Researchers have used a multi-omics approach to construct the first-ever predictive model of inflammatory bowel disease (IBD)

Researchers have constructed the first-ever predictive model of inflammatory bowel disease (IBD), revealing the complexity of its immune network through a multi-omics approach—a complexity that they hope will yield potential targets for new treatments.

The model consists of individual networks constructed using molecular data generated from intestinal tissue samples isolated from three different groups of patients with various stages of IBD, the researchers said. The data included DNA variation, gene expression, regulatory elements, and clinical information.

Gathering that data from the samples enabled the researchers to observe the effects of genes and regulatory elements on each other, and thus represent the entire network of immune activity. Investigators then modeled the precise biological networks involved in the immune component by identifying genes predicted to modulate network regulatory states associated with IBD.

The team prioritized and prospectively validated the top 12 genes predicted to alter the immune network, providing new insights into elements that regulate IBD.

“This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD,” the researchers wrote.

Their study, “A Functional Genomics Predictive Network Model Identifies Regulators of Inflammatory Bowel Disease,” was published Monday in Nature Genetics.

“Our predictive model serves as a repository of knowledge and understanding that facilitates learning more about the development and progression of IBD, including identifying master regulators of disease that can be explored as targets for treatment,” Eric Schadt, Ph.D., senior author of the study and CEO of Sema4, a Mount Sinai Health System spinout, said in a statement.

“These results demonstrate how much we stand to gain by organizing massive amounts of molecular and clinical data using advanced machine learning approaches that in turn can be queried to generate novel disease insights,” added Dr. Schadt, who is dean for precision medicine at Icahn School of Medicine at Mount Sinai.

Researchers from Icahn Mount Sinai and Sema4 leveraged clinical research and deep genomics data by collaborating with immunology investigators from Janssen Research & Development.

Sema4 was created earlier this year through the spinout of several genetic testing and data sciences components from Mount Sinai Health’s Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology.

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