Research Paper Volume 13, Issue 14 pp 18150—18190
Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues
- 1 Institute of Biomedicine – iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro 3810-193, Portugal
- 2 Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga 4710-057, Portugal
- 3 ICVS/3B’s–PT Government Associate Laboratory, Braga/Guimarães, Portugal
Received: February 19, 2021 Accepted: July 21, 2021 Published: July 29, 2021https://doi.org/10.18632/aging.203379
How to Cite
Copyright: © 2021 Ferreira et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
AD: Alzheimer’s Disease; ATP: Adenosine triphosphate; Bicor: Biweight Midcorrelation; BP: Biological Process; DEGs: Differentially Expressed Genes; ER: endoplasmic reticulum; FDR: False Discovery Rate; GEO: Gene Expression Omnibus; GO: Gene Ontology; GS: Gene Significance; HCC: hepatocellular carcinoma; KC: Kupffer cells; ME: Module Eigengene; MHCI: Major Histocompatibility Complex I; MM: Module Membership; MS: Multiple Sclerosis; NAFLD: Non-alcoholic fatty liver disease; PCA: Principal Component Analysis; RNA-Seq: RNA Sequencing; Serpina3n: serine (or cysteine) peptidase inhibitor, clade A, member 3N; Serpinf1: serine (or cysteine) peptidase inhibitor, clade F, member 1; Serping1: serine (or cysteine) peptidase inhibitor, clade F, member 1; TOM: Topological Overlap Matrices; VST: Variance Stabilizing Transformation; WGCNA: Weighted Gene Correlation Network Analysis; z.K: Standardized Connectivity.