PCK1 as a potential hub gene in distinguishing lactate metabolism between rheumatoid arthritis and osteoarthritis

Abstract

Background: Lactate is notably involved in the advancement of rheumatoid arthritis (RA) and osteoarthritis (OA). Nevertheless, the causal association between these conditions and lactate remains uncertain. This study aims to use Mendelian randomization (MR) to investigate their relationship with lactate and understand the genetic differences in lactate metabolism between them.

Methods: Genetic data for RA, OA, and lactate metabolism were obtained from GWAS, GEO, and MSigDB databases. MR analysis was performed using the inverse variance weighted (IVW) method. Differential gene expression analysis was conducted using the "limma" package, and Gene Set Enrichment Analysis (GSEA) was performed with GSEA software. Immune cell infiltration was assessed using the CIBERSORT platform. Validation of differentially expressed genes was carried out via Western blotting. Additionally, weighted gene co-expression network analysis (WGCNA) was employed to identify hub genes, while GO and KEGG analyses were performed to compare mechanistic differences between RA and OA. In vitro experiments were conducted to assess the effects of PCK1 on lactate secretion and cellular functions in RA-FLS.

Results: MR analysis indicated a causal relationship between RA and OA with lactate levels. Differential gene expression analysis revealed that PCK1 is a key gene underlying the metabolic differences in lactate levels between RA and OA. In vitro experiments demonstrated that knocking down PCK1 in RA-FLS affected lactate secretion, inhibited cell migration, and promoted apoptosis, suggesting its critical role in lactate metabolism. Additionally, GSEA analysis showed significant enrichment of PCK1 in the citrate cycle and gluconeogenesis signaling pathways in RA.

Conclusion: This study provides genetic evidence supporting the causal relationship between RA, OA, and lactate levels. Additionally, PCK1 is identified as a pivotal target implicated in the metabolic disparities of lactate between RA and OA, highlighting its potential significance in RA therapeutics.

Keywords: Lactate metabolism; Osteoarthritis; PCK1; Rheumatoid arthritis.

Figure 1. Research workflow chart.

Figure 2. Causal effect of RA and OA on the risk of increased lactate levels in Mendelian randomization analysis.

(A) The causal effect of RA on lactate. (B) The causal effect of OA on lactate. (C, D) Funnel plots representing the overall heterogeneity of Mendelian randomization estimates for the effect of RA and OA on lactate, respectively. CI, confidence interval; ^∗∗∗p < 0.001.

Figure 3. RA and OA lactate metabolism-related genes (LMRGs) selection and validation.

(A–C) Represent DEGs in RA/OA group, RA group, and OA group respectively. (D) The Venn diagram of differentially expressed LMRGs between RA and OA. (E, F) Validate the expression of PC and PCK1 in the GSE1919 dataset. (G, H) Protein expression of PCK1 in synovial tissues of both RA and OA was assessed using Western blot analysis. ^∗p < 0.05, ns, no significant difference.

Figure 4. The impact of downregulating PCK1 expression in RA-FLS on lactate levels and cellular functions.

(A) Verification of the knockdown efficiency of three siRNAs targeting PCK1 in RA-FLS using qPCR. (B) The effect of PCK1 knockdown on lactate metabolism in RA-FLS. (C, D) The influence of PCK1 knockdown on the migratory capacity of RA-FLS. (E, F) The effect of PCK1 knockdown on apoptosis in RA-FLS. Scale bar = 200 µm. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

Figure 5. The immune correlation of PCK1 in RA.

(A) The relative distribution of 22 immune cell types in synovial tissues of RA and OA. (B–E) The correlation of PCK1 with different immune cell types in RA.

Figure 6. GSEA enrichment plots.

The GSEA results revealed significant enrichment of genes associated with PCK1 in key signaling pathways in RA, including (A) the tricarboxylic acid cycle and (B) gluconeogenesis signaling pathway.

Figure 7. Identification of gene modules associated with RA and OA in the GEO dataset using WGCNA.

(A, D) Different colors under the gene tree represent gene co-expression modules. (B, E) Module-trait relationships were assessed by correlating module eigengenes with clinical traits, with each cell indicating the corresponding correlation coefficient and associated P value. (C, F) Scatter plots of module eigengenes in the brown and turquoise modules.

Figure 8. PPI network and enrichment analysis of RA common genes.

(A) The Venn diagram of the RA common genes. (B) GO and KEGG enrichment analysis of the RA common genes. (C) The PPI network of the RA common genes. Gray and yellow are Cluster 1 and 2, respectively.

Figure 9. PPI network and enrichment analysis of OA common genes.

(A) The Venn diagram of the OA common genes. (B) GO and KEGG enrichment analysis of the OA common genes. (C) The PPI network of the OA common genes. Gray and yellow are Cluster 1 and 2, respectively.