Home Physical Sciences Metformin inhibits knee osteoarthritis induced by type 2 diabetes mellitus in rats: S100A8/9 and S100A12 as players and therapeutic targets
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Metformin inhibits knee osteoarthritis induced by type 2 diabetes mellitus in rats: S100A8/9 and S100A12 as players and therapeutic targets

  • Xin Wang , Yu Qiao , Fahu Yuan , Yi Liu , Jun Hu , Qingfu Zhang , Fuyan Wang EMAIL logo and Zhigang Zhao EMAIL logo
Published/Copyright: April 4, 2024
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Open Chemistry
From the journal Open Chemistry Volume 22 Issue 1

Abstract

Type 2 diabetes mellitus (T2DM) is a significant risk factor for osteoarthritis (OA), and metformin, as the main therapeutic drug for T2DM, has shown positive effects on OA without a clear mechanism. This study aimed to explore the protective effects and mechanisms of oral metformin in T2DM-induced OA. We identified differentially expressed genes, using the GSE117999 and GSE98918 datasets, and protein–protein interaction networks were analyzed using the MCODE algorithm in cytospace to finalize the OA hub genes (S100A8, S100A9, and S100A12). To validate whether S100A8, S100A9, and S100A12 are potential targets of action for OA, we randomly divided 40 SD rats into a control group (CG, n = 10) and a T2DM group (n = 30). We modeled rats in the T2DM group with streptozotocin (35 mg/kg, i.p.) and a high carbohydrate and fat diet. Finally, 20 were randomly selected and divided into the T2DM group (n = 10) and the treated group (Met + T2DM, n = 10), and the treated group was given Met (180 mg/kg/day) by gavage for 8 weeks. We subsequently used histological assessment to show that oral metformin mitigated the development of T2DM-associated OA as indicated by the OA Research Society International score and articular cartilage thickness, and immunohistochemistry also confirmed that metformin significantly reduced the expression of S100A8, S100A9, and S100A12 in the knee joints of OA rats. In conclusion, metformin demonstrated a protective effect against OA in T2DM-induced rats, slowing knee OA progression by inhibiting S100A8, S100A9, and S100A12 expression. These findings suggest potential biological targets for future OA treatments.

Keywords: osteoarthritis; metformin; calgranulins; type 2 diabetes mellitus

1 Introduction

Osteoarthritis (OA) is the most common degenerative disease involving multiple joints, which seriously affects patients’ quality of life. Currently, it is believed that OA is closely related to age, obesity, mechanical factors, and genetic susceptibility [1]. The prevalence of OA is increasing year by year due to the increasing life expectancy globally [2]. OA is characterized by degeneration of articular cartilage, joint pain, and functional limitation, which seriously affects the quality of life of patients [1]. Currently, the clinical treatment of OA focuses on symptomatic relief, pain reduction, and improvement of joint function, but its pathophysiological process needs to be further investigated.

Type 2 diabetes mellitus (T2DM) is a common chronic metabolic disease characterized by insulin resistance and defective insulin secretion, leading to persistent elevation of blood glucose levels. In recent years, studies have shown a strong association between type 2 diabetes and OA [3]. It has been found that patients with T2DM are generally at a high risk of OA, which is associated with the two having common risk factors such as obesity and aging. Similarly, T2DM can be pathogenic for OA through mild chronic inflammation induced by oxidative stress and insulin resistance [3]. In classical rat models, T2DM can also develop high-fat diet-induced obesity, glucose intolerance, and insulin-resistance-associated OA [4]. Currently, a significant correlation between T2DM and OA has been found in a meta-analysis involving 1 million participants [5]. Moreover, T2DM is also an independent risk predictor for arthroplasty [6].

Metformin (Met) is a widely used antidiabetic drug with a favorable safety profile and is the first-line treatment for type II diabetes [7]. It has been found that Met, in addition to its blood glucose-lowering effects, also plays an active role in age-related diseases such as cardiovascular, cerebrovascular, and aging diseases [8]. It also plays a regulatory role in cellular physiological processes, such as inflammatory responses, oxidative damage, autophagy, and immune regulation [9,10,11]. Recent studies have suggested that Met may affect the development and progression of various diseases, including OA, by regulating intracellular signaling pathways [12]. A clinical study has shown that long-term use of Met improves knee cartilage loss and relieves knee symptoms in patients with OA [13,14]. At the same time, prospective studies have also found a significant reduction in the risk of total knee arthroplasty over 6 years in patients taking Met, as well as a lower rate of endosteal cartilage volume loss in patients taking Met [12]. These clinical evidence suggest that Met has a positive effect on the treatment of OA. However, the pathway by which Met plays a role in slowing the progression of OA remains unknown.

Currently, bioinformatics is widely used for the prediction of potential therapeutic targets for diseases, with the search for DEGs being one of the most commonly used means [14,15]. Therefore, our study hypothesized that metformin may alleviate type 2 diabetes-related OA by regulating the NF-κB signaling pathway by affecting S100A8 (Calgranulin A, MRP8), S100A9 (Calgranulin B, MRP14), and S100A12 (Calgranulin C), as validated by DEGs in combination with animal experiments. Calgranulins are predominantly expressed by neutrophils, monocytes, and activated macrophages. Calgranulins have some pro-inflammatory effects and at appropriate levels have antioxidant and protective functions, which help to regulate and restore the homeostatic environment of the body. Conversely, their overexpression may contribute to the chronic inflammatory process in certain diseases such as OA.

Our study will provide new insights into understanding the pathogenesis of T2DM-related OA as well as the therapeutic potential of metformin for the treatment of OA, and provide a theoretical basis for the development of more effective therapeutic strategies to improve the quality of life of patients.

2 Materials and methods

2.1 Microarray dataset collection and processing

The OA datasets GSE117999 and GSE98918 were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Both datasets were provided based on the GPL20844 platform (Agilent-072363 SurePrint G3 Human GE v3 8 × 60K Microarray 039494). Of these, GSE117999 included differentially expressed gene (DEG) expression in cartilage tissue from 24 patients (12 non-OA and 12 osteoarthritides). GSE98918 identified meniscus tissue from 24 patients (12 non-OA and 12 osteoarthritides). The probes were converted to gene symbols according to the annotation file, the data from the microarrays were log2 transformed, and the samples were normalized by the “remove batch effect” function of the limma package [16].

2.2 Identification of DEGs and overlapping genes

Significance analysis of DEGs between OA tissues and normal tissue samples was performed using the Limma package [16] in R language with the set criteria of ∣log2(FC)∣ ≥ 0.5 and adjusted p-value < 0.05. Volcano plots were performed using the R-package “ggplot” [17]. Venn diagrams of genes that were upregulated in OA diseases in the GSE117999 and GSE98918 datasets were performed using the R-package “eulerr” [18].

2.3 GO enrichment analysis and category network plot construction

Biological process (BP) enrichment of overlapping genes for gene ontology (GO) helps us to recognize the biological properties of genes. The “Cluster profiler” software package [19] was used to perform GO functional annotation and to construct a category network plot (cnetplot) linking genes to biological concepts, which helped us to understand the enrichment pathways involved in the genes.

2.4 PPI network construction and identification of hub genes

Overlapping genes obtained by taking the intersection of gene modules upregulated with OA in GSE117999 and GSE98918 were imported into the STRING online database (https://cn.string-db.org/) to construct a protein–protein interaction (PPI) network diagram. The confidence score ≥0.15 was regarded as the inclusion criterion, and the disconnected nodes were hidden. The results were imported into Cytoscape software (version 3.9.1) to construct visualized images. A k-means clustering algorithm was performed on them using molecular complex detection (MCODE) plug-in to find out the key subnetworks and genes, and the subnetworks with the highest network scores were analyzed downstream.

2.5 Expression of the S100 protein family in OA

We analyzed the expression of all S100 protein families in the GSE117999 and GSE98918 datasets using the Wilcoxon test for difference analysis, and finally, we used the “ggplot” software package to perform box plots of S100 family expression.

2.6 Experimental animals

The study was approved by the Ethics Committee of the Department of Medicine, Jianghan University (NO: JHDXLL2022-069), and the experiments were strictly conducted under its supervision. The 40 six-week-old male Sprague-Dawley (SD) rats (180–200 g) used in the experiment were purchased from Beijing Vital River Laboratory Animal Technology Co. All animals were housed in an SPF-grade animal laboratory with an ambient temperature controlled at 22 ± 1°C and a 12-h light/dark cycle. All SD rats were given free access to a planned diet. All SD rats were acclimatized for 1 week before the initiation of the experimental program.

2.7 Experimental design

After 1 week of acclimatization, all rats were randomly assigned into the control group (CG, n = 10) and T2DM group (n = 30). Control group: fed with a standard animal diet (10% of kcal derived from fat) for 6 months throughout the experiment. T2DM group: T2DM was induced in rats by a high carbohydrate and fat diet (HCFD) in combination with an intraperitoneal injection (i.p.) of streptozotocin (STZ) [20]. Thirty rats were first fed HCFD for 4 weeks. They were fasted the night before administration and received a single intraperitoneal injection of STZ (dose: 35 mg/kg) [21]. Blood was collected from the tail of the rats 72 h after the STZ injection, and fasting blood glucose was measured by using a Randox kit (SigmaAldrich), and fasting blood glucose > 16.7 mmol/L was adjudged to be type 2 diabetic rats [20].

After STZ injection, a total of 24 rats developed T2DM, from which 20 T2DM rats were randomly selected and subdivided into two groups (T2DM group, n = 10; treated group, n = 10). Control group (CG): maintained on standard diet feed. T2DM group: maintained on HCFD feed. The treatment group (Met + T2DM) was given metformin 180 mg/kg daily by gavage, and the rest were the same as the T2DM group for 8 weeks.

2.8 Measurement of blood levels of glucose and HbA1c

Blood glucose was detected by using a blood glucose detector (CONTOUR PLUS ONE, Bayer.) on the tail vein of rats. Rat HbA1c was measured using an ELISA kit (Cat. 80300; Crystal Chem, USA) according to the manufacturer’s instructions methods.

2.9 Sampling and tissue preparation

All rats were anesthetized and euthanized, and the knee joints were dissected, fixed in 4% paraformaldehyde for 72 h, and then decalcified in 5% hydrochloric acid for 3 weeks. Decalcified specimens were dehydrated in incremental grades of alcohol and paraffin-embedded using standard methods. Knee specimens were then sectioned on 5 μm-thick sagittal planes and stained with H&E and Safranin O-fast green to analyze the state of the tissue for the structural and pathologic analysis. The proteoglycan content in articular cartilage was assessed using Safranin O-fast green staining. The average thickness of articular cartilage in each group of rats was measured under a microscope, and the degree of arthritis in the rat knee was scored using the OA Research Society International (OARSI) score [22]. A combination of grade and stage metrics was used (Score = grade × stage, Grade: G1–G6, Stage: S1–S4, Score: 0–24 points) [23].

2.10 Immunohistochemistry

Immunohistochemical staining of tissue sections was performed by dewaxing paraffin sections to water according to the manufacturer’s instructions, followed by antigen repair using pepsin. Sections were incubated in 3% hydrogen peroxide for 25 min, then closed with 3% BSA for 30 min at room temperature, and then sections were incubated with rabbit polyclonal anti-S100A8 antibody (GB11421, Servicebio, China), rabbit polyclonal anti-S100A9 antibody (GB111149, Servicebio, China), and rabbit polyclonal S100A12 Antibody (GB113483, Servicebio, China), all of which were diluted at a ratio of 1:200 and incubated at 4°C overnight. The sections were washed in PBS for 15 min and then incubated with the corresponding secondary antibody for 50 min at room temperature. The sections were color developed using DAB color solution (G1212, Servicebio, China), and the nuclei were restained with hematoxylin for 3 min, and finally, they were dehydrated and sealed. Positively stained cells were observed under a microscope in five fields of view and quantitatively analyzed using the ImageJ image analysis system.

2.11 Molecular docking of metformin with S100 Calgranulins

To investigate whether metformin can act directly on S100 Calgranulins, we performed molecular docking of metformin with S100 Calgranulins. The corresponding crystal structures of the two proteins (S100A8/S100A9: PDB ID: 7QUV; S100A12: PDB ID: 2WCB) were obtained from the RCSB PDB database. The obtained protein crystals were subjected to protein preprocess, regenerate states of native ligand, H-bond assignment optimization, protein energy minimization, and removal waters, using the Protein Preparation Wizard module of Schrödinger software. The 2D sdf structure file of the compound Metformin was processed, and all its 3D chiral conformations were generated using the LigPrep module in Schrödinger. Subsequent molecular docking was performed using Schrödinger Maestro 13.5 software. The SiteMap module in Schrödinger was first used to predict the best binding site, and then the receptor grid generation module in Schrödinger was used to set the most suitable Enclosing box to wrap the predicted binding site perfectly, and the active sites of the two proteins were obtained on this basis. The processed ligand compound metformin was molecularly docked with the active sites of the two proteins (using the highest precision XP docking). The ligand compound metformin and the active sites of the two proteins were analyzed by MM-GBSA calculation.

2.12 Statistical analysis

All data are expressed as mean ± standard deviation and processed using SPSS version 26.0 (SPSS, Inc., Chicago, USA). The Shapiro–Wilk and Levene tests were used to evaluate the normality and homogeneity of the results between the two groups. One-way ANOVA was used for statistical analysis when the variables were normally distributed. P < 0.05 was regarded as statistically different for the results, and P < 0.01 was regarded as statistically significant for the results.

3 Results

3.1 Bioinformatic characterization of DEGs in the OA microarray dataset

We found 350 upregulated genes in GSE117999 (Figure 1a) and 916 upregulated genes in GSE117999 (Figure 1b) using Limma package. The upregulated DEGs in GSE117999 and GSE98918 were intersected using the “eulerr” software package and plotted in a Venn diagram (Figure 1c), resulting in 126 overlapping genes. The 126 overlapping genes were functionally enriched using the R-package “clusterProfiler,” and their GO BP and cnetplots were plotted (Figure 1d and e) to predict the potential biological functions and related pathways of these overlapping genes.

Figure 1 Bioinformatic characterization of DEGs in osteoarthritis microarray dataset. (a) Volcano map of DEGs for the GSE117999 dataset. (b) Volcano map of DEGs for the GSE98918 dataset. (c) Venn diagram of up-regulated genes in the GSE117999 and GSE98918 datasets. (d) GO BP enrichment analysis of 126 overlapping genes in the top10. (e) Cnetplot of the top 10 pathways. DEGs, differentially expressed genes; GO, gene ontology function; BP, biological process; Cnetplot, category network plot.
Figure 1

Bioinformatic characterization of DEGs in osteoarthritis microarray dataset. (a) Volcano map of DEGs for the GSE117999 dataset. (b) Volcano map of DEGs for the GSE98918 dataset. (c) Venn diagram of up-regulated genes in the GSE117999 and GSE98918 datasets. (d) GO BP enrichment analysis of 126 overlapping genes in the top10. (e) Cnetplot of the top 10 pathways. DEGs, differentially expressed genes; GO, gene ontology function; BP, biological process; Cnetplot, category network plot.

3.2 Identification of hub genes in the OA microarray dataset

To further identify hub genes associated with OA, 126 overlapping genes were imported into the STRING database to construct a protein–protein interaction (PPI) network, and their PPI information was imported into Cytoscape 3.9.1 by setting the confidence score ≥0.15 to construct a visualization image (Figure 2a). The MCODE plugin was used to find out the key sub-networks (Figure 2b), and the red cluster with the highest score (Score = 8) was selected for downstream analysis (Figure 2c). The confidence score ≥0.99 was set for the red cluster, and three hub genes, S100A8, S100A9, and S100A12, were finally obtained.

Figure 2 PPI network construction for overlapping genes and Hub genes identification for OA. (a) PPI network diagram of overlapping genes. (b) Identifying critical subnetworks using the MCODE plug-in. (c) The subnetwork with the highest MCODE score was screened from the PPI. (d) Top three hub genes. PPI, protein–protein interaction.
Figure 2

PPI network construction for overlapping genes and Hub genes identification for OA. (a) PPI network diagram of overlapping genes. (b) Identifying critical subnetworks using the MCODE plug-in. (c) The subnetwork with the highest MCODE score was screened from the PPI. (d) Top three hub genes. PPI, protein–protein interaction.

Combined with GO and cnetplot analyses in Figure 2d and e, we hypothesized that S100A8, S100A9, and S100A12 may play physiological roles by regulating NF-κB signaling pathway, and cell chemotaxis in OA patients.

3.3 Expression of the S100 protein family in OA

As all the hub genes we identified were members of the S100 protein family, we differentially analyzed the expression of all the S100 protein families in the two datasets to obtain box plots (Figure 3). Ultimately, we found that S100A8, S100A9, S100A12, and S100P were significantly upregulated in both groups of OA patients.

Figure 3 Expression of S100 protein family in osteoarthritis. (a) Expression of the S100 protein family in the GSE117999 dataset. (b) Expression of the S100 protein family in the GSE98918 dataset. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 3

Expression of S100 protein family in osteoarthritis. (a) Expression of the S100 protein family in the GSE117999 dataset. (b) Expression of the S100 protein family in the GSE98918 dataset. *P < 0.05; **P < 0.01; ***P < 0.001.

3.4 Induction of T2DM-related OA in rats

We induced the development of T2DM in rats by HCFD feeding combined with intraperitoneal injection of streptozotocin (STZ). After 24 weeks, the development of diabetes and OA was induced in both groups as confirmed by the detection of blood glucose, HbA1c, and pathological sections in the T2DM and Met + T2DM groups. The body weights of the rats in the T2DM group and Met + T2DM group were significantly lower compared to the CG group (Figure 4a). Blood glucose and HbA1c concentrations were significantly higher in the T2DM group and Met + T2DM group compared to CG (P < 0.001). However, blood glucose and HbA1c concentrations were significantly lower (P < 0.001) in the Met + T2DM group compared to the T2DM group (Figure 4b and c).

Figure 4 Results of body weight, blood glucose, and glycosylated hemoglobin measurements in SD rats. (a) Body weight change curve of SD rats. After 1 week of acclimatization, 30 of the rats were fed with high carbohydrate and fat diet (HCFD). They were injected with STZ at the fifth week to induce the production of T2DM. (b) HbA1c assay results of SD rats. (c) Glucose assay results in SD rats. Note: CG, control group; T2DM, T2DM group; Met + T2DM, treated group; *P < 0.05; ***P < 0.01; ****P < 0.001.
Figure 4

Results of body weight, blood glucose, and glycosylated hemoglobin measurements in SD rats. (a) Body weight change curve of SD rats. After 1 week of acclimatization, 30 of the rats were fed with high carbohydrate and fat diet (HCFD). They were injected with STZ at the fifth week to induce the production of T2DM. (b) HbA1c assay results of SD rats. (c) Glucose assay results in SD rats. Note: CG, control group; T2DM, T2DM group; Met + T2DM, treated group; *P < 0.05; ***P < 0.01; ****P < 0.001.

3.5 Histological results of metformin in alleviating T2DM-related OA

H&E staining and Safranin O-fast green staining of rat knee joint sections with OARSI score and knee cartilage thickness measurement could reflect the OA condition in rats. H&E staining of knee joint sections showed normal knee joint organization in the CG group (Figure 5a), and severe damage to the articular cartilage structure, such as irregularity of the articular cartilage surface, was seen in the T2DM group (Figure 5b). The Met + T2DM group was effective in improving the structure of the knee joint in rats (Figure 5c). The content and distribution of proteoglycans in the matrix of rat knee joints were reflected by Safranin O-fast green staining. Safranin staining of rats in the CG group showed uniform coloration of articular cartilage, suggesting that the content and distribution of proteoglycans were normal (Figure 5d). Proteoglycans in the matrix of rat knee joints in the T2DM group were significantly decreased (Figure 5e). The proteoglycan content was significantly elevated in the Met + T2DM group compared to the T2DM group, but its proteoglycan content was reduced compared to the CG group. This suggests that metformin can maintain proteoglycan production and delay the progression of OA, but cannot reverse the onset of OA.

Figure 5 H&E staining, Safranin O-fast green staining, OARSI score, and articular cartilage thickness in the knee joints of SD rats. (a–c), H&E staining (×4); (d and e), Safranin O-fast green staining (×4); control group (a and d); T2DM group (a and d); Met + T2DM group (a and d); (g) representation of the severity of OA disease with all groups of rats (mean ± SD, n = 5). (h) Histograms represent the quantitative analysis of the mean thickness of articular cartilage surfaces in all groups (mean ± SD, n = 5). *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 5

H&E staining, Safranin O-fast green staining, OARSI score, and articular cartilage thickness in the knee joints of SD rats. (a–c), H&E staining (×4); (d and e), Safranin O-fast green staining (×4); control group (a and d); T2DM group (a and d); Met + T2DM group (a and d); (g) representation of the severity of OA disease with all groups of rats (mean ± SD, n = 5). (h) Histograms represent the quantitative analysis of the mean thickness of articular cartilage surfaces in all groups (mean ± SD, n = 5). *P < 0.05; **P < 0.01; ***P < 0.001.

OARSI score and articular cartilage surface thickness were measured by histology of knee joints. The OARSI score in the T2DM group was 15.1 ± 4.01 significantly higher than that in the CG group, which was 0.3 ± 0.48 (P < 0.01), and also the cartilage thickness in the T2DM group (66.7 ± 10.59 μm) compared with that in the CG group (151.8 ± 11.62 μm) was significantly decreased. The OARSI score in the Met + T2DM group was significantly reduced to 7.5 ± 3.14 (P < 0.001), while the articular cartilage thickness (91.3 ± 7.53 μm) was also significantly elevated (P < 0.001) compared with the T2DM group (Figure 5g and h).

3.6 Metformin inhibits the expression of S100 Calgranulins

Based on the immunohistochemical staining, it was suggested that the expression of S100A8 (Figure 6a and d), S100A9 (Figure 6b and e), and S100A12 (Figure 6c and f) was significantly increased in the knee joints of OA rats in the T2DM group, compared with that in the CG group. The expression of S100A8 (Figure 6a and d), S100A9 (Figure 6b and e), and S100A12 (Figure 6c and f) was significantly decreased after oral Met. These results confirm that Met attenuates the expression of S100 Calgranulins, thereby relieving OA.

Figure 6 Immunohistochemistry of S100A8, S100A9, and S100A12 (×40). (a) IHC for S100A8. (b) IHC for S100A9. (c) IHC for S100A12. (d) Positive cell rate of S100A8 in three groups of rats (mean ± SD, n = 5). (e) Positive cell rate of S100A9 in three groups of rats (mean ± SD, n = 5). (f) Positive cell rate of S100A12 in three groups of rats (mean ± SD, n = 5). *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 6

Immunohistochemistry of S100A8, S100A9, and S100A12 (×40). (a) IHC for S100A8. (b) IHC for S100A9. (c) IHC for S100A12. (d) Positive cell rate of S100A8 in three groups of rats (mean ± SD, n = 5). (e) Positive cell rate of S100A9 in three groups of rats (mean ± SD, n = 5). (f) Positive cell rate of S100A12 in three groups of rats (mean ± SD, n = 5). *P < 0.05; **P < 0.01; ***P < 0.001.

3.7 Molecular docking results of metformin with S100 Calgranulins

Metformin binds to the surface of the active pocket of the S100A8/A9 proteins, forming hydrophobic forces with residues such as ILE14, a hydrogen bond with each of residues ALA84 and TYR47, and a hydrogen bond and a salt bridge with residue GLU15 (Figure 7a). Metformin binds to the surface of the active pocket of the S100A12 protein, forming a hydrophobic force with residue TYR86 of the S100A12 protein and a hydrogen bond with each of residues HIS89 and GLU91 (Figure 7b). Comprehensively analyzing the XP docking and MM-GBSA results, the docking score of metformin with S100A8/A9 was −2.669, and the MM-GBSA result was −11.20 kcal/mol, with higher docking scores and free energies of binding, which indicated that the binding of metformin to S100A8/A9 was less stable. Second, the docking score of metformin with S100A12 was −2.274, and the MM-GBSA result was −9.31 kcal/mol, with a higher docking score and binding free energy, indicating that metformin binding to S100A12 was also unstable. However, metformin binds better to S100A8/A9 than to S100A12 protein. The molecular docking results suggest that Met may play its physiological functions by binding to other proteins.

Figure 7 Molecular docking of metformin with S100A8/A9 and S100A12. (a) 2D and 3D molecular docking models of metformin with S100A8/A9. (b) 2D and 3D molecular docking models of metformin with S100A12. Yellow, hydrogen bonds; Red, salt bridges.
Figure 7

Molecular docking of metformin with S100A8/A9 and S100A12. (a) 2D and 3D molecular docking models of metformin with S100A8/A9. (b) 2D and 3D molecular docking models of metformin with S100A12. Yellow, hydrogen bonds; Red, salt bridges.

4 Discussion

OA is the most common type of arthritic disease. According to statistics, 300 million people worldwide are currently suffering from OA, and it is by far the most common cause of disability in the elderly [24]. The main clinical symptoms of OA include pain, transient morning stiffness, etc., which lead to disability and affect the patient’s quality of lives. This not only causes physical suffering to the patient but also has a huge economic impact, accounting for approximately 1–2.5% of the gross domestic product of Western countries [25]. Obesity and aging, which are the most important factors causing primary OA, are also high-risk factors for T2DM [1]. Obesity often leads to mechanical damage to the knee joint and is also present in the majority of patients with T2DM. As the age of the population continues to increase, the prevalence of T2DM and OA also increases significantly [3]. Metformin has been used as a first-line therapeutic agent for the treatment of T2DM for more than 60 years, and Met can have anti-inflammatory and anti-aging properties in addition to its hypoglycemic effects [26]. The use of Met in the treatment of OA has received widespread attention, and recent studies have found that Met can protect the mitochondrial function of chondrocytes through activation of the AMPK and SIRT1 pathways, and promoting the production of osteoblasts. It can also delay the progression of OA by inhibiting inflammation levels, regulating cellular autophagy and antioxidants [26,27]. Moreover, Met has been clinically validated in the treatment of OA [12]. In the present study, we validated the effect of metformin on a rat model of T2DM-associated OA through a combination of bioinformatics and animal model experiments. In this study, we conclusively identified S100A8, S100A9, and S100A12 as hub genes for OA and detected that the degree of OA in the rat knee joint was significantly alleviated, and the expression of S100A8, S100A9, and S100A12 was significantly reduced in the presence of Met. These findings provide new ideas for the treatment of OA in T2DM as well as a basis for subsequent related studies.

S100As is a family of calcium-binding proteins consisting of small-molecular-weight proteins [28]. S100 proteins undergo conformational changes upon binding to Ca2+ and bind to target proteins, thereby translating Ca2+ signaling into specific biological behaviors that are involved in the regulation of various cellular processes, including proliferation, apoptosis, differentiation, inflammation, cell cycle, Ca2+ homeostasis, and energy metabolism [29–32]. Currently, S100 proteins are important mediators of a wide range of diseases, including inflammatory arthritis, atherosclerosis, tumor growth, metastasis, and prognosis. The S100 family has been found to be important mediators in a range of diseases, including inflammatory arthritis, atherosclerosis, tumor growth, metastasis, and prognosis [33–36].

Calgranulins are expressed by neutrophils, monocytes, and macrophages and include three proteins, S100A8, S100A9, and S100A12. According to GO and cnetplot analysis, Calgranulins may play a role in OA by regulating the NF-κB signaling pathway and cell chemotaxis. And the expression of these proteins increased with the progression of OA [33]. The NF-κB signaling pathway is an important inflammatory regulatory pathway, which plays an important role in the inflammatory process [37]. It is considered to be an important link in the pathogenesis of T2DM and OA [38]. Recent studies have shown that the NF-κB signaling pathway also plays an important role in the development and progression of OA [39]. NF-κB affects cellular immune and inflammatory responses and promotes tissue destruction and joint pain by participating in the production of multiple inflammatory mediators and the regulation of the expression of inflammation-related genes [40]. The current study has found that metformin can ameliorate diseases related to aging, such as aging, by inhibiting the expression of NF-κB [41]. Moreover, the anti-inflammatory effect that metformin possesses may provide a potential therapeutic target for certain inflammatory disorders [42].

S100A8 and S100A9 are abundantly present in the cytoplasm of neutrophils, often in the form of S100A8/A9 heterodimers (calprotectin) [43]. S100A8/A9 can promote cell chemotaxis by upregulating the expression of adhesion molecules on endothelial cells (ECs), by inducing neutrophil chemokine IL-8, and by increasing vascular permeability through the downregulation of EC junction-associated proteins [44]. S100A8/A9 proteins play an important regulatory role in inflammation mainly by binding and activating Toll-like receptors and glycosylated end-product receptors and thus mediating intracellular inflammatory signaling and other pathways, are involved in the pathological processes of a variety of chronic inflammatory diseases, and play an important role in inflammatory responses and the process of intrinsic immunity of the organism [45,46]. Animal studies have found that S100A8/A9 can bind to TLR4 binding and increased the expression of MMPs, TNF-α, and IL-6 through the NF-κB signaling pathway, and the occurrence of inflammation-associated pain could be reduced by S100A8/A9 inhibitors [47]. S100A8/A9 stimulated the production of pro-inflammatory cytokines, such as TNF-α, in monocytes and macrophages, and the use of p38 MAPK inhibitors and NF-κB inhibitors could reduce the stimulation produced by S100A8/A9. Thus, S100A8/A9 can amplify proinflammatory cytokine responses through the NF-κB and p38 MAPK pathways [48]. Clinical studies have confirmed that serum S100A8/S100A9 is positively correlated with Western Ontario and McMaster Universities OA Index (WOMAC) score [49].

S100A12 promotes inflammation in the body mainly through the activation of mast cells and is regarded as a new mast cell activator [50]. Levels of S100A12 can also be regarded as a highly sensitive biomarker of inflammatory diseases [33]. In vitro experiments have revealed that S100A12 regulates leukocyte transport by upregulating the expression of CD11b and L-selectin shedding in neutrophils and also has a chemotactic effect on monocytes [33,51]. Calgranulins proteins are also the main branch of TLR4 activation in mammals [52]. In vitro experiments, the addition of S100A12 to OA chondrocytes increased the release of MMP-13 and VEGF and promoted cartilage degradation, which is involved in the development of OA. And the release of MMP-13 and VEGF in S100A12-induced OA cells was significantly reduced by the use of p38 inhibitors and NF-κB inhibitors [53]. And S100A12 in patients’ serum and synovial fluid may be a potential biomarker for predicting the onset and progression of knee OA [54]. In the present study, we found that in addition to S100A8/A9 and S100A12, lactotransferrin (LTF) may also be involved in the regulation of OA through the NF-κB pathway. Zhang et al. found that the expression content of LTF in meniscus increased with age in rats, and that knockdown of LTF inhibited the NF-κB pathway and alleviated senescence [55].

Our study still has some limitations. First, we analyzed through public databases and did not find a fully compatible dataset of OA caused by T2DM. To improve accuracy, more clinical samples for analysis and real sequencing data from clinical samples are needed in the future. Second, we did not further refine the study of different degrees of OA, and we need to explore the potential therapeutic targets of OA of different stages in more detail in the future. Finally, we found through molecular docking that metformin may not exert its physiological function directly by binding to calgranulins, and we need to further explore the direct target of Met for OA in the future.

5 Conclusion

We analyzed the OA dataset and animal experiments to ultimately conclude that S100A8, S100A9, and S100A12 play important roles in OA. Calgranulins can induce the migration of inflammatory cytokines such as leukocytes and IL-6 through cell chemotaxis, and also amplify the pro-inflammatory cytokine response through the NF-κB and p38 MAPK pathways that further aggravate the development of OA. The development of OA can be significantly attenuated by oral administration of metformin, which reduces the expression of calgranulins in articular cartilage and exerts a significant protective effect. However, through molecular docking, we found that the binding of Met to Calgranulins is not stable, and it may exert its physiological function by combining with other proteins. Calgranulins may be a new biomarker or therapeutic target for OA research in the future.

# X. Wang and Y. Qiao contributed equally to this work.

Acknowledgments

Not applicable.

  1. Funding information: This study was supported by the Chinese Medicine Research Project of the Hubei Provincial Administration of Chinese Medicine (2023-2024) (No: ZY2023F143); by the Graduate Scientific Research Foundation of Jianghan University. (No. KYCXJJ202330); by Excellent Discipline Cultivation Project of JHUN (No. 2023XKZ015); and by the Young Talent Development Program of Wuhan Fourth Hospital.

  2. Author contributions: Conceptualization: X. W., Y. Q., F. H. Y., F. Y. W., and Z. G. Z.; methodology: X. W., Y. Q., F. H. Y., F. Y. W., and Z. G. Z.; software: X. W., Y. Q., and J. H.; visualization: X. W., Y. Q., F. H. Y., and Q. F. Z.; validation: X. W. and Y. L.; formal analysis: X. W., Y. Q., and F. H. Y.; writing – original draft preparation: X. W., Y. Q., and F. H. Y.; writing – review and editing: F. H. Y., X. W., F. Y. W., Z. G. Z., and Y. Q.; funding acquisition: F. H. Y., Y. Q., and Y. L. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: The authors declared that they have no conflicts of interest in this work.

  4. Ethical approval: All animal studies were conducted in accordance with the regulations and guidelines of the Department of Medicine, Jianghan University, and approved by the Ethics Committee of the Department of Medicine, Jianghan University (No: JHDXLL2022-069).

  5. Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Received: 2023-12-15
Revised: 2024-03-12
Accepted: 2024-03-14
Published Online: 2024-04-04

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/chem-2024-0013
Keywords for this article
osteoarthritis; metformin; calgranulins; type 2 diabetes mellitus
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BY 4.0

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