Background: Cellular aging is characterized by oxidative stress and mitochondrial dysfunction. Pharmacological inhibition of cellular aging holds significant promise. Ginkgo biloba extract (GBE), a crude extract rich in bioactive natural products, has shown potential in mitigating age-related pathologies pharmacologically, However, its anti-aging effects, active components, and underlying mechanisms remain unclear.
Methods: Using Saccharomyces cerevisiae (BY4741/BY4742), a well-established model for studying cellular aging, we assessed GBE's effects on chronological lifespan, stress resistance, and reactive oxygen species (ROS). Active compounds in GBE aqueous solution were identified via LC-MS/MS and network pharmacology. A four-compound cocktail (G4C: quercetin, rutin, ginkgolide B, and isorhamnetin) was formulated based on pathway enrichment analysis. Mitochondrial function was evaluated via RNA-seq, oxygen consumption rate (OCR), membrane potential (ΔΨm), and mitochondrial ROS levels.
Results: GBE extended chronological lifespan in BY4741 by up to 73 %, enhanced oxidative and thermal stress resistance, and reduced ROS by 66 % in BY4741 and by 44 % in BY4742 strains. LC/MS analysis combining network pharmacology identified G4C components targeting longevity-related pathways. While individual compounds lacked efficacy, addition of G4C into medium improved oxidative stress resistance synergistically. G4C also extended lifespan by 40 % and reduced ROS by 46 % in yeast. RNA-seq revealed G4C downregulated oxidative phosphorylation genes. Functionally, G4C reduced mitochondrial ROS, preserved ΔΨm during aging, boosted OCR (basal/maximal respiration, ATP production), and modulated mitochondrial calcium, indicating enhanced mitochondrial function.
Conclusion: GBE's anti-aging effects stem from synergistic actions of quercetin, rutin, ginkgolide B, and isorhamnetin. This multi-compound cocktail enhances mitochondrial function, offering a novel strategy for combating cellular aging. Our findings support network pharmacology-guided design of multi-component anti-aging therapeutics.
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| Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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| Site | Modification | Modifier | Source | Reference |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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| Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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| Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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