Personalized nutrition has emerged in recent years as a key potential solution to a variety of diseases that originate in the gut, and Nebraska scientists are mining microbiome data to determine how individuals’ digestive systems might respond to different nutritional approaches.
Ultimately, these findings could help scientists and doctors recommend specific types of foods — say, yogurts — to individuals to nurture beneficial bacteria in their guts and stave off diseases such as diabetes and obesity.
Yanbin Yin, associate professor of food science at the University of Nebraska–Lincoln, has received a four-year, $1.2 million grant from the National Institutes of Health to continue his research. Yin’s lab develops computational models and informatics tools to identify carbohydrate-active enzymes — CAZymes — in the gut microbiome that can build, modify and break down various complex carbohydrates. These CAZymes are made by our gut bacteria to fully digest fibers in our diets. Bacterial digestion of these fibers produces metabolites, such as short-chain fatty acids, that have a significant influence on their human hosts’ health, Yin said.
“However, every human individual has different microbiome compositions and thus may respond to the same dietary fibers differently,” Yin said. “We aim to develop machine learning tools to help predict which human individuals may respond to which dietary fibers by analyzing their gut microbiome DNA sequences.”
Yin and his co-investigator, Yuzhen Zhou, assistant professor of statistics at Nebraska, will develop new computer software that can automate the data mining of these sequencing data for CAZymes and their gene clusters. Finding these gene clusters will contribute to addressing two fundamental personalized nutrition questions:
Creating new bioinformatics tools for predicting carbohydrate use will contribute to the arising microbiome-based personalized nutrition practice and aid in the development of therapeutic options to prevent and treat human metabolic disorders.
The software Yin is creating can analyze and classify CAZymes at a more detailed level than ever before. It looks for key features within genetic code to distinguish among different CAZyme groups and predict how the enzymes function.
The computer algorithms learn and improve as data is added. Yin started his research with a CAZyme database, CAZy, compiled from the scientific literature and maintained by other researchers. He’s been using the database to train his CAZyme discovery software and has packaged the software into a free, user-friendly website, dbCAN, for CAZyme researchers.
Yin has more than 10 years of experience in developing CAZyme bioinformatics tools and maintains the well-recognized CAZyme annotation database and web server. His CAZyme bioinformatics research has been funded by an NSF CAREER award.
Yin’s work is affiliated with the Nebraska Food for Health Center, launched in September 2016, which brings together faculty researchers from the University of Nebraska–Lincoln, the University of Nebraska Medical Center and the University of Nebraska at Omaha to tie gastrointestinal and biomedical research to agriculture, plant and animal breeding, and genetics.
Source: University of Nebraska-Lincoln