Fig 1.
Metabolic patterns in RA patients and normal subjects.
(A) The heat map shows the standard score for each metabolite of each RA patient and each normal subject. The standard score shows how the concentration of each metabolite is related to the mean value of the control group. Red color indicates that the metabolite is increased compared to the mean of the control group; green color indicates that the metabolite is decreased. Metabolites are sorted according to the initial letter of the metabolite. The standard score is truncated to -7.31/7.31 for clarity. (B) Orthogonal PLS-DA (OPLS-DA) score plot of the first two principal components of an analysis of metabolites from RA patients (R) and Normal subjects (N). The number after letter means the number of the sample. The horizontal axis and the vertical axis mean values of the first and the second principal components. The ellipse denotes the 95% significance limit of the model, as defined by Hotelling's t-test.
Table 1.
Different Metabolites in RA patients and normal subjects (VIP≥1, p<0.05).
Table 2.
Different enzymes in RA patients and normal subjects related to energy metabolism.
Fig 2.
Verification of different metabolites and enzymes in synovial fluid and synovial tissues from RA patients and normal subjects.
(A) Real-time PCR amplification was performed, the results were normalized for the amount of β-actin as internal control. Each value represents the average from 50 RA patients or 20 normal subjects. (B) Western blot of different enzymes. Protein samples were analyzed on 10% SDS–PAGE, followed by immunoblotting, left panel is the band image of enzymes from 3 RA patients and 3 normal subjects. The mean intensity of corresponding protein expression in RA patients (20 cases) and normal subjects (20 cases) was shown in the right panel. (C) The concentration of glucose and lactic acid were verified in synovial fluid from inactive RA patients (25 cases), active RA patients (25 cases) and normal subjects (20 cases) by colorimetric assay kit and ROC curve analysis. ROC curve analysis showed that the concentration of glucose and lactic acid in synovial fluid could be used as dependable biomarkers for the diagnosis of active RA, provided an AUC of 0.906 and 0.922. Sensitivity and specificity, which were determined by cut-off points, reached 84% and 96% in sensitivity and 95% and 85% in specificity, respectively.
Fig 3.
The effect of HIF-1α knockdown on the expression of PFKP, LDHA, CS, DLST, PGD, ACSL4, HADHA and ACADVL in FLS cells.
(A) Real-time PCR of enzymes in FLS cells transfected with scrambled siRNA or HIF-1α siRNA for 24hr, β-actin was used as a loading control. (B) Western blot of enzymes in FLS cells transfected with scrambled siRNA or HIF-1α siRNA for 48hr, β-actin was used as a loading control. All experiments were performed at least in triplicates, the data are presented as mean ± SD, *p<0.05, compared with control, the asterisks represent significant differences.
Fig 4.
Energy metabolism profiles obtained from RA patients and normal subjects based on comparative proteomics and metabolomics study.
The different metabolites and enzymes are marked with a rectangle and a circle, respectively. The number means the ratio of RA to normal. Arrow means the direction of reaction.