Genetic Control of Metabolic Flux in Response to Diet

PROJECT SUMMARY/ABSTRACT
Despite many years of research in humans and model organisms, there remains no clear consensus about which
diet is most compatible with human health. However, the premise of this statement is that a single diet is ideal
for everyone. Yet, among humans, there is a wide range in metabolic response to various diets, including body
weight, glucose tolerance, and plasma lipids. Genetically diverse mice show this same metabolic variability,
suggesting that genetics plays a key role in driving diet-responsiveness. In addition, the microbiome in both
humans and mice contributes to diet responsiveness through its metabolism of dietary nutrients and production
of potent metabolites. The premise of this project is that genetic interactions with diet and the gut microbiome
affect metabolic health. Using an outbred mouse model system that has as much genetic diversity as the entire
human population, the Diversity Outbred population, we will genetically map the gene loci that interact with diet
and the microbiome to affect cardiometabolic phenotypes. We will test two diets, a low-fat/high carbohydrate diet
and a high-fat/low carbohydrate diet. The mice will be phenotyped for glucose tolerance, insulin resistance,
weight gain, and circulating levels of lipids and metabolites. Using 15 stable isotope tracers, we will conduct
metabolic flux measurements using mass spectrometry-based isotopomer analysis, enabling us to interrogate
the major pathways of carbohydrate, lipid, and protein metabolism in multiple tissues. This will be the first time
metabolic flux has been subjected to a genetic screen. We will also map gut microbial composition, and gene
regulation in key metabolic tissues: liver, adipose, muscle and intestine. These studies will deliver
comprehensive genetic maps of these phenotypes. Through the identification of phenotypes that co-map, we
will perform mediation analysis to construct causal networks that link gene loci, metabolites, microbiome taxa
and physiological phenotypes. We will prioritize loci that are syntenic to human loci with significant metabolic
associations in GWAS. These results will provide metabolic markers that can help predict an individual’s
metabolic response to specific diets, the first step towards matching diets to individuals.

Grant Number: 5RC2DK125961-05
NIH Institute/Center: NIH
Principal Investigator: Alan Attie