Scientists say they have developed a mathematical model that can predict the effectiveness of microbiome therapies that manipulate the immune system through live bacteria. The team believes their work (“Computer-Guided Design of Optimal Microbial Consortia for Immune System Modulation”), published in eLife, could help doctors choose the most appropriate treatment for people with inflammatory or allergic diseases.
“Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome composition and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contribution of twelve Clostridia strains with known immune-modulating effect to Treg induction,” write the investigators.
“Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.”
Introduction of therapeutically potent bacteria into patients with infections or metabolic diseases is an emerging approach with great promise. But there are two challenges standing in the way of its success. First, the bacteria must be able to set up home alongside the already resident microbes. Second, in the context of autoimmune diseases, they must stimulate a range of immune responses that dampen down unwanted inflammation. This study focused on stimulating regulatory T cells, or Tregs.
Single bacterial strains are less effective than groups of different strains. But testing the huge number of potential bacterial combinations experimentally simply isn't feasible.
“In previous work, our collaborators and paper co-authors identified 17 different strains of bacteria that can generate the required immune response, but determining the best combinations from these strains would need more than 130,000 independent experiments,” explains senior author Vanni Bucci, Ph.D., assistant professor at the University of Massachusetts Dartmouth. “The goal of this study was to develop a mathematical model to rapidly and systematically select groups of bacteria that would optimally produce the desired immune response.”
The team built a model using published and newly generated data showing which bacterial strains were most efficient at colonizing the gut and at stimulating Treg cells in germ-free mice, both individually and together. They then combined this model with another that predicts the growth and expansion of bacterial colonies in mice over time.
This allowed them to determine both the growth of each bacterial strain in the mice and the extent of each strain's contribution to the increase in Treg immune cells. Based on this, they developed a way of scoring how well groups of bacteria colonize together and stimulate an immune response. They then tested every possible bacterial combination, generating a ranked list of bacterial combinations.
To measure the model's accuracy, they tested five different four-strain combinations of bacteria in germ-free mice. They found that the bacterial combinations with the highest scores predicted by the model not only stimulated immune cells more potently, but also colonized the gut more stably—proving the value of including both measures in the model.
“Treatment of immune or inflammatory diseases is not necessarily achieved by targeting a single biological function but will require simultaneous manipulation of multiple processes within the host immune system,” said lead author Richard Stein, Ph.D., research associate at the Dana-Farber Cancer Institute. “To our knowledge, this is the first model that allows for the simultaneous prediction of the dynamics of both the microbiota and the immune response. It can be considered a stepping stone to the development and rational design of microbiome therapies.”
