{"id":210712,"date":"2025-06-24T15:27:11","date_gmt":"2025-06-24T15:27:11","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/210712\/"},"modified":"2025-06-24T15:27:11","modified_gmt":"2025-06-24T15:27:11","slug":"study-reveals-rare-gene-variants-contributing-to-systemic-sclerosis-risk","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/210712\/","title":{"rendered":"Study reveals rare gene variants contributing to systemic sclerosis risk"},"content":{"rendered":"

\t\"\"Complex computational analyses help researchers uncover whether new genetic variants contribute to disease.<\/strong> Image courtesy of Unsplash.<\/p>\n

Systemic sclerosis (SSc) is a severe autoimmune disease with complex genetic causes. Some genetic contributors have been identified, but others remain unknown, which has impeded development of targeted treatments. In a new study published in\u00a0Annals of the Rheumatic Diseases<\/a>, researchers at Baylor College of Medicine and collaborating institutions used complementary approaches that integrate exome sequencing and evolutionary action machine learning to identify protein changes and their associated mechanisms in SSc.<\/p>\n

\"\"<\/a>Dr. Shamika Ketkar<\/strong><\/p>\n

Previous genome-wide association studies (GWAS) that analyzed the frequency of common genetic variants show the strongest genetic contributors located in the human leucocyte antigen (HLA) region on chromosome six. In this study, researchers led by first author\u00a0Dr. Shamika Ketkar<\/a>\u00a0performed GWAS using exome sequencing data from 2,559 SSc patient cases and 893 healthy control cases in the Scleroderma Family Registry and DNA Repository at the University of Texas Health Science Center at Houston. They aimed to find novel genes and rare variants contributing to SSc risk.<\/p>\n

Complementary approaches uncover new gene variants and their functional contribution<\/strong><\/p>\n

\u201cWhat truly surprised and excited us was the discovery of\u00a0MICB, a gene located within the HLA region but acting independently of the classical HLA genes.<\/p>\n

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MICB\u00a0had not previously been implicated in systemic sclerosis, and its identification represents a novel genetic contributor and a potential therapeutic target,\u201d said Ketkar, assistant professor of molecular and human genetics at Baylor.<\/p>\n<\/blockquote>\n

\"\"<\/a>Dr. Olivier Lichtarge<\/strong><\/p>\n

Collaborators in Spain replicated the findings using previously published European GWAS data comprising nearly 10,000 cases, further strengthening the significance of the findings. At Baylor,\u00a0Dr. Olivier Lichtarge<\/a>\u2019s lab used its evolutionary action-machine learning (EAML) framework to analyze the exome sequencing data and prioritize genes with variants highly predictive of SSc. The results once again pointed to\u00a0MICB, as well as other genes on chromosome six like\u00a0NOTCH4\u00a0and rare variants in genes enriched in interferon signaling (a key pathway in the immune system), including\u00a0IFI44L\u00a0and\u00a0IFIT5.<\/p>\n

\u201cWith our machine learning framework, we are not only identifying whether a variant occurs frequently, but also, using evolutionary data across all species, we are weighing the likelihood the variant is functionally disruptive to the protein and eventually to the patient,\u201d said Lichtarge, Cullen Chair and professor of molecular and human genetics, biochemistry and molecular biology and pharmacology.<\/p>\n

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We previously used this method in diseases with much larger genome data sets, like Alzheimer\u2019s disease and heart disease. In this study, we show that it can be effective in complex diseases with a smaller patient data set.\u201d<\/p>\n<\/blockquote>\n

To understand the functional impact of the genetic variants identified in the study, researchers integrated publicly available single-cell RNA sequencing data from SSc skin biopsies to identify the types of cells in which the genes are expressed. They also performed expression quantitative trait locus (eQTL) analysis using whole blood datasets to establish whether disease-associated variants regulated the expression of other genes.<\/p>\n

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MICB\u00a0and\u00a0NOTCH4\u00a0were found to be expressed in fibroblasts and endothelial cells, two cell types that play central roles in fibrosis and vasculopathy, key clinical features of SSc. These complementary analyses confirmed functional regulatory effects of identified risk genes.<\/p>\n<\/blockquote>\n

\"\"<\/a>Dr. Brendan Lee<\/strong><\/p>\n

\u201cTo solve complex diseases like SSc, we need to combine different approaches and machine learning with the analysis of large DNA, RNA and protein data sets to discover otherwise hidden targets for treatment,\u201d said corresponding author\u00a0Dr. Brendan Lee<\/a>, professor, chair and Robert and Janice McNair Endowed Chair of\u00a0molecular and human genetics<\/a>\u00a0at Baylor.<\/p>\n

Other authors who contributed to this work include Hongzheng Dai, Lindsay Burrage, David Murdock, Brian Dawson, Marialbert Acosta-Herrera, Martin Kerick, Javier Martin, Kevin Wilhelm, Jennifer Kay Asmussen, Regeneron Genetics Center, Shervin Assassi and Maureen D. Mayes. They are affiliated with one of the following institutions: Baylor College of Medicine, McGovern Medical School at UTHealth Houston, Institute of Parasitology and Biomedicine Lopez-Neyra and Regeneron Pharmaceuticals Inc.<\/p>\n

This work was funded by the National Institute of Arthritis, Musculoskeletal and Skin Diseases of the National Institutes of Health, the University of Texas Health Science Center and the Department of Defense Congressionally Directed Medical Research Program. See the publication for a full list of funding.<\/p>\n

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By Molly Chiu<\/a><\/strong><\/p>\n

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