What question did this study set out to answer?

The aim is to identify biomarkers and mechanisms influencing therapy response in Crohn's disease.

January 23, 2026

P0941A “Gut Microbiota-Bile Acid” Integrated Predictive Model for Forecasting Response to Immunotherapy in Crohn’s Disease

Key Points

The aim is to identify biomarkers and mechanisms influencing therapy response in Crohn's disease.
Collected fresh fecal samples from mice prior to disease modeling and treatment with α4β7 integrin antagonist.
Used metagenomic sequencing to analyze fecal DNA and liquid chromatography-mass spectrometry for serum metabolites.
Evaluated the predictive capability of gut microbiota and metabolites for Crohn’s disease treatment response.
Identified significant increases in taxa in the remission group compared to the model group.
Enrichment of specific genera like Lactobacillus and Clostridium in the remission group as potential biomarkers.
Detected 71 significantly differential metabolites; bile acid metabolite functions strongly correlated with treatment response.

Abstract

Abstract Background This study aimed to identify biomarkers and explore underlying mechanisms influencing the response to therapy in Crohn’s disease (CD) by integrating metagenomic sequencing and untargeted metabolomics, thereby providing new perspectives for CD treatment. Methods Fresh fecal samples were collected from mice in all groups prior to the construction of animal disease models and intervention with the α4β7 integrin antagonist. Blood samples were obtained via retro-orbital plexus puncture. DNA was extracted from the original fecal samples and subjected to metagenomic sequencing using the Illumina NovaSeq PE250 platform. Meanwhile, serum samples, which had been aliquoted and stored, were analyzed using liquid chromatography-mass spectrometry (LC-MS) for metabolite extraction, detection, and data analysis. Finally, the predictive capability of combining gut microbiota and metabolites for the response to CD treatment was evaluated. Results Metagenomic sequencing yielded 1,609,033 raw sequences, with a total gene length of 1179.26 Mbp. Compared to the model group, the number of taxa at various levels increased significantly in the remission group but showed no marked increase in the non-remission group. At the genus level, the remission group was enriched with Lactobacillus, Clostridium, Prevotella, and Mucispirillum schaedleri, whereas the non-remission group was enriched with Parabacteroides distasonis and Bacteroides, suggesting these as potential response biomarkers. Untargeted metabolomics identified 71 significantly differential metabolites. Comparative analysis of various baseline gut microbiota-enriched functions and enzymes between groups revealed that only gut microbiota-mediated bile acid metabolites were significantly associated with CD treatment response (P 0.05). Furthermore, this baseline data alone was sufficient to predict the response level (AUC 0.944). Conclusion The integrated predictive model, constructed based on gut microbiota-mediated alterations in bile acids, effectively predicts the response to immunotherapy in Crohn’s disease. Conflict of interest: Mr. Huang, Wei: No conflict of interest Lv, Xiaoping: No conflict of interest Han, Bing: No conflict of interest

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