Microbial Cross-Feeding Scores Uncover Interactions That Influence Gut Health

An international research team led by scientists at the Hudson Institute of Medical Research has found a way to determine which species of gut microbiota are important in certain diseases, and how they interact with other microorganisms 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 microorganism as an essential nutrients by another—and how these may be altered in disease. The researchers suggest that understanding such relationships could point to therapeutic approaches for a range of disorders including inflammatory bowel disease, infections, autoimmune diseases and cancer.

“There are roughly 1000 different bacterial species in a healthy gut—it’s a microscopic multicultural community with over a trillion individual members,” said research lead Samuel Forster, PhD. “Bacteria in our microbiomes exist as communities that rely on each other to produce and share key nutrients between them … We have developed a new computational way to understand these dependencies and their role in shaping our microbiome. This new method unlocks our understanding of the gut microbiome and provides a foundation for new treatment options that selectively remodel microbial communities.”

Forster’s team, working with collaborators at the Institute for Systems Biology, and at Monash University and Monash Health, reported their findings in Nature Communications, in a paper titled “Disease-specific loss of microbial cross-feeding interactions in the human gut.” In their paper the team concluded, “We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding

interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem … We show that our analytical framework identifies both known and novel microbiome-disease associations, providing a cost-efficient and mechanistically grounded strategy to prioritize experiments and guide clinical trials.”

The human gut contains hundreds of microbial species forming a complex and interdependent metabolic network, the authors explained. And more than half of the metabolites consumed by gut microbes are by-products of microbial metabolism, with the waste of one species serving as nutrients for other. This means that the loss of one species may impact on the survival or extinction of others. “Species interdependence can render microorganisms vulnerable to local extinction if a partner is lost unless alternative species are available to fill that niche,” the team continued. “Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized.”

Forster and colleagues have spent years studying the gut microbiome and working 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 is designed 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 taxa predicted to produce a given metabolite, normalized by the total number of involved taxa,” the authors noted.

For their reported study, and to obtain an overview of the association between cross-feeding interactions and different diseases, the team applied the MES technique to exiting datasets. “To obtain an overview of the association between cross-feeding interactions and different diseases, we performed a large-scale analysis of 1661 high-quality and deeply sequenced gut metagenome samples, including 871 healthy and 790 diseased individuals from 33 published studies, 15 countries and 11 disease phenotypes,” they wrote. Using the MES platform the team was able to identify and rank metabolic interactions that were significantly affected by loss of cross-feeding partners in 10 out of 11 diseases.

For example, hydrogen sulphide was highlighted as a key factor in Crohn’s disease (CD). The investigators discovered that the association may relate to loss of bacteria that use hydrogen sulphide, H₂S, not an increase in those species producing it, as was previously believed. “Our results suggest that CD patients lack microbial community members to support a healthy H₂S balance,” they wrote. The study in addition identified other associations between the microbiome interactions and disease, including some that hadn’t previously been known.

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

First author Vanessa Marcelino, PhD, says the new computational method for studying microbial communities was key to establishing the relationships. “This is a significant step in the development of complex microbial therapies. This approach allows us to identify and rank the key interactions between bacteria and use this knowledge to predict targeted ways to change the community.”

Forster and team have a long-standing relationship with Adelaide-based biotechnology company BiomeBank, which is working on new ways to treat and prevent disease by restoring gut microbial ecology. He commented, “Through the partnership between the Hudson Institute of Medical Research and BiomeBank, these insights into community structure will provide the opportunity for targeted intervention with rationally selected combinations of microbes,” Forster said. In their paper the team concluded, “We expect that metagenome-informed metabolic models, coupled with an assessment of microbial cross-feeding interactions, will help alleviate one of the main barriers in the development of microbiome therapies—prioritizing which species or metabolites to target. By focusing on restoring key aspects of the gut ecology, we may be able to introduce more effective and long-lasting changes in the human gut microbiome.”

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