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High expression of transcription factor EGR1 is associated with postoperative muscle atrophy in patients with knee osteoarthritis undergoing total knee arthroplasty

Journal of Orthopaedic Surgery and Research volume 19, Article number: 618 (2024) Cite this article

Abstract

Background

Muscle atrophy is a typical affliction in patients affected by knee Osteoarthritis (KOA). This study aimed to examine the potential pathogenesis and biomarkers that coalesce to induce muscle atrophy, primarily through the utilization of bioinformatics analysis.

Methods

Two distinct public datasets of osteoarthritis and muscle atrophy (GSE82107 and GSE205431) were subjected to differential gene expression analysis and gene set enrichment analysis (GSEA) to probe for common differentially expressed genes (DEGs) and conduct transcription factor (TF) enrichment analysis from such genes. Venn diagrams were used to identify the target TF, followed by the construction of a protein-protein interaction (PPI) network of the common DEGs governed by the target TF. Hub genes were determined through the CytoHubba plug-in whilst their biological functions were assessed using GSEA analysis in the GTEx database. To validate the study, reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and Flow Cytometry techniques were employed.

Results

A total of 138 common DEGs of osteoarthritis and muscle atrophy were identified, with 16 TFs exhibiting notable expression patterns in both datasets. Venn diagram analysis identified early growth response gene-1 (EGR1) as the target TF, enriched in critical pathways such as epithelial mesenchymal transition, tumor necrosis factor-alpha signaling NF-κB, and inflammatory response. PPI analysis revealed five hub genes, including EGR1, FOS, FOSB, KLF2, and JUNB. The reliability of EGR1 was confirmed by validation testing, corroborating bioinformatics analysis trends.

Conclusions

EGR1, FOS, FOSB, KLF2, and JUNB are intricately involved in muscle atrophy development. High EGR1 expression directly regulated these hub genes, significantly influencing postoperative muscle atrophy progression in KOA patients.

Introduction

Muscle atrophy, characterized by the reduction in muscle mass and strength, frequently occurs as a complication after total knee arthroplasty (TKA) in patients with knee osteoarthritis (KOA) [1, 2]. Although TKA has demonstrated significant improvements in pain, active function, and quality of life in patients, and the number of procedures performed annually continues to rise [3], research has demonstrated a high incidence of postoperative muscle atrophy that can persist for many years, particularly presenting as quadricep weakness [4]. The quadriceps muscle plays a critical role in knee extension and overall lower extremity function, yet it is particularly vulnerable to atrophy following surgery. This weakness may lead to abnormal gait, lower physical capacity, and an increased likelihood of falls and other postoperative complications [5, 6]. With more than 700,000 TKA procedures performed in the United States alone in 2012, and the number continuing to grow predictably, muscle atrophy-induced mobility difficulties have emerged as a significant public health concern [7, 8].

The relationship between KOA and muscle atrophy is multifaceted and bidirectional [9, 10]. While muscle weakness and atrophy may catalyze KOA development, the presence of KOA can negatively impact muscle function and further exacerbate muscle atrophy [2]. Advanced imaging techniques have exhibited that the cross-sectional area of muscles in the affected limb of KOA patients is reduced by 19% compared to their normal limb [11]. Several clinical studies suggest that muscle atrophy is a result of a combination of surgical trauma, immobilization, disuse, and altered neuromuscular function [1, 12, 13]. Nevertheless, the underlying biological mechanisms concerning muscle atrophy in post-TKA patients are yet to be definitively determined.

The primary objective of this study was to investigate the potential cellular and molecular mechanisms underlying muscle atrophy in patients with KOA following TKA. We adopted a bioinformatics approach and analyzed two datasets from the Gene Expression Omnibus (GEO) database, including the muscle atrophy and osteoarthritis datasets. Through comparative analysis, we identified common differentially expressed genes (DEGs) shared by both datasets and subjected them to transcription factor enrichment analysis. The enriched transcription factors (TFs) were intersected with the common DEGs to obtain the target TF most significantly expressed in both osteoarthritis and muscle atrophy. Subsequently, we leveraged protein-protein interaction (PPI) network analysis to identify potential hub genes and used gene set enrichment analysis (GSEA) to examine both DEGs and target TF for pathways associated with muscle atrophy. Ultimately, we validated the expression and function of the target TF leveraging reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and Flow Cytometry. This investigation adds to our understanding of the mechanism and diagnosis of muscle atrophy in post-TKA patients.

Materials and methods

Data collection

Two microarray datasets, GSE82107 and, were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo) using “osteoarthritis” and “muscle atrophy” as keywords. The GSE82107 dataset, utilized in the GPL570 platform, accommodated synovial tissue samples from ten patients diagnosed with osteoarthritis and seven healthy controls. The GSE205431 dataset, employed in the GPL24676 platform, encapsulated RNA-sequencing data of skeletal muscle tissue samples from a total of twenty subjects suffering from end-stage osteoarthritis, including non-surgical limb samples (musculus vastus lateralis, n = 20) and surgical limb samples (vastus medialis, tensor fasciae latae, or gluteus maximus, n = 20). Moreover, count and TPM expression matrices of RNA sequencing data from 803 healthy human skeletal muscle tissue samples were downloaded from the GTEx database.

Analysis of DEGs

The differential expression analysis was carried out using empirical Bayesian linear models within the “Limma” package of R software to detect DEGs in the osteoarthritis dataset (GSE82107). For the RNA-sequencing muscle atrophy dataset (GSE205431), the DESeq2 package, designed for data with a Poisson distribution, was utilized for differential expression analysis. The statistical significance threshold for DEGs was determined by adjusting the P-value (false discovery rate [FDR] < 0.05) and setting the expression to an absolute log2 fold change (FC) > 0.5. Volcano plots were used as a visual representation for all the DEGs. The DEGs common in both datasets were identified by a Venn diagram tool (https://bioinfogp.cnb.csic.es/tools/venny/) and presented through heatmaps.

GSEA for the two microarray datasets

GSEA ranks the differential expression levels between different sample groups using pre-defined gene sets to determine if the gene sets are enriched at the top or bottom of the ranked list. The Hallmark gene set was used in this research for enrichment analysis. The ClusterProfiler (3.14) R package was used to analyze GSEA on the GSE82107 and GSE205431 datasets to determine significant functional and pathway differences based on differential expressional analysis results. The normalization enrichment score (NES) and FDR were calculated by setting random numbers to 1000. A gene set was considered to be significantly enriched when fulfilling the conditions NES ≥ 1.0, P<0.05, and FDR ≤ 0.25.

Analysis of TF enrichment and identification of target TF

CheA3 web (https://maayanlab.cloud/chea3/) is a computational platform that enables the user to investigate the regulatory relationships between genes and transcription factors by entering a list of genes or selecting from predefined gene sets. It uses various algorithms and parameters to identify enriched transcription factors, gene ontology terms, and pathways. To enrich the corresponding transcription factors, the ChEA3 website was used to upload the common DEGs from the osteoarthritis and muscle atrophy datasets. The Venn diagram tool was utilized to obtain the intersection of enriched transcription factors with common DEGs to identify the TFs that exhibited significant differential expression across both datasets. Finally, the TF that could potentially regulate the most common DEGs was selected as the target TF.

Functional annotation of genes regulated by target TF

The Clue gene ontology (GO) method, which combines statistical tests with visual representations, is an effective approach used in biological research to interpret large datasets and reveal functional relationships between genes underlying complex diseases. This method is frequently used in biological research to uncover functional relationships between genes and to gain insights into underlying biological processes in complex diseases. To explain the potential biological mechanisms of these genes, the common DEGs regulated by the target TF were extracted, and gene ontology biological process (GO: BP) functional enrichment analysis was carried out using the Clue GO plug-in in Cytoscape software.

PPI network construction and hub target TF-regulated genes identification

A PPI refers to the process by which proteins form noncovalent bonds to create a protein complex. The construction of a PPI network facilitates the understanding of the molecular mechanisms of biological processes. The STRING database (http://www.string-db.org/) is an online resource used for providing comprehensive information concerning PPI and functional associations among various species. Common DEGs that potentially regulate TFs were incorporated into the STRING database to scrutinize their interactions and produce a PPI network. Cytoscape (Version 3.7.1) is an open-source tool used for analyzing and visualizing molecular interaction networks, where the outcomes of the STRING database were imported. CytoHubba plug-in was applied to identify the top five hub genes according to their weight coefficients by using the Maximum Clique Centrality (MCC) algorithm. Finally, Cytoscape visualizes the five hub genes and their closely associated common DEGs.

Exploring the biological characteristics of target TF

To investigate the biological function of the target TF, we selected 803 RNA sequencing data samples from the GTEx database that were derived from skeletal muscle tissue. We used the DESeq2 package to perform differential expression analysis with the median expression level of the target TF used as the grouping condition. Next, we performed the HALLMARK gene set enrichment analysis using the ClusterProfile package with screening conditions of P < 0.05 and FDR ≤ 0.25.

Experimental animals and grouping

This experimental study received approval from the Animal Ethics Committee of Sichuan University. The SD rats were primarily chosen for constructing the osteoarthritis model and conducting subsequent research. The study comprised three groups categorized by distinct expression levels of the early growth response gene-1 (EGR1): the normal group, the EGR1 overexpression group, and the EGR1 knockdown group. Rats in the normal group were obtained from the Experimental Animal Center of Sichuan University, while those in the EGR1 overexpression and knockdown groups were sourced from Cyagen Biosciences (Guangzhou) Inc. The experimental animals were individually housed in standard cages at the SPF Experimental Animal Center of Sichuan University (three rats per cage). They had ad libitum access to food and water under controlled temperature and humidity conditions and underwent a one-week acclimatization period within a 12-hour light and 12-hour dark cycle before the experiment.

Construction of the osteoarthritis model

Following rat anesthesia, the skin on the medial side of the knee joint was aseptically cleansed, and a longitudinal incision approximately three centimeters in length was performed. The infrapatellar fold, connecting to the intercondylar fossa, was incised to reveal the underlying anterior cruciate ligament. Using microscissors, the anterior cruciate ligament was transected near the femur. The successful detachment of the anterior cruciate ligament was confirmed through an anterior drawer test.

Experimental cells

The experimental cells were procured from Yagi Biotechnology (Shanghai, China) Inc, and genetic manipulation of the EGR1 gene was accomplished with lentiviral constructs. The cells were cultured in DMEM medium supplemented with 10% fetal bovine serum and 1% antibiotics (penicillin/streptomycin). They were maintained in a cell culture incubator under constant conditions of temperature (37 °C) and humidity, with a 5% CO2 atmosphere.

qRT-PCR

To validate the expression of target transcription factors, the extraction of total RNA from chondrocytes was done using the TRIzol reagent (Takara, Japan). The cDNA synthesis was performed with the use of the PrimeScript RT kit (Takara, Japan). The qRT-PCR was executed by following the instructions provided using the SYBR Green method (ES Science, China, QP002). The specific primer sequences used for the qRT-PCR with cDNA are presented in Table 1. The calculation of mRNA levels for the targeted gene involved the 2-ΔΔCt method, which was then normalized to GAPDH.

Table 1 The primer sequence for qRT-PCR amplification
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ELISA

The concentrations of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) were detected following the instructions provided with the ELISA kit. Briefly, the reaction pore was sequentially filled with the standard, sample, antibody, and HRP-streptavidin, and the mixture was incubated at 37 ℃. Subsequently, the chromogenic solution and stop solution were added to the reaction pore in a sequential manner. The OD value was then measured using an enzyme labeling instrument, and the protein concentration was calculated based on the standard curve.

Flow cytometry

The apoptosis rate was determined using flow cytometry. Following cell collection, flow cytometry analysis was conducted using the Annexin V/FITC apoptosis detection kit I (BD Biosciences, Franklin Lake, NJ, USA) in accordance with the manufacturer’s instructions. Annexin V single-positive cells were indicative of early apoptosis, propidium iodide (PI) single-positive cells were indicative of necrosis, and PI and Annexin V double-positive cells indicated late apoptosis. In order to assess skeletal muscle cell apoptosis in the context of osteoarthritis, skeletal muscle cells were incubated with 20 µ PE-labeled EGR1 (BD Bioscience) in the dark at room temperature. Subsequent analysis was performed using MODFitLT5.0 software (BD Biosciences).

Statistical analysis

All data were presented as the mean ± standard deviation (SD) assessed by the Shapiro-Wilk test for data normality. The Mann-Whitney U test was employed to compare two groups, while one-way analysis of variance (ANOVA) followed by the Bonferroni post-test was used for comparisons among multiple groups of samples. GraphPad Prism 9 software was utilized to plot and analyze the data. Statistical significance was denoted as * for P < 0.05, ** for P < 0.01, and *** for P < 0.001.

Results

Screening of DEGs

Screening for DEGs was performed according to the study design flowchart presented in Fig. 1. Using a cutoff criterion of an adjusted p-value of less than 0.05 and |logFC| > 0.5, a total of 1582 DEGs were identified from the GSE82107 dataset, of which 700 were upregulated and 882 were downregulated in the osteoarthritis group (Additional file 1). In the GSE205431 dataset, 1229 DEGs were identified, of which 1169 were upregulated and 60 were downregulated (Additional file 1). To validate the results, volcano plots (Fig. 2A) were drawn. The DEGs from the two datasets were intersected by a Venn diagram, resulting in 138 common DEGs (Fig. 2B and Additional file 2). Finally, the corresponding heat map is shown in Additional file 5.

Fig. 1
figure 1

The flowchart of the research process. GEO: Gene Expression Omnibus database; GSEA: Gene set enrichment analysis; DEG: differentially expressed gene; TF: Transcription Factor; EGR1: early growth response gene-1; GO: BP: gene ontology biological process; PPI: protein-protein interaction

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Fig. 2
figure 2

(A) Volcano plots, functional enrichment analysis, and gene set enrichment analysis of the GSE82107 dataset and the GSE205431 dataset; (B) Venn diagram and heatmap of 138 common differentially expressed genes (DEGs); (C) Venn diagram of 16 transcription factors (TFs) and differential expression of the target TF early growth response gene-1 (EGR1) in GSE82107

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GSEA of two microarray datasets

The GSEA was performed on the microarray datasets (GSE82107 and GSE205431) to identify the gene sets that were significantly differentially expressed between the osteoarthritis and muscle atrophy groups compared to the control group. The Hallmark gene set was used as the predefined gene set. In the GSE82107 dataset, the gene sets involved in epithelial mesenchymal transition (EMT), TNF-α signaling via NF-κB, and inflammatory response pathways were significantly activated in the osteoarthritis groups (Fig. 2A). Similarly, in the GSE205431 dataset, the activation of EMT, TNF-α signaling via NF-κB, and inflammatory response pathways was also observed in the muscle atrophy group (Fig. 2A). These results suggest similar biological processes in osteoarthritis and muscle atrophy. Furthermore, osteoarthritis may cause muscle atrophy through the activation of these three shared pathways.

EGR1 is a target TF

TFs play an essential role in regulating gene expression. To further explore the mechanism between osteoarthritis and muscle atrophy, we performed TF enrichment analysis on the 138 common DEGs. A total of 1632 TFs were enriched through the CheA3 transcription factor enrichment analysis website (Additional file 3). The enriched TFs were then intersected with the common DEGs using a Venn diagram intersection to obtain 16 TFs with significant differential expression in both the GSE82107 and GSE205431 datasets (Fig. 2C and Table 2). We finally selected EGR1 as the target TF due to the up to 90 common DEGs it regulates (Additional file 4). The differential expression of EGR1 was visualized using a box plot (Fig. 2C).

Table 2 Features of 16 transcription factors
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Functional annotation of EGR1 regulatory genes

To verify the functions of the 90 common DEGs that can be regulated by EGR1, biological function annotation analysis was performed using the Clue GO plug-in in the Cytoscape software. The GO: BP enrichment results showed that the 90 common DEGs were involved in biological processes related to muscle atrophy (Fig. 3A).

Fig. 3
figure 3

(A) Functional enrichment analysis of gene ontology biological process in 90 common differentially expressed genes (DEGs) regulated by early growth response gene-1 (EGR1). (B) Overview of the protein-protein interaction network of 90 common DEGs through Cytoscape. The larger size of the points, the higher degree of the genes; (C) Top 5 hub genes interaction networks; the darker the color, the more powerful the critical degree; the darker the color, the more powerful the critical degree

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Construction of PPI network and hub genes for EGR1 regulatory genes

The 90 common DEGs from the previous analysis were used to screen for proteins interacting with them and to construct a PPI network using the STRING database (Fig. 3B). The results were imported into the Cytoscape software, and node connectivity was calculated using the MCC algorithm in the CytoHubba plug-in. The five top hub genes were EGR1, FOS, FOSB, KLF2, and JUNB genes, which were colored red as nodes in the PPI network (Fig. 3C). These genes have strong interactions, with darker colors indicating higher rank, pointing towards a possible pathophysiological mechanism linking osteoarthritis to muscle atrophy.

Biological characteristics of EGR1

The GSEA of 803 sequencing data from skeletal muscle tissues in the GTEx database indicated upregulation of EMT, TNF-α signaling via NF-κB, inflammatory response, Interferon-γ response, allograft rejection, and Kras signaling. In addition, high EGR1 expression was positively correlated with apoptosis, estrogen response late, coagulation, and apical junction pathways (Fig. 4A and B).

Fig. 4
figure 4

Functional mining of early growth response gene-1 (EGR1). (A) Functional enrichment analysis of EGR1 in the GTEx database; (B) Biological pathway of EGR1 in the Hallmark gene set

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Validation of EGR1

qRT-PCR was utilized to preliminary detect hub gene expression in osteoarthritis chondrocytes (Fig. 5). The results indicated that the expression of EGR1 significantly increased in the osteoarthritis group and the muscle atrophy group compared to the healthy control group under normal EGR1 expression conditions, with a significant statistical difference (P < 0.01, Fig. 5A). The expression levels of other genes related to EGR1, including Fos, FosB, KLF2, and JunB, were also significantly higher compared to the normal control group, with statistically significant differences (P < 0.05, Fig. 5A). By manipulating EGR1 through overexpression and knockdown, we found that when the EGR1 gene was overexpressed, the other four genes’ expression levels significantly increased (Fig. 5B). Conversely, when the EGR1 gene was knocked down, the expression levels of the other four genes significantly decreased in response (Fig. 5C). These findings align with the results of bioinformatics analysis, suggesting that EGR1 functions as a transcription factor that regulates the expression of the other four genes, thereby impacting muscle atrophy in osteoarthritis.

Fig. 5
figure 5

Verification of the early growth response gene-1 (EGR1) gene was conducted using qRT-PCR. (A) The expression levels of relevant genes were assessed under normal expression of the EGR1 gene in the control, arthritis, and muscle atrophy groups. (B) The expression levels of relevant genes were analyzed under overexpression of the EGR1 gene in the control, arthritis, and muscle atrophy groups. (C) The expression levels of relevant genes were examined under knockdown of the EGR1 gene in the control, arthritis, and muscle atrophy groups. The significance levels were set at *p < 0.05, **p < 0.01, and ***p < 0.001. The error bars were used to represent the standard deviation. MA: muscle atrophy

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ELISA was employed to determine IL-1β and TNF-α expression levels in the synovial fluid of osteoarthritis patients (Fig. 6A-B). The results demonstrated that, under normal EGR1 expression conditions, the levels of IL-1β and TNF-α expression in the osteoarthritis group exhibited significant elevation compared to the control group. When the EGR1 gene was overexpressed or knocked down, the expression levels of IL-1β and TNF-α in osteoarthritis either increased or decreased significantly and these alterations were statistically significant (P < 0.05). This finding demonstrates the significant regulatory role of the EGR1 gene in the inflammatory pathway.

Fig. 6
figure 6

ELISA analysis was conducted to confirm the expression levels of IL-1β and TNF-α in synovial fluid. (A) The expression of IL-1β was examined under normal expression, overexpression, and knockdown of the early growth response gene-1 (EGR1) gene. (B) The expression of TNF-α was evaluated under normal expression, overexpression, and knockdown of the EGR1 gene. The significance levels were set at *P < 0.05, **P < 0.01, and ***P < 0.001. The error bars were used to represent the standard deviation

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Additionally, the influence of EGR1 on apoptosis in skeletal muscle cells was extensively investigated through the implementation of flow cytometry analysis (Fig. 7A and B). The obtained results revealed that manipulating the expression levels of the EGR1 gene in osteoarthritis chondrocytes led to a corresponding increase or decrease in the apoptotic rate of skeletal muscle cells, exhibiting statistically significant differences (P < 0.05). This implies that the EGR1 gene plays a dual role in not only regulating the inflammatory response in arthritis but also influencing the process of apoptosis.

Fig. 7
figure 7

Flow cytometry was conducted to evaluate the apoptotic status of skeletal muscle cells. (A) Apoptosis profiles of skeletal muscle cells were analyzed under conditions of normal expression, overexpression, and knockdown of the early growth response gene-1 (EGR1) gene. (B) Apoptosis statistics of skeletal muscle cells were assessed under conditions of normal expression, overexpression, and knockdown of the EGR1 gene

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Discussion

KOA is a prevalent musculoskeletal disorder that significantly impacts affected individuals through severe knee pain, stiffness, and dysfunction, ultimately leading to disability [14]. TKA is an effective intervention for advanced osteoarthritis that can improve patients’ pain and quality of life. However, postoperative muscle atrophy, mainly quadriceps atrophy, results in reduced muscle strength and mobility that diminishes the benefits of TKA [15,16,17]. Quadriceps weakness is a contributing factor to long-term postoperative disability assessment in KOA patients [5, 18, 19]. Patients with post-KOA are known to have muscle weakness and atrophy lasting several years [20]. Therefore, quadriceps weakness and atrophy concern clinicians treating KOA patients [21]. Previous research attributed quadriceps atrophy to disuse atrophy caused by joint pain [11, 22, 23], yet studies have proposed that it is a predisposing factor for developing KOA [24, 25]. Moreover, surgery-induced skeletal muscle trauma is suggested as another potential cause of muscle atrophy [12]. However, the biological pathogenesis and underlying mechanisms to develop muscle atrophy in patients who undergo KOA surgery remain unclear.

Our study aimed to identify critical genes involved in pathogenesis mechanisms underlying muscle atrophy. Two GEO datasets were analyzed using bioinformatics to screen for DEGs in both osteoarthritis and muscle atrophy groups systematically. The GSEA demonstrated that inflammatory-related pathways like EMT, TNF-α signaling via NF-κB, and inflammatory response were activated in both osteoarthritis and muscle atrophy disease states. We intersected DEGs in both datasets to obtain 138 common DEGs, which helped us identify genes jointly involved in these pathways. TF enrichment analysis was executed on the 138 common DEGs, and 16 transcription factors were found significantly enriched in osteoarthritis and muscle atrophy. Consequently, we identified EGR1, which regulates the most common DEGs, as the target transcription factor to investigate further pathways of muscle atrophy pathogenesis.

EGR1 is a multifunctional and critical TF involved in various physiological processes, including cell growth, differentiation, and apoptosis [26]. EGR1 spans approximately 3.8 Kb containing two exons and an intron. It contains a highly-conserved DNA structural domain of three Cys2-His2 type zinc finger structures on chromosome 18 in mice and chromosome 5 in humans [27,28,29]. EGR1 is predominantly expressed in different connective tissues, such as tendons, cartilage, and bone, regulating extracellular matrix functions to facilitate tissue development, homeostasis, and healing processes [26, 29, 30]. Numerous studies have investigated EGR1’s role in cartilage and bone, where it contributes to chronic diseases of articular cartilage degeneration [31,32,33,34]. EGR1 is highly expressed in synovial tissues, and articular cartilage of patients with osteoarthritis [27, 35]. Under interleukin-1β (IL-1) stimulation, EGR1 recruitment to the Pparg promoter downregulates PPAR expression, restraining its protective role in osteoarthritis [36]. In patients with osteoarthritis, excess EGR1 was reported to increase proteins and transcripts of type I collagen in synovial fibroblasts [37]. While the effects of EGR1 on human skeletal muscle regulation remain unclear, previous studies highlighted the role of EGR1 in the promotion of differentiation of bovine skeletal muscle-derived satellite cells [26]. Moreover, several studies have explored EGR1 in other muscle-wasting conditions. For example, research has shown that EGR1 is upregulated in skeletal muscle under conditions of mechanical stress or injury and plays a role in muscle regeneration and repair [38, 39]. This raises the possibility that EGR1 could be involved in the adaptive response of muscle tissue to the stresses associated with KOA, including atrophic changes.

To examine the mechanisms of EGR1-mediated gene regulation, we constructed a PPI network encoded by 90 DEGs. We employed the CytoHubba plug-in to identify hub genes that were predominantly EGR1, FOS, FOSB, KLF2, and JUNB. The Fos gene family includes four members, including FOS, FOSB, FOSL1, and FOSL2, and regulates cellular proliferation, differentiation, and transformation [40]. JUNB is a protein-encoded gene located in the nucleoplasm that positively modulates RNA polymerase II transcription [41]. It forms heterodimers with FOS family proteins to create the AP-1 transcription complex, enhancing DNA-binding activity, transcriptional activity, and AP-1 consensus sequence specificity [42, 43]. The AP-1 complex has been identified to facilitate the pathogenesis of osteoarthritis by binding to the promoter of inflammatory cytokines [44]. KLF2 encodes a zinc finger protein within the Kruppel family that is expressed during early mammalian development and functions in multiple growth and disease-related processes, such as adipogenesis, embryonic erythroid cell generation, epithelial cell integrity, inflammation, and T cell viability [45]. KLF2 is involved in osteoarthritis progression by regulating matrix metalloproteinases (MMPs), as reported by Takashi Aki et al. [46].

Finally, we explored the biological functions of EGR1 in the GTEx database. GSEA analysis revealed significant activation of inflammatory-related pathways, including EMT, TNF-α signaling via NF-κB, and the inflammatory response. Notably, the GSEA analysis of the osteoarthritis and muscle atrophy datasets included these three pathways. Therefore, EGR1 affects the process of muscle atrophy in osteoarthritis as it participates in these three pathways.

Through experimentation, the involvement of the EGR1 gene in osteoarthritis and muscle atrophy has been effectively validated. Firstly, the direct regulatory influence of the TF EGR1 on the FOS, FOSB, KLF2, and JUNB genes was confirmed using real-time fluorescence quantitative polymerase chain reaction. Secondly, the vital role of the EGR1 gene in inflammation regulation was substantiated through an enzyme-linked immunosorbent assay. This regulatory mechanism has been observed to impact various inflammatory pathways, such as EMT, TNF-α signaling via NF-κB, and the inflammatory response, significantly altering the expression of proteins associated with inflammation. Lastly, the regulatory significance of the EGR1 gene in skeletal muscle apoptosis was affirmed through a flow cytometry assay. These experimental findings are consistent with the outcomes of bioinformatics analysis.

In this study, we employed bioinformatics analysis to determine EGR1 as a key gene implicated in the muscle atrophy observed in patients with KOA. Nevertheless, there are still some limitations in the study design. Firstly, the possibility of bias should be acknowledged because of the small sample size utilized in the bioinformatics analysis. Secondly, further validation through in vivo and in vitro experiments is required. Nonetheless, current technology and capabilities only allow for the exploration of gene overexpression and knockdown via animal experiments, which may introduce some degree of error in comparison to studies involving humans. Additionally, confirmation of the results through collection of clinical samples is essential. Moreover, while variation in muscle atrophy-related gene expression across different stages of OA is a critical consideration, the scope of our study was limited to the end-stage of OA. Future studies could benefit from exploring gene expression dynamics over time and across different disease stages. Finally, a comprehensive understanding of the main targets is necessary to design effective treatment strategies for post-arthritic muscle atrophy resulting from knee osteoarthritis.

Conclusion

The high expression of the transcription factor EGR1 has been established as a direct regulator of FOS, FOSB, KLF2, and JUNB. This regulatory mechanism has been found to affect various inflammatory pathways, such as EMT, TNF-α signaling via NF-κB, and inflammatory response. These pathways, in turn, have an impact on the postoperative muscle atrophy process in patients with KOA. These findings suggest that EGR1 is not only a marker of muscle atrophy but also a potential therapeutic target. Future research should explore the clinical applications of EGR1 modulation, with the goal of developing personalized therapeutic strategies to reduce the burden of muscle atrophy.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

DEG:

Differentially expressed gene

EGR1:

Early growth response gene-1

ELISA:

Enzyme-linked immunosorbent assay

EMT:

Epithelial mesenchymal transition

FC:

Fold change

FDR:

False discovery rate

GEO:

Gene Expression Omnibus database

GO:

Gene ontology

GO: BP:

Gene ontology biological process

GSEA:

Gene set enrichment analysis

IL-1β:

Interleukin-1β

KOA:

Knee Osteoarthritis

MCC:

Maximum Clique Centrality

NES:

Normalization enrichment score

PI:

Propidium iodide

PPI:

Protein-protein interaction network

qRT-PCR-:

Reverse transcriptase real-time quantitative polymerase chain reaction

SD:

Standard deviation

TF:

Transcription factor

TNF-α:

Tumor necrosis factor

TKA:

Total knee arthroplasty

References

  1. de Souza Silva JM, Gil Alabarse PV, Nunes Teixeira VdO, Freitas EC, de Oliveira FH, da Chakr RM, Xavier RM. (2018) Muscle wasting in osteoarthritis model induced by anterior cruciate ligament transection. PLoS ONE, 13(4) (2018) https://doi.org/10.1371/journal.pone.0196682

  2. Franz A, Ji S, Bittersohl B, Zilkens C, Behringer M. (2022) Impact of a Six-Week Prehabilitation With Blood-Flow Restriction Training on Pre- and Postoperative Skeletal Muscle Mass and Strength in Patients Receiving Primary Total Knee Arthroplasty. Frontiers in Physiology, 13 (2022) https://doi.org/10.3389/fphys.2022.881484

  3. Sloan M, Premkumar A, Sheth NP. (2018) Projected Volume of Primary Total Joint Arthroplasty in the US, 2014 to 2030. Journal of Bone and Joint Surgery-American Volume, 100(17) (2018) 1455–1460 https://doi.org/10.2106/jbjs.17.01617

  4. Singla R, Niederer D, Franz A, Happ K, Zilkens C, Wahl P, Behringer M. (2023) The course of knee extensor strength after total knee arthroplasty: a systematic review with meta-analysis and -regression. Archives of Orthopaedic and Trauma Surgery, 143(8) (2023) 5303–5322 https://doi.org/10.1007/s00402-022-04750-5

  5. Kubo Y, Fujita D, Sugiyama S, Takachu R, Sugiura T, Sawada M, Yamashita K, Kobori K, Kobori M. (2024) Quadriceps Strength Loss Following Total Knee Arthroplasty as a Predictor of Three-Month Strength Recovery: A Secondary Analysis of a Randomized Controlled Trial. Cureus Journal of Medical Science, 16(8) (2024) https://doi.org/10.7759/cureus.68244

  6. Taniguchi M, Fukumoto Y, Kobayashi M, Kawasaki T, Maegawa S, Ibuki S, Ichihashi N, QUANTITY AND QUALITY OF THE LOWER EXTREMITY MUSCLES IN WOMEN WITH KNEE OSTEOARTHRITIS. (2015). Ultrasound in Medicine and Biology, 41(10) (2015) 2567–2574 https://doi.org/10.1016/j.ultrasmedbio.2015.05.014

  7. Inacio MCS, Paxton EW, Graves SE, Namba RS, Nemes S. (2017) Projected increase in total knee arthroplasty in the United States - an alternative projection model. Osteoarthritis and Cartilage, 25(11) (2017) 1797–1803 https://doi.org/10.1016/j.joca.2017.07.022

  8. Worlicek M, Koch M, Daniel P, Freigang V, Angele P, Alt V, Kerschbaum M, Rupp M. (2021) A retrospective analysis of trends in primary knee arthroplasty in Germany from 2008 to 2018. Scientific Reports, 11(1) (2021) https://doi.org/10.1038/s41598-021-84710-y

  9. Taniguchi M, Fukumoto Y, Yagi M, Motomura Y, Okada S, Okada S, Kobayashi M, Ichihashi N. Enhanced echo intensity in vastus medialis is associated with worsening of functional disabilities and symptoms in patients with knee osteoarthritis: a 3 years longitudinal study. Rheumatol Int. 2022;2022. https://doi.org/10.1007/s00296-022-05246-6.

  10. Ye H, Long Y, Yang J-M, Wu Y-L, Dong L-Y, Zhong Y-B, Luo Y, Wang M-Y. (2024) Curcumin regulates autophagy through SIRT3-SOD2-ROS signaling pathway to improve quadriceps femoris muscle atrophy in KOA rat model. Scientific Reports, 14(1) (2024) https://doi.org/10.1038/s41598-024-58375-2

  11. Lopes HS, Waiteman MC, Priore LB, Glaviano NR, Bazett-Jones DM, Briani RV, Azevedo FM. (2024) There is more to the knee joint than just the quadriceps: A systematic review with meta-analysis and evidence gap map of hamstring strength, flexibility, and morphology in individuals with gradual-onset knee disorders. Journal of Sport and Health Science, 13(4) (2024) 521–536 https://doi.org/10.1016/j.jshs.2023.08.004

  12. Dalle S, Koppo K. (2020) Is inflammatory signaling involved in disease -related muscle wasting? Evidence from osteoarthritis, chronic obstructive pulmonary disease and type II diabetes. Experimental Gerontology, 137 (2020) https://doi.org/10.1016/j.exger.2020.110964

  13. Xu J, She G, Gui T, Hou H, Li J, Chen Y, Zha Z. Knee muscle atrophy is a risk factor for development of knee osteoarthritis in a rat model. J Orthop Translation. 2020;22(2020):67–72. https://doi.org/10.1016/j.jot.2019.10.003.

    Article  CAS  Google Scholar 

  14. Pickering M-E, Chapurlat R. (2020) Where Two Common Conditions of Aging Meet: Osteoarthritis and Sarcopenia. Calcified Tissue International, 107(3) (2020) 203–211 https://doi.org/10.1007/s00223-020-00703-5

  15. Jensen C, Aagaard P, Overgaard S. (2011) Recovery in mechanical muscle strength following resurfacing vs standard total hip arthroplasty - a randomised clinical trial. Osteoarthritis and Cartilage, 19(9) (2011) 1108–1116 https://doi.org/10.1016/j.joca.2011.06.011

  16. Ueyama H, Kanemoto N, Minoda Y, Taniguchi Y, Nakamura H. (2020) 2020 Chitranjan S. Ranawat Award: Perioperative essential amino acid supplementation suppresses rectus femoris muscle atrophy and accelerates early functional recovery following total knee arthroplasty A PROSPECTIVE DOUBLE-BLIND RANDOMIZED CONTROLLED TRIAL. Bone & Joint Journal, 102B(6) (2020) 10–18 https://doi.org/10.1302/0301-620x.102b6.Bjj-2019-1370.R1

  17. Cheuy VA, Dayton MR, Hogan CA, Graber J, Anair BM, Voigt TB, Nelms NJ, Stevens-Lapsley JE, Toth MJ. (2023) Neuromuscular electrical stimulation preserves muscle strength early after total knee arthroplasty: Effects on muscle fiber size. Journal of Orthopaedic Research, 41(4) (2023) 787–792 https://doi.org/10.1002/jor.25418

  18. Joseph MN, Carmont MR, Tailor H, Stephen JM, Amis AA. Total knee arthroplasty reduces knee extension torque in-vitro and patellofemoral arthroplasty does not. J Biomech. 2020;104(2020). https://doi.org/10.1016/j.jbiomech.2020.109739.

  19. Kubo Y, Sugiyama S, Takachu R, Sugiura T, Sawada M, Kobori K, Kobori M. (2020) Effects of preoperative low-intensity training with slow movement on early quadriceps weakness after total knee arthroplasty in patients with knee osteoarthritis: a retrospective propensity score-matched study. Bmc Sports Science Medicine and Rehabilitation, 12(1) (2020) https://doi.org/10.1186/s13102-020-00223-7

  20. Thomas AC, Stevens-Lapsley JE. (2012) Importance of Attenuating Quadriceps Activation Deficits After Total Knee Arthroplasty. Exercise and Sport Sciences Reviews, 40(2) (2012) 95–101 https://doi.org/10.1097/JES.0b013e31824a732b

  21. Barber-Westin S, Noyes FR. (2019) Blood Flow-Restricted Training for Lower Extremity Muscle Weakness due to Knee Pathology: A Systematic Review. Sports Health-a Multidisciplinary Approach, 11(1) (2019) 69–83 https://doi.org/10.1177/1941738118811337

  22. Hughes L, Rosenblatt B, Gissane C, Paton B, Patterson SD. (2018) Interface pressure, perceptual, and mean arterial pressure responses to different blood flow restriction systems. Scandinavian Journal of Medicine & Science in Sports, 28(7) (2018) 1757–1765 https://doi.org/10.1111/sms.13092

  23. Loenneke JP, Wilson JM, Marin PJ, Zourdos MC, Bemben MG. (2012) Low intensity blood flow restriction training: a meta-analysis. European Journal of Applied Physiology, 112(5) (2012) 1849–1859 https://doi.org/10.1007/s00421-011-2167-x

  24. Oiestad BE, Juhl CB, Eitzen I, Thorlund JB. (2015) Knee extensor muscle weakness is a risk factor for development of knee osteoarthritis. A systematic review and meta-analysis. Osteoarthritis and Cartilage, 23(2) (2015) 171–177 https://doi.org/10.1016/j.joca.2014.10.008

  25. Culvenor AG, Ruhdorfer A, Juhl C, Eckstein F, Oiestad BE. (2017) Knee Extensor Strength and Risk of Structural, Symptomatic, and Functional Decline in Knee Osteoarthritis: A Systematic Review and Meta-Analysis. Arthritis Care and Research, 69(5) (2017) 649–658 https://doi.org/10.1002/acr.23005

  26. Zhang W, Tong H, Zhang Z, Shao S, Liu D, Li S, Yan Y. (2018) Transcription factor EGR1 promotes differentiation of bovine skeletal muscle satellite cells by regulating < i > MyoG gene expression. Journal of Cellular Physiology, 233(1) (2018) 350–362 https://doi.org/10.1002/jcp.25883

  27. Havis E, Duprez D. (2020) EGR1 Transcription Factor is a Multifaceted Regulator of Matrix Production in Tendons and Other Connective Tissues. International Journal of Molecular Sciences, 21(5) (2020) https://doi.org/10.3390/ijms21051664

  28. Kulkarni R. (2022) Early Growth Response Factor 1 in Aging Hematopoietic Stem Cells and Leukemia. Frontiers in Cell and Developmental Biology, 10 (2022) https://doi.org/10.3389/fcell.2022.925761

  29. Thomes PG, Donohue TM Jr. (2017) Role of Early Growth Response-1 in the Development of Alcohol-Induced Steatosis. Current Molecular Pharmacology, 10(3) (2017) 179–185 https://doi.org/10.2174/1874467208666150817112529

  30. Orgeur M, Martens M, Leonte G, Nassari S, Bonnin M-A, Boerno ST, Timmermann B, Hecht J, Duprez D, Stricker S. (2018) Genome-wide strategies identify downstream target genes of chick connective tissue-associated transcription factors. Development, 145(7) (2018) https://doi.org/10.1242/dev.161208

  31. Zhang H-Y, Liu Q, Yang H-X, Shi L-Q, Wang P, Xie M-J, Liu J-Q, Xu X-J, Liu X-D, Yu S-B, Jiao K, Zhang M, Xuan S-J, Xu Y-F, Zhang X, Liu Y-F, Zhang J, Wang M-Q. (2019) Early growth response 1 reduction in peripheral blood involving condylar subchondral bone loss. Oral Diseases, 25(7) (2019) 1759–1768 https://doi.org/10.1111/odi.13168

  32. Xu W, Wang X, Liu D, Lin X, Wang B, Xi C, Kong P, Yan J. (2023) Identification and validation of hub genes and potential drugs involved in osteoarthritis through bioinformatics analysis. Frontiers in Genetics, 14 (2023) https://doi.org/10.3389/fgene.2023.1117713

  33. Chen H, Ni Q, Li B, Chen L. (2021) Identification of differentially expressed genes in synovial tissue of osteoarthritis based on a more robust integrative analysis method. Clinical Rheumatology, 40(9) (2021) 3745–3754 https://doi.org/10.1007/s10067-021-05649-z

  34. Sun X, Huang H, Pan X, Li S, Xie Z, Ma Y, Hu B, Wang J, Chen Z, Shi P. (2019) EGR1 promotes the cartilage degeneration and hypertrophy by activating the Kruppel-like factor 5 and β-catenin signaling. Biochimica Et Biophysica Acta-Molecular Basis of Disease, 1865(9) (2019) 2490–2503 https://doi.org/10.1016/j.bbadis.2019.06.010

  35. Grimbacher B, Aicher WK, Peter HH, Eibel H. (1997) Measurement of transcription factor c-fos and EGR-1 mRNA transcription levels in synovial tissue by quantitative RT-PCR. Rheumatology International, 17(3) (1997) 109–112 https://doi.org/10.1007/s002960050018

  36. Nebbaki S-S, El Mansouri FE, Afif H, Kapoor M, Benderdour M, Duval N, Pelletier J-P, Martel-Pelletier J, Fahmi H. (2012) Egr-1 contributes to IL-1-mediated downregulation of peroxisome proliferator-activated receptor γ expression in human osteoarthritic chondrocytes. Arthritis Research and Therapy, 14(2) (2012) https://doi.org/10.1186/ar3788

  37. Chen S-J, Ning H, Ishida W, Sodin-Semrl S, Takagawa S, Mori Y, Varga J. (2006) The early-immediate gene < i > EGR- 1 is induced by transforming growth factor-β and mediates stimulation of collagen gene expression. Journal of Biological Chemistry, 281(30) (2006) 21183–21197https://doi.org/10.1074/jbc.M603270200.

  38. Rundqvist HC, Montelius A, Osterlund T, Norman B, Esbjornsson M, Jansson E. (2019) Acute sprint exercise transcriptome in human skeletal muscle. PLoS ONE, 14(10) (2019) https://doi.org/10.1371/journal.pone.0223024

  39. Wessner B, Ploder M, Tschan H, Ferunaj P, Erindi A, Strasser E-M, Bachl N. (2019) Effects of acute resistance exercise on proteolytic and myogenic markers in skeletal muscles of former weightlifters and age-matched sedentary controls. Journal of Sports Medicine and Physical Fitness, 59(11) (2019) 1915–1924 https://doi.org/10.23736/s0022-4707.19.09740-8

  40. Matsuoka K, Bakiri L, Bilban M, Toegel S, Haschemi A, Yuan H, Kasper M, Windhager R, Wagner EF. (2023) Metabolic rewiring controlled by c-Fos governs cartilage integrity in osteoarthritis. Annals of the Rheumatic Diseases, 82(9) (2023) 1227–1239 https://doi.org/10.1136/ard-2023-224002

  41. Lin Z, Miao J, Zhang T, He M, Wang Z, Feng X, Bai L. (2021) JUNB-FBXO21-ERK axis promotes cartilage degeneration in osteoarthritis by inhibiting autophagy. Aging Cell, 20(2) (2021) https://doi.org/10.1111/acel.13306

  42. Tsunoda M, Fukasawa M, Nishihara A, Takada L, Asano M. (2021) JunB can enhance the transcription of IL-8 in oral squamous cell carcinoma. Journal of Cellular Physiology, 236(1) (2021) 309–317 https://doi.org/10.1002/jcp.29843

  43. Ye F, Zeng Q, Dan G, Dong X, Chen M, Sai Y, Lin H, Zou Z. (2020) Nitrogen mustard prevents transport of Fra-1 into the nucleus to promote c-Fos- and FosB-dependent IL-8 induction in injured mouse epidermis. Toxicology Letters, 319 (2020) 256–263 https://doi.org/10.1016/j.toxlet.2019.10.006

  44. Huber R, Augsten S, Kirsten H, Zell R, Stelzner A, Thude H, Eidner T, Stuhlmueller B, Ahnert P, Kinne RW. (2021) Identification of New, Functionally Relevant Mutations in the Coding Regions of the Human Fos and Jun Proto-Oncogenes in Rheumatoid Arthritis Synovial Tissue. Life-Basel, 11(1) (2021) https://doi.org/10.3390/life11010005

  45. Gao X, Jiang S, Du Z, Ke A, Liang Q, Li X. (2019) KLF2 Protects against Osteoarthritis by Repressing Oxidative Response through Activation of Nrf2/ARE Signaling < i > In Vitro and < i > In Vivo. Oxidative Medicine and Cellular Longevity, 2019 (2019) https://doi.org/10.1155/2019/8564681

  46. Aki T, Hashimoto K, Ogasawara M, Itoi E. (2018) A whole-genome transcriptome analysis of articular chondrocytes in secondary osteoarthritis of the hip. PLoS ONE, 13(6) (2018) https://doi.org/10.1371/journal.pone.0199734

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Acknowledgements

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Funding

This research was funded by 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (grant No: 2023HXFH012, ZYGD23033). Funds were used to cover experimental reagents and consumables.

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  1. Xiao-yang Liu and Qiu-ping Yu contributed equally to this work and should be considered as equal first authors.

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  1. Department of Orthopedics, Orthopaedic Research Institute, West China Hospital, Sichuan University, #37 Guoxue Road, Chengdu, 610041, People’s Republic of China

    Xiao-yang Liu, Si-qin Guo, Xu-ming Chen, Wei-Nan Zeng & Zong-Ke Zhou

  2. Health Management Center, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China

    Qiu-ping Yu

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Si-qin Guo, Xu-ming Chen, Wei-nan Zeng and Zong-ke Zhou. The first draft of the manuscript was written by Xiao-yang Liu and Qiu-ping Yu. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Liu, Xy., Yu, Qp., Guo, Sq. et al. High expression of transcription factor EGR1 is associated with postoperative muscle atrophy in patients with knee osteoarthritis undergoing total knee arthroplasty. J Orthop Surg Res 19, 618 (2024). https://doi.org/10.1186/s13018-024-05109-9

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Journal of Orthopaedic Surgery and Research

ISSN: 1749-799X