Metabolic pathway and network analysis integration for discovering the biomarkers in pig feces after a controlled fruit-vegetable dietary intervention.

This study aimed to establish a strategy for identifying dietary intake biomarkers using a non-targeted metabolomic approach, including metabolic pathway and network analysis. The strategy was successfully applied to identify dietary intake biomarkers in fecal samples from pigs fed two doses of a polyphenol-rich fruit and vegetable (FV) diet following the Dietary Guidelines for Americans (DGA) recommendations. Potential biomarkers were identified among dietary treatment groups using liquid chromatography-high resolution mass spectrometry (LC-HRMS) based on a non-targeted metabolomic approach with metabolic pathway and network analysis. Principal component analysis (PCA) results showed significant differences in fecal metabolite profiles between the control and two FV intervention groups, indicating a diet-induced differential fecal metabolite profile after FV intervention. Metabolites from common flavonoids, e.g., (epi)catechin and protocatechuic acid, or unique flavonoids, e.g., 5,3',4'-trihydroxy-3-methoxy-6,7-methylenedioxyflavone and 3,5,3',4'-tetrahydroxy-6,7-methylenedioxyflavone, were identified as highly discriminating factors, confirming their potential as fecal markers for the FV dietary intervention. Microbiota pathway prediction using targeted flavonoids provided valuable and reliable biomarker exploration with high confidence. A correlation network analysis between these discriminatory ion features was applied to find connections to possible dietary biomarkers, further validating these biomarkers with biochemical insights. This study demonstrates that integrating metabolic pathways and network analysis with a non-targeted metabolomic approach is highly effective for rapid and accurate identification and prediction of fecal biomarkers under controlled dietary conditions in animal studies. This approach can also be utilized to study microbial metabolisms in human clinical research.