Abstract Background Arthrofibrosis is a debilitating complication after total knee arthroplasty (TKA) which becomes a considerable burden for both patients and clinical practitioners. Our study aimed to identify novel biomarkers and therapeutic targets for drug discovery. Methods Potential biomarker genes were identified based on bioinformatic analysis. Twelve male New Zealand white rabbits underwent surgical fixation of unilateral knees to mimics the joint immobilization of the clinical scenario after TKA surgery. Macroscopic assessment, hydroxyproline content determination, and histological analysis of tissue were performed separately after 3-days, 1-week, 2-weeks, and 4-weeks of fixation. We also enrolled 46 arthrofibrosis patients and 92 controls to test the biomarkers. Clinical information such as sex, age, range of motion (ROM), and visual analogue scale (VAS) was collected by experienced surgeons Findings Base on bioinformatic analysis, transforming growth factor-beta receptor 1 (TGFBR1) was identified as the potential biomarkers. The level of TGFBR1 was significantly raised in the rabbit synovial tissue after 4-weeks of fixation (p<0.05). TGFBR1 also displayed a highly positive correlation with ROM loss and hydroxyproline contents in the animal model. TGFBR1 showed a significantly higher expression level in arthrofibrosis patients with a receiver operating characteristic (ROC) area under curve (AUC) of 0.838. TGFBR1 also performed positive correlations with VAS baseline (0.83) and VAS after 1 year (0.76) while negatively correlated with ROM baseline (-0.76) in clinical patients. Interpretation Our findings provided novel biomarkers for arthrofibrosis diagnosis and uncovered the role of TGFBR1. This may contribute to arthrofibrosis prevention and therapeutic drug discovery. Keywords: Arthrofibrosis, Stiff knee, Biomarker, TGF-β signal pathway, TGFBR1 __________________________________________________________________ Research in context. Evidence before this study Total knee arthroplasty (TKA) is the most common surgical treatment for severe osteoarthritis or rheumatic arthritis of the knee joint. However, arthrofibrosis has been reported to be one of the most frequent complications in TKA, which lead to the limited range of motion (ROM) in knee joint flexion and extension. Previous meta-analysis manifested that the prevalence of arthrofibrosis after TKA ranges from 1 to 17%. But the best management is still unknown due to the poor understanding of the pathogenesis and the lack of reliable biomarkers. Recently, Banu Bayram and colleagues had successfully performed RNA-sequencing of posterior capsule specimens from 4 patients undergoing a revision TKA for arthrofibrosis (RTKA-A) and 4 patients for non-arthrofibrotic and non-infectious etiologies (RTKA-NA). These results paved the way for reliable arthrofibrosis biomarker identification. Added value of this study We identified TGFBR1 as the promising arthrofibrosis biomarker based on the overlapping of reported fibrosis gene database and expression data associated with arthrofibrosis ([34]GSE135854). Subsequently, we validated these genes in the animal model and figured out the expression of TGFBR1 increase over time. Moreover, a total of 46 arthrofibrosis patients and 92 control group patients were also involved in testing these biomarkers in the clinical scenario. These results illustrated that the TGFBR1 showed positive correlations with knee pain and negatively correlated with ROM in patients Implications of all available evidence For the first time, we identified the TGFBR1 as the biomarker of arthrofibrosis with high prediction accuracy. The expression of TGFBR1 is highly linked with the patients’ ROM before the revision surgery and knee pain both before and after revision surgery. These results also implicated the central role of TGFBR1 in the pathogenesis of arthrofibrosis and provided a promising therapeutic target for this disease. Alt-text: Unlabelled box 1. Introduction Arthrofibrosis (AF) is a well-known devastating postoperative complication after the total knee arthroplasty (TKA) which can be characterized by the excessive production of collagen resulting in fibrous scar tissue in the joint [35][1]. The proliferated fibrous scar tissue forms adhesions in knee joint capsules, tendons, and bursa around the joint, which leads to restricted joint motion and knee pain [36][2]. The prevalence of arthrofibrosis after TKA ranges from 1 to 17% [[37]3–[38]6], which causes significant disability and limitation in daily living. Even a small loss of 5° of knee flexion will seriously affect physical activities of patients and create considerable difficulties in sitting, driving, and stair climbing [39][7]. Together with the loss of ROM, constant pain of the knees was also commonly reported by the patients, which lead to a large proportion of revision surgery and places a heavy economic burden on the health care system [40][4]. Unfortunately, the conservative treatments to prevent the AF in the early stage are largely unknown due to the lack of specific biomarkers and reliable therapeutic targets. As the primary concerns in joint surgery, a plethora of attempts have been made to discover biomarkers. The first biomarker of arthrofibrosis, α-SMA (ASMA), was identified early in 2003 in the myofibroblasts [41][8] and performed significant difference in the AF patients compared to controls [[42]9,[43]10]. Beta-catenin was also defined as a reference for AF diagnosis and severity grading in 2013 by Ruppert and colleagues [44][9]. Besides, CD68 expressed in the sublining layer of synovial was known as the biomarker for diagnosis and indicator of inflammatory activity [[45]10,[46]11]. Matrix metalloproteinases (MMPs), tissue inhibitors of matrix metalloproteinases (TIMPs), and a disintegrin and metalloproteinases with thrombospondin (ADAMTS) also show clinical relevance of knee stiffness after TKA [47][12]. Despite the increase of AF biomarkers, there is still lack of highly specific and sensitive biomarkers for clinical use. Comprehensive molecular analyses of AF are needed for novel biomarker identification and therapeutic targets investigation. Recently, Banu Bayram and colleagues had successfully performed RNA-sequencing of posterior capsule specimens from 4 patients undergoing a revision total knee arthroplasty (TKA) for arthrofibrosis (RTKA-A) and 4 patients for non-arthrofibrotic and non-infectious etiologies (RTKA-NA) [48][13]. This finding uncovered the molecular changes in arthrofibrosis tissue and shed light on the biomarkers for diagnosis in the clinical scenario. The purpose of this study was to identify the biomarker genes from this expression data ([49]GSE135854), validated in animal models, and applied in clinical patients with both clinical symptoms and pathological confirmation of arthrofibrosis. Herein, we established a classic rabbit model of AF which had been widely used in other studies [50][14], [51][15], [52][16], [53][17], [54][18], [55][19]. Comparing with other animal models, rabbits are more reliable for joint fibrosis formation after fixation and more effective in measurement [[56]14,[57]20]. They also shared a similar knee anatomy structure as humans which were convenient for surgical procedure and synovial tissue obtain. In this study, we immobilized the knees for different time span (3-days, 1-week, 2-weeks, and 4-weeks) to explore the biomarker gene expression over time. This study aimed to bridge between the bioinformatics analyses and clinical scenarios. In clinical practice, these biomarkers information can be easily obtained during the treatment of arthrofibrosis such as revision TKA, arthroscopic, open lysis of adhesions, and manipulation under anesthesia (MUA). Our study may pave a new way for precise diagnosis and drug discovery of patients with AF after TKA. 2. Methods 2.1. Statement of ethics This study was designed and performed according to the registration and had been approved by the Ethic Committee of Jishuitan hospital (No.201811–09). All animal experiments were carried out with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health [58][21]. The case-control study part was also conducted with the permission of the Ethics Committee of the Jishuitan hospital (No.202011–02) and informed consent was received from the patients. For those who cannot write properly, the informed consent form was signed by their lineal relatives. 2.2. Gene expression omnibus data set selection The gene profiles were downloaded for The gene expression omnibus database (GEO, [59]http://www.ncbi.nlm.nih.gov/geo) to explore the early-stage diagnostic biomarker after TKA. Finally, we selected [60]GSE135854 based on the platforms of [61]GPL18573 Illumina NextSeq 500 (Homo sapiens) for further investigation [62][13]. [63]GSE135854 contained 4 patients with arthrofibrosis in their knee joint after TKA and 4 patients without arthrofibrosis after TKA as control. All these samples were standardized by the affy package of R programming software. ([64]https://www.r-project.org/) 2.3. Differentially expressed genes (DEGs) Identification Differentially expressed genes (DEGs) were identified with Bioconductor's package Limma package of R programming software in this study. The criteria for DEGs in these two profiles was set as the adjusted P-value < 0.05 (Benjamini-Hochberg method) or |logFC| > 1 based on the Bayes’ test. Hierarchical clustering analysis were identified by the pheatmap packages R programming software [65][22]. The shared DEGs were also shown with a Venn diagram ([66]http://bioinfogp.cnb.csic.es/tools/venny/). 2.4. GO and KEGG analysis Gene ontology and pathway enrichment analysis of these two gene profiles DEGs were conducted with the clusterProfiler packages of R programming software. As the enrichment functions of DEGs analysis, Go terms and KEGG pathway with FDR<0.05 were selected. 2.5. PPI network integration Protein-protein interaction (PPI) of the DEGs of these two profiles and the shared DEGs were constructed by Search Tool for the Retrieval of Interacting Genes database (STRING, [67]https://string-db.org/) [68][23]. Interactions possibility in STRING is quantified with four levels of confidence score (highest>0.9, high>0.7, medium>0.4, low>0.15) [69][24]. We set the high confidence score (>0.7) to have the best visual performance of the network integration. 2.6. The surgical procedure of animal model All animal models were established in accordance with the institutional animal research ethic and international guidelines. Twelve male New Zealand white rabbits (mean weight 2.5 kg) were randomly and equally divided into 4 groups by immobilization time: 3-days groups, 1-week groups, 2 weeks groups, and 4 weeks groups to explore the time sequence of gene expression. After anesthetized by intravenous injection of 20% urethane, the animals' surgery was performed according to the previous protocol [70][25]. Briefly, the fur around the right knee was shaved and the exposed skin was sterilized using iodophor. The surgical limbs in each group were immobilized by 1.2 mm Kirschner wires (K-wire) in the fully flexed position for 3-days, 1 week, 2 weeks groups, and 4 weeks groups. The animals after surgery were kept individually in cages that had full access to standard chow and water. 2.7. Measurement of the ROM The rabbits were humanely euthanized by intravenous administration of 20% urethane (5 g/kg) at the endpoint of each group. ^16,19After the K-wires were removed, the range of motion of the fixed knees and the control knees were measured according to the previous studies. With a looped wire hooked on the distal leg, which was 8 cm distal from the proximal tibia joint surface. A consistent force of 5 N was applied to the looped wire and the angle of femur and tibia was measured as the ROM. 2.8. Determination of hydroxyproline content The synovial membrane and fibrous scars were removed from both knees and 20 mg (wet weight) of the tissue were used as hydroxyproline content determination samples. As the previous study described [71][16], the samples were hydrolyzed with 6 mol/l HCl at 130°C for 12 h and then neutralized with 2.5-N NaOH, which methyl red was apply as the indicator. With 1 ml of chloramine T added, all the tissue samples and four known hydroxyproline standards were incubated for 20 min at room temperature. Subsequently, 1 ml p-dimethylaminobenzaldehyde solution was added in both sample and standards. With the spectrophotometer, the absorbance of the solution was determined at 558 nm. Based on the standard curve, the hydroxyproline content of the sample was calculated. 2.9. Histological analysis The synovial membrane and fibrous scars removed from 24 knees were fixed in the 10% buffered formalin and then embedded in paraffin. A total of 24 4-μm transverse sections which were perpendicular to the femoral axis were obtained and stained with hematoxylin-eosin. The synovial membrane and fibrous scar tissue were evaluated under microscopy at × 100 magnification. The histological images at × 400 magnification were obtained by the digital scanner (KF-PRO-005 Magscanner). 2.10. Immunohistochemistry evaluation Immunohistochemical stains were performed with an automated immunostainer (Autostainer 720, Labvision) according to standard heat-induced epitope retrieval and the avidin-biotin-peroxidase complex method. Antibodies used in the present study are α-SMA (Abcam CAT#ab32575, RRID: AB_722,538), TGFBR1 (Abcam CAT#ab31013, RRID: AB_778,352), TGFBR2 (Abcam CAT#ab186838, RRID: AB_2,728,775), which have been validated the pilot study beforehand. Sections were incubated overnight at 4°C and were counterstained with Hematoxylin (Thermo Electron Corporation, Pittsburgh, PA, US). The scoring procedure was applied as the previous studies mentioned about [72][26]. Two pathologists evaluated independently without informed of the group information beforehand. About 100 fibroblasts stained positively filed were defined as target filed which the percentage of total cells were carefully inspected. The Kruskal-Wallis test was applied to determine the difference between these groups. In this study, we set p-value<0.05 as statistically significant. 2.11. RT-PCR TRIzol® Reagent (Plant RNA Purification Reagentfor plant tissue) was used to isolate the RNA in these capsules and synovial membrane of each knee. The quality of the extracted RNA was determined by 2100 Bioanalyser (Agilent) and quantified with the ND-2000 (NanoDrop Technologies). RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, San Jose, CA) was applied to get the reverse transcription in accordance with the standard instructions. Specific primers (GAPDH: F—TCA CCA TCT TCC AGG AGC GA and R—CAC AAT GCC GAA GTG GTC GT; ACTA2(a-SMA) F—GACCGAATGCAGAAGGAG R—CGGTGGACAATGGAAGG; TGFBR1 (ALK5) F—CGACGGCGTTACAGTGTTTCT R—CCCATCTGTCACACAAGTAAA; ACVRL1 (ALK1) F—CCATCGTGAATGGCATCGT R—GGTCATTGGGCACCACATC) were selected as the previous studies mentioned [73][27]. 2.12. Patients’ enrollment and Intraoperatively samples collection The present case-control study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [74][28]. All the patients’ information in the pathology medical records database (PMRD) was screened. A total of 758 patients who underwent revision knee surgery in the Department of Adult Joint Reconstructive Surgery of Jishuitan hospital from January 2010 to April 2020 were evaluated. Among them, 46 patients were further enrolled according to the inclusion criteria and exclusion criteria ([75]Table 1) [[76]5,[77]29,[78]30]. The arthrofibrosis of this study was defined as the limited range of motion (flexion ROM<90 degree) due to the soft-tissue fibrosis. In addition, another 92 patients in the PMRD were also enrolled as a control group by individual matching according to sex and age groups (40–49, 50–59, 60–69, 70–79, and ≥80 years) (1:2 ratio). As one of the major complaints of the AF patients, knee pain was also taken into consideration in this study. A visual analogue scale (VAS) was applied in this study for the knee pain measurement [79][31]. The score of VAS ranges from 0 to 10, with higher scores indicating worse knee pain. Baseline clinical information such as sex, age, range of motion (ROM), and visual analogue scale (VAS) was collected and evaluated by experienced surgeons before the surgery. The ROM and VAS after surgery were collected with at least 1-year follow-up by the same surgeons. Synovial tissue was collected during the revision operation process with patients' informed consent. After fixed in 10% buffered formalin and embedded in the paraffin, this synovial tissue was kept in the sample bank of the JST hospital pathology department. Subsequently, immunohistochemistry and Histological evaluation of the obtained samples was performed as previously mentioned. Table 1. Inclusive and exclusive criteria of the AF patients. Number Inclusive criteria Exclusive criteria __________________________________________________________________ 1 Persistent limitation of flexion ROM<90° at minimum 1 year follow-up Prosthetic joint infection 2 Revision of TKA surgery for high degree of psychological strain and restriction of quality of life Revision of TKA surgery for instability or loosening of the implantation 3 Inform consent of the patients No obvious fibrous tissue under the microscope [80]Open in a new tab 2.13. Statistical analysis Clinical measurement data were reported with a mean, standard deviation, and 95% confidence interval (CI). All analyses in this study were calculated in R programming software (version 3.6.2) and used the tidyverse workflow [81][32]. The chi-squared test and t-test were applied for the comparison between two groups of categorical variables and continuous variables separately. Correlation analyses were conducted with the Corrplot package of R programming software [82][33] ([83]https://github.com/taiyun/corrplot). ROC curves were depicted and the area under the curve (AUC) was calculated based on the pROC package of R programming software [84][34]. The cut-off value in this study was defined as the value which corresponded to the maximum joint sensitivity and specificity on the ROC curve. The significant level was set to a P-value of <0.05 in all statistical analyses. 2.14. Role of funding source Funders of this study had no role in study design, data collection, data analyses, interpretation, or writing of the report. 3. Results 3.1. Identification of differentially expressed genes (DEGs) We inspected the RNA-sequencing data ([85]GSE135854) of posterior capsule specimens from 4 RTKA-A and 4 RTKA-NA patients and identified 6104 differentially expressed genes (3165 up-regulated and 2939 down-regulated DEGs). These results were displayed by heatmap in [86]Fig. 1a and volcano plot in [87]Fig. 1b. All of these up and down-regulated fibrosis DEGs were displayed in Supplementary Table 1. Fig. 1. [88]Fig 1 [89]Open in a new tab Identification of differentially expressed genes (DEGs). (a) Hierarchical clustering analysis of the DEGs between 4 RTKA-A and 4 RTKA-NA patients. (b) Volcano plot of the DEGs. The red points and blue points represented the significantly up and down-regulated genes (|LogFC|>2). (c)Gene ontology analysis of the DEGs. The size of the spot represents the gene counts and the color represents the adjusted p-value (Benjamini-Hochberg method) (For interpretation of the references to