Network of genes and proteins representing IBD regulators

Decoding IBD: A New Model Identifies Key Regulators of Inflammatory Bowel Disease

Kai Mendoza in Health & Wellness August 2025 • 4 min read.

"Researchers develop a functional genomics network model to predict and validate key genes driving inflammatory bowel disease, offering new insights for treatment."

Inflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, poses a significant challenge to healthcare due to its complex nature and varied presentation. Characterized by chronic inflammation of the gastrointestinal tract, IBD's exact causes remain elusive, although genetic predisposition, environmental factors, and immune system dysregulation are known to play crucial roles.

Previous genome-wide association studies (GWAS) have identified over 200 IBD-associated loci, yet these explain only a fraction of the heritability, underscoring the need for integrative approaches that consider the interplay of genes, regulatory elements, and environmental influences. The challenge lies in translating these genetic associations into a functional understanding of IBD pathogenesis.

Now, a groundbreaking study published in Nature Genetics presents a functional genomics predictive network model designed to identify key regulators of IBD. This model integrates diverse IBD datasets with functional and regulatory annotations to map causal relationships between genetic loci and disease mechanisms, offering a novel framework for understanding and potentially treating IBD.

Building a Predictive Network for IBD

Network of genes and proteins representing IBD regulators

The researchers constructed a predictive model focused on the immune component of IBD, leveraging molecular data from intestinal samples of IBD patients at different disease stages. This involved creating individual networks for each patient population and then using 'key driver analysis' to pinpoint genes predicted to modulate network regulatory states associated with IBD.

The network was built using data from three distinct cohorts:

RISK cohort: Treatment-naive pediatric patients.
CERTIFI cohort: Adult patients refractory to anti-TNF-α therapy.
Novel MSH population: Patients with advanced disease.

The researchers integrated IBD risk SNPs, expression quantitative trait loci (eQTLs), and cis-regulatory element (CRE) data to identify candidate causal IBD genes. This multi-layered approach allowed them to prioritize genes with strong evidence of regulatory function in the context of IBD.

Implications for IBD Treatment

This study marks a significant step towards a more comprehensive understanding of IBD. By identifying and validating key regulators within the immune network, the researchers have not only expanded the known players in IBD pathogenesis but also provided a framework for future research. The validated key driver set introduces new regulators of processes central to IBD and provides integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1038/ng.3947, Alternate LINK

Title: A Functional Genomics Predictive Network Model Identifies Regulators Of Inflammatory Bowel Disease

Subject: Genetics

Journal: Nature Genetics

Publisher: Springer Science and Business Media LLC

Authors: Lauren A Peters, Jacqueline Perrigoue, Arthur Mortha, Alina Iuga, Won-Min Song, Eric M Neiman, Sean R Llewellyn, Antonio Di Narzo, Brian A Kidd, Shannon E Telesco, Yongzhong Zhao, Aleksandar Stojmirovic, Jocelyn Sendecki, Khader Shameer, Riccardo Miotto, Bojan Losic, Hardik Shah, Eunjee Lee, Minghui Wang, Jeremiah J Faith, Andrew Kasarskis, Carrie Brodmerkel, Mark Curran, Anuk Das, Joshua R Friedman, Yoshinori Fukui, Mary Beth Humphrey, Brian M Iritani, Nicholas Sibinga, Teresa K Tarrant, Carmen Argmann, Ke Hao, Panos Roussos, Jun Zhu, Bin Zhang, Radu Dobrin, Lloyd F Mayer, Eric E Schadt

Published: 2017-09-11

Everything You Need To Know

What is the functional genomics network model?

The functional genomics network model is a newly developed tool that integrates various IBD datasets with functional and regulatory annotations. It's designed to map causal relationships between genetic loci and disease mechanisms. This model is crucial because it offers a new way to understand how genetic variations contribute to Inflammatory Bowel Disease (IBD), encompassing conditions like Crohn's disease and ulcerative colitis, and potentially how to treat it. By pinpointing key regulators, the model helps bridge the gap between genetic associations and a functional understanding of IBD.

What are 'key regulators' in this research, and why are they important?

Key regulators in the context of this research are genes that are predicted to significantly influence the network regulatory states associated with Inflammatory Bowel Disease (IBD). The researchers used 'key driver analysis' to find these genes by examining molecular data from intestinal samples of IBD patients at different disease stages. Identifying these key regulators is important because they are central to the development and progression of IBD. They represent potential targets for new therapies, offering a more targeted approach to treatment than previously possible.

What are the different patient cohorts used in this study, and what is the significance of each?

The study uses data from three patient cohorts: the RISK cohort (treatment-naive pediatric patients), the CERTIFI cohort (adult patients refractory to anti-TNF-α therapy), and a Novel MSH population (patients with advanced disease). Each cohort provides unique insights. The RISK cohort offers insights into early-stage disease, the CERTIFI cohort provides information on patients who did not respond to anti-TNF-α therapy, and the Novel MSH population allows for the study of advanced disease states. Combining data from these distinct groups enables a more comprehensive understanding of IBD across different patient populations and disease severities.

What types of data were integrated to identify candidate causal IBD genes?

The study integrated IBD risk SNPs, expression quantitative trait loci (eQTLs), and cis-regulatory element (CRE) data. IBD risk SNPs are genetic variations associated with the risk of developing IBD. eQTLs help identify how these genetic variations affect gene expression, and CRE data provides information about regulatory elements that control gene activity. Integrating these data types enables researchers to identify candidate causal IBD genes by prioritizing those with strong evidence of regulatory function within the context of IBD. This multi-layered approach strengthens the evidence linking specific genes to the disease.

What are the implications of this study for the treatment of Inflammatory Bowel Disease (IBD)?

The implications for Inflammatory Bowel Disease (IBD) treatment are significant. By identifying new regulators within the immune network, researchers have expanded the known players in IBD pathogenesis. This study offers a framework for future research, with the validated key driver set introducing new regulators of processes central to IBD. It provides integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD, leading to a deeper understanding of this complex condition and paving the way for the development of targeted therapies.