Abstract
Background/Objectives: Glucocorticoid-induced osteonecrosis of the
femoral head (GIOFH) is a debilitating condition resulting from
impaired bone metabolism and vascular disruption due to prolonged
glucocorticoid use. This study aimed to explore the therapeutic
potential of salvigenin, a flavonoid with antioxidative and
estrogen-like properties, in alleviating GIOFH by modulating estrogen
receptor alpha (ESR1) pathways. Methods: A network pharmacology
approach was utilized to identify salvigenin’s potential targets and
their association with GIOFH. Protein–protein interaction networks,
along with Gene Ontology and KEGG pathway analyses, were conducted to
clarify salvigenin’s multi-target mechanisms. Molecular docking and
dynamics simulations assessed the interaction between salvigenin and
ESR1. Experimental validation included in vitro assays on MG63 cells
treated with dexamethasone (Dex) to mimic GIOFH, evaluating oxidative
stress, apoptosis, osteogenic differentiation, and ESR1 expression.
Results: Network analysis identified ESR1, NOS3, and MMP9 as key hub
targets of salvigenin. Molecular docking and dynamics simulations
confirmed stable binding of salvigenin to ESR1. Salvigenin
significantly reduced Dex-induced oxidative stress and apoptosis in
osteoblasts while restoring osteogenic differentiation and ESR1
expression. Functional assays showed improved mineralized nodule
formation, ALP activity, and mitochondrial integrity in
salvigenin-treated cells. Conclusions: Salvigenin exhibits significant
therapeutic potential in addressing GIOFH through ESR1-mediated
pathways. These results offer a strong foundation for future
translational studies and the development of salvigenin-based therapies
for glucocorticoid-induced bone disorders.
Keywords: network pharmacology, salvigenin, osteonecrosis of femoral
head, osteoblast apoptosis, oxidative stress
1. Introduction
Glucocorticoid-induced osteonecrosis of the femoral head (GIOFH) is a
severe condition marked by femoral head collapse resulting from
disrupted bone metabolism and reduced blood supply, commonly caused by
extended glucocorticoid exposure [[34]1]. GIOFH accounts for a
significant proportion of non-traumatic osteonecrosis cases worldwide,
predominantly affecting middle-aged adults [[35]2]. According to
studies, the incidence of GIOFH has been reported to reach 9–40% in
patients undergoing prolonged glucocorticoid therapy, particularly
among those with conditions requiring long-term steroid treatment, such
as systemic lupus erythematosus and rheumatoid arthritis [[36]3].
Despite advances in glucocorticoid therapy, the associated risk of
GIOFH remains a critical clinical challenge, often leading to severe
disability and necessitating surgical interventions, such as total hip
replacement [[37]4]. The pathophysiology of GIOFH involves complex
interactions between oxidative stress, apoptosis of osteocytes,
vascular dysfunction, and disrupted bone remodeling
[[38]5,[39]6,[40]7,[41]8]. Prolonged glucocorticoid exposure triggers
excessive production of reactive oxygen species (ROS), disrupts
mitochondrial integrity, and inhibits angiogenesis, collectively
undermining the survival of osteoblasts and osteocytes [[42]9].
Moreover, glucocorticoids alter cellular survival pathways, notably the
PI3K/AKT and MAPK cascades, aggravating bone tissue injury [[43]10].
These multifaceted mechanisms underscore the urgent need for novel
therapeutic strategies targeting the molecular basis of GIOFH.
Emerging evidence indicates that estrogen receptor alpha (ESR1) is
crucial for preserving bone homeostasis and promoting vascularization
through its mediation of estrogen’s protective effects [[44]11]. ESR1
activation has been shown to counteract oxidative stress, promote
osteogenic differentiation, and enhance angiogenesis, all of which are
critical for preserving bone integrity in glucocorticoid-induced
conditions [[45]12]. Furthermore, ESR1 is involved in the regulation of
mitochondrial function and cellular antioxidant defenses, highlighting
its potential as a therapeutic target for GIOFH [[46]13].
Salvigenin, a naturally occurring flavonoid derived from medicinal
plants such as Artemisia species (e.g., Artemisia annua), Salvia
officinalis (sage), and Tanacetum parthenium (feverfew), has attracted
significant attention for its diverse pharmacological properties,
including anti-inflammatory, antioxidative, and estrogen-like
activities [[47]14]. Salvigenin has shown efficacy in regulating
oxidative stress and preventing apoptosis across diverse disease
models, such as neurodegenerative and cardiovascular disorders
[[48]15,[49]16]. Its structural similarity to estrogen-related
compounds suggests a potential role in activating ESR1, although its
specific molecular interactions and therapeutic potential in GIOFH
remain largely unexplored.
Network pharmacology, as a systems-level approach, provides a robust
framework for elucidating the multi-target mechanisms of salvigenin by
integrating chemical, biological, and pharmacological data [[50]17].
This method facilitates the discovery of critical targets and pathways
underlying disease mechanisms and therapeutic effects [[51]18].
Integrating molecular docking with experimental validation, network
pharmacology connects computational models to clinical applications,
providing novel perspectives for multi-target drug development
[[52]19].
This study utilized network pharmacology to explore the therapeutic
potential of salvigenin in modulating ESR1 and its associated pathways
to mitigate GIOFH. Protein–protein interaction (PPI) networks, Gene
Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway analyses were performed to develop a detailed
interaction map. Additionally, molecular docking, alongside both in
vitro and in vivo experiments, confirmed salvigenin’s efficacy in
alleviating glucocorticoid-induced oxidative stress, maintaining
osteocyte viability, and enhancing bone regeneration through ESR1
activation. These results offer valuable insights into salvigenin’s
role as a multi-target therapeutic agent for GIOFH and
glucocorticoid-induced bone diseases.
2. Materials and Methods
2.1. Prediction of Potential Targets for Salvigenin
The potential targets of salvigenin were identified from multiple
databases, including TCMSP ([53]https://old.tcmsp-e.com/tcmsp.php,
accessed on 1 December 2024), SuperPred
([54]https://prediction.charite.de/, accessed on 1 December 2024),
SwissTargetPrediction ([55]http://www.swisstargetprediction.ch/,
accessed on 1 December 2024), and BindingDB
([56]https://www.bindingdb.org/rwd/bind/index.jsp, accessed on 1
December 2024). These databases utilize the structural features of the
compound and previously known target data to provide a comprehensive
range of potential targets. Meanwhile, targets related to osteonecrosis
of the femoral head were retrieved from GeneCards
([57]https://www.genecards.org/, accessed on 1 December 2024) and
DisGeNET ([58]https://www.disgenet.org/, accessed on 1 December 2024)
using the keyword “Osteonecrosis of Femoral Head”. These sources
integrate multidimensional data linking genes to diseases, ensuring the
reliability and completeness of the retrieved target data. To
standardize the analysis, gene symbols from all identified targets were
unified using the UniProt database ([59]https://www.uniprot.org/,
accessed on 1 December 2024). Finally, common targets of salvigenin and
osteonecrosis of femoral head were identified through intersection
analysis, providing a foundation for investigating the molecular
mechanisms of salvigenin in treatment.
2.2. Building the Protein Interaction Network and Identifying Key Targets
To investigate the shared targets of salvigenin and osteonecrosis of
the femoral head, overlapping targets were analyzed using the STRING
database (Version 12.0, [60]https://cn.string-db.org/, accessed on 1
December 2024). The species was restricted to humans (Homo sapiens),
and a medium confidence score threshold (>0.9) was applied to ensure
high-quality PPI data. The network generated by STRING was exported and
visualized with Cytoscape 3.9.1. In Cytoscape, the CytoHubba plugin
evaluated the significance of each target within the PPI network. Among
the available algorithms, the maximal clique centrality (MCC) method,
known for its robustness in identifying hub nodes in complex networks,
was applied. Targets were ranked based on their MCC scores, producing a
prioritized list of key targets mediating salvigenin’s effects on
GIOFH. This approach aids in identifying candidates for further
experimental validation.
2.3. Differential Expression Analysis
The raw count data for [61]GSE112101 were retrieved from the Gene
Expression Omnibus (GEO) database. This dataset consists of
transcriptomic sequencing of nine primary human cell types treated with
glucocorticoids. For our analysis, we selected osteoblasts to
investigate the impact of glucocorticoid treatment on osteoblast gene
expression. Pre-processing and analysis were conducted using the DESeq2
package (version 1.38.0) in R. Initially, low-expression genes were
filtered out by retaining those with at least 10 counts in over 50% of
the samples. Library size normalization was applied using the
median-of-ratios method implemented in DESeq2. Differential expression
analysis was performed by fitting each gene to a negative binomial
generalized linear model, and significance was evaluated through the
Wald test. Genes with an adjusted p-value < 0.05 (Benjamini–Hochberg
correction) and an absolute log2 fold change > 1 were classified as
significantly differentially expressed. Data visualization included
volcano plots and heatmaps, generated using the ggplot2 package.
Subsequently, GO and KEGG pathway enrichment analyses were carried out
on these significantly altered genes to determine their functional
implications.
2.4. KEGG and GO Enrichment Analysis
KEGG pathway enrichment and GO functional annotation were performed
using the R packages clusterProfiler and org.Hs.eg.db. The shared
targets of salvigenin and GIOFH were first mapped from gene symbols to
ENTREZ IDs through the bitr function in the clusterProfiler package,
ensuring compatibility with downstream analyses. KEGG enrichment
analysis utilized the enrichKEGG function with parameters set to Homo
sapiens (hsa) and thresholds of p < 0.05 and q < 0.05. Similarly, GO
annotation was conducted across the biological process (BP), molecular
function (MF), and cellular component (CC) categories using the
enrichGO function. Pathways and GO terms meeting significance criteria
(p < 0.05) were visualized with the enrichplot and ggplot2 packages,
offering insights into the biological functions and mechanisms
underlying salvigenin’s therapeutic effects on GIOFH.
2.5. Gene Set Enrichment Analysis (GSEA)
GSEA was conducted using the clusterProfiler package (Version 4.8.3) in
R, focusing on KEGG and GO databases. The input data consisted of a
ranked gene list generated from the DESeq2 differential expression
results, sorted by log2 fold change values in descending order. Default
parameters were used for the analysis, and significantly enriched
pathways were identified with an FDR threshold of <0.25.
2.6. Molecular Docking
The crystal structure of the protein target was retrieved from the PDB
website (Protein Data Bank, [62]https://www.rcsb.org/, accessed on 1
December 2024), and the three-dimensional structure of the ligand was
downloaded from the PubChem database
([63]https://pubchem.ncbi.nlm.nih.gov/, accessed on 1 December 2024).
The protein structure was pre-processed using Pymol (version 2.5.0,
[64]https://pymol.org/, accessed on 1 December 2024), including the
removal of water molecules, small ligands, and other heteroatoms.
Hydrogen atoms and charges were added to the protein and ligand
structures using AutoDock Tools (version 1.5.6), and the ligand’s
rotatable bonds were identified and defined. The docking site was
determined based on the binding pocket of the co-crystallized ligand or
predicted through binding site prediction tools. The docking grid box
was centered on the active site, with dimensions set to accommodate the
ligand and ensure optimal sampling of the binding region. The docking
simulations were analyzed using Autodock Vina (version 1.1.2), with
exhaustiveness set to 8 to balance computational efficiency and
accuracy. Post-docking analysis was performed to evaluate binding free
energies (ΔG), and docking poses with the lowest ΔG values were
considered as the most probable binding conformations. The docking
results were visualized and dissected using Pymol and Discovery Studio
Visualizer (version 2019, [65]http://www.discoverystudio.net/, accessed
on 1 December 2024). Additionally, theoretical inhibition constants (Ki
and pKi) were calculated using the binding free energies based on the
following equations:
[MATH:
Ki
=eΔGRT :MATH]
(1)
[MATH:
pKi=−lg(Ki) :MATH]
(2)
Here, R represents the universal gas constant, and T represents the
absolute temperature. These analyses provided quantitative insights
into the binding affinity and potential inhibitory effects of the
ligand on the target protein.
2.7. Molecular Dynamics Simulations
Molecular dynamics (MD) simulations were conducted using Gromacs 2022
to investigate interactions between the protein and the small-molecule
ligand. The ligand was parameterized using the GAFF force field, while
the AMBER14SB force field and TIP3P water model were applied to the
protein and solvent to ensure the physicochemical accuracy of the
system. During system construction, all water molecules, small ligands,
and extraneous heteroatoms were removed, and periodic boundary
conditions were employed to simulate biomolecular behavior in an
infinite system. To maintain computational stability and precision,
hydrogen bonds were constrained using the LINCS algorithm, with a time
step of 2 femtoseconds. Electrostatic interactions were computed using
the Particle Mesh Ewald (PME) method, with a cutoff distance set to 1.2
nm. Non-bonded interactions were updated every 10 steps. The
temperature of the system was regulated at 298 K via the V-rescale
coupling method, while the pressure was stabilized at 1 bar using the
Berendsen coupling method, replicating physiological conditions. The
simulation was divided into two stages: equilibrium simulations under
NVT (constant volume and temperature) and NPT (constant pressure and
temperature) ensembles ensured system stabilization, followed by 100
nanoseconds of production MD simulations. System snapshots were saved
every 10 picoseconds to capture dynamic conformational changes. For
trajectory analysis, VMD and PyMOL were used to visualize the stability
of the ligand within the binding site of the protein. Binding free
energies (ΔG) were calculated using the g_mmpbsa tool to quantify the
binding affinity between the protein and ligand. Additionally, the
contribution of key residues at the ligand-binding site could be
extracted to provide insights into molecular interactions, offering
guidance for further optimization of drug candidates.
2.8. Cell Culture of MG63 Cells
The MG63 human osteoblast-like cell line was sourced from Procell Life
Science & Technology Co., Ltd. (Wuhan, China). The cells were
maintained in Dulbecco’s Modified Eagle Medium (DMEM, Servicebio,
Wuhan, China), supplemented with 10% fetal bovine serum (FBS, Procell,
Wuhan, China) and 1% penicillin–streptomycin solution (Servicebio,
China). Cultures were incubated at 37 °C in a humidified atmosphere
containing 5% CO[2]. The culture medium was replaced every two days to
maintain optimal cell health. When cells reached 80–90% confluency,
they were subcultured using 0.25% trypsin-EDTA (Servicebio, China). The
MG63 cell line was selected for this study because it is a widely used
human osteoblast-like cell model in bone metabolism research,
particularly in studies involving osteogenic differentiation,
apoptosis, and oxidative stress. MG63 cells retain key osteoblastic
characteristics and have been extensively utilized in in vitro models
of bone-related diseases, including those induced by glucocorticoids.
Their well-characterized biological properties and reproducibility make
them an appropriate choice for investigating the potential protective
effects of salvigenin on osteoblast function.
2.9. CCK-8
MG63 cells were utilized to assess the protective effects of salvigenin
(MedChemExpress, Shanghai, China) against dexamethasone (Dex,
MedChemExpress, Shanghai, China)-induced cytotoxicity, a cellular model
for GIOFH. Cells were seeded at a density of 5 × 10^3 cells per well in
96-well plates and cultured overnight in DMEM supplemented with 10% FBS
and 1% penicillin–streptomycin to allow adherence. Subsequently, the
cells were treated with 200 μM Dex for 24 h to induce cytotoxicity,
followed by an additional 24-h treatment with varying concentrations of
salvigenin (e.g., 0, 10, 20, 30, 40, 50 μM). Control groups received
equivalent volumes of DMSO without Dex or salvigenin. Cell viability
was evaluated using the Cell Counting Kit-8 (CCK-8, Beyotime, China)
following the manufacturer’s instructions. Briefly, 10 μL of CCK-8
solution was added to each well, and the plates were incubated at 37 °C
for 2 h. Absorbance was measured at 450 nm with a microplate reader
(ThermoFisher Multiskan FC, Shanghai, China).
The percentage of cell viability was calculated using the formula:
[MATH:
Cell Viabilit
y%=ODsample−ODb<
/mi>lank
ODcon<
/mi>trol−ODblank×100 :MATH]
2.10. Apoptosis Detection
Osteoblast apoptosis was evaluated using an Annexin V-FITC/PI
(Beyotime, Shanghai, China) dual-staining assay. MG63 cells were seeded
into 24-well plates at a density of 3 × 10^4 cells per well and treated
with 200 μM Dex at 37 °C for 24 h to induce apoptosis. After treatment,
cells were detached using trypsin, centrifuged at 1000 rpm for 5 min,
and washed twice with PBS. The cell pellet was resuspended in 500 μL of
1× binding buffer at a concentration of 1–5 × 10^5 cells/mL. To stain
the cells, 5 μL of Annexin V-FITC and 10 μL of propidium iodide (PI)
were added to the suspension. The mixture was incubated for 20 min at
room temperature in the dark to prevent photobleaching. Following
incubation, samples were immediately analyzed using a BD Influx flow
cytometer, and apoptotic populations were quantified with CytExpert 2.0
software (Beckman Coulter, Lane Cove, NSW, Australia). Cells positive
for Annexin V but negative for PI (Annexin V+/PI−) were categorized as
early apoptotic cells, while those positive for both Annexin V and PI
(Annexin V+/PI+) were identified as late apoptotic cells. The total
apoptosis rate was determined by summing the percentages of early and
late apoptotic cells. This method provided a precise quantification of
Dex-induced apoptosis and allowed evaluation of salvigenin’s protective
effects on osteoblast survival.
2.11. Alizarin Red S Staining
To investigate the effects of salvigenin on osteogenic differentiation
in dexamethasone (Dex)-treated MG63 cells, Alizarin Red S staining was
utilized to evaluate calcium deposition, a key indicator of matrix
mineralization. MG63 cells were plated in 6-well plates and cultured in
DMEM (Servicebio, China) supplemented with 10% FBS (Procell, China) and
1% penicillin–streptomycin (Servicebio, China). After 24 h of culture,
cells were treated with 200 μM Dex to induce GIOFH, followed by
salvigenin treatment. For osteogenic differentiation, the culture
medium was replaced with DMEM containing 10 mM β-glycerophosphate
(Solarbio, Beijing, China), 50 μg/mL ascorbic acid (Solarbio, Beijing,
China), and 10% FBS. The medium was refreshed every 2–3 days during the
treatment period. At the end of the experiment, cells were washed twice
with phosphate-buffered saline (PBS) and fixed in 4% paraformaldehyde
(Servicebio, China) at room temperature for 15 min. After fixation,
cells were stained with 2% Alizarin Red S solution (pH 4.2,
Sigma-Aldrich, Shanghai, China) for 20 min to detect calcium deposits.
Excess stain was carefully removed by rinsing the cells with distilled
water until the rinse solution became clear. Calcium deposition was
then observed and imaged under a light microscope to qualitatively
assess mineralization.
2.12. Alkaline Phosphatase (ALP) Staining
MG63 cells were seeded into 6-well plates and treated with 200 μM
dexamethasone (Dex) to establish an GIOFH model, followed by salvigenin
treatment in osteogenic differentiation medium. The differentiation
medium consisted of DMEM (Servicebio, China) supplemented with 10 mM
β-glycerophosphate, 50 μg/mL ascorbic acid, and 10% FBS (Procell,
China), with medium refreshed every 2–3 days. On day 7, cells were
fixed with 4% paraformaldehyde for 15 min at room temperature.
Following fixation, an alkaline phosphatase (ALP) staining kit (e.g.,
Beyotime, China) was used according to the manufacturer’s protocol.
After staining, cells were thoroughly rinsed with distilled water and
examined under a light microscope (OLYMPUS IX71, Tokyo, Japan) to
visualize ALP activity. This method provided a qualitative assessment
of the early osteogenic differentiation effects of salvigenin.
2.13. Western Blot Analysis
Western blotting was used to analyze protein expression levels in MG63
osteoblast-like cells. Total protein was extracted using RIPA buffer
with protease and phosphatase inhibitors (Servicebio, China). Protein
concentrations were measured using a BCA assay kit (Beyotime, China),
and equal amounts of protein were loaded onto a 15-lane SDS-PAGE gel
for separation and transferred onto PVDF membranes. To ensure
sufficient biological replicates while minimizing reagent waste, we
adopted a strategic loading approach. Protein samples were loaded into
designated lanes across the gel, and an additional set of samples was
run on the opposite side of the same gel, ensuring each group had at
least three independent biological replicates (n = 3) on a single
membrane. Although only two representative bands per group are
displayed, quantification was performed based on these replicates.
Membranes were blocked with 5% non-fat milk in TBST and incubated with
primary antibodies at 4 °C overnight, followed by HRP-conjugated
secondary antibodies at room temperature for 1 h. After washing,
protein bands were visualized using enhanced chemiluminescence (ECL,
Biosharp, Hefei, China). GAPDH was used as an internal control to
normalize protein loading, ensuring consistency across lanes. Since all
replicates were analyzed on the same membrane, interblot control was
not required. The sources and dilution ratios of all antibodies used in
this study are provided in [66]Table 1. Protein band intensities were
quantified using ImageJ software (Version 1.54g).
Table 1.
The source and dilution concentrations of primary antibodies.
Source Dilutions
ESR1 Proteintech 21244-1-AP 1:1000
Bax Proteintech 50599-2-Ig 1:8000
Bcl2 Proteintech 26593-1-AP 1:2000
Caspase3 Proteintech 25128-1-AP 1:1000
OPN Proteintech 30200-1-AP 1:2000
RUNX2 Proteintech 20700-1-AP 1:1000
GAPDH Proteintech 10494-1-AP 1:10,000
[67]Open in a new tab
2.14. Reactive Oxygen Species (ROS) Detection
Intracellular ROS levels were assessed using a commercially available
ROS detection kit (Beyotime, China), following the manufacturer’s
protocol. MG63 human osteoblast-like cells were cultured in 6-well
plates and exposed to 200 μM dexamethasone (Dex) to induce oxidative
stress, with subsequent salvigenin treatment. After the treatments,
cells were incubated with 10 μM DCFH-DA in serum-free medium at 37 °C
for 30 min in a dark environment to prevent probe degradation. Excess
dye was carefully removed through multiple PBS washes to ensure minimal
background interference. The fluorescence signal, indicative of
intracellular ROS levels, was visualized and captured using a
fluorescence microscope. Representative images were obtained for
qualitative comparisons of ROS generation across experimental groups.
This approach provided insights into the efficacy of salvigenin in
mitigating Dex-induced oxidative stress in MG63 cells.
2.15. Mitochondrial Membrane Potential (ΔΨm) Detection Using JC-1 Assay
The mitochondrial membrane potential (ΔΨm) of MG63 cells was evaluated
using the JC-1 assay kit (Beyotime, China) as per the manufacturer’s
instructions. MG63 cells were cultured in 6-well plates and exposed to
200 μM dexamethasone (Dex) to induce mitochondrial dysfunction,
followed by treatment with salvigenin. Post-treatment, cells were
incubated with JC-1 staining solution at 37 °C for 20 min in a dark
environment to ensure probe stability and prevent photobleaching.
Excess dye was removed through washing steps with JC-1 staining buffer
to eliminate non-specific fluorescence. Fluorescence signals,
indicating changes in mitochondrial membrane potential, were
immediately captured under a fluorescence microscope. This approach
allowed qualitative visualization of mitochondrial health and provided
insights into salvigenin’s protective effects against Dex-induced
mitochondrial damage.
2.16. Immunofluorescence Staining
Immunofluorescence staining was employed to visualize the expression
and localization of target proteins in MG63 human osteoblast-like
cells. Cells were cultured on sterilized glass coverslips placed in
6-well plates. Once cells reached 70–80% confluence, they were treated
with dexamethasone (Dex) and salvigenin according to the experimental
protocol. Following treatment, cells were fixed with 4%
paraformaldehyde for 15 min at room temperature to preserve cellular
structures. To facilitate antibody penetration, cells were
permeabilized using 0.1% Triton X-100 in PBS. Nonspecific binding sites
were blocked with 5% bovine serum albumin (BSA, Servicebio, China)
prepared in PBS. Primary antibodies specific to the target protein
(ESR1, Proteintech, Wuhan, China) were applied overnight at 4 °C. After
washing, fluorophore-conjugated secondary antibodies diluted in PBS
containing 1% BSA were incubated for 1 h at room temperature in the
dark to prevent photobleaching. Coverslips were mounted on glass slides
using a fluorescence mounting medium containing DAPI
(4′,6-diamidino-2-phenylindole, Servicebio, Wuhan China) to
counterstain nuclei. Fluorescence signals were captured using an
upright fluorescence microscope (Olympus BX53, Tokyo, Japan) equipped
with appropriate excitation/emission filters. Representative images
were obtained to evaluate the expression and subcellular localization
of the target protein, providing insights into salvigenin’s effects on
protein dynamics within MG63 cells.
2.17. Animal Model of GIOFH
Male Sprague–Dawley rats (8 weeks old, 200–250 g) were obtained from
the Wuhan Institute of Biological Products (Wuhan, China) and housed in
a controlled environment maintained at 22 ± 2 °C with a 12-h light/dark
cycle. The rats were fed standard laboratory chow with free access to
water and allowed to acclimate for one week prior to the experiment. To
establish the GIOFH model, dexamethasone (Dex, 5 mg/kg; Sigma-Aldrich,
St. Louis, MO, USA) was administered via intramuscular injection twice
weekly over a four-week period. Control animals received equivalent
volumes of saline injections. During the modeling process, rats were
observed daily for signs of stress or adverse reactions, and their body
weights were recorded weekly to monitor general health. At the
conclusion of the experiment, animals were euthanized via CO[2]
asphyxiation, and femoral heads were harvested for histological and
molecular analyses. Bone samples were fixed in 4% paraformaldehyde,
decalcified using 10% EDTA (pH 7.4), and embedded in paraffin for
sectioning. Histological assessment was performed on hematoxylin and
eosin (H&E)-stained sections to evaluate trabecular bone structure and
osteocyte viability.
2.18. Hematoxylin and Eosin (H&E) Staining
Femoral heads were immersed in 4% paraformaldehyde for fixation at room
temperature for 48 h and then decalcified in 10% EDTA (pH 7.4) for a
duration of 4 weeks. Decalcified samples were embedded in paraffin, and
tissue sections with a thickness of 5 μm were prepared for staining.
Hematoxylin and eosin (H&E) staining was conducted following
established protocols to analyze trabecular bone integrity and
osteocyte health. Tissue sections were subjected to deparaffinization
in xylene, followed by gradual rehydration using a series of ethanol
solutions. Hematoxylin staining was performed for 5 min to enhance
nuclear visibility, after which sections were rinsed in running water,
differentiated with 1% acid alcohol, and subsequently treated with
ammonia water to intensify nuclear contrast. Cytoplasmic components
were stained using eosin for 2 min, followed by sequential dehydration
in graded ethanol and xylene. To finalize the preparation, sections
were mounted with neutral resin and analyzed under a microscope
(Olympus BX53, Japan), providing insights into trabecular bone
microstructure and osteocyte morphology.
2.19. Immunohistochemistry (IHC)
Paraffin-embedded sections underwent deparaffinization in xylene and
rehydration through a graded ethanol series before being rinsed with
PBS. For antigen retrieval, the sections were immersed in citrate
buffer (pH 6.0) and heated in a microwave oven for 10 min. After
cooling to room temperature, sections were treated with 3% hydrogen
peroxide for 10 min to eliminate endogenous peroxidase activity,
followed by thorough washing with PBS. To minimize non-specific
binding, sections were incubated with 5% BSA (Servicebio, China) in PBS
for 30 min at room temperature. Primary antibodies specific to the
target protein (e.g., ESR1, Proteintech, China) were applied overnight
at 4 °C, diluted in PBS containing 1% BSA. The following day, sections
were washed multiple times with PBS and incubated with horseradish
peroxidase (HRP)-conjugated secondary antibodies (Proteintech, China)
for 1 h at room temperature. The signal was visualized using
diaminobenzidine (DAB) substrate solution (Beyotime, China) until a
brown color developed, followed by counterstaining with hematoxylin for
nuclear contrast. After washing, sections were dehydrated in a graded
ethanol series, cleared in xylene, and mounted with neutral resin.
Imaging was performed with an upright light microscope (Olympus BX53,
Japan) to assess the localization and expression of the target
proteins.
2.20. Statistical Analysis
All statistical evaluations were conducted using GraphPad Prism
(version 9.0) or R software (version 4.2.0). The results are expressed
as the mean ± standard deviation (SD) from a minimum of three
independent experiments unless stated otherwise. Comparisons between
two groups were performed using an unpaired Student’s t-test, while
one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test
was employed for analyses involving multiple groups. For datasets that
did not follow a normal distribution, non-parametric tests, such as the
Mann–Whitney U test or Kruskal–Wallis test, were utilized. Statistical
significance was defined as p < 0.05. Graphs were created with GraphPad
Prism, and all experiments were conducted in triplicate to ensure
reproducibility. Detailed statistical information, including p-values
and effect sizes, is provided in the corresponding figure legends.
3. Results
3.1. Recognition of Salvigenin Targets for GIOFH Treatment
The molecular structure of salvigenin is depicted ([68]Figure 1A). A
total of 383 GIOFH-related targets were identified, among which 18
overlapped with the predicted targets of salvigenin ([69]Figure 1B).
These overlapping targets suggest that salvigenin has potential
therapeutic relevance for GIOFH.
Figure 1.
[70]Figure 1
[71]Open in a new tab
Target recognition and enrichment analysis of salvigenin for GIOFH. (A)
Chemical structure of salvigenin. (B) Venn diagram showing the
intersection between GIOFH-related and salvigenin-predicted targets,
identifying 18 shared targets. (C) PPI network of the 18 overlapping
targets constructed utilizing the STRING database, with edge confidence
thresholds above 0.9. (D) Hub gene analysis of the PPI network using
MCC scoring, highlighting the top-ranked genes (e.g., ESR1, NOS3, and
MMP9) as critical targets of salvigenin. (E) MCC score ranking of the
18 overlapping genes, with ESR1 identified as the top hub gene. (F)
KEGG pathway enrichment analysis of the shared targets revealed
significant enrichment in critical pathways, including the estrogen
signaling pathway and the thyroid hormone signaling pathway. (G) Gene
Ontology (GO) functional enrichment analysis of the shared targets
highlights biological processes (top panel), cellular components
(middle panel), and molecular functions (bottom panel) significantly
linked to the therapeutic mechanisms of salvigenin.
3.2. Network Analysis of Key Targets
The protein–protein interaction (PPI) network of the 18 intersecting
targets was constructed utilizing the STRING database ([72]Figure 1C).
Significant interactions were observed among the targets, highlighting
their functional importance in GIOFH pathogenesis. Further analysis of
the core network using MCC scoring identified highly interconnected hub
genes ([73]Figure 1D). Genes, such as NOS3, MMP9, and ESR1, showed high
MCC scores, suggesting their central roles in salvigenin’s potential
therapeutic mechanisms ([74]Figure 1E). These hub genes are likely
critical mediators of the compound’s effects on GIOFH.
3.3. Functional and Pathway Enrichment Analysis
Pathway enrichment analysis indicated that salvigenin’s targets are
significantly enriched in key pathways, including the thyroid hormone
signaling pathway, estrogen signaling pathway, and fluid shear
stress-related pathways, among others ([75]Figure 1F). These pathways
are closely associated with critical biological processes underlying
GIOFH, such as oxidative stress regulation and bone metabolism. Gene
Ontology (GO) enrichment analysis categorized these targets into
biological processes, molecular functions and cellular components
([76]Figure 1G). Key biological processes included responses to
oxidative stress, regulation of collagen metabolism, and wound healing.
Enriched molecular functions such as transcription regulator activity
and serine hydrolase activity provide further insights into the
multi-faceted mechanism of salvigenin in GIOFH treatment.
3.4. Transcriptomic and Pathway Insights into Osteoblast Dysfunction Induced
by Glucocorticoid Treatment
RNA-seq analysis was conducted to investigate the transcriptomic
alterations in osteoblasts following glucocorticoid treatment.
Differential expression analysis recognized notable changes in gene
expression, as shown in the volcano plot ([77]Figure 2A). Enrichment
analysis uncovered that these genes were involved in pathways, such as
TNF signaling, apoptosis, and osteoblast differentiation, highlighting
the impact of glucocorticoids on essential biological processes
([78]Figure 2B). Gene Ontology (GO) analysis further identified
significant enrichment in processes, like epithelial cell
proliferation, oxidative stress regulation, and transcriptional
activity, all of which are critical for maintaining osteoblast function
([79]Figure 2C). Hierarchical clustering analysis revealed distinct
expression patterns among the significantly altered genes, with
co-regulated gene modules likely influenced by glucocorticoid treatment
([80]Figure 2D). Notably, GSEA outcomings demonstrated that the ovarian
steroidogenesis pathway was significantly activated ([81]Figure 2F),
whereas estrogen receptor-related modules, including G protein-coupled
estrogen receptor activity and nuclear estrogen receptor activity, were
markedly suppressed ([82]Figure 2H,I). These findings are particularly
significant given the pivotal role of ESR1 (estrogen receptor 1) in
osteoblast function and bone metabolism. Building on prior PPI network
results, ESR1 was recognized as a central hub gene with a high MCC
score ([83]Figure 1D). The suppression of ESR1 under glucocorticoid
treatment suggests that it may play a vital role in mediating the
detrimental effects of glucocorticoids on osteoblast function.
Additionally, glucocorticoid-induced processes, such as cellular stress
response, apoptosis regulation, and extracellular matrix remodeling,
were tightly linked to ESR1-regulated pathways ([84]Figure 2E,G).
Collectively, these findings underscore the critical role of ESR1 as a
promising therapeutic target for alleviating glucocorticoid-induced
osteoblast dysfunction.
Figure 2.
[85]Figure 2
[86]Open in a new tab
Transcriptomic and pathway insights into glucocorticoid-induced
osteoblast dysfunction. (A) Volcano plot showing differentially
expressed genes in osteoblasts following glucocorticoid treatment. (B)
KEGG enrichment analysis of DEGs, highlighting key pathways, such as
the TNF signaling pathway and osteoblast differentiation. (C) Gene
Ontology (GO) enrichment analysis of DEGs categorized into biological
processes, molecular functions, and cellular components. (D) Heatmap of
hierarchical clustering analysis showing distinct expression patterns
of DEGs across experimental conditions. (E) GSEA identifying
significantly enriched pathways, including TNF signaling and oxidative
stress-related pathways. (F) GSEA showing the upregulation of the
ovarian steroidogenesis pathway in glucocorticoid-treated osteoblasts.
(G) GSEA results indicating suppression of pathways related to G
protein-coupled and nuclear estrogen receptor activities. (H)
Enrichment plot for G protein-coupled estrogen receptor activity,
showing marked suppression under glucocorticoid treatment. (I)
Enrichment plot for nuclear estrogen receptor activity, demonstrating
its significant downregulation in response to glucocorticoids.
3.5. Molecular Docking and Dynamics Simulation of Salvigenin Binding to ESR1
To investigate the interaction between salvigenin and ESR1, molecular
docking and molecular dynamics (MD) simulations were conducted. The
docking results revealed that salvigenin binds within the
ligand-binding domain of ESR1, forming key hydrogen bonds and
hydrophobic interactions with residues, such as LEU347, THR347, LYS530,
LEU525, and VAL534, as shown in the docking pose and 2D interaction
diagram ([87]Figure 3A). These interactions suggest a stable and
specific binding mode for salvigenin to ESR1. MD simulations over a 100
ns trajectory confirmed the stability of the salvigenin–ESR1 complex.
The root mean square deviation (RMSD) analysis indicated that the
complex achieved equilibrium early in the simulation, with minimal
fluctuations compared to the free protein and ligand ([88]Figure 3B).
The radius of gyration (Rg) remained stable throughout, reflecting the
structural compactness of the complex ([89]Figure 3C). Additionally,
root mean square fluctuation (RMSF) analysis identified dynamic
flexibility in specific regions, highlighting the stability of the
ligand-binding domain ([90]Figure 3D). Protein–ligand interaction
dynamics, including distance and solvent-accessible surface area
(SASA), further validated the sustained binding of salvigenin to ESR1
([91]Figure 3E,F). The binding free energy decomposition revealed
favorable contributions from van der Waals and electrostatic
interactions ([92]Figure 3H), while key residues, such as GLU353,
ARG394, and ASP351, were identified as major contributors to the
binding energy ([93]Figure 3I). Notably, hydrogen bond analysis showed
consistent interactions between salvigenin and ESR1 throughout the
simulation, further supporting the stability of the complex ([94]Figure
3J). These results demonstrate that salvigenin establishes a stable and
specific interaction with ESR1, providing structural and energetic
evidence for its potential regulatory role in ESR1-mediated pathways.
Figure 3.
[95]Figure 3
[96]Open in a new tab
Molecular docking and molecular dynamics simulations of salvigenin
binding to ESR1. (A) Docking results showing salvigenin binding within
the ligand-binding domain of ESR1. Surface and ribbon models depict the
protein structure, while the detailed 2D interaction map highlights key
hydrogen bonds and hydrophobic interactions with residues LEU347,
THR347, LYS530, LEU525, and VAL534. (B) Root mean square deviation
(RMSD) plot showing structural stability of the protein–ligand complex,
free ligand, and free protein during 100 ns molecular dynamics (MD)
simulations. (C) Radius of gyration (Rg) analysis indicating the
compactness of the ESR1–salvigenin complex throughout the simulation.
(D) Root mean square fluctuation (RMSF) plot identifying flexibility
variations across ESR1 residues, highlighting stable regions in the
ligand-binding domain. (E) Distance analysis showing stable docking
interactions between the ligand and the active site during the MD
simulation. (F) Solvent-accessible surface area (SASA) analysis of the
protein–ligand complex, reflecting sustained interactions with the
solvent environment. (G) Molecular dynamics trajectory showing the
dynamic behavior of the ESR1–salvigenin complex, represented as a
time-lapse ribbon model. (H) Binding energy decomposition over
simulation time, indicating contributions from van der Waals,
electrostatic, and binding energy terms. (I) Per-residue binding energy
analysis highlighting key residues, such as GLU353 and ARG394,
contributing to salvigenin binding. (J) Hydrogen bond occupancy
histogram showing the frequency and stability of hydrogen bonds between
salvigenin and ESR1 throughout the simulation.
3.6. Salvigenin Protects Osteoblasts from Glucocorticoid-Induced Apoptosis
Through ESR1 Modulation
To assess the protective effects of salvigenin on osteoblast viability,
CCK-8 assays were performed. Glucocorticoid (Dex) treatment
significantly reduced cell viability in a dose-dependent manner
([97]Figure 4A), whereas salvigenin alone did not exhibit cytotoxic
effects at the tested concentrations ([98]Figure 4B). Co-treatment with
salvigenin significantly restored cell viability in Dex-treated
osteoblasts in a dose-dependent manner, suggesting its protective role
([99]Figure 4C). Immunofluorescence analysis showed that Dex treatment
dramatically downregulated ESR1 expression in osteoblasts, while
salvigenin co-treatment restored ESR1 expression levels ([100]Figure
4D). Western blot analysis further validated these results, showing
that ESR1 protein levels were significantly reduced by Dex but rescued
by salvigenin treatment ([101]Figure 4E,F). Additionally, salvigenin
modulated the expression of apoptosis-related proteins: Bax
(pro-apoptotic) levels were increased by Dex and reduced by salvigenin
([102]Figure 4G), whereas Bcl-2 (anti-apoptotic) levels were decreased
by Dex and upregulated by salvigenin ([103]Figure 4H). Caspase-3
activation, a hallmark of apoptosis, was significantly induced by Dex
and attenuated by salvigenin ([104]Figure 4I). Flow cytometry analysis
of apoptosis further supported these findings. Dex treatment led to a
significant increase in apoptotic cell populations, while co-treatment
with salvigenin markedly reduced apoptosis rates ([105]Figure 4J,K).
These results demonstrate that salvigenin protects osteoblasts from
glucocorticoid-induced apoptosis, potentially through the restoration
of ESR1 expression and modulation of apoptosis-related signaling
pathways.
Figure 4.
[106]Figure 4
[107]Open in a new tab
Salvigenin protects osteoblasts from glucocorticoid-induced apoptosis
and restores ESR1 expression. (A–C) Cell viability assays using CCK-8
(n = 3). (A) Dose-dependent cytotoxicity of dexamethasone (Dex) in MG63
cells. (B) Dose-response effects of salvigenin on cell viability,
showing no cytotoxic effects up to 50 μM. (C) Salvigenin protects
against Dex-induced cytotoxicity, restoring cell viability in a
dose-dependent manner. * p < 0.05 compared to control; # p < 0.05
compared to Dex. (D) Immunofluorescence analysis of ESR1 expression
(red) in MG63 cells under different treatments: control, Dex, and
Dex+salvigenin. Nuclei are stained with DAPI (blue). Merged images
highlight the restoration of ESR1 expression with salvigenin treatment.
(E–I) Western blot analysis of apoptosis-related proteins and ESR1. (E)
Representative protein bands for ESR1, Bax, Bcl-2, and Caspase-3, with
GAPDH as the loading control. Quantification of (F) ESR1, (G) Bax, (H)
Bcl-2, and (I) Caspase-3 protein levels, showing that salvigenin
reverses Dex-induced apoptotic signaling (n = 3). * p < 0.05, ** p <
0.01, *** p < 0.001. (J,K) Flow cytometry analysis of apoptosis using
Annexin V-FITC/PI staining. (J) Representative dot plots showing early
and late apoptotic cell populations under control, Dex, and
Dex+salvigenin treatments. (K) Quantification of total apoptosis rates,
with salvigenin significantly reducing Dex-induced apoptosis (n = 3).
** p < 0.01.
3.7. Salvigenin Restores Osteogenic Differentiation Impaired by
Glucocorticoids
To evaluate the effects of salvigenin on glucocorticoid-induced
inhibition of osteogenic differentiation, we conducted a series of
functional assays. Alizarin red staining revealed that Dex treatment
significantly reduced mineralized nodule formation in osteoblasts,
indicating impaired osteogenic capacity. Co-treatment with salvigenin
markedly restored mineralized nodule formation, suggesting its
potential to counteract glucocorticoid-induced osteogenic suppression
([108]Figure 5A, upper panel). Similarly, alkaline phosphatase (ALP)
staining showed decreased ALP activity in Dex-treated cells, which was
partially rescued by salvigenin co-treatment ([109]Figure 5A, lower
panel). Immunofluorescence analysis demonstrated that the expression of
osteogenic markers, such as osteopontin (OPN) and RUNX2 was
significantly reduced in Dex-treated osteoblasts. Salvigenin
co-treatment restored the expression of both OPN and RUNX2 to levels
comparable to the control group, highlighting its ability to enhance
osteogenic marker expression ([110]Figure 5B,C). These results
collectively suggest that salvigenin mitigates the deleterious effects
of glucocorticoids on osteogenic differentiation, potentially through
restoring osteogenic marker expression and mineralization capacity.
Figure 5.
[111]Figure 5
[112]Open in a new tab
Salvigenin restores osteogenic differentiation impaired by
glucocorticoids. (A) Alizarin Red S and ALP staining showing
mineralized nodule formation (top panel) and alkaline phosphatase
activity (bottom panel) in MG63 cells under control, Dex-treated, and
Dex+salvigenin conditions. Salvigenin restores osteogenic capacity
reduced by Dex. (B,C) Immunofluorescence analysis of osteogenic
markers. (B) Expression of osteopontin (OPN, red) and (C) runt-related
transcription factor 2 (RUNX2, red) in MG63 cells treated with Dex and
salvigenin. Nuclei were counterstained with DAPI (blue). Merged images
demonstrate salvigenin rescues osteogenic marker expression. (D–F)
Western blot analysis of OPN and RUNX2. (D) Representative protein
bands for OPN, RUNX2, and GAPDH (loading control). (E,F) Quantification
of OPN (E) and RUNX2 (F) protein levels, showing significant
restoration with salvigenin treatment (n = 3). * p < 0.05.
3.8. Salvigenin Suppresses Glucocorticoid-Induced Oxidative Stress in
Osteoblasts
To investigate the effects of salvigenin on glucocorticoid-induced
oxidative stress, we evaluated reactive oxygen species (ROS) levels and
mitochondrial morphology in osteoblasts. DCFH-DA staining revealed a
significant increase in intracellular ROS levels following Dex
treatment, indicating heightened oxidative stress. Co-treatment with
salvigenin markedly reduced ROS levels, suggesting its antioxidant
effects ([113]Figure 6A). Furthermore, JC-1 staining was used to assess
mitochondrial membrane potential, as normal, polarized mitochondria
exhibit red fluorescence, whereas depolarized mitochondria show green
fluorescence. Dex treatment led to mitochondrial depolarization, as
evidenced by an increase in green fluorescence and a decrease in red
fluorescence. In contrast, co-treatment with salvigenin partially
restored mitochondrial polarization, as indicated by an increased
red-to-green fluorescence ratio ([114]Figure 6B). These findings
demonstrate that salvigenin alleviates glucocorticoid-induced oxidative
stress in osteoblasts, potentially by reducing ROS levels and
preserving mitochondrial function, which may contribute to its
protective role in maintaining osteoblast viability and function.
Figure 6.
[115]Figure 6
[116]Open in a new tab
Salvigenin suppresses glucocorticoid-induced oxidative stress and
preserves mitochondrial integrity in osteoblasts. (A) Intracellular
reactive oxygen species (ROS) levels detected using DCFH-DA staining.
Dex treatment significantly increases ROS levels, while co-treatment
with salvigenin reduces oxidative stress. (B) Mitochondrial membrane
potential assessed via JC-1 staining. Normal, polarized mitochondria
exhibit red fluorescence, whereas depolarized mitochondria show green
fluorescence. Dex treatment induces mitochondrial depolarization,
increasing green fluorescence. Salvigenin co-treatment restores
mitochondrial polarization, as indicated by an increased red-to-green
fluorescence ratio.
3.9. Salvigenin Alleviates Glucocorticoid-Induced Femoral Head Necrosis
To assess the protective effects of salvigenin on
glucocorticoid-induced femoral head necrosis, we performed histological
and immunohistochemical analyses of the femoral head. Hematoxylin-eosin
(H&E) staining revealed improved trabecular bone structure.
Histological evaluation using H&E staining ([117]Figure 7A) showed
distinct differences among the experimental groups. In the control
group, the femoral head exhibited well-preserved trabecular bone with
dense osteocyte distribution within intact lacunae. However, in the
Dex-treated group, there were significant pathological changes,
including disorganized trabecular bone, increased empty lacunae, and
reduced osteocyte density, indicative of severe osteonecrosis. Notably,
co-treatment with salvigenin (Dex+salvigenin group) significantly
mitigated these pathological changes, demonstrating improved trabecular
structure, reduced empty lacunae, and enhanced osteocyte preservation.
These results suggest that salvigenin effectively protects against
glucocorticoid-induced femoral head necrosis. Immunohistochemical
staining of ESR1 was performed to evaluate the molecular mechanisms
underlying the protective effects of salvigenin ([118]Figure 7B). The
control group showed strong ESR1 expression, as indicated by dense
brown staining in osteocytes and surrounding bone matrix. In the
Dex-treated group, ESR1 expression was markedly diminished, consistent
with disrupted bone homeostasis and increased osteonecrosis.
Importantly, salvigenin co-treatment significantly restored ESR1
expression, with staining levels approaching those observed in the
control group. These findings indicate that salvigenin enhances ESR1
expression, which may contribute to its protective effects by promoting
bone homeostasis and mitigating glucocorticoid-induced bone damage.
Figure 7.
[119]Figure 7
[120]Open in a new tab
Salvigenin alleviates glucocorticoid-induced femoral head necrosis in
vivo. (A) Hematoxylin and eosin (H&E) staining of femoral head sections
showing trabecular bone structure and osteocyte distribution. The
control group exhibits well-preserved trabecular bone with intact
osteocytes, whereas the Dex-treated group shows disorganized
trabeculae, increased empty lacunae, and reduced osteocyte density.
Salvigenin co-treatment (Dex+salvigenin) significantly restores
trabecular bone integrity and osteocyte preservation. (B)
Immunohistochemical staining for ESR1 in femoral head sections. The
control group shows strong ESR1 expression (brown staining), which is
significantly reduced in the Dex-treated group. Salvigenin co-treatment
restores ESR1 expression levels, indicating its role in mitigating
glucocorticoid-induced bone damage.
4. Discussion
This study explored the potential therapeutic role of salvigenin in
glucocorticoid-induced osteonecrosis of the femoral head (GIOFH),
focusing on its effects on oxidative stress, osteoblast viability, and
osteogenic differentiation via the estrogen receptor alpha
(ESR1)-mediated pathway. Through a combination of network pharmacology,
molecular docking, experimental validation, and animal model studies,
we demonstrated that salvigenin exhibits multi-target pharmacological
activities, highlighting its promise as a therapeutic candidate for
GIOFH.
Network pharmacology analysis revealed that salvigenin’s therapeutic
potential is mediated by its interaction with key proteins, such as
ESR1, NOS3, and MMP9, which were identified as hub targets within the
protein–protein interaction (PPI) network. Functional enrichment
analysis further suggested that these targets are involved in crucial
biological processes, including oxidative stress regulation, bone
metabolism, and vascularization, all of which are impaired in GIOFH
[[121]20,[122]21]. Pathway analysis identified the estrogen signaling
pathway as a major mechanism underlying salvigenin’s effects. These
findings are consistent with previous studies highlighting the
protective role of estrogen receptor activation in maintaining bone
homeostasis and mitigating oxidative stress [[123]22,[124]23]. By
targeting ESR1, salvigenin may restore disrupted signaling pathways in
GIOFH, providing a mechanistic basis for its therapeutic effects.
Our in vitro experiments confirmed the protective role of salvigenin in
dexamethasone (Dex)-induced osteoblast apoptosis. Dex treatment
significantly increased apoptosis rates and altered the expression of
apoptosis-related proteins, such as upregulation of Bax and
downregulation of Bcl-2. Salvigenin co-treatment not only restored the
balance of these proteins but also inhibited caspase-3 activation,
suggesting its anti-apoptotic properties. Immunofluorescence and
Western blot analyses further showed that salvigenin rescued the
downregulation of ESR1 caused by Dex, underscoring its role in
restoring osteoblast viability through ESR1-mediated signaling
pathways. These results align with prior studies demonstrating that
ESR1 activation can counteract Dex-induced apoptosis and promote cell
survival [[125]24].
The ability of salvigenin to restore osteogenic differentiation in
Dex-treated osteoblasts was demonstrated through Alizarin Red S and ALP
staining, both of which showed significant recovery of mineralized
matrix formation and ALP activity. Additionally, salvigenin restored
the expression of osteogenic markers, such as RUNX2 and OPN, which are
critical regulators of bone formation. These findings suggest that
salvigenin counteracts the inhibitory effects of Dex on osteogenesis,
likely by modulating ESR1 and associated pathways. The restoration of
osteogenic differentiation is particularly important for bone
regeneration in GIOFH, where impaired osteoblast function plays a
central role in disease progression [[126]25,[127]26].
Oxidative stress is a key contributor to GIOFH, and Dex-induced ROS
production disrupts mitochondrial function, leading to osteoblast
apoptosis [[128]27]. Salvigenin’s antioxidative properties were evident
in its ability to significantly reduce ROS levels and restore
mitochondrial integrity in Dex-treated cells. The preservation of
mitochondrial membrane potential (ΔΨm) and morphology further supports
salvigenin’s role in mitigating oxidative damage. These effects may be
attributed to ESR1 activation, which is known to enhance cellular
antioxidant defenses and maintain mitochondrial function. By reducing
oxidative stress, salvigenin not only protects osteoblasts but also
creates a favorable environment for bone regeneration. However, while
our study demonstrated salvigenin’s protective effects against
GC-induced osteoblast injury, it remains unclear whether salvigenin
affects the broader therapeutic functions of glucocorticoids, such as
their anti-inflammatory and immunosuppressive effects. Future studies
should investigate whether salvigenin modulates glucocorticoid receptor
(GR) signaling in non-osteoblastic tissues, particularly in immune
cells, to determine its potential impact on the systemic effects of
glucocorticoid therapy.
In addition to in vitro findings, our animal model of GIOFH further
validated the therapeutic effects of salvigenin. Dexamethasone-treated
rats displayed typical features of GIOFH, including disrupted
trabecular bone structure and increased empty lacunae. Salvigenin
administration significantly mitigated these pathological changes, as
evidenced by histological evaluation through H&E staining. Furthermore,
immunohistochemical staining demonstrated that salvigenin restored ESR1
expression in the femoral head, aligning with its in vitro effects and
highlighting the consistency of its protective role in both cellular
and physiological contexts.
The findings of this study provide a strong rationale for further
investigation of salvigenin as a therapeutic agent for GIOFH. Its
multi-target mechanisms, coupled with its ability to modulate ESR1 and
associated pathways, suggest that salvigenin could address multiple
pathological aspects of GIOFH, including oxidative stress, apoptosis,
and impaired osteogenesis. Moreover, the integration of network
pharmacology, in vitro validation, and animal studies demonstrates the
utility of systems-level approaches in identifying and characterizing
novel therapeutic candidates. While these results are promising,
further studies are needed to evaluate the pharmacokinetics,
bioavailability, and long-term safety of salvigenin in vivo, as well as
its potential synergistic effects with existing treatments.
Despite the comprehensive nature of this study, several limitations
should be acknowledged. First, while the in vitro and animal model
analyses provide valuable insights, the complex interactions within the
human femoral head microenvironment require further investigation.
Future studies should include clinical trials to validate the
therapeutic effects of salvigenin in human GIOFH patients. Second, the
specific molecular interactions between salvigenin and ESR1 require
deeper exploration using advanced techniques, such as cryo-electron
microscopy. Thirdly, although salvigenin was shown to protect
osteoblasts from GC-induced damage, its potential interactions with the
immunosuppressive functions of glucocorticoids remain unexplored.
Future investigations should assess whether salvigenin influences
glucocorticoid receptor activation and downstream signaling pathways
beyond osteoblasts, particularly in immune cells and other target
tissues. Lastly, the potential off-target effects of salvigenin should
be systematically assessed to ensure its safety and efficacy as a
clinical candidate.
5. Conclusions
This study highlights the therapeutic potential of salvigenin in
glucocorticoid-induced osteonecrosis of the femoral head (GIOFH)
through its multi-target mechanisms, particularly its modulation of
ESR1-mediated pathways. By alleviating oxidative stress, preventing
osteoblast apoptosis, and promoting osteogenic differentiation,
salvigenin presents a promising approach for mitigating the adverse
effects of glucocorticoids on bone health. These findings hold
significant clinical relevance for orthopedists and rheumatologists
managing patients requiring long-term glucocorticoid therapy.
Specifically, salvigenin-based interventions could delay or reduce the
need for invasive surgical procedures (e.g., total hip replacement) by
preserving femoral head integrity, thereby improving patients’ quality
of life. For surgeons, this study provides a pharmacological rationale
to explore salvigenin as a neoadjuvant therapy to enhance bone
regeneration in early-stage GIOFH. Furthermore, the multi-target action
of salvigenin suggests its potential as a complementary therapy to
existing anti-resorptive agents (e.g., bisphosphonates) in preventing
glucocorticoid-induced bone complications. These findings provide a
strong foundation for future translational research focused on
developing salvigenin-based therapies for GIOFH and other
glucocorticoid-induced bone diseases.
Abbreviations
The following abbreviations are used in this manuscript:
GIOFH Glucocorticoid-induced osteonecrosis of the femoral head
ROS Reactive oxygen species
PI3K/AKT Phosphoinositide 3-kinase/protein kinase B
MAPK Mitogen-activated protein kinase
ESR1 Estrogen receptor alpha
PPI Protein–protein interaction
GO Gene Ontology
KEGG Kyoto Encyclopedia of Genes and Genomes
ONFH Osteonecrosis of the femoral head
TCMSP Traditional Chinese Medicine Systems Pharmacology
GEO Gene Expression Omnibus
DESeq2 Differential expression analysis with DESeq2
MCC Maximal clique centrality
BSA Bovine serum albumin
PBS Phosphate-buffered saline
HRP Horseradish peroxidase
DAPI 4′,6-diamidino-2-phenylindole
PVDF Polyvinylidene fluoride
TBST Tris-buffered saline with Tween 20
ECL Enhanced chemiluminescence
DMEM Dulbecco’s Modified Eagle Medium
FBS Fetal bovine serum
CCK-8 Cell Counting Kit-8
Dex Dexamethasone
ALP Alkaline phosphatase
H&E Hematoxylin and eosin
IHC Immunohistochemistry
DAB Diaminobenzidine
PME Particle Mesh Ewald
LINCS Linear Constraint Solver
SASA Solvent-accessible surface area
RMSD Root mean square deviation
RMSF Root mean square fluctuation
Rg Radius of gyration
ΔΨm Mitochondrial membrane potential
OPN Osteopontin
RUNX2 Runt-related transcription factor 2
Bcl-2 B-cell lymphoma 2
Bax Bcl-2-associated X protein
NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells
TNF Tumor necrosis factor
GSEA Gene Set Enrichment Analysis
GR Glucocorticoid receptor
[129]Open in a new tab
Author Contributions
Conceptualization, Z.Z., Y.Z. and R.H.; methodology, Z.Z. and C.Z.;
software, R.H.; validation, Z.Z., Y.Z. and C.Z.; formal analysis, Z.Z.
and Y.Z.; investigation, Z.Z. and R.H.; resources, J.C. and H.P.; data
curation, Z.Z.; writing—original draft preparation, Z.Z., Y.Z. and
R.H.; writing—review and editing, H.P. and J.C.; visualization, R.H.
and C.Z.; supervision, H.P. and J.C.; project administration, H.P.;
funding acquisition, H.P. and J.C. All authors have read and agreed to
the published version of the manuscript.
Institutional Review Board Statement
All animal experiments were approved by the Ethical Review Committee
for Laboratory Animal Welfare at Wuhan University People’s Hospital
(Approval No. WDRM20220106) and conducted in accordance with the
ethical guidelines outlined in the 8th edition of the Guide for the
Care and Use of Laboratory Animals (National Research Council,
Rockville, MD, USA, 2011).
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are available in the public databases mentioned in the manuscript.
Conflicts of Interest
The authors declare that there are no conflicts of interest.
Funding Statement
This study was supported by the National Natural Science Foundation of
China (Grant No. 81672154) and the Hubei Provincial Key Research and
Development Program (Grant No. 2021BCA147).
Footnotes
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referred to in the content.
References