bims-meglyc 2025-05-04 papers

Issue of 2025–05–04
three papers selected by
Silvia Radenkovic, UMC Utrecht

BMC Psychiatry. 2025 Apr 29. 25(1): 435

The long way to diagnosis: attention disorder, alcohol addiction or congenital disorder of glycosylation? A case report.

Timo Jendrik Faustmann, Lukas Hensel, Armin Bahic, Yoshinao Wada, Marianne Grüneberg, Thorsten Marquardt, Daniel Kamp, Leonhard Schilbach.

Attention deficit hyperactivity disorder (ADHD) is a relatively common disorder in clinical psychiatry. Patients often suffer from symptoms long before the diagnosis due to an overlap with other psychiatric differential diagnosis. Importantly, alcohol addiction and other illicit drug dependence and withdrawal symptoms mimicking ADHD symptoms should be ruled out. Here we present a rare case of a young female patient with symptoms of ADHD and an extremely high carbohydrate-deficient transferrin (CDT) of 19,6% (< 1,3%) indicating the presence of a congenital disorder of glycosylation (CDG). A thorough diagnostic workup excluded alcohol addiction as a cause of the constantly high CDT levels. The CDT test was positive due a transferrin mutation affecting the glycosylation site. Nevertheless, psychiatric symptoms can be due to metabolic disorders which should be considered. Further, substance-use disorders (SUD) are a critical and potentially complicated differential diagnosis concerning diagnostic procedures and treatment in ADHD.

Keywords: ADHD; CDG; CDT; Case report; Psychostimulants

DOI: https://doi.org/10.1186/s12888-025-06862-9

Orphanet J Rare Dis. 2025 Apr 30. 20(1): 207

A comprehensive update of genotype-phenotype correlations in PMM2-CDG: insights from molecular and structural analyses.

Tiago Oliveira, Ricardo Ferraz, Luísa Azevedo, Dulce Quelhas, João Carneiro, Jaak Jaeken, Sérgio F Sousa.

PMM2-CDG (phosphomannomutase 2-deficiency) is the most prevalent N-glycosylation disorder and results from impairments of PMM2 activity. This disease presents a large variety of pathogenic variants, which cause a wide phenotypical spectrum. This diversity, together with the low number of affected patients, raises the challenge of determining genotype-phenotype correlations in PMM2-CDG. This type of correlation could be highly significant in determining disease progression, prognosis, severity and in developing genome-personalized therapies. Structural analyses offer a valuable approach for assessing the pathogenic mechanisms within the PMM2 protein structure at a molecular level. Such an approach can reveal novel insights into the consequences of missense variants and their relationship with patients'phenotype. In this comprehensive review, we evaluate at a structural level 41 missense mutations in PMM2-CDG, examining their phenotypical characteristics and clinical severity, protein properties and interference at the enzymatic level. This work broadens the understanding of the intricate relationships between genotype and clinical manifestations of PMM2-CDG.

Keywords: Genotype–phenotype correlations; Missense variants; Molecular analysis; PMM2; PMM2-CDG

DOI: https://doi.org/10.1186/s13023-025-03669-5

J Mol Neurosci. 2025 Apr 29. 75(2): 60

Identification and Validation of Glycosylation‑Related Genes in Ischemic Stroke Based on Bioinformatics and Machine Learning.

Hui Zhang, Yanan Ji, Zhongquan Yi, Jing Zhao, Jianping Liu, Xianxian Zhang.

Ischemic stroke (IS) constitutes a severe neurological disorder with restricted treatment alternatives. Recent investigations have disclosed that glycosylation is closely associated with the occurrence and outcome of IS. Nevertheless, data on the transcriptomic dynamics of glycosylation in IS are lacking. The objective of this study was to undertake a comprehensive exploration of glycosylation-related genes (GRGs) in IS via bioinformatics and to assess their immune characteristics. In this study, through the intersection of genes from weighted gene co-expression network analysis, GRGs from five glycosylation pathways, and DEGs from differential expression analysis, 20 candidate GRGs were identified. Subsequently, through LASSO, Random Forest, and SVM-RFE, 3 hub GRGs (F5, PPP6C, and UBE2J1) were identified. Additional, a gene diagnostic model linked to glycosylation was developed and validated. The findings indicated that the diagnostic model could effectively distinguish between IS patients and healthy individuals in the training, validation, and merging datasets, indicating clinical relevance. Subsequently, by employing unsupervised clustering analysis, IS patients were classified into three clusters, and significant disparities were witnessed in immune cell infiltration among distinct clusters. In summary, this study successfully identified hub GRGs in IS and investigated the roles of these hub genes in the immune microenvironment, indicating potential clinical applications for IS.

Keywords: Glycosylation; Hub gene; Immune cell infiltration; Ischemic Stroke; Machine learning

DOI: https://doi.org/10.1007/s12031-025-02352-5