Deciphering the molecular network of Trichostatin A in regulating Alzheimer’s disease screening of core genes and mechanistic investigation based on multidimensional bioinformatics and molecular simulation

Abstract

Background Histone deacetylases (HDACs) regulate neuroprotection; however, Trichostatin A (TSA), an HDAC inhibitor, lacks clear molecular mechanisms and core targets in Alzheimer’s disease (AD), limiting clinical translation. This study aimed to decipher TSA’s AD-regulating network, screen core genes, and support AD early diagnosis and multi-target therapies. Methods TSA targets were computationally predicted. Five GEO AD datasets were analyzed for differential genes and core modules, and 130 machine learning algorithms were employed to identify core genes. Functional annotation, immune cell analysis, and single-cell expression profiling were conducted. Molecular docking and 100 ns molecular dynamics simulations verified TSA-protein interactions. Results 949 potential TSA targets were identified, overlapping with AD differential genes and enriching key pathways such as GABAergic synapse and tau phosphorylation. Eight machine learning-identified core genes (EFNA1, GABRB2, GABARAPL1, EGR1, CDK5, KCNC2, MET, GRIA2) exhibited a distinct AD expression pattern: synergistic downregulation of protective genes and unique upregulation of pathological EFNA1. These genes are implicated in neurotransmission, synaptic plasticity, tau clearance, and immune-neural crosstalk. Molecular dynamics simulations suggested TSA may not stably bind these candidates, implying its regulation relies on epigenetic mechanisms via HDAC1–3/6 inhibition, potentially restoring gene network balance and disrupting neuroinflammation-neurodegeneration cycles. Complex regulatory modes and cell type-specific expression were also observed. Conclusion This study provides preliminary insights into TSA’s putative mechanisms in AD intervention, highlighting the eight candidate core genes’ potential diagnostic and therapeutic value as AD biomarkers, supporting TSA’s multi-target therapy. All findings are computationally derived and require experimental verification.