Biomedical engineers at Duke University have demonstrated a method for systematically developing novel, complex combinations of probiotics and prebiotics to more effectively maintain gut health and treat various gastrointestinal diseases.
By tactically designing experiments and robotically automating thousands of parallel experiments to fill knowledge gaps that could make the model more accurate, the approach can reveal complex interactions between many microbial species, nutritional sources and the surrounding environment.
One of the challenges to designing probiotics and prebiotics that improve gut health is the substantial variability in our diet, organisms present in our gut, medications and other host factors. In a proof-of-concept study, researchers use this method to identify specific bacterial combinations and dietary fibers that together regularly promote the health and function of the human gut regardless of variability in the organisms present. The approach could be a boon for a rapidly growing prebiotic and probiotic industry that has long struggled to demonstrate consistent and predictable results for its customers.
"If you take a seed and plant it in soil that is depleted of nutrients and minerals, it might not grow very well," said Ophelia Venturelli, associate professor of biomedical engineering at Duke. "The same thing applies for putting microbes into a person's gut microbiome. This work aims to engineer both the environment and the bacteria to improve human health."
The results appear online July 27 in the journal Nature Chemical Biology.
The human gut is an amazingly complicated environment that hosts thousands of different species of microorganisms. Incredibly, the number of microbial cells in the human gut that roughly equal the number of human cells, weighing up to nearly half a pound.
To further complicate matters, the makeup of species and their relative ratios vary greatly from region to region, and even person to person, depending on a wide range of factors like diet, lifestyle and environmental exposures. Although there are high commonalities between bacterial groups in the human gut microbiome, different people harbor different bacterial species, leaving a large amount of variability that can make treating disruptions in their functions difficult.
And disruptions can be serious. Microbes in the gut prevent unwanted pathogens from taking hold while breaking down food like dietary fiber and complex carbohydrates that the stomach can't. This process provides nutrients essential for the health of gastrointestinal cells. Bacteria also produce other molecules that shape our immune system development, energy balance, cardiometabolic health and even our brain and neurological activities.
To address these issues, introducing healthy microbial communities and making sure they have the nutrients they need to thrive has become a major health industry, currently valued at about $130 billion worldwide . Demonstrating consistent results, however, has remained a challenge.
"Probiotics alone might not be able colonize a person's gut long enough to do anything helpful because there's so much living there already," Venturelli said. "There is a large uncertainty if these current methods for restoring gut microbiome health are effective at all, since outcomes are still largely unpredictable."
Venturelli's new approach is meant to bring more predictability to the whole enterprise.
In the new paper, Venturelli and her colleagues demonstrate a method for intelligently and systematically exploring this large design space by closing the loop between experiments and computational models. In benchtop experiments, her team forced various combinations of 15 different species of gut microbes to live and eat together. Their menu consisted of six types of dietary fibers known to affect the production of butyrate, a short-chain fatty acid critical to gut health.
Even with just 21 different variables on the table, the potential combinations of microbes and their diets easily counted well past the trillions. Faced with such an intractable problem, the researchers turned to machine learning, computer models, automated experiments and active learning—also known as Bayesian optimization.
Bayesian optimization is an approach that designs experiments to simultaneously fill gaps in the computer model and achieve specific goals. Armed with autonomous robotic experimental systems, the team was able to plow through five batches of high-throughput experiments—up to 390 conditions at the same time—to build their knowledge of interactions and outcomes.
"We found a lot of unexpected effects from interactions between the microbes and the fibers," Venturelli said. "These effects are very complicated and couldn't have been predicted before we ran the experiments."
The results revealed a combination of the dietary fiber inulin along with two inulin-hungry microbial species (Bacteroides uniformis and Anaerostipes caccae) interacted with a third bacterial species (Prevotella copri) to reliably produce the desired butyrate. What's more, this combination held strong regardless of what other species and factors were introduced.
The researchers are now working to see if this combination can effectively treat a mouse model of inflammatory bowel disease with promising early results. And beyond this specific combination, the researchers believe their approach could be used to discover many more promising combinations for a wide range of gastrointestinal disorders.
"There are companies combining bacteria and dietary fibers in their gut health products already, but they are not yet trying to identify and match the right synergies to the right problem," Venturelli said. "I believe using this process would help create more tailored—and more effective—solutions to a wide range of gastrointestinal issues."
This work was supported by the National Institutes of Health (R35GM124774, R01EB030340, R01DK13346) and the Army Research Office (W911NF-19-1-0269).
"Designing fiber–gut microbiome interactions with active learning." Bryce M. Connors, Jaron Thompson, Manasi Subhash Gangan, Nick Quinn-Bohmann, Sean M. Gibbons, Job Grant, Alejandro Castellanos-Sanchez, Jessica McCann, John Rawls and Ophelia S. Venturelli. Nature Chemical Biology, 2026. DOI: 10.1038/s41589-026-02272-4
