Molecular characterization and pathogenicity stratification of YWHAG missense variants in developmental and epileptic encephalopathy.
- 1Department of Neurology, The First Affiliated Hospital & Clinical Neuroscience Institute of Jinan University, Guangzhou, China.
- 2Department of Neurology, The First People's Hospital of Chenzhou, Chenzhou, Hunan, China.
- 3The Sixth Affiliated Hospital of Jinan University, Dongguan, Guangdong, China.
Abstract
Background
Variants in YWHAG cause severe developmental and epileptic encephalopathy (DEE) with early-onset seizures. Rapid and precise clinical classification of its missense variants presents a substantial challenge. This study sought to explore YWHAG spatiotemporal expression-pathogenicity correlation and reliable pathogenicity indicators for its variants to promote clinical genetic diagnosis.Methods
In this study, 55 YWHAG missense variants were curated, including 17 pathogenic/likely pathogenic variants that met ACMG guideline criteria and 38 benign variants from gnomAD. We performed protein structure modeling, spatiotemporal expression profiling, single-cell transcriptome analysis, PPI network construction, and functional enrichment analysis. We also evaluated 34 pathogenicity prediction tools using accuracy, balanced accuracy, AUC, and MCC.Results
Protein structure modeling revealed that hydrogen bond alterations occurred in both pathogenic and benign YWHAG variants, which could not reliably distinguish the two groups. YWHAG was highly expressed in the central nervous system with three distinct developmental peaks, shifting from both excitatory and inhibitory neurons to predominantly excitatory neurons during cerebral organoid maturation. The YWHAG protein regulated the MAPK, PI3K-Akt, Hippo pathways and cell cycle progression. Among the 34 evaluated tools, deep learning and ensemble algorithms outperformed traditional conservation-based predictors, with MetaSVM showing the best overall performance, MetaLR achieving excellent specificity and ESM1b attaining the highest AUC value.Conclusion
Meta-predictors may effectively predict YWHAG missense variant pathogenicity, and YWHAG spatiotemporal expression aligns with its DEE pathogenic role. These computational tools may provide reliable support for the pathogenicity assessment of YWHAG variants in clinical genetic diagnosis.
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BibTeX
@article{liu2026molecular,
title = {{Molecular characterization and pathogenicity stratification of YWHAG missense variants in developmental and epileptic encephalopathy.}},
author = {Wen-Hui Liu and Juan Zhou and Zhong-Yan Zhu and Qian-Ru Wen and Si-Qi Zhang and Yan Ding and Heng Meng},
journal = {Frontiers in genetics},
year = {2026},
month = oct,
doi = {10.3389/fgene.2026.1875740},
url = {https://europepmc.org/article/MED/42898427},
note = {CC BY},
}
RIS
TY - JOUR
TI - Molecular characterization and pathogenicity stratification of YWHAG missense variants in developmental and epileptic encephalopathy.
AU - Wen-Hui Liu
AU - Juan Zhou
AU - Zhong-Yan Zhu
AU - Qian-Ru Wen
AU - Si-Qi Zhang
AU - Yan Ding
AU - Heng Meng
PY - 2026
DA - 2026/10/05
JO - Frontiers in genetics
DO - 10.3389/fgene.2026.1875740
UR - https://europepmc.org/article/MED/42898427
AB - Background Variants in YWHAG cause severe developmental and epileptic encephalopathy (DEE) with early-onset seizures. Rapid and precise clinical classification of its missense variants presents a substantial challenge. This study sought to explore YWHAG spatiotemporal expression-pathogenicity correlation and reliable pathogenicity indicators for its variants to promote clinical genetic diagnosis.Methods In this study, 55 YWHAG missense variants were curated, including 17 pathogenic/likely pathogenic variants that met ACMG guideline criteria and 38 benign variants from gnomAD. We performed protein structure modeling, spatiotemporal expression profiling, single-cell transcriptome analysis, PPI network construction, and functional enrichment analysis. We also evaluated 34 pathogenicity prediction tools using accuracy, balanced accuracy, AUC, and MCC.Results Protein structure modeling revealed that hydrogen bond alterations occurred in both pathogenic and benign YWHAG variants, which could not reliably distinguish the two groups. YWHAG was highly expressed in the central nervous system with three distinct developmental peaks, shifting from both excitatory and inhibitory neurons to predominantly excitatory neurons during cerebral organoid maturation. The YWHAG protein regulated the MAPK, PI3K-Akt, Hippo pathways and cell cycle progression. Among the 34 evaluated tools, deep learning and ensemble algorithms outperformed traditional conservation-based predictors, with MetaSVM showing the best overall performance, MetaLR achieving excellent specificity and ESM1b attaining the highest AUC value.Conclusion Meta-predictors may effectively predict YWHAG missense variant pathogenicity, and YWHAG spatiotemporal expression aligns with its DEE pathogenic role. These computational tools may provide reliable support for the pathogenicity assessment of YWHAG variants in clinical genetic diagnosis.
SN - 1664-8021
ER -