Population-Based Characterization of Metabolic Pathways to Predict Pediatric Crohn's Disease Outcomes

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PROJECT SUMMARY/ABSTRACT Pediatric Crohn's Disease (CD) is a chronic, progressive disease which can have a severe impact on a child's growth, and development. Children are more likely to develop advanced forms of the disease within years of diagnosis. Treating pediatric Crohn's cases requires careful consideration as powerful anti-inflammatory treatments may have adverse effects and may not be necessary depending on severity of disease. Therefore, risk stratifying pediatric Crohn's populations, and predicting future subtypes, including structural manifestations of disease and a lack of response to treatments, is an urgent unmet need. While genomic markers of disease have been studied at length, exploration of the metabolic signature of pediatric Crohn's is less developed. Studies in recent years have identified metabolic changes which occur during Crohn's, including changes in lipid, amino acid, tricarboxylic acid and sulfur metabolism. But metabolic shifts have not been studied in enough detail or in large enough cohorts to become clinical biomarkers, especially for delineating subtypes of disease rather than Crohn's versus normal tissue. And although metabolic pathways are targetable and there are preliminary findings that blocking metabolic pathways (i.e., the mevalonate pathway), can be beneficial for Crohn's outcome, targeting metabolism has not become a widespread phenomenon. In this proposal, we will leverage computational methods to analyze transcriptomics data from large pediatric CD cohorts and map this data onto mathematical metabolic reconstructions to assess metabolic shifts. We hypothesize that identification of unique metabolic shifts in population-based cohorts will inform prediction of Crohn's subtypes, both structural and treatment-based. In Aim 1, we propose to build a novel computational metabolic network reconstruction that will be specific to the metabolic functioning of the ileum, a primary site of Crohn's pathology. This model will serve as a reference for understanding CD metabolic shifts but can also serve as a resource for other groups studying metabolism shifts in the small bowel. In Aim 2, we will leverage existing data from the large pediatric CD cohort, to computationally overlay transcriptomics from a range of subtypes onto our metabolic network reconstruction to assess shifts in metabolism. We will also recruit a prospective cohort of CD patients from both the University of Virginia and Emory University, collect tissue, perform RNA sequencing, and repeat our computational metabolic modeling to validate our analysis of archived data. These results will be further validated by mass spectrometry metabolomics and lipidomics. Finally, in Aim 3, we will profile the transcriptomic and metabolomic signatures of pediatric Crohn's-patient derived ileal organoids, to test if organoids are a valuable proxy for studying metabolic shifts in vivo for mechanistic intervention experiments. Together, these experiments will pave the way towards using high-throughput metabolic data to risk stratify pediatric Crohn's patient subtypes, which can facilitate personalized medicine treatment paradigms.