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
