New Genetic Insights into Systemic Sclerosis Through Exome Sequencing and Machine Learning

Systemic sclerosis (SSc) is a complex autoimmune disorder characterized by fibrosis and vasculopathy, with an intricate genetic landscape that has challenged researchers aiming to develop targeted therapies. Although some genetic factors have been identified, many remain elusive, hindering progress in personalized treatment strategies. A recent study published in the 0Annals of the Rheumatic Diseases1 presents a groundbreaking approach combining exome sequencing with advanced machine learning techniques to uncover novel genetic contributors to SSc.

Led by researchers at Baylor College of Medicine, the study analyzed exome sequencing data from 2,559 SSc patients and 893 healthy controls sourced from the Scleroderma Family Registry and DNA Repository at the University of Texas. The primary goal was to identify rare genetic variants and new genes associated with disease risk. Among the notable findings was the identification of MICB, a gene situated within the human leukocyte antigen (HLA) region on chromosome six, acting independently of classical HLA genes. This was particularly exciting since MICB had not been previously linked to SSc, suggesting it as a potential new target for therapeutic intervention.

Replication of these findings was successfully achieved using European GWAS data involving nearly 10,000 cases, reinforcing the importance of MICB as a key genetic factor. Further analysis using Baylor's evolutionary action machine learning (EAML) framework facilitated the prioritization of high-impact variants predictive of SSc, including genes involved in interferon signaling pathways such as IFI44L and IFIT5. These pathways are crucial in immune response regulation, and their involvement provides valuable insights into disease mechanisms.

Functional validation through integration of single-cell RNA sequencing data from SSc skin biopsies revealed that MICB and NOTCH4 genes are expressed in fibroblasts and endothelial cells. These cell types play central roles in fibrosis and blood vessel complications characteristic of SSc. Regulatory linkages between disease-associated variants and gene expression were further confirmed via eQTL analysis in blood datasets, establishing a direct connection between genetic variants and gene regulation.

This multifaceted approach underscores the power of combining genomic, transcriptomic, and computational methods to unravel complex disease genetics. Lead author Dr. Brendan Lee emphasized that leveraging machine learning on smaller and complex datasets can effectively identify novel therapeutic targets. Overall, this study offers significant advancements in understanding the genetic basis of systemic sclerosis and opens new avenues for developing targeted treatments.

(source: https://medicalxpress.com/news/2025-06-exome-sequencing-machine-genes-contributing.html)