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Sequencing the intestinal microbiome to target therapies

UMass scientists are developing mathematical models to predict which treatments may be most effective for enteric diseases

Like all extremely complex systems, the human microbiome is loaded with data that if analyzed correctly, can reveal useful and telling patterns. Vanni Bucci, PhD, assistant professor of computational biology at UMass Dartmouth, is collaborating with colleagues in the UMass Center for Microbiome Research to sequence the intestinal microbiome with the goals of targeting treatments for specific enteric diseases and prototyping probiotic cocktails that could be used as a substitute for antibiotic treatment. 


What is a microbiome?
Collectively, the microorganisms and their genes inhabiting a particular environment are called a microbiome. The microorganisms that live in and on an individual are referred to as the human microbiome. The human gut contains trillions of bacteria that have profound influences on immune development, health and disease.

The UMass Center for Microbiome Research
The UMass Center for Microbiome Research was created with support from the University of Massachusetts President’s Science and Technology Initiatives Fund to accelerate the understanding of the role that the microbiome plays in individual health. The center promotes innovative and collaborative research across the five UMass campuses, translates discoveries into practice and shares information about the latest microbiome research.


“What I look at is how can I transfer these large data sets into predictive tools that allow us to determine the risk of disease . . . as a function of microbiome composition and antibiotic perturbations,” said Dr. Bucci.

Antibiotic treatment can often lead to large shifts in the bacterial composition of the intestinal microbiome, wiping out the good along with the bad bacteria. When the good bacteria is reduced or eliminated, it creates an environment where intestinal infections can flourish. 

“DNA sequencing technologies have allowed us to survey this very complex community and a lot of data has been generated,” said Bucci. “The biggest problem—and that’s the focus of my research—is how to transfer the knowledge in this data from a purely descriptive analysis to where we can correlate microbiome shifts with disease to gain a more mechanistic understanding and actually gain the ability to make predictions.”

Bucci likens the type of model he and colleagues are building to models designed to predict the weather.

“In weather forecasting, people try to tell you if it’s going to rain tomorrow or not using mathematical models; we develop models that will hopefully tell us if, in a week or 10 days, in response to a specific antibiotic, we are going to encounter a specific disease,” Bucci said.

Related links on UMassMedNow:
Solving the complex problem of recurrent C. diff with a simple solution
Searching the microbiome for clues to managing inflammatory bowel disease
#UMassMedChat on the microbiome is Thursday at 11:30 a.m.