Identification of therapeutic targets for giant cell arteritis through integrated analysis of multi-omics datasets
Bi-Qing Huang1, Yi-Xiao Tian2, Lan-Juan Li3
1Research Units of Infectious Disease and Microecology, Chinese Academy of Medical Sciences Peking Union Medical College, Beijing 100000, China; State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences Peking Union Medical College, Beijing 100000, China.
Background:
Giant cell arteritis (GCA), the most common systemic vasculitis affecting elderly individuals, currently lacks specific therapies. This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.
Methods:
We constructed a multi-stage analytical framework encompassing 32 proteomic datasets (covering 2914 unique plasma proteins) and 6 transcriptomic datasets. Multi-omics integration strategies, including two-sample Mendelian randomization, colocalization analysis, and functional enrichment analysis, were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts. Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.
Results:
We identified 43 plasma proteins causally associated with GCA [false discovery rate (FDR) < 0.05], with 17 representing novel therapeutic targets. Through dual validation using proteome-wide association studies and transcriptome-wide association studies, we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns. Unc-51 like kinase 3 (ULK3) emerged as the strongest protective factor (odds ratio = 0.47, 95% confidence interval: 0.37-0.71) through autophagy regulation, while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab. Five targets have existing approved drugs (SLAMF7, ICAM1, IL18, IL6ST, CTSS). Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients, with cell-type-specific alterations in inflammatory gene expression, and TYMP as the most critical hub gene.
Conclusions:
This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA, identifying novel mechanisms including autophagy modulation and metabolic reprogramming, with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns. These findings require experimental validation before clinical translation.
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