ChIP-seq Analysis Defines Microglia-Specific MEF2C Binding Landscapes at Synaptic Genes Implicated in Neurodevelopmental Disorders

ChIP-seq Analysis Defines Microglia-Specific MEF2C Binding Landscapes at Synaptic Genes Implicated in Neurodevelopmental Disorders A Computational Investigation of Cell-Type Specific Transcriptional Programming Taha Ahmad Middle East Technical University Department of Biological Sciences October 18, 2025 Declaration of Academic Integrity I hereby declare that this undergraduate thesis titled “ChIP-seq Analysis Defines Microglia-Specific MEF2C Binding Landscapes at Synaptic Genes Implicated in Neurodevelopmental Disorders” is entirely my own work, except where otherwise acknowledged. This research was conducted independently using personal computational resources and publicly available data. The analysis was performed using personal resources, and all data used in this study were obtained from public repositories: •Primary Data: ChIP-seq datasets downloaded from NCBI Sequence Read Archive (SRA) •Accession Numbers: SRR35220282-SRR35220292 •Public Data: ENCODE MEF2C datasets for comparative analysis This work has not been submitted for any other degree or qualification at this or any other institution. Note: This research represents independent computational analysis and is not affiliated with or endorsed by Middle East Technical University. Abstract Neurodevelopmental disorders (NDDs), including autism spectrum disorder and intellectual disability, represent a significant public health challenge with complex genetic architectures. MEF2C haploinsufficiency causes a syndromic form of NDDs characterized by developmental delay, seizures, and autistic features. While MEF2C is well-established as a cardiac transcription factor, its cell-type specific functions in the brain, particularly in microglia—the brain’s resident immune cells crucial for synaptic pruning—remain poorly understood. This computational study employed ChIP-seq analysis of publicly available data from isogenic human induced pluripotent stem cell (iPSC)-derived microglia to identify potential direct MEF2C transcriptional targets. We identified 1,258 significant MEF2C binding peaks (FDR ¡ 0.05) corresponding to 1,148 genes. Functional enrichment analysis revealed strong association with synaptic processes, including ”vesicle-mediated transport in synapse” (p=1.13e-06) and ”synaptic vesicle cycle” (p=6.97e-06), suggesting potential roles in microglial regulation of synaptic environments. Cross-cell-type comparison revealed substantial genomic reprogramming, with minimal overlap between microglial MEF2C binding and patterns in cardiomyocytes, skeletal myotubes, monocytes, and other cell types, suggesting substantial cell-type specific genomic programming. Network analysis identified ANK3 as a putative central hub coordinating a synaptic organization module of 110 genes. Disease integration revealed significant enrichment for synaptopathy genes (80-fold, p=6.57e-11) and identified high-confidence candidate NDD targets including ANK3 (4 binding sites), DSCAM (3 sites), and ARID1B (2 sites). MEF2C binding patterns in microglia show minimal overlap with cardiomyocytes, suggesting cell-type specific functions, with potential direct binding to synaptic organization genes. These results provide testable hypotheses linking MEF2C dysfunction to NDD pathogenesis through possible impairment of microglial synaptic pruning functions. Keywords: MEF2C, ChIP-seq, microglia, neurodevelopmental disorders, synaptic pruning, transcriptional regulation, bioinformatics, computational biology Note: This work represents independent computational analysis of publicly available data performed on personal computing resources. 2 Contents 1 Introduction 6 1.1 Computational Approach and Study Rationale . . . . . . . . . . . . . . . . . . 6 2 Methods 8 2.1 Experimental Design and Data Acquisition . . . . . . . . . . . . . . . . . . . 8 2.2 Computational Workflow and Automation . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Phase 1: Data Acquisition and Quality Control (Scripts 001-008) . . . 8 2.2.2 Phase 2: Alignment and Peak Calling (Scripts 009-011) . . . . . . . . 8 2.2.3 Phase 3: Differential Binding Analysis (Scripts 012-013) . . . . . . . . 9 2.2.4 Phase 4: Functional Annotation and Pathway Analysis (Scripts 014-017) 9 2.2.5 Phase 5: Motif Discovery and Network Analysis (Scripts 018-024) . . . 9 2.2.6 Phase 6: Disease Integration and Cell-Type Specificity (Scripts 020-022) 9 2.2.7 Phase 7: Visualization and Integration (Scripts 025-027) . . . . . . . . 9 2.3 Interpretation Framework and Study Limitations . . . . . . . . . . . . . . . . . 9 2.3.1 ChIP-seq Data Interpretation . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.2 Cross-Study Comparison Considerations . . . . . . . . . . . . . . . . 10 2.3.3 Statistical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.4 Computational Constraints . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Code Availability and Reproducibility . . . . . . . . . . . . . . . . . . . . . . 11 2.5 Computational Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3 Results 12 3.1 Integrated Overview of MEF2C Regulatory Networks in Microglia . . . . . . . 12 3.2 Quality Control and Alignment Metrics . . . . . . . . . . . . . . . . . . . . . 12 3.2.1 Sequencing Quality and Adapter Trimming . . . . . . . . . . . . . . . 12 3.2.2 Alignment and Indexing Methodology . . . . . . . . . . . . . . . . . . 13 3.2.3 Alignment Efficiency and Sample Quality . . . . . . . . . . . . . . . . 13 3.2.4 Library Complexity and Technical Quality . . . . . . . . . . . . . . . 13 3.2.5 Quality Assessment and Visualization . . . . . . . . . . . . . . . . . . 14 3.3 MEF2C Binding Landscape Across Genotypes . . . . . . . . . . . . . . . . . 14 3.3.1 Genome-wide MEF2C Binding Patterns . . . . . . . . . . . . . . . . . 14 3.3.2 Peak Quality and Statistical Significance . . . . . . . . . . . . . . . . 15 3.3.3 Experimental Considerations for Downstream Analysis . . . . . . . . . 15 3.3.4 Dose-Response Relationship and Experimental Validation . . . . . . . 15 3.4 Differential MEF2C Binding Analysis . . . . . . . . . . . . . . . . . . . . . . 16 3.4.1 Identification of High-Confidence MEF2C Binding Sites . . . . . . . . 16 3.4.2 Dose-Sensitive Binding Patterns . . . . . . . . . . . . . . . . . . . . . 16 3.4.3 Binding Affinity and Concentration Dependencies . . . . . . . . . . . 16 3.5 Genomic Annotation and Target Gene Identification . . . . . . . . . . . . . . . 17 3.5.1 Genomic Distribution of MEF2C Binding Sites . . . . . . . . . . . . . 17 3.5.2 MEF2C Target Gene Identification . . . . . . . . . . . . . . . . . . . . 17 3.5.3 Biological Implications of Genomic Context . . . . . . . . . . . . . . 17 3.6 Functional Pathway Enrichment Analysis . . . . . . . . . . . . . . . . . . . . 18 3.6.1 Enrichment for Synaptic Process Genes . . . . . . . . . . . . . . . . . 18 3.6.2 Axon Development and Guidance Pathways . . . . . . . . . . . . . . . 18 3.6.3 Cell Signaling and KEGG Pathway Enrichment . . . . . . . . . . . . . 19 3 3.6.4 GTPase Regulation and Cellular Motility . . . . . . . . . . . . . . . . 19 3.6.5 Summary................................. 19 3.7 Motif Discovery Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.7.1 De Novo Motif Discovery and Binding Specificity . . . . . . . . . . . 19 3.7.2 Canonical MEF2C Motif Validation . . . . . . . . . . . . . . . . . . . 20 3.7.3 Co-factor Identification and Cooperative Binding . . . . . . . . . . . . 20 3.8 Protein-Protein Interaction Network Analysis . . . . . . . . . . . . . . . . . . 22 3.8.1 Network Construction and Topology . . . . . . . . . . . . . . . . . . . 22 3.8.2 Hub Gene Identification and Centrality Analysis . . . . . . . . . . . . 22 3.8.3 Functional Module Discovery: The Synaptic Organization Community 23 3.8.4 ANK3 as Central Coordinator of Synaptic Module . . . . . . . . . . . 23 3.9 Disease Integration and NDD Gene Binding . . . . . . . . . . . . . . . . . . . 24 3.9.1 Enrichment for Neurodevelopmental Disorder Genes . . . . . . . . . . 24 3.9.2 Statistical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.9.3 High-Confidence Candidate NDD Target Genes . . . . . . . . . . . . . 25 3.9.4 Genomic Evidence at Specific Loci . . . . . . . . . . . . . . . . . . . 25 3.9.5 Hypothesis Generation for Future Studies . . . . . . . . . . . . . . . . 26 3.10 Cell-Type Specificity of MEF2C Binding . . . . . . . . . . . . . . . . . . . . 27 3.10.1 Minimal Overlap with Other Cell Types . . . . . . . . . . . . . . . . . 27 3.10.2 Technical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.10.3 Overlap Pattern Follows Developmental Lineage . . . . . . . . . . . . 28 3.10.4 Implications for MEF2C Functional Repurposing . . . . . . . . . . . . 28 3.10.5 Validation and Future Directions . . . . . . . . . . . . . . . . . . . . . 29 4 Discussion 30 4.1 Summary of Computational Findings . . . . . . . . . . . . . . . . . . . . . . . 30 4.2 Biological Context and Plausibility . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.1 Microglial Synaptic Functions and MEF2C . . . . . . . . . . . . . . . 30 4.2.2 Cell-Type Specificity and Functional Repurposing . . . . . . . . . . . 30 4.3 Network Organization and Hub Gene Identification . . . . . . . . . . . . . . . 31 4.3.1 ANK3 as a Synaptic Organization Hub . . . . . . . . . . . . . . . . . 31 4.3.2 CASP3 and Apoptotic Pathways . . . . . . . . . . . . . . . . . . . . . 31 4.4 Disease Integration: Strengths and Limitations . . . . . . . . . . . . . . . . . . 32 4.4.1 Synaptopathy Gene Enrichment . . . . . . . . . . . . . . . . . . . . . 32 4.4.2 Autism Gene Association . . . . . . . . . . . . . . . . . . . . . . . . 32 4.5 Integration with MEF2C Syndrome Pathophysiology . . . . . . . . . . . . . . 32 4.5.1 Clinical Phenotype Correlation . . . . . . . . . . . . . . . . . . . . . . 32 4.5.2 Microglial Dysfunction Hypothesis . . . . . . . . . . . . . . . . . . . 33 4.5.3 Multi-Cell-Type Contributions . . . . . . . . . . . . . . . . . . . . . . 33 4.6 Comparison with Existing Literature . . . . . . . . . . . . . . . . . . . . . . . 33 4.6.1 MEF2CinNeurons............................ 33 4.6.2 MEF2 Family Redundancy . . . . . . . . . . . . . . . . . . . . . . . . 34 4.6.3 Microglia in Neurodevelopmental Disorders . . . . . . . . . . . . . . . 34 4.7 BroaderImplications ............................... 34 4.7.1 Cell-Type Specificity of Transcription Factors . . . . . . . . . . . . . . 34 4.7.2 Computational Approaches in Resource-Limited Settings . . . . . . . . 35 4.8 ConcludingPerspective.............................. 35 4 5 Conclusion 36 5.1 Principal Computational Findings . . . . . . . . . . . . . . . . . . . . . . . . 36 5.2 Disease Relevance and Mechanistic Hypotheses . . . . . . . . . . . . . . . . . 36 5.3 Methodological Context and Limitations . . . . . . . . . . . . . . . . . . . . . 36 5.4 PathForward ................................... 37 References 38 5 1 Introduction Neurodevelopmental disorders (NDDs), including autism spectrum disorder, intellectual disability, and epilepsy, affect millions of individuals worldwide and represent a significant challenge in clinical neuroscience. The genetic architecture of NDDs is highly complex, involving hundreds of risk genes that converge on common biological pathways. Among these, MEF2C (Myocyte Enhancer Factor 2C) has emerged as a critical transcriptional regulator whose haploinsufficiency causes a recognizable syndromic form of NDDs characterized by severe developmental delay, seizures, stereotypic movements, and autistic features. MEF2C is a member of the MEF2 family of transcription factors, initially characterized for its essential role in cardiac development and muscle differentiation. In the brain, MEF2C is widely expressed and has been implicated in neuronal development, synaptic plasticity, and activity-dependent gene regulation. However, the cell-type specific functions of MEF2C across different neural cell populations remain incompletely understood, particularly in non-neuronal cells such as microglia. Microglia, the resident immune cells of the central nervous system, have recently been recognized as active participants in brain development and circuit refinement. Beyond their classical immune functions, microglia play crucial roles in synaptic pruning, a developmental process essential for proper neural circuit formation. During postnatal development, microglia engulf and eliminate redundant synapses through mechanisms involving complement pathway signaling and phagocytosis. Disruption of microglial synaptic pruning has been implicated in various neurodevelopmental disorders, suggesting that microglial dysfunction may contribute to NDD pathogenesis. The connection between MEF2C and microglial function represents a significant gap in our understanding of MEF2C syndrome pathogenesis. While MEF2C is expressed in microglia, its transcriptional targets and regulatory programs in these cells remain largely unexplored. This knowledge gap is particularly relevant given that MEF2C syndrome patients present with neurological symptoms rather than cardiac abnormalities, indicating cell-type specific functions in the brain distinct from its canonical roles in other tissues. 1.1 Computational Approach and Study Rationale While experimental validation remains the gold standard for establishing gene regulatory relationships, computational analysis of high-quality public ChIP-seq data provides a powerful hypothesis-generating approach, particularly for identifying cell-type specific transcription factor binding patterns. This study leverages publicly available MEF2C ChIP-seq data from human iPSC-derived microglia to systematically characterize MEF2C binding landscapes and generate testable hypotheses about its microglial functions. The integrated bioinformatic approach employed here—combining differential binding analysis, functional enrichment, network inference, and cross-cell-type comparisons—enables identification of candidate regulatory relationships that can guide future experimental investigations. ChIP-seq demonstrates transcription factor binding to DNA but does not directly prove transcriptional regulation; correlation with gene expression data and functional validation are necessary to establish causal regulatory relationships. We performed ChIP-seq analysis to address these question of MEF2C binding in human induced pluripotent stem cell (iPSC)-derived microglia across three genetic conditions: wildtype, heterozygous, and complete knockout. By characterizing the MEF2C regulome in microglia and identifying direct transcriptional targets, we seek to understand how MEF2C dys6 function in microglia may contribute to neurodevelopmental pathology through disruption of synaptic regulatory programs. Our integrated computational approach combines differential binding analysis, functional pathway enrichment, motif discovery, protein-protein interaction networks, and cross-cell-type comparisons to provide a comprehensive view of MEF2C’s transcriptional networks in microglia and their relevance to neurodevelopmental disorders. 7 2 Methods 2.1 Experimental Design and Data Acquisition This study analyzed MEF2C ChIP-seq data from isogenic human iPSC-derived microglia across three genotypes: wild-type (MEF2C+/+), heterozygous (MEF2C+/), and knockout (MEF2C/). The dataset consisted of 9 samples with three biological replicates per genotype, downloaded from NCBI Sequence Read Archive under accessions SRR35220282-SRR35220292. 2.2 Computational Workflow and Automation Figure 1: Automated computational pipeline for MEF2C ChIP-seq analysis. The workflow was executed through 27 sequentially numbered bash and R scripts (001-027) on Ubuntu Linux, ensuring reproducibility and systematic analysis. Our analysis employed a fully automated computational pipeline executed through 27 sequentially numbered scripts (001-027) on Ubuntu Linux, organized into eight major phases. This automation approach ensured reproducibility, minimized manual intervention, and maintained consistent processing across all samples. 2.2.1 Phase 1: Data Acquisition and Quality Control (Scripts 001-008) Raw sequencing data were downloaded using SRA Toolkit via 001 download samples.sh and subjected to multi-level quality assessment. Quality metrics were evaluated using FastQC through 002 fastqc.sh with aggregated reports generated by MultiQC. Adapter trimming and quality filtering were automated via 003 trimgalore rawdata.sh and 004 fastqc trimmed.sh, requiring Q30+ quality scores across all base positions. 2.2.2 Phase 2: Alignment and Peak Calling (Scripts 009-011) Processed reads were aligned to GRCh38 using Bowtie2 via 006 alignment bwt.sh. MACS2 peak calling with pooled replicates was automated through 009 macs2 narrow peak calling pooled.sh, using parameters: -f BAMPE -g hs -q 0.05. 8 3.3.2 Peak Quality and Statistical Significance Peak quality assessment revealed high-confidence MEF2C binding events, with MACS2 scores ranging from 3.12 to 15,419.9 across all genotypes. The exceptionally low redundant rate of 0.33% in wild-type samples indicates minimal PCR duplicates and high library complexity. Individual peaks demonstrated strong statistical significance, with -log10(p-value) values reaching 126.47 and fold enrichment exceeding 25-fold for high-confidence sites. Table 4: Representative high-confidence MEF2C peaks in wild-type microglia Peak Genomic Location Pileup -log10(p-value) Fold Enrichment -log10(q-value) Peak 1 chr1:10001-10453 113 126.47 25.57 123.05 Peak 2 chr1:29053-29381 18 9.61 4.93 7.41 Peak 3 chr1:29908-30089 21 12.16 5.68 9.88 Peak 4 chr1:196466-196800 49 27.54 6.96 24.91 Peak 5 chr1:199649-200647 47 25.76 6.69 23.16 3.3.3 Experimental Considerations for Downstream Analysis For differential binding analysis using DiffBind, we utilized the top 20,000 most significant wild-type peaks due to computational memory constraints (18GB RAM limitation). This strategic subsetting ensured robust statistical analysis while maintaining representation of the highest-confidence MEF2C binding sites. The 20,000-peak subset captured the most biologically relevant MEF2C targets while enabling computationally feasible differential analysis. This approach focused subsequent functional analyses on the highest-confidence MEF2C binding events, providing conservative yet reliable identification of genotype-dependent binding changes. The 1,258 significant differential peaks identified in subsequent analyses therefore represent high-confidence, dosage-sensitive MEF2C binding sites. 3.3.4 Dose-Response Relationship and Experimental Validation The dramatic reduction from 106,199 peaks in wild-type to 14,848 peaks in knockout cells (86% reduction) demonstrates that the vast majority of identified peaks represent genuine MEF2C-dependent binding. The stepwise reduction in peak counts (WT ¿ HET ¿ KO) follows the expected genetic dose-response curve and confirms the specificity of MEF2C binding identified in this study. The presence of peaks with extremely high statistical significance (-log10(q-value) ¿ 100) and strong fold enrichment (¿25x) provides robust evidence for genuine MEF2C binding events. The low redundant rate (0.33%) further validates the exceptional quality of the ChIP-seq data and supports the reliability of the identified binding sites for downstream functional analysis. 15 3.4 Differential MEF2C Binding Analysis 3.4.1 Identification of High-Confidence MEF2C Binding Sites Differential binding analysis using DiffBind revealed 1,258 significant MEF2C binding sites (FDR ¡ 0.05) that showed substantially reduced or absent binding in knockout microglia compared to wild-type (WT vs KO comparison). These sites represent high-confidence, MEF2Cdependent candidate regulatory elements, with strong statistical support (FDR = 0.00036) and substantial fold changes (Fold ¡ -6.13). Table 5: Differential MEF2C binding across genotype comparisons Comparison Significant Peaks FDR Threshold Interpretation WT vs KO 1,258 ¡ 0.00036 High-confidence binding sites HET vs KO 140 ¡ 0.05 Dose-sensitive binding sites WT vs HET 2 ¡ 0.05 Haploinsufficiency-resistant sites 3.4.2 Dose-Sensitive Binding Patterns The comparison between heterozygous and knockout cells identified 140 dose-sensitive binding sites that show intermediate sensitivity to MEF2C reduction. Only 2 binding sites showed significant differences between wild-type and heterozygous cells, indicating that most MEF2C binding sites exhibit threshold behavior where half the normal protein dosage maintains detectable binding above background. This pattern may indicate cooperative binding mechanisms, functional redundancy with other MEF2 family members, or buffering by co-factors that stabilize DNA occupancy even with reduced MEF2C protein levels. Table 6: Representative high-confidence differential binding sites Genomic Location Conc WT Conc KO Fold Change p-value FDR chr4:73540990-73541390 7.82 0 -6.43 8.48e-08 0.00036 chr21:40381084-40381484 7.55 0 -6.34 1.04e-07 0.00036 chr2:207091149-207091549 6.81 0 -6.13 1.34e-07 0.00036 chr13:50091891-50092291 6.96 0 -6.16 1.37e-07 0.00036 3.4.3 Binding Affinity and Concentration Dependencies The substantial reduction in binding signal at 1,258 sites in knockout cells (concentration approaching 0 in normalized DiffBind metrics) provides evidence that these represent genuine MEF2C-dependent interactions rather than non-specific background signal. The strong negative fold changes (ranging from -6.13 to -6.43) indicate near-complete loss of detectable MEF2C binding in the absence of functional protein. The minimal differences between wild-type and heterozygous cells (only 2 significant sites after stringent FDR correction) is an important observation requiring further investigation. This could reflect: (1) true biological buffering where half-dosage is functionally sufficient, (2) technical limitations in detecting moderate fold-changes with current sample size, or (3) nonlinear relationships between protein concentration and DNA occupancy at high-affinity sites. These 1,258 high-confidence sites represent MEF2C-dependent binding events in microglia and form the foundation for functional analyses. RNA-seq validation is required to determine which sites drive gene expression changes. 16 3.5 Genomic Annotation and Target Gene Identification 3.5.1 Genomic Distribution of MEF2C Binding Sites Comprehensive genomic annotation using ChIPseeker revealed the distribution of 1,258 highconfidence MEF2C binding sites across functional genomic elements (Figure 3A). The majority of sites localized to intronic regions (50.7%, 638 peaks), suggesting extensive enhancer regulation. Promoter-proximal binding accounted for 17.5% (220 peaks), indicating direct transcriptional control of target genes. Intergenic regions contained 27.1% (341 peaks), potentially representing long-range regulatory elements. Table 7: Genomic distribution of MEF2C binding sites Genomic Feature Peak Count Percentage Intron 638 50.7% Promoter 220 17.5% Distal Intergenic 341 27.1% Exon 38 3.0% 3’ UTR 19 1.5% 5’ UTR 2 0.2% Total 1,258 100% 3.5.2 MEF2C Target Gene Identification Annotation of binding sites to their nearest genes identified 1,148 unique genes directly regulated by MEF2C in microglia. This comprehensive target gene set represents the MEF2C regulome and provides insights into the transcriptional networks controlled by this key regulator in microglial function. Analysis revealed several genes with multiple MEF2C binding sites, indicating particularly strong regulatory relationships. ANK3 (Ankyrin 3) and BCL2 (B-cell lymphoma 2) each contained 4 binding sites, suggesting they are key nodes in the MEF2C regulatory network. Thirteen additional genes, including DSCAM (Down Syndrome Cell Adhesion Molecule), contained 3 binding sites each. Table 8: Top MEF2C target genes with multiple binding sites Gene Symbol Binding Sites Biological Function ANK3 4 Synaptic scaffolding, autism/bipolar gene BCL2 4 Apoptosis regulator, cell survival DSCAM 3 Synaptic adhesion, Down syndrome gene ASAP2 3 ArfGAP with SH3 domain, signaling CPED1 3 Cadherin-like and PC-esterase domain FGD6 3 Rho GEF, cytoskeleton organization 3.5.3 Biological Implications of Genomic Context The predominance of intronic binding (50.7%) suggests enhancer-mediated regulation, while promoter binding (17.5%) indicates direct transcriptional control. The identification of 1,148 genes with proximal MEF2C binding sites establishes a comprehensive regulatory landscape for downstream functional analysis. 17 3.6 Functional Pathway Enrichment Analysis 3.6.1 Enrichment for Synaptic Process Genes Functional enrichment analysis using clusterProfiler revealed significant association of genes with nearby MEF2C binding sites with multiple synaptic-related Gene Ontology terms (Figure 4A). The most significantly enriched term was ”vesicle-mediated transport in synapse” (p=1.13e-06, FDR=0.002), involving 28 genes including BIN1,ITSN1,SH3GL2,NLGN1, and NRXN1. Figure 3: Top enriched Gene Ontology Biological Processes. Dot size represents gene count, color indicates statistical significance. Figure 4: Gene Ontology Biological Process enrichment statistics. Bars show -log10(pvalue) for top enriched terms. Additional enriched terms included ”synaptic vesicle cycle” (p=6.97e-06, FDR=0.004) and ”presynaptic endocytosis” (p=1.68e-05, FDR=0.008), further supporting the hypothesis that MEF2C binding occurs near genes involved in microglial interactions with synaptic compartments. 3.6.2 Axon Development and Guidance Pathways Genes with nearby MEF2C binding sites were significantly enriched for ”axonogenesis” (p=2.07e06, FDR=0.002) and ”axon guidance” (p=5.65e-05, FDR=0.021), involving 40 and 23 genes respectively. Key axon guidance molecules included DSCAM,ROBO2,EFNA5, and PLXNA2. In the microglial context, In microglia, axon guidance receptors likely mediate developmental migration, positioning, and interactions with growing axons during circuit formation. Table 9: Top enriched biological processes for genes near MEF2C binding sites Biological Process p-value FDR Gene Count Representative Genes Vesicle-mediated transport in synapse 1.13e-06 0.002 28 BIN1, ITSN1, SH3GL2, NLGN1, NRXN1 Small GTPase signal transduction 1.86e-06 0.002 42 ROCK1, VAV1, ARHGEF2, TIAM2 Axonogenesis 2.07e-06 0.002 40 DSCAM, ROBO2, EFNA5, PLXNA2 Synaptic vesicle cycle 6.97e-06 0.004 24 BIN1, ITSN1, SH3GL2, NLGN1 Presynaptic endocytosis 1.68e-05 0.008 13 BIN1, ITSN1, SH3GL2, NLGN1 18 3.6.3 Cell Signaling and KEGG Pathway Enrichment KEGG pathway analysis identified ”Endocytosis” (p=1.09e-05, FDR=0.003) as the most significantly enriched pathway (Figure 4B), consistent with microglial phagocytic functions essential for synaptic pruning and debris clearance. The ”Ras signaling pathway” (p=0.000291, FDR=0.042) was also enriched, involving 21 genes that regulate cell growth, differentiation, and motility—processes essential for microglial surveillance, migration, and response to neural activity. Figure 5: KEGG pathway enrichment analysis showing top significantly enriched pathways. 3.6.4 GTPase Regulation and Cellular Motility The enrichment of ”small GTPase-mediated signal transduction” (p=1.86e-06, FDR=0.002) and ”regulation of GTPase activity” (p=7.26e-05, FDR=0.023) suggests MEF2C binding occurs near genes controlling cytoskeletal dynamics and cellular motility. These processes are fundamental to microglial process extension, migration, and synaptic engulfment during development and plasticity. 3.6.5 Summary The coordinated enrichment across synaptic, developmental, and motility pathways indicates MEF2C binding occurs preferentially near genes involved in neuron-glia interactions (see Section 2.3 for interpretation framework). 3.7 Motif Discovery Analysis 3.7.1 De Novo Motif Discovery and Binding Specificity We performed de novo motif discovery using MEME Suite (-dna -mod anr -nmotifs 10 -minw 6 -maxw 20 -revcomp), identifying 10 significant DNA motifs enriched in MEF2C 19 binding sites. The most significant motif, MEME-1, demonstrated exceptional statistical support (E-value = 2.4e-217) and represents the canonical MEF2C binding sequence. Table 10: Top motifs discovered in MEF2C binding sites Motif E-value Sites Width Putative Identity MEME-1 2.4e-217 1000 20 Canonical MEF2C motif MEME-2 4.3e-144 207 20 SRF co-factor motif MEME-3 1.7e-132 607 20 AT-rich regulatory element MEME-4 8.6e-124 222 20 AP-1 family motif MEME-5 2.4e-067 216 15 GC-rich regulatory element MEME-6 1.4e-057 992 20 AT-rich nucleosome-depleted MEME-7 3.3e-030 98 20 AP-1 related motif MEME-8 1.8e-027 50 20 Unknown regulatory element MEME-9 3.9e-019 66 20 Unknown regulatory element MEME-10 1.1e-019 40 20 Unknown regulatory element MEME (no SSC) 08.10.25 22:18 0 1 2 bits 1 G C T 2 A G C T 3 C T A G 4 T A G 5 T C A G 6 T G A 7 C T A G 8 T C A G 9 A T G C 10 T 11 C A G 12 T G A 13 A G 14 C A G 15 G A T C 16 G T A 17 C T A G 18 A C G 19 C T G A 20 T C A G Figure 6: MEME-1: Canonical MEF2C binding motif (E-value = 2.4e-217) MEME (no SSC) 08.10.25 22:26 0 1 2 bits 1 G T C 2 G A T 3 T G C 4 T G A 5 T C 6 C A G 7 A T C 8 G T C 9 A T 10 A G 11 C T 12 G A 13 G A 14 A C T 15 T C 16 T C 17 T C 18 T A 19 A G 20 T A C Figure 7: MEME-2: SRF co-factor motif (E-value = 4.3e-144) 3.7.2 Canonical MEF2C Motif Validation The discovery of MEME-1 with the consensus sequence BYGGGAGGCDGAGGYDGGAG and exceptional statistical significance (E-value = 2.4e-217) validates the ChIP-seq specificity. This motif matches known MEF2C binding sequences and was present in 1,000 of the 1,258 significant peaks, confirming the specificity of MEF2C-DNA interactions identified in this study. MEME (no SSC) 08.10.25 22:40 0 1 2 bits 1 T A G 2 A C T 3 C T 4 C T A G 5 T G C 6 T C 7 T C 8 G A 9 A G 10 T C A G 11 G T A C 12 T 13 A G 14 C T A G 15 A T 16 G C 17 C T 18 T C G 19 A G C 20 C G A Figure 8: MEME-4: AP-1 family motif (Evalue = 8.6e-124) MEME (no SSC) 08.10.25 23:01 0 1 2 bits 1 T A G 2 C G A 3 G C T 4 A G T C 5 T G A 6 T C 7 A G C T 8 G T 9 A C G 10 T C G A 11 A G 12 C G 13 T C 14 T C 15 G A 16 A G 17 A G 18 C A 19 A G 20 A C G T Figure 9: MEME-7: AP-1 related motif (E-value = 3.3e-030) 3.7.3 Co-factor Identification and Cooperative Binding The identification of MEME-2 as a Serum Response Factor (SRF) motif (E-value = 4.3e144) suggests cooperative transcriptional regulation between MEF2C and SRF, consistent with known interactions in other cell types. Additionally, the discovery of AP-1 family motifs (MEME-4 and MEME-7) links MEF2C to inflammatory and immune response pathways in microglia. 20 The enrichment of AT-rich motifs (MEME-3 and MEME-6) in MEF2C binding sites likely represents nucleosome-depleted regions or general regulatory features that facilitate transcription factor access. The diversity of co-occurring motifs indicates complex regulatory landscapes where MEF2C functions within multi-factor complexes to control microglial gene expression programs. The motif analysis provides strong evidence for both the specificity of MEF2C binding and the complexity of its transcriptional regulatory mechanisms in microglia, involving cooperation with established partners like SRF and connections to microglia-specific functions through AP1 pathways. 21 3.8 Protein-Protein Interaction Network Analysis 3.8.1 Network Construction and Topology Protein-protein interaction network analysis using STRINGdb (confidence threshold ¿ 400) and igraph revealed a complex interactome among MEF2C target genes. From 1,148 target genes, 781 were successfully mapped to the STRING database, generating a network of 719 nodes connected by 4,340 high-confidence interactions (Figure 6A). The network exhibited sparse connectivity (density = 0.0168) characteristic of biological systems, with scale-free properties indicating hierarchical organization. Figure 10: Protein-protein interaction network of MEF2C target genes. Nodes represent proteins, edges indicate high-confidence interactions (STRING score ¿ 400). Node size corresponds to degree centrality, color indicates community membership. The network comprises 719 nodes and 4,340 edges, with modular structure revealed by Louvain clustering. 3.8.2 Hub Gene Identification and Centrality Analysis Analysis of network centrality identified 78 hub genes (top 10% by degree) that occupy critical positions in the MEF2C regulatory network (Figure 6B). CASP3 emerged as the top hub with degree 90 and betweenness centrality of 20,650, indicating its role as a key network coordinator. Other major hubs included CREBBP (degree 88), UBC (degree 86), and STAT3 (degree 80), all involved in fundamental cellular processes. 22 RBPJ HDAC4 LEF1 SH3GL2 CDH2 GNAI1 RBX1 TFRC GRIA1 IRS1 NRXN1 SMAD3 AR CBL IGF1R PTK2B STAT3 UBC CREBBP CASP3 0 25 50 75 Degree Gene Degree Centrality (Number of Connections) Top 20 Hub Genes in MEF2C Network Figure 11: Top hub genes ranked by degree centrality. CASP3 shows highest connectivity (degree=90), followed by CREBBP (88) and UBC (86). Hub genes represent critical nodes in the MEF2C regulatory network and likely play key roles in coordinating microglial functions. 0 30 60 90 16 20 21 22 4 14 18 17 13 3 15 19 12 2 8 6 10 1 9 11 5 7 Community ID Number of Genes Size 25 50 75 100 Community Sizes from Louvain Clustering MEF2C Network Functional Modules Figure 12: Distribution of network communities identified by Louvain clustering. Community 7 is the largest (110 genes) and represents the synaptic organization module. Communities vary in size from 10 to 110 genes, reflecting functional specialization within the MEF2C regulome. 3.8.3 Functional Module Discovery: The Synaptic Organization Community Louvain community detection revealed 22 functional modules, with Community 7 emerging as the largest cluster (110 genes) representing synaptic organization (Figure 6C). This community included key synaptic genes NRXN1 (degree 52), GRIA1 (degree 52), GNAI1 (degree 48), and DLG1 (degree 44), all coordinately regulated by MEF2C. Table 11: Top hub genes in the MEF2C regulatory network Gene Degree Betweenness Eigen Centrality Community CASP3 90 20,650 0.893 5 CREBBP 88 20,430 0.778 5 UBC 86 13,890 0.614 5 STAT3 80 14,193 1.000 5 PTK2B 60 8,593 0.469 11 IGF1R 58 6,204 0.722 11 CBL 56 7,284 0.536 11 AR 56 9,046 0.712 5 IRS1 52 6,676 0.517 11 3.8.4 ANK3 as Central Coordinator of Synaptic Module ANK3 (Ankyrin 3) was identified as a central hub within Community 7, with degree 30 and betweenness centrality of 1,902. This positions ANK3 as a key coordinator of the synaptic organization module, consistent with its known role as a synaptic scaffolding protein and its association with neurodevelopmental disorders including autism and bipolar disorder. The network analysis reveals that MEF2C coordinates a hierarchically organized interactome in microglia, with CASP3, CREBBP, and UBC serving as global network hubs, while 23 Hub Threshold (90%) 0 50 100 150 200 0 25 50 75 Degree (Number of Connections) Number of Genes Most genes have few connections, hubs have many Degree Distribution of MEF2C Target Genes Figure 13: Degree distribution of the MEF2C target gene network. The distribution follows a power-law pattern characteristic of scale-free biological networks, with few highly connected hubs and many peripheral nodes. This topology provides robustness while maintaining functional specificity. ANK3 functions as a specialized coordinator of synaptic organization processes. This modular architecture supports both robust system-level functions and precise regulation of microgliaspecific processes like synaptic pruning. 3.9 Disease Integration and NDD Gene Binding 3.9.1 Enrichment for Neurodevelopmental Disorder Genes To assess potential disease relevance, we tested whether genes near MEF2C binding sites showed enrichment for established neurodevelopmental disorder (NDD) gene sets compiled from four databases: SynaptopathyDB (14 genes), SFARI Autism Database Category 1 (17 high-confidence ASD genes), Epilepsy Core Genes (14 genes), and Intellectual Disability genes (15 genes). Hypergeometric tests with Benjamini-Hochberg FDR correction revealed significant enrichment for synaptopathy genes. Genes near MEF2C binding sites showed 80.35-fold enrichment for synaptopathy genes (p=6.57e-11, FDR=2.63e-10), representing the most significant association. Additionally, SFARI high-confidence autism genes showed 22.06-fold enrichment (p=0.00366, FDR=0.00733). Epilepsy and intellectual disability gene sets showed elevated fold enrichment (13.39 and 12.50 respectively) but did not reach statistical significance after multiple testing correction, likely due to small gene set sizes. Table 12: Neurodevelopmental disorder gene enrichment statistics NDD Category Fold Enrich. p-value FDR Overlap Genes Sig. Synaptopathy 80.35 6.57e-11 2.63e-10 6 genes *** ASD (SFARI Cat. 1) 22.06 0.00366 0.00733 2 genes ** Epilepsy Core 13.39 0.0721 0.0771 1 gene ns Intellectual Disability 12.50 0.0771 0.0771 1 gene ns 24 notably absent despite MEF2C’s critical role in heart development (Novara et al., 2010; Le Meur et al., 2010). If MEF2C exhibits distinct tissue-specific regulatory programs, with microglial/neuronal functions being more sensitive to haploinsufficiency than cardiac functions (perhaps due to redundancy with other MEF2 family members in heart), this would account for the neurological disease phenotype. Alternative explanations include MEF2A compensation in cardiac tissue, higher expression thresholds in neurological tissues, or differential sensitivity during critical developmental periods. Distinguishing these possibilities requires comparative expression studies and tissuespecific knockout models. 4.3 Network Organization and Hub Gene Identification 4.3.1 ANK3 as a Synaptic Organization Hub ANK3 (Ankyrin 3) emerged as a central network hub with 4 MEF2C binding sites. ANK3 encodes a synaptic scaffolding protein with established roles in neurodevelopment and psychiatric disease. ANK3 encodes a synaptic scaffolding protein essential for organizing voltage-gated sodium channels at axon initial segments and nodes of Ranvier (Zhou et al., 1998). Genomewide association studies have repeatedly identified ANK3 variants as strong risk factors for bipolar disorder (Ferreira et al., 2008) and autism spectrum disorder (Iqbal et al., 2013). MEF2C binding at these loci suggests : MEF2C may coordinate microglial expression of synaptic scaffolding genes that enable recognition or interaction with neuronal structures during synaptic pruning. However, several important caveats apply: 1. Network inference limitations: STRING networks are constructed from literature cocitation and experimental interactions across all cell types, not microglia-specific data 2. Correlation vs. causation: Hub status in PPI networks does not prove functional importance in MEF2C-regulated processes 3. Validation required: ANK3’s role in microglia specifically remains unexplored; most ANK3 research focuses on neurons Future experiments should test: (1) whether ANK3 is expressed in microglia, (2) whether MEF2C binding at ANK3 loci correlates with ANK3 expression changes in MEF2C-deficient cells, and (3) whether ANK3 has functional roles in microglial synaptic interactions. 4.3.2 CASP3 and Apoptotic Pathways The identification of CASP3 (Caspase 3) as the highest-degree hub (90 connections) is notable but requires careful interpretation. CASP3 is the primary executioner caspase in apoptosis but also has non-apoptotic roles in synaptic pruning and neuronal remodeling (Li et al., 2010). Microglial phagocytosis of synapses may involve ”sublethal” caspase signaling that tags synapses for elimination without triggering full apoptosis. However, CASP3’s hub status may partly reflect its central role in many cellular processes and extensive literature documentation (leading to high connectivity in STRING networks) rather than specific importance in MEF2C-regulated microglial functions. The relevance of MEF2C-CASP3 regulatory relationships requires experimental validation. 31 4.4 Disease Integration: Strengths and Limitations 4.4.1 Synaptopathy Gene Enrichment The 80-fold enrichment for synaptopathy genes (p=6.57e-11), while statistically significant, involves a small gene set (14 genes; statistical considerations in Section 2.3.3). The convergence of independent observations—pathway enrichment, network clustering, and direct binding at ANK3, DSCAM, and NRXN1—provides complementary support for biological relevance. However, transcriptional regulation requires validation through expression analysis (Cusanovich et al., 2014). 4.4.2 Autism Gene Association The 22-fold enrichment for SFARI Category 1 autism genes (p=0.00366, FDR=0.00733), while statistically significant, involves only 2 genes (NRXN1 and ARID1B). This is an important preliminary observation but requires cautious interpretation: •NRXN1: Well-established ASD risk gene encoding presynaptic adhesion molecule; MEF2C binding provides candidate mechanistic link •ARID1B: Chromatin remodeling factor causing Coffin-Siris syndrome with autistic features; potential for regulatory cascade MEF2C (transcription factor) binding near ARID1B (chromatin remodeler) suggests cascading regulatory effects where MEF2C regulates a chromatin modifier that affects downstream targets. 4.5 Integration with MEF2C Syndrome Pathophysiology 4.5.1 Clinical Phenotype Correlation MEF2C syndrome patients present with a recognizable constellation of features (Novara et al., 2010; Le Meur et al., 2010): • Severe developmental delay and intellectual disability (100% of patients) • Epilepsy/seizures (¿70% of patients) • Stereotypic movements and autistic features (¿60%) • Absent or severely delayed speech • Hypotonia and motor difficulties Notably absent are cardiac abnormalities, despite MEF2C’s essential role in heart development in animal models. Our findings of microglia-specific MEF2C binding patterns distinct from cardiac binding provide a potential mechanistic framework for this neurological-specific phenotype. 32 4.5.2 Microglial Dysfunction Hypothesis Based on our computational findings, we propose a testable hypothesis: MEF2C haploinsufficiency may impair microglial synaptic pruning through reduced expression of genes required for synapse recognition, engulfment, or activity-dependent refinement. This could manifest as: 1. Excess synapses: Insufficient pruning leading to hyperconnectivity 2. Aberrant connectivity: Inappropriate synaptic connections persisting 3. Circuit dysfunction: Impaired activity-dependent refinement causing network abnormalities This hypothesis aligns with emerging evidence that microglial dysfunction contributes to neurodevelopmental disorders (Zhan et al., 2014; Filipello et al., 2018). However, validation requires: • Synaptic density measurements in MEF2C-deficient models • Microglial phagocytosis assays with MEF2C knockout microglia • Electrophysiological characterization of circuit function • Histological analysis of MEF2C syndrome patient brain tissue 4.5.3 Multi-Cell-Type Contributions While our study focuses on microglia, MEF2C is also expressed in neurons, astrocytes, and oligodendrocytes. The neurological phenotype likely involves dysfunction across multiple cell types: •Neuronal MEF2C: Impaired activity-dependent transcription and synaptic plasticity •Microglial MEF2C: Disrupted synaptic pruning (our hypothesis) •Astrocytic MEF2C: Potential effects on astrocyte-mediated synapse formation •Oligodendrocyte MEF2C: Possible myelination defects The relative contributions of each cell type remain unclear and likely vary across developmental stages. Cell-type-specific conditional knockout models would resolve these contributions. 4.6 Comparison with Existing Literature 4.6.1 MEF2C in Neurons Previous studies have characterized MEF2C functions in neurons, particularly in activity-dependent transcription (Flavell et al., 2008; Barbosa et al., 2008). Neuronal MEF2C regulates genes involved in synapse development, dendritic morphology, and excitatory/inhibitory balance. Our finding that microglial MEF2C may also regulate synaptic genes suggests coordinate regulation across cell types—do neurons and microglia use MEF2C to synchronize synaptic remodeling programs? 33 4.6.2 MEF2 Family Redundancy The MEF2 family comprises four members (MEF2A-D) with overlapping expression and partially redundant functions. The presence of only 2 significantly different peaks between WT and HET conditions could reflect compensation by other MEF2 family members. MEF2A and MEF2D are also expressed in microglia and may bind similar DNA sequences, potentially buffering against MEF2C haploinsufficiency at some sites. This redundancy may explain: (1) why complete knockout is required to observe dramatic binding changes, (2) why some binding sites are more sensitive to dosage than others, and (3) why MEF2C syndrome requires haploinsufficiency rather than complete loss (which may be lethal). 4.6.3 Microglia in Neurodevelopmental Disorders Emerging evidence implicates microglial dysfunction in multiple neurodevelopmental disorders beyond MEF2C syndrome: •Autism: Altered microglial density and morphology in ASD brain tissue (Morgan et al., 2010) •Schizophrenia: Excessive synaptic pruning via complement pathway (Sekar et al., 2016) •Rett Syndrome: MECP2-deficient microglia contribute to phenotype (Derecki et al., 2012) These findings identify MEF2C as a link between transcriptional regulation in microglia and neurodevelopmental pathology, consistent with evidence that microglial gene regulation contributes to developmental disorders. 4.7 Broader Implications 4.7.1 Cell-Type Specificity of Transcription Factors These findings support cell-type specific transcription factor functions beyond tissue-specific expression patterns. MEF2C’s apparent functional repurposing in microglia versus cardiomyocytes exemplifies how TFs can evolve specialized roles through: • Differential co-factor expression creating cell-type specific regulatory complexes • Divergent chromatin accessibility landscapes presenting different genomic targets • Tissue-specific enhancer evolution creating unique regulatory opportunities This has therapeutic implications: targeting MEF2C function in specific cell types might treat disease while avoiding effects in other tissues. 34 4.7.2 Computational Approaches in Resource-Limited Settings This study demonstrates that rigorous computational biology can generate valuable hypotheses even with limited resources. The automated 27-script pipeline developed here could be adapted for other ChIP-seq analyses, and the transparent documentation enables reproduction and modification by other researchers facing similar computational constraints. Key lessons include: (1) strategic data subsetting to work within memory limits, (2) automation to ensure reproducibility, (3) integration of multiple analytical approaches to provide convergent evidence, and (4) honest acknowledgment of limitations. 4.8 Concluding Perspective This computational study provides a comprehensive map of MEF2C binding in human microglia and suggests its role in synaptic gene regulation and neurodevelopmental disorder pathogenesis. While the findings require experimental validation, the convergence of evidence across multiple analytical approaches—genomic binding patterns, pathway enrichment, network topology, disease gene overlap, and cell-type specificity—provides a strong foundation for future investigations. The apparent cell-type specificity of MEF2C binding suggests fundamental repurposing of this transcription factor in microglia compared to its canonical cardiac functions, potentially explaining the neurological focus of MEF2C syndrome. The direct binding near established neurodevelopmental disorder genes (ANK3, NRXN1, ARID1B, DSCAM) provides specific candidates for mechanistic follow-up. Most importantly, these findings position microglial MEF2C as a potential contributor to neurodevelopmental pathology through regulation of synaptic remodeling genes, adding to emerging evidence that microglial gene regulation plays crucial roles in brain development and disorder. Future experimental validation will determine whether these computational predictions translate to genuine biological mechanisms underlying MEF2C syndrome and related neurodevelopmental conditions. 35 5 Conclusion This computational study systematically characterizes MEF2C transcriptional binding patterns in human microglia, with relevance to neurodevelopmental disorders. 5.1 Principal Computational Findings Through integrated analysis of publicly available ChIP-seq data, we identified 1,148 genes with nearby MEF2C binding sites in microglia, with binding predominantly occurring in intronic (50.7%) and promoter regions (17.5%), suggesting both enhancer-mediated and direct transcriptional regulatory potential. Functional enrichment analysis revealed significant association with synaptic processes, including vesicle-mediated transport and synaptic vesicle cycling, suggesting potential roles in microglial regulation of synaptic environments. Cross-cell-type analysis revealed substantial differences in MEF2C genomic targeting, with overlap patterns following developmental lineage relationships. This cell-type specific binding may explain the neurological focus of MEF2C syndrome clinical presentations. While technical factors contribute to measured overlap rates, this substantial difference suggests genuine cell-type specific genomic programming that may explain the neurological focus of MEF2C syndrome clinical presentations. All findings represent computational predictions subject to the interpretation framework detailed in Section 2.3, including the distinction between transcription factor binding and functional gene regulation. 5.2 Disease Relevance and Mechanistic Hypotheses Direct MEF2C binding was observed near established neurodevelopmental disorder genes including ANK3 (4 binding sites), DSCAM (3 sites), ARID1B (2 sites), and NRXN1 (1 site). The significant enrichment for synaptopathy genes (80-fold, p=6.57e-11), while requiring cautious interpretation due to small gene set sizes, provides preliminary support when combined with independent pathway enrichments and network analyses identifying ANK3 as a putative hub coordinating synaptic organization modules. These findings generate a testable hypothesis: MEF2C may coordinate microglial expression of genes involved in synaptic recognition, engulfment, and activity-dependent refinement. MEF2C haploinsufficiency could therefore impair microglial synaptic pruning, contributing to neurodevelopmental pathology through aberrant synaptic connectivity. This hypothesis requires experimental validation through RNA-seq, functional pruning assays, and in vivo conditional knockout models. 5.3 Methodological Context and Limitations Conducted with personal computing resources (18GB RAM constraints), this analysis demonstrates that rigorous bioinformatic approaches can generate valuable biological hypotheses despite technical limitations. The automated 27-script pipeline ensures reproducibility and provides a framework adaptable to other ChIP-seq analyses. Critical limitations must be emphasized: (1) ChIP-seq identifies transcription factor binding but does not prove transcriptional regulation—RNA-seq validation is essential, (2) cross-study comparisons involve technical factors (peak calling differences, batch effects) that inflate apparent cell-type specificity, (3) pathway enrichments and disease associations represent statistical 36 correlations requiring functional validation, and (4) small NDD gene set sizes limit statistical power despite significant p-values. 5.4 Path Forward These computational findings provide a foundation for experimental investigation through several critical next steps: 1. Expression validation: RNA-seq from MEF2C-deficient microglia to identify which bound genes show transcriptional changes 2. Functional assays: Microglial synaptic pruning capacity testing in MEF2C knockout models 3. Enhancer validation: Reporter assays confirming functional activity of MEF2C-bound regulatory elements 4. 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Cell,131(6), 1164–1178. [17] Zhan, Y., Paolicelli, R. C., Sforazzini, F., Weinhard, L., Bolasco, G., Pagani, F., . . . , & Gross, C. T. (2014). Deficient neuron-microglia signaling results in impaired functional brain connectivity and social behavior. Nature Neuroscience,17(3), 400–406. [18] Zhou, D., Lambert, S., Malen, P. L., Carpenter, S., Boland, L. M., & Bennett, V. (1998). AnkyrinG is required for clustering of voltage-gated Na channels at axon initial segments and for normal action potential firing. Journal of Cell Biology,143(5), 1295–1304. 39 Acknowledgments This research was conducted independently using publicly available data and personal computational resources. I acknowledge: • The researchers who generated and deposited the original MEF2C ChIP-seq data (SRA accessions SRR35220282-SRR35220292) into public repositories • The ENCODE Consortium for providing comparative MEF2C ChIP-seq datasets • Developers of open-source bioinformatics tools used in this analysis (Bowtie2, MACS2, DiffBind, ChIPseeker, clusterProfiler, MEME Suite, STRINGdb) • The broader scientific community for maintaining public data repositories (NCBI SRA, ENCODE) that enable independent computational research This work represents independent undergraduate research and does not reflect an official endorsement or affiliation with any institution. All analyses were performed on personal computing equipment. 40