ALG13-CDG is an X-linked N-linked glycosylation disorder caused by pathogenic variants in the glycosyltransferase ALG13, leading to severe neurological manifestations. Despite the clear CNS involvement, the impact of ALG13 dysfunction on human brain glycosylation and neurodevelopment remains unknown. We hypothesize that ALG13-CDG causes brain-specific hypoglycosylation that disrupts neurodevelopmental pathways and contributes directly to cortical network dysfunction. We generated iPSC-derived human cortical organoids (hCOs) from individuals with ALG13-CDG to define the impact of hypoglycosylation on cortical development and function. Electrophysiological activity was assessed using MEA recordings and integrated with multiomic profiling, including scRNA-seq, proteomics, glycoproteomics, N-glycan imaging, lipidomics, and metabolomics. X-inactivation status was evaluated in both iPSCs and hCOs. ALG13-CDG hCOs showed reduced glycosylation of proteins involved in ECM organization, neuronal migration, lipid metabolism, calcium homeostasis, and neuronal excitability. These pathway disruptions were supported by proteomic and scRNA-seq data and included altered intercellular communication. Trajectory analyses revealed mistimed neuronal maturation with early inhibitory and delayed excitatory development, indicating an E/I imbalance. MEA recordings demonstrated early network hypoactivity with reduced firing rates, immature burst structure, and shortened axonal projections, while transcriptomic and proteomic signatures suggested emerging hyperexcitability. Altered lipid and GlcNAc metabolism, along with skewed X-inactivation, were also observed. Our study reveals that ALG13-CDG is a disorder of brain-specific hypoglycosylation that disrupts key neurodevelopmental pathways and destabilizes cortical network function. Through integrated multiomic and functional analyses, we identify early network hypoactivity, mistimed neuronal maturation, and evolving E/I imbalance that progresses to compensatory hyperexcitability, providing a mechanistic basis for seizure vulnerability. These findings redefine ALG13-CDG as disorders of cortical network instability, offering a new framework for targeted therapeutic intervention.

The ability of adeno-associated viruses (AAVs) to transduce host cells relies on interactions with glycan moieties on the cellular surface. Consequently, disrupted protein glycosylation, which is seen in a range of neurodevelopmental and neurodegenerative diseases, could impair transduction efficiency. Understanding how altered glycosylation impacts AAV binding is essential to optimize AAV-mediated therapeutic strategies. We used glycoproteomics data from cortical brain organoids and iCardiomyocytes of individuals with congenital disorders of glycosylation (CDG) (ALG13-, PMM2-, and PGM1-CDG) to examine the abundance of AAV-binding glycan species. Additionally, we assessed the abundance of coreceptors in proteomics data. We found that the abundance of AAV-binding glycan species was downregulated for all CDG subtypes, but this was significant only for AAV5-, AAV8-, and AAV9-binding glycan motifs in PGM1-CDG. The proteomics data showed significantly decreased abundance of the coreceptor PDGFRβ in ALG13-CDG. The downregulation of glycan species and AAV coreceptors in models of aberrant protein glycosylation underscores the need to optimize AAV selection for conditions with altered protein glycosylation, including CDG and neurodegenerative diseases such as Parkinson's and Alzheimer's disease.

Congenital disorders of glycosylation (CDGs) are a group of rare metabolic diseases recognized for their neurological presentations, including developmental delay and seizures. However, the link between glycosylation defects and cortical brain network pathology remains elusive. To address this unmet need, we generated iPSC derived human cortical organoids (hCOs) for ALG13-CDG, which is the second most common CDG that is also X-linked. To comprehensively understand the impact of glycosylation defects on cortical pathology in CDG, we combined electrophysiological recordings using multi-electrode arrays (MEA) with comprehensive molecular profiling via multiomics, including scRNA-seq, proteomics, glycoproteomics, N-glycan imaging, lipidomics, and metabolomics. X-inactivation status was also evaluated in both iPSCs and organoids. ALG13-CDG hCOs revealed reduced glycosylation of proteins critical for extracellular matrix (ECM), neuronal migration, lipid metabolism, calcium ion homeostasis, and neuronal excitability. Dysregulation in related pathways was corroborated by proteomics and scRNA-seq, which also showed altered communication patterns in these pathways. Trajectory analysis revealed an inversion in neuronal development, with early inhibitory and delayed excitatory development, indicating an excitatory and inhibitory (E/I) imbalance. MEA recordings demonstrated early network hypoactivity with reduced firing rates, immature burst dynamics, and shorter axonal extensions. Despite this, transcriptomic and proteomic data revealed upregulation of excitatory receptors suggesting latent hyperexcitability. Altered lipid and sugar (GlcNAc) metabolism and skewed X-inactivation were also observed. Our study provides the first evidence of glycosylation defects in an ALG13-CDG human cortical organoid (hCO) model and links these defects to disrupted neuronal developmental trajectories and dysregulation of key pathways essential for brain function. We identify mistimed neuronal maturation and an excitatory/inhibitory (E/I) imbalance as early drivers of network hypoactivity and immature burst dynamics, with downstream compensatory hyperexcitability that may contribute to seizure susceptibility. While specific to ALG13-CDG, these mechanisms likely extend to other glycosylation disorders with overlapping neurological features. This work offers new mechanistic insight into cortical dysfunction associated with impaired protein glycosylation and highlights potential targets for therapeutic intervention.

Congenital Disorders of Glycosylation (CDG) are a rapidly expanding group of inherited metabolic diseases caused by defects in glycosylation. Although over 190 genetic defects have been identified, effective treatments remain available for only a few. We hypothesized that integrative analysis of multi-omics datasets from individuals with various CDG could uncover common molecular signatures and highlight shared therapeutic targets. We compiled all publicly available RNA sequencing, proteomics and glycoproteomics datasets from patients with PMM2-CDG, ALG1-CDG, SRD5A3-CDG, NGLY1-CDDG, ALG13-CDG and PGM1-CDG, spanning different tissues, including induced cardiomyocytes, human cortical organoids, fibroblasts, and lymphoblasts. Differential expression and glycosylation analyses were performed, followed by Gene Set Enrichment Analysis (GSEA) to identify commonly dysregulated pathways. We then applied the EMUDRA drug prediction algorithm to prioritize candidate compounds capable of reversing these shared molecular signatures. We identified four glycoproteins with consistent differential glycosylation across all eight glycoproteomics datasets. Six glycosylation sites and glycan structures were recurrently altered across CDG and showed partial correction with treatment. Pathway analysis revealed shared disruptions in autophagy, vesicle trafficking, and mitochondrial function. EMUDRA predicted several repurposable drug classes, including muscle relaxants, antioxidants, beta-adrenergic agonists, antibiotics, and NSAIDs, that could reverse key pathway abnormalities, particularly those involving autophagy and N-glycosylation. Most dysregulated pathways were shared across CDG, suggesting the potential for common therapeutic strategies. Several candidate drugs targeting these shared abnormalities emerged from integrative analysis and warrant validation in future in vitro studies.

Congenital Disorders of Glycosylation (CDG) are a rapidly expanding group of inherited metabolic diseases caused by defects in glycosylation. Although over 190 genetic defects have been identified, effective treatments remain available for only a few. We hypothesized that integrative analysis of multi-omics datasets from individuals with various CDG could uncover common molecular signatures and highlight shared therapeutic targets. We compiled all publicly available RNA sequencing, proteomics and glycoproteomics datasets from patients with PMM2-CDG, ALG1-CDG, SRD5A3-CDG, NGLY1-CDDG, ALG13-CDG and PGM1-CDG, spanning different tissues, including induced cardiomyocytes, human cortical organoids, fibroblasts, and lymphoblasts. Differential expression and glycosylation analyses were performed, followed by Gene Set Enrichment Analysis (GSEA) to identify commonly dysregulated pathways. We then applied the EMUDRA drug prediction algorithm to prioritize candidate compounds capable of reversing these shared molecular signatures. We identified four glycoproteins with consistent differential glycosylation across all eight glycoproteomics datasets. Six glycosylation sites and glycan structures were recurrently altered across CDG and showed partial correction with treatment. Pathway analysis revealed shared disruptions in autophagy, vesicle trafficking, and mitochondrial function. EMUDRA predicted several repurposable drug classes, including muscle relaxants, antioxidants, beta-adrenergic agonists, antibiotics, and NSAIDs, that could reverse key pathway abnormalities, particularly those involving autophagy and N-glycosylation. Most dysregulated pathways were shared across CDG, suggesting the potential for common therapeutic strategies. Several candidate drugs targeting these shared abnormalities emerged from integrative analysis and warrant validation in future in vitro studies.