Exploration of the anti-insomnia mechanism of Ganoderma by central-peripheral multi-level interaction network analysis

Abstract Background Ganoderma ( Lingzhi in Chinese) has shown good clinical outcomes in the treatment of insomnia, restlessness, and palpitation. However, the mechanism by which Ganoderma ameliorates insomnia is unclear. We explored the mechanism of the anti-insomnia effect of Ganoderma using systems pharmacology from the perspective of central-peripheral multi-level interaction network analysis. Methods The active components and central active components of Ganoderma were obtained from the TCMIP and TCMSP databases, then screened to determine their pharmacokinetic properties. The potential target genes of these components were identified using the Swiss Target Prediction and TCMSP databases. The results were matched with the insomnia target genes obtained from the GeneCards, OMIM, DisGeNET, and TCMIP databases. Overlapping targets were subjected to multi-level interaction network analysis and enrichment analysis using the STRING, Metascape, and BioGPS databases. The networks analysed were protein-protein interaction (PPI), drug-component-target gene, component-target gene-organ, and target gene-extended disease; we also performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Results In total, 34 sedative-hypnotic components (including 5 central active components) were identified, corresponding to 51 target genes. Multi-level interaction network analysis and enrichment analysis demonstrated that Ganoderma exerted an anti-insomnia effect via multiple central-peripheral mechanisms simultaneously, mainly by regulating cell apoptosis/survival and cytokine expression through core target genes such as TNF, CASP3, JUN, and HSP90αA1; it also affected immune regulation and apoptosis. Therefore, Ganoderma has potential as an adjuvant therapy for insomnia-related complications. Conclusion Ganoderma exerts an anti-insomnia effect via complex central-peripheral multi-level interaction networks.