Gene Expression Meta-Analysis Reveals Concordance in Gene Activation, Pathway, and Cell-Type Enrichment in Dermatomyositis Target Tissues

Abstract

Objective: We conducted a comprehensive gene expression meta-analysis in dermatomyositis (DM) muscle and skin tissues to identify shared disease-relevant genes and pathways across tissues.

Methods: Six publicly available data sets from DM muscle and two from skin were identified. Meta-analysis was performed by first processing data sets individually then cross-study normalization and merging creating tissue-specific gene expression matrices for subsequent analysis. Complementary single-gene and network analyses using Significance Analysis of Microarrays (SAM) and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted to identify genes significantly associated with DM. Cell-type enrichment was performed using xCell.

Results: There were 544 differentially expressed genes (FC ≥ 1.3, q < 0.05) in muscle and 300 in skin. There were 94 shared upregulated genes across tissues enriched in type I and II interferon (IFN) signaling and major histocompatibility complex (MHC) class I antigen-processing pathways. In a network analysis, we identified eight significant gene modules in muscle and seven in skin. The most highly correlated modules were enriched in pathways consistent with the single-gene analysis. Additional pathways uncovered by WGCNA included T-cell activation and T-cell receptor signaling. In the cell-type enrichment analysis, both tissues were highly enriched in activated dendritic cells and M1 macrophages.

Conclusion: There is striking similarity in gene expression across DM target tissues with enrichment of type I and II IFN pathways, MHC class I antigen-processing, T-cell activation, and antigen-presenting cells. These results suggest IFN-γ may contribute to the global IFN signature in DM, and altered auto-antigen presentation through the class I MHC pathway may be important in disease pathogenesis.

Figure 1

Gene expression meta‐analysis pipeline. Abbreviations: GEO, Gene Expression Omnibus; SAM, Significance Analysis of Microarrays; FC, fold change; DEG, differentially expressed genes; WGCNA: Weighted Gene Coexpression Network Analysis.

Figure 2

Heatmaps of differentially expressed genes identified by Significance Analysis of Microarrays (SAM). Hierarchical clustering of significant differentially expressed genes (DEGs) in muscle (A) and skin (B) using the cutoff of the false discovery rate P value < 0.05 and fold change ≥ 1.3 demonstrates clustering of cases and controls and that the majority of DEGs in both tissues are upregulated.

Figure 3

There are 94 overlapping genes in muscle and skin enriched in common immune pathways. A, Gene symbols of 94 overlapping differentially expressed genes plotted by fold change in muscle and skin. B, Enrichment of 94 overlapping genes by overrepresentation analysis using the Reactome Database. P values are calculated using a hypergeometric distribution and corrected for multiple comparisons by the Benjamini‐Hochberg method.

Figure 4

Network maps demonstrating the most enriched terms in each tissue, muscle (A) and skin (B), and the upregulated genes found to be differentially expressed acting in these pathways. These maps demonstrate the interrelatedness of many of these immune pathways in both tissues and highlight the similarity of genes and pathways across the two tissues.

Figure 5

A, Heatmap demonstrating hierarchical clustering of significant modules identified by WGCNA in each tissue based on gene overlap. Correlation between module eigengene and disease status, sign*(correlation), is shown by the red to blue bar, where red represents modules most positively correlated with cases and blue represents modules negatively correlated with cases. The ‐log10 adjusted P value of this correlation is indicated by the yellow to orange bar. The degree of pairwise overlap in genes between muscle modules and skin modules computed using a hypergeometric test is denoted by grey coloring where modules with significant overlap in genes (P < 0.05) are denoted by an asterisk and the color range corresponds to –log10(P value) of the pairwise overlap. B, Enrichment of pathways from the Reactome database across a cluster of highly overlapping and interesting modules in each tissue (green box in pane A) demonstrating enrichment of shared pathways across tissues. P values are adjusted for comparisons across modules by the Benjamini‐Hochberg method.

Figure 6

Heatmaps of cell‐type enrichment calculated using xCell demonstrates clustering of cases and controls based on cell types in both muscle (A) and skin (B). Enrichment was strongest for dendritic cells and M1 macrophages in both tissues.