IDENTIFYING RELEVANT PATHWAYS AND BIOMARKERS IN CROHN’S DISEASE USING CONTEXTUALIZED METABOLIC NETWORK MODEL
Authors: McGrew Brooklyn , Shrivastava Aman , Fernandes Philip , Ehsan Lubaina , Sharma Yash , Payne Dawson , Dillard Lillian , Powers Deborah , Papin Jason , Kellermayer Richard , Griffiths Anne , Otley Anthony , Patel Ashish , Kirschner Barbara , Mack David , Ziring David , Moulton Dedrick , Markowitz James , Shapiro Jason , Hyams Jeffrey , Dotson Jennifer , Rosh Joel , Noe Joshua , Oliva-Hemker Maria , Pfefferkorn Marian , Heyman Melvin , Gulati Ajay , Baldassano Robert , Kim Sandra , Snapper Scott , Rabizadeh Shervin , Cohen Stanley , Guthery Stephen , Baker Susan , Walters Tom , Haberman Yael , Moore Sean , Kugathasan Subra , Denson Lee , Syed Sana 
Published: January 2021

Background

Candidate markers for Crohn’s Disease (CD) may be identified via gene expression-based construction of metabolic networks (MN). These can computationally describe gene-protein-reaction associations for entire tissues and also predict the flux of reactions (rate of turnover of specific molecules via a metabolic pathway). Recon3D is the most comprehensive human MN to date. We used publicly available CD transcriptomic data along with Recon3D to identify metabolites as potential diagnostic and prognostic biomarkers.

Methods

Terminal ileal gene expression profiles (36,372 genes; 218 CD. 42 controls) from the RISK cohort (Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn’s Disease) and their transcriptomic abundances were used. Recon3D was pruned to only include RISK dataset transcripts which determined metabolic reaction linkage with transcriptionally active genes. Flux balance analysis (FBA) was then run using RiPTiDe with context specific transcriptomic data to further constrain genes (Figure 1). RiPTiDe was independently run on transcriptomic data from both CD and controls. From the pruned and constricted MN obtained, reactions were extracted for further analysis.

Results

After applying the necessary constraints to modify Recon3D, 527 CD and 537 control reactions were obtained. Reaction comparison with a publicly available list of healthy small intestinal epithelial reactions (n=1282) showed an overlap of 80 CD and 84 control reactions. These were then further grouped based on their metabolic pathways. RiPTiDe identified context specific metabolic pathway activity without supervision and the percentage of forward, backward, and balanced reactions for each metabolic pathway (Figure 2). The metabolite concentrations in the small intestine was altered among CD patients. Notably, the citric acid cycle and malate-aspartate shuttle were affected, highlighting changes in mitochondrial metabolic pathways. This is illustrated by changes in the number of reactions at equilibrium between CD and control.

Conclusions

The results are relevant as cytosolic acetyl-CoA is needed for fatty acid synthesis and is obtained by removing citrate from the citric acid cycle. An intermediate removal from the cycle has significant cataplerotic effects. The malate-aspartate shuttle also allows electrons to move across the impermeable membrane in the mitochondria (fatty acid synthesis location). These findings are reported by previously published studies where gene expression for fatty acid synthesis is altered in CD patients along with mitochondrial metabolic pathway changes, resulting in altered cell homeostasis. In-depth analysis is currently underway with our work supporting the utility of potential metabolic biomarkers for CD diagnosis, management and improved care.