Xin Xing, a doctoral student in the Department of Statistics, uses advanced DNA sequencing technology to develop high-performance computation algorithms on the human microbiome – the collection of the many diverse types of micro-organisms that occupy almost every part of the human body.
Xing is especially interested in gut microbiome related to type-2 diabetes and inflammatory bowel disease.
“Many people know that diabetes can cause blindness, nerve pain, and lack of circulation in the limbs that can lead to amputation, Xing explains. “Fewer know that the disease can also contribute to problems throughout the digestive tract.”
For this reason, people with diabetes have a high risk of reflux, abdominal pain, nausea, ulcers, and diarrhea.
“While a lot of money has been invested on the genetic factors that affect diabetes, there is limited research on the environmental factors (such as the microbiome) that may contribute toward the disease,” says Xing.
“We know that the microbial distribution in the diabetes and the healthy gut is different. What we’d like to know is the causality- what changes are caused by the disease and how changes affect the course of the disease.”
Xing’s research is a balance between statistical methods and its application to real-world problems.
“Current technology allows us to collect more and more data, but also presents challenges for scientists to analyze this data and extract meaningful information from it.”
This balance between statistical theory and application, Xing explains, is why a doctoral degree from the University of Georgia was so appealing to him.
“The microbial world is very complex and like a mystery to me,” Xing says. “I’m looking for ways to use my statistics knowledge to understand the nature of microbiomes.”
Xing’s algorithm has been applied on two real-world data sets: one relating to type-2 diabetes patients and another to inflammatory bowel disease patients.
The algorithm successfully identified more than 2,000 microbial species, of which less than 10 percent have reference genomes available.
In total, 8 pathogenic microbial species have been identified by the algorithm. This includes 7 unknown species and 1 known species related to inflammatory bowel disease (Bateroides fragilis).
“Learning about the microbial communities may provide doctors with a new way to give fast and accurate diagnoses,” Xing explains. “In addition, by identifying these disease-related pathogens, we can help researches develop treatments.”
This research holds tremendous scientific promise since bulk DNA sequencing allows scientists to bypass the difficulties arising in cell cultivation (such as the rapid cell death of large amounts of microbial species once the environment has changed).
Following graduation, Xing will pursue a career in academia and continue his research in statistics, while teaching and sharing knowledge with students.