Microbial cross-feeding scores reveal interactions affecting gut health

An international team of researchers led by scientists at the Hudson Institute of Medical Research has found a way to determine which groups of gut microbes are important for certain diseases and how they interact with other microbes to create a healthy microbiome.

The team developed a computational metabolite exchange scoring system to identify microbial cross-feeding relationships (the use of metabolites produced by one microbe as an essential nutrient for another) and how these metabolites are altered in disease. Researchers say understanding this relationship could point the way to treatments for a range of diseases, including inflammatory bowel disease, infections, autoimmune diseases and cancer.

“There are approximately 1,000 different bacterial species in a healthy gut, a microscopic multicultural community with more than a trillion individual members,” said study leader Dr. Samuel Forster. “The bacteria in our microbiome exist as communities. , they depend on each other to produce and share key nutrients between them… We developed a new computational approach to understand these dependencies and their role in shaping our microbiomes. This new approach unlocks our Understanding the gut microbiome and laying the foundation for new treatment options to selectively reshape microbial communities.”

Associate Professor Samuel Foster of the Hudson Institute of Medical Research is developing new methods to understand interactions within the human gut microbiome. [Hudson Institute of Medical Research]

Working with collaborators from the Institute of Systems Biology, Monash University and Monash Health, Foster’s team nature communications, in a paper titled “Disease-specific loss of microbial cross-feeding interactions in the human gut.” “We propose that our conceptual framework will help prioritize in-depth analytical, experimental, and clinical goals with the goal of restoring microbial cross-feeding,” the team concluded in their paper.

Interactions represent a promising mechanism-based strategy for reestablishing healthy intestinal ecosystems… We show that our analytical framework identifies known and novel microbiome-disease associations, providing a cost-effective and based- Mechanistic strategies to prioritize experiments and guide clinical trials. “

The authors explain that the human gut contains hundreds of microbial species, forming complex and interdependent metabolic networks. More than half of the metabolites consumed by gut microbes are by-products of microbial metabolism, with waste products from one species serving as nutrients for another species. This means that the loss of one species may affect the survival or extinction of other species. “If partners are lost, species interdependence may leave microbes vulnerable to local extinction unless a replacement species fills the gap,” the team continued. “Many gut microbes critical to human health depend on rely on the nutrients produced by each other to survive; however, these cross-feeding interactions remain difficult to quantify and remain poorly characterized.”

Foster and colleagues have spent years studying the gut microbiome and figuring out which species perform which functions. To help understand the link between cross-feeding interactions and disease, the team developed a computational metabolite exchange score (MES) that can quantify microbiome interactions. The system aims to identify those microbial cross-feeding interactions that are most affected in disease. “MES is the product of the diversity of taxa predicted to consume and the diversity of taxa predicted to produce a given metabolite, normalized by the total number of taxa involved,” the authors state.

In the study they report, the team applied MES technology to an existing data set in order to obtain an overview of the association between cross-feeding interactions and different diseases. “To comprehensively understand the association between cross-feeding interactions and different diseases, we performed a large-scale analysis of 1661 high-quality and deeply sequenced intestinal metagenomic samples, including those from 33 published studies in 15 countries. 871 healthy and 790 diseased individuals and 11 disease phenotypes,” they wrote. Using the MES platform, the team was able to identify and rank metabolic interactions in 10 of 11 diseases that were significantly affected by the loss of cross-feeding partners.

For example, hydrogen sulfide is considered a key factor in Crohn’s disease (CD). The researchers found that this association may be related to a decrease in bacteria that use hydrogen sulfide, H .2S, there was not, as previously thought, an increase in the species that produced it. “Our results suggest that patients with Crohn’s disease lack members of the microbial community to support healthy H2S balance,” they wrote. The study also identified other associations between microbiome interactions and disease, including some previously unknown.

“Our framework identifies known and novel associations between the microbiome and disease, including links between colorectal cancer and microbial ethanol metabolism and rheumatoid arthritis and microbial-derived ribosylnicotinamide , and the link between Crohn’s disease and specific bacteria that metabolize hydrogen sulfide.”, the investigators further noted. “…we found that H2S, a gas previously associated with symptoms of CD and IBD, was the metabolite most affected by the loss of cross-feeding microbial partners…”

First author Vanessa Marcelino, Ph.D., said new computational methods for studying microbial communities are key to establishing this relationship. “This is an important step toward developing complex microbial therapeutics. This approach allows us to identify and sequence key interactions between bacteria and use this knowledge to predict targeted approaches to altering communities.”

Foster and team have a long-standing relationship with BiomeBank, an Adelaide-based biotech company that is researching new ways to treat and prevent disease by restoring gut microbial ecology. He commented: “Through this collaboration between the Hudson Institute for Medical Research and BiomeBank, these insights into community structure will provide opportunities for targeted intervention through rationally selected combinations of microorganisms,” Foster said. The team concluded in their paper: “We anticipate that metagenomic-based metabolic modeling, coupled with assessment of microbial cross-feeding interactions, will help alleviate one of the major barriers to microbiome therapeutic development – prioritizing targeting Which species or metabolites. By focusing on restoring key aspects of gut ecology, we may be able to make more effective and long-lasting changes to the human gut microbiome.”

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