Differential Expression Analyses on Aortic Tissue reveal Novel Genes and Pathways Associated with Abdominal Aortic Aneurysm Onset and Progression

Background: Abdominal aortic aneurysms (AAA) are focal dilatations of the abdominal aorta. They are normally asymptomatic and expand progressively, increasing their risk of rupture. Rupture of an AAA is associated with high mortality rates, but the mechanisms underlying the initiation, expansion and rupture of AAA are not yet fully understood. This study aims to characterize the pathophysiology of AAA and identify new genes associated with AAA initiation and expansion.

Methods: Our study used RNA-seq data on 140 samples, becoming the largest RNA-seq dataset for differential expression studies of AAA. We performed differential expression analyses between dilated and non-dilated aortic tissue samples, and between AAA of different diameters. We also studied the differences in splicing patterns between AAA and control samples, along with the contribution of genetic variants on gene expression in the diseased tissue.

Results: We identified 7,454 differentially expressed genes (DEGs) between AAA and controls, 2,851 of which were new compared to previous microarray studies. Notably, a novel cluster on adenosine triphosphate synthesis regulation emerged as strongly associated with AAA. Additionally, exploration of AAA of different diameters identified eight genes (EXTL3, ZFR, DUSP8, DISP1, USP33, VPS37C, ZNF784, RFX1) that overlapped with the DEGs between AAA and controls, implying association with both disease onset and progression. Seven genes (SPP1, FHL1, GNAS, MORF4L2, HMGN1, ARL1, RNASE4) with differential splicing patterns were also DEGs between AAA and controls, suggesting that splicing differences contribute to the observed expression changes and the disease development.

Conclusion: This study identifies new genes and pathways associated with AAA onset and progression and validates previous relevant roles of inflammation and intracellular calcium regulation. These findings provide insights into the complex mechanisms underlying AAA and indicate potential targets to limit AAA progression and mortality risk.

Fig. 1 Hierarchical clustering analysis results with all the DEGs between AAA and controls (A) and after removing DEG by ischemic time (B).

No conflict of interest has been declared by the author(s).

Acknowledgement

This work was supported by the Spanish Ministry of Science and Innovation (PID2019-109844RB-I00). MS-L is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CPII22/00007) and co-financed by the European Social Fund.

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Publication History

Article published online:
11 June 2024