Integrative UHPLC-HRMS and computational biology reveal ferroptosis and anoikis targeting by Wenpitongluo decoction in cardiorenal syndrome

Abstract

Background: The Wenpitongluo Decoction (WPTLD) was a classical herbal formula composed of medicinal herbs with both edible and therapeutic properties. It demonstrated clinical efficacy in treating Cardiorenal Syndrome (CRS), though its mechanism of action remained unclear. Although inflammatory and oxidative stress pathways in CRS have been intensively studied, the roles of ferroptosis and anoikis, which may be activated by these pathways, have received little attention.

Methods: First, the active components of WPTLD were obtained through the TCMSP and Herb databases, and then identified using UHPLC-HRMS. Subsequently, target prediction of the identified components was carried out via the SwissTargetPrediction platform. While CRS-related targets were retrieved from GEO, GeneCards, and PharmGKB. A gene library of ferroptosis- and anoikis-associated targets was established. Tissue-specific mRNA expression profiles were analyzed via BioGPS. Subsequently, protein-protein interaction (PPI) networks were constructed to identify core targets, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using Metascape. Finally, molecular docking assessed binding affinities between active components and core targets, with top-ranked complexes undergoing molecular dynamics (MD) simulations.

Results: Fifteen bioactive components and 39 component-disease interaction targets were identified, predominantly localized in kidney, thymus, lung, adipocytes, adrenal gland, and heart tissues. Topological analysis of PPI networks revealed eight core targets, including ferroptosis-/anoikis-associated SIRT1, PTGS2, and PRKCA. KEGG analysis highlighted critical pathways such as AMPK and PI3K-Akt signaling. Notably, molecular docking and MD simulations demonstrated stable binding between active compounds and core targets.

Conclusion: This study systematically deciphers WPTLD's anti-CRS mechanisms via targeting ferroptosis- and anoikis-related genes through multi-pathway modulation. These findings not only clarify the pathological roles of ferroptosis and anoikis in CRS but also provide a computational framework for developing therapeutic strategies.

Keywords: Wenpitongluo decoction; anoikis; cardiorenal syndrome; computational biology; ferroptosis; oxidative stress.

FIGURE 1

Flow chart of the study.

FIGURE 2

UHPLC-HRMS analysis results. (A) Content distribution and classification of herbal components; (B) EIC (Extracted Ion Chromatogram) of ellagic acid and its MS/MS spectrum compared with the LuMet-TCM standard library; (C) BPC (Base Peak Chromatogram) diagram in the positive ion mode; (D) BPC diagram in the negative ion mode.

FIGURE 3

WPTLD target screening and tissue network. (A) Component - disease target Venn diagram. (B,C) PPI network diagrams. (D) Interaction target - tissue/organ network diagram.

FIGURE 4

Enrichment analysis and WPTLD component screening. (A) KEGG pathway enrichment analysis. (B) GO enrichment analysis. (C) Component - target - pathway network. (D) PI3K - Akt signaling pathway.

FIGURE 5

Molecular docking result heatmap.

FIGURE 6

SIRT1 - Ellagic acid molecular docking model. (A) Docking model of Ellagic acid and SIRT1. (B) 2D structure of Ellagic acid. (C,D) Schematic diagrams of the docking results.

FIGURE 7

100 ns MD simulation analysis of the SIRT1 - Ellagic acid complex. (A) RMSD curve of the complex. (B) RMSD curve of the small molecule. (C) Rg curve of the complex. (D) SASA curve of the complex. (E) RMSF curve of SIRT1. (F) Hydrogen bond change curve of the complex.

FIGURE 8

Complex Gibbs free energy analysis. (A) 3D and 2D free energy landscapes of the SIRT1 - original activator complex. (B) 3D and 2D free energy landscapes of the SIRT1 - Ellagic acid complex. Blue - and purple - shaded areas indicate that the stable conformation of the complex can be mapped at lower energy within the minimum free energy zone. Weak or unstable interactions lead to multiple, rough clusters in the free energy landscape, while strong, stable interactions form single, smooth clusters.