Individuals can respond to diverse nutrients and dietary restrictions in markedly different ways. Some people easily gain weight, but others remain thin no matter what they eat. Additionally, metabolic diseases can differ dramatically among individuals in a population, for both rare single-gene Mendelian diseases and common multifactorial metabolic diseases such as obesity and type 2 diabetes. In large part, this variability suggests that individual genetic differences greatly affect the likelihood to get sick as well as the severity of the illness for both rare and common metabolic diseases across a population. It would be extremely valuable if one could identify both rare and common variants that contribute to individual responses to diet and to the acquisition of different types of metabolic diseases. Rare variants are usually identified by linkage mapping and whole- genome sequencing using families with affected individuals. By contrast, common variants are usually identified by genome-wide association studies using large populations of people with and without a disease. We will develop personalized metabolic network models for a large set of genetic individuals of the nematode C. elegans, both representing healthy metabolic state and mimicking an inborn error of human metabolism. With our experimental system and approach we will be able to derive predictions of both rare and common variation in a variety of metabolic traits influenced by nutrition. We will extensively validate such predictions using CRISPR/Cas9-mediated genome editing.

Public Health Relevance

Diverse individuals in a population respond to diet in unique ways, and genetic differences among individuals likely underlie variability in healthy metabolism as well as the propensity and severity of metabolic diseases. We will use nutrigenetic and nutrigenomic approaches to derive metabolic network models for diverse individuals using the experimentally tractable nematode Caenorhabditis elegans. These models will enable the first predictions of how nutrients influence metabolism in any animal system.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK115690-02
Application #
9566980
Study Section
Therapeutic Approaches to Genetic Diseases Study Section (TAG)
Program Officer
Karp, Robert W
Project Start
2017-09-20
Project End
2021-08-31
Budget Start
2018-09-01
Budget End
2019-08-31
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Massachusetts Medical School Worcester
Department
Genetics
Type
Schools of Medicine
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code