Researchers at Penn State College of Medicine have developed a new artificial intelligence algorithm that may lead to improved predictions and novel therapies for autoimmune diseases. The algorithm dives into the genetic code underlying the conditions to more accurately model how genes associated with specific autoimmune diseases are expressed and regulated and to identify additional genes of risk.
Their findings are published in Nature Communications in an article titled, “Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.”
“Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants,” the researchers wrote. “Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants.”
The researchers report that their algorithm outperforms existing methodologies and identified 26% more novel gene and trait associations.
“We all carry some DNA mutations, and we need to figure out how any one of these mutations may influence gene expression linked to disease so we can predict disease risk early. This is especially important for autoimmune disease,” explained Dajiang Liu, PhD, distinguished professor, vice chair for research, and director of artificial intelligence and biomedical informatics at the Penn State College of Medicine and co-senior author of the study. “If an AI algorithm can more accurately predict disease risk, it means we can carry out interventions earlier.”
EXPRESSO applies a more advanced artificial intelligence algorithm and analyzes data from single-cell expression quantitative trait loci, a type of data that links genetic variants to the genes they regulate. It also integrates 3D genomic data and epigenetics—which measures how genes may be modified by environment to influence disease—into its modeling. The team applied EXPRESSO to GWAS datasets for 14 autoimmune diseases, including lupus, Crohn’s disease, ulcerative colitis, and rheumatoid arthritis.
“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, PhD, assistant professor at the Penn State College of Medicine and senior author of the study.
The team then used this information to identify potential therapeutics for autoimmune disease.
“Most treatments are designed to mitigate symptoms, not cure the disease. It’s a dilemma knowing that autoimmune disease needs long-term treatment, but the existing treatments often have such bad side effects that they can’t be used for long. Yet, genomics and AI offer a promising route to develop novel therapeutics,” said Laura Carrel, PhD, professor of biochemistry and molecular biology at the Penn State College of Medicine and co-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. These drugs, already approved by the FDA as safe and effective for treating other diseases, could potentially be repurposed.
The research team is working with collaborators to validate their findings in a laboratory setting and, ultimately, in clinical trials.
