AI unlocks hidden links between gut bacteria and human health

Trillions of bacteria living in the gut have chemical conversations that affect from your immune system to your emotions, but scientists have been working to decode these microscopic communications.

Now, researchers at the University of Tokyo have developed an artificial intelligence system that can identify which gut bacteria produce specific chemicals that affect human health and have the potential to open up new paths for personalized medicine.

The challenge is shocking in scope. Although the human body contains about 30-40 trillion cells, the intestine alone holds about 100 trillion bacteria. These microorganisms produce thousands of molecular messengers called metabolites that circulate throughout the body, but still mysterious in mapping the chemicals produced by bacteria.

Neural networks comply with Bayesian statistics

“The problem is that we’re just starting to understand which bacteria produce what human metabolites and how these relationships change in different diseases,” explained Tung Dang, a project researcher at the university’s Department of Biological Sciences. “By accurately mapping these bacterial chemical relationships, we can potentially develop personalized treatments.”

The team’s solution, called Vbayesmm, combines neural networks with Bayesian statistics to screen through a large data set that contains information about bacterial populations and metabolite levels. Unlike the method that previously considered all bacteria as equally important, the new system uses what researchers call the “spike and tailor” method to identify only the most influential microbial players.

Think of it as having a spotlight that picks the most important actors on a crowded stage while dimming the background noise. The system automatically distinguishes key bacterial families that significantly influence metabolites with less-related microorganisms.

Disease applications in the real world

Vbayesmm’s methodology on test datasets from sleep disorders, obesity and cancer research has always outperformed existing analytical tools. For example, in a study of obstructive sleep apnea, the system identified specific bacterial families, such as lachnospileceae and opcillospilaceae, as key players in the production of bile acids that may cause metabolic interference in this case.

The meaning goes beyond sleep disorders. In obesity studies, the system highlights how high-fat diets can greatly transfer gut bacterial communities, especially the increase in the lachnospileceae population that affects bile acid metabolism, which may lead to changes in obesity-related metabolic problems.

Key benefits of the new approach include:

• Identification of core bacterial species from datasets containing tens of thousands of microorganisms
• Quantification of uncertainty in predictions, providing confidence measurements for experimental follow-up
• Processing scalability of large genomic datasets from advanced sequencing techniques
• Integrate multiple data types to reveal complex biological relationships

From data to personalized treatment

This study not only represents improved data analysis but also points to the future of specific bacterial or dietary interventions that physicians may tailor-made for individual patients’ microbial characteristics. Dang envisions “the ability to grow specific bacteria to produce beneficial human metabolites or design targeted therapies to modify these metabolites to treat the disease.”

However, computing demand is still very large. Analyze the most complex datasets (including nearly 60,000 bacterial species) and can be processed on a powerful computer system for up to five days of processing time. The team acknowledged this limitation, while noting that improving computing power will continue to reduce these barriers.

The system also assumes that bacterial species function independently, although gut microbes actually interact with each other in an incredibly complex network. Future versions will need to consider these complex microbial conversations while maintaining computational feasibility.

Extended microbial map

Going forward, researchers plan to collaborate with a more comprehensive set of chemical data to capture the full range of bacterial products, although this raises new challenges in determining whether a particular chemical comes from external sources like bacteria, the body, or diet.

The team also aims to make their system more robust when analyzing a diverse patient population and combining bacterial evolutionary relationships to improve predictions. The ultimate clinical goal remains to identify specific bacterial targets that can actually help patients with treatment or dietary interventions.

As our understanding of the role of the gut microbiome in human health continues to expand, tools such as Vbayesmm may be essential for turning complex biological data into practical medical applications. The study was published in the journal Bioinformatics and was supported by grants from the Japan Science and Technology Bureau.

For patients and clinicians, this work represents another step towards a precise medical approach that can leverage our human microbial partners to improve health outcomes, from basic research to practical medical applications that recognize the far-reaching impact of our bacterial partners.

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