Application of TPHP in bone metabolism

Abstract

The invention belongs to the technical field of medicines, and particularly relates to application of TPHP in bone metabolism. According to the application disclosed by the invention, the TPHP is found to have a remarkable inhibition effect on proliferation and invasion and migration of bone cells, and the TPHP damages proliferation, migration and invasion functions of MC3T3-E1 osteoblasts by inhibiting an MAPK / ERK signal channel and down-regulating related gene expression. The action effect of the TPHP is remarkable, and subsequent scientific research is facilitated. In addition, TPHP is easy to obtain and low in cost, related preparations and biological models are easy to prepare, the process is mature, industrialized production can be achieved, and the market acceptance degree is high.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and particularly relates to the application of TPhP in bone metabolism. Background Art

[0002] Bone metabolism balance is an important part of the body's metabolic balance, and the coupled balance of bone formation / bone resorption is the basis of normal bone reconstruction. However, when bone resorption is greater than bone formation, a high turnover rate of bone reconstruction occurs, which will lead to a decrease in bone mass and quality, causing osteoporosis. Osteoporosis is a systemic bone disease characterized by decreased bone density, increased bone brittleness, and destruction of bone microstructure. From a mechanistic perspective, osteoporosis is divided into primary osteoporosis and secondary osteoporosis, among which primary osteoporosis is divided into postmenopausal osteoporosis, senile osteoporosis, and idiopathic osteoporosis. Secondary osteoporosis is caused by diseases and / or drugs that affect bone metabolism and other causes.

[0003] In recent years, the role of environmental factors in the development of osteoporosis has received increasing attention. Studies have shown that heavy metal pollutants (such as lead, cadmium, and mercury) can interfere with bone cell function, inhibit osteoblast activity, and promote osteoclast differentiation, leading to increased bone resorption and decreased bone formation, ultimately causing osteoporosis (Snega Priya, Pratiksha Nandhini et al. 2023). Furthermore, people with long-term exposure to air pollutants such as PM2.5, PM10, and NO2 have shown an increased risk of osteoporosis and fractures (Mousavibaygei, Bisadi et al. 2023). Perfluorooctane sulfonic acid (PFOS) has also been shown to be significantly associated with reduced spinal bone mineral density in women, potentially interfering with bone mineralization by affecting thyroid hormone and vitamin D metabolism (Lin et al. 2014).

[0004] Triphenyl phosphate (TPhP) is a widely used organophosphorus flame retardant and plasticizer that has been detected in a variety of environmental media, including indoor air, dust, soil, and sewage treatment plant effluent (Guo et al., 2023; Ai et al., 2024). Due to its persistence and poor biodegradability, TPhP persists in the environment for long periods of time and exhibits significant bioaccumulation, posing a potential threat to human health and environmental safety (He et al., 2015; Chen et al., 2024). Studies have confirmed that TPhP exhibits significant developmental toxicity, interfering with cardiac development, skeletal formation, and embryonic development. Regarding the nervous system, TPhP can inhibit acetylcholinesterase activity, disrupt the integrity of the blood-brain barrier, and affect neurological function by inducing the release of inflammatory factors and oxidative stress (Argaw et al., 2006; Hong et al., 2018). In terms of the reproductive system, TPhP can disrupt hormonal balance, impair germ cell development, reduce fertility, and pose a carcinogenic risk (Chen, Hong et al. 2020, Kwon, Park et al. 2022, Shi, Wang et al. 2023). Notably, TPhP accumulates strongly in the liver, where it can cause metabolic disorders by affecting glucose and lipid metabolism and inducing oxidative stress (Morris, Medina-Cleghorn et al. 2014; Du, Zhang et al. 2016; Ramesh, Angitha et al. 2020; Wang, Sha et al. 2022). TPhP also exhibits genotoxic and immunotoxic effects, affecting DNA methylation and inducing DNA damage, and interfering with immune responses by modulating immune cell function and the expression of inflammatory factors (Hogberg, de Cássia da Silveira et al. 2021, Zhang, Chen et al. 2022).

[0005] However, despite the multiple mechanisms by which TPhP influences biological health, its effects on bone metabolism have not been systematically investigated. With the rapid development of bioinformatics, systematic global analysis based on multi-omics data has provided a new research strategy for studying the mechanisms of action of environmental pollutants. Therefore, understanding the pathophysiology of osteoporosis and identifying potential new treatment options for osteoporosis remain urgent tasks. Summary of the Invention

[0006] The present invention has been completed based on the discovery that TPhP has a significant inhibitory effect on the proliferation, invasion and migration of bone cells.

[0007] In a first aspect, the present invention provides use of TPhP in preparing an agent for inhibiting bone cell proliferation and migration.

[0008] Furthermore, the reagent is an experimental reagent.

[0009] Furthermore, the experimental reagent can be used for bone cell proliferation and migration model research.

[0010] In a second aspect, the present invention provides an agent for inhibiting the proliferation and migration of bone cells, wherein the active ingredient of the agent includes TPhP.

[0011] In a third aspect, the present invention provides use of a reagent for inhibiting bone cell proliferation and migration in preparing a model for inhibiting bone cell proliferation and migration, wherein the active ingredient of the reagent includes TPhP.

[0012] In a fourth aspect, the present invention provides an animal model of osteoporosis, wherein the preparation method of the model comprises TPhP treatment.

[0013] Furthermore, the animals include but are not limited to mice, rats, and rabbits.

[0014] In a fifth aspect, the present invention provides use of TPhP in preparing an animal model of osteoporosis.

[0015] Furthermore, the preparation method of the model includes TPhP treatment.

[0016] Furthermore, the animals include but are not limited to mice, rats, and rabbits.

[0017] Beneficial effects

[0018] The present invention discovered that TPhP significantly inhibits the proliferation, invasion, and migration of osteoblasts. TPhP impairs the proliferation, migration, and invasion of MC3T3-E1 osteoblasts by inhibiting the MAPK / ERK signaling pathway and downregulating the expression of related genes. TPhP's remarkable efficacy facilitates subsequent scientific research. Furthermore, TPhP is readily available and low-cost, and related formulations and biological models are readily prepared. The mature process allows for industrial-scale production and has high market acceptance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 .For PubChem results.

[0020] Figure 2 .Target screening for TPHP-induced osteoporosis.

[0021] Note: A is the gene set related to TPHP screened from the ChEMBL, STTCH and Swiss Target Prediction databases; B is the gene set related to osteoporosis screened from the GeneCard, OMIM and DisGeNET databases; C is the intersection of TPHP-related genes and osteoporosis-related genes.

[0022] Figure 3 Molecular Protein Interaction Network Analysis Notes: A) PPI network analysis of osteoporosis-related genes affected by TPHP. B) Volcano plot of differentially expressed genes in the GSE230665 dataset. C) Intersection of osteoporosis-related genes affected by TPHP and differentially expressed genes in the GEO dataset.

[0023] Figure 4 .Pathway enrichment analysis of target genes.

[0024] Note: A is a heat map of the 47 intersection genes of osteoporosis-related genes affected by TPHP and differentially expressed genes in the GEO dataset; BC is the GO enrichment analysis of the target genes, including biological process (BP), cellular component (CC), and molecular function (MF) categories; DE is the KEGG pathway enrichment analysis of the target genes, where the bubble size and histogram length represent the number of enriched genes.

[0025] Figure 5 To screen core target genes for TPHP-induced osteoporosis using machine learning algorithms. Note: AB are regression curves established using the LASSO regression model, with the optimal parameter value λ selected; six most suitable candidate core target genes were identified at the lowest point of the curve; C is the ROC curve of the model, verifying its accuracy; D is the ROC curve of the six candidate core genes of the model, verifying the scientificity and accuracy of the selected candidate genes.

[0026] Figure 6 . are the molecular docking results and binding energy between the core protein and TPHP.

[0027] Figure 7 .The effect of TPHP on osteoblast function.

[0028] Note: AB indicates that TPhP inhibits cell migration (scratch assay); C indicates that TPhP reduces cell proliferation (CCK-8 assay); DE indicates that TPhP inhibits migration and invasion (Transwell assay); F indicates that TPHP affects MAPK and EMT proteins by Western blot; GH indicates that the MEK / ERK activator (C16-PAF) rescues TPHP-inhibited migration / invasion (G) and proliferation (H), while the inhibitor (MEK-IN-6) does not; I indicates that Western blot confirms the regulation of MAPK / ERK in the rescue assay; J indicates that TPHP downregulates key target genes (*P<0.05, **P<0.01, ***P<0.001). DETAILED DESCRIPTION

[0029] The following is a further description of specific embodiments of the present invention. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the embodiments described below may be combined with each other as long as they do not conflict with each other.

[0030] The experimental methods in the following examples are conventional methods unless otherwise specified, and the experimental materials used in the following examples are commercially available unless otherwise specified.

[0031] Example 1 Identification of TPhP-induced osteoporosis targets A. Experimental methods

[0032] 1. Cell Culture

[0033] Mouse preosteoblastic MC3T3-E1 cells were routinely cultured in α-MEM complete medium containing 10% high-quality fetal bovine serum and 1% penicillin-streptomycin solution.

[0034] 2. Systematic Prediction and Analysis of TPhP Action Network

[0035] The standardized chemical structure and SMILES formula of TPhP were retrieved from the PubChem database. A ligand-based multi-target prediction strategy was employed, using the SWISS target prediction platform and the SEA-DOCK reverse docking system for high-throughput screening of potential targets. The species parameter was set to Homo sapiens, with a prediction probability threshold >0.6. Identified targets were imported into the UniProt database for standard annotation and functional classification, creating a dataset of potential TPhP targets.

[0036] 3. Multidimensional screening of bone metabolism-related targets

[0037] A systematic search was conducted using the DisGeNET disease gene association database and the TTD therapeutic target database using the keywords "osteoporosis," "bone metabolism," and "bone remodeling." Highly correlated genes involved in bone metabolism regulation were identified based on the level of evidence score (correlation score ≥ 0.4) to construct a bone metabolism-specific target library. Using Venn diagram analysis on the BioVenn online platform, overlapping genes between TPhP targets and bone metabolism-specific targets were identified, forming candidate key targets for TPhP-mediated bone metabolism regulation.

[0038] B. Test Results

[0039] Through comprehensive research, PubChem was systematically screened, and the toxicity of TPhP was preliminarily predicted and evaluated. PubChem showed a positive response to estrogen receptor a (ER) ( Figure 1 ).like Figure 2As shown in A, 1,659 potential TPhP-related targets were identified using the CHEMBL, STTCH, and Swiss TargetPrediction databases. Figure 2 As shown in Figure B, 970 osteoporosis-related genes were screened again from the GeneCard, OMIM, and DisGeNET databases. After thorough analysis and deduplication, 78 overlapping target molecules were finally identified, which were considered to be key candidates for TPhP to regulate bone metabolism. The distribution of TPhP and osteoporosis target molecules was visualized using a Venn diagram ( Figure 2 C).

[0040] Example 2 Construction and analysis of protein-protein interaction networks

[0041] A. Test Method

[0042] The identified overlapping genes were imported into the STRING database and a protein-protein interaction (PPI) network was constructed with a joint confidence threshold >0.4. Network visualization and topological analysis were performed using Cytoscape software (v3.10.1). Functional modules were identified using the MCODE clustering algorithm (degree cutoff = 2, node score cutoff = 0.2, k-core = 2). Core hub genes were identified using 12 ranking algorithms provided by the CytoHubba plugin, including degree, betweenness, and closeness centrality metrics.

[0043] B. Test Results

[0044] Using the STRING database, a PPI network of osteoporosis-related proteins was constructed, and its topological features were visualized using Cytoscape software. Figure 3 As shown in A, the network diagram clearly illustrates the correlation between related proteins, TPhP and osteoporosis. In order to further identify the key proteins in TPhP-induced osteoporosis, we first conducted an in-depth analysis of the osteoporosis-related gene set in the GEO database to identify differentially expressed genes ( Figure 3 B). By crossing these differentially expressed genes with the osteoporosis-related genes mentioned above, 47 target molecules significantly affected by TPhP in osteoporosis were finally identified ( Figure 3 C).

[0045] Example 3 Multi-layer functional enrichment and pathway analysis of potential targets

[0046] A. Test Method

[0047] Functional enrichment analysis of potential targets of TPhP was performed using the metscape platform and the DAVID annotation system. GO analysis encompassed three key aspects: biological process (BP), cellular component (CC), and molecular function (MF). Fisher's exact test was used to assess significance (P < 0.05) with correction for the false discovery rate (FDR). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was primarily focused on signaling pathways related to bone metabolism regulation, such as the Wnt, BMP, and MAPK pathways.

[0048] B. Test Results

[0049] like Figure 4 As shown in A, a detailed gene ontology (GO) functional analysis was performed using the DAVID database, focusing on human-specific aspects to study the biological functions and signaling pathways of 47 differentially expressed target proteins. Figure 4 B and Figure 4 As shown in C, the analysis revealed three major categories of functions: biological processes, cellular components, and molecular functions. These functions were ranked by significance level, and the top 10 most important entries in each category were visualized. The results showed that these molecules were mainly involved in biological processes including enzyme activity regulation, organ development, signal transduction, and protein modification; in terms of cell structure, they were mainly distributed in specialized membrane structures, enzyme complexes, and cell junctions; in terms of molecular function, they were mainly involved in various kinase activities and growth factor binding. In addition, KEGG pathway enrichment analysis visualized the signaling pathways involving these molecules, which were mainly enriched in MAPK signaling pathways, cell signal transduction, protein modification, and metabolic regulation pathways ( Figure 4 D and Figure 4 E).

[0050] Example 4 Core gene screening

[0051] A. Test Method

[0052] The LASSO (least absolute shrinkage and selection operator) regression algorithm was used to identify core osteoporosis-related genes. The LASSO regression model was constructed using R software and the "glmnet" package. The penalty parameter λ was determined by 10-fold cross-validation to minimize the mean square error. The optimal λ value was selected when the model achieved the best balance between bias and variance. To validate the screening results, receiver operating characteristic (ROC) curve analysis was performed on the identified candidate genes and the established osteoporosis model. The area under the curve (AUC) was calculated to evaluate the predictive accuracy of the model, and an AUC threshold of 0.7 was considered to indicate significant discriminatory power. The validation process was performed using the "pROC" package in R.

[0053] B. Test Results

[0054] The LASSO regression machine learning algorithm was used to screen core osteoporosis-related genes. The LASSO regression algorithm established an osteoporosis-related model and identified six candidate core target genes related to the disease (IGF1R, NR3C1, MAP3K1, BRAF, WNK4, CNR2) ( Figure 5 A and Figure 5 B). To verify the accuracy of the selected candidate genes, ROC tests were performed on the candidate genes and the osteoporosis model. The ROC curve results showed that the AUC value was greater than 0.7, confirming the scientific validity of the osteoporosis model construction ( Figure 5 C and Figure 5 D).

[0055] Example 5 Molecular docking of TPhP and core protein

[0056] A. Test Method

[0057] Molecular docking was performed using AutoDock software to investigate the binding properties of TPhP to six core target proteins (IGF1R, NR3C1, MAP3K1, BRAF, WNK4, and CNR2). The three-dimensional structures of these proteins were obtained from the Protein Data Bank (PDB). Prior to docking, all protein structures were preprocessed by removing water molecules and adding hydrogen atoms. The TPhP structures were energy minimized before docking simulations. Binding sites were defined based on surface cavities within each protein. Docking parameters were set to achieve optimal conformational sampling, and the most stable binding conformation was selected based on the lowest binding energy. A binding energy threshold of -5 kcal / mol was set, indicating excellent binding affinity. The three-dimensional configurations of the most stable binding conformations of TPhP to each target protein were visualized using PyMOL software. Molecular interactions and binding modes were comprehensively examined to assess the stability of the protein-ligand complexes.

[0058] B. Test Results

[0059] To investigate the interaction characteristics of TPhP with six key target proteins (IGF1R, NR3C1, MAP3K1, BRAF, WNK4, and CNR2), simulation analysis was performed using AutoDock software. It is well known that a binding energy below 0 kcal / mol indicates spontaneous binding between the receptor and ligand without the need for external energy, while a binding energy below -5 kcal / mol indicates good binding.

[0060] Molecular docking results showed that TPhP could bind tightly to the surface cavities of molecules such as IGF1R, NR3C1, MAP3K1, BRAF, WNK4 and CNR2, with binding energies lower than -5 kcal / mol (-7.5, -6.2, -6.1, -8.9, -8.9, -6.4 and -9.4 kcal / mol, respectively), indicating high binding stability. Figure 6 As shown, the three-dimensional visualization technology of PyMOL software clearly shows the most stable binding conformation, further confirming the close interaction between them.

[0061] Example 6 Cell function test

[0062] A. Test Method

[0063] 1. Evaluating the Effect of TPhP on Cell Proliferation Using CCK-8 Assay

[0064] MC3T3-E1 cells in logarithmic growth phase were cultured at 1×10 3 Cells were seeded into well plates at a density of 10 cells / well. After adherence, cells were treated with 0–100 μM TPhP. At 24, 48, 72, 96, and 120 hours, 10 μL of CCK-8 reagent was added to each well and incubated in the dark. Absorbance was measured at 450 nm using a microplate reader.

[0065] 2. Evaluation of the Effect of TPhP on Cell Invasion and Migration Using Transwell Chamber Assay

[0066] In the invasion assay, matrix rock was pre-coated on a polycarbonate membrane and incubated overnight. Serum-starved pre-treated cells were plated at 2 × 10 4 Cells were seeded at a rate of 100 cells / well in serum-free culture medium containing various concentrations of TPhP. The lower chamber was perfused with 600 μL of complete culture medium containing 10% fetal bovine serum as a chemoattractant. After 24 hours, cells on the upper surface of the membrane were gently removed, while infiltrating cells on the lower surface were fixed with methanol, stained with crystal violet, and counted in randomly selected fields under a microscope. Migration assays followed the same procedure without matrix metalloproteinase coating.

[0067] 3. Western Blot Analysis

[0068] Cells treated with TPhP were lysed on ice in RIPA buffer containing protease and phosphatase inhibitors, and the lysates were centrifuged and the supernatant collected. Protein concentration was quantified using the BCA assay, and 30 μg of denatured protein per sample was separated by SDS-PAGE and transferred to a PVDF membrane using a semi-dry transfer system. After blocking with 5% BSA in TBST, the membrane was incubated overnight with the specific primary antibody. After washing, an enzyme-conjugated secondary antibody (1:5000) was incubated at room temperature for a period of time. Signal development was performed using an enhanced chemiluminescence substrate and analyzed using ChemiDoc. TM The cells were visualized on the MP imaging system. Densitometric analysis was performed using ImageJ software, with β-actin serving as a loading control.

[0069] 4. Quantitative PCR (qPCR)

[0070] Total RNA was extracted using the TRIzol method, and 1 μg of RNA was reverse transcribed into cDNA. TM Gene expression was quantified using SYBR Green dye on the 3-well system. Relative expression was calculated using the 2-ΔΔCt2 method, using GAPDH as an internal control.

[0071] B. Test Results

[0072] To investigate the direct effects of TPhP on bone cells, MC3T3-E1 mouse osteoblasts were selected as a research model. Cells were exposed to TPhP at concentrations of 0, 10, 20, and 40 μM for 48 hours, and the effects were evaluated by wound healing assay and CCK-8 assay. The results showed that TPhP significantly inhibited MC3T3-E1 cell migration in a concentration-dependent manner ( Figure 7 A and Figure 7 B). Cell viability tests further confirmed that TPhP had a significant inhibitory effect on cell proliferation, which was particularly evident at higher concentrations ( Figure 7 C and 7H). Migration and invasion experiments confirmed the inhibitory effect of TPhP on the migration and invasion of osteocytes ( Figure 7 D. Figure 7 E and Figure 7 G), while Western blot results showed that TPhP could regulate the expression of emt-related proteins ( Figure 7 F) Based on the previous pathway enrichment analysis, we further investigated whether TPhP affects osteocyte function through the MAPK signaling pathway. Western blot results confirmed this hypothesis. Figure 7As shown in Figure 1, it shows that TPhP does affect bone cell function by regulating the MAPK signaling pathway. In addition, qPCR results showed that after 48 hours of TPhP exposure, the expression levels of six key target genes related to TPhP - IGF1R, NR3C1, MAP3K1, BRAF, WNK4 and CNR2 - were significantly downregulated ( Figure 7 J).

Claims

1. Application of TPhP in the preparation of reagents for inhibiting osteocyte proliferation and migration.

2. The use according to claim 1, wherein the reagent is an experimental reagent.

3. The use according to claim 2, wherein the experimental reagent is used for the study of bone cell proliferation and migration models.

4. An agent for inhibiting the proliferation and migration of bone cells, wherein the active ingredient of the agent comprises TPhP.

5. Use of a reagent for inhibiting bone cell proliferation and migration in preparing a model for inhibiting bone cell proliferation and migration, wherein the active ingredient of the reagent includes TPhP.

6. An animal model of osteoporosis, wherein the preparation method of the model comprises TPhP treatment.

7. The animal model according to claim 6, wherein the animals include mice, rats and rabbits.

8. Application of TPhP in preparing animal models of osteoporosis.

9. The use according to claim 8, wherein the preparation method of the model comprises TPhP treatment.

10. The use according to claim 8, wherein the animals include mice, rats and rabbits.

Images