Deciphering the molecular landscape of aortic aging: a meta-analysis of bulk RNA sequencing studies in mice.

Abstract

Global understanding of arterial aging remains limited despite its well-recognized medical and economic impact, hindering the development of effective mitigation strategies. The aorta, the major elastic artery, experiences a loss of homeostasis during aging that results from highly complex interactions. A promising approach to unravel the underlaying mechanisms is to investigate how aging affects the aortic transcriptome, leveraging the tremendous technological advances in RNA sequencing (RNA-seq). However, corresponding RNA-seq studies remain scarce and their interpretation hindered by a lack of standardization across experiments. In this study, we performed a meta-analysis of three publicly available bulk RNA-seq datasets from young and aged mouse aortas. After analyzing each dataset separately using a consistent bioinformatic pipeline, we combined differentially expressed genes across studies using Fisher's method. Subsequent gene set enrichment and protein-protein interaction analyses revealed coherent and functionally annotated changes in gene expression, notably associated with a broad immune response and possible infiltration of immune cells, extracellular matrix remodeling, osteochondrogenic signaling and mineralization, and glycolytic stress. We further identified a list of genes strongly altered by aging across studies. Our findings contribute to a better characterization of aortic aging at the molecular level and may support the development of targeted therapeutic strategies.