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© 2005 American Society for Nutrition J. Nutr. 135:3027S-3032S, December 2005

Supplement: International Conference on Diet, Nutrition, and Cancer

Nutrient-Gene Interaction: Tracer-Based Metabolomics1,2,3

Wai-Nang P. Lee and Vay Liang W. Go*,4

LABiomed Research Institute at Harbor-UCLA Medical Center, and * David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA

4To whom correspondence should be addressed. E-mail: lee{at}labiomed.org.

ABSTRACT

Understanding nutrient-gene interaction requires tools for both the study of nutrigenomics and the characterization of phenotype. Metabolomics or metabolite profiling is a powerful tool for characterizing metabolic phenotype, and tracer-based metabolomics is a subset of metabolomics that focuses on metabolite distribution and flux determination using tracers. In this review, the characterizations of metabolic phenotype by metabolite profiling and by metabolic flux measurements are compared. The rationale and methodologies of tracer-based metabolomics are explained. Tracer-based metabolomics provides a relational database of metabolites linked by the relationship of shared metabolic pathways, common substrates, and cofactors. Such a collection of flux measurements provides precise and accurate information on the operation of the cellular metabolic network and its response to genetic and nutrient environment changes. Nutrient-gene interaction can be studied using the concept of constraint-based modeling, which states that the observed metabolic phenotype is a consequence of constraints from genetic factors and the nutrient environment. Thus, genetic inheritance (genomic constraints) confers a wide range of possible phenotypes whereas selection by metabolic (structural and pathway relationship) and environmental (physical environment and nutrient availability) constraints determines the final observed phenotype. The study of the contribution from nutrient and genetic factors to the survival advantage of cancer cells using flux measurements is a critical first step in our understanding of the relationship between nutrient intake and cancer risk.

KEY WORDS: • glucose intermediary metabolism • ribose synthesis • isotopomer analysis • constraint-based modeling






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