BMC MedicineSystems BiologyPeer-reviewed journal article
Investigating intestinal epithelium metabolic dysfunction in Celiac Disease using personalized genome scale models
Chloe V. McCreeryiD, Drew S. AlessiiD, Katarina Mollo, Alessio FasanoiD, Ali R. ZomorrodiiD
Abstract
Celiac Disease (CeD) is an autoimmune condition characterized by an aberrant immune response triggered by the ingestion of gluten, which damages epithelial cells lining the small intestine. Small intestinal epithelial cells (sIECs) play a key role in various metabolic processes, including the enzymatic digestion and absorption of nutrients. Although nutritional malabsorption is widely recognized in CeD, the underlying disrupted metabolic processes remain largely undefined. To address this knowledge gap, we constructed personalized gender-specific genome-scale models of sIEC metabolism using transcriptional data from 42 subjects with active CeD, remission CeD, and healthy controls. We computationally simulated these models under a relevant diet for each group of subjects to assess the activity of 59 metabolic tasks essential for sIEC function and to profile metabolite secretion into the bloodstream and intestinal lumen. These investigations revealed significant variations in the activity of 25 metabolic tasks in active and remission CeD models. These tasks impact critical processes integral to sIEC function such as amino acid metabolism, nucleotide synthesis and DNA repair, ATP generation, and oxidative stress regulation. Additionally, we identified 54 metabolites with altered secretion profiles in CeD, encompassing amino acids, vitamins, antioxidants, and fatty acids. Furthermore, we pinpointed 22 FDA-approved drugs that target the genes associated with differentially active metabolic functions whose altered activities adversely affect sIECs in CeD, potentially helping to restore their normal activity. Our study unveils new insights into the metabolic reprogramming of sIECs in CeD, paving the way for therapeutic interventions targeting dysregulated metabolic processes.
- organism: human
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 7
- FWCI
- 5.66
- Field percentile
- top 4%
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