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

Aging (Albany NY). 2021 Jul 29;13(14):18150-18190. doi: 10.18632/aging.203379. Epub 2021 Jul 29.


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.

Keywords: WGCNA; aging; mus musculus; transcriptome; trendy.

Publication types

  • Research Support, Non-U.S. Gov't