bims-meglyc 2024-12-29 papers

Issue of 2024–12–29
four papers selected by
Silvia Radenkovic, UMC Utrecht

Stem Cell Reports. 2024 Dec 12. pii: S2213-6711(24)00324-2. [Epub ahead of print] 102380

Exploiting O-GlcNAc dyshomeostasis to screen O-GlcNAc transferase intellectual disability variants.

Huijie Yuan, Conor W Mitchell, Andrew T Ferenbach, Maria Teresa Bonati, Agnese Feresin, Paul J Benke, Queenie K G Tan, Daan M F van Aalten.

O-GlcNAcylation is an essential protein modification catalyzed by O-GlcNAc transferase (OGT). Missense variants in OGT are linked to a novel intellectual disability syndrome known as OGT congenital disorder of glycosylation (OGT-CDG). The mechanisms by which OGT missense variants lead to this heterogeneous syndrome are not understood, and no unified method exists for dissecting pathogenic from non-pathogenic variants. Here, we develop a double-fluorescence strategy in mouse embryonic stem cells to measure disruption of O-GlcNAc homeostasis by quantifying the effects of variants on endogenous OGT expression. OGT-CDG variants generally elicited a lower feedback response than wild-type and Genome Aggregation Database (gnomAD) OGT variants. This approach was then used to dissect new putative OGT-CDG variants from pathogenic background variants in other disease-associated genes. Our work enables the prediction of pathogenicity for rapidly emerging de novo OGT-CDG variants and points to reduced disruption of O-GlcNAc homeostasis as a common mechanism underpinning OGT-CDG.

Keywords: O-GlcNAc; OGT; OGT-CDG; neurodevelopment

DOI: https://doi.org/10.1016/j.stemcr.2024.11.010

medRxiv. 2024 Dec 13. pii: 2024.12.11.24318624. [Epub ahead of print]

A conserved role for ALG10/ALG10B and the N -glycosylation pathway in the sleep-epilepsy axis.

Congenital disorders of glycosylation (CDG) comprise a class of inborn errors of metabolism resulting from pathogenic variants in genes coding for enzymes involved in the asparagine-linked glycosylation of proteins. Unexpectedly to date, no CDG has been described for ALG10 , encoding the alpha-1,2-glucosyltransferase catalyzing the final step of lipid-linked oligosaccharide biosynthesis. Genome-wide association studies (GWAS) of human traits in the UK Biobank revealed significant SNP associations with short sleep duration, reduced napping frequency, later sleep timing and evening diurnal preference as well as cardiac traits at a genomic locus containing a pair of paralogous enzymes ALG10 and ALG10B . Modeling Alg10 loss in Drosophila, we identify an essential role for the N -glycosylation pathway in maintaining appropriate neuronal firing activity, healthy sleep, preventing seizures, and cardiovascular homeostasis. We further confirm the broader relevance of neurological findings associated with Alg10 from humans and flies using zebrafish and nematodes and demonstrate conserved biochemical roles for N -glycosylation in Arabidopsis . We report a human subject homozygous for variants in both ALG10 and ALG10B arising from a consanguineous marriage, with epilepsy, brain atrophy, and sleep abnormalities as predicted by the fly phenotype. Quantitative glycoproteomic analysis in our Drosophila model identifies potential key molecular targets for neurological symptoms of CDGs.

DOI: https://doi.org/10.1101/2024.12.11.24318624

J Inherit Metab Dis. 2025 Jan;48(1): e12836

Aldolase B Deficient Mice Are Characterized by Hepatic Nucleotide Sugar Abnormalities.

Amée M Buziau, Dirk J Lefeber, D Cassiman, M Estela Rubio-Gozalbo, Hanneke Kwast, Dean R Tolan, Casper G Schalkwijk, Martijn C G J Brouwers.

Hereditary fructose intolerance (HFI) is characterized by liver damage and a secondary defect in N-linked glycosylation due to impairment of mannose phosphate isomerase (MPI). Mannose treatment has been shown to be an effective treatment in a primary defect in MPI (i.e., MPI-CDG), which is also characterized by liver damage. Therefore, the aims of this study were to determine: (1) hepatic nucleotide sugar levels, and (2), the effect of mannose supplementation on hepatic nucleotide sugar levels and liver fat, in a mouse model for HFI. Aldolase B deficient mice (Aldob-/-) were treated for four weeks with 5% mannose via the drinking water and compared to Aldob-/- mice and wildtype mice treated with regular drinking water. We found that hepatic GDP-mannose and hepatic GDP-fucose were lower in water-treated Aldob-/- mice when compared to water-treated wildtype mice (p = 0.002 and p = 0.002, respectively), consistent with impaired N-linked glycosylation. Of interest, multiple other hepatic nucleotide sugars not involved in N-linked glycosylation, such as hepatic UDP-glucuronic acid, UDP-xylose, CMP-N-acetyl-beta-neuraminic acid, and CDP-ribitol (p = 0.002, p = 0.003, p = 0.002, p = 0.002), were found to have altered levels as well. However, mannose treatment did not correct the reduction in hepatic GDP-mannose levels, nor was liver fat affected. Aldob-/- mice are characterized by hepatic nucleotide sugar abnormalities, but these were not abrogated by mannose treatment. Future studies are needed to identify the underlying mechanisms responsible for the abnormal hepatic nucleotide sugar pattern and intrahepatic lipid accumulation in HFI. Trial Registration: PCT ID: PCTE0000340, this animal experiment is registered at (https://preclinicaltrials.eu/).

Keywords: aldolase B; congenital disorders of glycosylation; fructose intolerance; genetic diseases; glycosylation; nucleotide sugars

DOI: https://doi.org/10.1002/jimd.12836

Int J Med Inform. 2024 Dec 16. pii: S1386-5056(24)00428-3. [Epub ahead of print]195 105765

Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC-MS urinary metabolomics for diseases screening.

Haomin Li, Siyuan Gao, Dan Wu, Min Zhu, Zhenzhen Hu, Kexin Fang, Xiuru Chen, Zhou Ni, Jing Li, Beibei Zhao, Xuhui She, Xinwen Huang.

BACKGROUND: Gas chromatography-mass spectrometry (GC-MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC-MS for inborn errors of metabolism (IEM) screening is constrained by the rarity of IEM in population, and the difficult and specialized complexity of the interpretation of GC-MS organic acid profiles.
METHODS: Based on 355,197 GC-MS test cases accumulated from 2013 to 2021 in China, a random forest-based machine learning model was proposed, trained, and evaluated. Weighted undersampling or oversampling data processing and staged modeling strategies were used to handle the highly imbalanced data and improve the ability of the model to identify different types of rare IEM cases.
RESULT: In the first-stage model, which only identified positive cases without discriminating the specific IEM, the screening sensitivity was 0.938 (or 0.991 if abnormal cases were also included). The average sensitivity of the second-stage models that classify 11 particular IEMs is 0.992, with an average specificity and accuracy of 0.944 and 0.969, respectively. The SHAP values visualized for each model explain the basis for the differential diagnosis made by the model.
CONCLUSION: With sufficient high-quality data, machine learning models can provide high-sensitivity GC-MS interpretation and greatly improve the efficiency and quality of GC-MS based IEM screening.

Keywords: Disease screening; GC–MS; Imbalanced classification; Inborn error of metabolism; Machine learning

DOI: https://doi.org/10.1016/j.ijmedinf.2024.105765