Tuesday, 12 June 2018

Assessing effectiveness of microbiome therapies

Published in eLife, a paper presents a new mathematical model that can predict the effectiveness of microbiome therapies that manipulate the immune system through live bacteria.

To the authors' knowledge, this is the first model that allows for the simultaneous prediction of the dynamics of both the microbiota and the immune response, and can be "considered a stepping stone to the development and rational design of microbiome therapies".

Until now, it was experimentally intractable to identify the optimal combination of bacteria that would generate the desired anti-inflammatory treatment response. But researchers have now developed a model that predicted the most effective treatment in mice.

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 one such group of immune cells called 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.

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 more stably the gut – proving the value of including both measures in the model.

For details see: “Computer-guided design of optimal microbial consortia for immune system modulation.”

Posted by Dr. Tim Sandle

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