Enhancer-promoter Interactions Research Articles

TAD-dependent sub-TAD is required for enhancer-promoter interaction enabling the β-globin transcription.

Topologically associating domains (TADs) are chromatin domains in the eukaryotic genome. TADs often comprise several sub-TADs. The boundaries of TADs and sub-TADs are enriched in CTCF, an architectural protein. Deletion of CTCF-binding motifs at one boundary disrupts the domains, often resulting in a transcriptional decrease in genes inside the domains. However, it is not clear how TAD and sub-TAD affect each other in the domain formation. Unaffected gene transcription was observed in the β-globin locus when one boundary of TAD or sub-TAD was destroyed. Here, we disrupted β-globin TAD and sub-TAD by deleting CTCF motifs at both boundaries in MEL/ch11 cells. Disruption of TAD impaired sub-TAD, but sub-TAD disruption did not affect TAD. Both TAD and sub-TAD disruption compromised the β-globin transcription, accompanied by the loss of enhancer-promoter interactions. However, histone H3 occupancy and H3K27ac were largely maintained across the β-globin locus. Genome-wide analysis showed that putative enhancer-promoter interactions and gene transcription were decreased by the disruption of CTCF-mediated topological domains in neural progenitor cells. Collectively, our results indicate that there is unequal relationship between TAD and sub-TAD formation. TAD is likely not sufficient for gene transcription, and, therefore, sub-TAD appears to be required. TAD-dependently formed sub-TADs are considered to provide chromatin environments for enhancer-promoter interactions enabling gene transcription.

RAEPI: Predicting Enhancer-Promoter Interactions Based on Restricted Attention Mechanism.

Enhancer-promoter interactions (EPIs) are crucial in gene transcription regulation and cell differentiation. Traditional biological experiments are costly and time-consuming, motivating the development of computational prediction methods. However, existing EPI prediction methods inadequately capture the intricate direct interactions between enhancer and promoter sequences, which limits their prediction performance to some extent. In this work, we propose an innovative attention-based approach RAEPI, which uses convolutional neural networks to extract initial features of enhancers and promoters, combined with a specially designed Restricted Attention mechanism with Query-Key-Value constrained to simulate the interactions between them for further feature extraction. To improve cross-cell line prediction, we employ a transfer learning strategy for pre-training. Furthermore, we extracted sequence motifs to evaluate the RAEPI's effectiveness from a visualization perspective. Experimental results show that RAEPI achieves competitive prediction performance to existing methods on the benchmark dataset.

Fluctuating Chromatin Facilitates Enhancer-Promoter Communication by Regulating Transcriptional Clustering Dynamics.

Enhancers regulate gene expression by forming contacts with distant promoters. Phase-separated condensates or clusters formed by transcription factors (TFs) and cofactors are thought to facilitate these enhancer-promoter (E-P) interactions. Using polymer physics, we developed distinct coarse-grained chromatin models that produce similar ensemble-averaged Hi-C maps but with "stable" and "dynamic" characteristics. Our findings, consistent with recent experiments, reveal a multistep E-P communication process. The dynamic model facilitates E-P proximity by enhancing TF clustering and subsequently promotes direct E-P interactions by destabilizing the TF clusters through chain flexibility. Our study promotes physical understanding of the molecular mechanisms governing E-P communication in transcriptional regulation.

Active enhancers: recent research advances and insights into disease.

Precise regulation of gene expression is crucial to development. Enhancers, the core of gene regulation, determine the spatiotemporal pattern of gene transcription. Since many disease-associated mutations are characterized in enhancers, the research on enhancer will provide clues to precise medicine. Rapid advances in high-throughput sequencing technology facilitate the characterization of enhancers at genome wide, but understanding the functional mechanisms of enhancers remains challenging. Herein, we provide a panorama of enhancer characteristics, including epigenetic modifications, enhancer transcripts, and enhancer-promoter interaction patterns. Furthermore, we outline the applications of high-throughput sequencing technology and functional genomics methods in enhancer research. Finally, we discuss the role of enhancers in human disease and their potential as targets for disease prevention and treatment strategies.

Impact of interactions between Su(Hw)-dependent insulators on the transvection effect in <I>Drosophila melanogaster</i>

Transvection is a phenomenon of interallelic communication in which enhancers can activate a specific promoter located on a homologous chromosome. Insulators play a significant role in ensuring functional interactions between enhancers and promoters. In the presented work, we created the model where two or three copies of the insulator are located next to enhancers and promoters localized on homologous chromosomes. Using the Su(Hw) insulator as model, we showed that the functional interaction between a pair of insulators promotes enhancer-promoter trans interactions. The interaction between the three insulators, on the contrary, can lead to the formation of chromatin loops that sterically hinder the full enhancer-promoter interaction. The results of the work suggest the participation of insulators in the regulation of homologous chromosome pairing and in communication between distant genomic loci.

Prediction of YY1 loop anchor based on multi-omics features

The three-dimensional structure of chromatin is crucial for the regulation of gene expression. YY1 promotes enhancer-promoter interactions in a manner analogous to CTCF-mediated chromatin interactions. However, little is known about which YY1 binding sites can form loop anchors. In this study, the LightGBM model was used to predict YY1-loop anchors by integrating multi-omics data. Due to the large imbalance in the number of positive and negative samples, we use AUPRC to reflect the quality of the classifier. The results show that the LightGBM model exhibits strong predictive performance (AUPRC≥0.93). To verify the robustness of the model, the dataset was divided into training and testing sets at a 4:1 ratio. The results show that the model performs well for YY1-loop anchor prediction on both the training and independent testing sets. Additionally, we ranked the importance of the features and found that the formation of YY1-loop anchors is primarily influenced by the co-binding of transcription factors CTCF, SMC3, and RAD21, as well as histone modifications and sequence context.

DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.

Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convolutional neural network, BiLSTM, for enhancer identification. We first designed the DeepDualEnhancer method based only on the DNA sequence input. It mainly consists of a multi-scale Convolutional Neural Network, and BiLSTM to extract features by DNABert and embedding, respectively. Meanwhile, we collected new datasets from the enhancer-promoter interaction field and designed the method DeepDualEnhancer-genomic for inputting DNA sequences and genomic signals, which consists of the transformer sequence attention. Extensive comparisons of our method with 20 other excellent methods through 5-fold cross validation, ablation experiments, and an independent test demonstrated that DeepDualEnhancer achieves the best performance. It is also found that the inclusion of genomic signals helps the enhancer recognition task to be performed better.

Micro-capture-C in vessel wall cell types identifies mechanisms underlying coronary artery disease susceptibility loci

Abstract Genetics is estimated to account for 50% of Coronary Artery Disease (CAD) risk. Genome-Wide Association Studies (GWAS) have revealed 100s of genomic loci which are associated with CAD (1). However, moving from GWAS signals to causal genes has proved challenging, slowing progress in developing interventions for CAD and other diseases. The greatest challenge in interpreting GWAS data is that only 10% of variants are in protein-coding regions and 90% are in "non-coding" genomic regions. Many of these non-coding variants are found in regulatory elements such as enhancers. Enhancers are distal regulatory elements that bind transcription factors and regulate genes up to 1 million base pairs away. This is achieved by DNA looping bringing enhancers into physical proximity with target genes. However, it is not possible to predict which gene(s) a given enhancer will regulate based on genomic location alone. We aimed to develop a generalisable workflow to solve CAD GWAS signals accurately and sensitively, by identifying causal enhancer SNPs and linking them mechanistically to output genes in a cell-type specific manner. We combined cell-specific epigenomic mapping with machine learning to prioritise 21,959 SNPs from 861 candidate CAD loci, in endothelial cells (HUVECs) and smooth muscle cells (HCASMCs). Micro-Capture-C (MCC) was used to resolve enhancer-promoter interactions at base-pair resolution (2). MNase footprinting determined differential transcription factor binding at specific enhancer SNPs. In HCASMCs, 20 putative enhancers containing CAD SNPs were found to interact with 32 genes, and in HUVECs 25 enhancers interacted with 48 genes. These genes were enriched for pathways including TGFb signalling, RUNX3 activity, and MAPK6/MAPK4 signalling. To prove causality at specific loci, heterozygous SNPs were identified in the individual primary cell lines. The interaction frequency at these elements was statistically compared between risk and non-risk alleles. For example, at the 9p21.3 locus, an enhancer containing a non-risk T allele was found to interact more strongly with the CDKN2B gene, compared to the risk G allele. Interestingly, this imbalance between allele interaction was potentiated by stimulation with TGFb, a key mediator in atherosclerosis. Furthermore, the transcription factor footprint at the SNP location was altered by the risk allele, indicating the mechanistic cause of the CAD-associated change in CDKN2B expression. We demonstrate a combined computational and experimental approach to identify causal non-coding variants in CAD risk loci, at scale. Among the key outputs of this study is the identification of the risk variant (rs1537373), output gene (CDKN2B), and specific cell type (HCASMC) underlying the 9p21.3 locus association with CAD risk. Using this approach to translate GWAS signals into cell-type specific outputs genes, will lead to a better understanding of the pathogenesis of CAD, and new therapeutic opportunities.

An integrated view of the structure and function of the human 4D nucleome.

The dynamic three-dimensional (3D) organization of the human genome (the "4D Nucleome") is closely linked to genome function. Here, we integrate a wide variety of genomic data generated by the 4D Nucleome Project to provide a detailed view of human 3D genome organization in widely used embryonic stem cells (H1-hESCs) and immortalized fibroblasts (HFFc6). We provide extensive benchmarking of 3D genome mapping assays and integrate these diverse datasets to annotate spatial genomic features across scales. The data reveal a rich complexity of chromatin domains and their sub-nuclear positions, and over one hundred thousand structural loops and promoter-enhancer interactions. We developed 3D models of population-based and individual cell-to-cell variation in genome structure, establishing connections between chromosome folding, nuclear organization, chromatin looping, gene transcription, and DNA replication. We demonstrate the use of computational methods to predict genome folding from DNA sequence, uncovering potential effects of genetic variants on genome structure and function. Together, this comprehensive analysis contributes insights into human genome organization and enhances our understanding of connections between the regulation of genome function and 3D genome organization in general.

7690 Acute hormonal signaling-induced 3D genome organization controls transcriptional dynamics during adipocyte thermogenesis

Abstract Disclosure: Y. Zhang: None. R. Zheng: None. T. Tsuji: None. Y. Xing: None. C. Wang: None. J. Darcy: None. K. Chen: None. Y. Tseng: None. Brown adipose tissue (BAT) maintains body temperature by dissipating energy as heat, presenting a promising target against obesity and its associated metabolic disorders. When exposed to cold, the β3-adrenergic receptor (β3-AR) in brown adipocytes is stimulated, initiating a sequential signaling cascade and activating a thermogenic gene program essential for heat generation. Despite remarkable progress has been made towards understanding the engagement of transcriptional and epigenetic regulators during thermogenesis in response to β3-AR stimulation, the precise impact of three-dimensional (3D) genome organization, an increasingly important modulator of gene expression, on the activation of thermogenic gene program remains elusive. In this study, we employed high-resolution Micro-C in murine brown adipocytes to interrogate the rapidity of 3D genome reorganization in response to β3-AR hormonal signaling and its impact on transcriptional dynamics during thermogenesis. Our findings unveiled substantial rewiring of chromatin loops within 4 hours of β3-AR stimulation. These dynamic changes in chromatin loops, particularly enhancer-promoter (E-P) interactions, were coupled with gene activation during thermogenesis. Furthermore, we uncovered the mechanisms by which β3-AR hormonal signaling mediates 3D chromatin reorganization to regulate the thermogenic gene program. We found that β3-AR hormonal signaling induced the phosphorylation of p18Hamlet, a subunit of the SRCAP complex, facilitating the deposition of the histone variant H2A.Z into regulatory DNA regions. This nucleosome incorporation of H2A.Z further mediated nucleosome rearrangement, favoring the formation of nucleosome-depleted regions (NDRs) and enhancing the accessibility of regulatory DNA regions. As a result, H2A.Z chromatin incorporation facilitated interactions between cis-regulatory regions and gene promoters, such as E-P interactions, thereby activating thermogenic gene expression in response to β3-AR stimulation. Loss of H2A.Z disrupted loop dynamics, impeding gene activation and BAT thermogenic capacity. The conserved role of H2A.Z in human thermogenesis was validated, and we identified adiposity-related SNPs in human homologous loop anchors that could potentially be targeted for treating obesity. Taken together, our results unveil a previously underappreciated aspect of acute hormonal signaling-induced chromatin dynamics in orchestrating the thermogenic program in brown adipocytes. These findings not only enhance our understanding of molecular regulation of thermogenesis but also shed light on the role of rapid and dynamic chromatin reconfiguration in regulating the cellular response to external stimuli. Our study bridges a critical gap between 3D genome organization and gene activation in the thermogenic program mediated by acute β3-AR hormonal signaling. Presentation: 6/2/2024

Long non-coding RNA (LncRNA) and epigenetic factors: their role in regulating the adipocytes in bovine.

Investigating the involvement of long non-coding RNAs (lncRNAs) and epigenetic processes in bovine adipocytes can provide valuable new insights into controlling adipogenesis in livestock. Long non-coding RNAs have been associated with forming chromatin loops that facilitate enhancer-promoter interactions during adipogenesis, as well as regulating important adipogenic transcription factors like C/EBPα and PPARγ. They significantly influence gene expression regulation at the post-transcriptional level and are extensively researched for their diverse roles in cellular functions. Epigenetic modifications such as chromatin reorganization, histone alterations, and DNA methylation subsequently affect the activation of genes related to adipogenesis and the progression of adipocyte differentiation. By investigating how fat deposition is epigenetically regulated in beef cattle, scientists aim to unravel molecular mechanisms, identify key regulatory genes and pathways, and develop targeted strategies for modifying fat deposition to enhance desirable traits such as marbling and meat tenderness. This review paper delves into lncRNAs and epigenetic factors and their role in regulating bovine adipocytes while focusing on their potential as targets for genetic improvement to increase production efficiency. Recent genomics advancements, including molecular markers and genetic variations, can boost animal productivity, meeting global demands for high-quality meat products. This review establishes a foundation for future research on understanding regulatory networks linked to lncRNAs and epigenetic changes, contributing to both scholarly knowledge advancement and practical applications within animal agriculture.

Transient promoter interactions modulate developmental gene activation.

Transcriptional induction coincides with the formation of various chromatin topologies. Strong evidence supportsthat gene activation is accompanied by a general increase in promoter-enhancer interactions. However,it remains unclear how these topological changes are coordinated across time and space during transcriptional activation. Here, we combine chromatin conformation capture with transcription and chromatin profiling during an embryonic stem cell (ESC) differentiation time course to determine how 3D genome restructuring is related to transcriptional transitions. This approach allows us to identify distinct topological alterationsthat are associated with the magnitude of transcriptional induction. We detect transiently formed interactions and demonstrate by genetic deletions that associated distal regulatory elements (DREs), as well as appropriate formation and disruption of these interactions, can contribute to the transcriptional induction of linked genes. Together, our study links topological dynamics to the magnitude of transcriptional induction and detects an uncharacterized type of transcriptionally important DREs.

Combinatorial transcription factor binding encodes cis-regulatory wiring of mouse forebrain GABAergic neurogenesis.

Transcription factors (TFs) bind combinatorially to cis-regulatory elements, orchestrating transcriptional programs. Although studies of chromatin state and chromosomal interactions have demonstrated dynamic neurodevelopmental cis-regulatory landscapes, parallel understanding of TF interactions lags. To elucidate combinatorial TF binding driving mouse basal ganglia development, we integrated chromatin immunoprecipitation sequencing (ChIP-seq) for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and gene expression data, and functional enhancer assays. We identified sets of putative regulatory elements with shared TF binding (TF-pRE modules) that orchestrate distinct processes of GABAergic neurogenesis and suppress other cell fates. The majority of pREs were bound by one or two TFs; however, a small proportion were extensively bound. These sequences had exceptional evolutionary conservation and motif density, complex chromosomal interactions, and activity as invivo enhancers. Our results provide insights into the combinatorial TF-pRE interactions that activate and repress expression programs during telencephalon neurogenesis and demonstrate the value of TF binding toward modeling developmental transcriptional wiring.

Predictive Prioritization of Enhancers Associated with Pancreas Disease Risk.

Genetic and epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies. Despite recent advances, linking enhancer elements to target genes and predicting transcriptional outcomes of enhancer dysfunction remain significant challenges. Using 3D chromatin conformation assays, we generated an extensive enhancer interaction dataset for the human pancreas, encompassing more than 20 donors and five major cell types, including both exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating a quantitative, genome-wide analysis of enhancer connectivity. With these tree models, we developed a machine learning algorithm to estimate the impact of enhancer perturbations on cell type-specific gene expression in the human pancreas. Orthogonal to our computational approach, we perturbed enhancer function in primary human pancreas cells using CRISPR interference and quantified the effects at the single-cell level through RNA FISH coupled with high-throughput imaging. Our enhancer tree models enabled the annotation of common germline risk variants associated with pancreas diseases, linking them to putative target genes in specific cell types. For pancreatic ductal adenocarcinoma, we found a stronger enrichment of disease susceptibility variants within acinar cell regulatory elements, despite ductal cells historically being assumed as the primary cell-of-origin. Our integrative approach-combining cell type-specific enhancer-promoter interaction mapping, computational models, and single-cell enhancer perturbation assays-produced a robust resource for studying the genetic basis of pancreas disorders.

Network Analysis of Enhancer-Promoter Interactions Highlights Cell-Type-Specific Mechanisms of Transcriptional Regulation Variation.

Gene expression is orchestrated by a complex array of gene regulatory elements that govern transcription in a cell-type-specific manner. Though previously studied, the ability to utilize regulatory elements to identify disrupting variants remains largely elusive. To identify important factors within these regions, we generated enhancer-promoter interaction (EPI) networks and investigated the presence of disease-associated variants that fall within these regions. Our study analyzed six neuronal cell types across neural differentiation, allowing us to examine closely related cell types and across differentiation stages. Our results expand upon previous findings of cell-type specificity of enhancer, promoter, and transcription factor binding sites. Notably, we find that regulatory regions within EPI networks can identify the enrichment of variants associated with neuropsychiatric disorders within specific cell types and network sub-structures. This enrichment within sub-structures can allow for a better understanding of potential mechanisms by which variants may disrupt transcription. Together, our findings suggest that EPIs can be leveraged to better understand cell-type-specific regulatory architecture and used as a selection method for disease-associated variants to be tested in future functional assays. Combined with these future functional characterization assays, EPIs can be used to better identify and characterize regulatory variants' effects on such networks and model their mechanisms of gene regulation disruption across different disorders. Such findings can be applied in practical settings, such as diagnostic tools and drug development.

Abstract P364: Chromatin Interactions Between Regulatory Elements Influence Gene Expression in Human iPSC-Derived Endothelial and Vascular Smooth Muscle Cells

Most SNPs associated with blood pressure (BP) are located in noncoding regions of the human genome, where they may regulate gene expression in BP-relevant cell types. However, the mechanisms by which these SNPs influence BP regulation are unclear. To address this, we developed comprehensive epigenomic landscapes in human induced pluripotent stem cell (iPSC)-derived endothelial cells (iECs) and vascular smooth muscle cells (iVSMCs), incorporating DNA methylation analysis (RRBS), transcriptome analysis (RNA-seq), chromatin state profiling (ATAC-seq), and chromatin interaction mapping (Micro-C). We identified 5301, 8916, and 6046 chromatin loops in iECs and 5329, 7598, and 5228 loops in iVSMCs at 4, 8, and 16 kb resolutions (FDR < 0.05) respectively based on the Micro-C analysis. Remarkably, 82.4% and 83.8% of these chromatin contact regions in iECs and iVSMCs, respectively, harbored at least one known regulatory element such as an enhancer, promoter, or transcription factor binding site. Notably, 109 and 119 BP-associated SNPs were found within the chromatin contact regions of iECs and iVSMCs, respectively. We further investigated chromatin contact regions that involved interactions of regulatory elements. In both iECs and iVSMCs, lower methylation levels were observed in chromatin contact regions involving promoter-promoter (PP) and enhancer-promoter (EP) interactions, compared to enhancer-enhancer (EE), enhancer-transcription factor binding site (ET), and promoter-transcription factor binding site (PT) interactions. Additionally, greater chromatin accessibility was observed in chromatin contact regions involving PP and EP interactions. BP-associated SNPs were significantly enriched in chromatin contact regions involving PP and EP interactions. Moreover, genes proximal to chromatin contact regions involving PP or EP interactions exhibited higher expression than those near EE or other regulatory element interactions. These findings offer new insights into how chromatin interactions between regulatory elements define transcriptional states in human endothelial and vascular smooth muscle cells, providing a novel basis for understanding the genetic regulation of BP.

EPIMR: Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on Image Representation

EPIMR: Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on Image Representation

Transcriptional bursting: from fundamentals to novel insights.

Transcription occurs as irregular bursts in a very wide range of systems, including numerous different species and many genes within these. In this review, we examine the underlying theories, discuss how these relate to experimental measurements, and explore some of the discrepancies that have emerged among various studies. Finally, we consider more recent works that integrate novel concepts, such as the involvement of biomolecular condensates in enhancer-promoter interactions and their effects on the dynamics of transcriptional bursting.

Enhancer-Insulator Pairing Reveals Heterogeneous Dynamics in Long-Distance 3D Gene Regulation

Cells regulate fates and complex body plans using spatiotemporal signaling cascades that alter gene expression. Short DNA sequences, known as enhancers (50–1500 base pairs), help coordinate these cascades by attracting regulatory proteins that enhance the transcription by binding to distal gene promoters. In humans, there are hundreds of thousands of enhancers dispersed across the genome, which poses a challenging coordination task to prevent unintended gene activation. To mitigate this problem, the genome contains insulator elements that block enhancer-promoter interactions. However, there is an open problem with how the insulation works, especially as enhancer-insulator pairs may be separated by millions of base pairs. Based on recent empirical data from Hi-C experiments, this paper proposes a new mechanism that challenges the common paradigm that rests on specific insulator-insulator interactions. Instead, this paper introduces a stochastic looping model where insulators bind weakly to chromatin rather than other insulators. After calibrating the model to experimental data, we use simulations to study the broad distribution of hitting times between an enhancer and a promoter when insulators are present. We find parameter regimes with large differences between average and most probable hitting times. This makes it difficult to assign a typical timescale and hints at highly defocused regulation times. We also map our computational model onto a resetting problem that allows us to derive several analytical results. Besides offering new insights into enhancer-insulator interactions, our paper advances the understanding of gene regulatory networks and causal connections between genome folding and gene activation. Published by the American Physical Society 2024

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework

Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).