173. Global Transcriptomic Profiling Identifies Differential Gene Expression Signatures between Inflammatory and Non-inflammatory Aortic Aneurysms
Creators: name: Benjamin Hur1, 2
name: Koster3, Matthew J.
name: Jin Sung  Jang4, 5
name: Weyand3, Cornelia M.
name: Warrington3, Kenneth J.
name: Jaeyun Sung1,2,3
Publisher: name: Zenodo
Year: 2022
Subjects: Vasculitis
ANCA
Abstracts
Giant Cell Arthritis
MPA
GPA
EGPA
Takayasu
IgA vasculitis

description: <strong>Objective</strong><strong>:</strong> The goal of this study was to identify aortitis hallmark genes and pathways by utilizing a high-throughput gene expression profiling approach (RNA-seq). <strong>Methods: </strong>RNA-seq was performed on surgically resected aortic tissues from inflammatory (giant cell arteritis [GCA] with or without polymyalgia rheumatica [PMR] = 8; clinically isolated aortitis [CIA] = 17) and non-inflammatory (n = 25) aneurysms undergoing surgical aortic repair. Differentially expressed genes (DEGs) between the two patient groups were identified while controlling for clinical covariates. Gene set enrichment analysis was used to find enriched biological functions for the up/downregulated DEGs. A protein-protein interaction model, drug-gene target information, and DEGs were used to construct a pharmacogenomic network for identifying promising drug targets and potentially new treatment strategies in aortitis. <strong>Results: </strong>Overall, gene expression patterns were more associated with disease state than with other clinical characteristics. We identified 159 and 93 genes that were significantly upregulated and downregulated, respectively, in inflammatory aortic aneurysms compared to non-inflammatory aortic aneurysms. We found that the upregulated genes were enriched in immune-related functions, including response to infectious microbial agents; whereas the downregulated genes were enriched in neuronal processes. Notably, gene expression profiles of inflammatory aortic aneurysms from patients with GCA/PMR were no different than those from patients with CIA. Finally, our pharmacogenomic network analysis identified genes that could potentially be targeted by immunosuppressive drugs currently approved for other inflammatory diseases. <br> <strong>Conclusion</strong>: We performed the first global transcriptomics analysis in inflammatory aortic aneurysms from surgically resected aortic tissues. We identified signature genes and biomolecular processes, while finding that CIA may be a limited presentation of GCA. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions. Looking ahead, we expect system-level analyses using high-throughput technologies to elucidate druggable disease mechanisms and next-generation biomarkers for the precise diagnosis and treatment of aortitis. <strong>Disclosure</strong>: KJW receives clinical trial support from Kiniksa and Eli Lilly, and an honoraria from Chemocentryx. <strong>Figure 1.</strong> Data analysis pipeline and clustering results on genome-wide expression (transcriptome) profiles of inflammatory and non-inflammatory aortic aneurysms. (A) Study design to investigate transcriptomic differences between inflammatory and non-inflammatory aortic aneurysms. (B) PCA on gene expression profiles (26,475 total genes) from 50 surgically resected aortic tissue samples across the two patient groups (inflammatory aortic aneurysm, n = 25; non-inflammatory aneurysm, n = 25). (C) Hierarchical clustering on all 50 gene expression profiles shows that samples cluster together by disease condition (inflammatory/non-inflammatory aortic aneurysms) more so than by any other clinical characteristic (i.e., ACE/ARB use, aspirin use, sex, RD history, smoking history, and statin use). Heatmap of gene expression profiles is not shown due to space constraints. PCA: principal component analysis; ACE/ARB: angiotensin converting enzyme inhibitors/angiotensin receptor blockers; RD history: history of other rheumatic diseases