Global Transcriptomic Profiling Identifies Differential Gene Expression Signatures Between Inflammatory and Noninflammatory Aortic Aneurysms

Abstract

Objective: To identify hallmark genes and biomolecular processes in aortitis using high-throughput gene expression profiling, and to provide a range of potentially new drug targets (genes) and therapeutics from a pharmacogenomic network analysis.

Methods: Bulk RNA sequencing was performed on surgically resected ascending aortic tissues from inflammatory aneurysms (giant cell arteritis [GCA] with or without polymyalgia rheumatica, n = 8; clinically isolated aortitis [CIA], n = 17) and noninflammatory aneurysms (n = 25) undergoing surgical aortic repair. Differentially expressed genes (DEGs) between the 2 patient groups were identified while controlling for clinical covariates. A protein-protein interaction model, drug-gene target information, and the DEGs were used to construct a pharmacogenomic network for identifying promising drug targets and potentially new treatment strategies in aortitis.

Results: Overall, tissue gene expression patterns were the most associated with disease state than with any other clinical characteristic. We identified 159 and 93 genes that were significantly up-regulated and down-regulated, respectively, in inflammatory aortic aneurysms compared to noninflammatory aortic aneurysms. We found that the up-regulated genes were enriched in immune-related functions, whereas the down-regulated genes were enriched in neuronal processes. Notably, gene expression profiles of inflammatory aortic aneurysms from patients with GCA 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.

Conclusion: 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 and suggests future biomarker discovery directions for the precise diagnosis and treatment of aortitis.

Figure 1.. Data analysis pipeline and unsupervised 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 ascending 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.

Figure 2.. Differentially expressed genes (DEGs) and their functional categories reveal transcriptomic signatures of inflammatory aortic aneurysms.

(A) A total of 159 and 93 genes were identified to be significantly upregulated and downregulated, respectively, in inflammatory aortic aneurysms (Benjamini-Hochberg adjusted P-value < 0.01 and |log₂(fold-change in mean expression values)| > 2). (B) Protein classes and (C) biological pathways of the upregulated DEGs. (D) Protein classes and (E) biological pathways of the downregulated DEGs. DEGs and fold-changes were calculated by DEseq2 (v1.30.0) while controlling for ACE/ARB use, statin use, history of other RD. Functional classification of protein class and biological pathways was performed using the PANTHER Database (v16.0).

Figure 3.. Gene Ontology (GO) enrichment analysis reveals strong upregulation of immune response and downregulation of neuronal activity in inflammatory aortic aneurysms.

(A) Top 10 enriched GO terms of the upregulated DEGs (n = 159) and (B) their gene set similarities illustrated in a network diagram. (C) Top 10 enriched GO terms of the downregulated DEGs (n = 93) and (D) a network of their gene set similarities. Size of nodes in the network corresponds to the number of genes of each GO term. The width of the edge represents the number of genes common to both GO terms. Colors of nodes (pink to red) signify the statistical significance of GO enrichment. Gene set enrichment analysis on GO terms was performed in DAVID (v6.8).

Figure 4.. Transcriptomics analysis confirms higher expression of a subset of known target genes in aortitis.

Among the known targets of aortitis immunosuppressive drugs (e.g., JAK inhibitors, IL6 inhibitors, Th1/Th17 inhibitors, prednisone, and methotrexate), six genes (JAK1, JAK3, IL6R, IL23A, CD80, and CD86) were found to display differential expression between inflammatory and non-inflammatory aortic aneurysms. Square and circle nodes indicate drug names and gene symbols, respectively. Edges with a hammerhead indicate inhibition. The color (blue to red) of each node represents gene expression fold-changes between inflammatory and non-inflammatory aortic aneurysms.

Figure 5.. Pharmacogenomic network analysis uncovers the drug-gene interaction landscape in inflammatory aortic aneurysms.

The largest connected component in the DEG-based protein-protein interaction network is composed of 36 genes (nodes) and 92 interactions (edges). Using pharmacological information, 10 of those 36 genes were identified as ‘druggable’ (both directly and indirectly) with FDA-approved, pharmaceutical drugs. In the pharmacogenomic network, 29 out of 36 genes are related to the top 5 enriched GO terms. Nodes with green borders represent genes that are related to the top 5 GO terms: Inflammatory response, Cytokine activity, Positive regulation of response to stimulus, Regulation of immune system process, and Positive regulation of immune system process. The color (blue to red) of each node represents gene expression differences between inflammatory and non-inflammatory aortic aneurysms. The size of each node represents its degree, i.e., number of connections to other nodes. GO enrichment analysis was performed on 36 genes using DAVID (v6.8). The information of drug-target genes were obtained from DGIdb (v4.0).