Researchers, who used models to predict cross-feeding interactions in the gut (the consumption and secretion of hundreds of metabolites by the complex community of microbes), say predictions from such computational methods could eventually help doctors get a more complete understanding of gut health. The team published its study “Ecology-guided prediction of cross-feeding interactions in the human gut microbiome” in Nature Communications.

Previous studies have focused on determining the types of microbes that are present. Unfortunately, this information is not enough to understand the microbiome.

“Although it is possible to measure these metabolites experimentally, it is cumbersome and expensive,” notes Sergei Maslov, PhD, a professor of bioengineering at the University of Illinois at Urbana-Champaign (UIUC).

The researchers had previously published a study where they used experimental data from other studies to model the fate of metabolites as they pass through the gut microbiome. In the new study, they have used the same model to predict new microbial processes that have not been determined before.

“Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput,” write the investigators.

“Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning.”

“Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.”

“What we eat passes into our gut, and there is a cascade of microbes which release metabolites,” said Akshit Goyal, PhD, a postdoctoral fellow at MIT and a collaborator of the Maslov lab. “Biologists have measured these molecules in human stools, we have shown that you can use computational models to predict the levels of some.”

Measuring every metabolite and trying to understand which microbe might be releasing it can be challenging. “There is a large universe of possible cross-feeding interactions. Using this model, we can aid experiments by predicting which ones are more likely to occur in the gut,” continued Goyal.

The model was also supported by genomic annotations, which explain which microbial genes are responsible for processing the metabolites. “We are confident of our modeling predictions because we also checked whether the microbes contain the genes necessary for carrying out the associated reactions. About 65% of our predictions were supported by this information,” pointed out Veronika Dubinkina, a PhD student in bioengineering at UIUC.

The scientists are now working to improve the model by including more experimental data.

“Different people have different strains of gut microbes. Although these different strains have many genes in common, they differ in their capabilities,” explained Dubinkina. “We need to collect more data from patients to understand how different microbial communities behave in different hosts.”

“We are also interested in determining how fast the microbes consume and secrete the metabolites,” said Tong Wang, a PhD student in physics at UIUC. “Currently the model assumes that all the microbes consume metabolites at the same rate. In reality, the rates are different, and we need to understand them to capture the metabolite composition in the gut.”