The experiments were approved by our University Ethics Committee (Ethics Committee on Biomedical Research, West China Hospital of Sichuan University No.125 2020-(921)).

Ten patients with gouty arthritis of the knee who were admitted to our hospital from December 1, 2019, to May 30, 2020, and met the inclusion criteria were included. The clinical features, imaging changes, and arthroscopic manifestations of the patients with knee arthritis were retrospectively analyzed. Synovial fluid was collected during the arthroscopic procedure as sample. And 10 cases of relatively normal synovial fluid were selected as the control group at the same time. The general clinical and demographic data of the gouty arthritis patient group (T) and relatively normal group (N) are shown in Table 1. All patients were hospitalized. Acute episodes of gouty knee arthritis lasted no less than 3 days and the duration of GA was more than 1 year. Of the 10 patients with GA (mean age value = 42.3 ± 9.202), 10 (100%) were male. The mean BMI (27.35 ± 1.32 vs. 22.13 ± 2.33 kg/m2; p < 0.001) and uric acid (509.4 ± 114.5 vs. 316.2 ± 100.9; p < 0.001) were higher than in the control group. Also, the proportion of GA patients with smoking and drinking habits was much higher than that of healthy volunteers (smokers: 10% vs. 0%; drinkers: 10% vs. 0%). Serum biochemical parameters, including white blood cells (WBC), C-reactive protein (CRP), uric acid (UA), high/low-density lipoprotein (HDL/LDL), total cholesterol (TC) and triglycerides (TG), sedimentation (ESR), absolute monocyte count (MO#) and absolute lymphocyte count (LY#), were measured in all patients using a fully automated serum biochemistry analysis. In surgical removal of gout stones, all patients with gouty knee arthritis were ensured that urate crystals were removed.

Take 40 all of the joint solutions per sample and dilute with 400 μL of Binding Buffer (kit: Binding Buffer). Add 850 μL of Binding Buffer and allow it to flow through the column by gravity for activation. Add the diluted sample and allow it to flow gravitationally through the column. Rewash the column again with 600 μL of binding buffer to collect the eluted components from the previous three steps, i.e., the sample after albumin/IgG removal is vacuum freeze-dried. The freeze-dried sample was added to the solution and centrifuged at room temperature at 12,000 × g for 10 min, after which the supernatant was collected and centrifuged again. The supernatant was the total protein solution of the sample.

Determination of protein concentration: Calculate the standard curve based on the standard protein solution's known concentration and absorbance value and calculate the protein concentration value.

Trypsin digestion: 50 μg of protein is taken from each sample according to the measured protein concentration. DTT is added to the above protein solution to a final concentration of 4.5 mM, and incubated at 55 °C for 30 min. The same volume of iodoacetamide was then added to the solution. Add 6 times the volume of acetone to the above solution to precipitate the protein. Collecting the precipitate. Add 100 μL TEAB2 to re-solubilize the deposit. Add 1 mg/ml of Trypsin-TPCK by mass. Terminate the enzymatic reaction by adding phosphoric acid to adjust the pH is 3. The peptides were desalted using a SOLA™ SPE 96-well plate. The spectra output from the mass spectrometry were matched with the theoretical spectra generated by the fasta library to transform the machine signals into peptide and protein sequence information and then combined with the sequence information, peptide retention time, and fragment ion information to build the spectral library.

LC–MS/MS high resolution mass spectrometric detection. All samples after enzymatic digestion were mixed with equal amounts of peptides and fractions were separated in the mobile phase at pH = 10 using an Agilent 1100 HPLC system. Separation conditions Chromatographic column: Agilent Zorbax Extend—C18 narrow bore column, 1 × 150 mm, 5 μm. Detection wavelengths: UV 210 nm and 280 nm. mobile phase A: ACN-H2O (2:98, v/v), mobile phase B: ACN-H2O (90:10, v/v Gradient elution conditions: 0–10 min, 2% B; 10–10.01 min, 2–5% B; 10.01–37 min, 5–20% B; 37–48 min, 20–40% B; 48–48.01 min, 40–90% B; The fractions were collected at one-minute intervals from the 10th minute onwards in the order of 1–10 centrifuge tubes. A total of 10 fractions were collected, vacuum freeze-dried, and dried, and the samples were stored frozen for mass spectrometry. Before mass spectrometry injection, each sample was mixed at a volume ratio of iRT: sample to be tested = 1:10 and used as an internal standard. The enzymatically digested peptides of each sample were acquired separately on the machine, and the scan range was set to 350–1250 m/z with an isolation window of 26 m/z. The spectra output from the mass spectrometry were matched to the theoretical spectra generated by the FASTA library to convert the machine signals into peptide and protein sequence information and then combined with the sequence information, peptide retention time, and fragment ion information to build a library of spectra for DIA analysis. Processing of the raw DIA data was done using Spectronaut Pulsar software.

Remove samples stored at −80 °C, remove 100 μL of the sample, and add 10 μL each of internal standard (L-2-chlorophenyl alanine, 0.3 mg/mL; Lyso PC17:0, 0.01 mg/mL, both in methanol) and vortex for 10 s; Add 300 μL of precipitant protein methanol–acetonitrile (V: V = 2:1) and vortex for 1 min; Vortex and shake for 1 min; Extract by sonication in an ice-water bath for 10 min; Stand for 30 min at −20 °C; Centrifuge for 10 min (13000 rpm, 4 °C), evaporate 300 μL of supernatant, then re-dissolve with 200 μL of methanol–water (V: V = 1:4), vortex for 30 s and sonicate for 2 min. Centrifuge for 10 min (13,000 rpm, 4 °C). Aspirate 150 μL of supernatant using a syringe, filter using a 0.22 μm organic phase pinhole filter, and transfer to an LC injection vial and store at −80 °C until LC–MS analysis is performed. Quality control samples (QC) were prepared by mixing equal extracts from all samples, with each QC volume being the same as the sample. Metabolic analysis was performed using a liquid mass spectrometry system consisting of an ACQUITY UPLC ultra-performance liquid tandem AB Triple TOF 5600 high-resolution mass spectrometers.

We used the same method as before to perform Western blotting [28]. The primary antibodies we used were anti- LC3B (1:1000, Abclonal, A7198); anti- SQSTM1/p62 (1: 1000, Abclonal, A11483); anti- PLA2G2A (1:1000, Affinity Biosciences, DF6366); BeyoECL Plus (Beyotime, Beijing, China) was used for developing immunoblots, and a Tanon 2500RGel Imaging System (Tanon, Shanghai, China) was used to take pictures and store protein bands (n = 3).

Based on protein expression and metabolite response strength data, pearson correlation algorithm is used to calculate the correlation between protein expression and metabolite response strength data. Pearson correlation coefficient r > 0.6 or r < −0.6 is used to construct correlation network in r software environment, and then visualization using Cytoscape.

There were 979 genes and 20 samples in the original data from the differential proteins in proteomics. The genes with low expression fluctuation (standard deviation ≤ 0.5) were filtered; the remaining 862 genes were 20 samples. Set the power value from 1 to 30, and calculate the corresponding correlation coefficient and the network's average connectivity, respectively. A weighted co-expression network model was established based on the selected power values, and the 862 genes were finally divided into 9 modules. We used the WGCNA package of R language to complete the data analysis and data visualization, and R language and Python were used to complete the data visualization. Pearson correlation algorithm was used to calculate the correlation coefficient and p-value between characteristic genes and traits of modules. The absolute value of the correlation coefficient was greater than or equal to 0.3, and p-value was less than 0.05 as the threshold value, and the modules related to each trait were screened. For each trait-related module, the correlation between module Gene expression and corresponding trait Gene Significance (GS) was calculated, the correlation between module gene expression and Eigengene was calculated, and the correlation analysis of module trait was constructed according to the correlation.

All data are expressed as mean ± S.E.M. Statistical significance between groups was analyzed using a t-test or one-way ANOV a followed by a Tukey post hoc test to correct for multiple comparisons in GraphPad Prism 5 (San Diego, USA). p < 0.05 were considered statistically significant. An online tool based on R scripts, MetaboAnalyst 2.0 was used. Univariate statistical analysis: The Wilcoxon rank sum test and FC were used in this study to analyze the quantitative changes in metabolomic data. Final results were plotted on volcano plots at a difference multiple ≥ 1.5 and p < 0.05. P-values were corrected due to multiple simultaneous extensive hypothesis testing. Confidence protein: The null value in the data matrix is replaced by half of the minimum value, and the data is normalized by the normalize.quantiles function in R package'preprocessCore' after being processed by log2. Multivariate statistical analysis: PCA analysis was first used to observe and evaluate the overall distribution of all samples, and then comparative analysis of the data across groups was attempted in a supervised manner through PLS-DA modeling. OPLS-DA can better distinguish the differences between two groups of metabolites, mainly by orthogonalizing the samples in both groups. Differential metabolites were further screened with VIP > 1. To prevent overfitting of the constructed model, this study used round-robin interaction validation and ranking tests to judge the credibility of the modeling. The R2Y and Q2 obtained from the cross-loop validation were used to visually evaluate the quality of the model construction, and the response ranking test was used to evaluate the accuracy of the OPLS-DA model by random ranking, which was used to exclude bias caused by over-intervention of the grouped data by supervised learning methods. Orthogonal partial least squares—discriminant analysis OPLS-DA is a supervised statistical method of discriminant analysis. This method is modified on the basis of PLS-DA to filter out the noise irrelevant to the classification information, improve the analytical ability and effectiveness of the model, and maximize the differences between different groups within the model. On the OPLS-DA score chart, there are two principal components, namely, prediction principal component and orthogonal principal component. There is only one prediction principal component and multiple orthogonal principal components. OPLS-DA reflects the maximum difference between groups on t1, so it can directly distinguish the inter-group variation from t1, while the orthogonal principal component reflects the intra-group variation. The two groups of samples have significant differences in OPLS-DA score chart. GO functional enrichment analysis (ORA algorithm), KEGG pathway enrichment analysis (ORA algorithm), and network protein interaction analysis were performed to identify the specific pathways and functions of the differential proteins involved. Open database sources, including the Human Metabolome Database, Gene Ontology Resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database and MetaboAnalyst, were used to identify metabolic pathways. Upstream regulatory genes of synovial differential proteins were analyzed using IPA software and their activation and repression were predicted. The STRING database (https://cn.string-db.org/) was used to analyze the construction of protein interaction networks for differential proteins.