Study on the mechanism of action of the active ingredient of Calculus Bovis in the treatment of sepsis by integrating single-cell sequencing and machine learning.

BACKGROUND

Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechanisms of Calculus Bovis in sepsis using network pharmacology and RNA sequencing.

METHODS

Sepsis data from the China National GeneBank Database were analyzed for differentially expressed genes (FC ≥ 2, FDR < 0.05). Active components of Calculus Bovis were identified via the HERB and BATMAN-TCM databases, with target interactions assessed through protein-protein interaction (PPI) networks. GO and KEGG analyses identified pathway enrichments (P ≤ .01). Survival analysis using the GSE65682 database evaluated prognosis-related genes (P < .05). Four machine learning models (XGBoost, SVM, Decision Tree, KNN) were constructed to assess diagnostic potential, with AUC values evaluating accuracy. Immunofluorescence and single-cell RNA sequencing localized key genes, while molecular docking and molecular dynamics simulations (MD) assessed binding affinities and stability of Calculus Bovis compounds with target proteins.

RESULTS

We identified 593 targets for Calculus Bovis and 4329 sepsis-related genes, with 149 overlapping. Key genes ADAM17, CASP1, CD81, and MGMT were linked to improved prognosis (P < .05) and involved in inflammatory responses and pyroptosis (P ≤ .01). The XGBoost model achieved high diagnostic accuracy (AUC: training = 1.000, test = 0.964). Molecular docking showed strong binding (energy < -6.0 kcal/mol), and MD indicated stable interactions, particularly with ADAM17 and CD81.

CONCLUSION

This study highlights the potential of Calculus Bovis in sepsis treatment, identifying key genes as therapeutic targets.