General characteristic

As shown in Table 1, the proportion of hypertension group of AG, RG and AHU was more than the group of CTL. Comparative analysis suggested that the variables of BUN, TC, TG, ALT, AST and LDL-C had no statistical difference, while variables of UA, SCr, HDL-C were significantly different among these four groups (p < 0.05).

Table 1 Baseline characteristics of identified subjectsProteomic differences detected by iTRAQIdentify differentially expressed proteins (DEPs)

By iTRAQ proteomic analysis, a total of 9876 with unique peptides or polypeptide segments corresponding to 947 proteins were identified among AG, RG, AHU patients, and healthy controls (Table S1). Compared with CTL, we totally found 84 DEPs in the AG group, of which 63 proteins were up-regulated and 21 proteins were down-regulated. Compared with the CTL, we totally found 94 DEPs in the RG group, of which 32 proteins were up -regulated and 62 proteins were down-regulated. Compared with the CTL, in AHU group, we totally found 92 DEPs, of which 52 proteins were up-regulated and 40 proteins were down-regulated. Compared with the AG, in the AHU, we totally found 69 DEPs, of which 21 proteins were up-regulated and 48 proteins were down-regulated. The differential proteins in the clustering heat map were shown in Fig. 1.

Fig. 1

Cluster heat map of proteomics profiles in four comparison groups. Each row in the figure represents a protein, each column represents a set of samples. Regions of red or blue indicates that the differentially expressed protein is increased or decreased, respectively. White part represents there was no change in protein expression. A Cluster heat map of proteomics profiles in AG vs. CTL. B Cluster heat map of proteomics profiles in RG vs. CTL. C Cluster heat map of proteomics profiles in AHU vs. CTL. D Cluster heat map of proteomics profiles in AHU vs. AG

Gene ontology (GO) functional annotation analysis

To analyze the associated functions of the proteomics profiles in four groups, the DEPs underwent GO functional annotation based on Blast2GO software. Using Fisher’s exact test method, result of GO functional enrichment analysis could reveal the main biological processes (BP), cellular components (CC), and molecular function (MF) involved in DEPs in different groups (Fig. 2). According to the GO analysis, we found that in terms of biological processes, these proteins were mainly involved in lipid metabolism, endocytosis, vesicle-mediated transport, receptor-mediated endocytosis, anion transport, negative regulation of proteolysis, negative regulation of cell metabolic process, and negative regulation of catalytic activity. In terms of cell localization, these proteins were mainly located in extracellular space, blood microparticles, high-density lipoprotein particles, triglyceride-rich lipoprotein particles, and low-density lipoprotein particles. Significant changes occurred in some molecular functions like binding of lipid substances, lipid transport activity and peroxidase activity.

Fig. 2

The GO annotation results of DEPs in four comparison groups. The abscissa represents the GO Level 2 explanatory information, including biological process (red), molecular function (purple) and cellular component (orange). The ordinate (right) is a representation of the number of DEPs under each functional classification, and the ordinate (left) represents the percentage of DEPs under each functional classification in the total number of DEPs. A The GO annotation results of DEPs in AG vs. CTL. B The GO annotation results of DEPs in RG vs. CTL. C The GO annotation results of DEPs in AHU vs. CTL. D The GO annotation results of DEPs in AHU vs. AG

Kyoto encyclopedia of genes and genomes (KEGG) analysis of DEPs

In order to classify the functional annotations of the identified proteins, pathway analysis of DEPs was mainly conducted by KEGG analysis (Tables S2, S3, S4 and S5). The top significant pathways in each comparison groups were displayed in Fig. 3. Although the KEGG analysis provides a large number of pathway information from each comparison groups, the most representative pathway was peroxisome proliferator activated receptor (PPAR) signaling pathways and alcoholism pathway, because these two pathways occurred frequently among four comparison groups. Moreover, interestingly, histone H2A and histone H2B proteins were seen to be involved in alcoholism pathways and these two proteins were significantly increased in AG, RG and AHU compared with CTL, but were significantly decreased in AHU compared with AG (Table 2). This result revealed that histone H2A and histone H2B proteins may be involved in the core mechanism of gout onset through alcoholism pathway.

Fig. 3

KEGG pathway enrichment analysis of the DEPs. The ordinate is the name of the pathway in which the DEPs are involved, and the abscissa is the size of P value involved in the pathway. A The KEGG pathway enrichment results of DEPs in AG vs. CTL. B The KEGG pathway enrichment results of DEPs in RG vs. CTL. C The KEGG pathway enrichment results of DEPs in AHU vs. CTL. D The KEGG pathway enrichment results of DEPs in AHU vs. AG

Table 2 List of histone H2A and histone H2B in four comparison groups

Following this DEPs level trends, more proteins would be further validated by PRM analysis. Firstly, A venn diagram including the total DEPs from four comparison was generated to find the level trends we want. The detailed information of all proteins obtained from four comparison groups was presented in Fig. 4. In the venn diagram, 92 DEPs were shared in four comparison groups, which were significantly increased in AG, RG and AHU compared with CTL, but were significantly decreased in AHU compared with AG. Similarly, 53 DEPs were also shared in four comparison groups if the protein level trends become down-regulated in AG/CTL, RG/CTL, AHU/CTL, but up-regulated in AHU/AG. As shown in Fig. S2a, four groups were clearly separated from each other in PCA plot. Next, the supervised multivariate statistical method OPLS-DA was then employed to analyze each group (Fig. S2b ~ e). We found that the permutation test for OPLS-DA showed that the Q2 regression line had a negative intercept. Additionally, all R2 and Q2 values on the left were lower than the original points on the right, showing that the OPLS-DA model in the present study is valid (Fig. S2f ~ i). A total of 152 significantly DEPs (VIP > 1.0) were all successfully identified in the group of AG/CTL, RG/CTL and AHU/CTL (Fig. S2j). Then, 152 DEPs were further screened by a combination of veen result and protein sequence database searching based on DDA method. Finally, a list of 40 peptides was prepared for PRM validation (Table 3). Unfortunately, histone A and histone B was difficult to identified because its peptide spectrum matches (PSM) is lower from DDA database. (only the best scoring peptide to spectrum match for each LC/MS spectrum is considered as the potential peptide identification and is taken to the subsequent statistical validation).

Fig. 4

Venn diagram of unique and shared proteins of four comparison groups. The figure shows two different protein trends among four comparison groups to select proteins for further validated

Table 3 Selected 40 proteins to be verified by PRMPRM result

The PRM verified data were imported into skyline to check the peak shape of the target peptide segment and judge the spectral effect. The peak shape of some peptide segments was intact and the peak time was within the set retention time range, indicating the data quality was reliable (Supplementary Figure S1). Forty proteins related to gout process were found for PRM further analysis.

PRM analysis revealed that 14 proteins were identified to predict gout process significantly. The results, as shown in Fig. 5, the level of four proteins (Hyaluronan-binding protein 2, Myeloperoxidase (MPO), Carbonic anhydrase 1 (CA1), C-reactive protein) were significantly increased in AG, RG and AHU compared with the healthy group. Interestingly, these four proteins were also expressed higher in AG than in RG and AHU. Similarly, the expression levels of Apolipoprotein M, Serum albumin (ALB) and Hepatocyte growth factor activator exhibited a significant reduction in AG, RG and AHU compared with the healthy group. And these three proteins were also expressed lower in AG than in RG and AHU. Alpha-1-acid glycoprotein 1 (ORM1), Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) and Complement C2 presented no significant difference among healthy group, RG patients and AHU patients. However, in AG patients, the level of these two proteins were significantly lower than in the rest of three group. The levels of Complement component C8 beta chain (C8B) in AUR and AG patients were significantly lower than those of controls, which resulted in a significantly higher level in AG patients oppositely. More interestingly, the changes of Apolipoprotein C-IV and Thrombospondin-1 (THBS1) in RG and AHU patients were observed to increase compared with the healthy group but these two proteins displayed a significant reduction in AG patients compared with the healthy group. Finally, the level of Multimerin-1 (MMRN1) in AG patients was expressed lowest compared with the group of healthy control, RG and AHU.

Fig. 5

The comparison of protein expression by PRM. The ordinate is the group, and the abscissa is the intensity of protein level

In order to reveal the function of proteins, 40 differential proteins related to gout process were selected for protein–protein interaction (PPI) network analysis. By comparing proteins to STRING, the results showed that known proteins, such as THBS1, F2, FGA, SERPINF2, ORM2, ITIH4, ORM1 and MMRN1, account for a large weight in the network (Fig. 6). However, combined with the PRM results, THBS1, ITIH4, ORM1, MMRN1, MPO, CA1, ALB, C8B and Complement C2 were significantly related to gout process. Of all proteins, THBS1 exhibited the strongest regulatory ability above all others and complement and coagulation cascades performed the strongest regulatory ability above all pathways due to its higher interconnectedness in the network. The THBS1 might be the key biomarker to maintain the balance and stability of the gout process.

Fig. 6

PPI network of 40 DEPs in each comparison groups. In this PPI network, circle nodes represent proteins and the size of nodes represents value of betweenness centrality corresponding to interconnectedness. Red solid lines represent the interactions between proteins; Square nodes represent GO/KEGG term, the color of nodes represents P-value and blue dashed lines represent statistically significant signaling pathways involved in DEPs