Current molecular medicine · 2026
Network-based Transcriptomic Profiling of Fetal Astrocyte Differentiation Reveals Therapeutic Targets for Neurodegenerative Disease.
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Moftakhar-Bazkiaei Arya, Farzaneh Maryam
Аннотация
INTRODUCTION: Neurodegenerative disorders, including Alzheimer's Disease (AD), Parkinson's Disease (PD), and Age-related Macular Degeneration (AMD), are marked by the progressive loss of specific neuronal populations. Astrocytes, the glial cells surrounding neurons, play a critical role in maintaining neuronal health by providing neurotrophic support, producing antioxidants, and clearing waste. Dysfunctional astrocytes contribute to disease progression, yet their developmental trajectory and molecular regulation remain incompletely understood.
METHOD: This study aims to computationally characterize transcriptional differences between fetal astrocytes and neural stem cell lines to identify key regulatory genes, pathways, and therapeutic targets relevant to astrocyte-linked neurodegeneration. Using microarray data and bioinformatics pipelines, 359 Differentially Expressed Genes (DEGs) were identified, including 249 upregulated and 110 downregulated transcripts.
RESULTS: Protein-Protein Interaction (PPI) network analysis revealed ten hub genes- COL1A1, TIMP1, LOX, COL6A1, COL6A3, COL5A1, CD44, LTBP2, ACTA2, and PLAU-central to extracellular matrix remodeling and cell adhesion. Drug-gene interaction analysis linked these genes to compounds such as Estradiol valerate, Retinoic acid, and Calcitriol, suggesting therapeutic relevance.
DISCUSSION: Enrichment analysis highlighted transcriptional regulation, apoptosis, and ECM-receptor interaction as dominant biological themes. Key miRNA-mRNA interactions, including hsa-miR-877-5p and hsa-miR-767-5p targeting LOX and COL6A3 were also identified.
CONCLUSION: Overall, this study integrates transcriptomic profiling, network modeling, and drug-gene interaction analysis to uncover astrocyte-specific molecular targets, offering a computational framework for therapeutic exploration in neurodegenerative disease.
METHOD: This study aims to computationally characterize transcriptional differences between fetal astrocytes and neural stem cell lines to identify key regulatory genes, pathways, and therapeutic targets relevant to astrocyte-linked neurodegeneration. Using microarray data and bioinformatics pipelines, 359 Differentially Expressed Genes (DEGs) were identified, including 249 upregulated and 110 downregulated transcripts.
RESULTS: Protein-Protein Interaction (PPI) network analysis revealed ten hub genes- COL1A1, TIMP1, LOX, COL6A1, COL6A3, COL5A1, CD44, LTBP2, ACTA2, and PLAU-central to extracellular matrix remodeling and cell adhesion. Drug-gene interaction analysis linked these genes to compounds such as Estradiol valerate, Retinoic acid, and Calcitriol, suggesting therapeutic relevance.
DISCUSSION: Enrichment analysis highlighted transcriptional regulation, apoptosis, and ECM-receptor interaction as dominant biological themes. Key miRNA-mRNA interactions, including hsa-miR-877-5p and hsa-miR-767-5p targeting LOX and COL6A3 were also identified.
CONCLUSION: Overall, this study integrates transcriptomic profiling, network modeling, and drug-gene interaction analysis to uncover astrocyte-specific molecular targets, offering a computational framework for therapeutic exploration in neurodegenerative disease.