Combinatorial chemistry & high throughput screening · 2026
Exploring the Mechanism of Selenium-Biofortified Polygonatum Kingianum in Alzheimer's Disease: An Integrated Metabolomics and Network Pharmacology In Silico Study.
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Jia Zixuan, Xiao Qiang, Shi Guangdi, Wang Xiuzhi
Аннотация
BACKGROUND: Alzheimer's disease [AD] involves multifactorial pathogenesis such as Aβ deposition and Tau hyperphosphorylation, yet effective multi-target therapies remain scarce. The mechanisms by which selenium-biofortified Polygonatum kingianum [Se-PK] modulates AD pathways are poorly understood, limiting its clinical translation.
METHODS: An integrated in silico approach was employed: 1] UPLC-MS/MS metabolomics to identify differential metabolites in Se-PK [VIP > 1, fold change ≥2 or ≤0.5]; 2] network pharmacology to construct compound-target-pathway networks; and 3] molecular docking and dynamics simulations [AutoDock Vina, GROMACS] to assess binding stability.
RESULTS: We identified 92 differential metabolites, 87% of which were unclassified-including novel sulfur-containing/alkaloid-like compounds [e.g., Cerberin]. Five hub targets [EGFR, SRC, PIK3CA, HSP90AA1, STAT3] were enriched in PI3K/Akt signaling and other AD-related pathways [FDR < 0.01]. Se-PK likely modulates a multi-target axis: EGFR/PI3K [anti-apoptosis] → HSP90AA1 [proteostasis] → SRC/STAT3 [synaptic regulation], with high-affinity interactions such as Cerberin-EGFR [ΔG = -7.8 kcal·mol⁻¹; RMSD < 2.0 Å]. Unclassified metabolites like Schisanterin A [Degree = 45] showed broad target engagement, suggesting synergistic effects.
CONCLUSION: This study establishes a predictive "metabolomics-network pharmacology- dynamics" framework for elucidating the multi-target mechanisms of Se-PK against AD. While providing a methodological paradigm for natural product research, these in silico findings prioritize candidate compounds and pathways for future experimental validation, advancing precision phytotherapy in neurodegeneration.
METHODS: An integrated in silico approach was employed: 1] UPLC-MS/MS metabolomics to identify differential metabolites in Se-PK [VIP > 1, fold change ≥2 or ≤0.5]; 2] network pharmacology to construct compound-target-pathway networks; and 3] molecular docking and dynamics simulations [AutoDock Vina, GROMACS] to assess binding stability.
RESULTS: We identified 92 differential metabolites, 87% of which were unclassified-including novel sulfur-containing/alkaloid-like compounds [e.g., Cerberin]. Five hub targets [EGFR, SRC, PIK3CA, HSP90AA1, STAT3] were enriched in PI3K/Akt signaling and other AD-related pathways [FDR < 0.01]. Se-PK likely modulates a multi-target axis: EGFR/PI3K [anti-apoptosis] → HSP90AA1 [proteostasis] → SRC/STAT3 [synaptic regulation], with high-affinity interactions such as Cerberin-EGFR [ΔG = -7.8 kcal·mol⁻¹; RMSD < 2.0 Å]. Unclassified metabolites like Schisanterin A [Degree = 45] showed broad target engagement, suggesting synergistic effects.
CONCLUSION: This study establishes a predictive "metabolomics-network pharmacology- dynamics" framework for elucidating the multi-target mechanisms of Se-PK against AD. While providing a methodological paradigm for natural product research, these in silico findings prioritize candidate compounds and pathways for future experimental validation, advancing precision phytotherapy in neurodegeneration.