Muffels I J J, Budhraja R, Shah R, Radenkovic S, Morava E, Kozicz T
Department of Genetics and Genomics, Icahn school of Medicine at Mount Sinai, New York, NY, USA.
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States.
bioRxiv. 2025 Jul 10:2025.07.07.663468. doi: 10.1101/2025.07.07.663468.
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.
糖基化先天性疾病（CDG）是一组因糖基化缺陷导致的快速增多的遗传性代谢疾病。尽管已鉴定出190多种基因缺陷，但仅有少数疾病有有效的治疗方法。我们推测，对患有各种CDG的个体的多组学数据集进行综合分析，可能会揭示共同的分子特征，并突出共享的治疗靶点。
我们汇总了来自PMM2 - CDG、ALG1 - CDG、SRD5A3 - CDG、NGLY1 - CDDG、ALG13 - CDG和PGM1 - CDG患者的所有公开可用的RNA测序、蛋白质组学和糖蛋白质组学数据集，这些数据集来自不同组织，包括诱导心肌细胞、人类皮质类器官、成纤维细胞和淋巴母细胞。进行差异表达和糖基化分析，随后进行基因集富集分析（GSEA）以识别共同失调的途径。然后，我们应用EMUDRA药物预测算法对能够逆转这些共享分子特征的候选化合物进行优先级排序。
我们在所有八个糖蛋白质组学数据集中鉴定出四种糖蛋白，其糖基化差异一致。六个糖基化位点和聚糖结构在CDG中反复改变，并在治疗后显示出部分校正。通路分析揭示了自噬、囊泡运输和线粒体功能方面的共同破坏。EMUDRA预测了几种可重新利用的药物类别，包括肌肉松弛剂、抗氧化剂、β - 肾上腺素能激动剂、抗生素和非甾体抗炎药，这些药物可以逆转关键通路异常，特别是那些涉及自噬和N糖基化的异常。
大多数失调的通路在CDG中是共享的，这表明存在共同治疗策略的潜力。综合分析中出现了几种针对这些共享异常的候选药物，值得在未来的体外研究中进行验证。
