Systemic sclerosis (SSc) is a complex autoimmune disease with genetic causes that remain largely unidentified, hindering the development of targeted treatments. A recent study published in the Annals of the Rheumatic Diseases by researchers from Baylor College of Medicine and other institutions aims to address this gap. The study employs integrative exome sequencing and evolutionary action machine learning to identify protein changes and their mechanisms in SSc.
Led by Dr. Shamika Ketkar, the research team conducted genome-wide association studies (GWAS) using exome sequencing data from 2,559 SSc patients and 893 healthy controls at the University of Texas Health Science Center at Houston. Their objective was to uncover novel genes and rare variants linked to SSc risk.
“What truly surprised and excited us was the discovery and replication of MICB, a gene located within the HLA region but acting independently of the classical HLA genes. MICB had not previously been implicated in systemic sclerosis, and its identification represents a novel genetic contributor and a potential therapeutic target,” said Ketkar.
Collaborators in Spain validated these findings using European GWAS data from nearly 10,000 cases, reinforcing their significance. At Baylor, Dr. Olivier Lichtarge’s lab applied its evolutionary action-machine learning framework to analyze exome sequencing data, prioritizing genes with high-impact variants predictive of SSc. This analysis highlighted MICB along with other genes on chromosome six like NOTCH4 and rare missense variants enriched in interferon signaling pathways such as IFI44L and IFIT5.
“With our machine learning framework, we are not only identifying whether a variant occurs frequently but also weighing the likelihood that it is functionally disruptive to the protein,” stated Lichtarge.
To further understand these genetic variants' functional impact, researchers integrated single-cell RNA sequencing data from SSc skin biopsies to determine cell type-specific expression patterns of risk genes. They also performed expression quantitative trait locus (eQTL) analysis using whole blood datasets to link disease-associated variants with transcriptomic changes. The study found that MICB and NOTCH4 are expressed in fibroblasts and endothelial cells—central players in fibrosis and vasculopathy associated with SSc.
“To solve complex diseases like SSc, we need to combine different approaches,” said Dr. Brendan Lee.
Contributors include Hongzheng Dai, Lindsay Burrage, David Murdock, Brian Dawson, Marialbert Acosta-Herrera, Martin Kerick, Javier Martin, Kevin Wilhelm, Jennifer Kay Asmussen from various institutions including Baylor College of Medicine and Regeneron Pharmaceuticals Inc.
The study received funding from several sources including the National Institute of Arthritis, Musculoskeletal and Skin Diseases of NIH and others.
