タンパク質相互作用網とゲノムおよび分子データをマルチモーダルに統合し、異なるRAエンドタイプを発見
Javad Rahimikollu1,2, Priyamvada Guha Roy1,3, Akash Kishore1,2
1Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Background:
Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease characterized by clinical and molecular heterogeneity, notably in the presence of anti-cyclic citrullinated peptide antibodies (CCP). CCP-positive (CCP+) RA patients exhibit more severe disease progression and distinct treatment responses compared to CCP-negative (CCP-) patients. Although previous studies have investigated cellular and molecular differences between these subtypes, their genetic differences are understudied.
Methods:
We leveraged the RACER (Rheumatoid Arthritis Comparative Effectiveness Research) cohort, comprising 555 CCP+/RF+ and 384 CCP-/RF+ RA patients. Using a novel framework, we integrated a network-based genome-wide association study (GWAS) with multi-omic data to uncover corresponding genetic and molecular differences.
Findings:
We uncovered a significant heritability difference between these disease groups. Network-based GWAS uncovered 14 putative gene modules, including many genes outside the HLA loci, that explained genetic differences between CCP+/RF+ and CCP-/RF+ RA. Heritability partitioning and multivariate expression analyses validated 4 modules, highlighting novel genetic loci underlying phenotypic differences. Module functional significance was established using multiple orthogonal cohorts, underscoring their biological relevance.
Interpretation:
Our findings demonstrate the utility of network-based approaches in revealing differential genetic risk factors underlying CCP+/RF+ and CCP-/RF+ RA. Disease-associated gene modules detected in synovial tissue were also observed in peripheral blood, indicating joint-specific molecular programs are reflected systemically. This cross-tissue concordance highlights the potential for blood-based assays to capture pathogenic mechanisms active in the joints, enabling practical patient stratification. Our findings highlight why CCP+/RF+ and CCP-/RF+ RA patients exhibit distinct clinical courses and therapeutic responses, supporting precision-guided treatment strategy development in RA.
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