Detection of Metabolic Alterations in Non-tumor Gastrointestinal Tissue of the <i>Apc</i>^Min/+ Mouse by ¹H MAS NMR Spectroscopy

Abstract

In this study, we have used metabolic profiling (metabolomics/metabonomics) via high resolution magic angle spinning (HRMAS) and solution state (1)H NMR spectroscopy to characterize small bowel and colon tissue from the Apc(Min/+) mouse model of early gastrointestinal (GI) tumorigenesis. Multivariate analysis indicated the presence of metabolic differences between the morphologically normal/non-tumor tissue from approximately 10 week-old Apc(Min/+) mice and their wild-type litter mates. The metabolic profile of isolated lamina propria and epithelial cells from the same groups could also be discriminated on the basis of genotype. Accounting for systematic variation in individual metabolite levels across different anatomical regions of the lower GI tract, the metabolic phenotype of Apc(Min/+) lamina propria tissue was defined by significant increases in the phosphocholine/glycerophosphocholine ratio (PC/GPC, +21%) and decreases in GPC (-25%) and the gut-microbial cometabolite dimethylamine (DMA, -40%) relative to wild type. In the whole tissue, elevated lactate (+15%) and myo-inositol (+19%) levels were detected. As the metabolic changes occurred in non-tumor tissue from animals of very low tumor burden (<2 polyps/animal), they are likely to represent the specific consequence of reduced Apc function and very early events in tumorigenesis. The observed increase in PC/GPC ratio has been previously reported with immortalisation and malignant transformation of cells and is consistent with the role of Apc as a tumor suppressor. Phospholipase A2, which hydrolyses phosphatidylcholine to Acyl-GPC, is a known modifier gene of the model phenotype (Mom1), and altered expression of choline phospholipid enzymes has been reported in gut tissue from Apc(Min/+) mice. These results indicate the presence of a metabolic phenotype associated with "field cancerization", highlighting potential biomarkers for monitoring disease progression, for early evaluation of response to chemoprevention, and for predicting the severity of the polyposis phenotype in the Apc(Min/+) model.