Researchers have long sought a better understanding of how the microbiome is formed and behaves in the human gut.
Scientists from the University of Wisconsin-Madison have developed a platform to predict how microbial gut communities work that could lead to a better understanding of how to manipulate the properties of the gut ecosystem.
The human gut contains numerous microbes, with each one interacting with one another in a network of both positive and negative exchanges. Some interactions produce substances that are food for other microbes, while others produce toxins that kill neighboring microbes.
However, by better predicting microbe behavior, scientists could design a probiotic that persists in the gut or tailor a diet to positively influence human health.
“We know very little about the ecological interactions of the gut microbiome,” Ophelia Venturelli, a biochemistry professor at the University of Wisconsin-Madison, said in a statement. “Many studies have focused on cataloging all of the microbes present, which is very useful, but we wanted to try to understand the rules governing their assembly into communities, how stability is achieved, and how they respond to perturbations as well.”
In the study, the researchers selected 12 bacterial types present in the gut that represent the diversity of the gut microbiome.
The majority of the microbe chosen have been previously shown to significantly impact human health with associations to various diseases including diabetes, irritable bowel syndrome, Crohn’s disease and colon cancer.
Researchers collected data on pairwise interactions, meaning that each bacterial species was paired with one other bacterial species.
The team then fed a dynamic model data about the pairwise interactions, as well as data on each individual species, to decipher how all of the bacteria would likely interact when combined. They found the pairwise data alone was enough to predict how the larger community assembles.
Furthermore, the team also looked at what species seemed to be the most important in the community by measuring substances microbes produce called metabolites, and found that the metabolite data was unable to predict the role of important species in the community.
After testing the model’s predictive power by trying to estimate the characteristics of different combinations of the 12 chosen bacteria, the researchers found that the model did well at predicting dynamic behaviors.
“We found there’s a balance between positive and negative interactions and the negative interactions kind of provide a stabilizing force for the community,” Venturelli said. “We are beginning to understand the design principles of stability of the gut microbes and what allows a community to recover from perturbations.”
The researchers believe that they could better predict the interactions between microbes using computational tools, rather than performing laborious and time-consuming laboratory experiments. They also said they could begin to answer questions about how pathogens cause damage when they invade communities and then develop ways to prevent it.
“Without a model, we are basically just blindly testing things without really knowing what we are doing and what the consequences are when we are, for example, trying to design an intervention,” Venturelli said. “Having a model is a first step toward being able to manipulate the gut ecosystem in a way that can benefit human health.”
The study was published in Molecular Systems Biology.