Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

Abstract

Gene expression alterations occurring with aging have been described for a multitude of species, organs, and cell types. However, most of the underlying studies rely on static comparisons of mean gene expression levels between age groups and do not account for the dynamics of gene expression throughout the lifespan. These studies also tend to disregard the pairwise relationships between gene expression profiles, which may underlie commonly altered pathways and regulatory mechanisms with age. To overcome these limitations, we have combined segmented regression analysis with weighted gene correlation network analysis (WGCNA) to identify high-confidence signatures of aging in the brain, heart, liver, skeletal muscle, and pancreas of C57BL/6 mice in a publicly available RNA-Seq dataset (GSE132040). Functional enrichment analysis of the overlap of genes identified in both approaches showed that immune- and inflammation-related responses are prominently altered in the brain and the liver, while in the heart and the muscle, aging affects amino and fatty acid metabolism, and tissue regeneration, respectively, which reflects an age-related global loss of tissue function. We also explored sexual dimorphism in the aging mouse transcriptome and found the liver and the muscle to have the most pronounced gender differences in gene expression throughout the lifespan, particularly in proteostasis-related pathways. While the data showed little overlap among the age-dysregulated genes between tissues, aging triggered common biological processes in distinct tissues, which we highlight as important features of murine tissue physiological aging.

DNA methylation meta-analysis confirms the division of the genome into two functional groups

“…Currently, there are many theories that give their explanation of the causes of aging, but there is no unanimous opinion on the problem [12,13,14]. We share the viewpoint that in the case of a multicultural organism, aging is always accompanied by a decrease in the resources required by its cells for repair and tissue regeneration [15,16,17,18]. The reasons for such a decrease in resources are well explained in our theoretical model of the functional division of the metazoan genome [19,20].…”

Aging at Evolutionary Crossroads: Longitudinal Gene Co-expression Network Analyses of Proximal and Ultimate Causes of Aging in Bats

“…GCNs have already been used to unravel a broad range of subtle age-related transcriptional variations across tissues and across individuals within species ( Somel et al 2010 ; Baumgart et al 2016 ; Pacifico et al 2018 ; Huang et al 2019 ), with a traditional focus on the heuristic detection of network modules (densely connected sets of nodes), associated with aging ( Southworth et al 2009 ; Ferreira et al 2021 ). Here, we developed the use of GCNs for aging studies in another direction by exploiting different aspects of their architectures via comparisons of the general and local topological properties of GCNs constructed for different age-classes.…”

A spatiotemporal map of the aging mouse brain reveals white matter tracts as vulnerable foci

“…Network analysis was performed with the Weighted Gene Correlation Network Analysis (WGCNA) (Ferreira et al, 2021) package to identify significant modules that were associated with a specific aging group and brain region. Modules were independently detected in each brain region.…”