Icariside II attenuates renal fibrosis through mTOR signaling modulation: an integrated approach combining network pharmacology, molecular dynamics, and in vitro validation

Introduction

Renal fibrosis (RF), a defining pathological feature of chronic kidney disease (CKD), is characterized by abnormal extracellular matrix (ECM) accumulation and progressive disruption of renal architecture (1). This irreversible process encompasses sequential pathological events: tubular epithelial cell injury, inflammatory cell infiltration, myofibroblast activation and proliferation, excessive ECM deposition, tubular atrophy, loss of resident renal cells, and vascular rarefaction, ultimately leading to structural and functional renal failure (2). Despite its clinical significance, effective treatments for RF are limited, posing a significant global health burden (3). Current strategies, such as renin-angiotensin-aldosterone system (RAAS) inhibitors, offer only partial symptom relief and fail to halt disease progression, with prolonged use potentially exacerbating complications like hyperkalemia and worsening renal function (4). Thus, there is an urgent need to develop novel antifibrotic agents with multitarget capabilities and low toxicity.

The pathogenesis of RF involves dysregulation of multiple signaling pathways, including the well-established transforming growth factor β1 (TGF-β1)/Smad axis as a driver of fibrotic responses (5), and non-Smad pathways like the PI3K/AKT cascade, which also play critical roles in CKD-associated fibrosis (6). Natural product-derived compounds have emerged as promising drug candidates due to their structural and biological diversity (7). Recent studies highlight the therapeutic potential of phytoactive components in RF modulation. For example, astragaloside IV attenuates epithelial-mesenchymal transition (EMT) and ECM deposition by suppressing the AKT1/glycogen synthase kinase-3 beta (GSK-3β) pathway (8), while Qingshen granules ameliorate fibrosis by regulating the PI3K-AKT-mTOR signaling axis in dendritic cells (9).

Icariside II (ICA-II), a primary metabolite of icariin derived from Epimedium brevicornum Maxim (10), exhibits diverse pharmacological properties including antioxidant (11), antitumor (12), and anti-osteoporotic effects (13). Emerging evidence suggests its antifibrotic potential in a CKD rat model by modulating mitochondrial function through the peroxisome proliferator-activated receptor alpha/fatty acid oxidation axis (PPARα/FAO) axis (14). Tian et al. (15) showed that ICA-II promotes renal cortical and medullary cell proliferation in diabetic nephropathy by upregulating endothelial cell content, inhibiting the TGF-β/Smad/connective tissue growth factor (CTGF) cascade, and reducing oxidative stress, possibly through antioxidant activity and recruitment of endogenous 5-ethynyl-2’-deoxyuridine + (EdU+) progenitor cells to the renal microenvironment. However, the precise molecular mechanisms underlying ICA-II’s antifibrotic effects in RF remain incompletely understood.

Network pharmacology, a systems biology approach, allows systematic identification of drug-target-pathway interactions to decipher the multitarget mechanisms of herbal compounds. In this study, we integrate network pharmacology, molecular docking, molecular dynamics (MD) simulations, and in vitro experiments to comprehensively characterize ICA-II’s multitarget network. By elucidating its molecular mechanisms in RF, we aim to establish a solid theoretical foundation for the development of ICA-II-based therapeutic strategies targeting fibrotic diseases. We present this article in accordance with the MDAR reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-395/rc).

Methods

Prediction of ICA-II targets

We used ‘Icariside II’ as a search term in the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) to obtain the molecular structure of ICA-II. The pharmacological parameters of ICA-II, including absorption, distribution, metabolism, and excretion (ADME), were retrieved through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (https://tcmsp-e.com/). Additionally, we searched for ICA-II-related targets using keywords such as ‘Icariside II’ in databases including Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/), PharmMapper (www.lilab-ecust.cn/pharmmapper/submitfile.html), and SwissTargetPrediction (http://swisstargetprediction.ch/).

Identifying target genes linked to RF

We gathered human genes associated with RF from four databases: Online Mendelian Inheritance in Man (OMIM) (https://omim.org/), GeneCards (https://www.genecards.org/), Therapeutic Target Database (TTD) (https://db.idrblab.net/ttd/), and MalaCards (https://www.malacards.org). We used search terms such as ‘renal fibrosis’, ‘renal failure’, and ‘chronic kidney disease’ to retrieve relevant targets. The intersecting targets of ICA-II and RF were then visualized using the Draw Venn Diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) (16). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Construction of a protein-protein interaction (PPI) network

To construct the PPI network, we employed Search Tool for the Retrieval of Interacting Genes/Proteins​ (STRING) (https://string-db.org/), a database that consolidates both experimentally validated and computationally predicted PPI through systematic bioinformatic approaches. Specifically, we focused on PPI involving the therapeutic targets of ICA-II in the context of RF. We restricted our search to the species Homo sapiens and selected PPIs with confidence scores exceeding 0.4, while excluding disconnected nodes from the network visualization.

Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

We conducted enrichment analyses to investigate the functional characteristics of the identified genes. GO classification, covering biological processes, molecular functions, and cellular compartments, was performed using Metascape’s toolkit (https://metascape.org/gp/index.html). Pathway analysis was conducted using the KEGG resource. The results were visualized in the Bioinformatic database (http://www.bioinformatics.com.cn/). To illustrate the relationships between significant genes and enriched pathways, we created an interactive molecular network model using Cytoscape software (version 3.10.1).

Molecular docking

The 2D structures of ICA-II were downloaded from PubChem and TCMSP databases, then converted to 3D mol2 format using Chem3D (20.0) software. The 3D structures of ICA-II targets were obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank​ (RCSB PDB) database (http://www.rcsb.org/pdb/) and prepared in PyMol by removing inactive ligands. Both ligands and target proteins were imported into AutoDock Vina for docking, and the affinity and hydrogen bond interactions were visualized using PyMol.

MD simulations

The MD simulations of the complexes, obtained through molecular docking, were performed using ​GROningen MAchine for Chemical Simulations (GROMACS) 2022.3, following precedents set by previous studies (17). Briefly, the simulation system employed the Amber99sbildn force field and Tip3p water model. To neutralize the system’s total charge, an appropriate number of Na+ ions were added. Energy minimization was achieved using the steepest-descent method. After this minimization, equilibrium simulations were carried out using both isothermal isovolumic and isothermal isobaric ensembles. The MD simulations then ran for 100 ns, consisting of 5 million steps with a 2 fs step length at 300 K and 1 atmosphere of pressure. Upon completing the simulation, the software’s built-in tool analyzed the trajectory, calculating root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and hydrogen bonding.

Cell culture

The normal rat kidney-49 fibroblast (NRK-49F) and human kidney 2 (HK2) cell were purchased from Wuhan Pricella Biotechnology Co. These cells were cultured in ’s Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS), maintained at 37 ℃ with 5% CO₂. NRK-49F and HK2 cells were maintained in 0.2% FBS (DMEM) overnight before stimulation with 10 ng/mL TGF-β1 (100-21, PeproTech, New Jersey, USA) for 48 hours. Concurrently, the cells were exposed to ICA-II (1.25, 2.5, 5 and 10 µM), MHY1485 (5 µM) or not, depending on the experimental protocol.

Cell viability analysis

NRK-49F and HK2 cells were seeded in 96-well plates at a density of 1×10⁴ cells/well and serum-starved for 12 h using DMEM supplemented with 0.2% FBS. The cells were then treated with ICA-II at concentrations of 1, 2, 3, 5, 10 or 20 µM for 24 or 48 hours. Following treatment, 10% Cell Counting Kit-8 (CCK-8) solution (K1018, APExBIO, Texas, USA) in fresh DMEM was added to each well and incubated for 1–2 h. Absorbance at 450 nm was measured using a HEALES microplate reader.

Immunofluorescence analysis

To assess the impact of various ICA-II concentrations on NRK-49F and HK2 cells fibrosis, the cells (1×10⁵) were seeded on coverslips in 24-well plates. Fibrosis was induced by TGF-β1, and the cells were treated with ICA-II (5 and 10 µM) for 48 hours. Following treatment, the cells were fixed, permeabilized, blocked, and incubated with primary antibodies at 4 ℃ overnight. After removing the primary antibodies, fluorescent secondary antibodies were applied, and the coverslips were mounted using DAPI-containing mounting medium. Images were captured using an Olympus fluorescence microscope (Japan). Each experiment was repeated three times (n=3).

Western blot

Cells were lysed in RIPA buffer (G2002, Servicebio, Wuhan, China) containing protease and phosphatase inhibitors (G2007, Servicebio). Protein concentrations were then determined using the Bicinchoninic Acid Assay. The samples underwent electrophoresis on 6% or 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels before being transferred to polyvinylidene difluoride (PVDF) membranes. After transfer, the membranes were incubated for 30 minutes at 4 ℃ in a rapid blocking buffer (SFC0133, SerfullCare, Tianjin, China). After blocking, the membranes were incubated with anti-collagen-I (Col-I, 67288-1-Ig, Proteintech, Wuhan, China), anti-α-smooth muscle actin (α-SMA, 67735-1-Ig, Proteintech), anti-phospho-mTOR (Ser2448) (p-mTOR, 67778-1-Ig, Proteintech), anti-mTOR (mTOR, 66888-1-Ig, Proteintech), anti-phospho-p70 S6 kinase (S6K) (Thr389/Thr412) (p-S6K, TA3228S, Abmart, Shanghai, China), anti-p70 S6K (S6K, 14485-1-AP, Proteintech) and anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH, 60004-1-lg, Proteintech), anti-heat shock 70 kDa protein 1A (HSP70, 10995-1-AP, Proteintech) overnight at 4 ℃. After incubation with secondary antibodies, we detected the signals using electrochemiluminescence (ECL) reagents (34096, Thermo Fisher, MA, USA) and quantified them with ImageJ software (6.1.0).

Cellular thermal shift assay (CETSA)

CETSA was employed to assess target stabilization in cells upon compound interaction, as previously described (18). In summary, cells at 80% confluency, cultured in 100-mm dishes, were exposed to 20 µM ICA-II or DMSO for 120 minutes. Following treatment, cells were gathered in PBS containing protease inhibitors. The resulting cell suspension was distributed into 5 polymerase chain reaction (PCR) tubes and subjected to heating for 5 minutes at temperatures ranging from 42 to 54 ℃, in increments of 3 ℃. The samples underwent three freeze-thaw cycles in liquid nitrogen, followed by centrifugation at 17,000 g for 20 minutes at 4 ℃. The resulting soluble fractions were subjected to SDS-PAGE analysis and immunoblotting using an anti-mTOR antibody.

Statistical analysis

Results were presented as the mean ± standard deviation (SD). Multigroup comparisons were analyzed using the t-test (for two groups) or one-way analysis of variance (ANOVA) with SPSS 19.0 software. Data visualization was achieved using Graphpad Prism 9.5.0 software. A P value less than 0.05 was considered statistically significant.

Results

The targets of ICA-II

The overall study workflow is presented in Figure 1. Figure 2A illustrates the chemical structure of ICA-II, while its ADME-related properties were sourced from the TCMSP database (Table 1). By intersecting data from CTD, PharmMapper, and Swiss Target Prediction databases, we identified 252 unique targets for ICA-II after eliminating duplicates. Using Cytoscape 3.10.1 software, we constructed a network mapping ICA-II to its targets (Figure 2B).

Figure 1 Flowchart of network pharmacology and experimental study on the anti-fibrotic effects of ICA-II. Scale bar =50 μm. *, P<0.05; ^#, P<0.05. BP, biological process; CC, cellular component; CETSA, cellular thermal shift assay; DMSO, dimethyl sulfoxide; EPC, Edge Percolated Component; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GO, Gene Ontology; ICA-II, icariside II; KEGG, Kyoto Encyclopedia of Genes and Genomes; MCC, Maximum Clique Centrality; MD, molecular dynamics; MF, molecular function; MNC, Maximum Neighborhood Component; PPI, protein-protein interaction; Rg, radius of gyration; RMSD, root mean square deviation; RMSF, root mean square fluctuation; TGF-β1, transforming growth factor-beta 1; α-SMA, alpha-smooth muscle actin.

Figure 2 ICA-II molecular structure and target network. (A) The chemical structure of ICA-II. (B) The network formed by ICA-II and its targets. (C) Venn diagram showing the overlap between ICA-II-related targets and RF-related targets. ICA-II, icariside II; RF, renal fibrosis.

Identifying target genes linked to RF

We searched the GeneCards database for RF-related targets, screening 5,172 targets with scores ≥5.46. Additional targets were retrieved from OMIM, TTD, and MalaCards databases, totaling 226, 43, and 249, respectively. After removing duplicates, 5,223 disease-related targets remained. The Venn diagram revealed 142 overlapping targets between ICA-II and RF (Figure 2C).

Constructing the ICA-II-RF PPI network

The 142 conserved targets were analyzed for PPI network using STRING 11.5 (Figure 3A). PPI results were exported as .tsv files, filtered, and imported into Cytoscape 3.10.1 to create a visualized PPI network (Figure 3B). This network comprises 138 nodes (excluding single-edged targets) and 1,991 edges. Node color intensity reflects degree centrality: darker nodes indicate greater network importance. The average number of neighbors in the PPI network is 28.8, with 54 targets exceeding this value (highlighted in inner two circles). Cluster analysis using molecular complex detection (MCODE) revealed a highly connected subnetwork, grouping targets into three clusters (Figure 3C-3E). The cytoHubba plug-in calculated target significance using Degree, Edge Percolated Component (EPC), Maximum Clique Centrality (MCC), and Maximum Neighborhood Component (MNC) methods. The top 15 targets were identified, and a Venn diagram revealed 10 common targets (AKT1, mTOR, TP53, EGFR, ESR1, CASP3, CTNNB1, BCL2, HSP90AA1, and HSP90AB1) (Figure 3F,3G).

Figure 3 Analysis of common targets. (A) PPI network diagram of common targets. (B) Visual PPI network diagram. (C-E) Cluster analysis-based construction of a PPI network utilizing MCODE plugin [(C) Cluster 1, 41 nodes and 704 edges; (D) Cluster 2, 10 nodes and 15 edges; (E) Cluster 3, 12 nodes and 17 edges]. (F) Venn diagram of intersection among MCC, EPC, MNC, and degree. (G) 10 common targets. EPC, Edge Percolated Component; MCC, Maximum Clique Centrality; MCODE, molecular complex detection; MNC, Maximum Neighborhood Component; PPI, protein-protein interaction.

GO function and KEGG pathway results

To explore ICA-II’s mechanisms in RF treatment, we uploaded the common targets to Metascape for enrichment analysis. This yielded 2,115 GO terms and 205 KEGG pathways. Figure 4A presents the enriched GO terms across biological processes, cellular components, and molecular functions. The Sankey diagram summarizes the relationship between core targets and enriched KEGG pathways (Figure 4B). Notably, ICA-II influences various biological processes, including the PI3K-Akt signaling pathway, lipid metabolism, and atherosclerosis. The enrichment degree of KEGG was measured by P value and gene number.

Figure 4 GO, KEGG enrichment analysis. (A) The histogram of common target GO analysis. (B) Sankey diagram for KEGG enrichment analysis of top 20 signaling pathways of ICA-II against RF and 10 hub targets. BP, biological process; CC, cellular component; GO, Gene Ontology; ICA-II, icariside II; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; RF, renal fibrosis.

Target-pathway network enrichment analysis

We constructed a “target-pathway” network in Cytoscape 3.10.1 using the 10 shared target genes and the top 20 KEGG pathways (Figure 5). Ellipses represent targets, rectangles denote regulatory pathways, and edges indicate interactions between them. More edge connections signify a greater effect. This network reveals how ICA-II’s multiple targets and pathways interact in RF.

Figure 5 Target-pathway network. The hexagon, ellipse and rectangle nodes represent the drug, the common 10 targets, and the pathway, respectively. ICA-II, icariside II.

Molecular docking analysis of ICA-II with core targets

Following network pharmacology screening, 10 core target proteins were identified for molecular docking with ICA-II. Binding affinities were evaluated using docking scores (lower values indicate stronger molecular interactions, with scores <−6.0 kcal/mol typically denoting robust binding). All docked complexes exhibited favorable scores below this threshold (Table 2), validating the network-predicted interactions. Structural visualization of the ligand-receptor binding modes was performed using PyMOL (Figure 6A-6J), demonstrating stable conformational alignment between ICA-II and the active sites of the core targets. This computational evidence supports ICA-II’s potential to modulate key fibrotic pathways through direct protein interactions.

Figure 6 The molecular docking analysis reveals the binding interactions between ICA-II and the 10 core target proteins. (A-J) Molecular docking diagram of mTOR, AKT1, EGFR, ESR1, BCL2, CASP3, CTNNB1, TP53, HSP90AA1, HSP90AB1 and ICA-II. ICA-II, icariside II.

MD simulation

The mTOR protein, selected due to its binding affinity and biological importance in RF, was subjected to MD simulations (Figure 6). Analysis of RMSD demonstrated the structural stability of the mTOR-ICA-II complex, with an average RMSD of 1.0 Å throughout the simulation (Figure 7A). Examination of RMSF profiles highlighted localized movement in key residues of ICA-II-bound mTOR over the 100 ns simulation period (Figure 7B). The complex maintained a fairly steady Rg of approximately 19.0 Å, indicating minimal changes in overall protein structure (Figure 7C). Furthermore, hydrogen bond analysis showed that ICA-II consistently formed 3 hydrogen bonds with mTOR, with occasional occurrences of 4 hydrogen bonds (Figure 7D). Taken together, these results suggest a dynamically stable interaction between ICA-II and mTOR, characterized by both rigid core binding and flexible peripheral contacts.

Figure 7 Molecular dynamics simulation analysis of ICA-II-mTOR protein complex. (A) RMSD, (B) RMSF, (C) Rg, and (D) number of hydrogen bonds over time. ICA-II, icariside II; Rg, radius of gyration; RMSD, root mean square deviation; RMSF, root mean square fluctuation.

ICA-II prevents fibrosis in TGF-β1-treated NRK-49F cells

To assess ICA-II’s nephroprotective potential, we treated TGF-β1-stimulated NRK-49F cells with varying ICA-II concentrations. Cell viability, assessed by CCK-8 assay, revealed no cytotoxicity below 10 µM after 24 or 48 hours (Figure 8A,8B). Western blotting and immunofluorescence analyses showed that TGF-β1 upregulated Col-I and α-SMA expression, effects dose-dependently attenuated by ICA-II (Figure 8C-8H). These findings highlight ICA-II’s ability to counteract fibrotic protein synthesis without affecting cell viability. To determine whether ICA-II ameliorates fibrosis through the mTOR pathway, we assessed key signaling molecules by Western blot and immunofluorescence. TGF-β1 stimulation significantly increased p-mTOR and p-S6K levels, which were markedly reduced by ICA-II treatment (Figure 9).

Figure 8 ICA-II prevents RF in TGF-β1-treated NRK-49F cells (mean ± SD; n=3). (A,B) Effect of ICA-II on the viability of NRK-49F cells. Cells were challenged with increasing concentrations of ICA-II for 24 or 48 h and cell viability was measured by CCK-8 method (n=6). (C-E) Western blot analysis was applied to evaluate the collagen I and ɑ-SMA protein levels in NRK-49F cells after different concentrations of ICA-II treatment for 48 h. (F-H) Collagen I and ɑ-SMA expression were elevated by IF assays, scale bar =50 µm. Significant differences relative to the control group are denoted as ^#, P<0.05; ^##, P<0.01; ^###, P<0.001. Asterisks indicate statistical significance compared to the TGF-β1 group: *, P<0.05; **, P <0.01; ***, P<0.001 vs. TGF-β1. ɑ-SMA, alpha-smooth muscle actin; CCK-8, Cell Counting Kit-8; DAPI, 4’,6-diamidino-2-phenylindole; DMSO, dimethyl sulfoxide; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; ICA-II, icariside II; IF, immunofluorescence; RF, renal fibrosis; SD, standard deviation; TGF-β1, transforming growth factor-beta 1.

Figure 9 ICA-II treatment reverses TGF-β1-induced increases in p-mTOR and p-S6K levels in NRK-49F cells (mean ± SD; n=3). (A-C) Western blot analysis was applied to evaluate the p-mTOR and p-S6K protein levels in NRK-49F cells after different concentrations of ICA-II treatment for 48 h. (D-F) p-mTOR and p-S6K levels were elevated by IF assays, scale bar =50 µm. Significant differences relative to the control group are denoted as ^#, P<0.05; ^###, P<0.001. Asterisks indicate statistical significance compared to the TGF-β1 group: *, P<0.05; **, P <0.01; ***, P<0.001 vs. TGF-β1. DAPI, 4’,6-diamidino-2-phenylindole; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; ICA-II, icariside II; IF, immunofluorescence; SD, standard deviation; TGF-β1, transforming growth factor-beta 1.

ICA-II prevents fibrosis in TGF-β1-treated HK2 cells

To evaluate the potential anti-fibrotic effects of ICA-II on human renal cells, HK2 cells stimulated with TGF-β1 were treated with varying concentrations of ICA-II (Figure 10). The CCK-8 assay showed no significant cytotoxicity at concentrations up to 10 µM after either 24 or 48 hours of treatment (Figure 10A,10B). Western blot and immunofluorescence analyses indicated that TGF-β1 upregulates the expression of Col-I and α-SMA in HK2 cells, an effect that was dose-dependently attenuated by ICA-II (Figure 10C-10E,10H-10J). These findings suggest that ICA-II exhibits anti-fibrotic properties without compromising the viability of human proximal tubular epithelial cells. To investigate the role of the mTOR signaling pathway in the amelioration of fibrosis by ICA-II, western blot and immunofluorescence analyses were performed. The results demonstrated that TGF-β1 stimulation enhanced the phosphorylation levels of mTOR and S6K, and these increases were effectively reversed following ICA-II treatment (Figure 10C,10F-10H,10K,10L).

Figure 10 ICA-II ameliorates RF in TGF-β1-treated HK2 cells (mean ± SD; n=3). (A,B) Effect of ICA-II on the viability of HK2 cells. Cells were treated with increasing concentrations of ICA-II for 24 or 48 h, and cell viability was measured using the CCK-8 assay (n=6). (C-G) Western blot analysis of collagen I, α-SMA protein expression, and p-mTOR/p-S6K phosphorylation levels in HK2 cells after treatment with various concentrations of ICA-II for 48 h. (H-L) Immunofluorescence staining showing expression of collagen I and α-SMA, as well as p-mTOR and p-S6K phosphorylation levels; scale bar =50 µm. Significant differences compared to the control group are indicated by ^##, P<0.01; ^###, P<0.001. Asterisks denote statistical significance compared to the TGF-β1-treated group: *, P<0.05; **, P<0.01; ***, P<0.001. α-SMA, alpha-smooth muscle actin; CCK-8, Cell Counting Kit-8; DAPI, 4’,6-diamidino-2-phenylindole; DMSO, dimethyl sulfoxide; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; ICA-II, icariside II; RF, renal fibrosis; SD, standard deviation; TGF-β1, transforming growth factor-beta 1.

ICA-II attenuates RF in vitro by inhibiting mTOR pathway

To elucidate the role of the mTOR signaling pathway in the anti-fibrotic effects of ICA-II, the mTOR agonist MHY1485 (5 µM) was administered in a TGF-β1-induced fibrotic NRK-49F cell model. Results showed that MHY1485 significantly attenuated or even reversed the anti-fibrotic effects of ICA-II, indicating that mTOR signaling plays a critical role in mediating the anti-fibrotic properties of ICA-II (Figure 11A-11E). Furthermore, CETSA confirmed mTOR’s physical engagement and stabilization against thermal denaturation in NRK-49F cells exposed to ICA-II, aligning with the known principle that binding with a chemical agent enhances the thermal stabilization of cellular proteins in live cells (19) (Figure 11F).

Figure 11 ICAII mitigates TGF-β1-induced fibrosis in NRK-49F cells via mTOR pathway modulation (mean ± SD; n=3). (A-E) Western blot analysis showing the Col-I, α-SMA, p-mTOR and p-S6K protein levels in NRK-49F cells after 48 h of various treatments. (F) CETSA indicating mTOR target engagement by ICA-II in NRK-49F cells. Significant differences relative to the control group are denoted as ^#, P<0.05; ^###, P<0.001; ^####, P<0.0001. Asterisks indicate statistical significance compared to the TGF-β1 group: *, P<0.05; **, P<0.01; ****, P<0.0001 vs. TGF-β1. ^&, indicate statistical significance compared to the TGF-β1 + ICA-II group: ^&, P<0.05; ^&&&&, P<0.0001. ɑ-SMA, alpha-smooth muscle actin; CETSA, cellular thermal shift assay; DMSO, dimethyl sulfoxide; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; ICA-II, icariside II; SD, standard deviation; TGF-β1, transforming growth factor-beta 1.

Discussion

RF, a central pathological process in the progression of CKD towards end-stage renal failure, involves tubular EMT, abnormal ECM deposition, and the establishment of an inflammatory interstitial microenvironment (1). Clinical research has identified the activation of signaling pathways, such as TGF-β/Smad and PI3K-Akt/mTOR, as critical factors driving this fibrotic process (9). However, current monotherapy strategies targeting individual pathways face limitations due to suboptimal efficacy and off-target toxicities (5). Traditional agents like the mTOR inhibitor everolimus, while demonstrating some antifibrotic effects, are constrained by their mechanistic limitations and inconsistent clinical responses, emphasizing the need for more comprehensive therapeutic strategies (20). ICA-II, a biologically active metabolite derived from icariin in Epimedium (Huperziaceae), displays a range of biological activities, including antioxidant, anti-inflammatory, anti-apoptotic, and anti-cancer properties (21). Aberrant inflammation and oxidative stress are hallmark features of fibrotic progression. ICA-II demonstrates significant anti-fibrotic efficacy across multiple fibrosis models by inhibiting inflammatory cytokine release, mitigating oxidative stress, and reducing collagen deposition through multifaceted mechanisms including modulation of macrophage polarization, apoptosis, autophagy, and lipid metabolism. In pulmonary fibrosis, it suppresses M2 macrophage accumulation by inhibiting the WNT/β-catenin and PI3K/Akt pathways, thereby downregulating M2 markers (CD163, Arg-1, CD206) (22,23). For cardiac fibrosis, ICA-II alleviates oxidative stress by inhibiting the apoptosis signal-regulating kinase 1-c-jun N-terminal kinase (ASK1-JNK)/p38 pathway, downregulates pro-apoptotic proteins (Bax, p53), and counteracts hypertension- and hypertrophy-induced myocardial fibrosis and remodeling through suppression of TGF-β1/Smad2, nuclear factor kappa-B (NF-κB)/p65, matrix metalloproteinases/tissue inhibitor of metalloproteinase-1 (MMP/TIMP-1), and mTORC1/p70S6k signaling (24-27). In RF, it ameliorates lipotoxicity-induced interstitial fibrosis by upregulating PPARα and its downstream fatty acid oxidation proteins [carnitine palmitoyltransferase 1A (CPT-1α), short-chain acyl-CoA dehydrogenase (ACADSB)], thereby improving mitochondrial function (including antioxidant capacity and NAD⁺/NADH ratio) and inhibiting lipid deposition (14). Against radiation-induced bladder fibrosis, ICA-II reduces inflammatory mediators and collagen deposition, with its mechanism involving high-affinity interactions with key molecular targets such as histone h3.7 (H3F3C), interferon-stimulated gene 15 (ISG15), secreted phosphoprotein 1 (SPP1), and lipocalin 2 (LCN2) (28). Furthermore, ICA-II attenuates subarachnoid fibrotic remodeling and improves hemorrhagic hydrocephalus by inhibiting TGF-β1/p-Smad2/3 and CTGF expression (29). These findings underscore the substantial clinical potential of ICA-II as a multi-target, multi-organ protective anti-fibrotic agent.

Recent studies also suggest its potential in managing diabetes and its associated complications, particularly diabetic nephropathy (15,30). However, the precise molecular mechanisms through which ICA-II exerts its antifibrotic effects are still poorly understood. This study aims to bridge this gap by integrating network pharmacology with in vitro experimental validation, thereby providing a comprehensive understanding of ICA-II’s multitargeted regulatory mechanisms in RF and laying the groundwork for the development of innovative antifibrotic therapies.

A drug-target interaction network was systematically built by integrating multiple databases, revealing 252 potential targets for ICA-II. By referencing RF-specific gene databases such as GeneCards, OMIM, TTD, and MalaCards, we refined this list to 142 targets directly linked to fibrosis. Advanced analyses, including PPI networks, MCODE clustering, and cytoHubba algorithms, pinpointed 10 key hub targets: AKT1, MTOR, EGFR, ESR1, BCL2, CASP3, TP53, CTNNB1, HSP90AA1, and HSP90AB1. These targets stood out with high node degrees (≥10), highlighting their critical positions within the fibrosis regulatory network. Functional enrichment analyses (GO/KEGG) further illuminated ICA-II’s ability to modulate several fibrosis-related pathways, notably the PI3K-Akt signaling, lipid metabolism and atherosclerosis, focal adhesion, AGE-RAGE signaling in diabetic complications, EGFR tyrosine kinase inhibitor resistance, and the MAPK signaling pathway. Molecular docking studies provided evidence of strong binding affinities between ICA-II and all 10 targets, with binding energies less than −6.0 kcal/mol. Notably, the interactions with mTOR, BCL2, EGFR, AKT1, and HSP90AA1 were particularly potent, exhibiting binding energies below -12 kcal/mol.

mTOR, a member of the phosphatidylinositol 3-kinase-related kinase family, is a serine/threonine protein kinase. It consists of 2549 amino acids and encompasses various domains: a catalytic kinase domain, an FKBP12-rapamycin-binding (FRB) domain, an autoinhibitory domain, multiple HEAT repeat motifs, as well as FAT (FRAP-ATM-TRRAP) and FATC (FAT C-terminal) domains. The FRB domain specifically facilitates rapamycin’s inhibitory effect by binding to the immunosuppressive protein FKBP12 (31). mTOR operates within two distinct multiprotein complexes: mTOR complex 1 (mTORC1), comprising Raptor, mLST8, DEPTOR, and PRAS40, and mTOR complex 2 (mTORC2), which includes Rictor, mSin1, Protor, and DEPTOR. These complexes integrate signals related to growth factors, nutrients, and cellular energy status, thereby regulating cellular metabolism and growth (32). mTORC1 promotes protein synthesis by phosphorylating p70S6K (enhancing ribosome biogenesis and translational initiation) and 4E-BP1/2 (lifting their inhibition on eIF4E to activate cap-dependent translation), while also suppressing autophagy-related genes like Unc-51 like autophagy activating kinase 1 (ULK1) (33). Conversely, mTORC2 regulates cell survival through the phosphorylation of Akt at the Ser473 site and serum- and glucocorticoid-inducible kinase 1 (SGK1), and preserves cytoskeletal integrity by modulating actin-associated proteins such as p21-activated kinase 1/2 (PAK1/2) (31). Disruptions in this pathway have been linked to malignancies (including renal cell carcinoma and neuroendocrine tumors), metabolic disorders (such as insulin resistance and diabetes), and neurodegenerative diseases. Therapeutic strategies targeting this pathway, notably rapamycin (specific to mTORC1) and Torin1 (inhibiting both mTORC1 and mTORC2), have emerged as treatments for cancer and autoimmune diseases (34).

All resident renal cell types, including fibroblasts, tubular epithelial cells, pericytes, and endothelial cells, are involved in the initiation and progression of RF. This pathological process is marked by abnormal ECM deposition and the formation of an inflammatory interstitial microenvironment, with the mTOR signaling pathway playing a pivotal regulatory role (35). Research has shown that during RF, the mTOR pathway becomes hyperactivated due to hypoxia, oxidative stress, and profibrotic factors such as TGF-β and indoxyl sulfate, which drive excessive activation of mTORC1 in the kidney. This, in turn, promotes fibroblast transdifferentiation and fibrosis progression (36). mTOR inhibitors like rapamycin have been found to significantly ameliorate fibrosis in various nephropathy models, including ischemia-reperfusion injury, transplant nephropathy, doxorubicin-induced nephropathy, unilateral ureteral obstruction (UUO), and glomerular diseases (37). The biological effects of mTOR are mediated through two distinct complexes: mTORC1, primarily responsible for regulating cell metabolism and protein synthesis, and mTORC2, which controls cell survival and cytoskeletal remodeling (32). In glomerular mesangial cells, TGF-β1 activates the Pten-Akt-mTORC1 axis, leading to the induction of miR-21 expression. This drives mesangial cell hypertrophy and matrix expansion, accelerating glomerulosclerosis (38). In tubular epithelial cells, mTORC1 activation worsens fibrosis by inhibiting autophagy and promoting EMT, a process that can be reversed with rapamycin treatment (39). In renal fibroblasts (specifically the NRK-49F cell line), TGF-β1 activates both mTORC1 and mTORC2 in a time- and dose-dependent manner. Inhibition of this pathway using rapamycin significantly suppresses fibroblast activation (40). In UUO models, both mTORC1 and mTORC2 are activated in interstitial myofibroblasts, with mTORC1 overactivation being a key driver of interstitial fibrosis. Genetic deletion of Rictor, a core component of mTORC2, in fibroblasts reduces disease progression in UUO mice (41). Additionally, mTORC2 signaling in macrophages is crucial for M2 polarization, and its activation is closely linked to fibrosis induced by UUO or ischemia-reperfusion injury (42). Zhang et al. (43) demonstrated that ICA-II disrupts aberrant energy homeostasis in sarcoma cells by inhibiting the mTORC1–4E-BP1 axis, thereby suppressing cell proliferation. This process is primarily mediated through specific inhibition of mTORC1 rather than mTORC2. Experiments showed that knockdown of Raptor, a specific component of mTORC1, significantly attenuated the inhibitory effect of ICA-II on glycolysis, whereas knockdown of Rictor, a specific component of mTORC2, did not affect its efficacy. These results indicate that the core mechanism by which ICA-II regulates energy metabolism relies on the suppression of mTORC1 signaling. Furthermore, ICA-II promotes dephosphorylation of PRAS40 at Thr246, disrupts mTORC1 complex assembly, and inhibits its activity in a TSC2-independent manner, without affecting mTORC1 localization to lysosomes. Collectively, these findings suggest that ICA-II exerts a highly selective effect on the mTOR pathway, specifically targeting mTORC1 but not mTORC2.

Further studies should validate the specific inhibition of mTORC1 by ICA-II in RF models and human primary renal cells. Additionally, employing mTORC1/2 double-knockout models could help elucidate the interplay between these complexes in fibrosis, thereby providing a deeper theoretical foundation for developing anti-RF strategies based on mTORC1-specific inhibition.

Autophagy, a dynamic cellular process responsible for degrading damaged organelles and macromolecules, exhibits complex interactions with mTOR signaling. mTORC1 negatively regulates autophagy by promoting the formation of TOR-autophagy space coupling compartments (TASCCs). These TASCCs contribute to enhanced secretion of profibrotic cytokines and progression of fibrosis (44). In cases of severe renal injury, TASCC formation intensifies in tubular epithelial cells, thereby accelerating fibrotic processes (45).

Classical mTOR inhibitors such as rapamycin and everolimus have undergone extensive validation (46). However, recent investigations underscore the potential of natural compounds as multitargeted modulators of mTOR signaling (34). Vitexin, for instance, alleviates lipid metabolism disorders and hepatic injury in obese mice through the PI3K/AKT/mTOR/SREBP-1c pathway (47). Cordyceps cicadae, on the other hand, targets PI3K/mTOR-mediated autophagy to alleviate lupus nephritis (48). Additionally, formulations from traditional Chinese medicine (TCM) exhibit mTOR-dependent antifibrotic mechanisms. Qingshen granules and Dendrobium mixture hinder dendritic cell metabolism and function through the PI3K-AKT-mTOR pathway, ultimately activating renal to alleviate tubulointerstitial injury (9,49). Qi-dan-dihuang decoction improves diabetic nephropathy by modulating the p38MAPK/AKT/mTOR axis (50). These collective findings underscore the significance of mTOR as a pivotal target for multifaceted antifibrotic strategies. Furthermore, the network pharmacology approach employed in this study identifies mTOR as a potential target for ICA-II.

Other key hub targets, such as BCL2, EGFR, Akt, and HSP90AA1, hold significant importance in renal damage and recovery processes. Proteins from the BCL2 family are crucial regulators of apoptosis (51,52), particularly BNIP3, which adjusts mitochondrial autophagy, providing protection from acute kidney injury (53). BCL2 itself counterbalances diabetic nephropathy by regulating autophagy and apoptosis (54). EGFR, being a tyrosine kinase receptor, finds its place as a therapeutic target in renal cancer treatment owing to its involvement in cell multiplication and chemoresistance (55). Akt, a critical component of the PI3K/Akt pathway (56), contributes to the advancement of CKD via several mechanisms including inflammation, oxidative stress, EMT, and autophagy (57). Additionally, HSP90AA1, a preserved molecular chaperone, lessens collagen formation in cardiac fibroblasts and demonstrates potential antifibrotic effects in kidney tissue (58,59).

Molecular docking revealed that ICA-II interacts with the FRB domain of mTOR, potentially disrupting the mTOR complex’s conformation and inhibiting kinase activity. MD simulations further supported the formation of a stable ICA-II-mTOR complex, as indicated by an RMSD of less than 1.0 Å. In vitro experiments showed that ICA-II suppressed TGF-β1-induced expression of Col-I and α-SMA in NRK-49F cells in a dose-dependent manner and reduced the phosphorylation of p-mTOR/p-S6K, thus indicating mTOR pathway inhibition. Partial reversal of this effect by the mTOR activator MHY1485 suggests specificity. Additionally, CETSA indicated increased thermal stability of mTOR upon ICA-II treatment, suggesting direct physical interaction or conformational changes. These results broaden the therapeutic scope of natural small molecules targeting mTOR and pave the way for developing safe and effective antifibrotic agents.

This integrated approach, combining network pharmacology, molecular modeling, and experimental validation, establishes a “component-target-pathway-phenotype” framework. It serves as a methodological blueprint for elucidating the bioactive components of TCM. However, the study’s reliance on cell models poses limitations, necessitating future in vivo investigations to assess tissue-specific effects. Although mTOR’s central role is well-documented, further exploration is needed to understand ICA-II’s synergistic regulation of other key targets. Pharmacokinetic parameters also require investigation. Future research will employ single-cell transcriptomics and spatial multi-omics to delve into ICA-II’s cell-type-specific regulatory networks within the kidney.

Conclusions

Network pharmacology identified 10 potential therapeutic targets for ICA-II in RF (AKT1, mTOR, EGFR, ESR1, BCL2, CASP3, TP53, CTNNB1, HSP90AA1, and HSP90AB1). Computational simulations confirmed strong ICA-II-mTOR binding affinity and conformational stability. In vitro experiments validated ICA-II’s direct interaction with mTOR and its capacity to inhibit mTOR signaling, thereby attenuating fibrosis. This study provides a robust theoretical foundation for further mechanistic investigations into ICA-II’s therapeutic potential in RF.

Acknowledgments

We extend our heartfelt thanks to our families. Additionally, we wish to convey our deep appreciation and gratitude to the researchers and staff members involved in the development of the aforementioned software and databases.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-395/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-395/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-395/prf

Funding: This work was supported by Tianjin Education Commission Research Program Project (No. 2023YXZX06), Tianjin Institute of Urology Talent Funding Programme in 2023 (Nos. MYSRC202318 and MYSRC202319) and Tianjin High-Level Healthcare Talent Development Program (No. TJSQNYXXR-D2-146).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-395/coif). Z.C.X. serves as an unpaid executive editor of Translational Andrology and Urology from March 2025 to February 2027. All authors report that this work was supported by Tianjin Education Commission Research Program Project (No. 2023YXZX06), Tianjin Institute of Urology Talent Funding Programme in 2023 (Nos. MYSRC202318 and MYSRC202319) and Tianjin High-Level Healthcare Talent Development Program (No. TJSQNYXXR-D2-146). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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