Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues

Abstract

Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson's disease and Alzheimer's disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.

Keywords: GTEx; differential expression; differential targeting; gender; gene regulation; gene regulatory networks; sex differences; sexual dimorphism.

Figure 1.. Study Overview

Schematic overview of our study. See also Table S1.

Figure 2.. Sex Bias in Gene Expression

(A) Number of differentially expressed genes (DEGs) (absolute fold change ≥ 1.5 and FDR < 0.05), and male versus female tSNR across 29 tissues. The red line represents the tSNR value expected in the case of no male versus female differences. Error bars represent the standard deviation (SD) of tSNR values across 10,000 random samplings of 30 males and 30 females. (B) Number of DEGs and number of tissues that share DEGs. Figure inset: same as the main figure, but the minimum number of shared tissues shown is 4. (C) Top 20 female-biased DEGs based on the average log-fold-change expression values across all tissues. The bar plot shows the average ± SD. (D) Enriched GO terms in males (blue) and females (red). The three heatmaps show the 10 GO terms with the highest average normalized enrichment score (NES) across all tissues (consistent male enrichment), lowest average NES across all tissues (consistent female enrichment), and highest SD across all tissues (most variable sex enrichment across tissues). See also Tables S2 and S3.

Figure 3.. Sex-Biased Targeting in Gene Regulatory Networks

(A) Schematic representation of the three classes of differentially targeted (DT) genes: male biased, sex divergent, and female biased. (B) Scatterplots of all genes (n = 30,243), indicating the number of female-biased edges (x axis) and the number of male-biased edges (y axis) at FDR < 0.05. Genes with more than 5% of their edges significantly different between males and females (FDR < 0.05) were defined as DT; the number of DT genes in each tissue is noted underneath the tissue name. Blue points represent DT male-biased genes (the proportion of sex-biased edges in the male direction is greater than 0.6), yellow points represent DT sex-divergent genes (the proportion of sex-biased edges in the male- and female-biased directions is between 0.4 and 0.6), red points represent DT female-biased genes (the proportion of sex-biased edges in the female direction is greater than 0.6), and black points represent non-DT genes (less than 5% of the edges are sex biased). See also Tables S5 and S6 and Data S1.

Figure 4.. Differential Targeting by the TF MAZ in Brain (Basal Ganglia)

(A) Expression levels of PRSS30P (left) and MAZ to PRSS30P edge weights (right) across male and female brain (basal ganglia) samples. (B) Expression levels of FRG1B (left) and MAZ to FRG1B edge weights (right) across male and female brain (basal ganglia) samples. (C) Plots showing the expression of the target gene versus the MAZ to target gene edge weight. (D) Plots showing the expression of the target gene versus MAZ gene expression. In (C) and (D), each data point represents a single sample in brain (basal ganglia). (E) Scatterplot of MAZ target genes showing the genes’ differential expression levels (t statistics) by the genes’ differential edge weight targeting levels by MAZ (t statistics). Mismapping of sequencing reads to the sex chromosomes may result in spurious expression of Y chromosome genes in females. (F) Same as (E), but without sex chromosome genes. M, male; F, female. See also Figures S3–S5.

Figure 5.. Expression Levels and Expression-Targeting Correlation of MAZ across 29 Tissues

(A) Expression levels (average ± SD, left panel) and expression-targeting correlation values (R², right panel) across 29 tissues for MAZ. Green bars represent the correlation considering all target genes, and red dots represent the correlation considering only autosomal target genes. (B–D) Three example tissues that have different levels of MAZ expression-targeting correlation: (B) pituitary, (C) colon (transverse), and (D) adipose (subcutaneous). MAZ, a highly expressed TF with no differential expression by sex, exhibits tissue-dependent expression-targeting correlation. See also Figure S6.

Figure 6.. TF Differential Targeting Patterns across 29 Tissues

Expression-targeting correlation value (including both autosomal and sex chromosome genes) for each TF (green ticks) in each tissue. Tissues are ordered based on the ESR1 expression-targeting correlation value. For each tissue, the names of the top 3 TFs with the highest expression-targeting correlation values are annotated, and the locations of ESR1, ESR2, AR, and MAZ are marked by arrows. See also Table S8.

Figure 7.. Sex-Biased Targeting of Biological Processes in Brain (Basal Ganglia)

(A) Top 20 GO terms and 20 KEGG pathways enriched for genes differentially targeted by TFs with high expression-targeting correlation values (R² > 0.3). GO terms and KEGG pathways selected are those with the highest NES standard deviation across the TFs. Enrichment for males is shown in blue and for females is shown in red. (B) Top 10 male-biased (blue) and female-biased (red) TFs differentially targeting genes annotated to Parkinson’s disease, and Alzheimer’s disease. TFs were selected based on their FDR significance. See also Figure S7.