Investigating the causal relationship and potential shared diagnostic genes between primary biliary cholangitis and systemic lupus erythematosus using bidirectional Mendelian randomization and transcriptomic analyses

Abstract

Background: The co-occurrence of primary biliary cholangitis (PBC) and systemic lupus erythematosus (SLE) has been consistently reported in observational studies. Nevertheless, the underlying causal correlation between these two conditions still needs to be established.

Methods: We performed a bidirectional two-sample Mendelian randomization (MR) study to assess their causal association. Five MR analysis methods were utilized for causal inference, with inverse-variance weighted (IVW) selected as the primary method. The Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and the IVW Radial method were applied to exclude outlying SNPs. To assess the robustness of the MR results, five sensitivity analyses were carried out. Multivariable MR (MVMR) analysis was also employed to evaluate the effect of possible confounders. In addition, we integrated transcriptomic data from PBC and SLE, employing Weighted Gene Co-expression Network Analysis (WGCNA) to explore shared genes between the two diseases. Then, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment methods to perform on the shared genes. The Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm was utilized to identify potential shared diagnostic genes. Finally, we verified the potential shared diagnostic genes in peripheral blood mononuclear cells (PBMCs)-specific cell populations of SLE patients by single-cell analysis.

Results: Our MR study provided evidence that PBC had a causal relationship with SLE (IVW, OR: 1.347, 95% CI: 1.276 - 1.422, P < 0.001) after removing outliers (MR-PRESSO, rs35464393, rs3771317; IVW Radial, rs11065987, rs12924729, rs3745516). Conversely, SLE also had a causal association with PBC (IVW, OR: 1.225, 95% CI: 1.141 - 1.315, P < 0.001) after outlier correction (MR-PRESSO, rs11065987, rs3763295, rs7774434; IVW Radial, rs2297067). Sensitivity analyses confirmed the robustness of the MR findings. MVMR analysis indicated that body mass index (BMI), smoking and drinking were not confounding factors. Moreover, bioinformatic analysis identified PARP9, ABCA1, CEACAM1, and DDX60L as promising diagnostic biomarkers for PBC and SLE. These four genes are highly expressed in CD14+ monocytes in PBMCs of SLE patients and potentially associated with innate immune responses and immune activation.

Conclusion: Our study confirmed the bidirectional causal relationship between PBC and SLE and identified PARP9, ABCA1, CEACAM1, and DDX60L genes as the most potentially shared diagnostic genes between the two diseases, providing insights for the exploration of the underlying mechanisms of these disorders.

Keywords: Mendelian randomization; causal relationship; primary biliary cholangitis; systemic lupus erythematosus; transcriptomic data.

Figure 1

Schematic diagram of the study design.

Figure 2

Forest plot of bidirectional mendelian randomization.

Figure 3

Causal effect of PBC on the risk of SLE. (A) Scatter plot. (B) Forest plot. (C) Leave-one-out test. (D) Funnel plot.

Figure 4

Causal effect of SLE on the risk of PBC. (A) Scatter plot. (B) Forest plot. (C) Leave-one-out test. (D) Funnel plot.

Figure 5

Identification of differentially expressed genes. (A) A heatmap of the top 30 DEGs in GSE65391. (B) A heatmap of the top 30 DEGs in GSE119600. (C) Venn diagram shows that 107 genes overlap in the SLE and PBC. (D) Gene ontology enrichment analysis of these 107 genes.

Figure 6

Co-expression network analysis for differentially expressed genes. (A) Sample dendrogram and trait heatmap in GSE65391 (B) Sample dendrogram and trait heatmap in GSE119600. (C) Heatmap of the module-trait relationships in GSE65391. (D) Heatmap of the module-trait relationships in GSE119600. (E) Venn diagram identifies 112 shared genes by overlapping the hub modules of PBC and SLE. (F) Gene ontology enrichment analysis of these 112 genes.

Figure 7

Functional enrichment analyses of the shared genes. (A) GO analysis of the shared genes. (B) KEGG pathway enrichment analysis of the shared genes.

Figure 8

Expression pattern validation and diagnostic value. (A) Expression of PARP9, ABCA1, CEACAM1 and DDX60L in GSE65391. (B) ssGSEA score of the shared diagnostic genes in GSE65391. (C) Expression of PARP9, ABCA1, CEACAM1 and DDX60L in GSE119600. (D) ssGSEA score of the shared diagnostic genes in GSE119600. ssGSEA, single-sample gene set enrichment analysis; *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 9

The single-cell transcriptomic analysis of PBMCs in SLE patients. (A) UMAP plot illustrating the distribution of 14 identified cell populations within PBMCs. (B) Split UMAP plots showcasing the distribution of the 14 cell populations within PBMCs from SLE patients and healthy control groups. (C) Heatmap displaying the expression levels of PARP9, ABCA1, CEACAM1, and DDX60L genes in the SLE and healthy PBMC populations. (D) Violin plots representing AUCell scoring for PARP9, ABCA1, CEACAM1, and DDX60L across all PBMCs. (E, F) AUCell scoring and expression of PARP9, ABCA1, CEACAM1, and DDX60L across the 14 identified cell populations within PBMCs.