Studies on the Anti-Inflammatory Effect of Xiaoyao Pills in The Treatment of Postmenopausal Osteoporosis in Mice

Summary

Postmenopausal osteoporosis has become a global public health problem. The aim of this study is to explore the therapeutic effects and related mechanisms of the traditional Chinese medicine Xiaoyao pills on this condition.

Abstract

Osteoporosis is a common metabolic disease of elderly and postmenopausal women, with no obvious symptoms during its early stages. In the latter stages of this condition, the patients are prone to fractures, and this can seriously affect their health and quality of life. The worldwide increase in life expectancy has made osteoporosis a global concern. The Xiaoyao pills were previously
used in the treatment of depression. In addition, the drug appeared to have estrogen-like activity, which affected the expression of ALP, an early osteoblast-specific marker, and COL-1, a major component of bone extracellular matrix. Xiaoyao pills were assessed for their effects on postmenopausal osteoporosis (PMOM) in mice. The target information of each herbal component of Xiaoyao pills was accessed through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Information from GeneCards, OMIM, PharmGkb, TTD, DrugBank, and other websites was used to construct the regulatory network of the herbal complex through Cytoscape and String network to assess the protein interactions. Mice were ovariectomized, and treated with high and low doses of Xiaoyao pills and these were compared to controls. Their symptoms were assessed by immunocytochemistry of bone tissues. The results suggested that Xiaoyao pills had the ability to alleviate the symptoms of PMOM in ovariectomized mice through the IL-17 signaling pathway. This drug has the potential to become a novel therapeutic agent for the treatment of osteoporosis.

Introduction

The World Health Organization (WHO) defines osteoporosis (OP) as a disease characterized by a decrease in bone mass and deterioration of the microarchitecture of bone tissue, leading to an increase in bone brittleness and, thus an increased risk of fracture¹. The clinical significance of osteoporosis is that it can lead to fractures, which are associated with high mortality, morbidity, and economic costs². Postmenopausal osteoporosis (PMOP) is caused by a decrease in estrogen levels in women after menopause, which leads to an increase in osteoclast activity, resulting in bone loss and destruction of bone microstructure. This often causes osteoporosis with a serious impact on health³. Current therapies for PMOP include estrogen replacement therapy, bisphosphonates, and parathyroid hormone, but they can have varying degrees of adverse effects, insufficient long-term compliance, and high costs⁴. Therefore, affordable herbal medicine is a viable alternative for a large proportion of the population.

Xiaoyao pills are included in the Chinese Pharmacopoeia⁵, and these contain eight herbal components, including Chai Hu, Angelica sinensis, White paeonia lactiflora, Atractylodes macrocephala, Poria cocos, Menthae Herba, licorice, and fresh ginger. All these herbs are known to be effective in detoxifying the liver and strengthening the spleen, nourishing the blood, and regulating the menstrual cycle, and the mixture has also been used for treating depression⁶. However, the role of Xiaoyao pills in osteoporosis is unclear.

Early studies have suggested that inflammation can lead to bone loss⁷, and that the decline in bone density associated with this process may be accelerated by menopause. In addition, there is a strong relationship between the development of osteoporosis and inflammation. The inflammatory factor, interleukin-17 (IL-17), is a pro-inflammatory factor secreted by Th17 cells, a subset of CD4+ T lymphocytes. These cells are associated with several chronic inflammatory conditions, and they play an important role in the development of bone destruction in rheumatoid arthritis⁸. Additionally, IL-17 stimulates nuclear factor-κ B ligand receptor activator (RANKL), which regulates osteoclastogenesis, leading to greater bone resorption than bone formation⁹. IL-17 stimulates the expression of other osteoclastogenic cytokines such as TNFα, IL-1, IL-6, and IL-8. It has the ability to synergize with other inflammatory factors, making it an important inflammatory effector¹⁰.

Studies have also shown a link between Xiaoyao pills and inflammation. Shi et al.¹¹ and Fang et al.¹² have recently confirmed that Xiaoyao pills can reduce the levels of IL-6 and TNF-α, respectively. In another study of metabolism-associated steatohepatitis, it was reported that Xiaoyao pills could upregulate the expression of propionic acid, which in turn inhibits the expression of TNF-α and exerts an anti-inflammatory effect¹³. However, at present, it is not known whether Xiaoyao pills can regulate the development of PMOM by mediating an inflammatory response through IL-17, which was the aim of this study.

This study predicted the intersection of the targets of Xiaoyao pills and osteoporosis-related genes through network pharmacology and bioinformatics analysis and analyzed the intersecting genes for protein interactions, GO, and KEGG. Based on the predicted results, the expression of Act1 and IL-6, which are key proteins in the IL-17 signaling pathway, can be observed^14,15, as well as the bone turnover markers alkaline phosphatase (ALP) and collagen type I (COL-1), to observe the therapeutic efficacy of the Xiaoyao pills in the PMOM mice model.

Protocol

The Laboratory Animal Ethics Committee of the Youjiang Medical University for Nationalities approved the study protocol (approval number: 2022101502). Female C57BL/6 mice, aged 10-12 weeks, SPF class and body weights (22 ± 2) g, were housed in the SPF Class Animal Experiment Center of Youjiang Medical University for Nationalities. The experimental animals were maintained at a temperature of 24-26 °C and a relative humidity of 55% to 60%.

1. Traditional Chinese medicine systems pharmacology database and analysis platform

NOTE: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP; https://old.tcmsp-e.com/tcmsp.php) are Chinese medicine pharmacology platforms that contain Information on TCM ingredients, ADME-related properties, targets, and diseases^16,17.

1. Access the TCMSP web interface. Search for Herb's name, enter the name of the Chinese medicine, and click Search. Click on Latin Name in the search results. Perform screening on the results of the ingredients with the parameters OB ≥ 30% and DL ≥ 0.18.
2. Copy all the results and save them in TXT format, named Chinese Medicine Name_ingredients.txt.
3. Under Targets Information in the Related Targets tab, filter for Mol ID (i.e., the result in step 1.2).
4. Copy the filtered results and save them in TXT format named Chinese Medicine Name_targets.txt.
   NOTE: Through the above steps, all the eligible active ingredients and corresponding target information of traditional Chinese medicine (TCM) was obtained.
5. Place all of the above ingredients.txt files and targets.txt files in the same folder and place the Perl scripts (Supplementary Coding File 1) in that folder.
6. Open CMD, type the cd folder path, enter it, and run the Perl script. Get a new text file (allTargets.txt). This new text file (allTargets.txt) contains the herbal name, ingredient name, ingredient ID, and target.

2. UniProt database

NOTE: The UniProt database (https://www.uniprot.org/) contains human protein sequences annotated with functional information, and it is used to normalize the names of targets to their official names¹⁸.

1. Go to the UniProt web interface. Click the Search tab, select UniProtKB, and click Search.
2. Filter in the left sidebar, select Reviewed (Swiss-Prot) for Status and Human for Popular organisms. Click Download, select Download All, select TSV for Format, and click Download to download the annotation file.
3. Unzip the downloaded file to the current folder, open the unzipped file, and copy and paste it into a text file named ann.txt.

3. Drug target ID conversion

1. Put all the drug target files allTargets.txt obtained in section 1 and the UniProt annotation file ann.txt obtained in section 2 into a folder and put the Perl scripts (Supplementary Coding File 2) into it.
2. Open CMD, type cd + space + folder path, enter it, and run the Perl script to obtain a new text file (allTargets.symbol.txt). This new text file (allTargets.symbol.txt) contains the herbal name, ingredient ID, ingredient name, and gene ID.

4. Database search

1. GeneCards: Human Gene Database (GeneCards) database
   NOTE: The GeneCards database (https://www.genecards.org/) provides prediction and annotation information for human genes. It is used to access disease targets^19,20.
   1. Go to the GeneCards web interface. Type osteoporosis in the search box and click Search.
   2. Click Export and select Export to Excel.
   3. Open the downloaded file, copy the genes with Relevance score ≥ 1, and paste and save them into a file named GeneCards.txt.
2. OMIM database
   ​NOTE: The OMIM database (https://omim.org) contains human genes, genetic diseases and traits²¹.
   1. Go to the OMIM web interface. Click on GENE Map, type osteoporosis in the search box, and click Search.
   2. Click Download As and select Excel File.
   3. Open the downloaded file, copy the gene name in the Approved Symbol column, paste it and save it to a file named OMIM.txt.
3. PharmGkb database
   NOTE: PharmGkb (https://www.pharmgkb.org), the pharmacogenomics knowledge base, contains drug label annotations, drug-centered pathways, pharmacogenetic summaries, and relationships between genes, drugs, and diseases​²².
   1. Go to the PharmGkb website interface. Enter osteoporosis in the search box, click Search, and check Gene in the left sidebar.
   2. Enter all the results manually and save them to a file called PharmGkb.txt.
4. TTD: Therapeutic Target Database (TTD) database
   NOTE: The TTD database (https://idrblab.org/ttd/) provides information on protein and nucleic acid targets, targeted diseases, pathway information, and corresponding drugs for each target²³.
   1. Go to the TTD web interface. Type osteoporosis in the search box and click Search.
   2. Click Target Info under Target ID in the result, copy the Target Name, paste it, and save it to a file named TTD.txt. A total of 33 results were obtained. Save each result in the same way.
5. DrugBank Online (DrugBank) database
   NOTE: The DrugBank database (https://go.drugbank.com) contains information on drugs and drug targets, drug interactions, drug mechanisms, and drug metabolism²⁴.
   1. Go to the DrugBank website interface. Select Indications in the search tab, type osteoporosis in the search box, and click Search.
   2. Click on the entry Osteoporosis in the results for more information, click on the DRUGS AND TARGETS tab, and click on the Appropriate Link in the TARGET column of the table.
   3. Click Details in Protein, copy the Gene Name, paste it, and save it to a file named DrugBank.txt. Save each result in the same way.

5. Venn diagram

1. Disease-associated gene merging and Venn mapping
   1. Put the txt files saved as well as the script in the same folder.
   2. Open the R code (Merge Disease-Related Genes), copy and paste the path where the above txt file is stored into the line where the setwd is located in the R code.
   3. Open the R software, run the modified R code, and save. Set the gene, name the file Disease.txt.
   4. Open the Draw Venn Diagram website; in the input section, copy and paste the text content saved in sections 4 to 8 one by one into the list, naming it with the respective database, and clicking Submit.
   5. Click Save Image As PNG below the image, and save the text result in the same folder.
2. Intersection of drug targets and disease-associated genes
   1. Place the allTargets.symbol.txt file obtained in section 3 and the Disease.txt file obtained in step 5.1.3 in the same folder.
   2. Open the R code (drug target and disease-related gene take intersection), copy and paste the path where the above txt file is stored to the line where the setwd is located in the R code.
   3. Open the R software, run the modified R code, and save. Set the gene, name the file Drug_Disease.txt.
   4. Open the Draw Venn Diagram website. In the input section, copy and paste allTargets.symbol.txt and Disease.txt into the list and name them after their respective files.
   5. Click Save Image As PNG below the image, and save the text results in the same folder.

6. Construction of the regulatory network of TCM compounding

1. Place the allTargets.symbol.txt file obtained in section 3 in the same folder as the Disease.txt file and the corresponding Perl script (Supplementary Coding File 3).
2. Open CMD, type the cd file path, press Enter, and run the Perl script. Get four new txt files: net.geneLists.txt, net.molLists.txt, net.network.txt, and net.type.txt. The network.txt file contains the TCM ingredient ID, target gene, target relationship, and ingredient name. net.type.txt contains node name, attributes, and affiliation. net.geneLists.txt is the list of genes, and net.molLists.txt is the list of ingredients.
3. Copy the newly obtained text files in the same new folder.
4. Open Cytoscape 3.9.1 software, click File, click Import, and select Network Form File. Select net.network.txt, put the first column of Component ID as Source Node, the second column of Genes as Target Node, and the third column of Targeting Relationship as Interaction Type, and click OK.
5. Import net.type.txt from the Node Table window. Click the Select tab, select Nodes, select From ID List File, select net.geneLists.txt, and click Open.
6. Click the Layout tab, select Degree Sorted Circle Layout, and select Selected Nodes Only. Adjust both the height and width of the nodes to 70.
7. Click the Second Box in Height, select degree.layout in Column, select Continuous Mapping in Mapping Type, and double-click the Height Adjustment Window on the right side of Current Mapping to adjust the upper and lower limits of the height to a suitable range.
8. Repeat the operation with the Width option.
9. Click the Layout tab again and click Layout Tools > Adjust the Scale so that the nodes do not overlap.
10. Click the Upper Right Window of the software to make the node network unchecked, click the Select tab, select Nodes, select From ID List File, select net.molLists.txt, and click Open.
11. Click the Layout tab, select Group Attributes Layout, select Selected Nodes Only, and click Type.
12. Click Label Font Size in the left column and set Default Value to 12. Click Default Value in Image/Chart 1 in the left column.
13. Select Charts in the popup window, click Pie Chart, and select the Items in the Available Columns column except for degree. Layout on the selected columns column and click Apply.
14. Click on Border Paint in the left column and set the Default Value to #003EF8. Click on the Edge tab at the bottom of the left sidebar and set Width to 0.8.
15. Click File on the top toolbar, select Export, then select Network to Image.
16. In the pop-up window, select PNG format for Format, select the save directory and name the image network, adjust the Zoom in Image Size to the maximum 500%, select Transparent Background, and click OK to finish saving the image.

7. Protein-Protein Interaction Networks (PPI) constructs

1. Open the STRING website (https://string-db.org/), click Multiple Protein, click Browse in Upload a file, select the Drug_Disease.txt file obtained in step 5.2.3, and select Homo sapiens in Organisms. Click Search and click Continue.
2. Click Settings, set the minimum required interaction score to the highest confidence (0.900), and check Hide Disconnected Nodes in the network in the network display options. Click Update.
3. Make adjustments to the nodes in the network diagram so that there is no overlap or occlusion.
4. Click Exports and click Download as a high-resolution bitmap to get the protein interworking network map in PNG format. Also, download the TSV file as a short tabular text output. Save both files in a unified folder. The TSV file contains gene name, STRING internal ID, and scores for different attributes.

8. PPI network core construction

1. Place the TSV file obtained in section 7 and the required Perl scripts in a new folder.
2. Open Cytoscape 3.9.1 software, click File, select Import, click Network from File, select the Above TSV file, put the first column node1 as Source Node and the second column node2 as Target Node, and click OK.
3. Click Style in the left sidebar, click Default Value in Width, and set it to 60. Drag and drop the nodes so that there is no overlap and no occlusion in the network.
4. Click APPs in the top toolbar, click CytoNCA, and click Open.
5. In the left column, select Without Weight under Betweenness, Closeness, Degree, Eigenvector, Local Average Connectivity-based method, and Network, and click Analyze.
6. When the analysis is complete, click on Node Table in the lower right window, click on Export, save it to the folder, and name it score 1.
   NOTE: The CytoNCA plugin needs to be installed in Cytoscape software. To do so, click Apps in the top toolbar, click APP Manager, enter CytoNCA in the search box, check CytoNCA in the middle column of the returned results, and click Install.
7. Open score1, adjust the name column to the first column, copy the information in the table, paste it into a new text file, name it score1.txt, and save it.
8. Open the R code and copy and paste the path where the score1.txt file is located to the line where the setwd is located in the R code.
9. Open the R software and run the modified code to get two new files, score2.txt and score2.gene.txt. The score2.txt file contains the genes for which all program scores were greater than the median and the specific scores for each program. score2.gene.txt contains the genes that scored greater than the median for all items.
10. To continue in Cytoscape, click AnalysisPanel 1 in the lower right window, click Upload From File on the left side of the window, select the score2.gene.txt, click Open, and tap OK in the pop-up window.
11. Click Select Nodes at the bottom and click OK in the pop-up window.
12. Click File in the top toolbar, select Export, click Network to Image, adjust the Zoom (%) in Image Size to the maximum 500%, check Transparent Background, and save the file to the same folder, name it network1.
13. Click Create Sub-Network in the right sidebar of the lower window to create a sub-network, analyze the sub-network, click APPs in the toolbar at the top, click CytoNCA and click Open.
14. In the left sidebar, select Without Weight under Betweenness, Closeness, Degree, Eigenvector, Local Average Connectivity-based method, and Network, and click Analyze.
15. When the analysis is done, click Node Table in the lower right window, click Export, and save it in a new folder named score2.
16. Open score2, adjust the name column to the first column, copy the information in the table, paste it into a new text file, name it score2.txt, and save it.
17. Open the R code and copy and paste the path where the score2.txt file is located to the line where the setwd is located in the R code.
18. Open the R software and run the modified code to get two new files: score3.txt and score3.gene.txt. The score3.txt file contains the genes that scored greater than the median for all items and the specific scores for each item. score3.gene.txt contains the genes that scored greater than the median for all items.
19. To continue in Cytoscape, click AnalysisPanel 1 in the lower right window, click Upload From File on the left side of the window, select the score3.gene.txt, click Open, and tap OK in the pop-up window.
20. Click Select Nodes at the bottom and click OK in the pop-up window.
21. Click File in the top toolbar, select Export, click Network to Image, adjust Zoom (%) in Image Size to 500%, check Transparent Background, and save the file in the same folder as the one used in this step, naming it network2.
22. Click Create Sub-Network on the right sidebar of the window below to create a sub-network.
23. Click File in the toolbar at the top, select Export, click Network to Image, adjust the Zoom (%) in Image Size to the maximum of 500%, check Transparent Background, and save the file in the same folder as used in this step and name it network3.

9. Gene ID Conversion

1. Place the Drug_Disease.txt file in the same new folder as the required code file.
2. Open the R code (Supplementary Coding File 4) and copy and paste the path where the Drug_Disease.txt file is located to the line where the setwd is located in the R code.
3. Open the R software, run the modified code using the org.Hs.eg.db package to convert the gene ID, and run after the completion of a new file id.txt. The id.txt contains the symbol of the gene and the corresponding ID.

10. GO enrichment analysis

1. Place the id.txt file from the previous step in the same folder as the GO enrichment analysis code.
2. Open the R code (Supplementary Coding File 5) and set the working directory to id.txt and the path where the GO code is stored.
3. Open R software, run the modified code, and use clusterProfiler, org.Hs.eg.db, enrich the plot, ggplot2 plot, the GO enrichment analysis histograms, and bubble plots. Get three new files, GO.txt, barplot.pdf, and bubble.pdf, after the run is completed.
   NOTE: The GO.txt is the enrichment result file containing enrichment classification (BP, CC, MF), GO ID, GO name, gene proportion, background proportion, enrichment significance p-value, corrected p-value (p.adjust, value), gene ID (name), and number of genes enriched on each GO. barplot.pdf is the histogram, bubble. pdf is a bubble plot.

11. KEGG enrichment analysis

1. Place the id.txt file in the same folder as the KEGG enrichment analysis code.
2. Open the R code (Supplementary Coding File 6) and set the working directory to the path where id.txt and KEGG code are stored.
3. Open the R software, run the modified code, and use the clusterProfiler, org.Hs.eg.db, enrichplot, ggplot2 package to plot GO enrichment analysis histograms, bubble plots, and pathway plots. The run is completed with the production of three new files, KEGG.txt, barplot.pdf, and bubble.pdf.
   NOTE: The KEGG.txt file is an enrichment result file containing pathway ID, pathway description, gene proportion, background proportion, enrichment significance p-value, corrected p-value (p.adjust, qvalue), gene ID (name), and the number of genes enriched in each pathway. barplot.pdf is a bar chart, and bubble.pdf is a bubble chart.
4. Search IL-17 in the KEGG.txt file; the result shows that there is only one pathway; copy the pathway ID.
5. Open the R code, paste the IL-17 pathway ID at KEGGID, and use the pathview package to label the pathway map. Run the modified R code and get two new files, hsa04657.pathview.png and hsa04657.png.
   NOTE: The hsa04657.png file is the pathway map, hsa04657.pathview.png is the labeled pathway map, and those labeled in red are the genes present in the interactions network.

12. Preparation of Xiaoyao pills

NOTE: For the preparation method used, refer to Chinese Pharmacopoeia⁵.

1. Take 100 g of Chai Hu, 100 g of Angelica sinensis, 100 g of White Paeonia lactiflora, 100 g of stir-fried Atractylodes macrocephala, 100 g of Poria cocos, 80 g of Radix glycyrrhizae preparate, and 20 g of Menthae herba.
2. Using a stone mill or a powdering machine, crush the herbs to a fine powder; several herbs are combined in the above proportions and pulverized into a fine powder to naturally mix together. Use an 80-mesh drug sieve with an inner diameter of 180 µm ± 7.6 µm and sieve the pulverized powder. Mix thoroughly.
3. Take 100 g of ginger, add water, and boil it 2x for 20 min each time. Strain and set aside.
4. Take a medicinal plaque, dip a small broom in ginger water, brush it on the plaque, take the above powder, sprinkle it on the ginger water, and turn the plaque so that all the powder is wet and it can be molded into a ball shape.
   NOTE: The formation of the medicinal plaque is an essential step during the preparation of TCM pills. Most of the moso bamboo will be split into strips and woven into a skeleton. The bamboo skin, or tengpi, is woven into the surface, and it becomes rounded. The surface of the plaque should be thinly coated with tung oil and then brushed with a layer of varnish. This is dried to become watertight. The pills made in this manner will be round and smooth.
5. Brush on the ginger water again, sprinkle the powder, and rotate the plaque so that the drug is gradually rounded and enlarged. Place in a cool place for drying.

13. Establishment of the animal model

1. After 7 days of acclimatization, randomly divide the experimental mice into 4 groups, namely, the sham operation group (Sham, where only the fat around the ovaries was removed) and three groups of ovariectomized mice consisting of a model group (OVX) as well as low- and high-concentration Xiaoyao pills administration groups.
2. Anesthetize the mice with 3% sodium pentobarbital (40 mg/kg) injected intraperitoneally. Check that the mice enter the anesthesia state by observing generalized muscle relaxation, deep and slow breathing, and slowed movement. Apply eye ointment to the mice's eyes before surgery.
3. Place the animals in the prone position and shave the renal region of the back with an animal shaver. Perform local disinfection with 75% ethanol.
4. Make a longitudinal incision of approximately 1 cm bilaterally, close to the dorsal region of the kidney, and incise the fascia to separate the muscles and peritoneum.
5. Locate the upper part of the uterine horn and the fallopian tubes for ligation. Insert forceps into the incision for exploration, and locate the ovary, encased by adipose tissue in a bright red cauliflower pattern, with spirally arranged fallopian tubes below the ovary, connecting the ovary to the uterine horn²⁵. Remove the ovaries with surgical scissors and suture.
6. Remove a small amount of adipose tissue near the ovary as a control in the sham operation group. Observe animals until they regain consciousness. Place postoperative animals in separate cages until full recovery.
7. To prevent infection, provide mice with gentamicin for 3 days postoperatively. After the operation, ensure a normal diet and good growth environment according to the mice's actual condition. If there are any abnormalities, such as wound infection or loss of appetite, consult the instructor of the Laboratory Animal Center immediately.

14. Administration of medication

NOTE: According to Pharmacology Experimental Methodology²⁶, the conversion of human and animal dosages used was 9 g of Xiaoyao pills for a 70 kg adult, equivalent to one dose (dosage for a single administration).

1. For the low and high dose groups, use 0.683 g/kg and 2.73 g/kg, respectively, for a group of 8 animals. Use the left hand to immobilize the mouse so that the mouse's mouth is in a straight line with the esophagus. Holding the gavage needle in the right hand, gently insert it into the esophagus along the posterior wall of the pharynx from the corner of the mouse's mouth.
2. At this point, the direction of the gavage needle may be slightly altered to stimulate an induced swallowing action. Inject the drug. Perform this once daily for 12 weeks.
3. For the sham-operated and model groups, administer saline once a day for 12 weeks in an amount determined by the animal's weight.
4. Euthanize mice by cervical dislocation and clip their hind limbs. Skin the limbs to separate the muscle from the femur. Treat mice femurs with EDTA decalcification solution for 1 month and replace with fresh solution daily.

15. Hematoxylin-eosin staining (HE) staining

1. Dewax femoral tissues for 8 min and then place into gradient ethanol for 3 min; rinse with running water for 1 min.
2. Add Hematoxylin staining solution dropwise to the sections to completely cover the tissue, stain for 5 min, and wash with tap water
3. Add the hydrochloric acid differentiation solution to the tissue on the slide, ensuring that it completely covers the tissue. Stop when discoloration of the tissue is observed. Rinse with tap water.
4. Add Eosin dye solution dropwise and allow to cover the tissue completely, acting for 30 s to 2 min. Rinse the excess dye with running water.
5. Perform gradient ethanol dehydration with 75% anhydrous ethanol for 5 min, followed by anhydrous ethanol for 5 min, and clear using dewaxing solution for 3 min.
6. Drop an appropriate amount of neutral gum on the slide according to the size of the tissue, and lower the coverslip carefully, avoiding air bubbles.

16. Micro-CT and Immunohistochemistry analysis

1. Remove excess muscles and ligaments around the femur specimens from different groups of mice and fix the sample tissues in 4% paraformaldehyde for 24 h.
2. Air dry bone tissues and perform micro-CT by bone tissue scanning in order to obtain three-dimensional micro-CT data.
3. From the preserved mouse wax blocks, cut them into 6 µm thick continuous sections and bake the slices at 70 °C -72 °C for 30-60 min.
4. Dewax for 8 min, followed by 75% anhydrous ethanol treatment for 5 min, anhydrous ethanol for 5 min, and PBS rinse for 5 min for 3x.
5. Perform EDTA high-pressure repair for 15 min followed by PBS wash for 5 min for 3x.
6. Add 3% hydrogen peroxide dropwise to the tissue, incubate at room temperature for 10 min, and wash with PBS for 5 min for 3x.
7. Use an immunohistochemistry pen to draw a circle. Wash with PBS wash for 3 min for 3x.
8. Stain in primary antibody (anti-ALP rabbit pAb (1:200), anti-COL-1 rabbit pAb (1:200), anti-IL-17 rabbit pAb (1:275), anti-Act1 (1:500), anti-IL-6 rabbit pAb (1:500)) overnight, followed by PBS buffer rinse for 5 min for 3x.
9. Dropwise add the secondary antibody (HRP-conjugated Affinipure Goat Anti-Rabbit IgG (H+L; 1:2000)) and incubate at room temperature for 60 min. Wash with PBS buffer for 3 min for 3x.
10. Incubate in DAB ready-to-use for 3-5 min, then rinse with PBS.
11. Perform Hematoxylin re-staining for 3 min, followed by PBS rinse.
12. Use differentiation solution for about 10 s, followed by PBS rinse.
13. Add return blue solution for 10 s, followed by PBS rinse.
14. Perform 75% anhydrous ethanol treatment for 5 min, anhydrous ethanol for 5 min, and clear for 3 min. Use neutral resin for sealing slides.

Results

Active ingredients and targets of action of Xiaoyao pills
By searching the TCMSP database and screening according to the criteria of oral bioavailability (OB) ≥ 30% and drug-like properties (DL) ≥ 0.18, 125 active ingredients were found to exist in Xiaoyao pills. Among them, ingredients 6, 4, 9, 13, 2, 6, 83, and 2 were from White Paeonia lactiflora, Atractylodes macrocephala, Menthae herba, Chaihu, Angelica sinensis, Poria cocos, licorice an...

Discussion

According to statistics, osteoporosis causes 1.5 million fractures each year in the United States, and the vast majority of these occur in postmenopausal women²⁷. With an increase in the aging population, it is predicted that the majority of the world's future hip fractures will occur in Asia and that by 2050, the total number of these worldwide will reach 8.2 million²⁸. The cost of preventing fractures is almost equal to that of treating them, and the use of medication...

Materials

Name	Company	Catalog Number	Comments
1ml Sampler	Guangxi Beilunhe Medical Industrial Group Co.	JYQ001	For anesthesia in mice
4%polyformaldehyde	Beijing Solarbio Science & Technology Co.,Ltd.	P1110	For tissue fixation
6-0 absorbable suture (angled needle)	Shanghai Pudong Jinhuan Medical Supplies Co.	HZX-06	For postoperative suturing
Absorbent cotton ball	Winner	MIANQIU-500g	For sterilization and hemostasis
Adhesive slides	Jiangsu Shitai Experimental Equipment Co.	188105	For tissue sectioning
Amobarbital	Sigma Aldrich (Shanghai) Trading Co.	A-020-1ML	For anesthesia in mice
Bluing Solution	Beijing Solarbio Science & Technology Co.,Ltd.	G1866	Blue coloration of the nuclei of cells after the action of hematoxylin differentiation solution
C57BL/6 mice	Beijing Vital River Laboratory Animal Technology Co., Ltd.	(SCXK 2033-0063)	For use in animal experiments
Carbon steel surgical blades	Premier Medical Equipment Co.	SP239	For mouse surgery
CIKS/TRAF3IP2 Rabbit pAb	BIOSS ANTIBODIES	bs-6202R	Binds to Act1 in tissues
Cole's Hematoxylin Solution (For Conventional Stain)	Beijing Solarbio Science & Technology Co.,Ltd.	G1140	For staining paraffin sections
Collagen Type I Polyclonal antibody	proteintech	14695-1-AP	Binds to COL-1 in tissues
DAB Substrate kit,20x	Beijing Solarbio Science & Technology Co.,Ltd.	DA1010	For tissue color development
Disposable surgical sheet 50*60CM	Nanchang Xuhui Medical Equipment Co.	SP4529777	For mouse surgery
EDTA decalcification solution (pH 7.2)	Beijing Solarbio Science & Technology Co.,Ltd.	E1171-500ml	For tissue decalcification
Enhanced Endogenous Peroxidase Bloching Buffer	Beyotime Biotechnology	P0100B	Sequestration of tissue or cellular endogenous peroxidases
Environmentally friendly dewaxing clear liquid	Servicebio	G1128-1L	For dewaxing paraffin sections
Ethyl Alcohol	CHRON CHEMICALS	64-17-5 (CAS)	For dehydration of paraffin sections
General Purpose Antibody Diluent	Epizyme Biotech	PS119L	For antibody dilution
Hematoxylin Differentiation Solution	Servicebio	G1039-500ML	For differentiation after hematoxylin staining and removal of excessively bound and non-specifically adsorbed dye from tissues
Hematoxylin Eosin (HE) Staining Kit	Beijing Solarbio Science & Technology Co.,Ltd.	G1120-3*100ml	For tissue staining
High quality stainless steel surgical knife handle	Premier Medical Equipment Co.	SP0088	For mouse surgery
HRP-conjugated Affinipure Goat Anti-Rabbit IgG(H+L)	proteintech	SA00001-2	Binds to primary antibody and amplifies signal
IL-17A Polyclonal antibody	proteintech	13082-1-AP	Binds to IL-17 in tissues
IL-6 Polyclonal antibody	proteintech	21865-1-AP	Binds to IL-6 in tissues
Immunohistochemistry pen	Beijing Zhongshan Jinqiao Biotechnology Co.	ZLI-9305 (YA0310)	For drawing circles in immunohistochemistry
Medical surgical suture Non-absorbent (ball) 5-0 3.5m	Yangzhou Yuanlikang Medical Equipment Co.	FHX-5-2	For postoperative suturing
Medical Suture Needles Angle Needles 4*10 3/8	Chaohu Binxiong Medical Equipment Co.	FHZ612-4	For postoperative suturing
Neutral Balsam	Beijing Solarbio Science & Technology Co.,Ltd.	G8590	As a slice sealer
PBS(1X)	Shenzhen Mohong Technology Co.,Ltd	B0015	Buffer for slide washing and partial solution dilution
Protein Free Rapid Blocking Buffer (1X)	Epizyme Biotech	PS108P	Avoiding non-specific binding of proteins
Rabbit Anti-Bone Alkaline Phosphatase antibody	BIOSS ANTIBODIES	bs-6292R	Binds to alkaline phosphatase in tissues
Saline	Affiliated Hospital Youjiang Medical University For Nationalities	LHN500	For animals by gavage
Shaving/Electric clippers	HANGZHOU HUAYUAN PET PRODUCTS CO., LTD.	DTJ-002	For shaving mice
Stainless Steel Medical Needle Holder 14cm Coarse Needle	Premier Medical Equipment Co.	SP784	For mouse surgery
Stainless Steel Ophthalmic Forceps 10.5cm Curved (No Hook)	Zhuoyouyue	YKNWW-10.5	For mouse surgery
Stainless Steel Ophthalmic Scissors/Surgical Scissors 10CM Straight Tip	Premier Medical Equipment Co.	ZYJD-10-ZJ	For mouse surgery
Stainless Steel Tip Gastric Needle 12 Gauge 55mm Elbow		GWJ-12-55W	For use in mice by gavage
Tris-EDTA Antigen Repair Fluid (50x)	proteintech	PR30002	For antigen repair of paraffin sections
Wooden dissecting board 25*16cm		JP16*24	For mouse surgery
Xiaoyao pills	Jiuzhitang Co.,Ltd.	YPG-041	For animal drug delivery
Others
R 4.3.1			Data processing
Cytoscape3.9.1	National Resource for Network Biology		Building a regulatory network for traditional Chinese medicine
ImageJ 1.54f	National Institutes of Health		Image processing for immunohistochemistry results
Adobe Photoshop 24.0.0	Adobe		For image combination
GraphpadPrism 9.5	GraphPad Software		Statistical analysis of data
cellsens Dimension	OLYMPUS		For slicing and photographing
OLYMPUS BX53	OLYMPUS		For HE staining and immunohistochemical section photography