A new advanced artificial intelligence algorithm more accurately model how genes associated with specific autoimmune diseases are expressed and regulated and to identify additional genes of risk. The method outperforms existing methodologies and identified 26% more novel gene and trait associations.
Genetics often underpin disease development. Variations in DNA can influence gene expression, or the process by which the information in DNA is converted into functional products like a protein. How much or how little a gene is expressed can influence disease risk. "With this new method, we were able to identify many more risk genes for autoimmune disease that actually have cell-type specific effects, meaning that they only have effects in a particular cell type and not others," said Bibo Jiang, assistant professor at the Penn State College of Medicine and senior author of the study.
The team's work pointed to drug compounds that could reverse gene expression in cell types associated with an autoimmune disease, such as vitamin K for ulcerative colitis and metformin, which is typically prescribed for type 2 diabetes, for type 1 diabetes. These drugs, already approved by the Food and Drug Administration as safe and effective for treating other diseases, could potentially be repurposed.
Funding from the National Institutes of Health and the Artificial Intelligence and Biomedical Informatics pilot grant from the Penn State College of Medicine supported this work.Lida Wang, Chachrit Khunsriraksakul, Havell Markus, Dieyi Chen, Fan Zhang, Fang Chen, Xiaowei Zhan, Laura Carrel, Dajiang. J. Liu, Bibo Jiang.People with a third copy of chromosome 21, known as trisomy 21, are at high risk of developing acute myeloid leukemia , an aggressive form of blood cancer.