Osteoarthritis and Cartilage Open 4 (2022) 100295 Contents lists available at ScienceDirect Osteoarthritis and Cartilage Open journal homepage: www.elsevier.com/journals/osteoarthritis-and-cartilage-open/2665-9131 Synovial tissue metabolomic profiling reveal biomarkers of synovial inflammation in patients with osteoarthritis Jessica D. Murillo-Saich a, Roxana Coras a, b, Robert Meyer a, c, Cristina Llorente a, Nancy E. Lane d, Monica Guma a, b, c, * a Department of Medicine, School of Medicine, University of California, San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA b Department of Medicine, Autonomous University of Barcelona, Plaça Cívica, 08193 Bellaterra, Barcelona, Spain c San Diego VA Healthcare Service, San Diego, CA, 92161, USA d Department of Medicine, University of California, Davis, Sacramento, CA, 95817, USA A R T I C L E I N F O A B S T R A C T Keywords: Objective: Inflammatory responses are associated with changes in tissue metabolism. Prior studies find altered Inflammation metabolomic profiles in both the synovial fluid (SF) and serum of osteoarthritis subjects. Our study determined Fibrosis the metabolomic profile of synovial tissue (ST) and SF of individuals with osteoarthritis (OA) and its association Metabolomic with synovial inflammation. Synovial fluid Design: 37 OA ST samples were collected during joint replacement, 21 also had SF. ST samples were fixed in formalin for histological analysis, cultured (explants) for cytokine analysis by enzyme-linked immunosorbent assay, or snap-frozen for metabolomic analysis. ST samples were categorized by Krenn synovitis score and pic- rosirius red. CD68 and vimentin expression was assessed by immunohistochemistry and semi-quantified using Image J. Proton-nuclear magnetic resonance (1H NMR) was used to acquire a spectrum from ST and SF samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. Metaboanalyst 5.0, SPSS v26, and R (v4.1.2) were used for statistical analysis. Results: 42 and 29 metabolites were detected in the ST and SF respectively by 1H NMR. Only 3 metabolites, lactate, dimethylamine, and creatine positively correlated between SF and ST. ST concentrations of several me- tabolites (lactate, alanine, fumarate, glutamine, glycine, leucine, lysine, methionine, trimethylamine N-oxide, tryptophan and valine) were associated with synovitis score, mostly to the lining score. IL-6, acetoacetate, and tyrosine in SF predicted high Krenn synovitis scores in ST. Conclusion: Metabolomic profiling of ST identified metabolic changes associated with inflammation. Further studies are needed to determine whether metabolomic profiling of synovial tissue can identify new therapeutic targets in osteoarthritis. 1. Introduction an imbalance between synthesis and degradation of collagens, is also a hallmark of OA [6]. It leads to thickening and stiffening of the synovial Osteoarthritis (OA) is the most common form of arthritis and one of membrane, which is believed to be a major contributor to both joint pain the leading causes of impaired mobility among older adults [1]. The and joint stiffness. Macrophages and fibroblast-like synoviocytes (FLS) synovium in OA patients is characterized by synovial lining hyperplasia, react to molecules from degraded hyaline cartilage by producing a sublining with mononuclear and fibroblast cell infiltration, fibrosis, and pro-inflammatory mediators and fibrotic mediators. These in turn act on stromal vascularization [2]. Macrophages are shown to be the most both synoviocytes and chondrocytes to not only produce excess of pro- predominant immune cells in OA synovium. Macrophages are believed to teolytic enzymes that augment cartilage degradation, but also generate play a critical role in OA pathogenesis through the induction of inflam- additional inflammatory mediators to further amplify synovial inflam- matory mediators, growth factors, and proteinases [3–5]. mation and fibrosis [7]. Synovial fibrosis, which is characterized by fibroblast hyperplasia and Increasing evidence indicate that synovial inflammation [8] * Corresponding author. Department of Medicine, School of Medicine, University of California, San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA. E-mail address: mguma@health.ucsd.edu (M. Guma). https://doi.org/10.1016/j.ocarto.2022.100295 Received 25 June 2022; Received in revised form 4 July 2022; Accepted 5 July 2022 2665-9131/© 2022 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International (OARSI). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 contributes to OA progression [6]. Synovitis can occur prior to incident by boiling in citrate buffer pH 6 for 10 min with pressure cooker. Next, radiographic OA, and effusion synovitis 1–2 years prior to diagnosis is endogenous peroxidase was inactivated with 3% H2O2 in water for 10 significantly associated with subsequent OA development [9,10]. These min at room temperature. Sections were incubated with 3% BSA (bovine results suggest that inflammation is a part of the spectrum of OA serum albumin) for 1 h at room temperature, and then with the specific pathology. primary antibody overnight at 4  C. After washing, the secondary anti- Metabolism is important for cartilage and synovial joint function body was added for 30 min at room temperature and sections were [11]. Metabolic changes from a resting regulatory state to an active washed three times with PBS. 3,30-Diaminobenzidinetetrahydrochloride metabolic state both in chondrocytes and synoviocytes were reported in (DAB) (Vector, #SK-4100) was used as a chromogen. Sections were OA and implicated in its pathogenesis [11,12]. One of these metabolic lightly counterstained with hematoxylin, dehydrated and then mounted. changes is the upregulation of the glycolytic cycle due to the increased A non-related IgG was used as a negative control. The antibodies and demand in energy and the activation of glucose transporters in response working concentrations used are listed below. to hypoxia and proinflammatory cytokines, resulting in increased con- centration of glucose, lactate and pyruvate, among others [11,12].  CD68. Abcam#ab955 1/500 (Clone KP1) Emerging evidence indicates an important role for metabolites derived  Vimentin. Cell Signaling #D2H13 1/100 from key metabolic pathways in the regulation of inflammatory re- sponses and cell function. Glucose and choline metabolism, for instance, The slides were scanned using a NanoZoomer slide scanner using a support the expression of proinflammatory cytokines [13–17], while 40x brightfield objective. Semiquantitative determination of antibody tryptophan, and derivates of fumarate have been associated to an expression was performed using Image J Fiji, according to the method anti-inflammatory role [18]. Other metabolites have mixed properties, described by Alexander Crowe et al. [29] IHC images stained with he- like succinate, a metabolite that supports inflammation but also has matoxylin and DAB were deconvoluted and the expression of CD68 and immunomodulatory functions [19]. vimentin were calculated using a threshold for DAB of 170. Previous studies have shown metabolomic alterations in OA in Metabolite extraction from synovial tissue: Metabolite extraction different biofluids such as urine [20], synovial fluid (SF) [21,22], blood of ST samples was performed using a protein precipitation protocol. serum [23], and plasma [24], determined by nuclear magnetic resonance Approximately 14 mg of each sample was snap-frozen and stored at 80  (NMR) or mass spectrometry. However, a metabolomic characterization C before metabolite extraction. The tissue was ground with a hydro- of the OA synovial tissue and their association with histological features alcoholic mix using Beadblaster microtube homogenizer. Next, chloro- and association with synovial fluid metabolomics has not been per- form was added then shaken for 1 min. The samples were centrifuged for formed. This study determined the metabolomic profile in SF to predict 5 min at 1200 rpm to separate the polar layer which contains the polar inflammation of ST in individuals with OA. metabolites. This was evaporated using a speed vacuum (Thermo Fisher Scientific, Waltham, MA) and the metabolites were resuspended in 60 μL 2. Method of the standard 3-(Trimethylsilyl) propioninc-2,2,3,3,d4 acid sodium salt (TSP-d4) (Millipore Sigma, Cat#269913) at a concentration of 66.17 μM. Patients: Thirty-seven synovial tissue (ST) samples with twenty-one Then, 50 μL of each sample was added into a 1.7 mm BioSpin tube. paired synovial fluid (SF) samples were obtained during joint replace- Metabolite extraction from synovial fluid: The SF samples were ment in patients diagnosed with hip and knee OA [25] at the Veterans thawed on ice and polar metabolites were extracted using an ultrafil- Affairs (VA) San Diego Health Care System. Age and sex were also tration method with a 3 KDa filter device with 30 min of centrifugation at collected. ST samples were obtained from the suprapatellar area (knee) 12,000 rpm, 4  C. Then, 45 μL were taken from the filtered sample and and from the inferior capsular region (hip). The samples were kept on ice 15 μL of TSP-d4 standard 2 mM were added to obtain a final concen- until they were fixed in formalin for histological analysis, cultured (ex- tration of 0.5 mM. Finally, 50 μL of each sample were added into a 1.7 plants) for 24 h for cytokine analysis by enzyme-linked immunosorbent mm BioSpin tube. assay (ELISA), or snap-frozen for metabolomic analysis. SF samples Data acquisition, identification, and quantification of metabo- collected the same day were centrifuged at 4000 rpm for 10 min, cells lites: A 600 MHz Bruker Avance III spectrometer one-dimension proton- were discarded, and supernatant was stored at 80  C. Patients signed nuclear magnetic resonance (1H NMR) was used to acquire NMR spectra and informed consent and the protocol was approved by the VA Insti- of ST and SF samples. The standard Bruker pulse sequence “noe- tutional Review Board (IRB number H170130). The study was conducted sygppr1d” was used with a mixing time of 500 ms and 64 scans. A daily in accordance with the Helsinki Declaration of 1975, as revised in 2000. quality assurance procedure was performed before sample data acquisi- Histology: ST samples were fixed in formalin 10% (Thermo scientific, tion, involving temperature checks and calibration as well as shim and #5735) and embedded in paraffin. ST (5 μM) were stained using He- water suppression quality. The data acquisition was obtained in the NMR matoxylin and Eosin (H&E), and synovitis was evaluated according to the facility of Skaggs School of Pharmacy and Pharmaceutical Science, Uni- Krenn histopathological synovitis score [26]. Based on the Krenn score, versity of California San Diego. Software Chenomx NMR suite 8.5 pro- ST samples were classified as low (score 0–4) or high (score 5–9) degree fessional was used for metabolite identification and quantification. synovitis. Synovial lining and sublining infiltration (identified with H&E) Samples were normalized by vol/wt and the concentrations were re- were scored using a semiquantitative method from 0 to 3 and were ported in μM. grouped as low (<2) or high (2) scores. The slides were scored by two Quantification of inflammatory mediators in explants and SF different researchers using the microscope Motic BA400. Fibrosis was samples: Explants (around 15 mg) were cultured in triplicates with identified with picrosirius red staining. The picrosirius red staining complete media (Gibco Dubbeco's modified eagle medium #11965 with highlights the natural birefringence of collagen fibers when exposed to 10% fetal bovine serum (FBS), 1% glutamine, 1% pen strep and genta- polarized light. This allows evaluating the organization of collagen fibers micin). Supernatants were collected after 24 h and stored at 20  C for in the tissues and the semiquantification of type I and III collagen, cytokine analysis. SF samples were collected and centrifuged at 4000 rpm identifying fibers type I in red and type III in green [27,28]. Fibrosis was for 10 min, cells were discarded, and the supernatant was stored at 80  scored using a semiquantitative scoring from 0 to 3 and were grouped as C before analysis. Interleukin 6 (IL-6) (R&D, DY206), Interleukin 1 beta low collagen (<2) or high collagen (2) scores. (IL-1β) (R&D, #DY201), Tumor necrosis factor (TNF) (R&D, #DY210), C- Immunohistochemistry (IHC): IHC was used to quantify macro- X-C Chemokine ligand 2 (CXCL2) (R&D, #DY452), C–C Motif Chemokine phages and fibroblasts in the ST samples. IHC analysis was performed Ligand 2 (CCL2) (R&D, #DY279), and Matrix metallopeptidase 13 using 5 μM sections of formalin-fixed paraffin-embedded ST samples. (MMP13) (R&D, #DY511) were measured in both explant supernatants After deparaffinization and rehydration, antigen retrieval was performed and SF samples by ELISA following manufacturers' instructions. 2 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 Statistical analysis: Continuous variables are shown in mean  fibrosis and vice versa (Supplementary Table 2). Interestingly, hip sam- standard deviation (SD) and categorical variables are shown in per- ples tended to be more fibrotic than inflammatory, while knees shared centages. Student t-test was used to evaluate the differences between both inflammation and fibrotic characteristics (Supplementary Table 2). quantitative clinical variables, concentration of cytokines, and metabo- Both collagens (type I and III) were more abundant in high fibrotic tissue, lites between 2 groups, Differences between categorical variables were and the collagen I/III ratio was similar in all groups (Table 1, Supple- assessed using Chi-square with SPSS software v27. We also performed mentary Table 1). orthogonal partial least square discrimination analysis (OPLS-DA) and We also stained the synovial tissue with CD68 and vimentin to the variables important of projection (VIP) was performed to identify the determine the abundance and distribution of macrophages and fibro- metabolites that contribute to separation between different groups using blasts in the synovium, respectively (Fig. 1). Although not significant, in MetaboAnalyst v5. The strength of association between metabolites knees we observed an increased expression of CD68 (0.84  1.23 vs 1.35 presented in synovial tissue and synovial fluid, and between metabolites  1.18, p ¼ 0.17) and vimentin (1.59  1.40 vs 3.23  5.79, p ¼ 0.32) in and cytokines was measured with Pearson correlation using “gplots” inflamed tissues. Interestingly, when we compared the location of the package (https://cran.r-project.org/web/packages/gplots/gplots.pdf). abundance of these two cellular markers, we observed a tendency for To identify if SF features (metabolites and cytokines) that could predict higher expression of vimentin in the sublining of high synovitis samples the degree of synovitis (high versus low), a Random Forest predictive (1.71  0.73 vs 2.07  0.51, p ¼ 0.14), but not in the high fibrotic model was implemented using the “randomForest” package (https://cr samples (2.00  0.59 vs 1.78  0.70, p ¼ 0.39) (Table 1 and Supple- an.r-project.org/web/packages/randomForest/index.html). Boruta mentary Table 1). feature selection was first performed using the Boruta package (https:// cran.r-project.org/web/packages/Boruta/index.html) with maximum 3.2. Metabolomic profile of synovial tissue in inflamed and non-inflamed runs set at 250. The data was next partitioned in training (50% of the synovial samples data) and testing (50% of the data) datasets, and the Random Forest was built including only the metabolites selected by the Boruta algorithm. A total of 42 metabolites were detected in ST samples by 1H NMR. Next, the predictive ability of the model was evaluated using a receiver Multivariate approaches were conducted to capture the global picture of operation curve (ROC) analysis using the 21 samples with SF (https://cra 2 metabolite disturbances according to the degree of synovitis. Using an n.r-project.org/web/packages/pROC/index.html). OPLS-DA with all identified metabolites, 52.6% of the total variance across groups explained the separation. As observed in Supplementary 3. Results Figure 1A, this approach was able to discriminate the two groups analyzed including hips and knees, although a certain overlap existed. A 3.1. Histological classification of ST samples total of 14 metabolites had VIP scores >1 (Supplementary Figure 1B). Glucose was identified as the most important metabolite to discriminate A total of 37 synovial tissue (ST) samples were obtained from 28 between low and high synovitis, followed by glycine, dimethyl sulfone, knees and 9 hips. The average age of the individuals with OA was 66  dimethylamine, acetate, acetone and tryptophan. When only knees were 8.5 and 86.5% were men. ST samples were classified as either low (n ¼ included, glucose was also identified as the most important metabolite 22) or high (n ¼ 15) synovitis scores by H&E staining, and as either low and no major differences were observed (Supplementary Fig. 1C and D). (n ¼ 18) or high (n ¼ 19) fibrosis scores by picrosirius red staining (Fig. 1 We also performed an OPLS-DA according to fibrosis groups (Supple- and Supplementary Table 1). Age and sex were not different between mentary Figure 1E). 54.2% Of the total variance across groups was synovitis or fibrosis categories. Although all samples have both inflam- explained and a total of 13 metabolites had VIP scores >1 including hips mation and fibrosis features, the highly inflamed samples showed less and knees, o-phosphoethanolamine, acetoacetate, glucose, glutamine, Fig. 1. Histological classification of synovial tissue from osteoarthritis (OA) patients in inflamed (upper panels) or fibrotic (bottom panel) samples. Hematoxylin Eosin staining of synovial tissue, Picrosirius red staining on red the collagen fibers, immunohistochemistry (IHC) for CD68 as marker of macrophages and IHC for Vimentin as marker of fibroblasts. 3 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 Table 1 Comparison of histological characteristics according to inflammation and fibrosis in knee samples. Variables Inflammation P Fibrosis P Low synovitis N ¼ 14 High synovitis N ¼ 14 Low collagen N ¼ 14 High collagen N ¼ 14 Age, years 66.14  6.12 68.35  4.57 0.28 68.57  4.36 65.93  6.18 0.20 Male, n (%) 13 (92.9) 13 (92.9) 1.00 12 (85.7) 14 (100) 0.48 CD68 0.84  1.23 1.35  1.18 0.17 1.69  2.32 0.97  1.23 0.31 CD68 Lining 0.89  1.05 1.36  1.18 0.28 1.36  1.13 0.89  1.11 0.28 CD68 Sublining 1.00  0.64 0.96  0.41 0.86 0.93  0.43 1.03  0.63 0.61 Vimentin 1.59  1.40 3.23  5.79 0.32 3.11  5.81 1.71  1.44 0.39 Vimentin Lining 1.28  1.15 1.75  0.84 0.24 1.86  0.77 1.18  1.15 0.07 Vimentin Sublining 1.71  0.73 2.07  0.51 0.14 2.00  0.59 1.78  0.70 0.39 Total Collagen 1.96  0.60 1.32  0.61 0.009 1.07  0.38 2.21  0.32 < 0.001 Collagen type I 1.68  0.64 1.21  0.75 0.09 0.96  0.49 1.93  0.58 < 0.001 Collagen type III 1.39  0.52 0.96  0.46 0.03 0.96  0.46 1.39  0.52 0.03 Collagen I/III 1.36  0.70 1.40  0.87 0.87 1.15  0.73 1.61  0.77 0.12 Quantitative variables are expressed in mean  standard deviation and qualitative variables are expressed in count and percentage. Student t-test was used to compared quantitative variables and, Chi square for qualitative variables. Considering inflammation low synovitis <5 and high synovitis 5 according to Krenn score. Fibrosis low collagen <2, high collagen 2 according to picrosirius red staining. Table 2 Comparison of polar metabolites detected by 1H NMR in synovial tissue of knee samples between inflammation and fibrosis groups. Variables Inflammation P Fibrosis p Low synovitis N ¼ 14 High synovitis N ¼ 14 Low collagen N ¼ 14 High collagen N ¼ 14 3-Hydroxybutyrate 0.62  0.58 0.75  0.63 0.59 0.81  0.65 0.55  0.53 0.25 Acetate 17.14  14.33 15.27  14.47 0.73 15.29  14.23 17.12  14.57 0.74 Acetoacetate 0.14  0.10 0.22  0.13 0.07 0.21  0.14 0.15  0.09 0.20 Acetone 0.09  0.06 0.07  0.04 0.16 0.08  0.04 0.09  0.07 0.76 Alanine 4.09  1.78 6.17  2.93 0.03 6.35  2.73 3.91  1.85 0.01 Betaine 0.38  0.28 0.38  0.24 0.96 0.46  0.29 0.30  0.19 0.10 Choline 1.54  0.95 2.04  1.02 0.19 2.10  0.94 1.48  0.99 0.10 Citrate 1.08  0.37 1.37  0.53 0.11 1.39  0.49 1.06  0.40 0.06 Creatine 1.12  0.96 1.25  0.46 0.66 1.47  0.92 0.90  0.37 0.04 Creatine phosphate 0.28  0.12 0.40  0.19 0.06 0.37  0.16 0.31  0.18 0.37 Creatinine 0.28  0.26 0.30  0.22 0.77 0.31  0.21 0.27  0.27 0.68 Dimethylamine 0.16  0.10 0.11  0.05 0.12 0.12  0.07 0.14  0.10 0.57 Dimethyl sulfone 0.12  0.15 0.16  0.22 0.57 0.18  0.23 0.11  0.14 0.32 Formate 19.24  12.17 20.73  11.02 0.74 20.57  10.87 19.41  12.34 0.79 Fumarate 0.17  0.10 0.27  0.13 0.02 0.26  0.11 0.18  0.12 0.06 Glucose 9.53  3.68 7.47  6.05 0.29 8.24  6.05 8.77  3.96 0.79 Glutamate 7.35  3.76 11.17  6.25 0.06 11.11  5.88 7.41  4.39 0.07 Glutamine 2.55  1.67 4.75  2.81 0.02 5.13  2.73 2.17  1.04 0.001 Glycine 2.30  2.65 5.43  4.67 0.04 5.41  4.91 2.29  2.19 0.04 Isoleucine 0.62  0.38 1.00  0.44 0.02 1.06  0.41 0.56  0.34 0.002 Lactate 28.34  14.98 44.46  21.66 0.03 44.47  21.63 28.36  15.00 0.03 Leucine 2.00  1.12 2.73  1.10 0.09 2.97  1.02 1.76  0.97 0.003 Lysine 2.43  1.27 3.14  1.61 0.20 3.37  1.49 2.20  1.23 0.03 Methanol 5.94  6.65 45.22  140.14 0.30 45.42  140.07 5.74  6.79 0.30 Methionine 0.40  0.27 0.67  0.45 0.07 0.69  0.44 0.38  0.25 0.03 Myo-Inositol 7.59  6.57 11.48  5.82 0.11 12.97  6.04 6.10  4.83 0.003 O-Acetylcarnitine 0.23  0.25 0.32  0.21 0.31 0.31  0.22 0.24  0.24 0.40 OPhosphocholine 0.75  0.41 1.24  0.71 0.04 1.27  0.68 0.72  0.42 0.02 OPhosphoethanolamine 2.26  1.22 3.69  3.02 0.15 4.26  2.64 1.63  1.01 0.006 Oxypurinol 9.76  7.92 14.88  10.46 0.16 14.57  10.38 10.07  8.22 0.21 Phenylalanine 0.69  0.43 0.98  0.51 0.12 1.06  0.50 0.61  0.36 0.01 Proline 3.33  1.57 3.97  1.59 0.29 4.21  1.51 3.09  1.49 0.06 Pyruvate 0.23  0.08 0.31  0.17 0.10 0.30  0.16 0.24  0.10 0.20 Sarcosine 0.36  0.22 0.46  0.31 0.37 0.50  0.31 0.32  0.19 0.08 SG3PCh 0.60  0.34 0.70  0.35 0.43 0.78  0.36 0.52  0.29 0.05 Succinate 0.73  0.41 0.58  0.26 0.26 0.64  0.28 0.66  0.41 0.86 Threonine 1.43  1.04 1.82  0.90 0.30 2.04  0.88 1.20  0.90 0.02 Trimethylamine 0.05  0.03 0.08  0.09 0.24 0.08  0.09 0.05  0.03 0.21 TMNO 0.25  0.23 0.43  0.25 0.06 0.47  0.29 0.21  0.11 0.007 Tryptophan 0.44  0.15 0.43  0.25 0.91 0.49  0.23 0.35  0.07 0.20 Tyrosine 0.99  0.50 1.32  0.66 0.14 1.39  0.63 0.92  0.47 0.03 Uridine 1.04  0.56 1.43  0.87 0.09 1.47  0.86 0.99  0.54 0.09 Valine 1.74  0.81 2.39  1.12 0.01 2.54  1.08 1.59  0.70 0.01 Quantitative variables are expressed in mean  standard deviation. Student t-test was used to compare quantitative variables. Low synovitis <5 and high synovitis 5 according to Krenn scor. Low collagen <2, high collagen 2 according to picrosirius red staining. SG3PCh: sn-glycero-3-phosphocholine; TMNO: Trimethylamine N- oxide. Statistical difference was considered when p < 0.05. Concentrations are reported in μM. tyrosine, and phenylalanine among them (Supplementary Figure 1F). low and high collagen (Supplementary Figure 1G), and glutamine fol- When only knees were analyzed, we observed a good separation between lowed by isoleucine, leucine, phenylalanine, threonine, and glucose were 4 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 the most important metabolites for the separation (Supplementary with high synovitis (Table 3). Consistent with their histological charac- Figure 1H). teristics, knees presented higher concentrations of IL-6 (15820.01  To determine the significantly different metabolites between groups, 28631.64 vs 55669.32  44908.33, p ¼ 0.04), CXCL2 (568.21  684.17 metabolites were analyzed by univariate analysis. When we analyzed the vs 2219.42  1886.67, p ¼ 0.04) and CCL2 (388.04  329.18vs 1652.59 knees, lactate levels were increased in samples with high synovitis scores  1564.30, p ¼ 0.008) (Supplementary Table 7). (28.34  14.98 vs 44.46  21.66, p ¼ 0.03), as expected. Other me- The correlation between pro-inflammatory mediators and metabo- tabolites including alanine (4.09  1.78 vs 6.17  2.93, p ¼ 0.03), lites showed that IL-6 in explants was positively associated with several fumarate (0.17  0.10 vs 0.27  0.13, P ¼ 0.02), glutamine (2.55  1.67 metabolites, including alanine, betaine, fumarate, glutamine, glycine, vs 4.75  2.81, p ¼ 0.02), glycine (2.30  2.65 vs 5.43  4.67, p ¼ 0.04), leucine, lysine, methionine, myo-inositol, O-phosphocholine, o-phos- isoleucine (0.62  0.38 vs 1.00  0.44, p ¼ 0.02), o-phosphocholine phoethanolamine, phenylalanine, TMNO, tryptophan, tyrosine, uridine (0.75  0.41 vs 1.24  0.71, p ¼ 0.04) and valine (1.74  0.81 vs 2.39  and valine. In addition, TNF and CXCL2 from explants correlated with ST 1.12, p ¼ 0.01) were also significantly elevated in these samples (Table 2 TMNO, while IL-1β from explants correlated with ST tryptophan. We and Supplementary Table 3). Consistently, concentration of 18 metabo- identified less significant correlations between metabolites and these lites were decreased in high fibrotic ST samples (Table 2 and Supple- mediators in SF. IL-6 in SF showed an association with succinate and mentary Table 3). Differences in metabolites between hips and knees glutamate in SF. While leucine in SF showed negative correlation with were also observed (Supplementary Table 4). Of note, the metabolic TNF, IL-1β, and MMP13 (Supplementary figure 3). changes were driven mostly by the lining, as we did not observe many Correlation between metabolites and histological scores also showed differences when samples were stratified by sublining scores (Supple- a different pattern between ST and SF. While several metabolites from ST mentary Tables 5 and 6). were positively associated with inflammation and negatively with fibrosis, we observed the opposite in SF, except for lactate and fumarate 3.3. Correlation between synovial fluid and synovial tissue metabolic that had the same tendency (Supplementary Figure 4). Interestingly, the profiles expression of CD68 in ST was negatively associated with ST glucose and succinate, but positively with ST glycine, fumarate, methionine and 1 lactate. CD68 was also positively associated with acetate, lactate, formate H NMR analysis of SF obtained from 21 knee OA samples identified 29 of the 42 metabolites identified in tissue (Fig. 2). Several metabolites in SF. identified in ST including tryptophan, hydroxybutyrate, and betaine among others, were not identified in the paired SF. Correlation analysis 3.5. Synovial fluid as predictor of inflammation in synovial tissue between the SF and ST metabolites showed that lactate, dimethylamine, and creatine were positively correlated while formate, acetate, and valine We first compared the SF metabolites by degree of synovitis (using had a negative correlation between ST and SF samples (Fig. 3 and Sup- Krenn score) and fibrosis (using picrosirius red score) of the ST. As seen plementary Figure 2). in Supplementary Table 8, concentrations of leucine (86.98  17.24 vs 70.27  16.86, p ¼ 0.04) and tyrosine (48.15  9.07 vs 40.36  8.62, p 3.4. Inflammatory mediators and metabolites in synovial tissue and ¼ 0.06) were lower in SF from samples with high synovitis, while only synovial fluid reflect tissue histologic features the concentration of valine (123.06  29.20 vs 146.04  13.53, p ¼ 0.05) was higher in SF from samples with high collagen (Supplementary To determine the relationship between metabolites, proinflammatory Table 8). mediators, and histologic features, we quantified the levels of TNF, IL-6, To further determine whether the SF metabolite profiling was asso- IL-1β, CXCL2, CCL2 and MMP13 from synovial explants and SF by ELISA. ciated with the histological pattern of ST, multivariate approaches were Concentrations of these mediators were not significantly different be- conducted to capture the global picture of metabolite disturbances ac- tween samples with high or lower synovitis, although IL-6 concentration cording to the degree of inflammation. Using an OPLS-DA with all was higher in explants with high synovitis (33,045  35,699 vs 59,447  identified metabolites, 58.4% of the total variance across groups 52,096, p ¼ 0.15) and significantly lower in explants from fibrotic explained the separation. A clear separation was observed between the samples (64,045  49,708 vs 20,411  20,160, p ¼ 0.01) (Table 3). In SF, two degrees of synovitis (Fig. 4A), the metabolites responsible for the levels of IL-6 (190.8  140.9 vs 639.6  602.6, p ¼ 0.06) and MMP13 discrimination were acetoacetate, fumarate, dimethylamine, acetate, (119.2  109.05 vs 434.1  378.2, p ¼ 0.04) were elevated in samples succinate, choline, and glutamine. (Fig. 4B). On the other hand, an Fig. 2. Map of metabolites identified in synovial tissue and synovial fluid using one dimension proton-Nuclear magnetic resonance (1H NMR). The metabolites found only in synovial tissue are marked in blue, whereas the metabolites identified in both synovial tissue and synovial fluid are marked in green. 5 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 Fig. 3. Pearson correlation of synovial tissue (ST) and synovial fluid (SF) metabolites. The direction of association is showed by color where blue indicates a negative correlation and red indicates a positive correlation and the p value is indicated with the number diagonally on each square indicating the association of metabolites between ST and SF. X axis shows ST metabolites; Y axis shows SF metabolites. Table 3 Comparison of mediators between inflammation and fibrosis groups quantified in synovial fluid and synovial tissue by ELISA. SYNOVIAL FLUID Mediators Inflammation P Fibrosis p Low synovitis N ¼ 12 High synovitis N ¼ 9 Low collagen N ¼ 9 High collagen N ¼ 12 TNF 18.43  1.17 19.32  2.16 0.24 19.3  2.2 18.5  1.2 0.59 IL-6 190.8  140.9 639.6  602.6 0.06 547.8  597.4 259.6  282.4 0.37 IL-1β 4.62  0.42 9.99  16.84 0.37 10.0  16.8 4.60  0.43 0.34 CXCL2 169.7  163.2 130.7  40.7 0.49 134.6  40.3 166.7  164.0 0.18 CCL2 27.47  48.29 21.06  43.98 0.76 21.1  43.9 27.5  48.3 0.59 MMP13 119.2  109.05 434.1  378.2 0.04 332.27  337.4 195.6  266.6 0.46 SYNOVIAL TISSUE Mediators Inflammation P Fibrosis p Low synovitis N ¼ 14 High synovitis N ¼ 10 Low collagen N ¼ 13 High collagen N ¼ 11 TNF 23.82  9.32 22.91  12.19 0.84 24.37  12.34 22.49  7.93 0.67 IL-6 33,045  35,699 59,447  52,096 0.15 64,045  49,708 20,411  20,160 0.01 IL-1β 7.13  10.68 18.35  39.59 0.43 14.69  34.41 8.06  11.99 0.55 CXCL2 1426  1351 2094  2237 0.38 2208  2065 1077  1108 0.13 CCL2 1319  1632 1166  1206 0.81 1032  1076 1564  1841 0.45 MMP13 85.7  74.8 201.36  409.11 0.40 89.05  77.71 196.98  409.73 0.36 Quantitative variables are expressed in mean ± standard deviation. Student t-test was used to compare quantitative variables. Low synovitis < 5 and high synovitis  5 according to Krenn score. Low collagen < 2, high collagen  2 according to picrosirius red staining. TNF: Tumor necrosis factor alpha; IL-6: Interleukin 6; IL-1β: Interleukin 1 beta; CXCL2: C-X-C Motif chemokine ligand 2; CCL2: CC Motif chemokine ligand 2; MMP13: Matrix metallopeptidase 13. Statistical difference was considered when p < 0.05. Concentrations are reported in pg/mL. 6 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 Fig. 4. SF metabolomic profile discriminates between inflammation (A,C) and fibrotic (B,D) scores. Upper figures show orthogonal partial least square-discriminant analysis (OPLS-DA) separation between inflammation (A) and fibrosis (B) groups and bottom figures show the variables important in projection in discriminate inflammation (C) and fibrosis (D), where a variable importance in projection (VIP) score 1 were considered as important. overlap existed when divided according to the degree of fibrosis Metabolomic profiling from different biofluids has been explored as (Fig. 4C). Acetoacetate and fumarate were the most important metabo- biomarker of OA and radiographic severity in OA [30]. Plasma levels of lites in the model (Fig. 4D). succinate, tryptophan, and xanthurenic acid were described as bio- Finally, we built a Random Forest classifier to evaluate the capacity of markers to assist the diagnosis of OA [24] and higher concentrations of SF metabolites and mediators to predict the degree of synovitis. There glutamine, valine, and tyrosine were detected in the synovial fluid of were three SF mediators, namely tyrosine, acetoacetate, and IL-6 that end-stage osteoarthritic knees compared to hips [22]. Also low serum were able to classify the ST samples in high and low synovitis degree with levels of glycine and high levels of arginine were also associated with OA an accuracy of 82%. The ROC performed to evaluate the classifier model radiographic severity in OA patients [23], while perturbations of gluta- yielded an area under curve (AUC) of 0.93, equivalent to a good mine metabolism were found in knee OA patients with inflammation discriminative ability of the model (Fig. 5). (patients with knee stiffness and positive balloon sign) [20]. However, plasma, serum and urine levels of the metabolites can be influenced by a 4. Discussion variety of factors, such as diet, comorbidities, and microbiome, among others. Hence, the metabolomic profile in these biosamples could reflect In this work, we described the metabolomic profiling of OA synovial changes that are not specific to OA. samples and identified metabolic changes associated with inflammation. Here, we observed an increased concentration of several metabolites Of interest, IL-6, acetoacetate, and tyrosine in SF predicted high Krenn in ST from OA patients in samples with histological characteristics of synovitis scores in ST underscoring that the study of metabolomics and inflammation. For instance, TMNO and O-phosphocholine are metabo- inflammatory mediators in SF can help classify ST by synovial features lites derived from choline metabolism, which were related to inflam- and also provide information on the potentially activated pathways in the mation [31,32]. Other metabolites such as alanine, fumarate, glutamate, OA synovium. Since one of the main challenges in the management of OA glutamine, glycine, lactate, leucine, lysine, myo-inositol, phenylalanine, is the lack of effective treatment, due in part to the incomplete under- and valine were also associated with inflammation in ST. Lactate is a standing of the underlying mechanisms, the study of the metabolomic proinflammatory metabolite by reprogramming CD4 T cells, for instance, profile of the OA SF and ST can help better understand the metabolic as consequence of the glycolytic switch of rheumatoid arthritis (RA) FLS changes in the OA synovium and identify new therapeutic targets in OA. [33–35]. Previous studies reported that glutamine/glutamate are also 7 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 Fig. 5. Receiver operating characteristic curve of the Random Forest model used to assess predictive ability of the synovial fluid (SF) metabolites and mediators to predict degree of synovitis (N ¼ 21). A) Plot of the importance of features (metabolites and cytokines) selected by the Boruta algorithm in relation to the degree of synovitis. Red and yellow features are the ones that were finally rejected, while the green ones (Tyrosine, Acetoacetate and IL-6) are the ones deemed important. B) Receiving receiver operation curve (ROC) of the Random Forest predictive model with an area under curve (AUC) of 0.93. involved in the aggressive phenotype of RA [36]. Finally, valine and factors that drive these phenotypes are still unknown. Epigenetic, leucine, that are branched-chain amino acids (BCAA), participate, in high biomechanical and anatomical factors have been described as contribu- concentrations, in ROS generation and inflammation through the acti- tors [48]. vation of NF-KB [37]. Of interest, leucine was decreased in the SF of our Several metabolites correlated with the expression of CD68 and inflammatory samples. The high concentration in tissue suggests a vimentin. Macrophages and fibroblasts are the most common types of probable intake of leucine during inflammation. Low concentrations of cells in the synovial membrane. In OA, macrophages play a key role in small leucine-rich proteoglycans were suggested to participate in the pathogenesis and progression of the disease [5]. We observed an pathogenesis of OA, activating innate immune inflammation and the increased expression of CD68 in samples with high synovitis scores, extracellular collagen network [38]. Of interest, amino acids such as especially in the lining. Activated macrophages have an increased glycine and lysine were described as anti-inflammatory metabolites glycolytic influx [49,50]. Interestingly, glucose and succinate in ST were [39–42]. This observation suggests an increased activity of this pathway negatively associated with CD68 expression suggesting a consumption of in an attempt to suppress inflammation in the joint. these metabolites by macrophages. Succinate in macrophages triggers Only a few metabolites had a significant correlation between SF and the production of IL-1β via hypoxia-inducible factor 1 (HIF1-α) [51,52]. ST. Acetate, formate, and valine had a negative correlation between SF In SF of RA patients, succinate was described to activate macrophages and ST. These data could reflect the consumption of these metabolites by through GRP91 receptor [51]. However, succinate was also proposed as cells in tissue. On the other hand, lactate, dimethylamine, and creatine triggering an anti-inflammatory response in macrophages via the same positively correlated between ST and SF pointing out to the activation of receptor [19,53]. pathways associated to energy metabolism such as glycolysis and the Finally, IL-6, tyrosine and acetoacetate in SF were the metabolites creatine phosphate pathway [43]. Since SF reflects changes from other that discriminated between low and high synovitis. IL-6 is a critical joint tissues involved in OA pathogenesis, these results argue with the proinflammatory cytokine in the inflamed synovium thus it is not sur- value of SF as biomarker of ST metabolic changes and emphasize the prising to find it as a biomarker of inflammation in SF. Tyrosine is re- need of conducting metabolomics in tissue to better understand meta- ported to be associated with macrophage activation by its bolic drivers of OA synovitis. phosphorylation in RA. Interestingly, phenylalanine, which is hydroxyl- Data on the relation between metabolites with synovial fibrosis is ated by phenylalanine 4-hydroxylase (PAH) to tyrosine, and tyrosine lacking. Fibrosis is associated to tissue damage which leads to cellular works closely with IL-6R which interacts with IL-6 [54]. In addition, reprograming of fibroblasts to myofibroblasts [44]. Myofibroblasts are tyrosine along with phenylalanine in serum has been implicated in the characterized by the secretion of collagen that contributes directly to the activation of the immune system in other diseases [55,56]. Finally, formation of fibriosis [44]. We did not phenotype the ST fibroblasts, but β-hydroxybutyrate and acetoacetate have been associated with inflam- interestingly, vimentin levels was associated with inflammation scores mation by modulating NLRP3 inflammasome, which has been associated and not with fibrotic scores, especially in the synovial sublining, sug- to OA pathogenesis [57,58]. gesting that fibrosis could be the end result of chronic inflammation as There were some limitations in our study. Surgical synovial samples has been described in other diseases [45]. were obtained from suprapatellar area (knee) and from the inferior Some metabolites such as proline and glycine were associated with capsular region (hip) and may not be representative for the entire sy- fibrosis in other diseases [46]. Together, glycine, proline, and hydroxy- novial membrane. However, the process of the sample and the histology proline (which is produced through posttranslational hydroxylation of criteria was performed following the recommendations by the Outcome collagen proline residues) constitute over half of all amino acids in the Measures in Rheumatoid arthritis Clinical Trials (OMERACT) and the primary structure of collagen protein [47]. We found lower concentra- EULAR Synovitis Study Group (ESSG) consensus [8]. In this work, we tions of proline in the group with high collagen. However, as most of the could not investigate the relationship between histologic and metab- metabolites that were decreased in fibrotic samples were upregulated in olomic changes with clinical data, or the effect of current treatment and the inflammatory samples, our understanding of what drives fibrosis is comorbidities on inflammation and metabolites. Future studies, with limited. Of interest, while most of the samples from knees showed high detailed description of clinical outcomes will help validate our results inflammation scores, the samples from hips were more fibrotic. The and determine the clinical relevance of the current findings. Lastly, our 8 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 study includes only end-stage OA samples from patients who underwent metalloproteinases, and other destructive and inflammatory responses in osteoarthritis, Arthritis Res. Ther. 8 (6) (2006) R187. joint replacement. The increased accessibility to ultrasound-guided or [4] J. Bondeson, A.B. Blom, S. Wainwright, C. Hughes, B. Caterson, W.B. van den Berg, arthroscopic synovial biopsies in the outpatient clinic with low adverse The role of synovial macrophages and macrophage-produced mediators in driving effect rate has allowed the characterization of synovia cells in RA. We inflammatory and destructive responses in osteoarthritis, Arthritis Rheum. 62 (3) hope to be able to apply the same techniques to obtain tissue from earlier (2010) 647–657. [5] Y. Chen, W. Jiang, H. Yong, M. He, Y. Yang, Z. Deng, et al., Macrophages in stages of OA and explore the metabolic alteration at different stages of osteoarthritis: pathophysiology and therapeutics, Am J Transl Res 12 (1) (2020) the disease. 261–268. In summary, IL-6, acetoacetate, and tyrosine in SF predicted high [6] H. Zhang, D. Cai, X. Bai, Macrophages regulate the progression of osteoarthritis, Osteoarthritis Cartilage 28 (5) (2020) 555–561. Krenn synovitis scores in ST. The study of metabolomics and inflamma- [7] V. Di Nicola, Degenerative osteoarthritis a reversible chronic disease, Regen Ther tory mediators in SF as predictors of inflammation in ST can help classify 15 (2020) 149–160. ST by synovial features and provide information on the potentially [8] A. Najm, B. Le Goff, C. Orr, R. Thurlings, J.D. Canete, F. Humby, et al., Standardisation of synovial biopsy analyses in rheumatic diseases: a consensus of activated pathways in the OA synovium. Further in-depth studies are the EULAR Synovitis and OMERACT Synovial Tissue Biopsy Groups, Arthritis Res. necessary to understand whether or not these changes are driven directly Ther. 20 (1) (2018) 265. from inflamed tissue or can help identify new therapeutic targets. [9] A.C. Stout, M.F. Barbe, C.B. Eaton, M. Amin, F. Al-Eid, L.L. Price, et al., Inflammation and glucose homeostasis are associated with specific structural features among adults without knee osteoarthritis: a cross-sectional study from the Authors contribution osteoarthritis initiative, BMC Muscoskel. Disord. 19 (1) (2018) 1. [10] J. Sellam, F. Berenbaum, The role of synovitis in pathophysiology and clinical symptoms of osteoarthritis, Nat. Rev. Rheumatol. 6 (11) (2010) 625–635. Supervision of overall project: MG. Conception of design: MG. Patient [11] A. Mobasheri, M.P. Rayman, O. Gualillo, J. Sellam, P. van der Kraan, U. Fearon, The recruitment: NEL, RM. Sample collection: RC, JDM-S. Acquisition of 1H role of metabolism in the pathogenesis of osteoarthritis, Nat. Rev. Rheumatol. 13 NMR data: JDM-S. Analysis: JDM-S, RC, CL, MG. Interpretation of results: (5) (2017) 302–311. [12] L. Zheng, Z. Zhang, P. Sheng, A. Mobasheri, The role of metabolism in chondrocyte JDM-S, RC, CL, MG. Drafting the manuscript: JDM-S. Revising the dysfunction and the progression of osteoarthritis, Ageing Res. Rev. 66 (2021), manuscript: JDM-S, RC, NEL, MG. All authors read and approved the final 101249. manuscript. [13] M. Guma, E. Sanchez-Lopez, A. Lodi, R. Garcia-Carbonell, S. Tiziani, M. Karin, et al., Choline kinase inhibition in rheumatoid arthritis, Ann. Rheum. Dis. 74 (7) (2015) 1399–1407. Declaration of funding [14] R. Garcia-Carbonell, A.S. Divakaruni, A. Lodi, I. Vicente-Suarez, A. Saha, H. Cheroutre, et al., Critical role of glucose metabolism in rheumatoid arthritis This work was supported by the National Institute of Health (NIH), fibroblast-like synoviocytes, Arthritis Rheumatol. 68 (7) (2016) 1614–1626. [15] S. Umar, K. Palasiewicz, M.V. Volin, B. Romay, R. Rahat, C. Tetali, et al., Metabolic United States of America (R01AR073324 to MG, National Institute of regulation of RA macrophages is distinct from RA fibroblasts and blockade of Health (NIH), United States of America Diversity Supplement to JDM-S, glycolysis alleviates inflammatory phenotype in both cell types, Cell. Mol. Life Sci. T32AR064194 to RC, P50 AA011999, P30 DK120515 and D34HP31027 78 (23) (2021) 7693–7707. [16] M.F. Bustamante, P.G. Oliveira, R. Garcia-Carbonell, A.P. Croft, J.M. Smith, to CL, UCSD School of Medicine Microscopy Core - National Institute of R.L. Serrano, et al., Hexokinase 2 as a novel selective metabolic target for Neurological Disorders and Stroke (NINDS), United States of America rheumatoid arthritis, Ann. Rheum. Dis. 77 (11) (2018) 1636–1643. P30 NS047101, and University of California San Diego (UCSD), United [17] E. Sanchez-Lopez, Z. Zhong, A. Stubelius, S.R. Sweeney, L.M. Booshehri, L. Antonucci, et al., Choline uptake and metabolism modulate macrophage IL-1beta States of America Clinical and Translational Science Award (CTSA), and IL-18 production, Cell Metabol. 29 (6) (2019) 1350–13562 e7. United States of America - UL1TR001442). CL was also supported by the [18] C. Hoyle, J.P. Green, S.M. Allan, D. Brough, E. Lemarchand, Itaconate and fumarate American Association for the Study of Liver Diseases (AASLD), United derivatives inhibit priming and activation of the canonical NLRP3 inflammasome in macrophages, Immunology 165 (4) (2022) 460–480. States of America Pinnacle Research Award in Liver Disease (8998 GA). [19] K.J. Harber, K.E. de Goede, S.G.S. Verberk, E. Meinster, H.E. de Vries, M. van Weeghel, et al., Succinate is an inflammation-induced immunoregulatory Role of funding source metabolite in macrophages, Metabolites 10 (9) (2020). [20] S. Abdelrazig, C.A. Ortori, M. Doherty, A.M. Valdes, V. Chapman, D.A. Barrett, Metabolic signatures of osteoarthritis in urine using liquid chromatography-high The funding did not have any role in the study design, in the collec- resolution tandem mass spectrometry, Metabolomics 17 (3) (2021) 29. tion, analysis and interpretation of data; in the writing of the manuscript; [21] K.J. Noordwijk, R. Qin, M.E. Diaz-Rubio, S. Zhang, J. Su, L.K. Mahal, et al., Metabolism and global protein glycosylation are differentially expressed in healthy or in the decision to submit the manuscript for publication. and osteoarthritic equine carpal synovial fluid, Equine Vet. J. 54 (2) (2022) 323–333. Declaration of competing interest [22] P. Akhbari, M.K. Jaggard, C.L. Boulange, U. Vaghela, G. Graca, R. Bhattacharya, et al., Differences in the composition of hip and knee synovial fluid in osteoarthritis: a nuclear magnetic resonance (NMR) spectroscopy study of metabolic profiles, All the authors declare no conflict of interest. Osteoarthritis Cartilage 27 (12) (2019) 1768–1777. [23] K. Tootsi, K. Vilba, A. Martson, J. Kals, K. Paapstel, M. Zilmer, Metabolomic signature of amino acids, biogenic amines and lipids in blood serum of patients with Acknowledgments severe osteoarthritis, Metabolites 10 (8) (2020). [24] Z. Huang, Z. He, Y. Kong, Z. Liu, L. Gong, Insight into osteoarthritis through Brendan Duggan from UCSD NMR facility of Skaggs School of Phar- integrative analysis of metabolomics and transcriptomics, Clin. Chim. Acta 510 (2020) 323–329. macy and Pharmaceutical Science for sample preparation and data [25] R. Altman, E. Asch, D. Bloch, G. Bole, D. Borenstein, K. Brandt, et al., Development acquisition. of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association, Arthritis Rheum. 29 (8) (1986) 1039–1049. Appendix A. Supplementary data [26] V. Krenn, L. Morawietz, G.R. Burmester, R.W. Kinne, U. Mueller-Ladner, B. Muller, et al., Synovitis score: discrimination between chronic low-grade and high-grade Supplementary data to this article can be found online at https synovitis, Histopathology 49 (4) (2006) 358–364. [27] C.M. Lopez De Padilla, M.J. Coenen, A. Tovar, R.E. De la Vega, C.H. Evans, ://doi.org/10.1016/j.ocarto.2022.100295. S.A. Muller, Picrosirius red staining: revisiting its application to the qualitative and quantitative assessment of collagen type I and type III in tendon, J. Histochem. References Cytochem. 69 (10) (2021) 633–643. [28] R. Lattouf, R. Younes, D. Lutomski, N. Naaman, G. Godeau, K. Senni, et al., Picrosirius red staining: a useful tool to appraise collagen networks in normal and [1] R.F. Loeser, J.A. Collins, B.O. Diekman, Ageing and the pathogenesis of pathological tissues, J. Histochem. Cytochem. 62 (10) (2014) 751–758. osteoarthritis, Nat. Rev. Rheumatol. 12 (7) (2016) 412–420. [29] A.R. Crowe, W. Yue, Semi-quantitative determination of protein expression using [2] A. Mathiessen, P.G. Conaghan, Synovitis in osteoarthritis: current understanding immunohistochemistry staining and analysis: an integrated protocol, Bio Protoc 9 with therapeutic implications, Arthritis Res. Ther. 19 (1) (2017) 18. (24) (2019) e3465. [3] J. Bondeson, S.D. Wainwright, S. Lauder, N. Amos, C.E. Hughes, The role of synovial macrophages and macrophage-produced cytokines in driving aggrecanases, matrix 9 J.D. Murillo-Saich et al. Osteoarthritis and Cartilage Open 4 (2022) 100295 [30] S.A.A. Showiheen, A.R. Sun, X. Wu, R. Crawford, Y. Xiao, R.M. Wellard, et al., [45] I. Chimenti, S. Sattler, G. Del Monte-Nieto, E. Forte, Editorial: fibrosis and Application of metabolomics to osteoarthritis: from basic science to the clinical inflammation in tissue pathophysiology, Front. Physiol. 12 (2021), 830683. approach, Curr. Rheumatol. Rep. 21 (6) (2019) 26. [46] R.B. Hamanaka, G.M. Mutlu, The role of metabolic reprogramming and de novo [31] R. Coras, A. Kavanaugh, T. Boyd, D. Huynh, K.A. Lagerborg, Y.J. Xu, et al., Choline amino acid synthesis in collagen protein production by myofibroblasts: implications metabolite, trimethylamine N-oxide (TMAO), is associated with inflammation in for organ fibrosis and cancer, Amino Acids 53 (12) (2021) 1851–1862. psoriatic arthritis, Clin. Exp. Rheumatol. 37 (3) (2019) 481–484. [47] K.H. Bradley, S.D. McConnell, R.G. Crystal, Lung collagen composition and [32] R. Volchenkov, M. Dung Cao, K.B. Elgstoen, G.L. Goll, K. Eikvar, O. Bjorneboe, et synthesis. Characterization and changes with age, J. Biol. Chem. 249 (9) (1974) al., Metabolic profiling of synovial tissue shows altered glucose and choline 2674–2683. metabolism in rheumatoid arthritis samples, Scand. J. Rheumatol. 46 (2) (2017) [48] M. Hall, M. van der Esch, R.S. Hinman, G. Peat, A. de Zwart, J.G. Quicke, et al., How 160–161. does hip osteoarthritis differ from knee osteoarthritis? Osteoarthritis Cartilage 30 [33] M.A. Neveu, N. Beziere, R. Daniels, C. Bouzin, A. Comment, J. Schwenck, et al., (1) (2022) 32–41. Lactate production precedes inflammatory cell recruitment in arthritic ankles: an [49] A.K. Jha, S.C. Huang, A. Sergushichev, V. Lampropoulou, Y. Ivanova, imaging study, Mol. Imag. Biol. 22 (5) (2020) 1324–1332. E. Loginicheva, et al., Network integration of parallel metabolic and transcriptional [34] R. Haas, J. Smith, V. Rocher-Ros, S. Nadkarni, T. Montero-Melendez, F. D'Acquisto, data reveals metabolic modules that regulate macrophage polarization, Immunity et al., Lactate regulates metabolic and pro-inflammatory circuits in control of T cell 42 (3) (2015) 419–430. migration and effector functions, PLoS Biol. 13 (7) (2015), e1002202. [50] A. Viola, F. Munari, R. Sanchez-Rodriguez, T. Scolaro, A. Castegna, The metabolic [35] V. Pucino, M. Certo, V. Bulusu, D. Cucchi, K. Goldmann, E. Pontarini, et al., Lactate signature of macrophage responses, Front. Immunol. 10 (2019) 1462. buildup at the site of chronic inflammation promotes disease by inducing CD4(þ) T [51] A. Littlewood-Evans, S. Sarret, V. Apfel, P. Loesle, J. Dawson, J. Zhang, et al., cell metabolic rewiring, Cell Metabol. 30 (6) (2019) 1055–10574 e8. GPR91 senses extracellular succinate released from inflammatory macrophages and [36] A. Torres, B. Pedersen, I. Cobo, R. Ai, R. Coras, J. Murillo-Saich, et al., Epigenetic exacerbates rheumatoid arthritis, J. Exp. Med. 213 (9) (2016) 1655–1662. regulation of nutrient transporters in rheumatoid arthritis fibroblast-like [52] G.M. Tannahill, A.M. Curtis, J. Adamik, E.M. Palsson-McDermott, A.F. McGettrick, synoviocytes, Arthritis Rheumatol. 74 (7) (2022) 1159–1171. G. Goel, et al., Succinate is an inflammatory signal that induces IL-1beta through [37] O. Zhenyukh, E. Civantos, M. Ruiz-Ortega, M.S. Sanchez, C. Vazquez, C. Peiro, et HIF-1 alpha, Nature 496 (7444) (2013) 238–242. al., High concentration of branched-chain amino acids promotes oxidative stress, [53] N. Keiran, V. Ceperuelo-Mallafre, E. Calvo, M.I. Hernandez-Alvarez, M. Ejarque, inflammation and migration of human peripheral blood mononuclear cells via C. Nunez-Roa, et al., SUCNR1 controls an anti-inflammatory program in mTORC1 activation, Free Radic. Biol. Med. 104 (2017) 165–177. macrophages to regulate the metabolic response to obesity, Nat. Immunol. 20 (5) [38] G.X. Ni, Z. Li, Y.Z. Zhou, The role of small leucine-rich proteoglycans in (2019) 581–592. osteoarthritis pathogenesis, Osteoarthritis Cartilage 22 (7) (2014) 896–903. [54] C. Blanchetot, N. De Jonge, A. Desmyter, N. Ongenae, E. Hofman, A. Klarenbeek, et [39] J. Cheng, J.C. Tang, M.X. Pan, S.F. Chen, D. Zhao, Y. Zhang, et al., l-lysine confers al., Structural mimicry of receptor interaction by antagonistic interleukin-6 (IL-6) neuroprotection by suppressing inflammatory response via microRNA-575/PTEN antibodies, J. Biol. Chem. 291 (26) (2016) 13846–13854. signaling after mouse intracerebral hemorrhage injury, Exp. Neurol. 327 (2020), [55] P. Sirnio, J.P. Vayrynen, K. Klintrup, J. Makela, T. Karhu, K.H. Herzig, et al., 113214. Alterations in serum amino-acid profile in the progression of colorectal cancer: [40] N. Zhang, X. Su, K. Xu, [Comparison of the application between circular stapler and associations with systemic inflammation, tumour stage and patient survival, Br. J. linear stapler in Billroth II(anastomosis of distal gastrectomy], Zhonghua Wei Cancer 120 (2) (2019) 238–246. Chang Wai Ke Za Zhi 21 (2) (2018) 201–205. [56] C. Murr, T.B. Grammer, A. Meinitzer, M.E. Kleber, W. Marz, D. Fuchs, Immune [41] Z. Zhong, M.D. Wheeler, X. Li, M. Froh, P. Schemmer, M. Yin, et al., L-Glycine: A activation and inflammation in patients with cardiovascular disease are associated novel antiinflammatory, immunomodulatory, and cytoprotective agent, Curr. Opin. with higher phenylalanine to tyrosine ratios: the ludwigshafen risk and Clin. Nutr. Metab. Care 6 (2) (2003) 229–240. cardiovascular health study, J. Amino Acids 2014 (2014), 783730. [42] N.K. Khanna, B.R. Madan, Anti-inflammatory activity of DL-valine, Indian J. Exp. [57] Y. Onizawa, T. Katoh, R. Miura, K. Konda, T. Noguchi, H. Iwata, et al., Acetoacetate Biol. 16 (7) (1978) 834–836. is a trigger of NLRP3 inflammasome activation in bovine peripheral blood [43] H. Taegtmeyer, J.S. Ingwall, Creatine–a dispensable metabolite? Circ. Res. 112 (6) mononuclear cells, Vet. Immunol. Immunopathol. 244 (2022), 110370. (2013) 878–880. [58] Y.H. Youm, K.Y. Nguyen, R.W. Grant, E.L. Goldberg, M. Bodogai, D. Kim, et al., The [44] A. Maglaviceanu, B. Wu, M. Kapoor, Fibroblast-like synoviocytes: role in synovial ketone metabolite beta-hydroxybutyrate blocks NLRP3 inflammasome-mediated fibrosis associated with osteoarthritis, Wound Repair Regen. 29 (4) (2021) inflammatory disease, Nat Med 21 (3) (2015) 263–269. 642–649. 10