Decoding shared pathogenic networks of oxidative stress in neuropsychiatric disorders to prioritize multi-target therapeutics from natural products

Neurodegenerative and psychiatric disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and schizophrenia (SZ), are characterized by progressive neuronal loss and synaptic dysfunction. Despite their severity, effective disease-modifying treatments remain unavailable, largely due to the elusive nature of their underlying molecular mechanisms. To elucidate these mechanisms, we conducted an integrative systems biology analysis incorporating transcriptomic datasets, in silico proteomic networks, and inferred metabolomic profiles. Machine learning (ML) and deep learning (DL) models were employed to identify regulatory networks associated with oxidative stress, immune response, and synaptic signaling. Furthermore, network pharmacology approaches were applied to explore multi-target intervention strategies using bioactive compounds from traditional Chinese medicine (TCM). Our integrative analysis revealed extensive overlap in dysregulated biological processes across all four disorders, particularly involving oxidative stress and immune activation. We identified TP53, NFE2L2, and PPP3CA as central regulatory hubs driving these pathologies. Notably, computational predictions highlighted that TCM-derived compounds, specifically stigmasterol and dodecanoic acid, exhibit promising multi-target effects for modulating these oxidative and inflammatory responses. Subsequent in vivo experimental validation was performed exclusively to corroborate the disease-associated pathways and core gene dysregulation in an Aβ42-induced AD model. These findings demonstrated molecular and behavioral phenotypes consistent with our multi-dimensional computational predictions, establishing a robust mechanistic rationale that merits future in vivo pharmacological validation for the predicted bioactive compounds. This study highlights the utility of a multi-disease, multi-dimensional framework in uncovering shared pathogenic signatures. By integrating computational analytics with pharmacological modeling and experimental validation, we identified key regulatory genes and natural compounds with therapeutic potential. These findings provide a theoretical foundation for the development of multi-target, personalized treatment strategies against neurodegeneration. This figure illustrates an integrative multi-omics and network pharmacology framework designed to uncover shared mechanisms and multi-target natural compounds for neurodegenerative and psychiatric disorders (AD, PD, HD, SZ). (A) Cross-disease analysis reveals shared features, including oxidative stress and inflammatory dysregulation. (B) Layered multi-omics data (Transcriptomics, Proteomics, Metabolomics) are integrated and analyzed using advanced AI (Machine Learning, Deep Learning) to pinpoint key shared pathway clusters. (C) These pathways are governed by interconnected central hub genes: TP53, NFE2L2 (Nrf2), and PPP3CA (Calcineurin). (D) A network pharmacology approach identifies multiple multi-target natural compounds from Traditional Chinese Medicine (TCM)—Stigmasterol, Dodecanoic Acid, and Honokiol—which are predicted to regulate these hub nodes and pathways. (E) Computational predictions of shared mechanisms and compound therapeutic efficacy are validated in vivo using an Aβ42-induced mouse model, confirming behavioral improvement and molecular confirmation. The entire approach provides a personalized, multi-target strategy for complex neurological conditions.