Transcriptomic profiling of scleroderma monocytes reveals links with cardiovascular complications, implicating Notch and interferon pathways

Abstract

Objectives: Recent research has highlighted the critical role of monocytes and macrophages in driving both inflammatory and fibrotic processes in systemic sclerosis. This study seeks to elucidate the gene expression profiles of systemic sclerosis monocytes and their potential links to disease complications, with the ultimate goal of uncovering novel therapeutic targets.

Methods: A total of 48 systemic sclerosis patients and 15 controls were recruited and monocytes were isolated using CD14+ magnetic beads. Total RNA was extracted and bulk RNA-seq analysis was performed. Differential gene expression followed by unsupervised hierarchical clustering and pathway analysis was conducted, and correlations with clinical features were analyzed. Interferon signature score (IFN6) was calculated using the log transformed values of six genes (IFIT3, IFIT2, MX1, IFIH1, STAT2, and NCF1).

Results: We identified four distinct patient subgroups, relative to normal, two with inflammatory and two with non-inflammatory gene profiles. The inflammatory subgroups exhibited high expression of interferon-related genes and included all systemic sclerosis patients with pulmonary hypertension and most with cardiac involvement. In these patients, IFN6 was markedly elevated and showed a significant correlation with global longitudinal strain (GLS; r = -0.5, p = 0.006), a key indicator of cardiac function. Furthermore, pathway analysis identified an enrichment of the Notch signaling pathway among genes whose overexpression correlated with impaired global longitudinal strain.

Conclusion: These findings unveil a potential new mechanistic link between interferon activity, Notch signaling, and cardiac complications in systemic sclerosis, offering new insights into disease pathogenesis and potential therapeutic targets.

Keywords: Scleroderma; cardiovascular complications; disease characteristics; gene expression; monocytes.

Figure 1.

Differential gene expression in systemic sclerosis monocytes. (a) Volcano plot showing differentially expressed genes (p < 0.05,|log2 fold change| > 1.5) in systemic sclerosis (SSc) monocytes compared to healthy controls. (b) Gene set enrichment analysis results showing significantly enriched pathways in SSc monocytes.

Figure 2.

Enhanced sample separation using feature selection and dimensionality reduction techniques. (a) Principal component analysis (PCA) plots comparing gene expression data before and after feature selection. Left: PCA plot using all genes shows limited separation between systemic sclerosis (SSc) and healthy control (HC) samples. Right: PCA plot using genes selected by elastic net regularization demonstrates clear separation between SSc and HC groups. (b) Dendrogram showing hierarchical clustering of systemic sclerosis (SSc) patient samples based on monocyte gene expression profiles. Four main clusters (A–D) are identified.

Figure 3.

Functional analysis of differentially expressed genes in patient clusters. (a) cluster A, (b) Cluster B, (c) Cluster C and (d) Cluster D. For each cluster: Top: volcano plot showing the distribution of DEGs. Red dots indicate significantly upregulated genes, and blue dots indicate significantly downregulated genes. Bottom: gene ontology (GO) enrichment analysis results for the DEGs. The top enriched biological process terms are shown for upregulated (red) and downregulated (blue) genes. The x-axis represents the −log10(p-value) for each term, and the size of the dots corresponds to the number of genes associated with each term.

Figure 4.

Interferon score correlates with clinical features. (a) GLS average by cluster: boxplot illustrating the distribution of global longitudinal strain (GLS) average values across the four identified SSc patient clusters. Each dot represents an individual patient’s GLS measurement. (b) Interferon score (IFN6) by cluster: boxplot showing the distribution of interferon signature score (calculated based on expression of IFIT3, IFIT2, MX1, IFIH1, STAT2, and NCF) across the four identified patient clusters. Each dot represents an individual patient, with the red dot and error bars indicating the mean and standard error for each cluster. ANOVA p-value = 0.0031. (c) IFN6 by PH Status: boxplot comparing interferon signature scores between SSc patients with and without pulmonary hypertension (PH). The Wilcoxon test p-value = 0.0441. (d) Interferon score (IFN6) vs GLS average: scatter plot showing the relationship between interferon score (IFN6) and global longitudinal strain (GLS) average. Each dot represents an individual patient. The red line indicates the linear regression fit. Pearson’s correlation coefficient = −0.501, p-value = 0.0066.

Figure 5.

Correlation of gene expression with global longitudinal strain (GLS). (a) Volcano plot showing gene expression correlations with GLS in systemic sclerosis (SSc) patients. Significant correlations are defined by p < 0.05 and |r| > 0.4 (indicated by vertical dashed lines). Blue points represent statistically significant correlations. (b) Correlation plots for three key Notch pathway-related genes: NUMB, PSEN1, and DNER. Each plot shows the negative correlation between gene expression and GLS. (c) Pathway enrichment analysis of genes negatively correlating with GLS. The dot plot shows significantly enriched pathways from various databases (indicated by different colors), with dot size corresponding to −log10(FDR q-value). The analysis reveals enrichment of multiple Notch signaling pathways in different databases.