Sex dimorphism and tissue specificity of gene expression changes in aging mice

Abstract

Background: Aging is a complex process that involves all tissues in an organism and shows sex dimorphism. While transcriptional changes in aging have been well characterized, the majority of studies have focused on a single sex and sex differences in gene expression in aging are poorly understood. In this study, we explore sex dimorphism in gene expression in aging mice across three tissues.

Methods: We collected gastrocnemius muscle, liver and white adipose tissue from young (6 months, n = 14) and old (24 months, n = 14) female and male C57BL/6NIA mice and performed RNA-seq. To investigate sex dimorphism in aging, we considered two levels of comparisons: (a) differentially expressed genes between females and males in the old age group and (b) comparisons between females and males across the aging process. We utilized differential expression analysis and gene feature selection to investigate candidate genes. Gene set enrichment analysis was performed to identify candidate molecular pathways. Furthermore, we performed a co-expression network analysis and chose the gene module(s) associated with aging independent of sex or tissue-type.

Results: We identified both tissue-specific and tissue-independent genes associated with sex dimorphism in aged mice. Unique differentially expressed genes between old males and females across tissues were mainly enriched for pathways related to specific tissue function. We found similar results when exploring sex differences in the aging process, with the exception that in the liver genes enriched for lipid metabolism and digestive system were identified in both females and males. Combining enriched pathways across analyses, we identified amino acid metabolism, digestive system, and lipid metabolism as the core mechanisms of sex dimorphism in aging. Although the vast majority of age-related genes were sex and tissue specific, we identified 127 hub genes contributing to aging independent of sex and tissue that were enriched for the immune system and signal transduction.

Conclusions: There are clear sex differences in gene expression in aging across liver, muscle and white adipose. Core pathways, including amino acid metabolism, digestive system and lipid metabolism, contribute to sex differences in aging.

Keywords: Adipose tissue; Aging; Co-expression network analysis; Feature selection; Gene expression; Liver; Mice; Muscle; Sex dimorphism; Tissue-specific.

Fig. 1

Schematic figure showing the workflow of the study. Blue dots represent female samples and yellow dots represent male samples

Fig. 2

Differential expression analysis results for males and females in the old age group. The differential analysis was performed by using the limma-voom pipeline, which included tissue, sex and age group as factors in the design. Differentially expressed genes between females and males in the old age group were defined as genes that present absolute Fold Change greater than log₂(1.5) by t-test relative to a threshold with adjusted p-value less than 0.05. Volcano plots show the up- (blue) and down- (green) regulated genes when comparing female to male samples in three types of tissues, gastrocnemius muscle (A), liver (B), and white adipose tissue (C). Genes that were not determined as DEGs are colored in gray. By combining three sets of DEGs, we generated an Upset plot (D) showing intersecting sets, with five genes shared across the three DEG sets. Unique DEG sets were subjected to gene set enrichment analysis, and top ten pathways that were overrepresented and showed the least adjusted p-values were plotted for gastrocnemius muscle (E), liver (F), and white adipose tissue (G), respectively. The position of the dot at the x-axis shows the number of genes within the unique DEG set, color indicates the significance level and size represents the gene ratio in the complete gene set of the pathway

Fig. 3

Sex differences in aging in terms of differentially expressed genes (DEGs). The differential analysis was performed by using the limma-voom pipeline, which included tissues, sex and age group as factors in the design. DEGs under two conditions (comparisons of the old to the young group in either males or females) were defined as genes that present absolute Fold Change greater than log₂(1.5) by t-test relative to a threshold with adjusted p-value less than 0.05. The scatter plots show logFCs of all the genes in female and male comparison pairs. DEGs that were unique in females are colored in blue, with yellow for unique male DEGs and green triangle for common DEGs, in three types of tissues, gastrocnemius muscle (A), liver (B), and white adipose tissue (C). Genes that were not determined as DEGs are colored in gray. By combining the above six sets of DEGs, we generated an Upset plot (D) showing intersecting sets. E The shared DEGs across males and females for liver (n = 37) were subjected to gene set enrichment analysis, and top ten pathways that were overrepresented and showed the least adjusted p-values were plotted. The position of the dot at the x-axis shows the number of genes within the DEG set, color indicates the significance level and size represents the gene ratio in the complete gene set of the pathway

Fig. 4

List of 21 enriched KEGG pathways from identified differentially expressed gene (DEG) lists. DEG lists were derived from differential expression analysis in gastrocnemius muscle (Mus), liver (Liv), and white adipose tissue (Wat) from either comparing females to males in the old age group (labeled Sex diff. in aged mice) or unique sex specific genes in aging (comparing the old to young age group) (labeled Female or Male spe. in aging). Enriched KEGG pathways were determined by the overrepresentation of genes by Fisher’s exact test, where adjusted p-values were calculated using the Benjamini–Hochberg method and cutoff at 0.05 was applied. KEGG pathways that were detected in more than one gene set were obtained. The green dots represent the presence of the pathway within the enrichment analysis of the gene list. *The pathway was only detected in one type of tissue

Fig. 5

Gene module associated with aging. Gene co-expression network analysis was performed based on gene expression levels for somatic chromosome-related genes (n = 15,932) from all 56 samples, regardless of tissue and sex. The analysis resulted in 7 gene modules (A). Within each module, a co-expression network was derived and the first principal component of gene expression was denoted as module eigenvalue. B Forest plot shows the associations of eigenvalue of each module with age group and ME4 presented significant association with age group regardless of sex. Based on gene significance (correlation coefficient between gene expression level and age group) and module membership, hub genes were selected (C). These hub genes were then subjected to gene set enrichment analysis, and top ten pathways that were overrepresented and showed the least adjusted p-values were plotted (D). The position of the dot at the x-axis shows the number of genes within the DEG set, color indicates the significance level and size represents the gene ratio in the complete gene set of the pathway