Abstract Background Osteoarthritis (OA), the most prevalent joint disorder, is characterized by a complex etiology and a lack of safe and effective therapeutic interventions. Emerging evidence suggests that immune cell dysregulation plays a pivotal role in the pathogenesis of OA. Recent advancements in high-throughput sequencing technologies, along with the integration of machine learning into medical research, have provided novel insights into the molecular mechanisms underlying various diseases. However, the specific roles and mechanisms of immune-related factors in OA remain poorly understood. This study aims to identify potential biomarkers for the diagnosis and monitoring of OA progression and to explore targeted therapeutic strategies based on key genes associated with the disease. Results WGCNA and immune infiltration analysis identified SIK1 as a core gene involved in immune regulation during the progression of OA. In vitro experiments demonstrated that AICAR, an activator of SIK1, significantly suppressed inflammatory responses by modulating glucose and lipid metabolism in macrophages. A novel nanoliposome composite hydrogel, Gel@Lipo@AICAR, has been successfully developed for the targeted delivery of AICAR. The intra-articular administration of Gel@Lipo@AICAR demonstrated excellent biosafety and therapeutic potential in mitigating the progression of OA. Conclusions This study identifies SIK1 as a novel biomarker for diagnosing and monitoring the progression of OA. The anti-inflammatory effects of its agonist, AICAR, were validated, underscoring its role in reprogramming macrophage glucose and lipid metabolism. Furthermore, the development of Gel@Lipo@AICAR, a nanoliposome composite hydrogel, presents a promising therapeutic strategy for the treatment of OA. Graphical Abstract graphic file with name 12951_2025_3543_Figa_HTML.jpg [38]Open in a new tab Preparation of Gel@Lipo@AICAR and therapeutic efficacy in ACLT osteoarticular model mice Supplementary Information The online version contains supplementary material available at 10.1186/s12951-025-03543-3. Keywords: WGCNA, SIK1, AICAR, Macrophage metabolism, Nanoliposome composite hydrogel, Osteoarthritis Introduction Osteoarthritis (OA) is a degenerative joint disorder characterized by cartilage degradation, bone remodeling, and synovial inflammation [[39]1]. The pathogenesis of OA is multifactorial, involving a complex interplay of risk factors such as aging, obesity, genetic predisposition, acute joint injury, and chronic low-grade inflammation [[40]2–[41]5]. With the rapid advancement of high-throughput transcriptomic technologies, such as microarrays and RNA sequencing, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful tool for identifying potential therapeutic targets in complex diseases [[42]6–[43]8]. WGCNA is a systems biology approach that constructs scale-free networks by correlating gene expression profiles with clinical traits. This method enables the identification of co-expressed gene modules, the exploration of associations between gene networks and phenotypic traits, and the discovery of hub genes within these networks [[44]6–[45]8]. Furthermore, the integration of WGCNA with LASSO regression and immune infiltration analysis not only validates the expression of hub genes in disease contexts but also provides deeper insights into the underlying molecular mechanisms, thereby identifying immune cell populations and biomarkers relevant to early diagnosis and targeted therapy. Despite these advancements, there remains a critical lack of effective treatments capable of halting or reversing the progression of OA, underscoring the importance of exploring novel biomarkers for disease prevention, diagnosis, and therapeutic intervention. In clinical practice, pharmacological management remains the cornerstone of OA treatment, aimed at alleviating symptoms and improving joint function. However, conventional drug administration routes, such as oral and intravenous delivery, are often limited by low bioavailability and systemic side effects [[46]10]. Intra-articular drug delivery has emerged as a promising alternative, offering localized therapeutic effects while minimizing systemic exposure. Recent developments in tissue engineering have led to significant progress in the design of injectable hydrogels and liposomal drug delivery systems [[47]11, [48]12]. These advancements have culminated in the development of a novel drug delivery platform: biocompatible hydrogels encapsulating nanoscale liposomes. These liposomes, composed of phospholipid bilayers surrounding an aqueous core, provide effective protection against physiological degradation and extend the half-life of drugs. Moreover, their injectable nature enables sustained, site-specific drug release, thereby maximizing therapeutic efficacy [[49]13, [50]14]. This study aims to identify biomarkers associated with OA for disease prevention, diagnosis, and progression monitoring, as well as to explore therapeutic strategies targeting key genes. Differential gene expression (DEG) analysis was conducted utilizing microarray datasets from the Gene Expression Omnibus (GEO) database, augmented with clinical metadata. WGCNA was employed to identify co-expressed gene modules significantly associated with OA phenotypes. LASSO regression was subsequently applied to refine the candidate genes from the identified modules and DEGs, leading to the identification of Salt-inducible Kinase 1 (SIK1) as a pivotal hub gene. Single-sample gene set enrichment analysis (ssGSEA) was utilized to quantify immune cell infiltration within samples, highlighting macrophages as key immune cells relevant to OA pathogenesis and therapy. In vitro experiments were conducted to elucidate the anti-inflammatory mechanisms of acadesine (AICAR), a SIK1 activator, and its role in reprogramming glucose and lipid metabolism in macrophages. Finally, a nanoliposome composite hydrogel, Gel@Lipo@AICAR, was developed and validated for its therapeutic potential in the treatment of OA. Results Co-expression module construction and validation of hub genes A total of 46 DEGs (Table [51]S1) were identified through comparative analysis of the [52]GSE55457 and [53]GSE55235 datasets, which included transcriptomic profiles from 20 healthy controls and 26 OA patients. The DEGs were filtered using stringent thresholds (absolute log fold change|logFC| > 2; adjusted P < 0.05) (Fig. [54]1A), comprising 20 upregulated (red) and 26 downregulated (blue) genes (Fig. [55]1B). Functional enrichment analysis revealed that these DEGs were predominantly associated with inflammatory and immune regulatory processes. Gene Ontology (GO) analysis highlighted their involvement in leukocyte migration, cytokine activity, and chemokine-mediated signaling pathways (Fig. [56]1C). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further indicated enrichment in pro-inflammatory pathways, including those of IL-17, TNF-α, and NF-κB signaling, as well as Toll-like receptor and chemokine signaling cascades (Fig. [57]1D). These findings underscore the critical role of immune-inflammatory dysregulation in OA pathogenesis. Fig. 1. [58]Fig. 1 [59]Open in a new tab Construction of co-expression modules and validation of hub genes in osteoarthritis (OA). (A) Volcano plot of differentially expressed genes (DEGs) between healthy controls (n = 20) and OA patients (n = 26). Red/blue dots represent upregulated/downregulated genes (|logFC| > 2, adjusted P < 0.05). (B) Hierarchical clustering heatmap of DEGs across samples. (C) Gene Ontology (GO) enrichment analysis of DEGs, highlighting immune-inflammatory processes. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. (E) Topological overlap matrix (TOM) heatmap demonstrating scale-free network architecture (soft threshold power = 8). (F) Module-trait association heatmap correlating co-expression modules with OA phenotypes. (G) Module significance ranking, with the blue module showing the strongest association with OA. (H) Scatter plot of gene significance (GS) versus module membership (MM) in the blue module (R² = 0.82). (I) Venn diagram intersection of WGCNA module genes and DEGs (8 overlapping candidates). (J) LASSO regression coefficient profiles for hub gene selection. (K) SIK1 expression levels in training cohort (n = 10 per group). (L) ROC curve analysis of SIK1 diagnostic performance (AUC = 0.911). (M) Correlation heatmap between SIK1 expression and immune cell infiltration levels. (N) Violin plots comparing ssGSEA-derived immune scores between OA and control groups. Data presented as mean ± SD. Statistical significance: *, P < 0.05, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001 To systematically identify hub genes associated with OA, a weighted gene co-expression network was constructed using the WGCNA algorithm. A soft threshold power of 8 was selected to achieve a scale-free topology (Fig. [60]1E; scale-free fit R² = 0.88; Fig. [61]S1A-D). Hierarchical clustering of gene correlations identified 11 distinct co-expression modules (Fig. [62]1F). Among these, the blue module, which containing 30 genes exhibited the strongest correlation with OA phenotypes (Fig. [63]1G) and demonstrated high intramodular connectivity (gene significance [GS] = 0.5 vs. module membership [MM] = 0.8; Fig. [64]1H). Intersection analysis between WGCNA module genes and DEGs revealed eight overlapping candidates (Fig. [65]1I). LASSO regression further refined these to two potential hub genes: SIK1 and SLC2A3 (Fig. [66]1J). Receiver operating characteristic (ROC) curve analysis validated the diagnostic efficacy of SIK1 (AUC = 0.911; Fig. [67]1K-L), with consistent performance in the independent validation dataset [68]GSE82107 (AUC = 0.965; Fig. [69]S1E-F). Notably, SIK1 expression was significantly downregulated in OA patients compared to healthy controls. Supplementary analyses for SLC2A3 are provided in Figure [70]S1G-J. Based on these results, SIK1 was selected for subsequent experimental validation. A strong association has been observed between immune dysregulation and the severity of OA. Gene Set Enrichment Analysis (GSEA) has revealed that OA samples exhibit significantly elevated immune scores compared to control samples (Fig. [71]S1K-L). To delineate immune cell infiltration patterns, single-sample GSEA (ssGSEA) was performed on DEGs. Heatmap visualization (Fig. [72]1M) and immune score distributions (Fig. [73]S1L) demonstrated a pronounced infiltration of activated B cells, eosinophils, macrophages, and effector memory T cell subsets in OA patients. Correlation analysis (Fig. [74]1N) revealed that SIK1 expression was inversely correlated with regulatory T cells ( r = − 0.62, P < 0.001), neutrophils ( r = − 0.58, P < 0.001), and macrophages ( r = − 0.54, P < 0.001), suggesting its role in suppressing pro-inflammatory immune responses. These results collectively elucidate the cellular and molecular mechanisms underlying SIK1-mediated immunomodulation in the progression of OA. SIK1 activation by AICAR attenuates macrophage-mediated inflammation Increasing evidence suggests that immune regulation plays a significant role in the progression of osteoarthritis (OA), with macrophages being central to this process. During the development of OA, macrophages proliferate excessively and are abnormally recruited to the joints [[75]15]. The physiological functions of macrophage and their M1/M2 polarization are crucial in the precise regulation of inflammation and tissue damage repair during the development of OA [[76]15]. In the present study, based on WGCNA, we identified SIK1 as a key gene involved in the pathogenesis of immune regulation, particularly in macrophages associated with OA. Considering that salt-inducible kinases (SIKs) belong to the AMP-activated protein kinase (AMPK) family [[77]17], we employed the AMPK activator AICAR to explore and verify the cellular and molecular mechanisms by which SIK1 acts as a biomarker in immune-regulated inflammatory microenvironments that involve macrophages. We initially verified that AICAR could significantly activate the expression of SIK1 at both the gene and protein levels. The results indicated that the level of SIK1 expression displayed a distinct dependency on AICAR drug concentration, meaning that as the concentration of AICAR increased, the expression of SIK1 was markedly elevated (Fig. [78]2A-C). This finding established the foundation for the subsequent use of AICAR as an activator of SIK1. The scratch assay results indicated that inflammatory conditions could effectively trigger macrophage activation and migration. However, the SIK1 activator AICAR significantly reduced cellular wound closure, thereby decreasing the migration of inflammatory macrophages (Fig. [79]2D-E). The AICAR was demonstrated to reduce phagocytosis by activated macrophages in neutral red phagocytosis assays (Fig. [80]2F). The transwell assay (Fig. [81]2G-H) indicated that the cell migration rate in the AICAR group was significantly lower than that in the LPS group. Comparable results were observed in the Edu proliferation assay, where there were notably fewer newly proliferating cells (green) in the AICAR group than in the LPS group (Fig. [82]2I-J). Cytoskeleton staining revealed that the control cells displayed well-defined contours and uniform elliptical-shaped structures. In contrast, LPS-induced M1 macrophages displayed distinct phenotypic characteristics, including enlarged, spindle-shaped, fibrous, or dendritic features, alongwith prominent nuclei. Upon exposure to AICAR, the spindle-shaped, polarized cells underwent a gradual contraction, resulting in their morphological transformation into an elliptical shape. This finding suggests that AICAR may influence the morphometric polarization of macrophages towards the M1 phenotype under inflammatory conditions (Fig. [83]S2). Initial experiments revealed that the proliferation, migration, infiltration, and phagocytosis of macrophages within the inflammatory environment were significantly suppressed by the SIK1 activator AICAR. Fig. 2. [84]Fig. 2 [85]Open in a new tab Pharmacological activation of SIK1 by AICAR attenuates macrophage-driven inflammation. (A) Dose-dependent upregulation of SIK1 mRNA expression in macrophages treated with AICAR (0, 2.5, 5, 10 µM; 24 h). (B, C) Representative Western blot and densitometric quantification of SIK1 protein levels normalized to GAPDH. (D, E) Scratch assay images and quantified wound closure rates at 24 h post-treatment (scale bar: 500 μm). (F) Phagocytic capacity assessed by neutral red uptake (absorbance at 500 nm). (G, H) Transwell migration images and cell counts per field (scale bar: 100 μm). (I, J) EdU proliferation assay images (green: proliferating cells; blue: DAPI) and quantification of EdU^+ cells (scale bar: 100 μm). (K, L) Representative fluorescent images and quantified ROS levels using DCFH-DA probe (scale bar: 100 μm). (M) Nitric oxide (NO) concentration in cell supernatants measured by Griess assay. (N) Related mRNA expression levels of inflammatory mediators (IL-1β, IL-6, IL-23, IL-17, TNF-α, and iNOS). Data represent mean ± SD of three independent biological replicates. Statistical significance: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (one-way ANOVA with Tukey’s post-hoc test) In the pathogenesis of OA, inflammatory factors and mediators released by activated macrophages exacerbate the immune response and impact tissue metabolism and joint cavity function [[86]18, [87]19]. Reactive oxygen species (ROS), an inflammatory mediator, plays an important role in regulating macrophage polarisation during chronic inflammation [[88]20]. We utilized fluorescent probes to assess the expression levels of ROS in various cell groups and discovered that, in contrast to the LPS group, the fluorescent signals from AICAR-treated cells were diminished, indicating a notable reduction in ROS expression (Fig. [89]2K-L). Concurrently, NO, a signaling molecule indicative of inflammatory activity [[90]21], exhibited a significant decline in the supernatants of AICAR-treated cells (Fig. [91]2M). The RT-qPCR results indicated that IL-1β, a pivotal initiator of the inflammatory response, and IL-6, a synergistic signaling molecule, were markedly activated, resulting in an exponential increase in the expression of related RNAs in the macrophages of the LPS group. AICAR could reverse this increased expression (Fig. [92]2N). The same trend was observed for the pro-inflammatory signaling molecules IL-17 and IL-23, which are capable of recruiting various types of immune cells, and the inflammatory factors TNF-α and iNOS, which exert powerful destructive effects on tissue cells. In contrast, within the AICAR treatment group, the expression of these pro-inflammatory factors was reduced to varying extents. These outcomes indicated that AICAR (the SIK1 activator) could effectively reduce macrophage migration and infiltration, suppress macrophage M1 polarization, and decrease the expression of relevant inflammatory mediators and factors, thereby mitigating the inflammatory response. AICAR modulates macrophage inflammatory responses through glycolipid metabolic reprogramming To further investigate the potential mechanism by which AICAR alleviates macrophage inflammation, we conducted untargeted metabolomics analyses on metabolites from control and AICAR-treated groups under inflammatory conditions. Utilizing public databases such as HMDB ([93]http://www.hmdb.ca/) and BMRB ([94]http://www.bmrb.wisc.edu/), two major groups of metabolites identified by NMR were classified (Fig. [95]3A). Principal Component Analysis (PCA) serves as a pattern recognition method to differentiate between various groups in an unsupervised manner. The results indicated that the control and AICAR-treated groups were distinctly separated in the PCA scatterplot (Fig. [96]3B). A combination of Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) (Fig. [97]3C) and S-plot analysis (Fig. [98]3D) was employed to identify and characterize metabolites, with potentially differentiated metabolites being screened using a t-test (p < 0.05) and a variable importance in projection (VIP) value (> 1). Significant alterations in the intracellular levels of eight metabolites were noted in AICAR-treated cells compared to the control group, specifically: Glutamine, Succinate, Pyruvate, 1,3 -Bisphosphoglycerate, Choline, Phenylalanine, GPCc, and PCc (Fig. [99]3E-L). Pyruvate, the end product of glycolysis, serves as the primary functional substance in glucose metabolism under aerobic conditions. Succinate, an intermediate product of the tricarboxylic acid cycle, primarily contributes to the oxidative energy supply post-glycolysis and is involved in the β-oxidation of fatty acids, leading to the complete oxidation of acetyl coenzyme A. Likewise, 1,3-Bisphosphoglycerate is an intermediate metabolite in glycolysis [[100]22]. All these metabolite substances exhibited varying degrees of reduction upon stimulation by AICAR. In contrast, choline metabolites such as Choline, GPCc, and PCc significantly increased in the AICAR group. A similar trend was observed for the amino acid metabolites Glutamine and Phenylalanine. It was also noted that these differential metabolites were predominantly enriched in pathways, including the Glucose-Alanine Cycle, Phosphatidylcholine Biosynthesis, Alanine Metabolism, Warburg Effect, and Citric Acid Cycle (Fig. [101]3M). Metabolic pathway analysis (MetPA) revealed that the primary pathways involved in AICAR’s regulation of macrophage metabolis include Alanine, aspartate, and glutamate metabolism, the Citrate cycle (TCA cycle), Glycerophospholipid metabolism, as well as Phenylalanine, tyrosine, and tryptophan biosynthesis (Fig. [102]3N). As previously mentioned, metabolomics has revealed that differential metabolites are primarily concentrated in glucose metabolism and glycerophospholipid metabolism pathways. This suggested that AICAR’s role in modulating the macrophage inflammatory response may be achieved by regulating the reprogramming of glycolipid metabolism. Fig. 3. [103]Fig. 3 [104]Open in a new tab Metabolomic analysis and associated differential metabolites. (A) Metabolomic Analysis Process. (B) PCA scores plot, (C) OPLS scores plot, (D) S-plot derived from cell treated with AICAR. (E-L) Differential metabolites after AICAR treatment. (M) Metabolite enrichment analysis. (N) Metabolic pathway analysis. Data represent mean ± SD of three independent biological replicates. Statistical significance: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (one-way ANOVA with Tukey’s post-hoc test) Fabrication and characterization of gel@lipo@aicar Intra-articular injection of therapeutic drugs is an effective strategy for OA treatment. The form of the drug carrier in local administration dictates the drug release profile and the frequency of intra-articular injections [[105]23]. To counteract the drawbacks associated with administering the drug alone, we developed an injectable biomaterial, Gel@Lipo@AICAR, which possesses drug-loading and controlled-release capabilities, and we validated the potential application of AICAR in OA treatment. The characterization results for Gel@Lipo@AICAR are presented in Fig. [106]4. The transmission electron microscope (TEM) image of the Lipo@AICAR nanoparticles in Fig. [107]4A shows that the size of the vesicle-like particles ranges from approximately 60 to 120 nm. The particle size distribution results presented in Fig. [108]4B indicated that the average particle size for both Liposome and Lipo@AICAR was around 100 nm, which was consistent with the observations from the TEM. In Fig. [109]4C and Fig. [110]S3A-B, the average zeta potential of Lipo@AICAR was approximately − 50 mV, comparable to the − 56 mV of liposome particles. It was demonstrated that drug loading did not significantly affect the particle size and zeta potential of liposome particles. The full-wavelength scanning of the UV spectrum revealed that drug AICAR and Lipo@AICAR had two closely aligned peaks. The results of Fig. [111]4D provided evidence for the successful loading of AICAR into liposomes. Figure [112]4E showed that the thermoresponsive Gel@Lipo@AICAR were in a solution state at 4 °C but transformed into a gel state at 37 °C, and had excellent injectability, paving the way for subsequent successful joint cavity injections. Rheological characterization was conducted to investigate the gel transition properties, confirming the gelling properties of the Gel and Gel@Lipo@AICAR. The results revealed that initially, the elastic modulus (G’) of the hydrogels was lower than the viscous modulus (G’’). However, the crossover of elastic modulus and viscous modulus indicated that the sol-gel transition occurred at 18 °C (the critical temperature). Finally the elastic moduli for hydrogels were 10 times larger than the viscous moduli, substantiating the formation of stable hydrogels (Fig. [113]4F-G). The surface of the scanning electron microscope (SEM) images indicated that the lyophilized hydrogel Gel and Gel@Lipo@AICAR exhibited a highly interconnected porous network structure, with pore sizes ranging from 20 to 80 μm (Fig. [114]4H-I). 500mM Lipo@AICAR was doped into a hydrogel to investigate the drug release profile of AICAR. The loading efficiency of AICAR in liposomes was first characterized using High Performance Liquid Chromatography (HPLC). Based on the standard curve, the loading efficiency of AICAR in liposomes can be calculated to be approximately 52.5% (Fig. [115]S3B). Meanwhile, it was evident from the release profile of the AICAR hydrogel that the drug release experienced an explosive stage in the first week. Subsequently, the curve tends to flatten, and the drug release gradually decreases, entering a relatively stable phase that lasts until nearly one month (Fig. [116]4J). Fig. 4. [117]Fig. 4 [118]Open in a new tab Characterisation of Lipo@AICAR and Gel@Lipo@AICAR. (A) TEM image for the Lipo@AICAR. Scale bars: 100 nm (B) Particle size distributions of the Liposomes and Lipo@AICAR. (C) The Zeta potential for the liposomes and Lipo@AICAR particles. Data are represented as means ± SD of three replicate experiments. (D) Full-wavelength scanning of the UV spectrum for AICAR and Lipo@AICAR. (E) Thermo-responsiveness and injectability of Gel and Gel@Lipo@AICAR. (F-G) Variation of elastic modulus (G’) and viscous modulus (G’’) of Gel and Gel@Lipo@AICAR plotted as a function of the temperature. (H-I) SEM image for the Gel and Gel@Lipo@AICAR. Scale bars: left: 50 μm and right: 20 μm. (J) Drug release profile of Gel@Lipo@AICAR In vivo biocompatibility and biosafety of gel@lipo@aicar To evaluate the therapeutic effect of the hydrogel in vivo, we first verified that Gel implantation alone had no significant effect on articular cartilage, synovium, or joint mobility in mice (Fig. [119]S4). Subsequently, we established an anterior cruciate ligament transection model and injected the hydrogels into the joints post-surgery. Gait analysis was performed on mice after intra-articular injection with hydrogels to assess knee motion. As shown in Fig. [120]5A, a screenshot of the gait for each group of mice was presented to demonstrate that knee motion was not significantly affected in any group. Simultaneously, the right hind (RH) limb that had been intra-articularly injected with hydrogels was analyzed independently. The parameters indicating the right hind limb functions, including stand time, break time, average stride length, average print area, and median foot pressure, were obtained (Fig. [121]5B-F). The aforementioned parameters showed no significant differences among the samples. The gait analysis results indicated that intra-articular injection of hydrogels would not affect knee motion. Subsequently, HE staining was performed to evaluate the biocompatibility of Gel@Lipo@AICAR in vivo (Fig. [122]5G). The staining results indicated that the hydrogel did not cause any significant harm for liver and kidney organs or tissues, thus proving its non-toxic effect in the body. Cell viability and cytoskeletal integrity assessments demonstrated the biocompatibility of hydrogel materials. Supplementary Figure S5A and S5C revealed > 90% cell survival rates after 24-hour co-culture, with preserved cellular morphology observed through cytoskeletal staining under normal culture conditions (Fig. [123]S5B). The hemolysis assay demonstrated that all hydrogel groups maintained normal erythrocyte morphology (biconcave disc shape) without any hemolytic activity, as evidenced by microscopic examination and comparison with Triton X-100 positive controls. (Fig. [124]S5D-E). Hematological parameters, including white blood cells (WBC), red blood cells (RBC), hemoglobin (HGB), and platelets (PLT), as well as biochemical indices such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood creatinine (CREA), and blood urea (UREA), were all within the normal range across all groups. (Fig. [125]5H-O). These phenomena further confirmed the excellent in vivo biocompatibility and safety of Gel@Lipo@AICAR. Fig. 5. [126]Fig. 5 [127]Open in a new tab In vivo biocompatibility and biosafety of Gel@Lipo@AICAR. (A) Representative gait screen shot of each group mice. (B) Quantification of right hind (RH) average stance time. (C) Quantification of right hind (RH) average break time. (D) Quantification of right hind (RH) stride length. (E) Quantification of right hind (RH) average print area. (F) Quantification of right hind (RH) average median foot pressure. (G) Staining of HE sections of mice liver and kidney. Scale bar 100 μm. Data are represented as means ± SD of six replicate experiments. (H-O) Serum levels of biomarkers reflecting liver and kidney function (ALT, AST, CREA and UREA) and haematological parameters (WBC, RBC, HGB, PLT) in mice. Sham group: mouse joint cavity injection with phosphate-buffered saline (PBS), ACTL group: anterior cruciate ligament transection model in mice, Gel group: Simple hydrogel injection into the joint cavity after ACLT model in mice; Gel@Lipo group: Hydrogel-carried blank liposomes injection into the joint cavity after ACLT model in mice; Gel@Lipo@AICAR group: Hydrogel-carried AICAR-loading liposomes injection into the joint cavity after ACLT model in mice. Data are represented as means ± SD of six replicate experiments Intra-articular injection with gel@lipo@aicar attenuate cartilage destruction Histological analyses (H&E and Safranin-O/fast Green) revealed preserved cartilage architecture in Gel@Lipo@AICAR-treated groups, with no evidence of progressive cartilage degradation typical in early-stage osteoarthritis pathogenesis [[128]24]. For HE staining, the Gel@Lipo@AICAR hydrogel-injected group exhibited less cartilage erosion, and the ratio of hyaline cartilage (HC) to calcified cartilage (CC) served as a more intuitive indicator of the extent of osteoarticular cartilage destruction and degeneration [[129]25]. Compared with the other ACLT groups, the hydrogel group showed a certain degree of reversal in the elevated HC/CC ratio, indicating that the hydrogel effectively alleviated the cartilage wear and tear caused by the ACLT model (Fig. [130]6A and F). The uniformity of the stained cartilage tissue is also one of the essential indicators for evaluating the degradation degree of cartilage in the OA model. Histological analysis revealed that Gel@Lipo@AICAR-treated joints maintained smooth and continuous cartilage architecture with homogeneous ECM staining, contrasting with ACLT-induced articular cartilage disorganization and heterogeneous ECM coloration (Fig. [131]6B). The relative content of glycosaminoglycans (GAG) statistically responsible for regulating cartilage synthesis and delaying cartilage degradation showed the same trend based on the above staining (Fig. [132]6G). OARSI scores, a severity score used to evaluate osteoarthritis, were shown in Fig. [133]6H. Both the Gel@Lipo@AICAR and Sham groups exhibited lower scores compared to the other treatment groups, demonstrating the favorable therapeutic effect of the Gel@Lipo@AICAR group in slowing the progression of OA. The balance between anabolic and catabolic metabolism of the cartilage matrix is crucial for maintaining cartilage homeostasis [[134]26]. Immunofluorescent staining of knee specimens was utilized to evaluate metabolic homeostasis within the cartilage. The Gel@Lipo@AICAR group demonstrated the most favourable outcome in terms of the upregulation of COL2A1 (red) (Fig. [135]6C and I) and a substantial decrease in MMP13 (green) (Fig. [136]6D and J) compared to the ACTL groups. Immunofluorescence staining of the marker SIK1 (Fig. [137]6E and K) was decreased in the ACTL group and increased in the Gel@Lipo@AICAR group further confirming the release of the SIK1 agonist AICAR for the treatment of OA by enhancing cartilage anabolism and inhibiting catabolism. Fig. 6. [138]Fig. 6 [139]Open in a new tab Intra-articular injection with Gel@Lipo@AICAR attenuate cartilage destruction. (A) Representative images of Hematoxylin-eosin (HE) staining. Scale bar: 50 μm (B) Safranin O-fast green (SOFG) staining of mouse knee joints. Scale bar: top 100 μm; bottom 50 μm. (C-E) Immunofluorescence staining images for COLIIA1, MMP13 and SIK1 proteins. Scale bar: 50 μm. (F) The ratio of hyaline cartilage (HC) and calcified cartilage (CC) of articular cartilage. (G) Quantitative analysis of relative glycosaminoglycan (GAG) content. (H) Osteoarthritis Research Society International (OARSI) Modified Mankin scores of articular cartilage in each group. (I-K) Quantitative analysis of Immunofluorescence staining of MMP13, COLIIA1 and SIK1 proteins. Data represent mean ± SD of six independent biological replicates. Statistical significance: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (one-way ANOVA with Tukey’s post-hoc test) Gel@Lipo@AICAR relief synovitis in OA Accumulating evidence suggests that synovial inflammation is correlated with the pathogenesis and progression of OA [[140]27]. Macrophage accumulation in the intimal lining, reflecting mostly proliferative synovial tissue, is the principal morphological characteristic of synovitis [[141]28]. Similarly, H&E (Fig. [142]7A) and Masson staining (Fig. [143]7B) were performed to evaluate the effect of Gel@Lipo@AICAR hydrogel on the synovial membrane. Masson’s trichrome staining revealed progressive cartilage degeneration in ACLT joints, characterized by synovial hyperplasia with inflammatory cell infiltration and fibrosis (increased red-stained collagen deposition), which correlated with cartilage structural deterioration. Meanwhile, in the Gel@Lipo@AICAR group, these degrees of lesions were attenuated to varying degrees, and the total synovitis score, which reflects indicators of the degree of synovial cellular, vascular, and fibrous tissue proliferation, showed the same results (Fig. [144]7E). Macrophages within the synovium dictate the expression of inflammatory markers during synovitis. The polarization of M1 macrophages worsens disease progression [[145]29]. To this end, immunofluorescence staining was conducted to investigate macrophage accumulation and phenotypic characteristics in OA synovial tissue. Quantitative immunohistochemistry revealed elevated F4/80 + M1 macrophages in ACLT synovium (vs. controls), indicative of synovial M1 macrophage polarization, while Gel@Lipo@AICAR treatment markedly reduced these inflammatory parameters, correlating with quantitative fluorescence intensity analysis (Fig. [146]7C and F). In contrast, CD206 + M2 macrophage levels remained downregulated with no significant differences between OA model and Gel@Lipo@AICAR-treated groups, suggesting M1 macrophage infiltration predominated during OA progression. AICAR alleviated inflammation through proliferation inhibition, chemotaxis suppression, and pro-inflammatory cytokine reduction, but did not modulate M1/M2 balance (Fig. [147]S6). Elevated SIK1 expression was also observed in the synovial tissue of the Gel@Lipo@AICAR group (Fig. [148]7D and G), demonstrating the role of the SIK1 agonist AICAR in OA synovitis. Fig. 7. [149]Fig. 7 [150]Open in a new tab Gel@Lipo@AICAR relief synovitis in OA. (A) Representative picture of HE staining of synovium. Scale bar: 100 μm. (B) Masson staining of synovium. Scale bar: 100 μm. (C) Immunofluorescence staining images for F4/80 mark in synovium.Scale bar: 100 μm. (D) Immunofluorescence staining images for SIK1 proteins in synovium. Scale bar: 100 μm. (E) Total synovitis score for each synovial group according to the degree of proliferation of synovial cells, blood vessels and fibrous tissue. (F-G) Quantitative analysis of Immunofluorescence staining of F4/80 mark and SIK1 proteins. Data represent mean ± SD of six independent biological replicates. Statistical significance: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (one-way ANOVA with Tukey’s post-hoc test) Discussion As a member of the AMP-activated protein kinase (AMPK) family, salt-inducible kinase 1 (SIK1) orchestrates pleiotropic cellular processes through serine/threonine phosphorylation events, spanning from inflammatory resolution to metabolic adaptation [[151]30, [152]31]. Crucially, our study reveals that pharmacological activation of SIK1 by 5-aminoimidazole-4-carboxamide ribonucleoside (AICAR) establishes a novel therapeutic paradigm for OA. This connection operates through three synergistic mechanisms: First, SIK1 serves as a molecular rheostat for macrophage polarization. While prior studies showed SIK1 inhibition facilitates CRTC3 dephosphorylation to drive M2 polarization, we demonstrate that AICAR-mediated SIK1 activation conversely suppresses M1 polarization (↓IL-1β/IL-6/TNF-α). This bidirectional regulation suggests SIK1 activity thresholds determine macrophage fate. Second, phosphorylation at Ser577 enables SIK1 to function as a metabolic sensor [[153]33–[154]35]. Our metabolomics data reveal that AICAR-activated SIK1 coordinates glycolipid reprogramming - suppressing glycolysis (↓pyruvate ) while enhancing phospholipid synthesis (↑PCc ). Third, the SIK1-AICAR axis exhibits a dual role in joint protection: it reduces synovial M1 infiltration and it maintains the balance between cartilage anabolism and catabolism. Notably, while SIK1’s role in bone remodeling via CRTC1-CREB-Id1 and osteoclastogenesis is established [[155]35, [156]36], our work reveals AICAR-activated SIK1 as a gatekeeper of macrophage immunometabolism in OA. This finding expands the therapeutic scope of AMPK activators beyond their classical metabolic roles. Macrophage metabolic reprogramming can elucidate the effects of key metabolic pathways on the regulation of different polarisation states and related functions of macrophages [[157]37]. Numerous studies have shown that changes in cellular immune metabolism may be an important factor in regulating macrophage polarization [[158]38]. The metabolic pathways involved in cellular immune metabolism mainly include glycolysis, tricarboxylic acid (TCA) cycle, fatty acid metabolism and amino acid metabolism. For instance, it was demonstrated that efficient glycolysis (or the Warburg effect) is maintained within LPS-activated M1-type macrophages, Pyruvate kinase M2 regulates HIF1α activity and IL-1β induction and is a critical determinant of the Warburg effect in LPS-activated macrophages [[159]40, [160]41], while glycolysis during inflammatory responses promotes the production of cellular inflammatory factors and maintains the cellular polarity of M1-type macrophages [[161]42, [162]43]. In the tricarboxylic acid (TCA) cycle, succinic acid, as an intermediate metabolite of the TCA cycle, is capable of responding to LPS stimulation, which can mediate the intra-mitochondrial transition from ATP synthesis to mitochondria-derived ROS (mROS) production, driving the inflammatory response and thus further promoting the progression of macrophages towards pro-inflammatory state [[163]44, [164]45]. Similarly in fatty acid metabolism, it has been shown that Mitochondrial STAT3 exacerbates LPS-induced macrophage polarisation by driving CPT1a-mediated fatty acid oxidation [[165]46]. Changes in metabolites of these pathways are closely related to the inflammatory immune response function of macrophages, further revealing the complex interactions between metabolic reprogramming and immune responses [[166]47]. In the inflammation model in this study, along with the activation of SIK1, AICAR not only effectively inhibited macrophage glycolytic activity and thus reduced metabolites such as pyruvate and 1,3-dihydroxyacetone in the glycolytic pathway, but also significantly reduced the expression of cellular inflammatory factors such as IL-1β, IL-6 and TNF-α. Similarly, from the experimental results, the addition of AICAR avoided the accumulation of reactive oxygen species in the mitochondrial oxidative stress as well as the high accumulation of succinic acid in the tricarboxylic acid cycle, thus slowing down the inflammatory process. The significant attenuation of macrophages from a pro-inflammatory state was mediated by the addition of AICAR, which was also reflected in increased choline synthesis alterations. It is undeniable that choline improves the utilisation of fatty acids and prevents abnormal fat accumulation and choline uptake is critical for phospholipid remodelling and for maintaining macrophage metabolism under inflammatory conditions [[167]48]. In addition it has been shown that glutamine metabolism may promote macrophage M2-type polarisation through α-ketoglutarate-mediated alteration of macrophage epigenetic pathways [[168]49], which is also reflected in changes in the levels of the differential metabolite glutamine in AICAR-regulated macrophage polarization. From the above results, it can be inferred that AICAR may exert an anti-inflammatory effect by altering the reprogramming of macrophage glucose and lipid metabolism to regulate macrophage polarization. OA is primarily treated clinically with the systemic or local administration of analgesics or anti-inflammatory drugs to alleviate symptoms [[169]50]. Local intra-articular drug injection is an effective method, losses occur rapidly and injections are frequent [[170]51]. Consequently, injectable biomaterials with drug-carrying and controlled-release properties, particularly liposomes with amphiphilic drug delivery carriers and biomimetic injectable hydrogels, have garnered significant interest in the treatment of OA. Hydrogel drug delivery systems can take advantage of the beneficial therapeutic effects of drug delivery and are already in clinical use. Hydrogels can spatially and temporally control the release of various therapeutic drugs, including small molecule drugs, large molecule drugs, and cells [[171]52]. Lipid-based spherical vesicle systems offer excellent advantages in drug delivery systems for small molecules, peptides, genes and monoclonal antibodies due to their superior biodegradability and biocompatibility [[172]53]. For example: injectable recombinant block polymer gel for sustained delivery of therapeutic protein in post-traumatic OA [[173]54], photocrosslinking spherical hydrogel-encapsulated targeting peptide-modified engineered Exo (W-Exo@GelMA) exhibit notable potential in OA therapy [[174]55]. Previous studies showed that thermosensitive nanocomposite hydrogel and hydrogel microspheres loaded with drug has been developed for anti-inflammatory effects and cartilage protection in the treatment of OA [[175]56–[176]58]. Such evidences strongly support the extraordinary benefits of injectable hydrogel drug delivery systems in the treatment of OA. Nevertheless, considering the disadvantages of injecting drugs into the joints, the liposome containing AICAR, doped into a temperature-sensitive injectable hydrogel (Gel@Lipo@AICAR), was synthesized using a mild and non-toxic method. This method facilitates the protection of the protein activity of biomolecules (e.g., certain fragile proteins or drugs) from physiological deterioration, allowing for prolonged drug release through local injection. Meanwhile, the hydrogel skeleton protects the integrity of the liposomes, improves their stability, and is suitable for the local administration of the drug, which extends the range of the application. The temperature responsive liposome hydrogel makes the drug delivery system more intelligent, convenient and controllable. Limitation This study still has several limitations. Our findings indicate AICAR mediates anti-inflammatory effects via macrophage glucose/lipid metabolic reprogramming, but its regulatory role and underlying pathway in macrophage polarization remain unclear. Furthermore, this study identifies SIK1 as a novel biomarker for OA progression and validates the anti-inflammatory efficacy of its targeted agonist AICAR both in vitro and in vivo. However, our findings are confined to preclinical models, clinical validation is required to establish correlations between SIK1 expression dynamics in OA synovium and disease progression, thereby elucidating SIK1’s pathological role in OA pathogenesis. Conclusion Based on WGCNA and immune infiltration analysis, we explored the significant significance of SIK1 as a novel marker in the prevention and diagnosis of osteoarthritis, and revealed the molecular mechanisms played in and agonist AICAR in the regulation of macrophage immune infiltration and reprogramming of glucose and lipid metabolism. Eventually, a liposomal hydrogel drug delivery system carrying the SIK1 activator AICAR was successfully prepared, providing a promising therapeutic option for OA treatment. Materials and methods Data downloading and differentially expressed gene (DEG) Access the Gene Expression Omnibus (GEO) database to download OA and normal specimen data, transform the probe matrix into a gene expression matrix by annotation, batch correct and merge [177]GSE55457 and [178]GSE55235 data to obtain the training dataset sets and [179]GSE82107 as the validation data sets. Both of sets were analysed using the limma package with the filter conditions:|logFC|> 2 and adj.P.Val < 0.05 for DEGs (shown in Table [180]S1). GO and KEGG enrichment analysis All DEGs were mapped to the “clusterProfiler” R package, and searched for significantly enriched GO and KEGG pathways at p < 0.05 level. Visualisation of biological processes (BP), cellular components (CC) and molecular functions (MF) in GO term analysis after definition. Weighted co-expression network construction The “WGCNA” R package was used to construct Weighted co-expression network. First extract expression data of corrected DEGs and remove missing values. Calculate the distance between genes using Pearson correlation coefficient and perform clustering. Set fit index and the Optimal soft threshold and verify its accuracy and convert the adjacency matrix into a topological overlap matrix (TOM). Configure the minimum number of genes per gene network module to 60 and then identify and merge the modules according to the hierarchical clustering. Calculate MM values (correlation between genes and modules) and GS values (importance of genes) and set screening conditions (MM = 0.8, GS = 0.5) to choose core genes for the most relevant modules for clinical traits. LASSO regression analysis Co-expression network of modular core genes intersected with DEGs to obtain key genes for osteoarthritis disease. Lasso regression models are constructed using the glmnet package, and genes characteristic of the disease are output after cross-validating. The signature genes were variance analysed separately and the receiver operating characteristic (ROC) curves were plotted to validate the accuracy of the genes to diagnose osteoarthritic in the training and validation datasets. Immune infiltration analysis The gene set enrichment score (GSE) was generated by utilizing single-sample Gene Set Enrichment Analysis (ssGSEA) to rank the expression of all genes in DEGs in descending order, calculating and weighting the scores, and then intersecting score with the expression of the disease signature genes for difference and correlation analysis. Data from DEGs were pooled with immune gene sets using the enrichplot package for analysis. The top five highest scoring gene collections for both groups were displayed by defining enrichment scores < 0 for the control group and OA > 0 (FDR < 0.25, adjusted p < 0.05 and|NES| > 1 was considered significant). Materials and chemicals RAW 264.7 macrophage cell lines were obtained from Beyotime Biotechnology. Fetal bovine serum, penicillin and streptomycin, 0.25% trypsin for cell culture were from Gibico. Lipopolysaccharide (LPS), cholesterol, lecithin, trisodium phosphate anhydrous, propylene oxide and chitin were purchased from Sigma-Aldrich Biochemical. Anhydrous ethanol, methanol was purchased from Shanghai Macklin Biochemical. AICAR was purchased from MedChemExpress. Neutral Red Staining Solution and Crystalline violet dye were obtained from Beyotime Biotechnology. 5-Ethynyl-2′-deoxyuridine (EdU) kit was purchased from YEASEN Biochemical. RNA extraction kit was purchased from AG Biochemical. Reactive oxygen species assay kit, NO assay kit, Hematoxylin and Eosin (H&E) staining test kit, Safranine O-Fast Green staining test kit and Masson’s trichrome staining test kit were purchased from Solarbio Biochemical. The immunofluorescence staining primary antibodies were purchased from Proteintech Biochemical, including SIK1, COL2A1, MMP13 and F4/80 antibodies. The secondary antibodies, including Alexa Fluor 488 - and 594-conjugated were purchased from Thermo Fisher Scientific. Cell culture and treatment RAW 264.7 cells were cultured in medium containing 10% foetal bovine serum and 1% penicillin-streptomycin, and then incubated at 37 ℃ and 5% CO2. RAW 264.7 cells were added with 1 µg/mL LPS to construct the inflammation model and then added with AICAR drug for subsequent experiments. Scratch wound-healing assay RAW 264.7 cell suspensions were evenly distributed in 6-well plates (density: 3 × 10^5 cells) and manufacturing a vertical line scratch monolayer cells with the pipette tips when cells were cultured to confluence. Photographs of the gaps were recorded for each group at 0 H, 12 H and 24 H after treatment with different conditions. The change in width of the scratched area was measured by using Image J software. The rate of migration area was calculated as follows: Migration area (%) = (A[0] – A[n])/A[0] × 100, where A[0] represents the area of initial wound area, and an represents the residual area of wound at the metering point. Neutral red phagocytosis experiment RAW 264.7 cells were plated in 96-well plates and incubated with different stimulation conditions for 24 h. After incubation with 0.1% neutral red solution for 30 min, the cells were washed three times with PBS and shaken for 30 min with 150 µL of cell lysis solution (ethanol: acetic acid = 1:1). The absorbance of the plate was measured at 560 nm to assess the phagocytic capacity of macrophages. Cell invasion assay Cells (1 × 10^4 per well) were inoculated in the ECM Gel-coated upper compartment and incubated for 12 h with the lower compartment containing 10% FBS medium, Cells attached to the inner side of the membrane in the upper chamber were wiped away with cotton swab while those cells adhering to the upper surface of the filter membranes were fixed in formaldehyde and stained with crystal violet for 30 min. The invading cells were counted in three different randomly selected fields of view. Cytoskeleton staining Raw 264.7 cell were inoculated into 24-well plates at a density of 1 × 10^4 per well. The cells were then subjected to permeabilization by treatment with 0.5% Triton X-100 for 15 min. Subsequently, a 30 min incubation period with a 1% BSA solution, diluted with Phalloidin-TRITC dye, was carried out. Prior to fluorescence microscopy imaging, the cell nuclei were retained with DAPI. Reactive oxygen species (ROS) detection The reactive oxygen species assay kit was used to detect intracellular ROS production in different groups of cells. Cell suspensions after treatment under different conditions were incubated for 30 min against light by adding DCFH-DA mixture to load fluorescent probes. Cells were washed with serum-free medium and observed under a fluorescent microscope. Measurement of nitric oxide An indirect indicator of NO production by measuring the concentration of nitrite after the reaction according to the instructions of the NO assay kit. An equal amount of Griess reagent (1% sulfanilamide, 0.1% N-1-naphthalenediamine dihydrochloride, and 2.5% H[3]PO[4]) was added to 50 µL of culture supernatant of RAW 264.7 cells subjected to different combinations of stimulation conditions. The Nitrite levels of the sample was obtained by measuring the absorbance at 540 nm under the standard curve of sodium nitrite. 5-Ethynyl-2′-deoxyuridine (EdU) staining The effect of different conditions on cell proliferation after treatment with Raw 264.7 was examined with the Yefluor 488 Edu Cell Imaging Kit. DNA replication activity was directly measured by the “Click” reaction between the fluorescent dye and Edu. Cells were first labelled with 10 µM Edu working solution 2 h and then fixed in neutral paraformaldehyde for 30 min, neutralised with 50 µL of 2 mg/mL glycine for 5 min, washed twice with 3% BSA in PBS and incubated with 0.5% Triton X-100 for 10 min and finally incubated with 100 mL of Click-iT reaction mixture and Hoechst (nuclear stain) for 30 min at 37 °C in dark field. EdU-stained cells (green fluorescence) and DAPI-stained cells (blue fluorescence) were observed under microscope and proliferation was quantified by ratio. Real-time quantitative polymerase chain reaction (RT-qPCR) Total RNA was extracted from cultured cells utilizing the AG RNAex Pro Reagent in accordance with the guidelines provided by the manufacturer. The optical density (OD) of the total RNA was measured, and an OD260/OD280 > 1.8 was employed for subsequent quantification through reverse transcription polymerase chain reaction (RT-qPCR). Subsequently, the Nanodrop 2000 was used to quantify the RNA. Then, 600 ng of the total RNA of different samples was utilized to synthesize cDNA through reverse transcription. GAPDH was used as an internal control. Quantitative real-time PCRs were performed using the QuantStudio 1 system from Thermo Fisher Scientific. Then, the PCR was performed using the following conditions: step 1 (denaturation): 95 °C, 30 s; step 2 (amplification): 95 °C for 10 s and 60 °C for 30 s, 40 cycles; step 3 (cooling): 4 °C ∞. DNA was quantified using 2^−∆∆CT method with gene sequence shown in Table [181]S2). Western blot Drug-treated cells were collected and lysed with RIPA lysis buffer containing a mixture of protease and phosphatase inhibitors for 30 min on ice. Protein concentration was determined and quantitative protein samples were uploaded onto a precast SDS-PAGE gel, and the proteins were separated by electrophoresis for 70 min and then transferred to a PVDF membrane, blocked with 5% BSA for 1 h. The membrane was incubated overnight with primary antibody SIK1, and then the IgG HRP secondary antibody was incubated at RT for 60 min. After ECL development, the results were analysed using Image J software, and the grey values of the protein bands were calculated. Cell collection for NMR analysis 4 × 10^7 cells and 5 mL of supernatant from different groups of stimuli were collected and immediately frozen in liquid nitrogen and stored at -80 °C. The cell samples were repeatedly freeze-thawed 5 times before adding 1 mL of pre-cooled 1:2 methanol/H[2]O quenching solvent and subjected to cell disruption for 5 min underd ultrasonic instrument (sonicate 5 s, stop 9 s). After centrifugation at 13 000 r/min for 10 min, the supernatant was collected and the procedure was repeated with the addition of 1 mL of aqueous methanol to the precipitate. 2 mL of cell supernatant was freeze-dried directly. The lyophilised powder of cells and culture supernatant was added to the lyophilised powder containing 0.005% and 0.02% TSP and 10% D[2]O respectively and centrifuged at 13,000 r/min for 10 min at 4 °C and the supernatant was transferred to an NMR tube for measurement. Metabolomics analysis Metabolomics analysis was performed using a Bruker 600-MHzAVANCE III NMR spectrometer (Bruker, Germany). The ^1H NMR spectra were acquired using water suppression 1D noesygppr1d pulse sequence with 64 scans, 12345.7 Hz spectral width, 65,536 spectral size points, and a 1.0 s relaxation delay.The ^1 H NMR data were first processed by MestReNova software withmanually phased, baseline corrected and residual water (δ 4.65–5.0) removed. ^1 H NMR chemical shifts in the spectra were referenced to TSP. Then, the data matrix was acquired by segmented at 0.01-ppm intervals across δ 0.68–9.00 and all data points were normalized. Afterward, a text was exported into Simca-P 13.0 software for principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA). The differential metabolites were identified according to VIP-value (> 1) and t-test (p < 0.05). Visit the webpage ([182]https://www.metaboanalyst.ca/) for subsequent metabolic pathway analyses. Synthesis and characterization of lipo@AICAR Liposomes were prepared based on previous studies. Briefly, 20 mg of cholesterol and 80 mg of lecithin were completely dissolved in 10 mL of dichloromethane in a round-bottomed flask, and the organic solvents were removed by rotary evaporation for 1 h at 37 °C 5 mL of deionised water dissolved with 500 mM AICAR drug was dropped in a flask and emulsified to form micron-sized multi-lamellar liposomes (Lipo@AICAR) by sonication for one hour at room temperature. The morphology of Liposomes was examined by transmission electron microscopy (TEM) (JEM 1230, Electronics & America GATAN, Japan) and the samples were negatively stained with 2% phosphotungstic acid. The average size, polydispersity index, and Zeta potential of Lipo@AICAR weredetermined by means of Dynamic Light Scattering (Zeta-sizer Nano S90, Malvern Instruments, Worcestershire, UK). The full band absorbance was recorded using a NanoDrop One microvolume UV-Vis spectrophotometer (Thermofisher Scientific, USA). For the HPLC (LC 20 A, Shimadzu, Japan) test, a concentration gradient curve for AICAR dissolved in DI water was first established; the AICAR concentration in the Lipo@AICAR dialyzed DI water was detected, and the AICAR loading efficiency in Lipo@AICAR was calculated according to the following equation: (M[t] − M[n]) / M[t] × 100%, where M[t] is the total amount of AICAR added to prepare Lipo@AICAR nanoparticles, and Mn is related to the non-loaded AICAR amount detected in dialyzed DI water. Preparation and characterisation of gel@lipo@AICAR Hydroxypropyl chitin (HPCH), the raw material for hydrogels, is synthesised via the reaction of chitin and propylene oxide in an aqueous NaOH/urea solution. In brief, 11.4 g propylene oxide was added dropwisely to 100 g 2 wt% chitin solution in 11 wt% NaOH/4 wt% urea solution at 2°C for 24 h, The mixed solution was stirred at 15°C for 6 h and then neutralised by 3 mol/L HCl in an ice bath, and finally dialysed in distilled water and freeze-dried to obtain pure HPCH. The prepared Lipo@AICAR were added to the aqueous HPCH solution, mixed thoroughly and incubated at 37°C for 5 min to form hydrogel which labelled as Gel@Lipo@AICAR. High-resolution SEM (Thermo Fisher Scientific Apreo S, USA) was employed to investigate the morphology of hydrogel. The elastic and loss moduli G’ and G’’ with temperatures were recorded by arotational rheometer (HAAKE MARS III). The Gel@Lipo@AICAR drug release content in deionised water was recorded at set time points and based on a standard curve to obtain a cumulative AICAR drug release profile. Animal model The C57BL/6J mice were obtained from Southern Medical University (SMU), and approved by the Laboratory Animal Centre of Nanfang Hospital of Southern Medical University (Ethics Number: IACUC-LAC-20231220-002) In this study, male C57BL/6J mice (8–12 weeks) were randomly divided into 5 different groups with 6 mice in each group, underwent sham surgery and Anterior cruciate ligament transection (ACLT) of the right knee to create OA models. Briefly, different groups of hydrogels (sham-operated treated with phosphate-buffered saline (PBS), ACLT-operated treated with PBS, ACLT-operated treated with blank hydrogels (Gel), ACLT-operated treated with Gel@Lipo, and ACLT-operated treated with Gel@Lipo@AICAR were injected post operation. The volume of IA injection was 10 µL for each mouse. Finally, all mice were sacrificed 4 weeks after the knee-surgery and the right knee, liver, kidneys, and whole blood of each mouse were harvested for further evaluation. Gait analysis Gait analysis of mice after intra-articular injection of hydrogels was performed by an automatic animal gait analysis system. After mice woke up from anaesthesia after different hydrogel intra-articular injections, footprints of mice walking spontaneously were collected. Each mouse was individually placed on the walkway and walked freely from one side to the other while gait changes were recorded and analysed using the software. Right hind (RH) average stance time, right hind average break time, right hind average stride lenth, right hind average print area, and median foot pressure were recorded in each group of mice. Haematological test Mouse blood was collected by cardiac puncture under anaesthesia; blood was mixed with each hydrogel to form a haemolysis assay and blood smears were made to observe red blood cell morphology under microscope. Peripheral whole blood was divided into two fractions; one fraction was analysed for blood cells using an automated blood analyser (Mindray BC-5000 vet). The other portion was centrifuged (2000 rpm for 10 min) to obtain serum, which was analysed using a chemistry analyser. Histological analysis Knee samples were firstly fixed in 4% PFA solution for 24 h, Samples were decalcified with 10% ethylenediaminetetraacetic acid (EDTA) (pH 8.0) at 4 °C for 14 days and embedded in paraffin. Sagittal sections of 4 μm thick samples were cut and processed for staining. After conventional dewaxing to water, the paraffin sections were subjected to Hematoxylin- Eosin (H&E), Safranine O-Fast Green and Masson’s trichrome staining. Finally, the histological sections were observed and imaged under a normal light microscope. Immunofluorescence staining After deparaffinization and rehydration, slide samples were subjected to antigen repair in Tris EDTA repair solution (pH = 9) at 70 °C for 45 min. were blocked in with 3% bovine serum albumin (BSA) (30 min), followed by incubation with F4/80 (28463-1-AP; 1:200), CD206 (18704-1-AP; 1:100), COLIIA1 (28459-1-AP; 1:200), MMP13 (18165-1-AP; 1:200), and SIK1 (51045-1-AP; 1:100) were incubated overnight at 4 °C. Further, the slides were incubated with Alexa Fluor 488-conjugated secondary antibody (A-11008, 1:200) or Alexa Fluor 594-conjugated secondary antibody (A-11012, 1:200) at room temperature for 1 h. Finally, the nuclei were stained with hoechst and observed by fluorescence microscope. Statistics analysis All statistical analyses were conducted using R (version 4.2.3) and GraphPad Prism 9 (GraphPad Software, La Jolla, CA, United States). Independent t test was used to analyze differences between two groups when the data were normally distributed. Otherwise, the Mann–Whitney test was used. ANOVA analyzed the data between multiple groups, and the LSD method was used for pairwise comparison. Number of repetitions of all experiments n ≥ 3. Data are presented as the mean ± SD. (*, P < 0.05; **, P < 0.01; ***, P < 0.001 and ****, P < 0.0001). Electronic supplementary material Below is the link to the electronic supplementary material. [183]Supplementary Material 1^ (4.7MB, docx) Abbreviations OA Osteoarthritis WGCNA Weighted gene co-expression network analysis SIK1 Salt-inducible kinase 1 AICAR 5-aminoimidazole-4-carboxyamide ribonucleoside ROS Reactive oxygen species DEG Differential gene expression GEO Gene Expression Omnibus LASSO Least absolute shrinkage and selection operator ssGSEA Single-sample gene set enrichment analysis GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes LPS Lipopolysaccharide Edu 5-Ethynyl-2′-deoxyuridine RT-qPCR Real-time quantitative Polymerase Chain Reaction PCA Principal Component Analysis OPLS DA-Partial Least Squares Discriminant Analysis MetPA Metabolic pathway analysis ACLT Anterior cruciate ligament transection Author contributions Yong Fu, Hangtian Wu, WangJun, and Bin Yu conceived and designed the experiments. Yong Fu, Jiahui Hou, and Hangtian Wu performed and wrote the manuscript. Jiahui Hou and Qinmeng Yang analyzed the data. Yanpeng Lin and Nie Rui prepared all the Figures. All authors reviewed and agreed upon the manuscript. Funding This study was supported by the Guangdong Basic and Applied Basic ResearchFoundation (2022A1515110904 and 2021B1515230004), National Key R&D Program of China (2022YFC2504305). Data availability No datasets were generated or analysed during the current study. Declarations Compliance with ethics requirement All animal experiments in this study were performed in accordance with institutional guidelines and approved by the Laboratory Animal Centre of Nanfang Hospital of Southern Medical University (KYLL-2019(KS)-368). Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Yong Fu, Jiahui Hou and Qinmeng Yang contributed equally to this work. Contributor Information Hangtian Wu, Email: 547868739@qq.com. Bin Yu, Email: yubin@smu.edu.cn. References