Modeling Tay-Sachs Disease in Astrocyte-like Cells Reveals Significant Changes in the Transcriptomic Profile.
Overview
Abstract
Tay-Sachs disease is a rare genetic disorder characterized by the accumulation of GM2 ganglioside in neuronal lysosomes due to deficient β-hexosaminidase A (HexA) activity. Progressive GM2 storage leads to severe neurodegeneration, including developmental delay, motor weakness, seizures, ataxia, and early death, typically by five years of age. Previous studies have elucidated several neuronal mechanisms, including apoptosis, endoplasmic reticulum stress, neuroinflammation, and demyelination, these investigations have focused almost exclusively on neurons. However, other components of the central nervous system, particularly astroglia, may play a critical role in disease pathophysiology as suggested by studies in related lysosomal storage disorders. To address this gap, we generated an astrocyte-like model deficient in HexA by targeted knockdown of the HEXA gene in U87MG astrocytoma cells. The resulting cell line recapitulates key pathological features, including lysosomal accumulation, increased neutral lipid content, reduced mitochondrial mass, and elevated reactive oxygen species production. Transcriptomic analysis revealed significant alterations in pathways associated with neuronal degeneration, synaptic organization, mitochondrial dysfunction, and ganglioside metabolism. In summary, this model reproduces some classical cellular alterations reported in Tay-Sachs disease and could potentially provide novel insight into astrocyte involvement in its pathophysiology. These findings support the relevance of non-neuronal cells in disease pathophysiology and establish this system as a valuable platform for screening potential novel mechanisms and therapeutic approaches. Furthermore, this approach highlights the importance of integrating cell type specific models to better understand disease heterogeneity and providing insights into the progressive neurodegeneration of Tay-Sachs disease, positioning this model as a valuable tool for studying its underlying pathophysiology.
Reproduced under the paper's license (CC BY), from the paper cited above.
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Data
Datasets cited
- geo:GSE329156, at NCBI GEO; found in “Data Availability Statement”
Data Availability Statement
The data supporting this study are openly available at the Gene Expression Omnibus (GEO) database with Accession number GSE329156 (https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 10 MeSH terms, 1 funder, 80 references.
Cite
This paper
Suárez-García, D. A., Espejo-Mojica, A. J., & Alméciga-Díaz, C. J. (2026). Modeling Tay-Sachs Disease in Astrocyte-like Cells Reveals Significant Changes in the Transcriptomic Profile. International journal of molecular sciences, 27(14), 6503. https://
BibTeX
@article{suarezgarcia202
author = {Suárez-García, Diego A and Espejo-Mojica, Angela J and Alméciga-Díaz, Carlos J},
title = {{Modeling Tay-Sachs Disease in Astrocyte-like Cells Reveals Significant Changes in the Transcriptomic Profile}},
journal = {International journal of molecular sciences},
year = {2026},
month = jul,
volume = {27},
number = {14},
pages = {6503},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1422-0067},
doi = {10.3390/
url = {https://
pmid = {42511849},
pmcid = {PMC13411418}
}
RIS
TY - JOUR
AU - Suárez-García, Diego A
AU - Espejo-Mojica, Angela J
AU - Alméciga-Díaz, Carlos J
TI - Modeling Tay-Sachs Disease in Astrocyte-like Cells Reveals Significant Changes in the Transcriptomic Profile
T2 - International journal of molecular sciences
J2 - Int J Mol Sci
PY - 2026
DA - 2026/
VL - 27
IS - 14
SP - 6503
SN - 1422-0067
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/
UR - https://
LA - en
ER -
CSL-JSON
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"title": "Modeling Tay-Sachs Disease in Astrocyte-like Cells Reveals Significant Changes in the Transcriptomic Profile",
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"author": [
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"given": "Angela J"
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"ISSN": "1422-0067",
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"URL": "https://
"language": "en",
"issued": {
"date-parts": [
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