Regulator combinations identify systemic sclerosis patients with more severe disease

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Systemic sclerosis (SSc) is a complex autoimmune disease characterized by fibrosis and organ damage.
• Previous research identified four intrinsic molecular subsets of SSc based on skin gene expression.
• The regulators driving these subsets and their clinical associations remain largely uncharacterized.

Purpose of the Study:

• To develop a computational framework for assessing gene expression regulator activity in SSc.
• To identify associations between regulator activity and SSc clinical outcomes.
• To determine if regulator activity can define SSc molecular subsets and predict disease severity.

Main Methods:

• Computational analysis of gene expression data to calculate regulator activity scores.
• Correlation analysis of regulator activity with SSc molecular subsets (inflammatory, fibroproliferative, limited, normal-like).
• Association of regulator activity with clinical variables, including modified Rodnan skin score (MRSS) and interstitial lung disease (ILD).

Main Results:

• Regulator activity scores successfully recapitulated the intrinsic SSc molecular subsets.
• Distinct sets of regulators were identified for each subset.
• Regulator activity correlated with MRSS and ILD likelihood, identifying patient subgroups with more severe phenotypes.
• Patients with severe ILD showed a higher likelihood of forced vital capacity decline.

Conclusions:

• Gene expression regulator activity is linked to SSc molecular subsets and clinical manifestations.
• This framework can identify SSc patients with more severe disease, including those at risk for lung function decline.
• Understanding regulator activity offers insights into SSc pathogenesis and potential therapeutic targets.