Protein-centric omics analysis reveals circulating complements linked to non-viral liver diseases as potential therapeutic targets

Abstract

Background/aims: To evaluate the causal correlation between complement components and non-viral liver diseases and their potential use as druggable targets.

Methods: We conducted Mendelian randomization (MR) to assess the causal role of circulating complements in the risk of non-viral liver diseases. A complement-centric protein interaction network was constructed to explore biological functions and identify potential therapeutic options.

Results: In the MR analysis, genetically predicted levels of complement C1q C chain (C1QC) were positively associated with the risk of autoimmune hepatitis (odds ratio 1.125, 95% confidence interval 1.018-1.244), while complement factor H-related protein 5 (CFHR5) was positively associated with the risk of primary sclerosing cholangitis (PSC;1.193, 1.048- 1.357). On the other hand, CFHR1 (0.621, 0.497-0.776) and CFHR2 (0.824, 0.703-0.965) were inversely associated with the risk of alcohol-related cirrhosis. There were also significant inverse associations between C8 gamma chain (C8G) and PSC (0.832, 0.707-0.979), as well as the risk of metabolic dysfunction-associated steatotic liver disease (1.167, 1.036-1.314). Additionally, C1S (0.111, 0.018-0.672), C7 (1.631, 1.190-2.236), and CFHR2 (1.279, 1.059-1.546) were significantly associated with the risk of hepatocellular carcinoma. Proteins from the complement regulatory networks and various liver diseaserelated proteins share common biological processes. Furthermore, potential therapeutic drugs for various liver diseases were identified through drug repurposing based on the complement regulatory network.

Conclusion: Our study suggests that certain complement components, including C1S, C1QC, CFHR1, CFHR2, CFHR5, C7, and C8G, might play a role in non-viral liver diseases and could be potential targets for drug development.

Keywords: Complement system proteins; Drug repositioning; Liver diseases; Mendelian randomization analysis.

Figure 1.

Schematic of the study design. GWAS, genome-wide association study; MASLD, metabolic dysfunction-associated steatotic liver disease; AIH, autoimmune hepatitis; ALC, alcohol-related cirrhosis; PSC, primary sclerosing cholangitis; PBC, primary biliary cirrhosis; HCC, hepatocellular carcinoma; cis-pQTLs, cis-acting protein quantitative trait loci; SNP, single nucleotide polymorphism.

Figure 2.

Summary of the IVW results based on the cis-pQTL instruments. Color of tiles are scaled according to MR beta estimates, and white tiles are displayed for associations without available instruments. A single asterisk indicates a nominally significant association (P<0.05), and two asterisks indicate that the association is significant after Bonferroni adjustment for multiple comparisons. IVW, Inverse-variance weighted; cis-pQTLs, cis-acting protein quantitative trait loci; MR, Mendelian randomization; AIH, autoimmune hepatitis; ALC, alcohol-related cirrhosis; PSC, primary sclerosing cholangitis; PBC, primary biliary cirrhosis; MASLD, metabolic dysfunction-associated steatotic liver disease; HCC, hepatocellular carcinoma.

Figure 3.

Consistent causal relationships and complement pathway visualization. (A) Consistent significant causal relationships estimated using different mendelian randomization (MR) approaches are depicted. Each point on the graph represents the odds ratio (OR) value, while the horizontal lines passing through the points indicate the corresponding 95% confidence interval. The robustness of the observed associations between circulating complement components and various non-viral liver diseases is reinforced by the consistent results across distinct MR techniques. (B) Visualization of the identified associations within the complement pathway, showcasing the dynamic interplay of circulating complement components linked to various non-viral liver diseases. The schematic representation provides an intuitive insight into the alterations of complement components associated with distinct liver diseases. This visualization enhances our understanding of the complex molecular relationships underlying the pathogenesis of non-viral liver diseases. AIH, autoimmune hepatitis; ALC, alcohol-related cirrhosis; PSC, primary sclerosing cholangitis; PBC, primary biliary cirrhosis; MASLD, metabolic dysfunction-associated steatotic liver disease; HCC, hepatocellular carcinoma.

Figure 4.

Network construction and drug repositioning for AIH, PSC, and MASLD. (A) The top 4 common enriched biological processes identified between the AIH-associated proteins and proteins from the C1QC regulatory network. (B) Sankey diagram illustrating the discovery process of potential therapeutic drugs for AIH based on the C1QC regulatory network through drug repurposing. (C) The top 5 common enriched biological processes identified between the PSC-associated proteins and proteins from the C8G-CFHR5 regulatory network. (D) Sankey diagram illustrating the discovery process of potential therapeutic drugs based on the C8G-CFHR5 regulatory network through drug repurposing. (E) The top 3 common enriched biological processes identified between the MASLD-associated proteins and proteins from the C8G regulatory network. (F) Sankey diagram illustrating the discovery process of potential therapeutic drugs for MASLD based on the C8G regulatory network through drug repurposing. GO, gene ontology; AIH, autoimmune hepatitis; PSC, primary sclerosing cholangitis; MASLD, metabolic dysfunction-associated steatotic liver disease; C1QC, complement C1q subcomponent subunit C; C8G, complement component C8 gamma chain; CFHR5, complement factor H-related protein 5.

Figure 5.

Network construction and drug repositioning for ALC and HCC. (A) The top 11 common enriched biological processes identified between the ALC-associated proteins and proteins from the CFHR1-CFHR2-C1QC regulatory network. (B) Sankey diagram illustrating the discovery process of potential therapeutic drugs based on the CFHR1-CFHR2-C1QC regulatory network through drug repurposing. (C) The top 5 common enriched biological processes identified between the HCC-associated proteins and proteins from the C1S-CFHR2-C7 regulatory network. (D) Sankey diagram illustrating the discovery process of potential therapeutic drugs based on the C1S-CFHR2-C7 regulatory network through drug repurposing. GO, gene ontology; ALC, alcohol-related cirrhosis; HCC, hepatocellular carcinoma; C1QC, complement C1q subcomponent subunit C; CFHR1, complement factor H-related protein 1; CFHR2, complement factor H-related protein 2, C1S, complement C1s subcomponent; C7, complement component 7.

Figure 6.

Enrichment analysis of gene expression linked to complement components in liver diseases. (A) This heatmap offers a detailed analysis of GO biological process enrichment. It focuses on gene sets related to non-viral liver diseases and differential gene expression in the liver, in relation to several complement components (C1QC, C1S, CFHR1, CFHR2, CFHR5, C7, and C8G). The heatmap skillfully visualizes the top 20 enriched term clusters within the GO biological process category, derived from these specific gene sets. (B) This heatmap depicts KEGG pathway enrichment across various input gene lists. It is color-coded to represent P-values, displaying the top 20 clusters of KEGG pathway enrichment. Each cluster is marked by its most significantly enriched pathways, with a nuanced color gradient used to effectively convey the p-value significance. GO, gene ontology; KEGG, Kyoto encyclopedia of genes and genomes; AIH, autoimmune hepatitis; PSC, primary sclerosing cholangitis; MASLD, metabolic dysfunction-associated steatotic liver disease; ALC, alcohol-related cirrhosis; HCC, hepatocellular carcinoma; C1QC, complement C1q subcomponent subunit C; C1S, complement C1s subcomponent; CFHR1, complement factor H-related protein 1; CFHR2, complement factor H-related protein 2, CFHR5, complement factor H-related protein 5; C7, complement component 7; C8G, complement component C8 gamma chain.