AI Tool Could Help Identify Specific Gut Bacterial Targets for Treatment of Diseases

The human body hosts trillions of bacteria, particularly in the gut, which have a significant role in digestion and various other aspects of health. These gut bacteria produce a variety of metabolites that act as molecular messengers, influencing processes such as metabolism, immune function, brain activity, and mood. However, understanding the exact relationships between bacteria and the metabolites they produce is still in its infancy.

As gut bacteria are highly diverse and interact in complex ways, it’s challenging to pinpoint how these microbes influence human health and diseases. The difficulty in mapping these relationships hinders the development of targeted treatments. Researchers have been seeking methods to better understand the bacteria-metabolite interactions and how they can be applied in personalized treatments. Now, for the first time, researchers have used a special kind of artificial intelligence (AI) to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify.

The tool called VBayesMM has been developed by a team of researchers from the University of Tokyo (Tokyo, Japan) to help map the complex relationships between gut bacteria and metabolites. Using a Bayesian neural network, VBayesMM analyzes large datasets to identify key bacterial players affecting metabolite production. The system automatically distinguishes between the significant bacteria that influence metabolites and the vast background of less relevant microbes.

VBayesMM also accounts for uncertainty in the predictions, providing more accurate and reliable results compared to other existing methods. The tool has been tested on real data from studies on sleep disorders, obesity, and cancer, consistently outshining other techniques and uncovering bacterial families linked with known biological processes.

The findings, published in Nature Communications, show that the tool outperformed existing analytical methods by reliably identifying bacteria which align with biological processes and by acknowledging uncertainty in predictions. This approach gives researchers greater confidence in the results, reducing the risk of overconfident and potentially incorrect conclusions.

The tool offers promising applications in personalized healthcare, where it could help identify bacterial targets for treatments or dietary interventions. Moving forward, the researchers plan to enhance VBayesMM by incorporating more comprehensive chemical datasets and improving its robustness for diverse patient populations, ultimately transitioning from basic research to practical medical applications.

“The problem is that we’re only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases,” said Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences. “By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases.”

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