Understanding Our Unique Response to Foods
Our gut microbiome – the trillions of microbes in our digestive system – differs substantially even between people, even identical twins, and responds in unique ways to dietary intake. To design microbiome-informed personalized diets to optimize our health, the Gibbons Lab, with NIH funding, is studying how the microbiome influences our individual responses to diet.
Dr. Sean Gibbons with members of his lab. Photo credit: Scott Eklund / Red Box Pictures.
The gut microbiome plays a key role in human health, helping to modulate our immune responses and fend off disease. Much of the microbiome’s influence appears to be exerted through the production of metabolites – small molecules generated when the microbiome breaks down food. Researchers are just beginning to understand how the thousands of metabolites produced in our gut affect our health and how to optimize their composition through dietary modifications, probiotics, or other interventions. To study these processes, ISB researchers are developing CyberGut. CyberGut incorporates individualized information on gut tissue gene expression, microbiome composition, and diet to generate predictions of personalized responses to dietary or probiotic interventions designed to improve health.
- Funded by National Institutes of Health
- Led by Sean Gibbons, PhD
- Key Collaborators
- Johanna Lampe, PhD, Fred Hutchinson Cancer Center
- Pieter Dorrestein, PhD, University of California, San Diego
- Priyanka Baloni, PhD, Purdue University
CyberGut: A human-microbiome metabolic model to study and optimize individual nutrition and health
The microbes in the human gut break down nutrients and yield tens of thousands of metabolites that are absorbed into the body. These processes, and how to optimize them through interventions such as customized diets or probiotics, are active areas of scientific investigation.
Sean Gibbons, PhD, and his team at ISB are developing computational simulations of the complex interactions between diet, the microbiome, metabolites, and the human host. These community-scale metabolic models (MCMMs) offer an efficient way to predict the effects of various interventions on microbiome composition, microbial metabolite production, and human health.
In one key project, Gibbons and his colleagues are developing CyberGut, an MCMM that can be tailored to the individual with highly personalized information on diet, microbiome composition, and, if available, colonic tissue gene expression. The need for personalization is high, as microbiome and dietary composition — and the body’s reactions to them — vary substantially among individuals.
CyberGut is being built on a model published earlier by Gibbons and his colleagues called MICOM (a portmanteau of Microbial Community).1 MICOM inputs nutritional information based on a standardized diet and data on the type of microbes in the gut and their abundances, derived from stool DNA sequencing. The model outputs information on metabolic interactions among the microbes, including the net production of metabolites available for absorption by the body. The model can also generate predictions about the outcomes of interventions, such as dietary changes, prebiotic fiber supplementation, or taking probiotics. MICOM is widely used by the research community and has been downloaded more than 300,000 times.
Gibbons and his colleagues are currently refining CyberGut to include individualized dietary information, using a tool designed in his lab called MEDI (metagenomic estimation of dietary intake) that infers dietary intake from food-derived DNA in stool. The researchers also aim to bolster the model with improved data on human host metabolism, such as the use and production of metabolites (their flux) in the blood, key organs and different intestinal regions.
To accomplish these aims, researchers are tapping into detailed microbiome, metabolic, tissue-specific gene expression, and dietary data from existing cohorts of people who are healthy or have various conditions. The scientists are also culturing stool samples in an ex vivo (outside the body) experimental validation platform across a cohort of 100 healthy individuals, to test the effects of various dietary and pharmaceutical interventions, which can be used to validate and refine model predictions.
CyberGut will be a valuable research tool and could become a platform for the design of personalized interventions designed to favorably alter the microbiome and its metabolic output. In the near-term, CyberGut will be used to power a physician- and consumer-facing digital platform, called “My Digital Gut,” to optimize health through dietary changes, probiotics, or other interventions.
Key milestones to date include:
- Using MICOM to accurately predict individual susceptibility to colonization by the intestinal pathogen Clostridioides difficile, as well as response to a probiotic intervention.2
- Showing that MICOM can predict personalized profiles of the production of short-chain fatty acids (anti-inflammatory microbial metabolites crucial for human health). Personalized butyrate production was strongly associated with cardiometabolic health markers in a large (N>3,000) cohort (higher butyrate was associated with lower LDL cholesterol, lower levels of insulin resistance, lower inflammation, and lower blood pressure). These findings provide a proof-of-concept for how MCMMs can be leveraged to design personalized nutritional interventions that optimize human health.3
- Generating MEDI (metagenomic estimation of dietary intake), a method to estimate dietary intake from the analysis of the DNA of consumed food items in the gut.4 MEDI is used to produce a map of the organisms an individual ate and their metabolic composition, and will be incorporated into CyberGut. The approach also has broader potential to supersede current methods of determining dietary intake, which typically rely on time-consuming and biased self-reporting data.
By integrating modeling with their validation platform, Gibbons and his colleagues are building a unique system with the potential to yield highly accurate predictions about individualized interventions to optimize health, fend off disease, and possibly even extend the human healthspan. CyberGut, by providing personalized predictions, may change how multiple diseases are treated in the future, including conditions related to digestion, immunity, cardiovascular health and cancer.
Citations
- Diener C, Gibbons SM, Resendis-Antonio O. MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota. mSystems 2020 doi: 10.1128/msystems.00606-19
- Carr A, Baliga NS, Diener C, Gibbons SM. Personalized Clostridioides difficile engraftment risk prediction and probiotic therapy assessment in the human gut. bioRxiv 2024 doi: 10.1101/2023.04.28.538771v3
- Quinn-Bohmann N, Wilmanski T, Ramos Sarmiento K, Levy L, Lampe JW, Gurry T, Rappaport N, Ostrem EM, Venturelli OS, Diener C, Gibbons S. Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut. Nat Microbiol. 2024 doi: 10.1038/s41564-024-01728-4
- Diener C, Gibbons SM. Metagenomic estimation of dietary intake from human stool. bioRxiv 2024 doi: 10.1101/2024.02.02.578701v1
Technologies Developed
CyberGut, a computational model that can predict tailored interventions to optimize the composition of an individual’s microbiome and its associated metabolites, thereby potentially improving health. CyberGut extends an earlier model of microbiome metabolism, MICOM (microbial community), to include host metabolism.
MEDI (metagenomic estimation of dietary intake), a method to estimate dietary intake from the analysis of the DNA of consumed food items in the stool.
Contact Dr. Sean Gibbons
Associate Professor
ISB