An integrated multi-omics analysis identifies protein biomarkers and potential drug targets for psoriatic arthritis

Abstract

Psoriatic arthritis (PsA) is a complex, chronic immune-mediated inflammatory arthropathy that currently lacks definitive biomarkers and treatment targets. Identifying biomarkers and treatment targets is urgently needed for effectively managing PsA. Here, we conducted a multi-omics approach to identify protein biomarkers and potential drug targets for psoriatic arthritis. Proteome-wide Mendelian randomization (MR) analysis revealed seven plasma protein biomarkers significantly associated with PsA. Specifically, genetically predicted lower levels of NEO1 were linked to an increased PsA risk, whereas the remaining six proteins (IL23R, ERAP2, IFNLR1, KIR2DL3, CLSTN3, and POLR2F) exhibited a positive association with PsA risk. PPI analysis further supported these findings. Notably, druggability assessment revealed that scopoletin and esculetin were the two most significant drugs associated with ERAP2. Single-cell RNA-seq analysis revealed expression of IL23R, ERAP2, CLSTN3, and POLR2F in distinct T-cell subgroups of PBMCs derived from PsA patients. Furthermore, phenome-wide association studies (PheWAS) analysis assessed the potential side effects and safety as potential drug targets. Interestingly, experimental evidence showed that IFNLR1 expression is significantly upregulated under simulated inflammatory conditions. This study employed proteome-wide mendelian randomization to identify seven plasma proteins associated with PsA, including IL23R, ERAP2 and IFNLR1, offering potential insights for personalized PsA treatment strategies.

Fig. 1. Study overview.

This figure provides a comprehensive overview of the study design, highlighting the key stages of the research process, including data collection and analysis.

Fig. 2. MR results showing the associations between plasma proteins and PsA risk.

A The volcano plot illustrates the impact of seven candidate plasma proteins associated with PsA, derived from MR analyses using either IVW or Wald ratio. The forest plot displays the estimated effects of seven candidate proteins from the MR analysis. B displays the results from the discovery dataset. C displays the results from the replication dataset.

Fig. 3. Functional interaction network analysis of PsA-related proteins.

The PPI network was constructed using GeneMANIA. Each node is color-coded to signify the functional pathway associated with the respective gene.

Fig. 4. Single-cell transcriptomics analysis reveals differential expression patterns of candidate proteins in T cell subtypes of PsA patients.

A 2D visualization of single-cell types by UMAP, with a total of 11 cell types. Different colors represent distinct cell types. B The UMAP plot illustrates the expression patterns of four candidate proteins across distinct T cell subtypes.

Fig. 5. PheWAS analysis of associations between candidate proteins and various disease outcomes.

Each point in the plot represents a disease. Points above the dashed line indicate statistically significant disease associations. Different colors represent distinct disease system categories.

Fig. 6. mRNA expression changes of candidate genes following IL-17A and IL-23 stimulation.

Analysis of ERAP2, IFNLR1, and KIR2DL gene expression in PBMCs from a healthy donor showed significant upregulation of IFNLR1 expression following 48 h of co-stimulation with IL-17A and IL-23, relative to the control group. ERAP2 expression also demonstrated an upward trend. Experiments were repeated three times. Error bars represent standard deviation (SD). Statistical significance was designated as follows: *p < 0.05; **p < 0.01; ***p < 0.001.