels in cerebellar GCs, we analyzed these epigenomic marks in Kmt2d^fl/fl and Atoh1-Cre Kmt2d^fl/fl cerebella using IF staining. These analyses showed that Kmt2d ACKO reduced the levels of these marks in P5, 1-month-old, and 4-month-old cerebella while having no obvious effect on their levels in P1 cerebella (Fig. 3, A to C). To assess whether the effect of Kmt2d ACKO on H3K4me1, H3K27ac, and H3K4me3 is specific, we analyzed H3K36me3 and H3K79me3 in Kmt2d^fl/fl and Atoh1-Cre Kmt2d^fl/fl cerebella using IHC and IF. Kmt2d ACKO did not affect H3K36me3 and H3K79me3 in 1- and 4-month-old cerebella (fig. S3, A and B). These results suggest that KMT2D temporally and specifically regulates the epigenomic marks H3K4me1, H3K27ac, and H3K4me3 after P1.

Fig. 3. — (A to C) Immunofluorescent images of *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* cerebella stained using antibodies against H3K4me1, H3K27ac, and H3K4me3. P1 (A), P5 (A), 1-month-old (B), and 4-month-old (C) cerebella were analyzed. (D) The effect of *Kmt2d* loss on in vitro neurite outgrowth of GCP/GC-enriched *Kmt2d*^fl/fl neurospheres (P5). Neurospheres were infected with Ad-GFP or Ad-Cre-GFP viruses for 48 hours and cultured for the indicated numbers of days. (E to G) IHC of Ki-67 (cell proliferation marker) in *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* cerebella. P5 (E), 1-month-old (F), and 4-month-old (G) cerebella were analyzed. IHC data were quantified. (H) The effect of *Kmt2d* loss on the proliferation of GCP/GC-enriched *Kmt2d*^fl/fl cerebellar cells (P5). Neurospheres were infected with Ad-GFP or Ad-Cre-GFP viruses for 48 hours and cultured for the indicated numbers of days. Data are presented as the mean ± SEM (error bars) (n ≥ 3). *P < 0.05, **P < 0.01, and ***P < 0.001. GL, granule cell layer; ML, molecular layer; yellow bars, 50 µm; orange bars, 100 µm; d, days.

Kmt2d loss inhibits GC differentiation while increasing the proliferation of GC progenitors

To further assess the effect of Kmt2d loss on GC differentiation, we harvested the Kmt2d^fl/fl cerebellum (~P5), enriched the cerebellar GC lineage cells by removing non-neuronal cells, and treated cells with Ad–Cre–green fluorescent protein (GFP) or Ad-GFP viruses. P5 cells were used because an optimal age for GC culture is P4 to P6 when the number of GC progenitors (GCPs) is high (62, 63). Isolated GC lineage cells can form nonadherent spherical clusters of cells called neurospheres (64). We compared the differentiation between Ad-Cre-GFP– and Ad-GFP–treated GC lineage cells in vitro. Our results demonstrated that in vitro Ad-Cre–mediated deletion of Kmt2d markedly impaired the neurite outgrowth of cerebellar GCs, indicating that KMT2D positively regulates cerebellar GC differentiation (Fig. 3D).

Although Kmt2d ACKO did not alter the overall morphology of the cerebellum and its lobules (fig. S1C), Kmt2d ACKO inhibited GC differentiation. Similarly, in Atoh1-Cre En2^fl/fl mice, the number, placement, and architecture of the lobules in the cerebellum were relatively normal (65), although EN2 (a critical neuronal transcription factor) positively regulates differentiation and neurite outgrowth of cerebellar GCs as well as cerebellar zonal patterns (66, 67). It has been shown that En2 ACKO increases the proliferation of GCPs (68). Therefore, it is plausible that Kmt2d ACKO alters the proliferation states of GC lineage cells. To test this, we compared Ki-67–positive cell numbers between Kmt2d^fl/fl cerebella and Atoh1-Cre Kmt2d^fl/fl cerebella—Ki-67 is a proliferation marker of cells, including GCPs. Although Kmt2d ACKO did not induce any obvious cerebellar tumors for 1.0–1.5 years (fig. S1C), Kmt2d ACKO increased the Ki-67 signal in P5 and 1-month-old GC layers (Fig. 3, E and F). However, Kmt2d ACKO had no obvious effect on the Ki-67 signal in 4-month-old GC layers (Fig. 3G), perhaps because most of GCPs exited the cell cycle in Atoh1-Cre Kmt2d^fl/fl cerebella. To validate this result, we examined the effect of Kmt2d loss on cerebellar cell proliferation in vitro. For this, we enriched Kmt2d^fl/fl GC lineage cells (P5), treated them with Ad-Cre-GFP and Ad-GFP, and performed a cell proliferation assay. We found that Ad-Cre-GFP–mediated Kmt2d loss increased cell proliferation (Fig. 3H). Because GCPs proliferate until about P15 (69) and exit the cell cycle to differentiate into GCs (2), GCP proliferation was likely increased by Kmt2d loss. Together, these results indicate that Kmt2d loss increases GCP proliferation, likely predisposing GCPs to a transformed state.

Kmt2d ACKO impairs neuron differentiation programs during GC development

To delineate the KMT2D-mediated regulation of gene expression in cerebellar GCs, we harvested Kmt2d ^fl/fl and Atoh1-Cre Kmt2d ^fl/fl cerebella from 1- and 4-month-old mice and enriched the GCs. We then compared the gene expression profiles between Kmt2d ^fl/fl GCs and Atoh1-Cre Kmt2d ^fl/fl GCs using the transcriptome sequencing technique RNA-seq. In both 1- and 4-month-old GCs, Kmt2d ACKO down-regulated the neuronal differentiation program, the neuronal development program, and other neuronal programs (e.g., those required for neuron projection), which we collectively call “neuronal differentiation programs” (Fig. 4A). In addition, in alignment with the negative impact of Kmt2d ACKO on cerebellar dependent behaviors (Fig. 1, F and G), Kmt2d ACKO also down-regulated the gene expression program for walking behavior (Fig. 4A). In parallel, Kmt2d ACKO up-regulated differentiation inhibitory programs (Fig. 4B). These results supported the presence of defective GC states in the Atoh1-Cre Kmt2d^fl/fl cerebellum.

Fig. 4. — (A and B) Gene ontology analysis for genes that were down-regulated (A) or up-regulated (B) at least twofold by *Kmt2d* ACKO. RNA-seq (n = 3) was performed using GCs enriched from 1- and 4-month-old *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* cerebella. (C to E) Expression analysis of neuron differentiation genes (C), cerebellum-enriched genes (D), and other transcription factor genes (E) in *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* GC-enriched cerebellar cells. GCs were enriched from P1, 1-month-old, and 4-month-old cerebella. Quantitative RT-PCR was performed. (F) Quantitative RT-PCR analysis of *Kmt2d* expression in GCs enriched from mouse cerebella at different ages. (G to I) Expression analysis of neuron differentiation genes (G), cerebellum-enriched genes (H), and other transcription factor genes (I) after treatment of *Kmt2d^fl/fl* GCP/GC-enriched cerebellar cells (~P5) with Ad-GFP or Ad-Cre viruses. Neurospheres were infected with viruses for 48 hours and cultured for 48 hours. Quantitative RT-PCR was performed. Data are presented as the mean ± SEM (error bars) (n ≥ 3). *P < 0.05, **P < 0.01, and ***P < 0.001. m, months.

To confirm our RNA-seq results, we performed quantitative reverse transcription polymerase chain reaction (RT-PCR) using freshly enriched mouse cerebellar GCs (P1, 1 month old, and 4 months old). In agreement with our RNA-seq results, RT-PCR results showed that Kmt2d ACKO down-regulated multiple neuronal differentiation program genes, including genes important for neuron differentiation (e.g., En2, Pax6, Gprin1, and Slitrk1), cerebellum-enriched genes (e.g., Car7, Tnc, and Zfp521) (70), and other transcription factor genes (e.g., Myt1l) in 1- and 4-month-old GC-enriched cells. In contrast, Kmt2d ACKO had no significant effect on gene expression in P1 GCs (Fig. 4, C to E). This result was in accordance with the lack of an obvious effect of Kmt2d ACKO on the cellular levels of histone marks (H3K4me1, H3K27ac, and H3K4me3) in the P1 cerebellum. Kmt2d expression was increased in a time-dependent manner (Fig. 4F). These results suggest that Kmt2d ACKO affects gene expression in GCs in a temporal manner (after P1). To further substantiate these results, we determined the effect of in vitro Kmt2d deletion on gene expression in enriched GC lineage cells (P5). We achieved in vitro acute Kmt2d deletion by treating cells with Ad-Cre viruses. For in vitro versus in vivo deletion, in vitro acute deletion can be used to determine a short-term (possibly primary) effect, whereas in vivo deletion (e.g., Kmt2d ACKO) can be performed to assess a long-term physiological effect. The effect of in vitro acute Kmt2d deletion on gene expression was similar to that of Kmt2d ACKO in 1- and 4-month-old GC-enriched cells (Fig. 4, G to I).

The neuronal transcription factors EN2, PAX6, and MYT1L are major downstream effectors of KMT2D

To identify major downstream effectors of KMT2D, we searched for transcription regulators that are known to affect neuron differentiation among KMT2D-activated genes. En2, Pax6, and Myt1l were down-regulated by Kmt2d ACKO (Fig. 4, C and E). EN2 is required for differentiation of cerebellar GCPs (68). PAX6 is an important transcription factor that is needed for cellular polarization during the formation of parallel fibers of cerebellar GCs and for the proper formation of the external GC layers (71, 72). EN2 and PAX6 can act as transcriptional activators (73, 74). MYT1L maintains neuronal identity by down-regulating the expression of many non-neuronal genes in cultured hippocampal neurons (75). MYT1L is a transcription factor required for the reprograming of fibroblasts and other somatic cells into induced neuronal cells (76).

Immunohistochemical analysis confirmed that the neuronal transcription factors EN2, PAX6, and MYT1L were down-regulated by Kmt2d ACKO (Fig. 5, A to C). To vigorously assess the KMT2D-mediated regulation of En2, Pax6, and Myt1l expression, we examined whether KMT2D rescues En2, Pax6, and Myt1l expression in Kmt2d-deleted cerebellar GCs. Previously, others and we reported that a functional but smaller KMT2D (herein called mini-KMT2D) restored the defective differentiation of KMT2D-depleted cells (31) and up-regulated gene expression via enhancer activation (38). We have also reported that mutant mini-KMT2D with mutations in its tandem PHD domain are catalytically inactive. Thus, we ectopically expressed mini-KMT2D or its mutant form in Kmt2d-deleted cerebellar GCs. Results demonstrated that mini-KMT2D but not mutant mini-KMT2D significantly restored expression levels of En2, Pax6, and Myt1l in Kmt2d-deleted cerebellar GCs (fig. S4A).

We then examined the effect of EN2, PAX6, and MYT1L reexpression on the differentiation of Kmt2d-deleted GC-enriched cells (P5). Our results showed that their reexpression significantly restored the differentiation of Kmt2d-deleted GC-enriched cells and the expression of neuronal differentiation genes tested in the same cells (Fig. 5, D to F, and fig. S4, B to D). These results suggest that EN2, PAX6, and MYT1L act as key downstream effectors of KMT2D.

KMT2D positively regulates super-enhancers and broad H3K4me3 at En2, Pax6, and Myt1l

To gain epigenomic insights into how KMT2D controls gene expression in cerebellar GCs, we performed cleavage under targets and release using nuclease (CUT&RUN) to profile enhancer marks (H3K27ac and H3K4me1), the promoter mark H3K4me3, and KMT2D using 1-month-old Kmt2d^fl/fl and Atoh1-Cre Kmt2d^fl/fl GC-enriched cells. CUT&RUN is a chromatin profiling technique that involves antibody-directed DNA cleavage by nuclease-fused protein A, release of specific protein-DNA complex, and DNA sequencing (77). CUT&RUN results demonstrated that the neuronal transcription factor genes En2, Pax6, and Myt1l were associated with clusters of H3K27ac and H3K4me1 peaks (see red-outlined rectangles in Fig. 6, A and B). As these clusters outside the promoters ranged from 10 to 40 kb, they can be considered super-enhancers (21, 22). In addition, the promoter regions of these genes were occupied by broad H3K4me3 (10 to 40 kb). Kmt2d ACKO reduced enhancer signals (H3K27ac and H3K4me1 peaks) and H3K4me3 peaks that were associated with En2, Pax6, and Myt1l in mouse cerebella (Fig. 6, A and B). In agreement with this, the KMT2D CUT&RUN results showed that KMT2D occupied super-enhancers and broad H3K4me3 associated with En2, Pax6, and Myt1l (Fig. 6, A and B). Genome-wide analysis showed that KMT2D peaks were localized in the promoters, genic regions (exonic and intronic regions), and intergenic regions (fig. S5A). Gene ontology of KMT2D-occupied genes overlapped largely with ontology of genes down-regulated by Kmt2d ACKO but much less with that of genes up-regulated by Kmt2d ACKO (fig. S5, B and C). KMT2D peaks were colocalized with H3K4me1, H3K27ac, and H3K4me3 peaks in many genes (fig. S6A). In line with IF data (Fig. 3, A to C), Kmt2d ACKO decreased the average H3K27ac signals for both typical enhancers and super-enhancers as well as the average signals of H3K4me1 and H3K4me3 (Fig. 6, C and D, and fig. S6, A to E). These results indicate that KMT2D directly activates the expression of En2, Pax6, Myt1l, and many other genes by positively programming super-enhancers/enhancers and H3K4me3 peaks in the mouse cerebellum.

Fig. 6. — (A and B) Genome browser tracks of CUT&RUN data for H3K27ac, H3K4me1, H3K4me3, and KMT2D at *En2*, *Pax6*, and *Myt1l* in *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* GC-enriched cerebellar cells (1-month-old mice). CUT&RUN signals in *Atoh1-Cre Kmt2d^fl/fl* GC-enriched GCs were very weak because highly reduced signals of H3K27ac, H3K4me1, H3K4me3, and KMT2D in *Atoh1-Cre Kmt2d^fl/fl* GCs might be around or below the detection limit of the CUT&RUN technique. (C and D) Heatmaps of H3K27ac CUT&RUN reads (C) and average intensity curves of H3K27ac CUT&RUN peaks (D) at typical enhancers and super-enhancers in *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* GC–enriched cerebellar cells (1-month-old mice). (E) Quantitative RT-PCR for eRNA levels in *En2*, *Pax6*, and *Myt1* enhancers in *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* GC-enriched cells (1-month-old mice). Data are presented as the mean ± SEM (error bars) (n ≥ 3). *P < 0.05 and **P < 0.01.

RNA polymerase II generates transcripts from enhancer regions in a bidirectional manner (2). These transcripts are called enhancer RNAs (eRNAs), and eRNA expression levels can represent enhancer activities (2). Therefore, we determined the effect of Kmt2d ACKO on eRNA expression from the enhancers of En2, Pax6, and Myt1l using quantitative RT-PCR. Results demonstrated that Kmt2d ACKO down-regulated eRNA levels in enhancer regions of En2, Pax6, and Myt1l (Fig. 6E), suggesting that Kmt2d ACKO decreases enhancer activity in these genes.

Kmt2d ACKO alters the proportions and gene expression programs of GC clusters and cell non-autonomously changes those of other cerebellar cell clusters

scRNA-seq is an unbiased approach that defines the gene expression profile of a single cell, allowing for assessment of changes in cell proportions, gene expression, and cell identity. To examine the effect of Kmt2d ACKO on cerebellar cells at the single-cell level, we isolated cells from Kmt2d^fl/fl and Atoh1-Cre Kmt2d^fl/fl P5 cerebella (female and male) and performed scRNA-seq (~300 million reads per sample) (Fig. 7A). We obtained single-cell transcriptomes of 10,383 and 6048 cells from Kmt2d^fl/fl and Atoh1-Cre Kmt2d^fl/fl cerebella, respectively (Fig. 7B). Unbiased cell clustering led to 12 distinct cell clusters (fig. S7A). On the basis of the expression of marker genes (5, 78), we annotated 11 of these cell clusters, including GCP and GC clusters (GC1 and GC2) (Fig. 7, C and D, and fig. S7A). Unlike GC1 and GC2, the GCP cluster was characterized by cell hyperproliferation (assessed using G₂-M scores) (fig. S7B). In accordance with defective GC differentiation and increased GCP proliferation in Atoh1-Cre Kmt2d^fl/fl mice (Fig. 3, D to F and H), scRNA-seq–based cell proportion analyses showed that Kmt2d ACKO reduced GC proportions while increasing GCP proportions (Fig. 7E and fig. S7C). Kmt2d ACKO had a cell non-autonomous effect on the proportions and transcriptomes of other cell types, such as oligodendrocytes (Fig. 7E and fig. S7D). Because Kmt2d ACKO affected cell proportions in similar ways between male and female mouse cerebellar cells (fig. S7C), we combined their data. As KMT2D is highly expressed in GCs (Fig. 1, A and B) and ACKO is known to mainly affect the cerebellar GC lineage (54, 55), we focused on further assessing the effect of Kmt2d ACKO on the GC lineage.

Fig. 7. — (A) Workflow for scRNA-seq data analysis of mouse cerebellar cells. Cells from two to four cerebella (about P5) per genotype were isolated and pooled. FACS, fluorescence-activated cell sorting. (B) Integrated UMAP of scRNA-seq data for *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* cerebella. Two datasets were integrated and batch-corrected using the Harmony package. (C) Dot plot of the expression of the representative marker genes in each cell cluster. (D) Cell cluster annotations displayed on UMAP. GC1, GC2, and GCP clusters are outlined with dotted lines. (E) Analysis of cell proportions of cell clusters using *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* scRNA-seq data. GABAergic cells may include GABAergic progenitors and interneurons.

We performed cell lineage trajectory inference analysis using RNA velocity analysis, which leverages RNA splicing kinetics to infer the directionality of cell lineages (79). In agreement with GCP proliferation increased by Kmt2d ACKO, RNA velocity analysis showed that Kmt2d ACKO markedly diminished the transition from GCP to GC1/GC2 (Fig. 8A). Moreover, pseudotime analysis suggested that Kmt2d ACKO inhibited the transition from GCP to GC1/GC2 (Fig. 8B). Furthermore, the effect of Kmt2d ACKO on gene expression programs in GCs was similar between scRNA-seq and bulk RNA-seq (Fig. 8C and fig. S8A versus Fig. 4A). Consistent with bulk RNA-seq data, scRNA-seq analysis showed that Kmt2d ACKO down-regulated multiple neuronal differentiation program genes, including genes important for neuron differentiation (e.g., En2, Pax6, Gprin1, and Slitrk1), other transcription factor genes (e.g., Myt1l), and cerebellum-enriched genes (e.g., Car7, Tnc, and Zfp521) (Fig. 8D and fig. S8, B and C).

Fig. 8. — (A) RNA velocity analysis using *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* scRNA-seq data (P5). RNA velocity arrows embedded on UMAPs are shown. (B) Pseudotime analysis using *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* scRNA-seq data (P5). Each cell was aligned along a trajectory, and a specific pseudotime value was assigned to each cell to indicate its position along the trajectory. Expected root cells and endpoint cells are indicated. (C) Fast gene set enrichment analysis showing gene expression programs down-regulated by *Kmt2d* ACKO in scRNA-seq data of GC1 cluster cells (P5). (D) Violin plots showing the transcriptional profiles of *En2*, *Pax6*, *Gprin1*, *Slitrk1*, and *Myt1l* in *Kmt2d^fl/fl* and *Atoh1-Cre Kmt2d^fl/fl* scRNA-seq data of GC1 cluster cells (P5). ****P < 0.0001. (E) A hypothetical mechanistic view of KMT2D-mediated regulation of cerebellar GCs. KMT2D may positively program not only epigenomic signals (green peaks) for super-enhancers/enhancers but also H3K4me3 signals (blue peaks) at the gene promoters. KMT2D-programmed enhancers interact with their corresponding promoters (blue curved arrow) to activate expression of neuronal differentiation (ND) genes (e.g., *Pax6*, *En2*, and *Myt1l*). Neuronal transcription factors (e.g., PAX6, EN2, and MYT1L) may facilitate the differentiation of GCs from GCPs. Red right angle arrow, transcription start site.

DISCUSSION

In this study, our results showed that Kmt2d loss in the cerebellar GC lineage strongly inhibited GC differentiation in the cerebellum in mice, indicating an essential role for KMT2D in cerebellar GC development. This finding is in accordance with our other results showing that KMT2D was expressed at higher levels in the cerebellar GC lineage than in the other mouse brain regions. Mechanistically, KMT2D temporally regulates the expression of many neuronal differentiation genes. In particular, neuronal transcription factors, such as EN2, PAX6, and MYT1L, act as crucial downstream effectors of KMT2D during cerebellar GC differentiation as they could substantially rescue the differentiation of Kmt2d-deleted GC cells and the expression of neuronal differentiation genes in the same cells. Our scRNA-seq results support the strong impact of Kmt2d ACKO on neuronal differentiation programs in cerebellar GCs and also indicate that Kmt2d ACKO affects other types of cerebellar cells in a cell non-autonomous manner. These findings reveal a previously uncharacterized neuron differentiation mechanism by which KMT2D temporally activates neuronal differentiation programs to induce in vivo GC differentiation (Fig. 8E).

Previous studies have shown that KMT2D loss or KMT2D deficiency negatively affects the generation or differentiation of neural stem/progenitor cells (80, 81) and impairs the differentiation of neural crest cells (56). However, neural stem/progenitor cells differentiate into both neurons and non-neurons (e.g., astrocytes and oligodendrocytes) (82), and neural crest cells give rise to anterior cranial structures. Thus, KMT2D-mediated regulation of cerebellar neuron differentiation has been poorly characterized. Our results indicate that KMT2D directly activates En2, Pax6, Myt1l, and other neuronal differentiation genes in cerebellar GCs, the most numerous neurons that represent 62 to 73% of all neurons of the mouse brain. Notably, our results indicate that gene regulatory pathways, such as KMT2D-En2, KMT2D-Pax6, and KMT2D-Myt1l, are important for cerebellar GC differentiation. These pathways are quite distinct from medulloblastoma-suppressive pathways down-regulated by brain-wide loss of Kmt2d. In the medulloblastoma-suppressive pathways, KMT2D up-regulates the transcriptional corepressor DNMT3A to down-regulate oncogenic Ras activators and increases the expression of the transcriptional corepressors BCL6 and SIRT1 to repress cell-proliferative genes (39). In addition, KMT2D-regulated gene expression programs in cerebellar GCs are largely different from those regulated by KMT2D in other types of cells (33, 83). For example, KMT2D positively regulates the fat cell differentiation program for adipogenesis (33). Together, our findings indicate that KMT2D-mediated spatiotemporal regulation of neuronal differentiation program genes is specific for differentiation of cerebellar GCs.

KMT2D can positively regulate enhancer marks H3K4me1 and H3K27ac to activate gene expression (33–35, 37, 39, 60). We have shown that Kmt2d loss down-regulates super-enhancers at tumor suppressor genes (e.g., Dnmt3a and Bcl6) in medulloblastoma and lung tumor cells (39). However, little is known about how super-enhancers/enhancers are regulated during neuron differentiation in vivo. In the present study, our results indicate that KMT2D-regulated neuronal transcription factor genes (e.g., En2, Pax6, and Myt1l) are associated with super-enhancers/enhancers in the cerebellum and that KMT2D positively regulates super-enhancers/enhancers associated with these genes. In addition, our results indicate that the promoters of these genes are co-occupied by broad H3K4me3 and KMT2D in the cerebellum and that KMT2D up-regulates broad H3K4me3 peaks at the same genes. Together, these findings reveal a previously unknown neuronal epigenome-regulatory mechanism in which KMT2D activates key neuronal transcriptional factor genes by positively regulating super-enhancers/enhancers and H3K4me3 peaks during cerebellar GC differentiation (Fig. 8E).

Our results also indicate that Kmt2d ACKO increases cell proliferation signals in the GC layers while impairing GC differentiation and that Kmt2d loss augments the in vitro proliferation of GCPs. As cell hyperproliferation is a hallmark of cancer predisposition (84), Kmt2d ACKO is likely to predispose GCPs to a cancerous state. However, Kmt2d ACKO did not induce any obvious medulloblastoma. We have previously shown that brain-wide loss of Kmt2d using Nestin-Cre Kmt2d^fl/fl mice induced spontaneous medulloblastoma (39), and it has also been shown that KMT2D suppresses medulloblastoma development in mice (51). Thus, the present study, along with these previous studies, suggests that medulloblastoma genesis in Nestin-Cre Kmt2d^fl/fl mice is caused by both cancer predisposition by Kmt2d loss in the cerebellar GC lineage and medulloblastoma-promoting impact by Kmt2d loss in other brain cells. In this regard, although KMT2D mutations (8 to 10%) occur in medulloblastoma (52, 53), these KMT2D alterations may not be sufficient to induce medulloblastoma in the cerebellum. If so, how do cerebellar KMT2D alterations contribute to cerebellar medulloblastoma development? A likely answer is that cerebellar KMT2D alterations cooperate with additional oncogenic events in the cerebellum to induce medulloblastoma. In this regard, we have recently shown that heterozygous Kmt2d loss cooperates with heterozygous loss of the medulloblastoma suppressor gene Ptch to promote medulloblastoma development (85).

Our scRNA-seq results reported here showed that Kmt2d ACKO reduced the proportions of GC clusters in the cerebellum while altering their transcriptomes and also increased the proportion of the GCP cluster. In addition to this cell-autonomous effect, Kmt2d ACKO had a cell non-autonomous effect on the proportions and transcriptomes of other cerebellar cell types. For example, the proportions of astrocytes and oligodendrocytes were increased by Kmt2d ACKO. Our scRNA-seq analysis also suggested that Kmt2d ACKO increased the proportions of Purkinje cells. Moreover, our IHC results showed that Kmt2d ACKO negatively affected Purkinje cell development. This is consistent with previous studies indicating that GCs play an important role in the development of Purkinje cells (58, 86). Considering our findings demonstrating the impact of Kmt2d ACKO on the cerebellum and given that KMT2D mutations occur frequently in patients with Kabuki syndrome, cell-autonomous and cell non-autonomous effects of KMT2D deficiency on the proportions and states of several types of cerebellar cells could contribute to Kabuki syndrome–like characteristics and behavioral aberrations of Atoh1-Cre Kmt2d^fl/fl mice. Although cerebellar anomalies can occur in patients with Kabuki syndrome (43–45), whether and how cerebellar KMT2D alterations result in the abnormal features of Kabuki syndrome have yet to be studied. Our findings would be relevant to the molecular pathogenesis underlying this cerebellum-associated disease. Regarding the cell non-autonomous effects of KMT2D deficiency, in the future, resolving how GCPs or GCs affect other cell types in the cerebellum would be interesting.

MATERIALS AND METHODS

Mouse models

The genetically engineered mice used in this study were Atoh1-Cre (011104, the Jackson Laboratory, Bar Harbor, ME, USA) and Kmt2d^fl/fl mice (39). Male Atoh1-Cre mice were bred to generate Atoh1-Cre Kmt2d^fl/+ and Atoh1-Cre Kmt2d^fl/fl mice. All mice were maintained on a mixed C57BL/6 × 129Sv background. Oligonucleotides used for genotyping are listed in table S1. Mice were housed in an Association for Assessment and Accreditation of Laboratory Animal Care-accredited facility and handled according to National Institutes of Health guidelines for the care and use of laboratory animals.

Mouse study approval

The Institutional Animal Care and Use Committee of The University of Texas MD Anderson Cancer Center approved the use and study of all the animals involved (protocol no. 1043-RN04).

Measurement of facial length, facial angle, and head circumference

Facial length and angle and head circumference were measured using anesthetized mice as described by Shpargel et al. (56). Facial length (nasal tip to ear base) and facial angle (nasal-ocular line to ear base) were analyzed using lateral view photographs with ImageJ software. Head circumference was measured using a flexible tape placed around the head at its widest point. Measurements were taken in triplicate, and averages were recorded.

Balance beam test

The balance beam test was performed as described previously (87, 88) to assess the fine motor coordination and balance of mice. This test is used to examine the ability of a mouse to remain upright and walk on relatively narrow and elevated beams with three different widths (11, 16, and 28 mm). At either end of the beams, an escape platform is attached. During a single day of testing, each mouse was subjected to three 60-s trials at a minimum of 10-min interval between the trials. The average time of all three trials was used as the test score for each mouse. A test trial was given to familiarize each mouse with the beam. For each trial, the mouse was placed in the center of the beam and then released. The time each mouse remained on the beam was recorded. If a mouse remained on the beam for the entire trial, the maximum time of 160 to 300 seconds was recorded.

Footprint test

The footprint test was performed to assess mouse gait and motor coordination as described by Sugimoto and Kawakami (89). Mice were briefly trained to walk through a custom-made runway (60 cm long, 10 cm wide, and 10 cm high) enclosed with acrylic walls ending in a darkened box. The floor of the runway was lined with white paper to capture their footprints. The forepaws and hindpaws of the mice were painted with nontoxic ink using different colors for the forelimbs and hindlimbs. Each mouse was allowed to traverse the runway, and clear footprints were collected during three consecutive runs. After the runs, the footprints were allowed to dry, and the mice were cleaned of residual ink. Stride length, step width, and paw overlap were measured from the footprints using a digital caliper. Data were analyzed to assess differences in gait parameters.

IHC and IF

Mice at 1 month of age and 4 months of age were perfused with a 4% paraformaldehyde solution to fix the tissues. Subsequently, serial sections of the cerebellum were cut at a thickness of 40 μm using a cryostat and collected as free-floating samples. For the histological analysis of paraffin-embedded sections of the cerebellum, which were cut at a thickness of 8 μm, a standard hematoxylin and eosin staining procedure was performed. Immunohistochemical or immunofluorescent analysis was carried out. The paraffin-embedded sections were subjected to antigen retrieval using an antigen retrieval solution, followed by blocking in a solution containing 3% bovine serum albumin and 0.1% Triton X-100 for 1 hour. The primary antibodies used are specified in table S1. Staining was quantified using the Aperio Nuclear Algorithm after scanning the images using an Aperio Imagescope (Leica Biosystems, Deer Park, IL, USA).

Enrichment and culture of cerebellar GC lineage cells

To enrich cerebellar GCs, murine cerebellar tissues were processed using the Neural Tissue Dissociation Kit (P) (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s protocol with slight modifications. Briefly, cerebella were dissected from mice, washed in cold phosphate-buffered saline supplemented with 0.5% bovine serum albumin, and transferred into gentleMACS C tubes (Miltenyi Biotec) containing the enzyme mix from the dissociation kit. Tissues were mechanically dissociated using a gentleMACS Dissociator (Miltenyi Biotec) and incubated at 37°C for 15 min with gentle agitation to achieve enzymatic digestion. The tissue suspension was then triturated with fire-polished glass pipettes and further incubated for an additional 10 min at 37°C. Following enzymatic dissociation, the cell suspension was passed through a 70-μm cell strainer to remove debris and undissociated tissue fragments. Cells were centrifuged at 300g for 10 min at 4°C and resuspended in cold phosphate-buffered saline containing 0.5% bovine serum albumin. GC lineage cells were enriched from the dissociated tissue using the Neuron Isolation Kit (Miltenyi Biotec), which removes nonneuronal cells using antibodies specific for those cells (e.g., astrocytes, oligodendrocytes, microglia, endothelial cells, and fibroblasts). According to the isolation kit manufacturer’s data, neurons (including GC lineage cells as the vast majority of neurons) can be enriched to up to 93 to 99%. Cells were plated at a density of 5 × 10⁵ cells per dish in neurobasal medium (Thermo Fisher Scientific, Waltham, MA, USA) with B27 supplement, epidermal growth factor (20 ng/ml), basic fibroblast growth factor (20 ng/ml), and heparin (2 μg /ml). Within 3 to 5 days, the cells self-organized into neurospheres.

RNA-seq and quantitative RT-PCR

Total RNA was isolated from cerebellar tissues using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA). RNA samples were sequenced by using a HiSeq 2000 (Illumina, San Diego, CA, USA). Raw FASTQ files were processed using the nf-core/rnaseq pipeline (v3.7). Adapter sequences were removed using Trim Galore. Reads were aligned to GRCm38/mm10 (mouse) using STAR and used to quantify gene counts using RSEM. Gene expression values between samples were normalized using the geometric method in Cuffdiff. Analysis was performed using the Pluto platform (https://pluto.bio). Gene ontology and pathway enrichment analyses were performed to unravel functional implications of the differentially expressed genes. Each gene ontology term with a P value less than 5 × 10⁻² was considered to be significantly enriched. RT-PCR was performed as described previously (59). Oligonucleotides used for RT-PCR are listed in table S1.

CUT&RUN

CUT&RUN assays were performed using a CUTANA ChIC/CUT&RUN Kit. CUT&RUN libraries were prepared using adapters (New England Biolabs, Ipswich, MA, USA) as described previously (10). Libraries were multiplexed together, and sequencing was performed using a Hiseq2000 or HiSeq4000 (Illumina). Analysis was performed using the Pluto platform. The sources of antibodies used for these experiments are listed in table S1.

eRNA analysis

Because enhancers are located in the gene body in the genes, anti-sense eRNAs in enhancer regions were measured using quantitative RT-PCR. Oligonucleotides used for RT-PCR are listed in table S1.

scRNA-seq, data processing, clustering, and annotation

Cerebellar cells were obtained from mouse cerebella (~P5) through the meticulous process of cerebellum dissection, cell straining, and plating. A cDNA library was produced using the 10x Genomics kit in the Single Cell Genomics Core at Baylor College of Medicine and sequenced using an Illumina NovaSeq (Novogene, Sacramento, CA, USA) and mapped to the GRCm38/mm10 genome. Raw scRNA-seq data were processed using Cell Ranger for demultiplexing, barcode processing, and gene counting. Preprocessing and clustering were performed using the Python package Scanpy (90). uniform manifold approximation and projection (UMAP) was used for dimensionality reduction, and cells were clustered using either Seurat or Scanpy. Clusters were annotated on the basis of marker gene expression. Each dataset was preprocessed, normalized, and annotated separately using marker gene information. Clustering for each dataset was performed independently using the Leiden algorithm (91).

Cells expressing fewer than 200 genes or with more than 20% mitochondrial reads were excluded. Genes present in fewer than three cells were also removed. Gene expression data for each cell were normalized via log transformation, and mitochondrial read percentages were regressed out before data scaling. Dimensionality reduction and clustering using the Leiden algorithm were performed, followed by lineage annotation based on marker gene expression, using the sc.tl.rank_genes_groups function with the Wilcoxon rank-sum test.

RNA velocity and cell lineage tracing

RNA velocity analysis (79) was performed to trace cell lineages. Cells were filtered using a minimum shared counts threshold of 20 (min_shared_counts = 20) and the 2000 most highly variable genes (n_top_genes = 2000). Dimensionality reduction was applied using the default parameters of the scVelo and Scanpy packages. RNA velocity was calculated using both the dynamical and negative binomial (negbin) models, with clustering performed using the Leiden algorithm. Consistent parameters were applied across all datasets for RNA velocity analysis (n_neighbors = 10, n_pcs = 5). Velocity streams were analyzed and visualized using scVelo’s dynamical model (92), and pseudotime analysis was conducted and plotted to depict cell states, distinguishing between differentiation and dedifferentiation, using scVelo (92).

Fast gene set enrichment analysis

Fast gene set enrichment analysis was conducted using the R package fgsea (bioRxiv; doi: 10.1101/060012), based on a list of differentially expressed genes generated by Scanpy. Enrichment scores were calculated and visualized using fgsea.

Cell cycle scoring

The cell cycle scores for individual cell were calculated using the score_genes_cell_cycle function in scanpy and gene sets provided by Tirosh et al. (93).

Statistical analysis

Prism software was primarily used for conducting statistical analyses. The mean and SEM from a minimum of three independent experiments (or biological replicates) were presented. The two-tailed Student’s t test was used to determine statistical significance, unless stated otherwise. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 represent statistically significant differences. A two-sided log-rank test was used to determine the statistical significance of differences between survival curves of different mouse models. Survival curves and median survival were calculated and plotted using the Kaplan-Meier method in Prism software (GraphPad Software, Boston, MA, USA).

Acknowledgments

We are thankful to Z. Han and S. Zhang at UT MD Anderson Cancer Center for technical assistance and to D. R. Norwood (Scientific Publications Services, Research Medical Library, UT MD Anderson Cancer Center) for the editing assistance.

Funding: This work was supported in part by grants from the NIH to M.G.L. (R01CA262324), J.-I.P. (R01CA193297, R01CA278967, and R01CA278971), and R.V.S. (R01NS119301 and R01NS127435). We thank the IDDRC Neuropathology Core (core grant award number P50HD103555) at Baylor College of Medicine for histology and image scan services.

Author contributions: S.S.D.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, visualization, supervision, and project administration; K.P.K. and J.J.: Formal analysis, investigation, data curation, and visualization; C.B.-A.: Investigation and project administration; T.L.: Investigation and resources; S.A.: Investigation and project administration; K.C.: Investigation and writing—review and editing; R.V.S.: Conceptualization, methodology, validation, formal analysis, investigation, resources, writing—review and editing, project administration, and funding acquisition; J.-I.P.: Methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization, supervision, project administration, and funding acquisition; M.G.L.: Conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization, supervision, project administration, and funding acquisition.

Competing interests: R.V.S. serves on the Interim Governing Board of the Raynor Cerebellum Project (Fort Worth, TX, USA). The authors declare that they have no other competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. In addition, CUT&RUN and scRNA-seq raw data would be available through the Gene Expression Omnibus (CUT&RUN data: GSE282804; scRNA-seq data: GSE281630).

Supplementary Materials

This PDF file includes:

Figs. S1 to S8

Table S1

sciadv.adu7174_sm.pdf^{(2.4MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figs. S1 to S8

Table S1

sciadv.adu7174_sm.pdf^{(2.4MB, pdf)}

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PERMALINK

KMT2D temporally activates neuronal transcriptional factor genes to mediate cerebellar granule cell differentiation

Shilpa S Dhar

Kyung-Pil Ko

Jinho Jang

Calena Brown-Abel

Tao Lin

Sharad Awasthi

Kaifu Chen

Roy V Sillitoe

Jae-Il Park

Min Gyu Lee

Roles

Abstract

INTRODUCTION

RESULTS

Atoh1-Cre–mediated knockout of Kmt2d causes facial peculiarities, microcephaly, reduced body size, and defective cerebellum-dependent behaviors in mice

Fig. 1. Kmt2d ACKO induces facial anomalies, reduced body size, shortened survival, and defective cerebellum-relevant behaviors in mice.

Kmt2d ACKO has a negative impact on cerebellar GCs and Purkinje cells and reduces cellular levels of H3K4me1, H3K27ac, and H3K4me3 marks in cerebellar GCs

Fig. 2. Kmt2d ACKO negatively affects cellular markers of cerebellar GCs and Purkinje cells.

Fig. 3. Kmt2d ACKO down-regulates cellular levels of KMT2D-regulated epigenomic marks and GC differentiation while increasing GCP proliferation.

Kmt2d loss inhibits GC differentiation while increasing the proliferation of GC progenitors

Kmt2d ACKO impairs neuron differentiation programs during GC development

Fig. 4. Kmt2d ACKO diminishes neuronal differentiation programs.

The neuronal transcription factors EN2, PAX6, and MYT1L are major downstream effectors of KMT2D

Fig. 5. EN2, PAX6, and MYT1L act as major downstream effectors of KMT2D.

KMT2D positively regulates super-enhancers and broad H3K4me3 at En2, Pax6, and Myt1l

Fig. 6. Kmt2d ACKO reduces enhancer signals associated with the En2, Pax6, and Myt1l genes.

Kmt2d ACKO alters the proportions and gene expression programs of GC clusters and cell non-autonomously changes those of other cerebellar cell clusters

Fig. 7. Kmt2d ACKO alters the proportion of GC, GCP, and other cerebellar cell clusters.

Fig. 8. Kmt2d ACKO alters cell trajectory and gene expression programs of GC clusters.

DISCUSSION

MATERIALS AND METHODS

Mouse models

Mouse study approval

Measurement of facial length, facial angle, and head circumference

Balance beam test

Footprint test

IHC and IF

Enrichment and culture of cerebellar GC lineage cells

RNA-seq and quantitative RT-PCR

CUT&RUN

eRNA analysis

scRNA-seq, data processing, clustering, and annotation

RNA velocity and cell lineage tracing

Fast gene set enrichment analysis

Cell cycle scoring

Statistical analysis

Acknowledgments

Supplementary Materials

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REFERENCES AND NOTES

Associated Data

Supplementary Materials

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